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Practice Full Report

Promoting health and well-being in healthy people 2030, associated data.

Supplemental Digital Content is Available in the Text.

Healthy People 2030 describes a vision and offers benchmarks that can be used to track progress toward the goal of all people in the United States achieving their full potential for health and well-being across the life span. This vision can be realized through evidence-based interventions and policies that address the economic, physical, and social environments in which people live, learn, work, and play. Securing health and well-being for all will benefit society as a whole. Gaining such benefits requires eliminating health disparities, achieving health equity, attaining health literacy, and strengthening the physical, social, and economic environments. Implementation of Healthy People 2030 will by strengthened by engaging users from many sectors and ensuring the effective use and alignment of resources. Promoting the nation's health and well-being is a shared responsibility—at the national, state, territorial, tribal, and community levels. It requires involving the public, private, and not-for-profit sectors.

Healthy People provides science-based national objectives with 10-year targets for improving the health of the nation. Healthy People 2030—the fifth edition of the Healthy People initiative—describes a vision and offers benchmarks that can be used to track progress toward the goal of helping all people in the United States achieve their full potential for health and well-being across the life span. Healthy People 2030 expresses an expanded focus on health and well-being and an understanding that health and well-being for all people is a shared responsibility. This vision can be achieved through evidence-based interventions and policies that address the economic, physical, and social environments in which people are born, live, learn, work, play, worship, and age. High-quality data that are accurate, timely, and accessible are required to record and report on progress 1 over the course of the decade and to direct interventions to populations that are most likely to benefit from them.

Healthy People sets the federal agenda for the nation's health, guides its direction and allocation of resources, informs federal data collection and programmatic activities, and provides a model for promoting health and well-being at the state and local levels. The initiative's emphasis on promoting health and well-being signals to the nation that it is time to work across sectors to achieve health equity. This decade Healthy People 2030 is a resource for all sectors.

As part of the development of Healthy People 2030, the US Department of Health and Human Services (HHS) sought guidance from the Secretary's Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 (Secretary's Advisory Committee), a federal advisory committee composed of nonfederal, independent subject matter experts. The Secretary's Advisory Committee presented recommendations to the HHS Secretary for developing and implementing the objectives for 2030. The Secretary's Advisory Committee convened regularly between December 2016 and September 2019, with meetings open to the public.

Health promotion has been a cornerstone of the Healthy People initiative since its inception in 1979. The Secretary's Advisory Committee recommended that the focus of Healthy People 2030 expand beyond health promotion to the broader purpose of promoting “health and well-being.” The process that has been called health promotion no longer focuses on health alone, but now leads to health and well-being for individuals in addition to society as a whole. This offers a chance to balance the needs of individuals and society. Society is defined as “a voluntary association of individuals for common ends.” 2 Health and well-being are elements among the common ends that motivate us, as individuals, to act for the good of all. In return for participating in society, individuals expect fair and just opportunities to be as healthy and well as possible. This article provides insights into defining health and well-being, promoting health and well-being, fostering user collaboration to improve health and well-being, and measuring health and well-being, in addition to implications for policy and practice.

The Secretary's Advisory Committee produced 2 detailed briefs that offered guidance for promoting health and well-being. Secretary's Advisory Committee members, joined by additional subject matter experts, developed these 2 briefs. The original documents are available on the HealthyPeople.gov Web site. 3 , 4

Defining Health and Well-being

Healthy People 2030 refers to health and well-being in every aspect of the framework, including the vision, mission, foundational principles, plan of action, and overarching goals. 5 The expanded role for health and well-being in Healthy People 2030 was supported by the Secretary's Advisory Committee's recommendations and its definition of health and well-being as how people think, feel, and function—at a personal and social level—and how they evaluate their lives as a whole. 6 How people think, feel, and function affects their beliefs about whether their lives have meaning and purpose 7 , 8 (Table ​ (Table1). 1 ). This definition recognizes the multilevel nature of health and well-being. It acknowledges that social structures, such as families, neighborhoods, communities, organizations, institutions, policies, economies, societies, cultures, and physical environments, strongly influence health and well-being. Such influence is reciprocal between individual, social, and societal health and well-being. *

The terms “health” and “well-being” describe separate but related states; health influences well-being and, conversely, well-being affects health. 9 Health incorporates both physical and mental conditions; it implies fitness under changing circumstances, such as degradation of the physical, social, or economic environments, and must be safeguarded against threats from illness, injury, or death. Safety, as a result, is an important determinant of health. Well-being is both a determinant and an outcome of health. 10 It encompasses objective and subjective elements and reflects many aspects of life and states of being. These include physical and mental, as well as emotional, social, financial, occupational, intellectual, and spiritual, elements. 11 The terms apply to individuals as well as to groups of people (eg, families, communities) and environments (eg, physical, social, economic).

The World Health Organization defines health promotion as:

The process of enabling people to increase control over, and to improve, their health. 12 Health promotion ... covers a wide range of social and environmental interventions that are designed to benefit and protect individual people's health and quality of life by addressing and preventing the root causes of ill health, not just focusing on treatment and cure. 12

The World Health Organization identifies 3 key elements for health promotion: good governance for health; health literacy; and healthy cities. Adding the concept of well-being to this definition emphasizes that promotion of health and well-being takes place across different environments and users.

Promoting Health and Well-being

The concept of promoting health and well-being at both personal and systems levels has evolved over history, starting with ancient and classical civilizations. 13 Policy strategies for promoting health have been proposed since the 1970s. 14 More than 3 decades ago, the Ottawa Charter for Health Promotion described health as a “resource for everyday life, not the objective of living.” It noted that prerequisites for health include “peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice, and equity.” 15 This guidance remains relevant today. Promoting well-being requires engaging an expanded and diverse array of users, disciplines, and sectors that extend beyond public health, such as mental health, housing, childcare/education, business, and aging.

Interventions to promote health and well-being occur at the individual, site-specific community, and societal levels. They address economic, social, and physical environmental and political factors (“determinants of health”) that influence health and well-being. Promoting health and well-being is critical because determinants of health—the physical, social, and economic circumstances in which people are born, live, learn, work, play, worship, and age—have disparate effects on vulnerable populations. These factors interact to affect people disproportionately based on race and class. All sectors are needed to remedy such disparities and achieve health equity.

At the individual level, interventions to promote health and well-being might focus on health behaviors, employment, housing, food security, or childcare. These interventions also would apply to the community level since they target settings where people spend their time, including home, school, work, or places where they socialize such as community centers and parks. These interventions can address designs of the built environment for ease of access and to ensure safety. The Robert Wood Johnson Foundation's Culture of Health initiative is one such national model. The Foundation defines a culture of health as one in which “good health and well-being flourish across geographic, demographic, and social sectors; fostering healthy equitable communities guides public and private decision making; and everyone has the opportunity to make choices that lead to healthy lifestyles.” 16

The concept of promoting health and well-being has evolved over the decades (Figure). Health and well-being operate on more than 1 level. Broader conditions shape individual experiences of health and well-being, and organized efforts can influence those conditions. Social structures, such as families, neighborhoods and communities, and policies, economies, and cultures also play important roles. 17 – 21

How the Concept of Health Promotion Has Evolved Over Decades of Healthy People

Engaging users from many sectors and ensuring the effective use and alignment of resources will strengthen implementation of Healthy People 2030. To promote health and well-being for all people and foster equity and social justice, socioecological factors and determinants of health must be addressed at all levels. A dynamic mix of resources will be needed for long-term improvements to livability (eg, stable housing, healthy food, clean air, education, living wage jobs) and for urgent needs (eg, acute care for illness or injury, food assistance, shelter, addiction treatment, disaster relief). Such resources will need to address a more diverse range of factors than in the past.

All too often, communities and institutions function in a reactive and responsive mode, deferring or delaying long-term investments. This way of functioning generates persistent needs for urgent services, along with pressure to maintain them. Collaborative decision-making across sectors can optimize the positive impact of resources and reduce the number of crises that happen in the first place. Identifying evidence-based programs to promote health and well-being among users can serve common interests, help users expand their thinking about solutions, and set priorities for limited time, money, and other scarce resources.

Multisectoral Collaborations to Improve Health and Well-being

Achieving population-level improvements in the coming decade will require users working at all levels to function across sectors and establish or participate in multisectoral collaborations. Such efforts can improve outcomes—not only in the health sector but also in nonpublic health or health care sectors, such as education, economics, the environment, and social cohesion. Collaboration among various users groups can benefit all partners by creating win-win solutions that recognize the interrelatedness of population health status with factors that lie outside the health care and public health systems.

Achieving optimal health and well-being requires efforts that include partners from different sectors, who operate at multiple levels (eg, state, local, community) and address the circumstances of people's lives. † Such efforts could span the behavioral, psychosocial, socioeconomic, cultural, and political circumstances of the population. No single actor has sole ownership of, accountability for, or capacity to sustain the health and well-being of an entire population. 22 – 24 The 10 “causes of the causes” of poor health comprise psychological influences (eg, social gradient, stress, and social exclusion), as well as elements of community infrastructure, such as food and transportation. 25 Thus, success depends on strengthening the capacity of communities to cocreate their own futures. 26

The COVID-19 pandemic is a case study of the reciprocal, complex relationships between the health of individuals and the health of society as a whole, as well as the resulting unintended consequences. An individual's decision not to wear a mask at a grocery store or other indoor gathering place can result in the virus' spread to other people who are present. Defining some workers as essential and required to work, such as those who work in grocery stores, transportation, health care, and in other occupations that require interaction with the public, increases the risk of infection for many low-wage earners. When essential workers are compensated with low wages, lack of financial viability creates challenges to their overall health and well-being. When health insurance is tied to employment and unemployment is soaring, unemployed people often delay seeking care. When older adults stay in isolation to avoid the possibility of infection, they can experience loneliness, depression, and mental health issues. When schools are closed and children stay at home, those who lack Internet connectivity are at risk of falling behind in their schoolwork. Those who receive free school lunches may go hungry.

To help local health departments identify strategies for promoting population health and well-being and addressing determinants of health, the National Association of County and City Health Officials (NACCHO) identified 9 domains of determinants, 27 as well as data sources for each (Table ​ (Table2). 2 ). Healthy People users at the state and tribal levels may find NACCHO's domains and data sources useful for identifying and acting upon opportunities to improve and monitor measures of health and well-being. These include indicators that are important to the success of other sectors, such as high school graduation, crime reduction, and economic prosperity.

Measuring Health and Well-being

Monitoring and documenting changes to the population's health and well-being will require the use of new data sources and types of measures. The way people evaluate their own lives as a whole is one indicator of health and well-being. Yet, systems that are outside of an individual's control shape the exposures, choices, and services that people experience. An important distinction exists between individuals' subjective ratings of their own health and well-being and the objective conditions that surround and support people as they strive to improve their health and well-being.

Measures of progress that go beyond those specific to public health and health care settings will require tapping into existing data sources across other domains and sectors. For example, data used by agricultural extension offices, planning departments at all levels, schools, businesses, parks and recreation agencies, transportation systems, the Bureau of the Census, aging services, and the financial sector, among others, can inform health and well-being. Data partnerships between public health, health care settings, and other sectors can often benefit collaborators by providing a much richer source of information for each partner as well as for the entire partnership. 28

Healthy People 2020 used functional measures, including Healthy Life Expectancy, ‡ Summary Mortality and Population Health, § and Disparities, as global health measures for assessing progress. Earlier iterations of Healthy People used life expectancy and other measures. ∥ Holistic evaluations of health and well-being status of individuals, communities, and systems require broad measures, such as life satisfaction or social cohesion. 29 – 33 Assessing progress toward improved health and well-being must consider health disparities, health literacy, multisectoral policies, and determinants of health and well-being.

Realizing the potential of Healthy People 2030 will require accurate data from credible sources at all levels, with a renewed emphasis on local action. There are barriers to generating high-quality data (eg, funding, staffing, technology). Healthy People supports local action by providing guidance for consistent data collection methods and measures, as well as examples of best practices and innovations. A data partnership infrastructure and network focused on Healthy People objectives could address and respond to new developments in data sources and data analytics. For example, a data partnership could expand the availability of locally relevant data, stimulate access to new data sources to measure determinants of health and health equity, and enable linkage of geographic and demographic data in presentation formats for Healthy People users.

Partners would be able to share data, methods, and analyses and access guidance on data developments relevant to all 3 Healthy People objective types—core, developmental, and research. A data partnership infrastructure and network that links national, tribal, state, territorial, and local data through partnerships and collaborations could enhance the nation's capacity to identify and record the achievement of Healthy People objectives and overarching goals.

Healthy People 2030 continues the Healthy People initiative's tradition of serving as a catalyst for action by expanding the focus of health promotion to promoting health and well-being (see Supplemental Digital Content file, available at http://links.lww.com/JPHMP/A716 ). This emphasizes the need to shift from a disease-specific orientation to more upstream policy efforts. Healthy People 2030 offers data, objectives, and tools for creating well-being and a healthier nation. Realizing the potential of Healthy People 2030 will require the active involvement of a variety of public and private institutions and organizations, including national, tribal, state, territorial, and local health departments. Health departments at all levels can contribute to this work by engaging multiple sectors in the implementation and monitoring of objectives.

Discussions within the public health community, and between public health and other sectors, around defining health and well-being offer opportunities to engage partners that historically have not been involved in Healthy People. Engaging new partners in the Healthy People initiative will require those who traditionally have led the initiative to understand what those partners need to succeed, communicate how new partners' goals complement those of Healthy People, and convey how engaging with Healthy People can benefit the new partners. For example, partnering to improve high school graduation rates benefits the education and public health sectors, as well as the financial sector and potentially the criminal justice system. Accomplishing that goal might involve engaging with the telecommunications sector to support students' access to affordable Internet service. By engaging in such partnerships, everyone would become more familiar with the goals of other sectors and discover more win-win opportunities.

In their health improvement plans, public health departments at all levels should think broadly about which partners from other sectors could help them advance health and well-being goals, while considering what public health can offer those sectors in achieving their own goals. For example, in Maryland, each county has been charged with having a local health improvement coalition that brings together key users to achieve locally identified needs for health and well-being and to eliminate health disparities. Organizations and individuals often need to see value for investing their time and resources before they agree to participate. Involving partners early allows them to be part of identifying issues and finding solutions.

Open access data portals at the state level are proliferating and can inform decision makers as well as the public. These data portals and related data dashboards provide community leaders and residents with current geographically tracked data and tools that support assessments and linkages to evidence-based interventions. These data initiatives offer yet another opportunity for partners to convene and develop collaborative programs for their respective populations.

One of Healthy People 2030's foundational principles is that “the health and well-being of all people and communities are essential to a thriving, equitable society.” Achieving health and well-being for all will benefit society as a whole. Achieving such benefits requires eliminating health disparities, achieving health equity, attaining health literacy, and strengthening the physical, social, and economic environments. Promoting the nation's health and well-being is a shared responsibility—at the national, state, territorial, tribal, and community levels. By enlisting the involvement of the public, private, and not-for-profit sectors in efforts to promote the health and well-being of our populations, we will improve the health of the nation and the achievement of Healthy People 2030's targets.

Implications for Policy & Practice

• Across the field of public health, the focus on health promotion should be expanded to include health and well-being.
• No one sector has the ability, responsibility, or needed expertise to promote health and well-being for all. Multisectoral approaches are needed to address the social, economic, and physical determinants of health and well-being.
• It will be critical to identify common data sources and indicators that can be used to measure and evaluate trends in health and well-being.

Supplementary Material

* Other definitions exist of the terms “health” and “well-being,” respectively. This is the definition proposed for Healthy People 2030, and it considers “health and well-being” as a single term.

† In the coming decade, Healthy People 2030 will highlight innovative and successful state- and local-level efforts through HealthyPeople.gov, webinars, and other channels.

‡ Healthy Life Expectancy (HLE) includes the following: HLE free from activity limitations at birth/age 65 years; HLE free from disability at birth/age 65 years; HLE in good or better health at birth/age 65 years.

§ Summary Mortality and Population Health includes the following: life expectancy at birth/age 65 years; any activity limitation at birth/age 65 years; any disability at birth/age 65 years; percentage in fair or poor health at birth/age 65 years.

∥ Healthy People 2010 used Life Expectancy, Healthy Life Expectancy, and Disparities. Healthy People 2000 used Years of Healthy Life; Disparities; and Clinical Preventive Services.

This article is based on 2 briefs that were prepared by the Secretary's Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 and are available online at HealthyPeople.gov . The authors acknowledge and thank the following contributors to these original briefs: Tom Kottke, MD, MSPH; Bobby Milstein, PhD, MPH; Rebecca Rossom, MD, MSCR; Matt Stiefel, MPA, MS; and Elaine Auld, MPH, MCHES.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site ( http://www.JPHMP.com ).

• Open access
• Published: 19 June 2020

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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Trinity College Dublin, Dublin, Ireland

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Felicia A. Huppert

Well-being Institute, University of Cambridge, Cambridge, UK

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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Additional file 1: figure s1.

. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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The Science of Well-Being

Melissa Hartman

Luke Kalb has spent over a decade focusing on the measurement, treatment, and epidemiology of mental health crises. It was only after earning his PhD and joining the faculty in  Mental Health and at  Kennedy Krieger Institute that he began exploring positive mental health and well-being—an experience that, he says, “stopped me in my tracks cold.”

“I was shocked to find a robust body of scientific research on well-being,” says Kalb, PhD ’17, MHS ’08, especially since he didn’t encounter the topic in any of his training. He became convinced by the evidence that there is a dimension of mental life beyond the absence of disease—and a role for public health beyond the mitigation of mental illness and crises. With funding from the Herbert Bearman Foundation, he designed the first course at the School that was solely focused on well-being:  Public Health and the Good Life .

The course was launched last year, in the heart of the pandemic. As Kalb met with students virtually and heard about their challenges in everyday life, it became clear there is a wide need for practices we can all use to protect our mental health during stressful, uncertain times.

Here, Kalb shares some important ideas that students take from the course, along with some evidence-based strategies and tools to try.

• Take advantage of the well-being toolkit. There are many evidence-based practices—including mindfulness and meditation—that can improve well-being and prevent the onset of psychological distress. The Calm app (which Johns Hopkins offers for free to all faculty and staff) is a great place to start.  
• Cultivate relationships. One of the most important influences on our well-being is our relationships with others. However, we are living in a world of deep isolation and discord. Staying in close contact with loved ones is critical, whether for a walk in the neighborhood or a phone or video call. Finding new social outlets, like joining clubs or attending socials (even if they’re virtual), can be especially helpful for students or others who are transitioning to a new location.
• Avoid the comparison trap. A number of biases are often baked into our thinking, and we need to be aware of them. For instance, we are prone to social comparisons. Historically, due to limited transportation, we could compare ourselves only to our neighbors. Now, social media allows us to compare ourselves to the most rich and famous people in the world. Those unrealistic comparisons and self-judgments can be distressing.  
• Don’t overlook the basics. Many techniques to improve our well-being are readily available to us but not often discussed, such as protecting your sleep and leveraging gratitude. These simple practices can have profound downstream effects.

Public Health and the Good Life will be offered again starting in January 2022. New for this year: a focus on mHealth technologies. Kalb will bring in mHealth expert Johannes Thrul, PhD, MS , an assistant professor in Mental Health; and Omar Jalazada, co-founder CEO of  Kin , to talk about how we can leverage digital peer supports to promote lasting behavior change.  

Melissa Hartman is the managing editor of Hopkins Bloomberg Public Health magazine and associate director of editorial at the Johns Hopkins Bloomberg School of Public Health.

• The Intersection of Mental Health and Chronic Disease
• Mental Wellness at Work
• SEE Change: Improving Health Through Self-Empowerment

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Sustainable Development Goal 3

Ensure healthy lives and promote well-being for all at all ages.

Sustainable Development Goal 3 is to “ensure healthy lives and promote well-being for all at all ages”, according to the United Nations .

The visualizations and data below present the global perspective on where the world stands today and how it has changed over time.

The UN has defined 13 targets and 28 indicators for SDG 3. Targets specify the goals and indicators represent the metrics by which the world aims to track whether these targets are achieved. Below we quote the original text of all targets and show the data on the agreed indicators.

Target 3.1 Reduce maternal mortality

Sdg indicator 3.1.1 maternal mortality ratio.

Definition of the SDG indicator: Indicator 3.1.1 is the “maternal mortality ratio” in the UN SDG framework .

The maternal mortality ratio refers to the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.

Data for this indicator is shown in the interactive visualization.

Target: By 2030 “reduce the global maternal mortality ratio to less than 70 per 100,000 live births” per year.

More research: The Our World in Data topic page on Maternal Mortality gives a long-run perspective over the last centuries and presents research on the causes and consequences of the deaths of mothers.

Additional charts

• Number of maternal deaths by region
• Number of maternal deaths by country

SDG Indicator 3.1.2 Skilled birth attendance

Definition of the SDG indicator: Indicator 3.1.2 is the “proportion of births attended by skilled health personnel” in the UN SDG framework .

This indicator is measured as the ratio of the births attended by skilled health personnel (generally doctors, nurses, or midwives) who are trained in providing quality obstetric care, to the number of live births in the same period.

More research: Research, discussed in the Our World in Data topic page on Maternal Mortality , shows that skilled staff can reduce maternal mortality.

Target 3.2 End all preventable deaths under 5 years of age

Sdg indicator 3.2.1 under-5 mortality rate.

Definition: Indicator 3.2.1 is the “under-5 mortality rate” in the UN SDG framework .

The under-5 mortality rate measures the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.

Target: By 2030, “end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births.”

More research: Child mortality is covered more broadly, and with a longer-term perspective in the Our World in Data topic page on Child Mortality .

• Number of under-five deaths
• Number of under-five deaths by region
• Child mortality rate by sex

SDG Indicator 3.2.2 Neonatal mortality rate

Definition of the SDG indicator: Indicator 3.2.2 is the “neonatal mortality rate” in the UN SDG framework .

The neonatal mortality rate is defined as the probability per 1,000 that a child born in a given year will die during the first 28 days of life, if subject to the age-specific mortality rates of that period.

Data on this indicator is shown in the interactive visualization.

More research: The Our World in Data topic page on Child Mortality includes a section on neonatal mortality.

• Number of neonatal deaths
• Number of neonate deaths by region

Target 3.3 Fight communicable diseases

Sdg indicator 3.3.1 hiv incidence.

Definition of the SDG indicator: Indicator 3.3.1 is the “number of new HIV infections per 1,000 uninfected population, by sex, age and key populations” in the UN SDG framework .

Data for this indicator is shown in the interactive visualization, by age group in the first chart and for the 15-49 age group in the second chart. You can change the country shown in the first chart by clicking the “Change country” button in the upper left hand corner.

Target: The target for 2030 is to “end the epidemic of AIDS” across all countries. 1

The targeted level of reduction is defined by UNAIDS as a 90% reduction in new HIV infections over 2010 levels. For all age groups combined, this would imply a target of around .03 per 1,000, or 3 new infections for every 100,000 uninfected people.

More research: HIV is covered in detail by the Our World in Data topic page on HIV/AIDS .

• Share of population infected with HIV
• HIV/AIDS death rates
• Number of HIV/AIDS deaths

SDG Indicator 3.3.2 Tuberculosis incidence

Definition of the SDG indicator: Indicator 3.3.2 is “tuberculosis incidence per 100,000 population” in the UN SDG framework .

Tuberculosis incidence is the number of new and relapse cases of tuberculosis (TB) per 100,000 people, including all forms of TB.

Target: The 2030 target is to “end the epidemic of tuberculosis” in all countries. 1

The World Health Organization's End TB Strategy defines this targeted level of reduction as a decrease in incidence of 80% over 2015 levels. This would imply a target of around 28 cases per 100,000 population globally.

• Tuberculosis death rates
• Number of tuberculosis deaths

SDG Indicator 3.3.3 Malaria incidence

Definition of the SDG indicator: Indicator 3.3.3 is “malaria incidence per 1,000 population” in the UN SDG framework .

Malaria incidence is the number of new cases of malaria in one year per 1,000 people at risk.

Target: By 2030 “end the epidemic of malaria” in all countries. 1

To achieve this target, the WHO Global Technical Strategy has set a target of reducing incidence by 90% by 2030 from 2015 levels. This would imply a target of 6 or fewer cases of malaria per 1,000 people globally in 2030.

More research: More information on global and national trends in malaria prevalence, deaths and interventions can be found at the Our World in Data topic page on Malaria .

• Malaria death rates
• Number of malaria deaths

SDG Indicator 3.3.4 Hepatitis B incidence

Definition of the SDG indicator: Indicator 3.3.4 is “Hepatitis B incidence per 100,000 population” in the UN SDG framework .

Hepatitis B incidence is the number of new cases of hepatitis B in one year per 100,000 people in a given population. This is measured indirectly as the share of children under 5 years of age with an active Hepatitis B infection, as measured by an Hepatitis B surface antigen test.

Target: By 2030 “combat hepatitis” in all countries with a focus on hepatitis B. 1 The targeted level of reduction, however, is not defined.

• Hepatitis death rates

SDG Indicator 3.3.5 Neglected tropical diseases

Definition of the SDG indicator: Indicator 3.3.5 is the “number of people requiring interventions against neglected tropical diseases” in the UN SDG framework .

This is defined as the number of people who require interventions (treatment and care) for any of the 20 neglected tropical diseases (NTDs) identified by the WHO NTD Roadmap and World Health Assembly resolutions. Treatment and care is broadly defined to allow for preventive, curative, surgical or rehabilitative treatment and care.

Target: By 2030 “end the epidemic of neglected tropical diseases (NTDs)” in all countries. 1

The associated WHO target is a 90% reduction in the number of people requiring interventions against NTDs from 2010 baseline levels. This implies a target of 219 million people needing interventions against NTDs in 2030.

• Number of people requiring interventions for NTDs by region

Target 3.4 Reduce mortality from non-communicable diseases and promote mental health

Sdg indicator 3.4.1 mortality rate from non-communicable diseases.

Definition of the SDG indicator: Indicator 3.4.1 is the “mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease” in the UN SDG framework .

This is defined as the percent of 30-year-old-people who would die before their 70th birthday from cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that they would experience current mortality rates at every age and would not die from any other cause of death (e.g. injuries or HIV/AIDS).

Target: By 2030 “reduce by one third premature mortality from non-communicable diseases through prevention and treatment” in all countries. 2

More research: Further data and research on non-communicable diseases can be found at the Our World in Data topic pages on Causes of Death , Burden of Disease , and Cancer .

• Cancer death rates
• Cardiovascular disease (CVD) death rates
• Stroke death rates

SDG Indicator 3.4.2 Suicide rate

Definition of the SDG indicator: Indicator 3.4.2 is the “suicide mortality rate” in the UN SDG framework .

The suicide mortality rate is the number of deaths from suicide measured per 100,000 people in a given population.

Target: By 2030 “promote mental health and wellbeing”. 2 There is no defined target level of reduction for this indicator.

More research: Further data and research on suicide, mental health and wellbeing can be found at the Our World in Data topic pages on Suicide , Mental Health and Happiness and Life Satisfaction .

• Number of suicide deaths
• Share of population with depression

Target 3.5 Prevent and treat substance abuse

Sdg indicator 3.5.1 coverage of treatment interventions for substance use disorders.

Definition of the SDG indicator: Indicator 3.5.1 is the “coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders” in the UN SDG framework .

This is the share of people with substance use disorders in a given year who receive treatment in the form of pharmacological, psychosocial, rehabilitation or aftercare services. Data coverage in household surveys of substance use disorders is limited in many countries, and efforts are currently in progress to better estimate this indicator.

Data for this indicator is shown in the interactive visualizations. The first chart shows the share of the population with an alcohol use disorder in each country, and the second chart shows coverage of treatment interventions for certain types of substance use disorder for the countries where this data is available.

Target: By 2030 “strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol” across all countries. However, there is no defined target level for this indicator.

More research: The Our World in Data topic page on Substance Use provides data on substance use disorder prevalence and as well as more limited data coverage of treatment interventions.

SDG Indicator 3.5.2 Alcohol consumption per capita

Definition of the SDG indicator: Indicator 3.5.2 is the “harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol” in the UN SDG framework .

More research: Further data and research on alcohol consumption and alcohol use disorders can be found at the Our World in Data topic page on Alcohol Consumption .

• Share of population with alcohol use disorders
• Share of population with drug use disorders
• Prevalence of substance use disorders by sex

Target 3.6 Reduce road injuries and deaths

Sdg indicator 3.6.1 halve the number of road traffic deaths.

Definition of the SDG indicator: Indicator 3.6.1 is the “death rate due to road traffic injuries” in the UN SDG framework .

Road traffic deaths include vehicle drivers, passengers, motorcyclists, cyclists and pedestrians.

Data for this indicator is shown in the first chart in the series of interactive visualizations. The second chart shows the absolute number of road traffic deaths for additional context.

Target: By 2020 “halve the number of global deaths and injuries from road traffic accidents.”

While most SDG targets are set for 2030, this was set to be achieved for 2020.

Note that the SDG Indicator is the rate of road deaths while the target is set for the absolute number of road deaths. Because of this, the interactive visualization shows, in the first chart, the road traffic death rate, and in the second chart, the number of road traffic deaths.

• Road traffic deaths by user

Target 3.7 Universal access to sexual and reproductive care, family planning and education

Sdg indicator 3.7.1 family planning needs.

Definition of the SDG indicator: Indicator 3.7.1 is the “proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods” in the UN SDG framework .

This indicator incorporates two components, the prevalence of modern methods of contraception, and the share of women of reproductive age who want to stop or delay childbearing but are not using any method of contraception.

It is measured as the percent of women of reproductive age (15-49 years) who are currently using at least one modern contraceptive method, out of the total population of women who have demand for contraceptive methods (defined as those using contraception of any form or who have unmet need for contraception).

Target: By 2030 “ensure universal access to sexual and reproductive healthcare services, including for family planning, information and education.” 3

More research: Further data and research can be found at the Our World in Data topic page on Fertility Rate .

• Unmet need for contraception
• Contraception prevalence, any methods

SDG Indicator 3.7.2 Adolescent birth rate

Definition of the SDG indicator: Indicator 3.7.2 is the “adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age group” in the UN SDG framework .

Data for this indicator is shown in the interactive visualizations, which show, in the first chart, adolescent birth rates per 1,000 women aged 10-14 years old, and in the second chart, women aged 15-19 years old.

Target: By 2030 “ensure universal access to sexual and reproductive healthcare services, including for family planning.” 3

Target 3.8 Achieve universal health coverage

Sdg indicator 3.8.1 coverage of essential health services.

Definition of the SDG indicator: Indicator 3.8.1 is “coverage of essential health services” in the UN SDG framework .

Coverage of essential health services is defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population.

The Universal Health Coverage (UHC) Service Coverage Index is used to track progress on this indicator. The index is on a scale from 0 to 100, where 100 is the optimal value, and calculated from the geometric mean of 14 indicators measuring the coverage of essential services including reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access.

Target: By 2030 “achieve universal health coverage including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.”

More research: Further data and research can be found at the Our World in Data topic page on Financing Healthcare .

SDG Indicator 3.8.2 Household expenditures on health

Definition of the SDG indicator: Indicator 3.8.2 is the “proportion of population with large household expenditures on health as a share of total household expenditure or income” in the UN SDG framework .

Two thresholds are used for defining large household expenditures: greater than 10% or 25% of total household expenditure or income.

The interactive visualizations show data for the 25 and 10 percent thresholds.

Target: By 2030 “achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.”

• Out-of-pocket expenditure on healthcare
• Risk of catastrophic expenditure for surgical care
• Risk of impoverishing expenditure for surgical care

Target 3.9 Reduce illnesses and deaths from hazardous chemicals and pollution

Sdg indicator 3.9.1 mortality rate from air pollution.

Definition of the SDG indicator: Indicator 3.9.1 is the “mortality rate attributed to household and ambient air pollution” in the UN SDG framework .

This is measured as the number of deaths attributed to indoor and outdoor air pollution per 100,000 people, accounting for differences in the age structure of different populations.

Data for this indicator is shown in the series of interactive visualizations, first for household and ambient air pollution combined, then for each separately, and then with a comparison of the two types of pollution in the final chart.

Target: By 2030 “substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.” There is, however, not a defined target level for this indicator.

More research: Further data and research can be found at the Our World in Data topic pages on Air Pollution and Indoor Air Pollution .

• Mortality rate from ambient particulate air pollution
• Number of deaths from outdoor air pollution
• Mortality rate from indoor air pollution
• Number of deaths from indoor air pollution

SDG Indicator 3.9.2 Mortality rate from unsafe water, sanitation, hygiene (WASH)

Definition of the SDG indicator: Indicator 3.9.2 is the “mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene” in the UN SDG framework .

This indicator is defined as the number of deaths per 100,000 people that are attributed to unsafe water, unsafe sanitation, and lack of hygiene (defined as exposure to unsafe Water, Sanitation, and Hygiene for All (WASH) services). This definition includes deaths from diarrhoea, intestinal nematode infections, malnutrition and acute respiratory infections.

Target: By 2030 “substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.” There is, however, not a defined quantified target level for this indicator.

More research: Further data and research can be found at the Our World in Data topic page on Water Access, Resources and Sanitation .

• Mortality rate attributable to unsafe water
• Mortality rate attributable to unsafe sanitation

SDG Indicator 3.9.3 Mortality rate from unintentional poisoning

Definition of the SDG indicator: Indicator 3.9.3 is the “mortality rate attributed to unintentional poisoning” in the UN SDG framework .

This measures the annual number of deaths per 100,000 people that are attributed to unintentional poisonings.

Target 3.a Implement the WHO framework convention on tobacco control

Sdg indicator 3.a.1 prevalence of tobacco use.

Definition of the SDG indicator: Indicator 3.a.1 is the “age-standardized prevalence of current tobacco use among persons aged 15 years and older” in the UN SDG framework .

This measures the share of people aged 15 and older who currently use any tobacco product, whether smoked or smokeless tobacco. This includes both people who use tobacco on a daily basis as well as those who use it on a non-daily basis but have used it at some point in the last 30 days before the survey. Age-standardization accounts for differences in age distributions between countries.

Target: By 2030 “strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate.” There is no specified target level of tobacco use for this indicator.

More research: Further data and research can be found at the Our World in Data topic page on Smoking .

• Daily smoking in people aged 10 or older
• Share of men who smoke
• Share of women who smoke
• Death rate from tobacco smoking
• Deaths attributed to smoking and secondhand smoke

Target 3.b Support research, development and access to affordable vaccines and medicines

Sdg indicator 3.b.1 vaccine coverage.

Definition of the SDG indicator: Indicator 3.b.1 is the “proportion of the target population covered by all vaccines included in their national programme” in the UN SDG framework .

The UN currently includes the four following vaccines in this indicator: three-dose diphtheria, pertussis, and tetanus (DPT3); second-dose measles vaccine; recommended dose of pneumococcal conjugate vaccine (PCV3) and recommended dose of human papillomavirus vaccine.

Data on this indicator is shown across the four interactive visualizations.

Target: By 2030 “provide access to affordable essential medicines and vaccines.” 4

For this indicator, this means universal coverage of the vaccines noted above (if included in national vaccination programmes) must be achieved by 2030.

SDG Indicator 3.b.2 Development assistance to medical research & basic healthcare

Definition: Indicator 3.b.2 is the “total net official development assistance (ODA) to medical research and basic health sectors” in the UN SDG framework .

This indicator is measured as disbursements of official development assistance (ODA) and other official flows to the medical research and basic health sectors.

Official development assistance refers to flows to countries and territories on the Organization for Economic Co-operation and Development’s Development Assistance Committee (DAC) and to multilateral institutions which meet a set of criteria related to the source of the funding, the purpose of the transaction, and the concessional nature of the funding.

Data for this indicator is shown for recipient countries.

Target: By 2030 “support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, [and] provide access to affordable essential medicines and vaccines.” 4

SDG Indicator 3.b.3 Availability of essential medicines

Definition: Indicator 3.b.3 is the “proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis” in the UN SDG framework .

This indicator measures the share of surveyed healthcare facilities that had essential medicines available for purchase at prices, such that no extra daily wages would be needed for the lowest paid unskilled government sector worker to purchase a monthly dose treatment of this medicine after fulfilling their basic needs represented by the national poverty line.

The list of 32 essential medicines used in calculation is from the 2017 Model List of Essential Medicines from the WHO Expert Committee on Selection and Use of Essential Medicines, which updates its list of essential medicines every two years. Availability and affordability of specific medicines are weighted in the overall calculation based on the regional burden of disease.

Target: By 2030 “provide access to affordable essential medicines for all.” 4

Target 3.c Increase health financing and support health workforce in developing countries

Sdg indicator 3.c.1 health worker density.

Definition: Indicator 3.c.1 is “health worker density and distribution” in the UN SDG framework .

Health worker density is the size of the health workforce per 1,000 people. It is measured here based on the density of physicians, surgeons, nurses and midwives, dentistry and pharmaceutical personnel.

Target: By 2030 “substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries.”

• Nurses and midwives (per 1,000 people)
• Surgical workforce (per 100,000 people)
• Dentistry personnel (per 1,000 people)
• Pharmaceutical personnel (per 1,000 people)

Target 3.d Improve early warning systems for global health risks

Sdg indicator 3.d.1 health emergency preparedness.

Definition: Indicator 3.d.1 is the “International Health Regulations (IHR) capacity and health emergency preparedness” in the UN SDG framework .

The IHR Core capacity index is measured in terms of 15 capacities, where each capacity is measured as the average implementation score across a set of indicators. Countries self-report progress in the following 15 capacities: (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies.

Target: By 2030 “strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.”

SDG Indicator 3.d.2 Bloodstream infections due to antimicrobial-resistant organisms

Definition of the SDG indicator: Indicator 3.d.2 is the “percentage of bloodstream infections due to selected antimicrobial-resistant organisms” in the UN SDG framework .

This is measured as the share of people who are found to have a bloodstream infection due to certain antimicrobial-resistant organisms (methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli resistant to 3rd-generation cephalosporin), among those seeking care whose blood sample is collected and tested.

Data for this indicator is shown in the interactive visualizations.

Full text: “By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases.”

Full text: “By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.”

Full text:” By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes.”

Full text: “Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all.”

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SDG 3: Good Health & Well-Being

Sustainable Development Goal 3 (SDG 3) is one of the 17 Sustainable Development Goals established by the United Nations in 2015. The official wording of SDG 3 is: “To ensure healthy lives and promote well-being for all at all ages.” SDG 3 research focuses on key targets like: reducing maternal mortality, ending all preventable deaths for children under five, fighting communicable diseases, reducing mortality from non-communicable diseases, and promoting mental health — all with the aim of stopping needless suffering from preventable diseases and premature death.

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Springer Nature's SDG OA Book Collection

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Because OA’s enhanced and equitable visibility means that research can reach the broader audience, and findings can be translated into actionable strategy. Explore OA books that support sustainable development and learn more about publishing your book OA - including funding options.

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You can add impact and power to your SDG-related research when you publish it at Springer Nature, and alongside leading research (like the examples above). Research published OA at Springer Nature gets more exposure . For example, research published in fully OA Springer Nature journals are downloaded over 7,000 times on average (up to 5x more than competitors) and cited 7.39 times on average.

Publishing with Springer Nature gives you:

• A range of journals , from Nature, Springer, and BMC, both hybrid and OA. The journals with SDG content curated above are a small selection of journals you can publish in. Clicking through on them will give you the information — including journal metrics, submission guidelines and more — that you need to plan your next submission.
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GOAL 3: GOOD HEALTH AND WELL-BEING

Ensure healthy lives and promote well-being for all at all ages.

Goal 3 aims to ensure healthy lives and promote well-being for all, at all ages. Health and well-being are important at every stage of one’s life, starting from the beginning. This goal addresses all major health priorities: reproductive, maternal, newborn, child and adolescent health; communicable and non-communicable diseases; universal health coverage; and access for all to safe, effective, quality and affordable medicines and vaccines.

SDG 3 aims to prevent needless suffering from preventable diseases and premature death by focusing on key targets that boost the health of a country’s overall population. Regions with the highest burden of disease and neglected population groups and regions are priority areas. Goal 3 also calls for deeper investments in research and development, health financing and health risk reduction and management.

UNICEF’s role in contributing to Goal 3 centres on healthy pregnancies ( maternal mortality and skilled birth attendant), healthy childhoods (under-five and neonatal mortality) as well as vaccine coverage. UNICEF also contributes to monitoring elements of the universal health coverage indicator.

UNICEF is custodian for global monitoring of two indicators that measure progress towards Goal 3 as it relates to children: Indicator 3.2.1 Under-five mortality rate and Indicator 3.2.2 Neonatal mortality rate. UNICEF is also co-custodian for Indicator 3.1.2 Proportion of births attended by skilled health personnel and for Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.

Child-related SDG indicators

Target 3.1 by 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births, maternal mortality ratio (number of maternal deaths per 100,000 live births).

• Indicator definition
• Computation method
• Comments & limitations

Explore the data

Maternal mortality refers to deaths due to complications from pregnancy or childbirth. Accurate measurement of maternal mortality remains challenging and many deaths still go uncounted. Many countries still lack well functioning civil registration and vital statistics (CRVS) systems, and where such systems do exist, reporting errors – whether incompleteness (unregistered deaths, also known as “missing”) or misclassification of cause of death – continue to pose a major challenge to data accuracy.

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth.

Maternal deaths: The annual number of female deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, expressed per 100,000 live births, for a specified time period.

Maternal death: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management (from direct or indirect obstetric death), but not from accidental or incidental causes.

Pregnancy-related death: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death. Late maternal death: The death of a woman from direct or indirect obstetric causes, more than 42 days, but less than one year after termination of pregnancy

The maternal mortality ratio can be calculated by dividing recorded (or estimated) maternal deaths by total recorded (or estimated) live births in the same period and multiplying by 100,000. Measurement requires information on pregnancy status, timing of death (during pregnancy, childbirth, or within 42 days of termination of pregnancy), and cause of death. The maternal mortality ratio can be calculated directly from data collected through vital registration systems, household surveys or other sources. There are often data quality problems, particularly related to the underreporting and misclassification of maternal deaths. Therefore, data are often adjusted in order to take these data quality issues into account. Some countries undertake these adjustments or corrections as part of specialized/confidential enquiries or administrative efforts embedded within maternal mortality monitoring programmes.

For countries with data available on maternal mortality, the expected proportion of non-HIV- related maternal deaths was based on country and regional random effects, whereas for countries with no data available, predictions were derived using regional random effects only.

Estimation of HIV-related indirect maternal deaths For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and post-delivery. There is also some evidence from community studies that women with HIV infection have a higher risk of maternal death, although this may be offset by lower fertility. If HIV is prevalent, there will also be more incidental HIV deaths among pregnant and postpartum women. When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died (i.e. among all HIV-related deaths occurring during pregnancy, childbirth and puerperium).

For observed PMs, we assumed that the total reported maternal deaths are a combination of the proportion of reported non-HIV-related maternal deaths and the proportion of reported HIV- related (indirect) maternal deaths, where the latter is given by a*v for observations with a “pregnancy-related death” definition and a*v*u for observations with a “maternal death” definition.

The extent of maternal mortality in a population is essentially the combination of two factors:

1. The risk of death in a single pregnancy or a single live birth. 2. The fertility level (i.e. the number of pregnancies or births that are experienced by women of reproductive age).

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures (1) above.

By contrast, the maternal mortality rate (MMRate) is calculated as the number of maternal deaths divided by person-years lived by women of reproductive age. The MMRate captures both the risk of maternal death per pregnancy or per total birth (live birth or stillbirth), and the level of fertility in the population.

In addition to the MMR and the MMRate, it is possible to calculate the adult lifetime risk of maternal mortality for women in the population. An alternative measure of maternal mortality, the proportion of deaths among women of reproductive age that are due to maternal causes (PM), is calculated as the number of maternal deaths divided by the total deaths among women aged 15–49 years.

Related Statistical measures of maternal mortality:

Maternal mortality ratio (MMR): Number of maternal deaths during a given time period per 100,000 live births during the same time period.

Maternal mortality rate (MMRate): Number of maternal deaths divided by person-years lived by women of reproductive age.

Adult lifetime risk of maternal death: The probability that a 15-year-old woman will die eventually from a maternal cause.

The proportion of deaths among women of reproductive age that are due to maternal causes (PM): The number of maternal deaths in a given time period divided by the total deaths among women aged 15–49 years.

Click on the button below to explore the data behind this indicator.

Skilled birth attendant – Proportion of births attended by skilled health personnel

• Sources of discrepancies

Having a skilled health care provider at the time of childbirth is an important lifesaving intervention for both women and newborns. Not having access to this key assistance is detrimental to women’s and newborns’ health because it could cause adverse health outcomes such as the death of the women and/or the newborns or long lasting morbidity. Achieving universal coverage is therefore essential for reducing maternal and newborn mortality and morbidity.

Proportion of births attended by skilled health personnel (generally doctors, nurses or midwives but can refer to other health professionals providing childbirth care) is the proportion of childbirths attended by skilled health personnel. According to the current definition (1) these are competent maternal and newborn health (MNH) professionals educated, trained and regulated to national and international standards.

They are competent to:

(i) provide and promote evidence-based, human-rights based, quality, socio-culturally sensitive and dignified care to women and newborns;

(ii) facilitate physiological processes during labour and delivery to ensure a clean and positive childbirth experience; and

(iii) identify and manage or refer women and/or newborns with complications.

Discrepancies are possible if there are national figures compiled at the health facility level. These would differ from the global figures, which are typically based on survey data collected at the household level. In terms of survey data, some survey reports may present a total percentage of births attended by a skilled health professional that does not conform to the MDG definition (e.g., total includes provider that is not considered skilled, such as a community health worker). In that case, the percentage delivered by a physician, nurse, or a midwife are totalled and entered into the global database as the MDG estimate. In some countries where skilled attendant at birth is not available, birth in a health facility (institutional births) is used instead. This is frequent among Latin American countries, where the proportion of institutional births is very high. Nonetheless, it should be noted that institutional births may underestimate the percentage of births with skilled attendant.

TARGET 3.2 By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births

Under-five mortality rate.

Mortality rates among young children are a key output indicator for child health and well-being, and, more broadly, for social and economic development. This is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition.

Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births

The under-five mortality rate as defined here is, strictly speaking, not a rate (i.e. the number of deaths divided by the number of population at risk during a certain period of time), but a probability of death derived from a life table and expressed as a rate per 1000 live births.

The UN Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from national data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates. It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME applies the Bayesian B-splines bias-reduction model to empirical data to derive trend estimates of under-five mortality for all countries. See references for details.

For the underlying data mentioned above, the most frequently used methods are as follows:

Civil registration: The under-five mortality rate can be derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data to calculate death rates, which are then converted into age-specific probabilities of dying.

Census and surveys: An indirect method is used based on a summary birth history, a series of questions asked of each woman of reproductive age as to how many children she has ever given birth to and how many are still alive. The Brass method and model life tables are then used to obtain an estimate of under-five and infant mortality rates. Censuses often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates.

Surveys: A direct method is used based on a full birth history, a series of detailed questions on each child a woman has given birth to during her lifetime. Neonatal, post-neonatal, infant, child and under-five mortality estimates can be derived from the full birth history module.

The UN IGME estimates are derived based on national data. Countries often use a single source as their official estimates or apply methods different from the UN IGME methods to derive estimates. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data has good quality.

Many countries lack a single source of high-quality data covering the last several decades. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of under-five mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country. Each new survey or data point must be examined in the context of all other sources, including previous data. Data suffer from sampling or non-sampling errors (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also projects estimates to a common reference year. In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.

Neonatal mortality rate

The neonatal mortality rate is the probability that a child born in a specific year or period will die during the first 28 completed days of life if subject to age-specific mortality rates of that period, expressed per 1000 live births.

Neonatal deaths (deaths among live births during the first 28 completed days of life) may be subdivided into early neonatal deaths, occurring during the first 7 days of life, and late neonatal deaths, occurring after the 7th day but before the 28th completed day of life.

The UN Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from nationally representative data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates. It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME produces neonatal mortality rate estimates with a Bayesian spline regression model which models the ratio of neonatal mortality rate / (under-five mortality rate – neonatal mortality rate). Estimates of NMR are obtained by recombining the estimates of the ratio with the UN IGME-estimated under-five mortality rate. See the references for details.

Civil registration: Number of children who died during the first 28 days of life and the number of births used to calculate neonatal mortality rates.

Censuses and surveys: Censuses and surveys often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates.

Many countries lack a single source of high-quality data covering the last several decades. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of neonatal mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country. Each new survey or data point must be examined in the context of all other sources, including previous data. Data suffer from sampling or non-sampling errors (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also projects estimates to a common reference year. In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.

TARGET 3.3 By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

Estimated incidence rate (new hiv infection per 1,000 uninfected population).

This indicator is used to measure progress towards ending the AIDS epidemic. The overarching goal of the global AIDS response is to reduce the number of people newly infected to fewer than 500,000 in 2020 and fewer than 200,000 in 2030. Monitoring the rate of people newly infected over time measures the progress towards achieving this goal. Disaggregation by sex, age and key populations is important to characterize how the epidemic is evolving, to monitor equity of access to services and to support the planning of programme responses in specific age groups such as children under five, adolescents and young adults, as well as key populations.

Annual number of new HIV infections per 1,000 uninfected population

Longitudinal data on individuals are the best source of data but are rarely available for large populations. Special diagnostic tests in surveys or from health facilities can be used to obtain data on HIV incidence. HIV incidence is thus modelled using the Spectrum software.

Malaria incidence per 1,000 population

This indicator is used to measure trends in malaria morbidity and to identify locations where the risk of disease is highest. With this information, programmes can respond to unusual trends, such as epidemics, and direct resources to the populations most in need. This data also serves to inform global resource allocation for malaria such as when defining eligibility criteria for Global Fund finance.

Incidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk each year.

Case of malaria is defined as the occurrence of malaria infection in a person whom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. The population considered is the population at risk of the disease.

Malaria incidence (1) is expressed as the number of new cases per 100,000 population per year with the population of a country derived from projections made by the UN Population Division and the total proportion at risk estimated by a country’s National Malaria Control Programme. More specifically, the country estimates what is the proportion at high risk (H) and what is the proportion at low risk (L) and the total population at risk is estimated as UN Population x (H + L).

The total number of new cases, T, is estimated from the number of malaria cases reported by a Ministry of Health which is adjusted to take into account (i) incompleteness in reporting systems (ii) patients seeking treatment in the private sector, self-medicating or not seeking treatment at all, and (iii) potential over-diagnosis through the lack of laboratory confirmation of cases. The procedure, which is described in the World malaria report 2009 (2), combines data reported by NMCPs (reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health-service use.

𝑇=( a + (𝑐 × 𝑒) ⁄ 𝑑) × (1 + h ⁄𝑔 + ((1−𝑔−h)/2) ⁄ 𝑔)

where: a is malaria cases confirmed in public sector b is suspected cases tested c is presumed cases (not tested but treated as malaria) d is reporting completeness e is test positivity rate (malaria positive fraction) = a/b f is cases in public sector, calculated by (a + (c x e))/d g is treatment seeking fraction in public sector h is treatment seeking fraction in private sector i is the fraction not seeking treatment, calculated by (1-g-h)/2 j is cases in private sector, calculated by f x h/g k is cases not in private and not in public, calculated by f x i/g T is total cases, calculated by f + j + k.

To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the Test positivity rate value and standard deviation defined as 0.244 × Test positivity rate 0.5547 and truncated to be in the range 0, 1.

Reporting completeness was assumed to have one of three distributions, depending on the range or value reported by the NMCP. -If the range was greater than 80% the distribution was assumed to be triangular, with limits of 0.8 and 1 and the peak at 0.8. – If the range was greater than 50% then the distribution was assumed to be rectangular, with limits of 0.5 and 0.8. -Finally, if the range was lower than 50% the distribution was assumed to be triangular, with limits of 0 and 0.5 and the peak at 0.5 (3).

If the reporting completeness was reported as a value and was greater than 80%, a beta distribution was assumed with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% round the mean value.

The proportions of children for whom care was sought in the private sector and in the public sector were assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation calculated from the range of the estimated 95% confidence intervals (CI) divided by 4. The proportion of children for whom care was not sought was assumed to have a rectangular distribution, with the lower limit 0 and upper limit calculated as 1 minus the proportion that sought care in public or private sector.

Values for the proportion seeking care were linearly interpolated between the years that have a survey, and were extrapolated for the years before the first or after the last survey. Missing values for the distributions were imputed using a mixture of the distribution of the country, with equal probability for the years where values were present or, if there was no value at all for any year in the country, a mixture of the distribution of the region for that year. The data were analysed using the R statistical software.

Confidence intervals were obtained from 10000 drawns of the convoluted distributions. (Afghanistan, Bangladesh, Bolivia (Plurinational State of), Botswana, Brazil, Cambodia, Colombia, Dominican Republic, Eritrea, Ethiopia, French Guiana, Gambia, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, Lao People’s Democratic Republic, Madagascar, Mauritania, Mayotte, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, Philippines, Rwanda, Senegal, Solomon Islands, Timor-Leste, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen and Zimbabwe. For India, the values were obtained at subnational level using the same methodology, but adjusting the private sector for an additional factor due to the active case detection, estimated as the ratio of the test positivity rate in the active case detection over the test positivity rate for the passive case detection. This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010. Bangladesh, Bolivia, Botswana, Brazil, Cabo Verde, Colombia, Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Rwanda, Suriname and Venezuela (Bolivarian Republic of) report cases from the private and public sector together; therefore, no adjustment for private sector seeking treatment was made.

For some high-transmission African countries the quality of case reporting is considered insufficient for the above formulae to be applied. In such cases estimates of the number of malaria cases are derived from information on parasite prevalence obtained from household surveys.

First, data on parasite prevalence from nearly 60 000 survey records were assembled within a spatiotemporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data distribution on interventions such as ITNs, antimalarial drugs and IRS. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2016 (see http://www.map.ox.ac.uk/making-maps/ for methods on the development of maps by the Malaria Atlas Project).

Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence.

The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 2016.

Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries to obtain both national and regional estimates of malaria cases. (Benin, Cameroon, Central African Republic, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Guinea, Kenya, Malawi, Mali, Mozambique, Niger, Nigeria, Somalia, South Sudan, Sudan, Togo and Zambia). For most of the elimination countries, the number of indigenous cases registered by the NMCPs are reported without further adjustments. (Algeria, Argentina, Belize, Bhutan, Cabo Verde, China, Comoros, Costa Rica, Democratic People’s Republic of Korea, Djibuti, Ecuador, El Salvador, Iran (Islamic Republic of), Iraq, Malaysia, Mexico, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Suriname, Swaziland and Thailand).

The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by: – the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100% – the extent of malaria diagnostic testing (the number of slides examined or RDTs performed) – the use of private health facilities which are usually not included in reporting systems. – the indicator is estimated only where malaria transmission occurs.

TARGET 3.7 By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes

Adolescent birth rate (number of live births to adolescent women per 1,000 adolescent women).

Reducing adolescent fertility and addressing the multiple factors underlying it are essential for improving sexual and reproductive health and the social and economic well-being of adolescents. Preventing births very early in a woman’s life is an important measure to improve maternal health and reduce infant mortality. Furthermore, women having children at an early age experience a curtailment of their opportunities for socio-economic improvement, particularly because young mothers are unlikely to keep on studying and, if they need to work, may find it especially difficult to combine family and work responsibilities. The adolescent birth rate also provides indirect evidence on access to pertinent health services since young people, and in particular unmarried adolescent women, often experience difficulties in access to sexual and reproductive health services.

The adolescent birth rate represents the risk of childbearing among females in a particular age group. The adolescent birth rate among women aged 15-19 years is also referred to as the age-specific fertility rate for women aged 15-19

The adolescent birth rate represents the risk of childbearing among females in a particular age group. The adolescent birth rate (ABR) is also referred to as the age-specific fertility rate (ASFR) for ages 15-19 years, a designation commonly used in the context of calculation of total fertility estimates. A related measure is the proportion of adolescent fertility, measured as the percentage of total fertility contributed by women aged 15-19.

The adolescent birth rate is computed as a ratio.

Numerator – the number of live births to women aged 15-19 years Denominator – the estimate of the exposure to childbearing by women aged 15-19 years

The computation is the same for the age group 10-14 years. The numerator and the denominator are calculated differently for civil registration, survey and census data.

In the case of civil registration data, the numerator is the registered number of live births born to women aged 15-19 years during a given year, and the denominator is the estimated or enumerated population of women aged 15-19 years.

In the case of survey data, the numerator is the number of live births obtained from retrospective birth histories of the interviewed women who were 15-19 years of age at the time of the births during a reference period before the interview, and the denominator is person-years lived between the ages of 15 and 19 years by the interviewed women during the same reference period.

The reported observation year corresponds to the middle of the reference period. For some surveys without data on retrospective birth histories, computation of the adolescent birth rate is based on the date of last birth or the number of births in the 12 months preceding the survey.

With census data, the adolescent birth rate is computed on the basis of the date of last birth or the number of births in the 12 months preceding the enumeration. The census provides both the numerator and the denominator for the rates. In some cases, the rates based on censuses are adjusted for under-registration based on indirect methods of estimation.

For some countries with no other reliable data, the ‘own-children’ method of indirect estimation provides estimates of the adolescent birth rate for a number of years before the census.

For a thorough treatment of the different methods of computation, see Handbook on the Collection of Fertility and Mortality Data, United Nations Publication, Sales No. E.03.XVII.11 (publicly accessible at http://unstats.un.org/unsd/publication/SeriesF/SeriesF_92E.pdf ). Indirect methods of estimation are analyzed in Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2 (publicly accessible at http://www.un.org/esa/population/publications/Manual_X/Manual_X.htm ).

Discrepancies between the sources of data at the country level are common and the level of the adolescent birth rate depends in part on the source of the data selected.

For civil registration, rates are subject to limitations which depend on the completeness of birth registration, the treatment of infants born alive but that die before registration or within the first 24 hours of life, the quality of the reported information relating to age of the mother, and the inclusion of births from previous periods.

The population estimates may be subject to limitations connected to age misreporting and coverage. For survey and census data, both the numerator and denominator come from the same population. The main limitations concern age misreporting, birth omissions, misreporting the date of birth of the child, and sampling variability in the case of surveys.

With respect to estimates of the adolescent birth rate among females aged 10-14 years, comparative evidence suggests that a very small proportion of births in this age group occur to females below age 12. Other evidence based on retrospective birth history data from surveys indicates that women aged 15-19 years are less likely to report first births before age 15 than women from the same birth cohort when asked five years later at ages 20–24 years.

TARGET 3.8 Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all

Proportion of the target population covered by essential health services.

Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population).

Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The concern is with all people and communities receiving the quality health services they need (including medicines and other health products), without financial hardship.

Indicator 3.8.1 is for health service coverage and indicator 3.8.2 focuses on health expenditures in relation to a household’s budget to identify financial hardship caused by direct health care payments. Taken together, indicators 3.8.1 and 3.8.2 are meant to capture the service coverage and financial protection dimensions, respectively, of target 3.8. These two indicators should be always monitored jointly.

The indicator is an index reported on a unitless scale of 0 to 100, which is computed as the geometric mean of 14 tracer indicators of health service coverage.

The index of health service coverage is computed as the geometric means of 14 tracer indicators. The 14 indicators are listed below and detailed metadata for each of the components are given online ( http://www.who.int/healthinfo/universal_health_coverage/UHC_Tracer_Indicators_Metadata.pdf ) and Annex 1. The tracer indicators are as follows, organized by four broad categories of service coverage:

I. Reproductive, maternal, newborn and child health 1. Family planning: Percentage of women of reproductive age (15−49 years) who are married or in- union who have their need for family planning satisfied with modern methods 2. Pregnancy and delivery care: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times 3. Child immunization: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine 4. Child treatment: Percentage of children under 5 years of age with suspected pneumonia (cough and difficult breathing NOT due to a problem in the chest and a blocked nose) in the two weeks preceding the survey taken to an appropriate health facility or provider

II. Infectious diseases 5. Tuberculosis: Percentage of incident TB cases that are detected and successfully treated 6. HIV/AIDS: Percentage of people living with HIV currently receiving antiretroviral therapy 7. Malaria: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden] 8. Water and sanitation: Percentage of households using at least basic sanitation facilities

III. Noncommunicable diseases 9. Hypertension: Age-standardized prevalence of non-raised blood pressure (systolic blood pressure <140 mm Hg or diastolic blood pressure <90 mm Hg) among adults aged 18 years and older 10. Diabetes: Age-standardized mean fasting plasma glucose (mmol/L) for adults aged 18 years and older 11. Tobacco: Age-standardized prevalence of adults >=15 years not smoking tobacco in last 30 days (SDG indicator 3.a.1, metadata available here)

IV. Service capacity and access 12. Hospital access: Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population 13. Health workforce: Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre (partial overlap with SDG indicator 3.c.1, see metadata here) 14. Health security: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained (SDG indicator 3.d.1, see metadata here)

The index is computed with geometric means, based on the methods used for the Human Development Index. The calculation of the 3.8.1 indicator requires first preparing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values. The 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators additional rescaling is required to obtain appropriate values from 0 to 100, as follows: – Rescaling based on a non-zero minimum to obtain finer resolution (this “stretches” the distribution across countries): prevalence of non-raised blood pressure and prevalence of non- use of tobacco are both rescaled using a minimum value of 50%. rescaled value = (X-50)/(100-50)*100 – Rescaling for a continuous measure: mean fasting plasma glucose, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.1 mmol/L). rescaled value = (7.1 – original value)/(7.1-5.1)*100 – Maximum thresholds for rate indicators: hospital bed density and health workforce density are both capped at maximum thresholds, and values above this threshold are held constant at 100. These thresholds are based on minimum values observed across OECD countries. rescaled hospital beds per 10,000 = minimum(100, original value / 18*100) rescaled physicians per 1,000 = minimum(100, original value / 0.9*100) rescaled psychiatrists per 100,000 = minimum(100, original value / 1*100) rescaled surgeons per 100,000 = minimum(100, original value / 14*100) Once all tracer indicator values are on a scale of 0 to 100, geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. If the value of a tracer indicator happens to be zero, it is set to 1 (out of 100) before computing the geometric mean. The following diagram illustrates the calculations.

Note that in countries with low malaria burden, the tracer indicator for use of insecticide-treated nets is dropped from the calculation.

These tracer indicators are meant to be indicative of service coverage, not a complete or exhaustive list of health services and interventions that are required for universal health coverage. The 14 tracer indicators were selected because they are well-established, with available data widely reported by countries (or expected to become widely available soon). Therefore, the index can be computed with existing data sources and does not require initiating new data collection efforts solely to inform the index.

TARGET 3.9 By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

Mortality rate attributed to household and ambient air pollution.

As part of a broader project to assess major risk factors to health, the mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuel use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, as well as kerosene.

Air pollution is the biggest environmental risk to health. The majority of the burden is borne by the populations in low and middle-income countries.

The mortality attributable to the joint effects of household and ambient air pollution can be expressed as: Number of deaths, Death rate. Death rates are calculated by dividing the number of deaths by the total population (or indicated if a different population group is used, e.g. children under 5 years).

Evidence from epidemiological studies have shown that exposure to air pollution is linked, among others, to the important diseases taken into account in this estimate: – Acute respiratory infections in young children (estimated under 5 years of age) – Cerebrovascular diseases (stroke) in adults (estimated above 25 years) – Ischaemic heart diseases (IHD) in adults (estimated above 25 years) – Chronic obstructive pulmonary disease (COPD) in adults (estimated above 25 years); and – Lung cancer in adults (estimated above 25 years)

The mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuels use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood kerosene, wood, coal, animal dung, charcoal, and crop wastes.

Attributable mortality is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed, proportion of population relying primarily on polluting fuels for cooking).

This allows calculation of the ‘population attributable fraction’ (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in that case of both the annual mean concentration of particulate matter and exposure to polluting fuels for cooking).

Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths), gives the total number of deaths that results from exposure to that particular risk factor (in the example given above, to ambient and household air pollution).

To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).

The mortality associated with household and ambient air pollution was estimated based on the calculation of the joint population attributable fractions assuming independently distributed exposures and independent hazards as described in (Ezzati et al, 2003).

The joint population attributable fraction (PAF) were calculated using the following formula: PAF=1-PRODUCT (1-PAFi) where PAFi is PAF of individual risk factors.

The PAF for ambient air pollution and the PAF for household air pollution were assessed separately, based on the Comparative Risk Assessment (Ezzati et al, 2002) and expert groups for the Global Burden of Disease (GBD) 2010 study (Lim et al, 2012; Smith et al, 2014).

For exposure to ambient air pollution, annual mean estimates of particulate matter of a diameter of less than 2.5 um (PM25) were modelled as described in (WHO 2016, forthcoming), or for Indicator 11.6.2.

For exposure to household air pollution, the proportion of population with primary reliance on polluting fuels use for cooking was modelled (see Indicator 7.1.2 [polluting fuels use=1-clean fuels use]). Details on the model are published in (Bonjour et al, 2013).

The integrated exposure-response functions (IER) developed for the GBD 2010 (Burnett et al, 2014) and further updated for the GBD 2013 study (Forouzanfar et al, 2015) were used. The percentage of the population exposed to a specific risk factor (here ambient air pollution, i.e. PM2.5) was provided by country and by increment of 1 ug/m3; relative risks were calculated for each PM2.5 increment, based on the IER. The counterfactual concentration was selected to be between 5.6 and 8.8 ug/m3, as described elsewhere (Ezzati et al, 2002; Lim et al, 2012). The country population attributable fraction for ALRI, COPD, IHD, stroke and lung cancer were calculated using the following formula :

PAF=SUM(Pi(RR-1)/(SUM(RR-1)+1)

where i is the level of PM2.5 in ug/m3, and Pi is the percentage of the population exposed to that level of air pollution, and RR is the relative risk.

The calculations for household air pollution are similar, and are explained in detailed elsewhere (WHO 2014a).

An approximation of the combined effects of risk factors is possible if independence and little correlation between risk factors with impacts on the same diseases can be assumed (Ezzati et al, 2003). In the case of air pollution, however, there are some limitations to estimate the joint effects: limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non- linear interactions (Lim et al, 2012; Smith et al, 2014). In several regions, however, household air pollution remains mainly a rural issue, while ambient air pollution is predominantly an urban problem. Also, in some continents, many countries are relatively unaffected by household air pollution, while ambient air pollution is a major concern. If assuming independence and little correlation, a rough estimate of the total impact can be calculated, which is less than the sum of the impact of the two risk factors.

TARGET 3.b Support the research and development of vaccines and medicines for the communicable and non‑communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all

Proportion of the target population covered by all vaccines included in their national programme.

This indicator aims to measure access to vaccines, including the newly available or underutilized vaccines, at the national level. In the past decades all countries added numerous new and underutilised vaccines in their national immunization schedule and there are several vaccines under final stage of development to be introduced by 2030. For monitoring diseases control and impact of vaccines it is important to measure coverage from each vaccine in national immunization schedule and the system is already in place for all national programmes, however direct measurement for proportion of population covered with all vaccines in the programme is only feasible if the country has a well-functioning national nominal immunization registry, usually an electronic one that will allow this coverage to be easily estimated. While countries will develop and strengthen immunization registries it is a need for an alternative measurement.

Coverage of DTP containing vaccine (3rd dose): Percentage of surviving infants who received the 3 doses of diphtheria and tetanus toxoid with pertussis containing vaccine in a given year.

Coverage of Measles containing vaccine (2nd dose): Percentage of children who received two dose of measles containing vaccine according to nationally recommended schedule through routine immunization services in a given year.

Coverage of Pneumococcal conjugate vaccine (last dose in the schedule): Percentage of surviving infants who received the nationally recommended doses of pneumococcal conjugate vaccine in a given year.

Coverage of HPV vaccine (last dose in the schedule): Percentage of 15 years old girls received the recommended doses of HPV vaccine. Currently performance of the programme in the previous calendar year based on target age group is used.

In accordance with its mandate to provide guidance to Member States on health policy matters, WHO provides global vaccine and immunization recommendations for diseases that have an international public health impact. National programmes adapt the recommendations and develop national immunization schedules, based on local disease epidemiology and national health priorities. National immunization schedules and number of recommended vaccines vary between countries, with only DTP polio and measles containing vaccines being used in all countries.

The target population for given vaccine is defined based on recommended age for administration. The primary vaccination series of most vaccines are administered in the first two years of life.

Coverage of DTP containing vaccine measure the overall system strength to deliver infant vaccination. Coverage of Measles containing vaccine ability to deliver vaccines beyond first year of life through routine immunization services. Coverage of Pneumococcal conjugate vaccine: adaptation of new vaccines for children Coverage of HPV vaccine: life cycle vaccination

WHO and UNICEF jointly developed a methodology to estimate national immunization coverage form selected vaccines in 2000. The methodology has been refined and reviewed by expert committees over time. The methodology was published and reference is available under the reference section. Estimates time series for WHO recommended vaccines produced and published annually since 2001.

The methodology uses data reported by national authorities from countries administrative systems as well as data from immunization or multi indicator household surveys.

The rational to select a set of vaccines reflects the ability of immunization programmes to deliver vaccines over the life cycle and to adapt new vaccines. Coverage for other WHO recommended vaccines are also available and can be provided.

Given that HPV vaccine is relatively new and vaccination schedule varies from countries to country coverage estimate will be made for girls vaccinated by ag 15 and at the moment data is limited to very few countries therefore reporting will start later.

To ensure healthy lives and promote the well-being of all children, UNICEF has four key asks that encourage all governments to:

• Strengthen primary healthcare systems to reach every child
• Focus on maternal, newborn and child survival
• Prioritize child and adolescent health and well-being, including mental health
• Support responses to reduce the impact on children and families of natural disasters, complex emergencies and demographic shifts

Learn more about  UNICEF’s key asks for implementing Goal 3

See more Sustainable Development Goals

ZERO HUNGER

GOOD HEALTH AND WELL-BEING

QUALITY EDUCATION

GENDER EQUALITY

CLEAN WATER AND SANITATION

AFFORDABLE AND CLEAN ENERGY

DECENT WORK AND ECONOMIC GROWTH

REDUCED INEQUALITIES

CLIMATE ACTION

PEACE, JUSTICE AND STRONG INSTITUTIONS

PARTNERSHIPS FOR THE GOALS

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Healthy? Maybe. But are you flourishing?

Caitlin McDermott-Murphy

Harvard Correspondent

The answer, according to a groundbreaking new global study, might be more complex than you think

What’s your blood pressure?

For most people, this is an easy question, a fundamental measurement taken at every doctor’s visit. Many supermarkets have free stations to check it. Even smart watches can gather this metric anywhere, anytime.

Now answer this: What’s your purpose in life?

That data, according to a group of researchers at Harvard University and Baylor University, might be just as important as blood pressure in gauging what the scholars view as human well-being. That is to say, the sum total of your physical and mental health, along with your happiness and life satisfaction, sense of meaning and purpose, character and virtue, and close social relationships. This view of overall health is the focus of their new $43.4 million Global Flourishing Study to be launched this month — the largest, most culturally and geographically diverse of its kind. The team will follow roughly 240,000 participants from 22 countries over five years to gather data on which individuals or nations are flourishing and why, or why not.

“Health is more than the absence of disease,” according to the Centers for Disease Control and Prevention . Well-being is harder — but not impossible — to measure. While previous studies have tried, the Global Flourishing Study, whose partners include the survey giant Gallup  and the  Center for Open Science , is the first to take a global, longitudinal approach in an attempt to find causal links between well-being and specific character traits — like extroversion or optimism — practices, communities, relationships, or religions. If successful, the survey could later be administered as a kind of diagnostic test to prescribe interventions, similar to exercise and heart-healthy diets for cardiovascular disease.

People in poorer, developing countries typically have a greater sense of meaning and purpose. They also tend to have stronger relationships. “We don’t score very highly on that in the United States,” said Tyler VanderWeele, director of the Human Flourishing Program at Harvard.

Kris Snibbe/Harvard Staff Photographer

“We study physical health very well,” said the project’s co-director, Tyler VanderWeele, the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology and director of the Human Flourishing Program at Harvard. “We also study income and wealth very well.” But while these are no doubt important, people also care about being happy, having a sense of meaning and purpose, and trying to be a good person. “Why aren’t we studying these topics with the same level of empirical rigor as we study physical health and income?”

One reason is because it’s difficult. Measuring happiness, purpose, or love requires more than a medical instrument. Centuries of philosophical and theological texts offer varying and valuable takes on the meaning of life, which is why VanderWeele enlisted a senior philosopher to help develop the survey questions. The director hopes this modern effort will result in more quantitative, measurable answers to this age-old question.

“What we measure shapes what we talk about, what we focus on, what we aim for, and policies put in place to achieve it,” he said.

GDP might be as ubiquitous a metric as blood pressure, but because a country has a high GDP doesn’t always mean its citizens have a high level of well-being. People in wealthier developed countries, for example, often have higher levels of happiness and life satisfaction. But people in poorer, developing countries typically have a greater sense of meaning and purpose. They also tend to have stronger relationships. “We don’t score very highly on that in the United States,” said VanderWeele.

To study these seemingly nebulous qualities the way scientists study disease, the multidisciplinary team designed a survey in which participants respond to statements like: “I am content with my friendships and relationships,” “I feel that I’m a person of worth,” and “I have forgiven those who hurt me.” There are also more familiar questions like, “How often do you worry about safety, food, or housing?” and “About how many cigarettes do you smoke each day?”

Some critics still have doubts about whether the study can effectively measure seemingly more subjective qualities, like love. “To that, I would say, ‘Let’s see what we get,’” said Matthew Lee, director of empirical research for the Human Flourishing Program.

Stephanie Mitchell/Harvard Staff Photographer

Translating these concepts across cultures has not always been easy. Germany, for example, has two different words for “happiness,” neither of which map exactly to the English definition. Love doesn’t have one universal definition, either: There’s romantic love; love between parent and child; love of country; and spiritual love. And in some countries where humility and privacy are highly valued, said Matthew Lee, a lecturer of sociology and the director of empirical research for the Human Flourishing Program, individuals might tailor responses to avoid seeming boastful about how they’re doing or attention-seeking if they are having difficulties.

“So how do we incorporate all of that into one study?” Lee said. “The answer is we don’t. But we can become more aware of the limitations of what we’re trying to do.”

In an attempt to head off problems, the research team solicited feedback from scholars around the world and ran cognitive interviews and pilot tests to learn whether respondents in various countries interpreted the questions differently. Now, after three years, the first survey is finally set for launch, and the data will be open-access and available to anyone.

Some critics still have doubts about whether the study can effectively measure seemingly more subjective qualities, like love.

“To that, I would say, ‘Let’s see what we get,’” said Lee. “Happiness keeps going down, especially in the United States. If we’re not prioritizing deep, fulfilling, loving relationships, then [at least] our salaries can go up. And we can have bigger houses.”

But does that mean we have meaning in life? As Lee suggested, we’ll find out.

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From economic wealth to well-being: exploring the importance of happiness economy for sustainable development through systematic literature review

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• Shruti Agrawal   ORCID: orcid.org/0000-0002-1620-9429 1 , 5 ,
• Nidhi Sharma 1 , 5 ,
• Karambir Singh Dhayal   ORCID: orcid.org/0000-0002-0000-4330 2 &
• Luca Esposito   ORCID: orcid.org/0000-0001-5983-6898 3 , 4  

The pursuit of happiness has been an essential goal of individuals and countries throughout history. In the past few years, researchers and academicians have developed a huge interest in the notion of a ‘happiness economy’ that aims to prioritize subjective well-being and life satisfaction over traditional economic indicators such as Gross Domestic Product (GDP). Over the past few years, many countries have adopted a happiness and well-being-oriented framework to re-design the welfare policies and assess environmental, social, economic, and sustainable progress. Such a policy framework focuses on human and planetary well-being instead of material growth and income. The present study offers a comprehensive summary of the existing studies on the subject, exploring how a happiness economy framework can help achieve sustainable development. For this purpose, a systematic literature review (SLR) summarised 257 research publications from 1995 to 2023. The review yielded five major thematic clusters, namely- (i) Going beyond GDP: Transition towards happiness economy, (ii) Rethinking growth for sustainability and ecological regeneration, (iii) Beyond money and happiness policy, (iv) Health, human capital and wellbeing and (v) Policy push for happiness economy. Furthermore, the study proposes future research directions to help researchers and policymakers build a happiness economy framework.

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1 Introduction

Happiness is considered the ultimate goal of human beings (Ikeda, 2010 ; Lama, 2012 ). All economic, social, environmental and political human activities are aligned towards achieving this goal. This fundamental pursuit of human life introduces a new scope of research, namely the ‘happiness economy’ (Agrawal and Sharma 2023 ). The happiness economy is an emerging economic domain wherein many countries are working to envision and implement a happiness-oriented framework by expanding how they measure economic success, which includes wellbeing and sustainability (Cook and Davíðsdóttir 2021 ; Forgeard et al., 2011 ). The investigation of happiness, life-satisfaction and subjective well-being has witnessed increasing research interest across the disciplines- from psychology, philosophy, psychiatry, and cognitive neuroscience to sociology, economics and management (Diener 1984 ; Hallberg and Kullenberg, 2019 ).

In the post-Covid era, the world seeks an enormous transformation shift in the public system (Costanza 2020 ). However, public authorities need more time to realize such needs. To experience the ‘policy transformation’ within the coming few years, we require a paradigm shift that helps warm peoples’ hearts and minds. The new economic paradigm can penetrate the policy processes in advanced economies and every part of the world affected by the epidemic with the support of intellectuals, researchers, entrepreneurs and professionals.

OECD ( 2016 ) proposed a well-being economy framework to measure living conditions and people’s well-being. In 2020, developed countries like Finland, New Zealand, Iceland, Scotland and Wales have become members of the Wellbeing Economy Government (WEGo) (Abrar 2021 ). Since then, the network of government and international authorities across the globe has gained a quick momentum concerning an increasing tendency about a growing tendency to concentrate governmental decisions around human well-being rather than wealth and economic growth (Coscieme et al. 2019 ; Costanza et al. 2020 ).

In light of these circumstances, the purpose of this article is to describe the concept of a “happiness economy” or one that seeks to give everyone fair possibilities for growth, a sense of social inclusion, and stability that can support human resilience (Coyne and Boettke 2006 ). It provides a promising route towards improved social well-being and environmental health and is oriented towards serving individuals and communities (Skul’skaya & Shirokova, 2010 ). Moreover, the happiness economy paradigm is a transition from material production and consumption of commodities and services as the only means to economic development towards embracing a considerable variety of economic, social, environmental and subjective well-being dynamics that are considered fundamental contributors to human happiness (Atkinson et al., 2012 ; King et al., 2014 ; Agrawal and Sharma 2023 ). In following so, it reflects the ‘beyond growth’ approach that empathizes with the revised concept of growth, which is not centred around an increase in income or material production; instead it is grounded in the philosophy of achieving greater happiness for more people (Fioramonti et al. 2019a ).

Whereas the other critiques of economic growth emphasize contraction, frugality and deprivation, the happiness economy relies on a cumulative approach of humanity, hope and well-being, with a perceptive to build a ‘forward-looking’ narrative of ways for humans to live a happy and motivated life by inspiring the cumulative actions and encouraging policy-reforms in the measuring growth of an economy (Stucke 2013 ). Agrawal et al. ( 2023a , b ) explore the domain of happiness economics through a review of the various trends coupled with the future directions and highlight why it needs to be supported for a well-managed economic system and a happy society.

In this paper, we define a “happiness economy as an economy that aims to achieve the well-being of individuals in a nation, promoting human happiness, environmental up-gradation, and sustainability. Alternatively, as an economy where the wellbeing of people counts more than the goals of production and income”. Moreover, we have examined the existing body of research on the happiness economy and analyzed the emerging research themes related to rethinking the conventional approach to economic growth. We conclude by discussing how the happiness economy concept has been accepted so far and realizing its importance by triggering policy reforms at the societal level, by outlining potential future directions that might be included into the current national post-growth policies.

Various researchers and experts in the field of happiness economy support the idea that there is a lack of thorough studies related to the concept, definitions, and themes of the happiness economy model in the nations. This gap has motivated us to conduct a SLR in order to identify the evolution in the domain of happiness economy and to identify the emerging themes in this context. Therefore, this present study seeks to offer a holistic outline of the emerging research area of the happiness economy and helps to understand how the happiness economy can accelerate sustainable development. With the following research questions, this study seeks to give an all-encompassing review of this subject.

What is the annual publication trend in this domain and the most contributing authors, journals, countries etc?

Which themes and upcoming research areas are present in this field?

What directions will the happiness economics study field go in the future?

The SCOPUS database was used to achieve the above research objectives. We have selected 257 articles for examination by hand-selecting the pertinent keywords and going over each one. In the methods section, a thorough explanation of the procedures for gathering, reviewing, and selecting documents is provided.

The remainder of this paper is structured as follows; A thorough survey of the literature on the happiness economy is provided in Sect.  2 . The research approach employed in the study is presented in Sect.  3 . A thorough data analysis of the research findings is given in Sect.  4 . After discussing the results in Sect.  5 , Sect.  6 suggests areas for further research in this field. The study is summarised with a conclusion in Sect.  7 . Section  8 outlines the study’s limitation.

2 Literature review

The supporters of conventional economic growth proclaim that the material production of goods and services and consumption is vital to enhancing one’s living standards. The statement is true to some degree, mainly in countries of enormous deprivation. Some studies have found significantly less correlation between growth and happiness after fulfilling minimum threshold needs (Easterlin 1995 ; Kahneman and Krueger, 2006 ; Inglehart et al., 2008 ). These studies recommend that rather than concentrating solely on economic growth, governmental policy should give priority to non-economic aspects of human existence above a particular income level. According to some researchers, it is challenging to distinguish between the use and emissions of natural resources and economic growth (absolute decoupling) because of the interdependence between socioeconomic conditions and their biophysical basis (Wiedenhofer et al. 2020 ; Wang and Su, 2019 ; Wu et al., 2018 ). However, a shred of increasing evidence shows that it could be possible for humans to maintain a quality of life and a decent standard of living inside the ecological frontier of the environment, given that a contemporary perspective on the production and use of materials are adopted in conjunction with more fair wealth distribution (Millward-Hopkins et al. 2020 ; Bengtsson et al., 2018 ; Ni et al., 2022 ).

The scholarly discourse and institutional framework on the relationship between happiness and economic progress are synthesised in the happiness economy (Frey and Gallus 2012 ; Sohn, 2010 ; Clark et al., 2016 ; Easterlin, 2015 ; Su et al., 2022 ). From a happiness economy perspective, extreme materialism is unsustainable as it significantly impacts natural resources and hinders social coherence and individuals psychological and physical well-being (Fioramonti et al. 2022a ). Additionally, inequalities within countries have grown, while psychological suffering has increased, especially during accelerated growth (Vicente 2020 ; Galbraith, 2009 ). The modern world is witnessing anxiety, depression, wars, reduction of empathy, climate change, pandemics, loss of social bonds and other psychological disorders (Brahmi et al., 2022 ; Santini et al., 2015 ).

It has been scientifically proven that cordial human relations, care-based activity, voluntary activities and the living environment immensely impact a person’s health and societal well-being (Bowler et al. 2010 ; Keniger et al., 2013 ). Ecological economists demonstrated that free ecosystem services have enhanced human well-being (Fang et al. 2022 ). Social epidemiologists have long argued that an increase in inequalities has a negative influence on society while providing equality tends to improve significant objective ways of well-being, from healthier communities to happier communities, declining hate and crime and enhancing social cohesion, productivity, unity and mutual trust (Aiyar and Ebeke 2020 ; Ferriss, 2010 ).

From moving beyond materialistic growth, the happiness economy promotes, appreciates, and protects the environmental, societal, and human capital contributions that lead to cummalative well-being. In a happiness economy framework, a multidimensional approach is needed to evaluate the level of development based on the environmental parameters, health outcomes, as well as public trust, hope, value-creating education and social bonds (Agrawal and Sharma 2023 ; Bayani et al. 2023 ; Lavrov, 2010 ). Such factors have consistently been excluded from any traditional concept or assessment of economic growth. As a result, countries have promoted more industrial activities that deteriorate the authentic ways of human well-being and, hence, the foundations of economic progress.

An excess of production can create a detrimental effect on climate and people’s health, thereby creating a negative externality for society (Fioramonti et al. 2022b ). Moderation of output may be more efficient and desirable than hyper/over-production, as the former can reduce negative environmental externalities (e.g. waste, climate change) and create positive externalities (e.g. employment of the local resources and community) (Kim et al. 2019 ; Kinman and Jones, 2008 ). Moreover, people can also be productive in other contexts outside of the workplace, such as as volunteers, business owners, artists, friends, or members of the community (Fioramonti et al. 2022a ).

Various scholars and scientific research have established that the essential contributions to happiness in one’s life are made by natural surroundings, green and blue spaces, eco-friendly environment, healthy social relations, spirituality, good health, responsible consumption and value-creating education (Helliwell et al. 2021 ; Francart et al., 2018 ; Armstrong et al., 2016 ; Gilead, 2016 ; Giannetti et al., 2015 ). Unfortunately, existing conventional growth theories have ignored all these significant contributions. For example, GDP considers natural ecosystems as economically helpful only up until they are mined and their products are traded (Carrero et al. 2020 ). The non-market benefits they generate, such as natural fertilization, soil regeneration, climate regulation, clean air and maintenance of biodiversity, are entirely ignored (Boyd 2007 ; Hirschauer et al., 2014). The quality time people spend with their families and communities for leisure, educating future generations and making a healthy communal harmony is regarded meaningless, even in the event that they are important to enhance people’s well-being and, hence, to assist any dimension of economic engagement (Griep et al. 2015 ; Agrawal et al., 2020 ). Similarly, if an economy is focusing on people’s healthy lifestyle (for example, by providing comfortable working hours, improving work-life balance, emphasizing mental health, focusing on healthy food, reducing pollution, and promoting sustainable consumption), it is not considered in sync with the growth paradigm (Roy 2021 ; Scrieciu et al., 2013; Shrivastava and Zsolnai 2022 ; Lauzon et al., 2023 ).

Among the latest reviews, Bayani et al. ( 2023 ) highlight that the economics of happiness helps reduce the country’s financial crime by providing a livelihood that reduces financial delinquency. Chen ( 2023 ) highlights that smart city performance enhances urban happiness by adopting green spaces, reusing and recycling products, and controlling pollution. The study by (Agrawal and Sharma 2023 ) proposed a conceptual framework for a happiness economy to achieve sustainability by going beyond GDP. Similarly, Fioramonti et al. ( 2019b ) explored going beyond GDP for a transition towards a happy and well-being economy. The article by Laurent et al. ( 2022 ) has intensively reviewed the well-being indicators in Rome and proposed a conceptual framework for it.

Table  1 provides a thorough summary of the prior review studies about the happiness economy and its contribution to public policy and sustainable development.

3 Research methodology

In the current study, we have adopted an integrative review approach of SLR and bibliometric analysis of the academic literature to get a detailed knowledge of the study, which could also help propose future research avenues. The existing scientific production’s qualitative and quantitative context must be incorporated for a conclusive decision. The study by Meredith ( 1993 ) defines that SLR enables an “integrating several different works on the same topic, summarising the common elements, contrasting the differences, and extending the work in some fashion”. In the present study, the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) is applied to perform the SLR to follow systematic and transparent steps for the research methodology, as shown in Fig.  1 . The PRISMA technique includes the identification, screening, eligibility, and exclusion criteria parts of the review process.

Additionally, examples of the data abstraction and analysis processes are provided (Mengist et al. 2020 ; Moher et al., 2015 ). The four main phases of the PRISMA process are eligibility, identification, screening, and data abstraction and analysis. Because the PRISMA technique employs sequential steps to accomplish the study’s purpose, it benefits SLR research. Moreover, the bibliometric analysis helps summarise the existing literature’s bibliographic data and determine the emerging condition of the intellectual structure and developing tendencies in the specified research domain (Dervis 2019 ).

3.1 Identification

The step to conduct the PRISMA is the identification of the relevant keywords to initiate the search for material. Next, search strings for the digital library’s search services are created using the selected keywords. The basic search query is for digital library article titles, keywords, and abstracts. Next, a Boolean AND or OR operator is used to generate the search string (Boolean combinations of the operators may also be used).

There are different search databases to conduct the review studies, such as Scopus, Sage, Web of Science, IEEE, and Google Scholar. Among all the available search databases, we have used the Scopus database to identify the articles; since 84% of the material on Web of Science (WoS) overlaps with Scopus, very few authors have addressed the benefits of adopting Scopus over WoS (Mongeon and Paul-Hus 2016 ). Scopus is widely used by academicians and researchers for quantitative analysis (Donthu et al. 2021 ). It is the biggest database of scientific research and contains citations and abstracts from peer-reviewed publications consisting of journal research articles, books and conference articles (Farooque et al., 2019 ; Dhayal et al., 2022 ; Brahmi et al., 2022 ). The following search term was used: (TITLE-ABS-KEY (“happiness economy” OR “economics of happiness” OR “happiness in economy” OR “economy of happiness” OR “economy of wellbeing” OR “wellbeing economy” OR “wellbeing in economy” OR “beyond growth”). This process yields 380 artciles in the initial phase.

3.2 Screening

The second phase is completed by all identified articles from the Scopus database obtained from the search string in the identification phase. The publications are either included or excluded throughout the screening process based on the standards established by the authors and with the aid of particular databases. Exclusion and inclusion criteria are shown during the screening phase to identify pertinent articles for the systematic review procedure. The timeline of this study’s selected articles is from 1995 to 2023. The first article related to the research domain was published in 1995. The second criterion for the inclusion includes the types of documents. In the present research, the authors have regarded only peer-reviewed journals and review articles. Other types of articles, such as books, book chapters, conference articles, notes, and editorials, are excluded to maintain the quality of the review. The third inclusion and exclusion criterion is based on language. All the non-English language documents are excluded to avoid translation confusion; hence, only the English language articles are considered for the final review. After the screening process, 297 articles are obtained.

3.3 Eligibility

Articles are manually selected or excluded depending on specific criteria specified by the authors during the eligibility process. During the elimination process, the authors excluded the articles that did not fit into the scope of review after manual screening of the articles. Two hundred fifty-seven articles were selected after the eligibility procedure. These selected articles are carefully reviewed for the study by reviewing the titles, abstracts, and standards from earlier screening processes.

3.4 Data abstraction and analysis

Analysis and abstraction of data are part of the fourth step. Finally, 257 papers were taken into account for final review. After that, the studies are culled to identify pertinent themes and subthemes for the current investigation by thoroughly reviewing each article’s text. An integrative review is a form of study that combines mixed, qualitative, and quantitative research procedures. It is carried out as shown in Fig.  1 . R-studio Bibliometrix and VOSviewer version 1.6.18 were used to evaluate the final study dataset corpus of 257 articles. Since the Bibliometrix software package is a free-source tool programmed in the R language. It is proficient of conducting comprehensive scientific mapping. It also contains several graphical and statistical features with flexible and frequent updates (Agrawal et al. 2023a , b ).

Extraction of articles and selection process

This section provides an answer to the first research question, RQ1, by indicating the main information of corpus data, research publication trends, influential prolific authors, journals, countries and most used keywords, etc. (Refer to Tables  2 , 3 and 4 ) and (Refer to Figs.  2 , 3 , 4 , 5 and 6 ).

4.1 Bibliometric analysis

Table  2 shows the relevant information gathered from the publication-related details. It presents the cognitive knowledge of the research area, for instance, details about authors, annual average publication, average citations and collaboration index. By observing the rate of document publishing, the study illustrates how much has already been done and how much remains to be investigated.

The annual publication trend is shown in Fig.  2 . It is reflected that the first article related to happiness in an economy was released in the year 1995 when (Bowling 1995 ) published the article “What things are important in people’s lives? A survey of the public’s judgements to inform scales of health related quality of life” where the article discussed “quality of life” and “happiness” as an essential component of a healthy life. Oswald ( 1997 ) brought the concept of happiness and economics together and raised questions such as “Does money buy happiness?” or “Do you think your children’s lives will be better than your own?”. Eventually, the gross national product of the past year and the coming year’s exchange rate was no longer the concern; instead, happiness as the sublime moment became more accurate (Schyns 1998 ; Easterlin, 2001; Frey and Stutzer, 2005 ). Post-2013, we can see exponential growth in the publication trend, and the reason behind the growth is the report published by the “ Stiglitz-Sen-Fitoussi” Commission, which has identified limitations of GDP and questioned the metric of wealth, economic and societal progress. The affirmed questions have gained the attention of researchers and organizations, and thus, they have explored the alternatives to GDP. As a result, the “Organization for Economic Co-operation and Development” (OECD) have proposed a wellbeing framework. Some research work has significantly impacted that time, contributing to the immense growth in this research area (Sangha et al. 2015 ; Spruk and Kešeljević, 2015 ; Nunes et al., 2016 ).

Publication trend

Table  3 shows the top prolific journals concerning the topmost publications in the domain of happiness economy for the corpus of 257 articles, namely “International Journal of Environmental Research and Public Health”, “Ecological Economics”, “Ecological Indicators”, “Sustainability” and “Journal of Cleaner Production” with 5, 4, 4,4 and 4 articles respectively (Refer to Table  4 ). Moreover, the most influential journals with maximum citations are “Nature Human Behavior”, “Quality of Life Research”, “Journal of Applied Behavior Analysis”, “Journal of Cleaner Production” and “Ecological Economics”, with 219, 205, 186, 154 and 142 citations, respectively. “Journal of Cleaner Production” and “Ecological Economics” are highly prolific and the most influential journals in the happiness economy research domain.

Table  4 shows the most influential authors. Baños, R.M. and Botella, C. are the two most contributing authors with maximum publications. For the maximum number of citations, Zheng G. and Coscieme L. are the topmost authors for their research work. The nations were sorted according to the quantity of publications, and Fig.  3 showed where the top ten countries with the highest number of publications are listed originated. It can be seen from the figure that the United Stated has contributed the maximum publications, 66, followed by the United Kingdom with 41 articles, followed by Germany with 32 articles. It is worth noting that emerging nation such as India and China have also made significant contributions.

Top ten contributing countries

Figure  4 shows semantic network analysis in which the relationships between words in individual texts are performed. In the present study, we have identified word frequency distributions and the co-occurrences of the authors’ keywords in this study. We employed co-word analysis to find repeated keywords or terms in the title, abstract, or body of a text. In Fig.  5 , the circle’s colour represents a particular cluster, and the circle’s radius indicates how frequently the words occur. The size of a keyword’s node indicates how frequently that keyword appears. The arcs connecting the nodes represent their co-occurrence in the same publication. The greater the distance between two nodes, the more often the two terms co-occur. It can be seen that “happiness” is linked with “growth” and “life satisfaction”. The nodes of “green economy”, “ecological economics”, and “climate change” are in a separate cluster that shows they are emerging areas, and future studies can explore the relationship between happiness economy with these keywords.

Co-ocurrance of author’s keyword (Author’s compilation)

4.2 Thematic map analysis through R studio

The thematic analysis map, as shown in Fig.  5 , displays, beneath the author’s keywords, the visualisation of four distinct topic typologies produced via a biblioshiny interface. The thematic map shows nine themes/clusters under four quadrants segregated in “Callon’s centrality” and “density value”. The degree of interconnectedness between networks is determined by Callon’s centrality, while Callon’s density determines the internal strength of networks. (Chen et al. 2019 ). The rectangular boxes in Fig.  5 represent the subthemes under each topic or cluster that are either directly or indirectly connected to the major themes, based on the available research. In the upper-right quadrant, four themes have appeared, namely “circular economy”, “well-being economy”, “depression”, and “sustainable development”, they fall under the category of motor themes since they are extremely pertinent to the research field, highly repetitious, and well-developed. When compared to other issues with internal linkages but few exterior relations, “urban population” in the upper-left quadrant is seen as a niche concern since it is not as significant. This cluster may have affected the urban population’s happiness (Knickel et al. 2021 ). “Social innovation” is categorised as an emerging or declining subject with low centrality and density, meaning it is peripheral and undeveloped. It is positioned in the lower-left quadrant. Last but not least, the transversal and fundamental themes “happiness economy”, “subjective well-being”, and “climate change” in the lower-right quadrant are seen to be crucial to the happiness economy study field but are still in the early stages of development. As a result, future research must place greater emphasis on the quantitative and qualitative growth of the study area in light of the key themes that have been identified.

Thematic map analysis

4.3 Science mapping through cluster analysis

In the study, science mapping was conducted to examine the interrelationship between the research domains that could be intellectual (Aria and Cuccurullo 2017 ; Donthu et al. 2021 ). It includes various techniques, such as co-authorship analysis, co-occurrence analysis, bibliographic coupling, etc. We have used R-Studio for the study’s temporal analysis by cluster analysis. To answer RQ2, the authors have performed a qualitative examination of the emerging cluster themes through the science mapping of the existing research corpus of 257 articles by performing bibliographic coupling of documents. Bibliographic coupling analysis helps identify clusters reflecting the most recent research themes in the happiness economy field to illuminate the field’s current areas of interest.

The visual presentation of science mapping relied on VoSviewer version 1.6.18 (refer to Fig.  6 ). Five significant clusters emerged in this research domain (refer to Table  5 ). Going beyond GDP: Transition towards happiness economy, rethinking growth for sustainability and ecological regeneration, beyond money and happiness policy, health, human capital and wellbeing and Policy-Push for happiness economy. A thorough examination identified cluster analyzes has also assists us in identifying potential future research proposals. (Franceschet 2009 )

4.4 Cluster 1: Going beyond GDP: transition towards happiness economy

It depicts from the green colour circles and nodes, where seven research articles were identified with a common theme of beyond GDP that can be seen in Fig.  6 . Cook and Davíðsdóttir ( 2021 ) investigated the linkages between the alternative measure of the beyond growth approach such as a well-being economy prespective and the SDGs. They proposed a conceptual model of a well-being economy consisting of four capital assets interrelated with SDGs that promote well-being goals and domains. To extend the concept of going beyond GDP, various economic well-being indicators are being aligned with the different economic, environmental, and social dimensions to target the set goals of SDG. It is found that the “Genuine Progress Indicator” (GPI) is consider as the most extensive method that covers the fourteen targets among the seventeen’s SDG’s. Cook et al. ( 2022 ) consider SDGs to represent the classical, neoclassical and growth-based economy model and as an emerging paradigm for a well-being economy. The significance of GDP is more recognized within the goals of sustainable development.

GPI is considered an alternative indicator of economic well-being. On this basis, excess consumption of high-quality energy will expand macro-economic activity, which GDP measures. For such, a conceptual exploration of the study is conducted on how pursuing “Sustainable Energy Development” (SED) that can increase the GPI results. As the study’s outcome, according to the GPI, SED will have a significant advantage in implementing energy and environment policy and will also contribute to the advancement of social and economic well-being. Coscieme et al. ( 2020a ) explored the connection between the unconditional growth of GDP and SDG. The author considered that policy coherence for sustainable development should lessen the damaging effects of cyclic manufacturing on the ecosystem. Thus, the services considered free of charge in the GDP model should be valued as a component of society. Generally, such services include ecosystem services and a myriad of “economic” functions like rainfall and carbon sequestration. To work for SDG 8, defined by the “United Nations Sustainable Development Goals” (UNSDGs), a higher GDP growth rate would eventually make it more difficult to achieve environmental targets and lessen inequality. Various guidelines were proposed to select alternative variables for SDG-8 to enhance coherence among all the SDG and other policies for sustainability.

Fioramonti et al. ( 2019a ) state their focus is to go beyond GDP toward a well-being economy rather than material output with the help of convergence reforms in policies and economic shifts. To achieve the SDG through protecting the environment, promoting equality, equitable development and sharing economy. The authors have developed the Sustainable Well-Being Index (SWBI) to consolidate the “Beyond GDP” streams as a metric of well-being matched with the objectives to achieve SDG. The indicators of well-being for an economy have enough possibility to connect current transformations in the economic policies and the economy that, generally, GDP is unable to capture.

Fioramonti et al. ( 2022a ) investigate the critical features of the Wellbeing Economy (WE), including its various parameters like work, technology, and productivity. Posting a WE framework that works for mainstream post-growth policy at the national and international levels was the study’s primary goal. The authors have focused on building a society that promotes well-being that should be empowering, adaptable, and integrative. A well-being economic model should develop new tools and indicators to monitor all ecological and human well-being contributors. A multidimensional approach including critical components for a well-being economy was proposed that creates value to re-focus on economic, societal, personal, and natural aspects. Rubio-Mozos et al. ( 2019 ) conducted in-depth interviews with Fourth Sector business leaders, entrepreneurs, and academicians to investigate the function of small and medium-sized businesses and the pressing need to update the economic model using a new measure in line with UN2030. They have proposed a network from “limits to growth” to a “sustainable well-being economy”.

4.5 Cluster 2: Rethinking growth for sustainability and ecological regeneration

Figure  6 depicts it from blue circles and nodes, wherein four papers were identified. Knickel et al. ( 2021 ) proposed an analytical approach by collecting the data from 11 European areas to examine the existing conditions, difficulties, and anticipated routes forward. The goal of the study is to define the many ideas of a sustainable well-being economy and territorial development plans that adhere to the fundamental characteristics of a well-being economy. A transition from a conventional economic viewpoint to a broader view of sustainable well-being is centred on regional development plans and shifting rural-urban interactions.

Pillay ( 2020 ) investigates the new theories of de-growth, ecosocialism, well-being and happiness economy to break the barriers of traditional economic debates by investigating ways to commercialise and subjugate the state to a society in line with non-human nature. The significant indicator of Gross National Happiness (GNH) is an alternative working indicator of development; thus, the Chinese wall between Buddha and Marx has been built. They questioned the perspective of Buddha and Marx, whether they were harmonized or became a counter-hegemonic movement. In order to determine if the happiness principle is grounded in spiritual values and aligns with the counter-hegemonic ecosocialist movement, the author examined the ecosocialist perspective. Shrivastava and Zsolnai ( 2022 ) have investigated the theoretical and practical ramifications of creative organisations for well-being rooted in the drive for a well-being economy. Wellbeing and happiness-focused economic frameworks are emerging primarily in developed countries. This new policy framework also abolishes GDP-based economic growth and prioritizes individual well-being and ecological regeneration. To understand its application and interpretation, Van Niekerk ( 2019 ) develops a conceptual framework and theoretical analysis of inclusive economics. It contributes to developing a new paradigm for economic growth, both theoretically and practically.

4.6 Cluster 3: ‘Beyond money’ and happiness policy

It depicts pink circles and nodes, wherein five articles were identified, as shown in Fig.  6 . According to Diener and Seligman ( 2004a ) economic indicators are critical in the early phases of economic growth when meeting basic requirements is the primary focus. However, as society becomes wealthier, an individual’s well-being becomes less dependent on money and more on social interactions and job satisfaction. Individuals reporting high well-being outperform those reporting low well-being in terms of income and performance. A national well-being index is required to evaluate well-being variables and shape policies systematically. Diener and Seligman ( 2018 ) propounded the ‘Beyond Money’ concept in 2004. In response to the shortcomings of GDP and economic measures, other quality-of-life indicators, such as health and education, have been created. The national account of well-being has been proposed as a common path to provide societies with an overall quality of life metric. While measuring the subjective well-being of people, the authors reasoned a societal indicator of the quality of life. In this article, the authors have proposed an economy of well-being model by combining subjective and objective measures to convince policymakers and academicians to enact policies that enhance human welfare. The well-being economy includes quality of life indicators and life satisfaction, subjective well-being and happiness.

Frey and Stutzer ( 2000 ) perceived the microeconomic well-being variables in countries. In the study, survey data was used from 6000 individuals in Switzerland and showed that the individuals are happier in developed democracies and institutions (government federalization). They analyzed the reported subjective well-being data to determine the function of federal and democratic institutions on an individual’s satisfaction with life. The study found a negative relationship between income and unemployment. Three criteria have been employed in the study to determine happiness: demographic and psychological traits, macro- and microeconomic factors, and constitutional circumstances. Thus, a new pair of determinants reflects happiness’s effect on individuals’ income, unemployment, inflation and income growth.

Happiness policy, according to Frey and Gallus ( 2013b ), is an intrinsic aspect of the democratic process in which various opinions are collected and examined. “Happiness policy” is far more critical than continuing a goal such as increasing national income and instead considered an official policy goal. The article focuses on how politicians behave differently when they believe that achieving happiness is the primary objective of policy. Frey et al. ( 2014 ) explored the three critical areas of happiness, which are positive and negative shocks on happiness, choice of comparison and its extent to derive the theoretical propositions that can be investigated in future research. It discussed the areas where a more novel and comprehensive theoretical framework is needed: comparison, adaptation, and happiness policy. Wolfgramm et al. ( 2020 ) derived a value-driven transformation framework in Māori economics of wellbeing. It contributes to a multilevel and comprehensive review of Māori economics and well-being. The framework is adopted to advance the policies and implement economies of well-being.

4.7 Cluster 4: Health, human capital and wellbeing

It is depicted as a red colour circle and nodes in Fig.  6 , and only three papers on empirical investigations were found. Laurent et al. ( 2022 ) investigated the Health-Environment Nexus report published by the “Wellbeing Economy Alliance”. In place of increased production and consumption, they suggested a comprehensive framework for human health and the environment that includes six essential paths. The six key pathways are well-being energy, sustainable food, health care, education, social cooperation and health-environment nexus. The proposed variables yield the co-benefits for the climate, health and sustainable economy. Steer clear of the false perception of trade-offs, such as balancing the economy against the environment or the need to save lives. McKinnon and Kennedy ( 2021 ) focuses on community economics of well-being that benefits entrepreneurs and employees. They investigated the interactions of four social enterprises that work for their employees inside and within the broader community. Cylus et al. ( 2020 ) proposed the opportunities and challenges in adopting the model of happiness or well-being in an economy as an alternative measure of GDP. Orekhov et al. ( 2020 ) proposed the derivation of happiness from the World Happiness Index (WHI) data to estimate the regression model for developed countries.

4.8 Cluster 5: Policy-push for happiness economy

It is depicted as an orange circle and nodes in Fig.  6 , and only five papers on empirical and review investigations were found. Oehler-Șincai et al. ( 2023 ) proposed the conceptual and practical perspective of household-income-labour dynamics for policy formulation. It discusses the measurement of well-being as a representation of various policies focusing on health, productivity, and longevity. It focuses on the role of policy in building the subjective and objective dimensions of well-being, defines the correlation between well-being, employment policies, and governance, is inclined to the well-being performance of various countries, and underscores present risks that jeopardize well-being. Musa et al. ( 2018 ) have developed a “community happiness index” by incorporating the four aspects of sustainability—economic, social, environmental, and urban governance—as well as the other sustainability domains, such as human well-being and eco-environmental well-being. From then onwards, community happiness and sustainable urban development emerged. Chernyahivska et al. ( 2020 ) developed strategies to raise the standard of living for people in countries undergoing economic transition by using the quality of life index. The methods uncovered are enhancing employment opportunities and uplifting the international labour market in urban and rural areas, prioritizing human capital, eliminating gender inequality, focusing on improving the individual’s health, and enhancing social protection. Zheng et al. ( 2019 ) investigated the livelihood and well-being index of the population that makes liveable conditions and city construction in society based on people’s happiness index. The structure of a liveable city should be emphasised on sustainable development. The growth strategy in urban areas is an essential aspect of building a liveable city. Frey and Gallus ( 2013a ) criticised the National Happiness Index as a policy goal in a country because it cannot be measured and thus fails to measure the true happiness of people. To measure real happiness, the government should establish living conditions that enable individuals to become happy. The rule of law and human rights must support the process.

The structure of a liveable city should be emphasized in sustainable development. The growth strategy in urban areas is an essential aspect of building a liveable city. Frey and Gallus ( 2013a ) criticized the National Happiness Index as a policy goal in a country because it cannot be measured and thus fails to identify the true individuals happiness. To measure real happiness, the government should establish living conditions that enable individuals to be happy. The process needs to be supported by human rights and the rule of law.

Visualization of cluster analysis

5 Discussion of findings

Concerns like the improved quality-of-life and a decent standard of living within the ecological frontier of the environment have various effects on individuals overall well-being and life satisfaction. The ‘beyond growth’ approach empathized with the revised concept of growth, which is based on the idea of maximising happiness for a larger number of people rather than being driven by a desire for financial wealth or production. In that aspect, the notion of happiness economy is designed that prioritizes serving both people and the environment over the other. This present article has focused on the beyond growth approach and towards a new economic paradigm by doing bibliometric and visual analysis on the dataset that was obtained from Scopus, helping to determine which nations, publications, and authors were most significant in this field of study.

In this field of study, developed nations have made significant contributions as compared to the developing nations. In total, 59 countries have made the substantial contribution to the beyond growth approach literature an some of them have proposed their respective national well-being economy framwework. Among 59 countries the United States and the United Kingdom have been crucial to the publishing. With the exception of five of the top 10 nations, Europe contributes the most to scientific research. The existing research shows the inclination of developed and developing countries to build a new economic paradigm that goes beyond growth by prioritizing the happiness level at individual as well as at collective level.

The most prolific journals in this research domain are the “International Journal of Environmental Research” and “Public Health” with the total publication of 5 and 4. The top two cited journals were the “ Nature Human Behavior” with 219 citations and the “Quality of Life Research” with 205 citations. Due to various economic and non-economic factors, these journals struggled to strike a balance between scientific accuracy and timeliness, and it became vital to spread accurate and logical knowledge. For, example, discussing the relationship between inequality and well-being, exploring the challenges and opportunites of happiness economy in different countries, assessing the role of health in all policies to support the transition to the well-being economy. Visualization of semantic network analysis of co-ocurrance of authors keywords from the VOSviewer showed the future research scope to explore the association between happiness economy along with green economy, climate change, spirituality and sustainability. However, in the thematic mapping, the motor themes denotes the themes that are well-developed and repetative in research, such as, well-being economy, depression, sustainable development and circular economy. The basic themes depicts the developing and transveral themes such as happiness economy, subjective well-being and climate condition. As a result, future research must place greater emphasis on the theoretical and practical expansion of the research field in view of the determined major subjects.

The present study have performed the cluster analysis to identify the emerging research themes in this domain through VOSviewer that helps to analyze the network of published documents. Based on published papers, the author can analyse the interconnected network structure with the use of cluster analysis. We have identified the top five clusters from the study. Each cluster denote the specific and defined theme of the research in this domain. In cluster 1, the majorly of the authors are working in the area of going beyond GDP and transition towards happiness economy, which consists of empirical and review studies. Cluster 2 represents that authors are exploring the relationship between rethinking growth for sustainability and ecological regeneration to evaluate the transition from a conventional economic thought to a broader view of sustainable well-being which is centred on regional development plans and shifting rural-urban interactions. In cluster 3, the authors are exploring the beyond money and happiness policy themes and identified the shortcomings of GDP and economic measures, other quality-of-life indicators, such as health and education. They have proposed the well-being index to evaluate the well-being variables and shape socio-economic policies systematically. The authors have proposed an economy of well-being model by combining subjective and objective measures to convince policymakers and academicians to enact policies that enhance human welfare. The well-being economy includes quality of life indicators and life satisfaction, subjective well-being and happiness. In cluster 4, the authors are working of related theme of Health, human capital and wellbeing, whereby they have put up a comprehensive framework for health and the environment that includes several important avenues for prioritising human and ecological well-being over increased production and consumption. In cluster 5, the authors have suggested the policy-push for happiness economy in which they have identified the conceptual and practical perspective of household-income-labour dynamics for policy formulation. Majorly of the authors in this clutster have focused on the role of policy in building the subjective and objective dimensions of well-being, defines the correlation between well-being, employment policies, and governance, is inclined to the well-being performance of various countries, and underscores present risks that jeopardize well-being. Hence, the present study will give academics, researchers, and policymakers a thorough understanding of the productivity, features, key factors, and research outcomes in this field of study.

6 Scope for future research avenues

The emergence of a happiness economy will transform society’s traditional welfare measure. Such changes will generate more reliable and practical means to measure the well-being or welfare of an economy. After a rigorous analysis of the existing literature, we have proposed the scope for future research in Table  6 .

7 Conclusion

In 2015, the United Nations proposed the pathbreaking and ambitious seventeen “Sustainable Development Goals” (SDGs) for countries to steer their policies toward achieving them by 2030. In reality, economic growth remains central to the agenda for SDGs, demonstrating the absence of a ground-breaking and inspirational vision that might genuinely place people and their happiness at the core of a new paradigm for development. As this research has reflect, there are various evidence that the happiness economy strategy is well-suited to permeate policies geared towards sustainable development. In this context, ‘happiness’ may be a strong concept that ensures the post-2030 growth will resonate with the socioeconomic and environmental traits of everyone around the world while motivating public policies for happiness.

The current research has emphasized the many dynamics of the happiness economy by using a bibliometric analytic study of 257 articles. We have concluded that the happiness economy is an emerging area that includes different dimensions of happiness, such as ecological regeneration, circular economy, sustainability, sustainable well-being, economic well-being, subjective well-being, and well-being economy. In addition to taking into consideration the advantages and disadvantages of human participation in the market, a happiness-based economic system would offer new metrics to assess all contributions to human and planetary well-being. In terms of theoretical ramifications, we suggest that future scholars concentrate on fusing the welfare and happiness theory with economic policy. As countries are predisposed to generate disharmony and imbalance, maximizing societal well-being now entails expanding sustainable development. Since the happiness economy is still a relatively novel field, it offers numerous potential research opportunities.

8 Limitations

Similar to every other research, this one has significant restrictions as well. We are primarily concerned that all our data were extracted from the Scopus database. Furthermore, future research can utilize other software like BibExcel and Gephi to expound novel variables and linkages. Given the research limitations, this article still provides insightful and relevant direction to policymakers, scholars, and those intrigued by the idea of happiness and well-being in mainstream economics.

The study offers scope for future research in connecting the happiness economy framework with different SDGs. Future studies can also carry empirical research towards creating a universally acceptable ‘happiness economy index’ with human and planetary well-being at its core.

Data availability

Data not used in this article.

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Shruti Agrawal & Nidhi Sharma

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Karambir Singh Dhayal

Department of Economics and Statistics, University of Salerno, Fisciano, Italy

Luca Esposito

Karelian Institute, University of Eastern Finland, Joensuu, Finland

Erasmus Happiness Economics Research Organisation, Erasmus University, Rotterdam, Netherlands

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All authors contributed to the study conception and design. Shruti Agrawal: Conceptualization, Material preparation, Data Collection, Formal analysis, Methodology, Writing - Original Draft, Review and Editing. Nidhi Sharma: Validation, Project Administration, Supervision, and Writing - Review & Editing. Karambir Singh Dhayal: Validation, Formal analysis, Methodology, Writing - Review and Editing. Luca Esposito: Validation, Writing - Review and Editing. The first draft of the manuscript was written by Shruti Agrawal and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

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Agrawal, S., Sharma, N., Dhayal, K.S. et al. From economic wealth to well-being: exploring the importance of happiness economy for sustainable development through systematic literature review. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01892-z

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Maintaining health and well-being as we age

Although many older adults demonstrate high levels of resilience, they are also more prone to social isolation and loneliness than any other age group. Having strong social connections is especially important for mental health and well-being as we age, and is associated with lower instances of depression and anxiety.

Simon Fraser University (SFU) professor of mental health and aging Theodore D. Cosco researches a range of factors that promote healthy aging and resilience in older adults, from digital interventions to physical activity. He leads the Precision Mental Health Lab , a transdisciplinary research group dedicated to community-engaged and innovative technological approaches to improve well-being across all age groups.

One of his major research projects is studying data from the Canadian Longitudinal Study on Aging (CLSA). Cosco is a co-investigator on the CLSA, a national, long-term study of more than 50,000 Canadians who were 45 to 85 years old when the program began in 2009. Over 160 researchers from 26 universities across Canada are involved in the CLSA.

Cosco and colleagues, including three PhD students he supervises:  Lucy Kervin , Shawna Hopper , and Indira Riadi have found that during the coronavirus pandemic, the decreased ability to participate in social and physical activity was associated with increased risk of depression and anxiety in older adults.

These findings are outlined in Worsened ability to engage in social and physical activity during the COVID-19 pandemic and older adults’ mental health ,   published in Innovation in Aging .

We spoke to professor Cosco about his research.

What did your research reveal about older adults’ diminished ability to engage in physical and social activities during the coronavirus pandemic?

Our team used data from 24,108 participants surveyed during the first nine months of the COVID-19 pandemic and found roughly 22% screened positively for depression and 5% for anxiety.

Generally, older adults who reported worsened ability to participate in social and physical activities during the pandemic had poorer mental health outcomes than those whose ability remained the same or improved. We also found that participating in these activities had a buffering effect on depression and anxiety.

How does this research apply now that the pandemic is behind us? Do you have recommendations?

Our findings highlight the importance of fostering social and physical activity resources to mitigate the negative mental health impacts of future pandemics or other major life stressors that may affect the mental health of older adults. Beyond the pandemic these results highlight the importance of staying socially and physically active. You do not need to be socializing seven nights a week, nor do you need to be running marathons. Doing anything is better than nothing, so finding ways to integrate socializing and exercising into one’s life is an excellent strategy. Pick up the phone, walk to the shops, or find a way that you can integrate activity into one’s own life.

How do you approach the study of vast amounts of data from the CLSA? Do you have specific research questions to investigate, or does the study reveal topics that you want to pursue?

When working with large datasets, it is crucial to understand the types of data included, their collection dates and their sources. Once familiar with the available data, you can delve into current research and literature to formulate hypotheses. With extensive datasets, specificity in your initial hypotheses and deliberate in your analysis approaches are vital. Because of the dataset's size and the significant statistical power it provides, running numerous models to explore every possible outcome can often lead to “statistically significant” findings that occur by chance. This practice, known as “fishing” or “data dredging,” is discouraged because it may result in misleading associations. Therefore, it's important for us to be very purposeful in testing our hypotheses to avoid these issues.

In a previous Scholarly Impact of the Week, you discussed how during the pandemic older adults and their families quickly adopted the use of technology to increase connectedness. Is this trend still going strong, and do you have new insights on technology and older people?  

During the pandemic, older adults and their families rapidly embraced technology to stay connected, a trend that remains strong today. This period really spotlighted both the advantages and limitations of our current technology. It became clear that tech companies need to move away from a one-size-fits-all approach. Products specifically designed with older adults in mind—taking into account their unique needs and preferences tend to be more successful. These intentionally crafted tools are not only more widely accepted but also have a more significant impact. The pandemic has shown us the importance of such tailored technology solutions in enhancing social connectedness for older populations.

For more: See professor Cosco’s previous Scholarly Impact of the Week article, Understanding the impacts of COVID-19 on older adults , and visit the Precision Mental Health Lab web page.  

SFU's Scholarly Impact of the Week series does not reflect the opinions or viewpoints of the university, but those of the scholars. The timing of articles in the series is chosen weeks or months in advance, based on a published set of criteria. Any correspondence with university or world events at the time of publication is purely coincidental.

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Greater Good Science Center • Magazine • In Action • In Education

Three New Ideas About Happiness and Well-Being

Last month, around 1,000 people from over 56 countries gathered at the International Positive Psychology Association’s 8th World Congress (IPPA) in Vancouver, Canada, to share cutting-edge insights on the science of well-being.

This year more than ever, there was an acknowledgement that the ways we think about happiness—and the studies that have been conducted—are mainly based on Western ideas and Western participants. This not only marginalizes the experiences of different communities, argued some researchers, but deprives everyone of the fullest picture of what it means to live well and be well.

How might our goals and our everyday decisions change if we thought about well-being in new ways? What kind of benefits might we see in our own lives, and in our communities? Here are some emerging ideas about happiness and well-being that you might want to ponder in your own life. 

1. Psychological richness

What makes a good life? In positive psychology at least, there have been two main answers to that question: a happy life or a meaningful one.

A happy life brings you comfort, joy, security, and fun—you feel good and satisfied. Meanwhile, a meaningful life is more about feeling purposeful and significant, like you made a difference. Of course, our lives are usually a mix of both, with some people valuing one kind of pursuit more than the other.

But a few years ago, Shigehiro Oishi, a researcher at the University of Virginia, asked a provocative question: Is there a third kind of good life, one that isn’t defined by happiness or meaning?

He was motivated by a few puzzling findings, including research suggesting that conservatives and Facebook users with less diverse social networks tend to be happier. Did that mean that liberal thinking and diverse connections weren’t paths to a good life?

Eventually, his explorations led him to what he calls a psychologically rich life : a life of curiosity, adventure, novelty and variety, exploration, and openness. Our lives become more psychologically rich when we study abroad , read certain kinds of books, change our perspectives, and experience dramatic life events. If your life is psychologically rich, you’d probably say that you’ve seen and learned a lot. 

If this pathway to a good life piques your interest, think about adding more spontaneity and playfulness into your day; find new things to try and learn, and be open to where they might take you. While happiness and meaning may fluctuate more alongside the ups and downs of life, psychological richness may be something we can slowly build up over time, new experience by new experience.

2. Balance and harmony

Every year, the Gallup World Poll asks people from over 150 countries about their well-being, querying them about how satisfied they are with life and whether they smiled, laughed, or felt enjoyment the day before (among other things).

Implicit in these questions is a certain idea about what well-being is, which is also reflected in the recommendations you might see in the media about how to be happier. It’s a kind of energetic happiness that is characterized by feelings of excitement, enthusiasm, and elation.

But a group of researchers are trying to study and elevate a different kind of happiness that has been somewhat neglected in well-being research: quiet feelings of calm, balance, and harmony.

Starting in 2020, the Global Well-Being Initiative has worked with Gallup to ask people how calm, content, at peace, in balance, and in harmony they feel in their daily lives. And they’ve learned some interesting things: For example, when you ask people around the world whether they’d prefer an exciting life or a calm life, most say they’d like a calm life. Even though this idea of calm is inspired by Eastern notions of well-being, it is relevant to people globally. And people who experience more balance, harmony, and peace are more satisfied with their lives.

What does that mean for us? If you aspire to more calm, some research suggests it’s more often found in the moment—when you practice mindfulness, or tune in to what you appreciate about your life right now. It may also mean embracing negative emotions, as part of the full spectrum of what it means to be human.

3. Connection to your culture

While there’s much to learn from cultures around the world about what makes a good life, we can’t forget about our own culture. In fact, more and more research is finding that engaging in cultural practices and connecting to your cultural identity is a strong pathway to well-being. 

At IPPA, we heard about the First Nations Mental Wellness Continuum Framework, which was created by community leaders in Canada as a way to understand First Nations well-being. “The cultural values, sacred knowledge, language, and practices of First Nations are essential determinants of individual, family, and community health and wellness,” their report states.

In one study, for example, Indigenous participants who engaged in cultural practices like prayer, smudge, sweat lodge, and fasting increased their wellness by 8-10%, reported Carol Hopkins at IPPA.

Other studies back this up. For example, research finds that Indigenous youth can find healing, strength, and well-being by engaging with their culture, traditions, and community, and that the Latino values that caregivers nurture in their children help them grow up to be kind and connected to others. According to another new study mentioned at IPPA, identifying as Black helps people feel more satisfied with life even when they’re experiencing financial hardship.

“People have greater well-being when they’re involved in passing on knowledge to young people, engaging with their community, participating in cultural events, and developing a strong sense of identity and self-worth,” write Elizabeth Doery and her coauthors.

All these insights remind us that there isn’t one well-defined pathway to a good and happy life, and that we can learn a lot if we’re open and curious enough to see what other paths are out there.

About the Author

Kira M. Newman

Kira M. Newman is the managing editor of Greater Good . Her work has been published in outlets including the Washington Post , Mindful magazine, Social Media Monthly , and Tech.co, and she is the co-editor of The Gratitude Project . Follow her on Twitter!

Three Insights from the Frontiers of Positive Psychology

How Your Life Is Shaped by the Emotions You Want to Feel

Three Emerging Insights About Happiness

How a Strong Latinx Identity Helps Kids Thrive

How the Science of Well-Being Is Evolving

How a Strong Indigenous Identity Helps Kids Thrive

Why gardening can grow your mental well-being and cultivate friends

Research has found that gardening in the front yard, where the fruits of your labor are more visible, may foster social connections and better mental health.

Looking for a simple change that can improve your physical, mental and emotional health? Try gardening.

People garden indoors and out, in different weather and climes and with different intensities and goals. Research consistently shows that gardening has a positive effect on mental health and well-being. And emerging research suggests that gardening may also be a way into healthy behavioral changes writ large.

Why is gardening such a healthy pursuit? Research suggests that there are two main pathways that lead gardeners to mental well-being. One is through the connection with nature and its aesthetic beauty. But another, perhaps surprisingly, is how gardening can also be a way for us to connect with other people.

“I feel like it’s just about bringing the pieces back together of what makes us human,” said Jonathan Kingsley , senior lecturer of health promotion at Swinburne University of Technology in Australia.

Why people enjoy gardening

Gardening can be a rich, multisensory experience, and gardeners typically cite the gardens as a source of pleasure and joy , escape or curiosity and learning.

“It’s the taste, the texture, the sensation … wind on your face and your hair, just feeling the elements of nature. And it helps people feel alive, awakening in some way,” said Jill Litt , a senior researcher at the Barcelona Institute for Global Health. “These are things that are very therapeutic.”

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Recent research suggests that the smells of nature may impact well-being, and nature sounds such as birdsong also boost mental health.

Like other nature-based activities, gardening may derive some of its benefits from reducing stress . The attention restoration theory hypothesizes that natural stimuli may decrease mental fatigue by gently holding our attention with “ soft fascination .”

But one trait that makes gardening stand out is that it “requires active participation” and “getting the hands in the dirt,” said Litt, who is also a professor of environmental studies at the University of Colorado at Boulder. “You have to do something.”

With weeding, watering, digging, sowing, pruning and other such horticultural duties, gardening can be a physically demanding hobby. And physical exercise has also been shown to improve mental health.

Growing greens and cultivating connections

Gardening may not only help connect us to nature, but with other humans. Community gardening in a shared space can build trust, as people look after one another’s plots of land, and offer help and advice. This social growth is slow and steady, grounded by a shared purpose, a sense of belonging and learning. “All of it’s textbook on how you build strong relationships,” Litt said. And the “garden calls them to come back, because they have a responsibility.”

But even gardening at home is linked to greater social connectedness. In earlier research , Litt and her colleagues found that home gardeners were more socially involved — more likely to communicate with local elected officials or participate in parent-teacher associations, for example — than non-gardeners.

Home gardeners were also more likely to positively rate the aesthetics of their neighborhood. Both the increased social involvement and aesthetics ratings were associated with better health. Participating in community gardening further enhanced these effects.

Other research has found that gardening in the front yard, where the fruits of your labor are more visible, may also foster social connections and better mental health, said Lauriane Suyin Chalmin-Pui , an independent researcher in Britain specializing in the influence of gardening on well-being.

In one study , Chalmin-Pui and her colleagues transformed 38 bare front yards into gardens for 42 participants. Three months later, the study participants reported lower stress and had healthier cortisol patterns.

The gardens provided more opportunities for participants to bump into their neighbors, and the plants provided an easy conversation starter. When Chalmin-Pui followed up with the participants after a year and a half, she found that people had gotten to know their neighbors.

Some had lived on the same street for 10 years. “But it was only after they both got plants in their front yard that they actually struck up a conversation,” Chalmin-Pui said.

Chalmin-Pui recalled another study participant who was dealing with mental health issues and physical disability. The woman told her that the plants were a “lifesaver” and that “it was the first time that she had felt human in years.”

“She felt that she was keeping them alive,” Chalmin-Pui said. “And the fact that she was keeping them alive meant that she was capable of doing something.”

Gardening as a way to lasting behavioral change

Many of the studies investigating the health benefits of gardening are observational and correlational, so it is difficult to know whether it was the gardening that caused the health changes or whether certain types of people who already had these health behaviors were more drawn to gardening.

In the first randomized controlled trial testing the effect of community gardening on health, Litt and her colleagues worked with 37 community gardens in the Denver and Aurora, Colo., area to more directly test how gardening impacts health. For the study, 291 participants who had not gardened within the past two years were randomly selected to receive a community garden plot or remain on the waitlist.

Compared to waitlisted participants, those who gardened had increased moderate to vigorous physical activity — on average, 40.6 minutes more per week. They also consumed more fiber — about 1.4 grams of roughage each day. After one season of gardening, they also reported lower levels of stress and anxiety.

Though the size of behavioral change was modest, it was a tangible start in line with other health behavior interventions. “We see gardens as an agent of health behavior change,” Litt said.

After the data collection ended, the waitlist participants were also given a garden plot, and over half started gardening the following season, Litt said.

How much gardening do you need?

Researchers are still digging up the details on what “dose” of gardening reaps the most mental health benefit.

In a study published last year surveying 4,919 middle-aged and older adults in Australia, Kingsley and his colleagues reported that gardening for at least 2.5 hours each week was associated with better self-reported mental well-being and life satisfaction. These benefits were stronger for adults 64 and older.

The time in your garden oasis is “competing against other forces that are impacting your mental health every day,” Kingsley said. Though the study was correlational, Kingsley theorizes that 2.5 hours per week in the garden may be a sweet spot to meet that threshold.

For beginners, you can start small. Just a few potted plants indoors is still gardening. Some plants, like mint, are vigorous growers and may be easier for beginner gardeners to keep alive. But growing plants you personally enjoy is probably key, Chalmin-Pui said.

And don’t be afraid to get your hands dirty and make mistakes.

Gardening is “a kind of trial and error and just experience thing, which is life,” Kingsley said. “You’ll have lots of failures and wins in this. And that’s just what life is.”

Do you have a question about human behavior or neuroscience? Email [email protected] and we may answer it in a future column.

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Vegetables and Fruits

• Vegetables and fruits are an important part of a healthy diet, and variety is as important as quantity.
• No single fruit or vegetable provides all of the nutrients you need to be healthy. Eat plenty every day.

A diet rich in vegetables and fruits can lower blood pressure, reduce the risk of heart disease and stroke, prevent some types of cancer, lower risk of eye and digestive problems, and have a positive effect upon blood sugar, which can help keep appetite in check. Eating non-starchy vegetables and fruits like apples, pears, and green leafy vegetables may even promote weight loss. [1] Their low glycemic loads prevent blood sugar spikes that can increase hunger.

At least nine different families of fruits and vegetables exist, each with potentially hundreds of different plant compounds that are beneficial to health. Eat a variety of types and colors of produce in order to give your body the mix of nutrients it needs. This not only ensures a greater diversity of beneficial plant chemicals but also creates eye-appealing meals.

Tips to eat more vegetables and fruits each day

• Keep fruit where you can see it . Place several ready-to-eat washed whole fruits in a bowl or store chopped colorful fruits in a glass bowl in the refrigerator to tempt a sweet tooth.
• Explore the produce aisle and choose something new . Variety and color are key to a healthy diet. On most days, try to get at least one serving from each of the following categories: dark green leafy vegetables; yellow or orange fruits and vegetables; red fruits and vegetables; legumes (beans) and peas; and citrus fruits.
• Skip the potatoes . Choose other vegetables that are packed with different nutrients and more slowly digested  carbohydrates .
• Make it a meal . Try cooking new  recipes that include more vegetables. Salads, soups, and stir-fries are just a few ideas for increasing the number of tasty vegetables in your meals.

5 common questions about fruits and vegetables.

Vegetables, fruits, and disease, cardiovascular disease.

There is compelling evidence that a diet rich in fruits and vegetables can lower the risk of heart disease and stroke.

• A meta-analysis of cohort studies following 469,551 participants found that a higher intake of fruits and vegetables is associated with a reduced risk of death from cardiovascular disease, with an average reduction in risk of 4% for each additional serving per day of fruit and vegetables. [2]
• The largest and longest study to date, done as part of the Harvard-based Nurses’ Health Study and Health Professionals Follow-up Study, included almost 110,000 men and women whose health and dietary habits were followed for 14 years.
• The higher the average daily intake of fruits and vegetables, the lower the chances of developing cardiovascular disease. Compared with those in the lowest category of fruit and vegetable intake (less than 1.5 servings a day), those who averaged 8 or more servings a day were 30% less likely to have had a heart attack or stroke. [3]
• Although all fruits and vegetables likely contributed to this benefit, green leafy vegetables, such as lettuce, spinach, Swiss chard, and mustard greens, were most strongly associated with decreased risk of cardiovascular disease. Cruciferous vegetables such as broccoli, cauliflower, cabbage, Brussels sprouts , bok choy, and kale ; and citrus fruits such as oranges, lemons, limes, and grapefruit (and their juices) also made important contributions. [3]
• When researchers combined findings from the Harvard studies with several other long-term studies in the U.S. and Europe, and looked at coronary heart disease and stroke separately, they found a similar protective effect: Individuals who ate more than 5 servings of fruits and vegetables per day had roughly a 20% lower risk of coronary heart disease [4] and stroke, [5] compared with individuals who ate less than 3 servings per day.

Blood pressure

• The  Dietary Approaches to Stop Hypertension (DASH) study [6] examined the effect on blood pressure of a diet that was rich in fruits, vegetables, and low-fat dairy products and that restricted the amount of saturated and total fat. The researchers found that people with high blood pressure who followed this diet reduced their systolic blood pressure (the upper number of a blood pressure reading) by about 11 mm Hg and their diastolic blood pressure (the lower number) by almost 6 mm Hg—as much as medications can achieve.
• A randomized trial known as the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart) showed that this fruit and vegetable-rich diet lowered blood pressure even more when some of the carbohydrate was replaced with healthy unsaturated fat or protein. [7]
• In 2014 a meta-analysis of clinical trials and observational studies found that consumption of a vegetarian diet was associated with lower blood pressure. [8]

Numerous early studies revealed what appeared to be a strong link between eating fruits and vegetables and protection against cancer . Unlike case-control studies, cohort studies , which follow large groups of initially healthy individuals for years, generally provide more reliable information than case-control studies because they don’t rely on information from the past. And, in general, data from cohort studies have not consistently shown that a diet rich in fruits and vegetables prevents cancer.

• For example, over a 14-year period in the Nurses’ Health Study and the Health Professionals Follow-up Study, men and women with the highest intake of fruits and vegetables (8+ servings a day) were just as likely to have developed cancer as those who ate the fewest daily servings (under 1.5). [3]
• A meta-analysis of cohort studies found that a higher fruit and vegetable intake did not decrease the risk of deaths from cancer. [2]

A more likely possibility is that some types of fruits and vegetables may protect against certain cancers.

• A study by Farvid and colleagues followed a Nurses’ Health Study II cohort of 90,476 premenopausal women for 22 years and found that those who ate the most fruit during adolescence (about 3 servings a day) compared with those who ate the lowest intakes (0.5 servings a day) had a 25% lower risk of developing breast cancer. There was a significant reduction in breast cancer in women who had eaten higher intakes of apples, bananas , grapes, and corn during adolescence, and oranges and kale during early adulthood. No protection was found from drinking fruit juices at younger ages. [9]
• Farvid and colleagues followed 90, 534 premenopausal women from the Nurses’ Health Study II over 20 years and found that higher fiber intakes during adolescence and early adulthood were associated with a reduced risk of breast cancer later in life. When comparing the highest and lowest fiber intakes from fruits and vegetables, women with the highest fruit fiber intake had a 12% reduced risk of breast cancer; those with the highest vegetable fiber intake had an 11% reduced risk. [10]
• After following 182,145 women in the Nurses’ Health Study I and II for 30 years, Farvid’s team also found that women who ate more than 5.5 servings of fruits and vegetables each day (especially cruciferous and yellow/orange vegetables) had an 11% lower risk of breast cancer than those who ate 2.5 or fewer servings. Vegetable intake was strongly associated with a 15% lower risk of estrogen-receptor-negative tumors for every two additional servings of vegetables eaten daily. A higher intake of fruits and vegetables was associated with a lower risk of other aggressive tumors including HER2-enriched and basal-like tumors. [11]
• A report by the World Cancer Research Fund and the American Institute for Cancer Research suggests that non-starchy vegetables—such as lettuce and other leafy greens, broccoli, bok choy, cabbage, as well as garlic, onions, and the like—and fruits “probably” protect against several types of cancers, including those of the mouth, throat, voice box, esophagus, and stomach. Fruit probably also protects against lung cancer. [12]

Specific components of fruits and vegetables may also be protective against cancer. For example:

• A line of research stemming from a finding from the Health Professionals Follow-up Study suggests that tomatoes may help protect men against prostate cancer, especially aggressive forms of it. [12] One of the pigments that give tomatoes their red hue—lycopene—could be involved in this protective effect. Although several studies other than the Health Professionals Study have also demonstrated a link between tomatoes or lycopene and prostate cancer, others have not or have found only a weak connection. [14]
• Taken as a whole, however, these studies suggest that increased consumption of tomato-based products (especially cooked tomato products) and other lycopene-containing foods may reduce the occurrence of prostate cancer. [12] Lycopene is one of several carotenoids (compounds that the body can turn into vitamin A) found in brightly colored fruits and vegetables, and research suggests that foods containing carotenoids may protect against lung, mouth, and throat cancer. [12] But more research is needed to understand the exact relationship between fruits and vegetables, carotenoids, and cancer.

Some research looks specifically at whether individual fruits are associated with risk of type 2 diabetes. While there isn’t an abundance of research into this area yet, preliminary results are compelling.

• A study of over 66,000 women in the Nurses’ Health Study, 85,104 women from the Nurses’ Health Study II, and 36,173 men from the Health Professionals Follow-up Study—who were free of major chronic diseases—found that greater consumption of whole fruits—especially blueberries, grapes, and apples—was associated with a lower risk of type 2 diabetes. Another important finding was that greater consumption of fruit juice was associated with a higher risk of type 2 diabetes. [15]
• Additionally a study of over 70,000 female nurses aged 38-63 years, who were free of cardiovascular disease, cancer, and diabetes, showed that consumption of green leafy vegetables and fruit was associated with a lower risk of diabetes. While not conclusive, research also indicated that consumption of fruit juices may be associated with an increased risk among women. (16)
• A study of over 2,300 Finnish men showed that vegetables and fruits, especially berries, may reduce the risk of type 2 diabetes. [17]

Data from the Nurses’ Health Studies and the Health Professional’s Follow-up Study show that women and men who increased their intakes of fruits and vegetables over a 24-year period were more likely to have lost weight than those who ate the same amount or those who decreased their intake. Berries, apples, pears, soy, and cauliflower were associated with weight loss while starchier vegetables like potatoes, corn, and peas were linked with weight gain. [1] However, keep in mind that adding more produce into the diet won’t necessarily help with weight loss unless it replaces another food, such as refined carbohydrates of white bread and crackers.

Gastrointestinal health

Fruits and vegetables contain indigestible fiber, which absorbs water and expands as it passes through the digestive system. This can calm symptoms of an irritable bowel and, by triggering regular bowel movements, can relieve or prevent constipation. [18] The bulking and softening action of insoluble fiber also decreases pressure inside the intestinal tract and may help prevent diverticulosis. [19]

Eating fruits and vegetables can also keep your eyes healthy, and may help prevent two common aging-related eye diseases—cataracts and macular degeneration—which afflict millions of Americans over age 65. [20-23] Lutein and zeaxanthin, in particular, seem to reduce risk of cataracts. [24]

• Bertoia ML, Mukamal KJ, Cahill LE, Hou T, Ludwig DS, Mozaffarian D, Willett WC, Hu FB, Rimm EB. Changes in intake of fruits and vegetables and weight change in United States men and women followed for up to 24 years: analysis from three prospective cohort studies. PLoS medicine . 2015 Sep 22;12(9):e1001878.
• Wang X, Ouyang Y, Liu J, Zhu M, Zhao G, Bao W, Hu FB. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ . 2014 Jul 29;349:g4490.
• Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, Colditz GA, Rosner B, Spiegelman D, Willett WC. Fruit and vegetable intake and risk of major chronic disease. Journal of the National Cancer Institute . 2004 Nov 3;96(21):1577-84.
• He FJ, Nowson CA, Lucas M, MacGregor GA. Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: meta-analysis of cohort studies. Journal of human hypertension . 2007 Sep;21(9):717.
• He FJ, Nowson CA, MacGregor GA. Fruit and vegetable consumption and stroke: meta-analysis of cohort studies. The Lancet . 2006 Jan 28;367(9507):320-6.
• Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, Bray GA, Vogt TM, Cutler JA, Windhauser MM, Lin PH. A clinical trial of the effects of dietary patterns on blood pressure. New England Journal of Medicine . 1997 Apr 17;336(16):1117-24.
• Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, Conlin PR, Erlinger TP, Rosner BA, Laranjo NM, Charleston J. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. JAMA . 2005 Nov 16;294(19):2455-64.
• Yokoyama Y, Nishimura K, Barnard ND, Takegami M, Watanabe M, Sekikawa A, Okamura T, Miyamoto Y. Vegetarian diets and blood pressure: a meta-analysis. JAMA internal medicine. 2014 Apr 1;174(4):577-87.
• Farvid MS, Chen WY, Michels KB, Cho E, Willett WC, Eliassen AH. Fruit and vegetable consumption in adolescence and early adulthood and risk of breast cancer: population based cohort study. BMJ . 2016 May 11;353:i2343.
• Farvid MS, Eliassen AH, Cho E, Liao X, Chen WY, Willett WC. Dietary fiber intake in young adults and breast cancer risk. Pediatrics . 2016 Mar 1;137(3):e20151226.
• Farvid MS, Chen WY, Rosner BA, Tamimi RM, Willett WC, Eliassen AH. Fruit and vegetable consumption and breast cancer incidence: Repeated measures over 30 years of follow‐up. International journal of cancer . 2018 Jul 6.
• Wiseman M. The Second World Cancer Research Fund/American Institute for Cancer Research Expert Report. Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective: Nutrition Society and BAPEN Medical Symposium on ‘Nutrition support in cancer therapy’. Proceedings of the Nutrition Society . 2008 Aug;67(3):253-6.
• Giovannucci E, Liu Y, Platz EA, Stampfer MJ, Willett WC. Risk factors for prostate cancer incidence and progression in the health professionals follow‐up study. International journal of cancer . 2007 Oct 1;121(7):1571-8.
• Kavanaugh CJ, Trumbo PR, Ellwood KC. The US Food and Drug Administration’s evidence-based review for qualified health claims: tomatoes, lycopene, and cancer. Journal of the National Cancer Institute . 2007 Jul 18;99(14):1074-85.
• Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, van Dam RM, Sun Q. Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ . 2013 Aug 29;347:f5001.
• Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care . 2008 Apr 3.
• Mursu J, Virtanen JK, Tuomainen TP, Nurmi T, Voutilainen S. Intake of fruit, berries, and vegetables and risk of type 2 diabetes in Finnish men: the Kuopio Ischaemic Heart Disease Risk Factor Study–. The American journal of clinical nutrition . 2013 Nov 20;99(2):328-33.
• Lembo A, Camilleri M. Chronic constipation. New England Journal of Medicine . 2003 Oct 2;349(14):1360-8.
• Aldoori WH, Giovannucci EL, Rockett HR, Sampson L, Rimm EB, Willett AW. A prospective study of dietary fiber types and symptomatic diverticular disease in men. The Journal of nutrition . 1998 Oct 1;128(4):714-9.
• Brown L, Rimm EB, Seddon JM, Giovannucci EL, Chasan-Taber L, Spiegelman D, Willett WC, Hankinson SE. A prospective study of carotenoid intake and risk of cataract extraction in US men–. The American journal of clinical nutrition . 1999 Oct 1;70(4):517-24.
• Christen WG, Liu S, Schaumberg DA, Buring JE. Fruit and vegetable intake and the risk of cataract in women–. The American journal of clinical nutrition . 2005 Jun 1;81(6):1417-22.
• Moeller SM, Taylor A, Tucker KL, McCullough ML, Chylack Jr LT, Hankinson SE, Willett WC, Jacques PF. Overall adherence to the dietary guidelines for Americans is associated with reduced prevalence of early age-related nuclear lens opacities in women. The Journal of nutrition . 2004 Jul 1;134(7):1812-9.
• Cho E, Seddon JM, Rosner B, Willett WC, Hankinson SE. Prospective study of intake of fruits, vegetables, vitamins, and carotenoidsand risk of age-related maculopathy. Archives of Ophthalmology . 2004 Jun 1;122(6):883-92.
• Christen WG, Liu S, Glynn RJ, Gaziano JM, Buring JE. Dietary carotenoids, vitamins C and E, and risk of cataract in women: a prospective study. Archives of Ophthalmology . 2008 Jan 1;126(1):102-9.

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