essay on effect of poverty on health

Poverty and Health

Poverty is a major cause of ill health and a barrier to accessing health care when needed. This relationship is financial: the poor cannot afford to purchase those things that are needed for good health, including sufficient quantities of quality food and health care. But, the relationship is also related to other factors related to poverty, such as lack of information on appropriate health-promoting practices or lack of voice needed to make social services work for them.

Ill health, in turn, is a major cause of poverty. This is partly due to the costs of seeking health care, which include not only out-of-pocket spending on care (such as consultations, tests and medicine), but also transportation costs and any informal payments to providers. It is also due to the considerable loss of income associated with illness in developing countries, both of the breadwinner, but also of family members who may be obliged to stop working or attending school to take care of an ill relative. In addition, poor families coping with illness might be forced to sell assets to cover medical expenses, borrow at high interest rates or become indebted to the community.

Strong  health systems  improve the health status of the whole population, but especially of the poor among whom ill health and poor access to health care tends to be concentrated, as well as protect households from the potentially catastrophic effects of out-of-pocket health care costs. In general, poor health is disproportionately concentrated among the poor.

The World Bank’s work in the area of health equity and financial protection is defined by the  2007 Health, Nutrition and Population Strategy . The strategy identifies “preventing poverty due to illness (by improving financial protection)” as one of its four strategic objectives and commits the Bank’s health team, both through its analytical work and its regional operations, to addressing vulnerability that arises from health shocks.

The strategy also stresses the importance of equity in health outcomes in a second strategic objective to "improve the level and distribution of key health, nutrition and population outcomes... particularly for the poor and the vulnerable".

The Bank supports governments to implement a variety of policies and programs to reduce inequalities in health outcomes and enhance financial protection. Generally, this involves mechanisms that help overcome geographic, social and psychological barriers to accessing care and reducing out-of-pocket cost of treatment. Examples include:

• Reducing the direct cost of care at the point of service, e.g. through reducing/abolishing user fees for the poor or expanding health insurance to the poor (including coverage, depth and breadth).
• Increasing efficiency of care to reduce total consumption of care, e.g. by limiting “irrational drug prescribing,” strengthening the referral system, or improving the quality of providers (especially at the lower level).
• Reducing inequalities in determinants of health status or health care utilization, such as reducing distance (through providing services closer to the poor), subsidizing travel costs, targeted health promotion, conditional cash transfers.
• Expanding access to care by using the private sector or public-private partnerships.

The Bank’s health team also promotes the monitoring of equity and financial protection by publishing global statistics on inequalities in health status, access to care and financial protection, as well as training government officials, policymakers and researchers in how to measure and monitor the same.

Examples of how World Bank projects have improved health coverage for the poor and reduced financial vulnerability include:

The  Rajasthan Health Systems Development Project resulted in improved access to care for vulnerable Indians. The share of below-poverty line Indians in the overall inpatient and outpatient load at secondary facilities more than doubled between 2006 and 2011, well exceeding targets. In the same period, the share of the vulnerable tribal populations in the overall patient composition tripled.

The  Georgia Health Sector Development Project  supported the government of Georgia in implementing the Medical Insurance Program for the Poor, effectively increasing the share of the government health expenditure earmarked for the poor from 4% in 2006 to 38% in 2011. It also increased the number of health care visits of both the general population and the poor, but by more for the poor (from 2 per capita per year to 2.6) than for the general population (from 2 to 2.3) over the same time period.

The  Mekong Regional Health Support Project  helped the government of Vietnam to increase access to (government) health insurance from 29% to 94% among the poor, as well as from 7% to 68% among the near-poor. Hospitalization and consultation rates, at government facilities, also increased among both the poor and near-poor.

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Economic Stability

About This Literature Summary

This summary of the literature on Poverty as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Discrimination , Employment , Housing Instability , and Incarceration literature summaries. 

Related Objectives (4)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Reduce the proportion of people living in poverty — SDOH‑01
• Increase employment in working-age people — SDOH‑02
• Increase the proportion of children living with at least 1 parent who works full time — SDOH‑03
• Reduce the proportion of families that spend more than 30 percent of income on housing — SDOH‑04

Related Evidence-Based Resources (3)

Here's a snapshot of the evidence-based resources related to topics covered in this literature summary. Browse all evidence-based resources .

• Social Determinants of Health: Tenant-Based Housing Voucher Programs
• New Perspectives on Creating Jobs: Final Impacts of the Next Generation of Subsidized Employment Programs
• Strengthening TANF Outcomes By Developing Two-Generation Approaches To Build Economic Security

Literature Summary

The United States measures poverty based on how an individual’s or family’s income compares to a set federal threshold. 1 For example, in the 2021 definition, people are considered impoverished if their individual income is below $12,880 or their household income is below $26,500 for a family of 4. 2 After 5 consecutive years in decline, the U.S. poverty rate increased to 11.4 percent in 2020, or a total of 37.2 million people. 3  

Poverty often occurs in concentrated areas and endures for long periods of time. 1 Some communities, such as certain racial and ethnic groups, people living in rural areas, and people with disabilities, have a higher risk of poverty for a myriad of factors that extend beyond individual control. 1 , 4 – 8 For example, institutional racism and discrimination contribute to unequal social and economic opportunities. 4 Residents of impoverished communities often have reduced access to resources that are needed to support a healthy quality of life, such as stable housing , healthy foods , and safe neighborhoods. 1 , 4 , 9 Poverty can also limit access to educational and employment opportunities, which further contributes to income inequality and perpetuates cyclical effects of poverty. 1  

Unmet social needs, environmental factors, and barriers to accessing health care contribute to worse health outcomes for people with lower incomes. 10 , 11 For example, people with limited finances may have more difficulty obtaining health insurance or paying for expensive procedures and medications. 12 In addition, neighborhood factors, such as limited access to healthy foods and higher instances of violence , can affect health by influencing health behaviors and stress. 12  

Across the lifespan, residents of impoverished communities are at increased risk for mental illness, chronic disease, higher mortality, and lower life expectancy. 9 , 13 – 17 Children make up the largest age group of those experiencing poverty. 18 , 19 Childhood poverty is associated with developmental delays, toxic stress, chronic illness, and nutritional deficits. 20 – 24 Individuals who experience childhood poverty are more likely to experience poverty into adulthood, which contributes to generational cycles of poverty. 25 In addition to lasting effects of childhood poverty, adults living in poverty are at a higher risk of adverse health effects from obesity, smoking, substance use, and chronic stress. 12 Finally, older adults with lower incomes experience higher rates of disability and mortality. 6 One study found that men and women in the top 1 percent of income were expected to live 14.6 and 10.1 years longer respectively than men and women in the bottom 1 percent. 26

Poverty is a multifaceted issue that will require multipronged approaches to address. Strategies that improve the economic mobility of families may help to alleviate the negative effects of poverty. 27 – 29 For example, tax credits such as the Earned Income Tax Credit and Child Tax Credit alleviate financial burdens for families with lower and middle incomes by reducing the amount of taxes owed. 30 In addition, federal social assistance programs are designed to provide safety net services and specifically benefit individuals and families with lower incomes. 31 Two of the nation’s largest social assistance programs are Medicaid, which provides health coverage, and the Supplemental Nutrition Assistance Program (SNAP), which provides food assistance. Medicaid and SNAP serve millions of people each year and have been associated with reductions in poverty along with overall health benefits. 32 , 33 In order to reduce socioeconomic inequality, it may also be important to address factors that are associated with the health status of poor communities. 27 Additional research and interventions are needed to address the effects of poverty on health outcomes and disparities. 

U.S. Department of Agriculture, Economic Research Service. (n.d.) Rural poverty & well-being . Retrieved December 13, 2021, from https://www.ers.usda.gov/topics/rural-economy-population/rural-poverty-well-being/

U.S. Department of Agriculture, Office of the Assistant Secretary for Planning and Evaluation. (2021, February 1). 2021 Poverty guidelines . https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references/2021-poverty-guidelines

Shrider, E. A., Kollar, M., Chen, F., & Semega, J. (2021, September 14). Income and poverty in the United States: 2020 . U.S. Census Bureau. https://www.census.gov/library/publications/2021/demo/p60-273.html

Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186 (1), 69–101. https://doi.org/10.1111/j.1749-6632.2009.05339.x

Kaiser Family Foundation. (n.d.). Poverty rate by race/ethnicity . https://www.kff.org/other/state-indicator/poverty-rate-by-raceethnicity/

Minkler, M., Fuller-Thomson, E., & Guralnik, J. M. (2006). Gradient of disability across the socioeconomic spectrum in the United States. New England Journal of Medicine, 355 (7), 695–703. https://doi.org/10.1056/NEJMsa044316

Brucker, D. L., Mitra, S., Chaitoo, N., & Mauro, J. (2015). More likely to be poor whatever the measure: Working-age persons with disabilities in the United States. Social Science Quarterly, 96 (1), 273–296. https://doi.org/10.1111/ssqu.12098

Rank, M. R., & Hirschl, T. A. (2015). The likelihood of experiencing relative poverty over the life course. PLoS ONE, 10 (7), e0133513. https://doi.org/10.1371/journal.pone.0133513

Singh, G. K., & Siahpush, M. (2006). Widening socioeconomic inequalities in US life expectancy, 1980–2000. International Journal of Epidemiology, 35 (4), 969–979. https://doi.org/10.1093/ije/dyl083

Phelan, J. C., Link, B. G., & Tehranifar, P. (2010). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(Suppl 1) , S28–S40. https://doi.org/10.1177/0022146510383498

Thompson, T., McQueen, A., Croston, M., Luke, A., Caito, N., Quinn, K., Funaro, J., & Kreuter, M. W. (2019). Social needs and health-related outcomes among Medicaid beneficiaries. Health Education & Behavior: The Official Publication of the Society for Public Health Education, 46 (3), 436–444. https://doi.org/10.1177/1090198118822724

Khullar, D., & Chokshi, D. A. (2018). Health, income, & poverty: Where we are & what could help . Health Affairs Health Policy Brief. https://doi.org/10.1377/hpb20180817.901935

Braveman, P. A., Cubbin, C., Egerter, S., Williams, D. R., & Pamuk, E. (2010). Socioeconomic disparities in health in the United States: What the patterns tell us. American Journal of Public Health, 100 (Suppl 1), S186–S196. https://doi.org/10.2105/AJPH.2009.166082

Belle, D., & Doucet, J. (2003). Poverty, inequality, and discrimination as sources of depression among U.S. women. Psychology of Women Quarterly, 27 (2), 101–113. https://doi.org/10.1111/1471-6402.00090

Caughy, M. O., O’Campo, P. J., & Muntaner, C. (2003). When being alone might be better: Neighborhood poverty, social capital, and child mental health. Social Science & Medicine, 57 (2), 227–237. https://doi.org/10.1016/S0277-9536(02)00342-8

Ward-Smith, P. (2007). The effects of poverty on urologic health. Urologic Nursing, 27 (5), 445–446.

Mode, N. A., Evans, M. K., & Zonderman, A. B. (2016). Race, neighborhood economic status, income inequality and mortality. PLoS ONE, 11 (5), e0154535. https://doi.org/10.1371/journal.pone.0154535

Kaiser Family Foundation. (n.d.). Poverty rate by age . https://www.kff.org/other/state-indicator/poverty-rate-by-age/

Cellini, S. R., McKernan, S. M., & Ratcliffe, C. (2008). The dynamics of poverty in the United States: A review of data, methods, and findings. Journal of Policy Analysis and Management, 27 (3), 577–605.   https://onlinelibrary.wiley.com/doi/abs/10.1002/pam.20337

Eamon, M. K. (2001). The effects of poverty on children’s socioemotional development: An ecological systems analysis. Social Work, 46 (3), 256–266.

Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self-regulation, and coping. Child Development Perspectives, 7 (1), 43–48. https://doi.org/10.1111/cdep.12013

Shaw, D. S., & Shelleby, E. C. (2014). Early-starting conduct problems: Intersection of conduct problems and poverty. Annual Review of Clinical Psychology, 10 (1), 503–528. https://doi.org/10.1146/annurev-clinpsy-032813-153650

Justice, L. M., Jiang, H., Purtell, K. M., Schmeer, K., Boone, K., Bates, R., & Salsberry, P. J. (2019). Conditions of poverty, parent-child interactions, and toddlers’ early language skills in low-income families. Maternal and Child Health Journal, 23 (7), 971–978. https://doi.org/10.1007/s10995-018-02726-9

Council on Community Pediatrics, Gitterman, B. A., Flanagan, P. J., Cotton, W. H., Dilley, K. J., Duffee, J. H., Green, A. E., Keane, V. A., Krugman, S. D., Linton, J. M., McKelvey, C. D., & Nelson, J. L. (2016). Poverty and child health in the United States. Pediatrics, 137 (4), e20160339. https://doi.org/10.1542/peds.2016-0339

Wagmiller Jr, R. L., & Adelman, R. M. (2009). Childhood and intergenerational poverty: The long-term consequences of growing up poor . National Center for Children in Poverty. https://www.nccp.org/publication/childhood-and-intergenerational-poverty/

Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., & Cutler, D. (2016). The association between income and life expectancy in the United States, 2001–2014. JAMA, 315 (16), 1750–1766. https://doi.org/10.1001/jama.2016.4226

Yoshikawa, H., Aber, J. L., & Beardslee, W. R. (2012). The effects of poverty on the mental, emotional, and behavioral health of children and youth: Implications for prevention. The American Psychologist, 67 (4), 272–284. https://doi.org/10.1037/a0028015

Riccio, J. A., Dechausay, N., Greenberg, D. M., Miller, C., Rucks, Z., & Verma, N. (2010). Toward reduced poverty across generations: Early findings from New York City’s conditional cash transfer program . MDRC.

Love, J. M., Kisker, E. E., Ross, C. M., Schochet, P. Z., Brooks-Gunn, J., Paulsell, D., Boller, K., Constantine, J., Vogel, C., Fuligni, A. S., & Brady-Smith, C. (2002). Making a difference in the lives of infants and toddlers and their families: The impacts of early Head Start. Volumes I–III: Final technical report and appendixes and local contributions to understanding the programs and their impacts . U.S. Department of Health and Human Services, Administration for Children and Families.

Maag, E., & Airi, N. (2020). Moving forward with the earned income tax credit and child tax credit: Analysis of proposals to expand refundable tax credits. National Tax Journal, 73 (4), 1163–1186. https://doi.org/10.17310/ntj.2020.4.11

Blank, R. M. (2002). Evaluating welfare reform in the United States. Journal of Economic Literature, 40 (4), 1105–1166.

Currie, J., & Chorniy, A. (2021). Medicaid and Child Health Insurance Program improve child health and reduce poverty but face threats. Academic Pediatrics, 21 (8), S146–S153. https://doi.org/10.1016/j.acap.2021.01.009

Keith-Jennings, B., Llobrera, J., & Dean, S. (2019). Links of the Supplemental Nutrition Assistance Program with food insecurity, poverty, and health: Evidence and potential. American Journal of Public Health, 109 (12), 1636–1640. https://doi.org/10.2105/AJPH.2019.305325

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Poverty, health, and covid-19

Read our latest coverage of the coronavirus outbreak.

• Related content
• Peer review
• Margaret Whitehead , W H Duncan professor of public health ,
• David Taylor-Robinson , professor of public health and policy ,
• Ben Barr , professor of applied public health research
• Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, UK
• Correspondence to: M Whitehead mmw{at}liverpool.ac.uk

Yet again, poor families will be hardest hit by the pandemic’s long economic fallout

Covid-19 does not strike at random—mortality is much higher in elderly people, poorer groups, and ethnic minorities, and its economic effect is also unevenly distributed across the population. The economic fallout is likely to be felt for years. Without concerted preventive action worse off families and communities will be disproportionately affected, increasing health inequalities in the UK and globally.

Even before covid-19, extremely disturbing trends in health were emerging in England. Growing child poverty, homelessness, and food poverty led to an unprecedented rise in infant mortality, mental health problems, and stalling life expectancy, especially for women in the poorest areas and cities. 1 These were the same areas where 10 years of austerity measures had hit the poorest groups the hardest. Larger cuts in government funding to local authorities with higher proportions of children in poverty meant a reduction in spending on vital preventive services in areas where they were needed most. 2 The pandemic arrived in the middle of this worrying scene and amplified existing inequalities.

Exposure to infection is unequal. People in precarious, low paid, manual jobs in the caring, retail, and service sectors have been more exposed to covid-19 as their face-to-face jobs cannot be done from home. 3 Overcrowded, poor quality housing in densely populated areas have often added to their increased risk. 4 Poorer communities have also been more vulnerable to severe disease once infected because of higher levels of pre-existing illness. Increased rates of infection have led to greater loss of income linked to disruptions to work and job loss, but the immediate financial pressure of covid-19 has gone far beyond this.

Containment and lockdown measures have disproportionately affected low income families with young children. 5 Recent research identified the extra costs involved in having children at home for longer without access to vital free services, requiring increased spending on food, heating, and occupying children indoors. Over a third of low income families with children increased their spending during 2020, while 40% of high income families without children reduced theirs. 6

Rising demand for universal credit exposed the inadequacy of current levels of benefits. The UK government increased universal credit payments by £20 (€23; $28) a week to compensate for extra expenses during lockdown, but as yet the increase is only temporary. Food poverty increased, with free school meals—an essential nutritional boost for many low income families—having to be replaced by emergency measures to prevent children going hungry during school closures. Government support for this scheme has been precarious, and at times the measures have been inadequate to maintain the health of growing children. 7

Long term forecast

Predicted long term economic effects include loss of future earnings and unemployment, pushing more adults, particularly parents, into poverty. The effect of the pandemic on employment is predicted to be 10 times greater than that of the 2008 financial crisis, 8 which led to a sharp increase in suicides and mental illness. 9 The pandemic induced recession is likely to have a similarly damaging effect on mental health.

By far the most devastating long term costs of the pandemic are likely to fall on today’s children as they grow, develop, and forge their own economic futures. 2 5 Child poverty is already the biggest threat to child health and development in the UK and globally, 10 so the predicted increase is concerning. 11 A combination of worse financial strain within families and stay-at-home pandemic policies is causing immediate harm to the development and mental health of children, with some younger children regressing in basic skills. 12 Currently, one in six children and young people have mental health problems 13 as their lives are “put on hold,” with clear implications for their long term health and earnings.

Lost learning will cause the greatest damage to the qualifications and job prospects of pupils who are already disadvantaged. Calling for a “massive national policy response,” 14 the Institute for Fiscal Studies estimated that missing half a year of school could mean losing £40 000 in lifetime earnings, with negative effects concentrated among children from disadvantaged backgrounds.

The common framing of action as a trade-off between protecting health or protecting the economy is a false dichotomy: international evidence shows that the virus must be under control for the economy to recover. 15 We need to protect the worse off in society from the adverse consequences falling disproportionately on them, especially by giving every child the best start in life. This could include, in the immediate future, retaining the universal credit uplift, raising the pupil premium, and introducing intensive measures to help disadvantaged pupils catch up on lost learning, including addressing the digital divide.

In the medium term, the large numbers of people out of work and those whose ability to work is reduced because of the long term effects of covid-19 will need effective support and training to return to work. Reinvesting in children’s preventive services such as Sure Start children’s centres and improved access to a range of mental health services will be crucial. But above all we must avoid reintroducing austerity measures to fix the economy, which would again fall heaviest on the most disadvantaged groups and communities, widening health inequalities still further. Instead, we must “build back fairer.” 16

Competing interests: We have read and understood BMJ policy on declaration of interests and have no interests to declare.

Provenance and peer review: Commissioned; not externally peer reviewed.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

• Taylor-Robinson D ,
• Whitehead M
• ↵ Bambra C, Monford L, Alexiou A, et al. COVID-19 and the Northern Powerhouse. Northern Health Science Alliance, 2020. https://www.thenhsa.co.uk/app/uploads/2020/11/NP-COVID-REPORT-101120-.pdf .
• ↵ Whitehead M, Taylor-Robinson D, Barr B. Covid-19: We are not “all in it together”—less privileged in society are suffering the brunt of the damage. BMJ Opinion 22 May 2020. blogs.bmj.com/bmj/2020/05/22/covid-19-we-are-not-all-in-it-together-less-privileged-in-society-are-suffering-the-brunt-of-the-damage/
• ↵ Daras K, Alexiou A, Rose TC, Buchan I, Taylor-Robinson D, Barr B. How does vulnerability to covid-19 vary between communities in England? Developing a small area vulnerability index (SAVI). Social Science Research Network 2020. [Preprint.] doi: 10.2139/ssrn.3650050
• Hefferon C ,
• Bennett D ,
• ↵ OECD. Employment outlook 2020. http://www.oecd.org/employment-outlook/2020/
• Scott-Samuel A ,
• ↵ Unicef. COVID-19 impacts on child poverty. 2020. https://www.unicef.org/social-policy/child-poverty/covid-19-socioeconomic-impacts
• ↵ Francis-Devine B. Poverty in the UK: statistics. House of Commons Library, 2021. https://commonslibrary.parliament.uk/research-briefings/sn07096/
• ↵ Ofsted Chief Inspector. “We cannot furlough young people’s learning.” Education in the media. https://dfemedia.blog.gov.uk/2021/01/03/ofsted-chief-inspector-we-cannot-furlough-young-peoples-learning/
• ↵ NHS Digital. Mental health of children and young people in England, 2020: Wave 1 follow up to the 2017 survey. NHS Digital, 2020. https://digital.nhs.uk/data-and-information/publications/statistical/mental-health-of-children-and-young-people-in-england/2020-wave-1-follow-up
• ↵ Aum S, Lee SY, Shin Y. Inequality of fear and self-quarantine: is there a trade-off between GDP and Public Health? Centre for Economic Policy Research, 2020. https://ideas.repec.org/p/cpr/ceprdp/14679.html .
• Goldblatt P ,

Poverty and child welfare

• Understanding poverty
• Child poverty in Ontario
• Poverty in Indigenous and racialized communities
• Effects of poverty on children

Effects of poverty on families

• Child welfare and poverty
• Working with low-income families
• More resources

Poverty can negatively impact families and caregivers in a number of ways:

• As with children, adults who live in poverty experience worse health outcomes, including higher mortality rates  and increased risk of mental health conditions (e.g. depression, substance use disorders). The stress of poverty, coupled with inadequate health care access and limited financial resources for treatment, further exacerbates health conditions and makes parenting even more challenging
• Poverty can create considerable stress for families. As per the family stress model , poverty can contribute to interparental conflict, which plays a key role in family dynamics and can be a precursor to negative child outcomes. Conflict can also arise between children and parents because of economic pressures. For example, children may resent parents for having to work late or not being able to provide small luxuries. Finally, the living conditions associated with poverty - notably overcrowded housing and housing instability - can negatively affect all family relationships, including sibling relationships
• Poverty can make it difficult for parents to maintain a work-life balance that allows them to spend time at home caring for their children and to be active and involved with school, extracurricular activities, and community life. Parents on a low income are more likely to work long hours in  precarious jobs  that do not provide basic supports like parental leave and sick pay. Low-income workers typically also have less flexibility and choice than other parents (for example, they must rely on public transportation and do not have access to work-from-home options)
• Low-income fathers and paternal family members may be at risk of reduced family involvement due to negative perceptions they may have regarding their value and ability to fill the role of father as economic provider. It's important to note that the relationship between poverty and father involvement is complicated, as structural violence and other systemic barriers also play a role. Recent research also indicates that, despite racist and classist stereotypes about "deadbeat dads," the majority of low-income fathers are involved with their children once the definition of fatherhood is expanded beyond financial contributor

Spotlight on low-income parenting

• Parenting on a low income [2012] This report from About Families looks at the impact of low income on parenting. more... less... https://web.archive.org/web/20180412174026/https://aboutfamilies.files.wordpress.com/2012/03/about-families-report-parenting-on-a-low-income.pdf
• The relationship between poverty and parenting [2007] Summary and link to a report from the Joseph Rowntree Foundation, an organization working to solve UK poverty.
• Your helicopter parenting means you’re privileged [2018] This Maclean's article discusses heliciopter parenting and how it is not an option for many low-income families in Canada.
• Vast majority of impoverished fathers involved with their children [2016] Brief summary of a research study that found, despite stereotypes, many low-income fathers are involved in the lives of their children

Links and resources

• Inter-parental relationships, conflict, and the impacts of poverty [2017]
• Full house? How overcrowded housing affects families [2005]

• << Previous: Effects of poverty on children
• Next: Child welfare and poverty >>
• Last Updated: Sep 22, 2022 10:32 AM
• URL: https://oacas.libguides.com/poverty

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11 Poverty, Health, and Healthcare

Darrell J. Gaskin is an associate professor of health economics at Johns Hopkins University.

Eric T. Roberts is a graduate student in the department of health policy and management at Johns Hopkins University.

• Published: 28 December 2012
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This article discusses the relationship between poverty status and healthcare. The article is organized as follows. Section 2 reviews the empirical literature on the relationship between poverty and health. Section 3 discusses and gives an overview of Medicaid, the nation's public health insurance program for poor and disabled individuals. It then discusses five major issues that health economists have studied about the Medicaid program, including its impact on health outcomes for the poor and the extent to which it crowds out private insurance. Section 4 offers a conceptual framework to explain the relationships between poverty and health, and poverty and healthcare. Section 5 presents analyses of the association between poverty status and health and healthcare utilization by using the 2009 National Health Interview Survey and 2006 Medical Expenditure Survey. Section 6 concludes with a discussion of how public policy can address the problems created by the poverty-health and poverty-healthcare relationships.

1. Introduction

Health and healthcare disparities by socioeconomic status are pervasive. Poverty status influences every aspect of a person’s health, from the risk of dying at birth to the likelihood of dying from a chronic disease. Poverty status determines access to food, housing, healthcare, and community-level risks that influence a person’s general health status and risks of mortality and morbidity. Poverty status affects access to healthcare, the quality of healthcare, and healthcare outcomes. This chapter discusses the relationship between poverty status and healthcare and suggests ways in which society should address how those in poverty have difficulty with their healthcare.

This chapter has six sections. Section 2 reviews the empirical literature on the relationship between poverty and health. This section draws on the fields of economics, public health, health-services research, and sociology. We discuss the health-wealth gradient and provide an overview of research that explores the relationship between poverty and mortality, chronic conditions, self-reported health status, and other priority conditions. Section 3 discusses and gives an overview of Medicaid, the nation’s public health insurance program for poor and disabled individuals. We then discuss five major issues that health economists have studied about the Medicaid program, including its impact on health outcomes for the poor and the extent to which it crowds out private insurance. Section 4 offers a conceptual framework to explain the relationships between poverty and health, and poverty and healthcare. Section 5 presents analyses of the association between poverty status and health and healthcare utilization by using the 2009 National Health Interview Survey and 2006 Medical Expenditure Survey. Specifically, we present findings showing the association between poverty status, and access to office-based care, hospital-based care, dental and pharmacy care, and preventive and screening services. Section 6 concludes the chapter with a discussion of how public policy can address the problems created by the poverty-health and poverty-healthcare relationships. In particular, we outline policies in the Affordable Care Act as well as other strategies that policy makers should consider. Finally, we suggest directions for future research with a focus on the potential implications of health reform for low-income populations.

2. Literature Review: Poverty and Health

In 2009, 14.3 percent of the US population was poor and another 4.4 percent was near poor (between 100 and 125 percent of the federal poverty level) (DeNavas-Walt, Proctor, Smith, and US Census Bureau 2010 ). This means that over one in six persons in the nation are at risk of poor health and poor access to healthcare. Poverty can negatively affect every aspect of a person’s health because it negatively impacts the determinants of health, that is, access to quality food, clothing, shelter, transportation, healthcare, and a healthy environment. These are key risk factors for mortality and morbidity. The gradient between socioeconomic status and health is a well-established empirical observation (Adler and Ostrove 1999 ; Deaton 2002 ; Duncan et al. 2002 ; Mechanic 1989 ). The literature on the association between socioeconomic status and health indicates that the poor and disadvantaged are most at risk (Deaton 2002 ). Mortality, morbidity rates, and self-reported health status are inversely related to many correlates of socioeconomic status such as income, wealth, education, and social class (Kitagawa and Hauser 1973 ; Williams and Collins 1995 ). According to the National Longitudinal Mortality Study, people with family incomes in 1980 of less than $5,000 had a life expectancy 25 percent lower than those with family incomes above $50,000 (Roget et al., 1992 ). Data from the Panel Survey of Income Dynamics from 1984–94 shows a nearly four-fold difference in the median wealth of households when the head of household reported excellent health ($127,900) compared to when the head of household reported poor health ($34,700) (Smith 1999 ). While there is strong evidence that the gradient exists at all income/wealth levels, the relationship is nonlinear with effects diminishing across income and wealth distributions (Deaton 2002 ; Rodgers 2002 ; Smith 1999 ; Wilkinson 1986 , 1990 , 1992 , 2000 and 2002 ). The strongest effects were observed among the poor and weakest among the affluent.

The negative health consequences for the poor may be greater in areas with substantial income inequality. Several researchers have found that mortality has a strong association with income inequality (Cooper et al. 2001 ; Fang et al. 1999 ; Franzini et al. 2001 ; Wilkinson 1990 , 1992 , 2000 ). Rodgers ( 2002 ) reported that there is as much as a five-year difference in average life expectancy between members of relatively egalitarian societies and members of relatively less egalitarian societies. Theoretically, income inequality adversely affects population health because it erodes community cohesion and social capital (Kawachi et al. 1997 ; Putnam 2000 ). People who live in areas with high levels of income inequality are less likely to trust their neighbors. This lack of trust is associated with increased risk of mortality (Szreter and Woolcock 2004 ). This finding, however, has been disputed. Other researchers have not found a positive relationship between income inequality and mortality after adjusting for other factors such as race, education, and urbanization (Deaton and Lubotsky 2002 ; Deaton and Paxson 2001 ; Mellor and Milyo 2001 ; Muller 2002 ). These studies suggest that the association between income inequality and mortality found in area-level cross-sectional data analyses is an ecological fallacy. Research focusing on individuals also casts doubt on the association between income inequality and health. LeClere and Soobader ( 2000 ) reported that, after adjusting for family income and other individual covariates, they did not find a relationship between income inequality and health for African Americans of all ages, elder whites, and middle-aged whites who resided in areas with low- to moderate-income inequality. LaVeist and colleagues ( 2011 ) found that community factors account for a substantial portion of health disparities observed between African Americans and whites. The authors found that race-based disparities in health status and access to care, which typically are observed in national surveys, substantially disappeared in a survey of residents of a low-income, socioeconomically integrated neighborhood in Baltimore. Their findings highlight the importance of examining community-level determinants of health.

Higher mortality and morbidity rates among poor persons are also related to their labor market opportunities. The two Whitehall studies of British civil servants documented the strong relationship between occupation and health status (Wilkinson, 1986 ). The first Whitehall study, which began in 1967 and focused exclusively on men, found a steep inverse relationship between employment grade and poor health outcomes, including mortality for many diseases. The second Whitehall study, which was conducted 20 years later and included women, demonstrated that there was a four-fold higher relative risk of morbidity from the lowest to the highest grade (Marmot et al. 1991 ). Psychosocial factors such as work-related stress and limited social support networks are offered as explanations for the observed gradient. Singh-Manoux and colleagues ( 2003 ) present evidence that low subjective social status is a strong predictor of ill health. Subjective social status reflects individuals’ self-assessment of their position measured by money, education, and occupation in society relative to others.

2.1. Effects of Education on Health

Because education affects income, wealth, and occupational status, it may be the case that education, and not income, is protective of health (Fuchs 1989 , 1993 ; Garber 1989 ; Grossman 1975 ). A study using data from the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study found that increasing education by one additional year increases life expectancy at age 35 by at least 1.2 years (Lleras-Muney 2001 ). Ross and Mirowsky ( 1999 ) tested three models to explain the relationship between education and health: the quantity model, the credential model, and the selectivity model. Each of these models works through the labor market. The quantity model argues that schooling builds human capital and this leads to better labor market success. In turn, labor market success promotes a sense of personal control, social support, and a healthy lifestyle. In the credential model, formal education is a screening tool used by employers to make hiring decisions (Spence 1973 ). Thus, an extra year of schooling only improves labor market opportunities if it results in a degree. In the selectivity models, it is the quality of the education, the extent of the college’s credential or network, the school culture, and selection of better students that improve job market opportunities. Ross and Mirowsky found evidence to support the quantity model. They identify full-time employment, fulfilling work, high household income, high level of personal control, and social support as possible mediators. Adjusting for these variables reduces the estimated effect of years of school on perceived health status by 60 percent. Ross and Mirowsky did not estimate the effect of schooling on these mediators to see how much of the variables’ effect can be attributed to education.

In addition to work and economic conditions, education also affects health through social psychological resources of perceived control, social support, and health lifestyle (Murrell and Meeks 2002 ; Ross and Wu 1995 ). In two national samples of US households, Ross and Wu ( 1995 ) demonstrated that education is strongly and positively associated with two measures of health—self-reported health status and physical functioning. By comparing a base regression model that includes education and demographic information to ones that successively add work and economic conditions, measures of social-psychological resources, and health lifestyle measures, they conclude that the diminishing coefficient on education is evidence that these other factors are mediators. Including social-psychological resources and health lifestyle measures reduces the estimated effect of education by 36 percent. It is hard to interpret the results of this analysis because the change in the coefficient on education could simply be due to omitted variable bias. The main limitation of this analysis is that it is basically a cross-sectional analysis. A true longitudinal analysis would examine the effects of educational attainment on these pathway variables and would attempt to parse out the portion of the impact of these pathway variables that is due to education.

2.2. Long-Term Effects of Being Born Poor

Poor adult health may be a legacy of childhood poverty. Barker ( 1997 ) investigates the lasting effects of the fetal environment on future health. He and his colleagues argue that an embryo depends on a steady supply of nutrients and oxygen and that the size of the uterus is also important. A critical period of intra-uterine life occurs when cells are dividing rapidly. A reaction to lack of nutrients or oxygen is to slow rates of cell development of some organs, thereby “programming” the body to the onset of later life diseases such as coronary heart disease, stroke, diabetes, and hypertension (Barker et al. 1989 ).

A study of the health-wealth gradient in children in the United States by Case and colleagues found that family poverty worsens the effects of childhood diseases. For instance, poverty tends to increase the number of days an asthmatic child spends in bed. The study found that family poverty slowed gains in health as children matured. The authors also examined how family income, child’s age, poor health at birth, and interactions of these factors influence a child’s health status. The study found that poverty moderates the relationship between poor health at birth and current health status (Case, Lubotsky, and Paxson 2002 ).

These results lend support to the hypothesis that childhood poverty can have sustained health consequences during a person’s adulthood. In a British 1946 national cohort study, events of early childhood have been shown to be predictors of cardiovascular, respiratory, and neurological health for middle-aged adults (Wadsworth and Kuh 1997 ). Other studies concur that childhood exposure to poverty is associated with long-term adverse health outcomes (Power et al. 1999 ; Rahkonen et al. 1997 ).

2.3. Poverty and Health Outcomes

Poverty impedes access to healthcare and is itself associated with poor health outcomes. For example, impoverished cancer patients are less likely to be diagnosed at an early disease stage, often receive less timely treatment, and have lower survival rates than nonpoor cancer patients (Albano et al. 2007 ; American Cancer Society 2008 ; Bradley et al. 2001 ). Poor persons with diabetes are likely to have poor glycemic control and to suffer higher rates of complications such as amputations and blindness (Agency for Healthcare Research and Quality 2010 ; Braunwald et al. 2004 ; Elders and Murphy 2001 ; Ostchega et al. 2008 ). Socioeconomic status is negatively associated with poor outcomes and lower quality of life for cardiac patients (Macabasco-O’Connell et al. 2010 ; Shaw et al. 2008 ; Skodova et al. 2009 ). Hypertensive patients were less likely to have their blood pressure controlled and were at greater risk for complications and mortality (Bell et al. 2004 ; Colhoun et al. 1998 ; Sharma et al. 2004 ). Birth outcomes are associated with poverty status. Poverty elevates the risk of infant mortality, low birth weight, and preterm birth (Hughes and Simpson, 1995 ; Olson et al., 2010 ).

3. Medicaid Overview

Poor and near-poor persons in the United States depend on Medicaid and the State Children’s Health Insurance Program (SCHIP) for access to healthcare services. Medicaid and SCHIP finance healthcare services for low-income families, individuals with disabilities, and elderly poor adults. Medicaid covers more than 58 million persons and SCHIP covers an additional 5 million children in near-poor families. In 2010, Medicaid provided coverage for approximately 56 percent of low-income children and 21 percent of low-income adults. Medicaid also supplements Medicare benefits for about 9 million low-income seniors and younger individuals with disabilities, known as “dual eligibles.” Historically, childless adults have been excluded from Medicaid eligibility; however, as a result of the 2010 Patient Protection and Affordable Care Act (PPACA), those with incomes below 133 percent of the federal poverty level (FPL) will be brought into the program beginning in 2014.

The federal government and states jointly finance Medicaid and SCHIP. The Centers for Medicare and Medicaid Services (CMS) oversees the programs and ensures that they meet federal standards. Each state, however, is responsible for administering its own programs. CMS mandates that Medicaid provide a minimum set of benefits to enrollees, although states can choose to supplement these benefits. Medicaid’s benefits package is more generous than low-cost private insurance plans, and it requires little to no enrollee cost sharing. Covered services for children in both Medicaid and SCHIP are comprehensive, including basic healthcare services (physician, hospital, laboratory, and x-ray services; early and periodic screening; diagnostic and treatment services for individuals under age 21; and nursing facility services for persons over 21). States have the option of covering other services such as prescription drugs, dental, vision, physical therapy, and prosthetic devices. While the benefit package is generous, recipients face barriers to care because low reimbursement rates deter provider participation (Bronstein et al. 2004 ; Mitchell 1991 ; Perloff et al. 1997 ).

Medicaid cost taxpayers about $339 billion in 2009. The federal and state shares of the program’s cost are determined by the federal matching assistance percentage (FMAP), which varies by state based on states’ personal income levels, and the FMAP ranges from 50 to 75 percent. On average, the federal government finances 57 percent of the program’s costs, while states contribute the remaining 43 percent. To manage the ongoing cost of financing the program, states have increasingly contracted with health maintenance organizations (HMOs), on a capitated basis, to manage benefits for Medicaid enrollees. Under capitation, HMOs receive a fixed monthly payment per Medicaid enrollee, independent of the services provided. This flat fee structure helps Medicaid programs control costs and may also help to achieve more predictable expenditures. As of 2010, nearly 75 percent of Medicaid beneficiaries were enrolled in managed care plans. Most enrollees in Medicaid managed care plans are women and their children; similarly, the majority of children in the SCHIP program are enrolled in HMOs. The so-called Medicaid managed care population tends to have relatively low healthcare expenditures. Dual eligibles, who represent 15 percent of Medicaid’s enrolled population, account for 40 percent of program costs. The states’ ability to finance their share of Medicaid costs becomes especially challenging during recessions, because higher unemployment increases Medicaid enrollment, while, simultaneously, tax revenues decline.

Economists have studied several aspects of the Medicaid program. We focus on six major issues: (1) Does Medicaid enrollment improve health outcomes for the poor? (2) How do eligibility criteria, family circumstances, and availability of charity care affect Medicaid take-up rates? (3) How does Medicaid reimbursement affect provider participation and the availability of services? (4) Does Medicaid crowd out the demand for private insurance? (5) How has managed care impacted Medicaid? (6) How is Medicaid involved in the structure of the heathcare safety net, and what are other determinants of access to free and reduced-cost care for those without access to Medicaid coverage?

3.1. Medicaid and Health Outcomes

The infant mortality rate in the United States has declined from 25.9 deaths per 1,000 live births in 1960 to 6.8 deaths per 1,000 live births in the 2000s (National Center for Health Statistics, 2008). This substantial decline is driven by a number of factors, including improvements in the availability and quality of healthcare for pregnant mothers and newborns, as well as reductions in maternal smoking and education gains for women. Some of the improvement in birth outcomes can be attributed to the introduction of Medicaid, and expansions of the program in the late 1980s and 1990s had helped mothers and their children. Medicaid has been shown to have a protective effect against poverty’s harmful influence on health. The benefits provided by Medicaid, however, do not accrue evenly across the population.

Using data on newborn health and mortality, Lin ( 2009 ) examined which factors contributed to reductions in infant mortality and health disparities from the 1980s to 2000. The study assessed the gap in infant mortality rates and physician-assessed scores of infant health for children born to mothers with high levels of educational attainment versus mothers with low levels of education. Infant health was measured with an Apgar score, which reflects the condition of a newborn child’s breathing, heart rate, skin condition, muscle tone, and response to stimuli. Using a regression-based decomposition model, Lin explored which parental and insurance status characteristics contributed to widening or narrowing disparities in infant mortality and Apgar scores by maternal education level. Having an African American mother widened the gap by 4.8 percent, while having a Hispanic mother reduced the gap by 5.9 percent. Notably, the greatest single contributor to reductions in the health gap was adequate prenatal care, accounting for 37 percent of the convergence in infant health outcomes.

Expansions in Medicaid eligibility and the introduction of SCHIP have increased access to care for low-income children during their developmental years. Currie, Decker, and Lin ( 2008 ) assessed how Medicaid eligibility expansions for young children in the late 1980s and early 1990s changed the effect of income as a predictor of a child’s health status and use of primary care, as well as health during adolescence. The authors estimated probability and two-stage least squares models that included as independent variables family income and interactions of family income, child age, and an indicator for the time period of the observation. The study found income to be a consistent predictor of health status and utilization. Moreover, the significance of income did not diminish in models that also controlled for Medicaid eligibility. The authors observed that eligibility expansions during early childhood, however, did exercise a protective effect against poverty on health status during adolescence. Another model revealed a significant effect of expansions in Medicaid eligibility during early childhood (ages 2–4) on health status between the ages of 9 and 17. Quite reasonably, the effect of the Medicaid expansion on use of healthcare services was more immediate (Currie et al. 2008 ).

This finding supports the hypothesis that childhood poverty has a cumulative effect on health over the life cycle. Early and sustained investment in a child’s health, enabled by expanded Medicaid eligibility, counteracted—but did not completely eliminate—the health-wealth gradient in Currie, Decker, and Lin’s analysis. Likewise, Barker ( 1997 ) found that interventions earlier in life to enhance nutrition and development produce greater health gains than interventions at later stages of development. Barker’s epidemiological findings are supported by Case, Lubotsky, and Paxson ( 2002 ), who found that low parental income prior to and immediately following birth was a strong predictor of the child’s poor health. Further, improvements in health with age are slowed by poverty. The authors found that Medicaid coverage only mitigates, but does not remove, the effect of income on health. Consequently, they speculated that the best defense against poor child health is an early and continual absence of household poverty, which can promote ongoing investment in health.

A challenge that researchers often face in estimating the effect of Medicaid enrollment on health and healthcare utilization is the absence of a true experimental study, in which a population is randomized to intervention and control groups. A recent study by Finkelstein and colleagues ( 2011 ) took advantage of a Medicaid expansion by the Oregon Health Plan, which in 2008 allowed low-income adults who were not categorically eligible for Medicaid (by federal law) to apply for a limited number of openings in the state’s Medicaid program by lottery. The random assignment of applicants to Medicaid or no insurance offers a true randomized, controlled experimental design. The study also used longitudinal data spanning the time of insurance assignment, enabling the authors to make causal inferences about the effect of gaining insurance among a population of low-income adults. The authors analyzed the effect of Medicaid enrollment on hospital admissions, outpatient visits, use of prescription drugs, self-reported health, and the accumulation of unpaid medical debt.

The authors estimated the effect of Medicaid enrollment on these outcome variables using two-stage models. The first stage used the lottery to estimate the likelihood of insurance coverage. The second stage estimated the impact of having insurance on the various outcome variables. Because the lottery was random, it served as an excellent instrument to isolate the effect of gaining Medicaid coverage on healthcare utilization, financial liabilities, and health status. The authors found that gaining insurance via Medicaid increased an individual’s probability of having a hospital admission, but this increase was concentrated in nonemergency admissions. They speculated that this reflected individuals’ price sensitivity to voluntary, as opposed to emergency, medical care. Because Medicaid imposes minimal cost-sharing requirements on enrollees, enrollment in the program reduced financial barriers to accessing nonemergency forms of care. The study also found that gaining insurance increased the use of preventive services, prescription drugs, and outpatient services, as well as improved patient-reported general health status and mental health. Finally, gaining Medicaid coverage reduced the amount of medical debt sent to collection agencies, relative to the control group. These results are especially salient to policy makers and researchers in light of the national Medicaid expansion planned for 2014. The Oregon study suggests that Medicaid expansion can increase the poor’s healthcare utilization and improve their health status relatively quickly (Finkelstein et al. 2011 ). Future research will need to examine the long-term effects of this population’s uptake of insurance.

3.2. Determinants of Medicaid Take-Up Rates

Remler, Rachlin, and Glied ( 2001 ), in a review of the literature on take-up rates for public safety-net programs, concluded that the value of Medicaid benefits for potential enrollees, the administrative hassles associated with enrolling, and the availability and clarity of information about the program were all factors in determining Medicaid take-up rates. They also concluded that culture and stigma were not important predictors of enrolling in Medicaid. Aizer ( 2003 , 2007 ) showed that community health outreach workers can improve take-up rates, especially among non-English speakers. She also demonstrated that these outreach programs can improve healthcare utilization by reducing hospital stays that may have been prevented had the patient received timely and appropriate primary care. Remler and colleagues suggest that a passive automatic enrollment process would be more effective at ensuring that eligible persons were enrolled. The administrative barriers associated with Medicaid enrollment affect many uninsured children. Sommers ( 2007 ) reported that 42 percent of uninsured children who were eligible for Medicaid or SCHIP in 2006 had been enrolled during the pervious year. Sommer notes that this high rate of disenrollment is caused by requirements that parents re-enroll their children in the program annually. Likewise, Bindman and colleagues ( 2008 ) noted that frequent re-enrollment requirements for children in California’s Medicaid program disrupted access to primary and care.

Having children in the household is a strong predictor of an adult’s enrollment in Medicaid. This is largely a function of the guidelines that states established for Medicaid eligibility, which provide the most generous entry criteria for women and their children. Rask and Rask ( 2000 ) found that the presence of children and family size increased low-income families’ probabilities of enrolling in Medicaid. A child’s enrollment in Medicaid or SCHIP may also depend on parental enrollment. Sommers ( 2006 ) found that a child’s probability of losing Medicaid coverage, despite being eligible on the basis of family income, was 37 percent lower when a parent was also enrolled in Medicaid. Sommers’s study underscores the fact that Medicaid enrollment is a family phenomenon. Parent and child enrollment in public insurance can be self-reinforcing, and efforts to increase child coverage may need to factor in a parent’s incentives to enroll.

3.3. Supply of Medicaid Services

Medicaid is expected to add some 16 million new enrollees in 2014, almost one-half of the current uninsured, legal-resident population. Yet it is not clear if healthcare providers will be able to furnish the services needed to care for this population, or how the effects of the expansion will be distributed across the US healthcare system. Baker and Royalty ( 2000 ) used a model originally developed by Sloan and colleagues ( 1978 ) to interpret the effect of changes in reimbursement rates and expansions in Medicaid eligibility on the supply of “private” physician services for Medicaid patients. Consistent with economic theory, they conclude that increasing Medicaid’s reimbursement rates would cause physicians to see more Medicaid patients. The effect of an eligibility expansion without higher provider reimbursement rates is less clear. If the expansion simply adds formerly uninsured individuals to Medicaid, the model suggests that the number of Medicaid patients seen in private practices would remain unchanged. Care for new Medicaid enrollees would be handled by doctors practicing in “public” facilities, such as clinics or hospitals. If some individuals chose to drop private coverage and enroll in Medicaid, however, the model predicts that private physicians will see more Medicaid patients, who would have switched from private to public insurance. This phenomenon of switching from private to public insurance is termed “crowd-out,” and is discussed in Section 3.4 .

Baker and Royalty tested their hypotheses about reimbursement rates and eligibility expansions empirically by using the 1987 and 1991 Survey of Young Physicians. The survey asked participating doctors to report the percentage of Medicaid and poor patients they saw in their practices. The authors estimated that a 10 percent increase in the ratio of Medicaid to private insurer reimbursement rates would increase the number of poor patients seen in private practices by 3.4 percent, consistent with the theoretical model. Accounting for some reduction in public provider visits, the higher reimbursement rate was estimated to increase total physician office visits for the poor by 2.5 percent.

This finding is consistent with results from Currie, Gruber, and Fischer ( 1995 ), who suggest that increasing Medicaid reimbursements gives doctors an incentive to provide more prenatal care to pregnant women, with the effect of lowering the infant mortality rate. Similarly, Aizer and colleagues ( 2005 ) found that, in California, the introduction of supplemental payments to hospitals that treated a disproportionate share of poor patients (known as Disproportionate Share Hospital, or DSH, payments) gave private hospitals an incentive to treat a larger share of Medicaid enrollees. Following the introduction of DSH payments, hospital segregation declined and neonatal mortality among African Americans improved. In this study, segregation was measured as dissimilarity in the insurance characteristics of delivering mothers in a particular hospital, relative to the insurance characteristics of mothers in each woman’s county of residence. Quast and colleagues ( 2008 ) found that Medicaid providers that reimbursed on a fee-for-service (FFS) basis offered more well-child visits to children; however, capitated payments improved children’s compliance with asthma medication. These findings may reflect the fact that FFS-based reimbursement gives providers incentives to provide a high volume of care, such as office-based visits, while capitated payment systems encourage the use of care to prevent potentially expensive hospitalizations related to long-term diseases.

Other researchers have observed a weaker association between reimbursement rates and Medicaid participation, as well as greater barriers to mobility between different types of providers. Bronstein, Adams, and Florence ( 2004 ) studied the impact of SCHIP’s creation on Medicaid participation among physicians in Alabama and Georgia who were already a part of the states’ Medicaid networks. In Alabama, whose SCHIP program mirrored private insurance plans, the authors found that SCHIP enrollment did not have a significant effect on Medicaid participation among the state’s traditional Medicaid providers. In Alabama, the authors conclude, SCHIP enrollees gained access to networks of physicians that were previously accessed by the privately insured, resulting in little impact on the demand for services faced by established Medicaid providers. In urban communities in Georgia, however, physician participation in Medicaid declined as SCHIP enrollment grew. This suggests that there was some displacement of Medicaid with SCHIP enrollees, as theory would predict when SCHIP reimbursement rates are higher.

3.4. Medicaid Crowd-Out

Health economists, health services researchers, and policy makers have been concerned with the extent to which Medicaid take-up crowds out enrollment in private insurance plans. Crowd-out is formally defined as the percentage of persons who are enrolled in Medicaid despite being able to purchase private insurance. Conceptually, a Medicaid expansion induces crowd-out because eligible individuals are able to obtain free healthcare benefits with little or no cost sharing, compared to private insurance coverage, which includes paying premiums, co-pays, co-insurance, and deductibles. Limited physician participation and the administrative burden associated with enrolling in Medicaid probably deter crowd-out, however. The incentive to seek Medicaid coverage is probably greater for individuals and families who would otherwise purchase insurance in the nongroup market, where premiums are often much higher and not offset by employer contributions.

If the objective of a Medicaid expansion is to increase insurance coverage, a high crowd-out rate suggests that the expansion is inefficient, because it results in replacement of private insurance coverage and a lower rate of coverage expansion among those who cannot afford private insurance. Assessed in light of other objectives, however, crowd-out may not be as serious a concern. For example, because public insurance eliminates most or all enrollee cost-sharing requirements, which are typically a feature of private insurance, a low-income family that switches enrollment from private to public insurance may face fewer financial obstacles in seeking care. Also, low-income families may use the savings from reduced cost-sharing to purchase other essential goods that promote good health such as quality food, shelter, transportation, and clothing.

The research on crowd-out has been motivated by two waves of reform to the Medicaid program. The first, which occurred in 1987, decoupled Medicaid eligibility from enrollment in the Aid to Families with Dependent Children (AFDC) program, and it allowed states to expand Medicaid eligibility to pregnant women and low-income children. This was followed by federally mandated expansions in Medicaid eligibility for young, low-income children. The second wave of coverage expansions occurred with the 1997 creation of SCHIP, which dramatically expanded coverage for children, and with the passage of the Personal Responsibility and Work Opportunity Reconciliation Act, which allowed states to further expand Medicaid eligibility to adults and children. Studies based on these different expansions have estimated crowd-out rates between 4 percent and 60 percent (Gruber and Simon 2008 ).

Cutler and Gruber ( 1996 a) published one of the first articles to address this issue. They analyzed the effect of the first wave of Medicaid expansions on crowd-out for children and women aged 15–44. Using data from the Current Population Survey (CPS; March 1996), they identified a subset of the population of women and children that became newly eligible for Medicaid coverage between 1987 and 1992. Low-income populations that were eligible for Medicaid throughout the expansions, or that remained ineligible for public insurance, served as controls. Cutler and Gruber estimated the impact of an individual’s Medicaid eligibility on his or her propensity to be covered by Medicaid, to have private insurance coverage, or to be uninsured. Complicating the analysis was the fact that Medicaid eligibility is endogenous, since factors associated with demand for insurance, such as income, are correlated with Medicaid eligibility. The authors also sought to address the imprecision of measuring eligibility, which in practice is determined monthly, using annual income data. To address these concerns, the authors developed a state-level measure of Medicaid generosity using a random national sample, which they used as an instrumental variable in lieu of person-level eligibility. With this technique, Cutler and Gruber estimated a crowd-out rate of 49 percent.

Efforts to replicate Cutler and Gruber’s work have yielded a broad range of crowd-out estimates. The variations in estimates are due to estimation techniques, the data, and the definition of crowd-out. Thorpe and Florence ( 1999 ) noted that Cutler and Gruber’s analysis relied on cross-sectional data, which provide an aggregate picture of insurance coverage but not information on insurance switching within a family over time. It is possible, for example, that families drop their private insurance coverage for certain members who gain Medicaid eligibility. Thorpe and Florence addressed this latter concern directly in their study of the first Medicaid expansion’s effect on crowd-out for children. They used panel data from the National Longitudinal Survey of Youth (NLSY), which followed a cohort of young people beginning in 1979. The insurance statuses of the original cohort members and their children are collected in each year of the panel, which spanned the first wave of Medicaid expansions. The authors used the parents’ insurance statuses as an estimate of their children’s insurance coverage in the absence of eligibility expansions. They found that the parents of nearly all poor children (families with incomes under 100 percent of the FPL) and of most near-poor children (families with incomes between 100 percent and 200 percent of the FPL) who enrolled in Medicaid had lost employer-sponsored insurance. Job loss explained most of the decline in parents’ loss of private insurance coverage. The authors estimated that only 16 percent of the parents of near-poor children enrolling in Medicaid continued to retain private insurance coverage, suggesting a much lower crowd-out rate than Cutler and Gruber’s estimate.

Blumberg, Dubay, and Norton ( 2000 ) estimated crowd-out rates separately for previously insured and uninsured groups of low-income children, in addition to a combined group, by using panel data from the Survey of Income and Program Participation (SIPP). The authors employed a differences-in-differences study design, using low-income children who were slightly older than the cohort that gained Medicaid eligibility as the control group. Among children who were previously insured, the authors estimated a crowd-out rate of 23 percent. The authors found no significant evidence of crowd-out in the group of previously uninsured children. That is, formerly uninsured children who enrolled in Medicaid were unlikely to have otherwise gained private coverage. The pooled crowd-out estimate that the authors calculated, which effectively averaged the crowd-out rates for the first two groups, was 4.4 percent.

Looking at the second wave of Medicaid expansions in the late 1990s, Gruber and Simon ( 2008 ) estimated the crowd-out rate for children who became newly eligible for Medicaid or SCHIP to be between 61 percent and 68 percent. This result followed from a modeling strategy similar to Cutler and Gruber ( 1996a ), but which also assumed that eligibility of an individual family member for Medicaid or SCHIP will increase the likelihood that other family members will enroll. This assumption that coverage of other family members improves a child’s continuity of Medicaid enrollment has been borne out in research by Sommers ( 2006 ) and by Carroll and colleagues ( 2007 ). Without family-level eligibility effects, Gruber and Simon estimated a crowd-out rate between 24 percent and 37 percent. With family-level eligibility effects, the crowd-out estimate would increase to 61–68 percent, indicating that making an entire family eligible for Medicaid provides greater incentives for enrollment. Busch and Duchovny ( 2005 ) found that, in states that raised income eligibility levels for parents in the late 1990s, 24 percent of the take-up in Medicaid coverage was due to a reduction in private insurance coverage.

Concerns about crowd-out caused policy makers to enact anti-crowd-out rules in SCHIP. Gruber and Simon ( 2008 ) analyzed these potential disincentives to crowd-out in their study of the SCHIP expansions in the late 1990s. They found that increasing cost sharing resulted in a significant reduction in the probability of take-up. They concluded, however, that longer waiting periods may simply increase the amount of time a family waits to gain eligibility of SCHIP, without significantly reducing crowd-out. Lo Sasso and Buchmueller ( 2004 ) estimated a 47 percent crowd-out rate of SCHIP expansion on private health insurance. They noted that states that implemented an anti-crowd-out policy of a six-month waiting period reduced crowd-out to almost zero.

The literature on the crowd-out effect of public insurance is similar to research on the impact of other safety-net programs on health insurance enrollment. Rask and Rask ( 2000 ) found that, among groups of individuals with incomes up to 400 percent of the FPL, medically needy programs and welfare generosity increased the probability of Medicaid enrollment, while the availability of an uncompensated care funds increased the probability of being uninsured. The authors also found that the presence of a public hospital, typically a major provider of charity care, lowered the probability of enrolling in Medicaid. These findings suggested that the availability of charity care acts as a substitute for Medicaid and private insurance enrollment. Similarly, Herring ( 2005 ) and Chernew and colleagues ( 2005 ) found that the availability of charity care was a significant predictor of an individual’s decision to forego health insurance enrollment.

3.5. Medicaid Managed Care

Since its first introduction in Medicaid plans in the 1980s, managed care has grown to become the dominant model for delivering healthcare services to Medicaid beneficiaries. About three-quarters of Medicaid recipients are enrolled in managed care plans. Two managed care models operate in Medicaid: fully capitated managed care, in which an HMO bears financial risk for the care of enrollees, and primary care case management (PCCM), in which primary care providers are paid a supplemental fee to coordinate the care of their patients. Commonly cited rationales for employing managed care are (1) to enhance access to, and use of, primary and preventive care services, (2) to reduce expensive healthcare utilization, and (3) to provide states with more predictable program expenses (Kaiser Commission on Medicaid and the Uninsured, 2010 ).

Managed care may improve access to essential healthcare services via two pathways: facilitating care coordination, and providing financial incentives to primary care providers to increase the amount of Medicaid patients they see in their practices. A regression-based comparison of HMO versus FFS Medicaid enrollees found higher rates of primary care access and use, contact with specialists, and receipt of flu shots among the disabled (i.e., SSI-eligible) HMO Medicaid enrollees in urban areas. These benefits largely disappeared among rural HMO enrollees who also had significantly higher emergency-room use than the FFS comparison group. Similar effects were observed for PCCM patients, and again, the benefits were mostly confined to urban areas (Coughlin et al. 2008 ). One study of a Medicaid HMO’s pregnancy management program, which focused on care coordination, found that it significantly lowered the odds of infants’ low birth weight (Mason et al. 2011 ). In Washington, D.C., disabled children in a Medicaid managed care plan, which provided both care coordination and enhanced reimbursement for primary care services, were significantly more likely to have the recommended number of pediatrician visits for their age group versus FFS enrollees (Mitchell et al. 2008 ). Another study that involved the same population found a significantly higher likelihood of receiving dental exams and care in the managed care population, although both the managed care and FFS groups continued to have suboptimal use of dental care (Mitchell and Gaskin 2008 ).

The techniques employed by managed care providers to increase utilization of primary and preventive care should, in theory, help to avoid more costly medical care down the line. Studies have produced mixed findings about the cost savings that can be attributed to Medicaid managed care. The Lewin Group’s ( 2009 ) synthesis of 24 evaluations of managed care programs found evidence of cost savings between 0.5 percent and 20 percent, compared to traditional FFS Medicaid plans. By contrast, a difference-in-differences regression analysis found that expenditures actually increased slightly in Florida counties that implemented a managed care demonstration program, compared to those that retained FFS Medicaid. Populations enrolled for at least three months, however, cost less in managed care than FFS plans, potentially demonstrating that continuity of enrollment determines whether a managed care program can generate savings (Harman et al. 2011 ).

Aizer, Currie, and Moretti ( 2007 ) examined the effect of California’s adoption of Medicaid managed care plans on the delivery of prenatal services and birth outcomes. In an effort to control costs, California moved many patients in its traditional FFS Medicaid program, Medi-Cal, into managed care plans in the 1990s. Most counties entered into contracts with several private insurers to administer their managed care plans. Five counties, however, ultimately adopted a single, publicly run managed care plan known as the County Organized Health System (COHS). The authors used patient-level longitudinal data for mothers who had more than one singleton pregnancy before and after the introduction of managed care to examine the effect of this transition to managed care on the women’s pregnancies. The authors found that the adoption of managed care significantly lowered a mother’s probability of receiving first trimester prenatal care. In counties that used multiple private insurers, the probability of low birth weight, a short-gestation pregnancy, and neonatal death all increased following the adoption of managed care. These negative effects diminished in an analysis that focused only on COHS plans, although the probability of low birth weight remained elevated. These results cast a skeptical light on the notion that managed care plans provide better preventive care that is supposed to reduce the risk of adverse health outcomes among their enrollees. Because of the limited financing available for Medicaid patients and insurers’ incentives to control costs, managed care plans may still not improve access to care for vulnerable populations.

Lastly, Herring and Adams ( 2011 ) examined the effect of Medicaid HMO penetration on medical utilization and expenditures in urban markets. They found no significant effect on a Medicaid enrollee’s expenditures from the degree of HMO penetration in a given market. Increases in the market penetration of HMOs with ≥75 percent Medicaid enrollment raised medical practitioner visits and reduced some measures of hospital utilization but, unexpectedly, increased emergency room visits. Greater market penetration of HMOs with 〈75 percent Medicaid enrollment increased inpatient surgeries but lowered outpatient surgical procedures.

4. Conceptual Framework

Poverty status primarily influences healthcare use in two ways: through its impact on health production, and through its impact on the demand for healthcare services. Poverty status reflects family income that determines one’s ability to purchase goods and services required to maintain health, including medical services. The demand for healthcare is a derived demand that is rooted in the demand for good health. Hence, to model demand for healthcare, one must begin with an individual’s health-production function. For simplification purposes, suppose health is a function of healthcare services and a composite good. The composite good includes food, housing, recreation, and other goods and services that influence one’s health. These goods and services can have both a positive or negative influence, for example, one can eat fresh fruits and vegetables, or one can indulge in double chocolate fudge cake. Individuals choose their consumption of the composite good and healthcare services that maximizes their utility subject to their budget constraints. Based on this simple framework, one can derive demand functions for healthcare services and the composite good that are based on the prices and income from the budget constraints and from the parameters and exogenous factors in the health-production and utility functions (Grossman 1972a , 1972b ).

Poverty status, a measure of household income relative to household size, affects an individual’s or family’s budget constraints and consequently the demand for healthcare services. As long as healthcare services are normal goods, declines in income will reduce the demand for these services. The income effect, however, is counterbalanced by poverty’s negative impact on health status. The reduction in purchasing power due to poverty lowers the poor’s demand for goods and services such as quality housing, food, transportation, and so on, which improve health. Lower consumption of these goods and services reduce health status and increase healthcare needs. This is true for the most part unless affluent persons choose to purchase goods and services that are unhealthy for them (e.g., excessive alcohol, tobacco, illegal drugs, and foods with high fat and high cholesterol content). This negative relationship between health status and income is probably a phenomenon for very high-income individuals. The poor’s health is probably subject to the impact of a low value composite good. Consequently, poverty lowers health status, which increases the need for healthcare services.

If elements of the composite good and healthcare services are substitutes in the production of health, poorer persons may substitute services provided by Medicaid or by safety-net institutions for other components of the composite good. For example, higher-income diabetic and hypertensive patients may be able to manage their conditions with better diets and health habits (i.e., high-quality food and regular exercise at a health club). Poorer diabetics and hypertension sufferers, who may live in areas without access to high-quality food or safe environments for exercise, may rely more heavily on healthcare services to manage their conditions.

5. The Relationship between Poverty Status and Healthcare Utilization

We analyze here the Medical Expenditure Panel Survey (MEPS) to illustrate the relationship between healthcare utilization and poverty status. The MEPS is an annual longitudinal survey that covers the civilian noninstitutionalized population within the United States. It is conducted by the Agency for Healthcare Research and Quality (AHRQ) and is based on a subsample of the National Health Interview Survey. The MEPS is widely used as an authoritative source of information on the nation’s use of healthcare services. AHRQ uses the MEPS to monitor the nation’s progress on disparities in healthcare by race and ethnicity, economic status, and geography (Cohen et al. 1996 ). More information about the MEPS is available on AHRQ’s website ( www.meps.ahrq.gov/mepsweb ). In this analysis, we use the 2006 MEPS adult nonelderly sample, which consisted of 23,264 noninstitutionalized adults between the ages of 18 and 64.

To measure healthcare, we use 16 variables that cover the five following domains:

office-based care—physician and nonphysician

hospital-based care—inpatient, outpatient, and emergency room

dental care

pharmacy services

preventive and screening services—cholesterol check, routine checkup, flu shot, prostate antigen test (PSA), pap smear, clinical breast exam, mammogram, blood stool, and sigmoidoscopy or colonoscopy

The dependent variables we analyze are dichotomous, as follows. For office-based, hospital, dental, and pharmacy services, we construct indicators for whether the respondent had at least one visit during the previous 12 months. We employ variable time frames to analyze the use of preventive services. The indicator variables for flu shots, PSA tests, and routine checkups cover the previous 12 months. The variables for pap smears, clinical breast exams, and mammograms indicate whether these services were received within the previous three years. We use a five-year window for the cholesterol check. The blood stool and sigmoidoscopy or colonoscopy variables indicate whether the respondent ever had these tests. For each preventive service, we only consider the recommended population (e.g., mammograms for women ages 40 years and older; PSA tests for men ages 40 years and older).

Poverty status is the primary independent variable of interest. We include it in the model as a categorical variable: poor (less than 100 percent of the FPL), near-poor (100 to 125 percent of the FPL), low-income (125 to 200 percent of the FPL), middle-income (200 to 300 percent of the FPL) or high-income (greater than 300 percent of the FPL) participants. High-income individuals are the reference group in the regression models. We also examine the association between healthcare use and two additional socioeconomic variables that are closely associated with poverty status: educational attainment and health insurance status. Educational attainment is measured by using a set of categorical variables: 8 years or less, 9 to 12 years, some college, and a college degree. (The reference group is persons with a high school diploma or GED.) We categorize health insurance status as private, public (Medicaid, Medicare, other public), and uninsured. Private insurance is the reference category.

The other domains we control for are demographic characteristics, employment status, location, functional status, and the presence of chronic health conditions. Race and ethnicity, gender, and marital status are included as categorical variables with white, female, and married persons as the respective reference groups. The race and ethnicity categories are Hispanics and non-Hispanic African Americans and Asians. Marital status is divided into three groups: married, never married, and widowed or divorced/separated. To control for differences in healthcare use related to age, we use age minus 18 years and age minus 18 years squared. Employment status is divided into three categories: full-time, part-time, and not employed, with full-time employment as the reference group. We use several variables as measures of respondents’ health status. Respondents rate their general health status and mental health status on a five-point scale: poor, fair, good, very good, and excellent. For each, we create a set of categorical variables combining poor and fair health, and good and very good health, and we use excellent health as the reference category. We use three measures of functional status. The first two indicate whether the respondent received help or supervision with activities of daily living (ADLs) or with instrument activities of daily living (IADLs). ADLs are activities such as bathing, dressing, getting around the house, walking, climbing stairs, grasping objects, reaching overhead, lifting, bending or stooping, or standing for long periods of time. IADLs are activities such as using the telephone, paying bills, taking medications, preparing light meals, doing laundry, or going shopping. The third functional status measure indicates whether respondents had problems working, doing homework, or going to school. The presence of a chronic condition is ascertained by patients’ responses to a question that asks if a “doctor or healthcare professional” had informed them that they had hypertension, heart problems, stroke, cancer, asthma, diabetes, and/or joint pain. We create two variables: whether a respondent had only one chronic condition, and whether a respondent had two or more chronic conditions.

We estimate the impact of poverty status, educational attainment, and health insurance status on healthcare utilization. Because almost all persons age 65 and older are enrolled in Medicare, we estimate models for nonelderly adults and seniors separately. The data analysis is conducted with STATA 10 statistical software. We estimate separate regression models for each outcome using logistic regression. Logistic regression is selected because the dependent variables are dichotomous, and because of the convenience of reporting the associations as odds ratios (OR). We use the sampling weights and account for the complex survey design of the MEPS by using STATA’s survey regression procedures to produce national estimates.

Consistent with prior studies, Tables 11.1 and 11.2 illustrate the wealth-health gradient. Health status declines with an increase in poverty status. In 2006, poor adults had the lowest self-reported general and mental health status and had the lowest functional status. Data from the National Health Interview Survey (NHIS) show that, in 2009, poor and near-poor adults had higher prevalence rates for asthma, heart disease, hypertension, arthritis, diabetes, chronic joint pain, lower back pain, vision problems, hearing loss, and dental problems (Pleis et al. 2010 ). Cancer is the only condition for which nonpoor adults have a highest prevalence rate. The greater cancer disease burden for the more affluent is probably due to their higher survival rates and early detection rates relative to poor cancer patients. Poor cancer patients are less likely to have their cancer detected at early stages when it is treatable and therefore have shorter survival times than more affluent cancer patients.

Despite their poor health status, poor adults are less likely to use healthcare services when compared to more affluent adults (see Table 11.3 ). With the exception of hospital stays and emergency room visits, poor and near-poor adults are less likely to use 14 of the 16 services examined in this study. In Tables 11.4 through 11.9 , we report the ORs for poverty status, education level, and health insurance status adjusted for demographic, socioeconomic, and health factors. The regression analyses reveal the same associations between poverty status and healthcare utilization. We present here the results for office-based services followed by hospital-based services, dental and pharmacy services, and then preventive services.

Notes: Cell entries are percentages. Standard errors are in parentheses.

Source: Authors’ calculations from Cohen et al. ( 2006 )

Source: Summary Health Statistics for US Adults, National Health Interview Survey, 2009.

5.1. Office-Based Services

Table 11.4 reports that, in comparison to high-income nonelderly adults, the odds of poor nonelderly adults having at least one office-based physician visit and nonphysician visit during the year are 26.4 percent and 28.7 percent lower, respectively. Even for poor seniors who are covered by Medicare, there is a difference in the use of office-based services. Poor seniors are less likely to have at least one office-based physician visit (OR = 0.562) and office-based nonphysician visit (OR = 0.609), versus the high-income reference group. There is also a positive gradient between educational attainment and the odds of having an office-based visit. Although the estimates are not always significant, the relationship appears to be monotonically increasing, where the odds of using office-based services increase as educational attainment increases from no high school to an advanced degree. Persons with no high school training, who are the least educated group in our models, are the most disadvantaged. Nonelderly and elderly adults with less than a ninth-grade education have 28.2 percent and 39.7 percent lower odds, respectively, of a physician visit than adults with a high school diploma. Compared to privately insured adults, the uninsured are significantly less likely to use office-based services. The uninsured are almost 70 percent less likely to use office-based physician services and more than 53 percent less likely to use office-based nonphysician services.

Notes: These are results from logistic regression models that control for age, gender, race and Hispanic origin, urban/rural location, employment status, region, health status, and functional status.

The results reported in the table are the exponentiated results of logistic regressions. Statistical significance is determined on the logarithmic scale.

Standard errors are in parentheses.

Significance levels denoted as follows:

Source: Cohen et al. ( 2006 )

5.2. Hospital-Based Services

Table 11.5 indicates that the association between poverty status and hospital use is different. The poverty status association with outpatient services is similar to that of office-based services; however, for hospital stays and emergency room visits the association is different. For outpatient services, the poor elderly are nearly 30 percent less likely to use services than their high-income counterparts. The ORs for poor and near-poor nonelderly adults are less than one but statistically insignificant; however, the odds for low-income nonelderly adults is approximately 28 percent lower compared to high-income nonelderly adults. For hospital stays, nonelderly poor adults are more likely to have at least one stay or visit during the year than high-income adults. The pattern of the ORs suggests that the likelihood of a hospital stay declines with a higher poverty status. We observe a similar pattern for emergency room visits, although the estimates are not statistically significant. For seniors, the odds of a hospital stay and emergency room visits for the poor and near-poor are not statistically different from one, and the patterns of the estimates do not suggest an underlying monotonic association.

The impact of educational attainment and health insurance status on hospital-based services is similar to the results for office-based services, particularly for nonelderly adults. The pattern of estimates for hospital stays and outpatient services suggests that as education increases the use of hospital services increases. Nonelderly adults with no high school diploma are less likely to use outpatient (OR = 0.717) and emergency room services (OR = 0.716). We do not find the same association for seniors. Similar to office-based services, the uninsured are less likely to have a hospital stay and outpatient visit. We do not find the same result with emergency room visits, but this is consistent with hospitals’ legal obligation under the Emergency Medical Treatment and Active Labor Act, which requires hospitals to treat persons with an emergency medical condition regardless of the patients’ ability to pay for treatment.

These are results from logistic regression models that control for age, gender, race and Hispanic origin, urban/rural location, employment status, region, health status, and functional status.

5.3. Dental and Pharmacy Services

Use of dental services has a strong negative association with poverty status, as shown in Table 11.6 . The odds of using dental services decline as poverty status increases. The poor are most disadvantaged, followed by the near-poor, low-income, and middle-income participants, who are all less likely to have a dental visit than high-income adults. This association is the same for nonelderly adults and for seniors. This is an expected finding, given that private health insurance and Medicare do not provide dental coverage and because Medicaid dental coverage is inadequate as relatively few dentists participate in the program. Consequently, nonelderly adults and seniors must have out-of-pocket costs for dental care, which produces strong poverty status effects. We do not find a strong relationship between poverty status and the use of pharmacy services. The low-income and middle-income groups appear to be the most vulnerable. This finding may also be the product of how private health insurance, Medicare, and Medicaid provide coverage for pharmacy services. Poor and near-poor nonelderly adults may have first dollar coverage through Medicaid or access to indigent pharmacy assistance programs. High-income persons have more resources to afford the co-pays and deductibles associated with Medicare and private health insurance, while the nonelderly in the middle of the income spectrum may lack support from public programs or may not have the independent means to purchase prescription drugs.

* p 〈 0.05.

The impacts of educational attainment and health insurance status parallel the associations observed for office-based and hospital-based services. Adults with less education are most likely to use fewer dental and pharmacy services. The patterns of the ORs indicate monotonic relationships (i.e., as educational attainment declines, the odds of using dental or pharmacy services declines). The results are particularly strong for dental services. Adults with no high school are 44 percent (for the nonelderly) and 47 percent (for the elderly) less likely to have a dental visit. The ORs for prescription drug use follow a similar pattern, although the results are not significant for elderly adults. Among nonelderly adults, however, those with a college education (adults with some college and those with a college degree) are more likely have at least one prescription filled during the year. Similar to the other healthcare services, the uninsured are vulnerable. They have a 68 percent lower odds of using dental care and a 61 percent lower odds of using pharmacy services, compared to the privately insured.

5.4. Prevention and Screening Services

Table 11.7 indicates that nonelderly poor adults are less likely to receive preventive services. For services that pertain to the entire population and that can be performed during a routine physician visit, poor nonelderly adults have 54 percent lower odds of receiving a cholesterol check and 31 percent lower odds of receiving a routine checkup. This association does not hold for flu shots, for which only low-income adults are at risk. This may be due to public health departments’ efforts to ensure that poor people have access to flu vaccines. These free programs are typically targeted to poor and near-poor populations. We do not find statistically significant differences by poverty status among elderly adults. The education and health insurance effects are similar to other healthcare services. The patterns of ORs for the education variables indicate a monotonic association, where persons with lower educational attainment are most at risk. This association is strongest for nonelderly adults. Uninsured nonelderly adults have lower odds for receiving all three services.

For gender- and age-specific screening services, such as a clinical breast exam, PSA, PAP smear, or mammogram, the impact of poverty status varies by age as reported in Table 11.8 . Among the nonelderly, poor, low-income, and middle-income adults are less likely to receive these services than high-income adults, with the exception of clinical breast exams. In general, we do not observe significantly lower odds of the use of these services among the elderly. We observe the education-to-health-service-use gradient among these services, too. The patterns of ORs show that college-educated adults are more likely to receive these services. This association holds for both nonelderly and elderly adults. Similar to other services, the uninsured are significantly less likely to use these services.

1 Adults ages 20–64.

Source: Cohen et al. ( 2006 ).

Adults ages 41 to 64.

Adults ages 40 to 64.

As reported in Table 11.9 , poverty status is associated with the receipt of screenings for digestive disorders and colon cancer. Specifically, compared to high-income adults, the odds that a poor adult received a sigmoidoscopy or colonoscopy are 32 percent lower in the nonelderly sample and 35 percent lower among elderly adults. The education-to-health-services gradient is observed for these services as well. Persons without a high school diploma are the most at risk and persons with more than a bachelor’s degree are least at risk. Similar to all the other services, uninsured nonelderly adults are most vulnerable.

In summary, we find that poor and near-poor nonelderly adults are at significant risk of not receiving healthcare and preventive services. For the elderly, poverty status sometimes is not a disadvantage, especially for services that are covered by Medicare or Medicaid. Dental services are particularly subject to the poverty – health services gradient. We also find a consistent relationship between educational attainment and health services use. Adults without a high school diploma are disadvantaged while college-trained adults are more likely to use healthcare services. Finally, uninsured nonelderly adults are uniformly less likely to use services compared to privately insured nonelderly adults. The disadvantage is large and statistically significant, ranging from 35 percent to 60 percent lower odds, depending on the service.

6. Conclusion

The problems that poverty creates for health and healthcare use can be addressed with demand-side and supply-side policy levers. On the demand side, government can implement policies to expand health insurance coverage for the poor and to facilitate their use of healthcare services. On the supply side, government can implement policies to increase the number of healthcare providers for the poor and underserved communities. The Patient Protection and Affordable Care Act of 2010 (ACA) attempts to use both demand and supply remedies to improve the poor’s access to healthcare.

On the demand side, the ACA expands Medicaid coverage to poor individuals who are not currently eligible for the program. Virtually all US citizens under age 65 with incomes below 133 percent of the FPL will become eligible for Medicaid beginning in 2014. The ACA also authorizes states to create health insurance exchanges to organize and facilitate individual health insurance market purchases. The federal government will give individuals up to 400 percent of FPL tax credits and out-of-pocket subsidies to help them purchase plans in the exchanges. Plans offered on the exchanges must provide preventive services, such as immunizations and screenings, without cost-sharing requirements. States also have the option of insuring adults between 133 and 200 percent of the FPL in a Basic Health Plan. The Basic Health Plan would be a public plan similar to Medicaid or SCHIP. States could use 95 percent of the federal government subsidy these adults would have received in the health insurance exchange to support the Basic Health Plan. This essentially gives states the option of expanding their Medicaid coverage to adults up to 200 percent of the FPL.

The exchanges will make insurance more affordable by pooling risk across participants and lowering administrative costs for insurance carriers. Adults with incomes that are more than 133 percent of the FPL will be required to obtain health insurance through an employer or via an exchange. Those with incomes below 400 percent of the FPL will be eligible to receive tax-credit subsidies for insurance purchased in an exchange. The subsidies are structured to reflect individual income and the cost of insurance in the exchange. The ACA is expected to lower the proportion of the US population that is uninsured to 7 percent from its current level of 15 percent. One half of newly insured adults will receive coverage through Medicaid, growing the number of Medicaid enrollees by 30 percent from 2010 levels.

On the supply side, the ACA provides $11 billion over five years to expand the healthcare safety net through community health centers and the National Health Service Corp. Federally qualified health centers (FQHCs) are a primary source of care for the uninsured and for Medicaid patients. The funding for FQHCs will be used to both expand the number of health centers in operation and to recruit and train medical personnel. To encourage more private providers to participate in Medicaid, reimbursement rates for primary care physicians will be increased temporarily in 2013 and 2014. States are also encouraged to implement patient-centered medical homes for their dual eligible populations with multiple chronic conditions in order to improve their quality of treatment and possibly lower their cost of care.

The implementation of the ACA should spur numerous new studies. Economists and health services researchers engaged in evaluating the ACA must address a number of questions, several of which are outlined below. Has the ACA effectively reduced the number of uninsured people and improved their access to healthcare services? How did the expansion of the insured population impact persons who had insurance prior to implementation of the ACA? Did the Medicaid expansion crowd out private insurance participation? The variability of health benefit exchanges across states will invite studies comparing the efficiency of such exchanges. The ACA created a Center for Medicare Innovation that is charged with studying new healthcare delivery and payment methods that provide care as efficiently as possible to high-need populations. Potential programs to be evaluated include medical homes for individuals with multiple chronic conditions, as well as bundled payment systems that enhance provider coordination across multiple delivery settings. Evaluating these programs will be of particular interest to health services researchers, economists, and policy makers.

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• v.1(3); 2009 Aug

Impact of poverty, not seeking medical care, unemployment, inflation, self-reported illness, and health insurance on mortality in Jamaica

Paul andrew bourne.

Department of Community Health and Psychiatry, Faculty of Medial Sciences, The University of the West Indies, Andrew, Jamaica WI.

Background:

An extensive review of the literature revealed that no study exists that has examined poverty, not seeking medical care, inflation, self-reported illness, and mortality in Jamaica. The current study will bridge the gap by providing an investigation of poverty; not seeking medical care; illness; health insurance coverage; inflation and mortality in Jamaica.

Materials and Method:

Using two decades (1988-2007), the current study used three sets of secondary data published by the (1) Planning Institute of Jamaica and the Statistical Institute of Jamaica (Jamaica Survey of Living Conditions) (2) the Statistical Institute of Jamaica (Demographic Statistics) and (3) the Bank of Jamaica (Economic Report). Scatter diagrams were used to examine correlations between the particular dependent and independent variables. For the current study, a number of hypotheses were tested to provide explanation morality in Jamaica.

The average percent of Jamaicans not seeking medical care over the last 2 decades was 41.9%; and the figure has been steadily declining over the last 5 years. In 1990, the most Jamaicans who did not seek medical care were 61.4% and this fell to 52.3% in 1991; 49.1% in 1992 and 48.2% the proceeding year. Based on the percentages, in the early 1990s (1990-1994), the percent of Jamaicans not seeking medical care was close to 50% and in the latter part of the decade, the figure was in the region of 30% and the low as 31.6% in 1999. In 2006, the percent of Jamaicans not seeking medical care despite being ill was 30% and this increased by 4% the following year. Concomitantly, poverty fell by 3.1 times over the 2 decades to 9.9% in 2007, while inflation increased by 1.9 times, self-reported illness was 15.5% in 2007 with mortality averaging 15,776 year of the 2 decades. There is a significant statistical correlation between not seeking medical-care and prevalence of poverty (r = 0.759, p< 0.05). There is a statistical correlation between not seeking medical care and unemployment; but the association is a non-linear one. The relationship between mortality and unemployment was an unsure one, with there being no clear linear or non-linear correlation. The findings revealed that there is a strong direct association between not seeking medical care and inflation rate (r = 0.752). A strong negative statistical correlation was found between mortality and prevalence of poverty (r=0.717). There is a non-linear statistical association between not seeking medical care and illness/injury.

Conclusions:

Not seeking medical care is not a good indicator of premature mortality; but that this percentage must be excess of 55%. While this study cannot confirm a clear rate of premature mortality, there are some indications that this occurs beyond a certain level of not seeking care for illness.

Introduction

Health (medical) care-seeking behaviour of people is not only an indicator of their willingness to preserve life but it is crucial to personal, societal and national development. The health of an individual affects all area of his/her life and extends to the family, community, society and the nation. The cost of ill-health is not only borne by the individual; but the entire society. Ill-health means less time on the job; lowered production and productivity; reduced Gross Domestic Product and savings; high health care expenditure; switching of expenditure from education and other social development to health care; and this can further increase poverty for an individual or his/her family. Health therefore holds a key to social and economic development. Hence, long life must be supported by a healthy individual or population. It is this interrelationship among health, life expectancy, social and economic development that account for a demand in health care services.

Life expectancy is computed from mortality data, and so healthy life expectancy means the delaying of mortality. Mortality statistics provides an insight into morbidity patterns as well as the health of a person or a population. It also provides a basis upon which we can estimate the burden of premature deaths[ 1 , 2 ]; lifestyle practices; and health care-seeking behaviour[ 3 ]. The Caribbean is experiencing health transition which accounts for reduction in fertility and mortality, and the changing pattern of diseases from communicable to non-communicable disease as the leading cause of death[ 2 , 4 ]. The Caribbean is not atypical in regards to aforementioned pattern as the[ 1 ] argued that 80% of chronic disease deaths occur in low-to-middle income countries, and that this has a serious influence on the causes of premature mortality.

Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica published in the Jamaica Survey of Living Conditions[ 5 ] revealed that in 2007, 15.5% of Jamaicans reported an illness/injury compared 9.7% in 1997. Of the 15.5% of Jamaicans who reported health conditions, 66% of them sought medical care. Of those who sought health care, 40.5% went to public facilities compared to 51.9% who attended private health care facilities. Interestingly the typologies of diseases were asthma (8.7%); diabetes mellitus (12%); hypertension (22.4%); and arthritis (8.8%). Concomitantly, 33.9% of Jamaicans who did not seek care reported that they were unable to afford it; 30.2% mentioned that they preferred home remedy and 6.0% remarked that they had no time. According to Fraser[ 6 ], the prevalence of hypertension in the Caribbean was 28% and 55% for those over 25 years and 40 years respectively. This explains Fraser's call for an aggressive management drive to address the prevention of those health conditions, which was equally echoed by other scholars[ 7 , 8 ].

Morrison[ 9 ] titled an article ‘Diabetes and hypertension: Twin Trouble’ in which he established that diabetes mellitus and hypertension have now become two problems for Jamaicans and in the wider Caribbean. This situation was equally collaborated by Callender[ 10 ] at the 6 th International Diabetes and Hypertension Conference, which was held in Jamaica in March 2000. They found that there is a positive association between diabetic and hypertensive patients - 50% of individuals with diabetes had a history of hypertension[ 10 ]. Prior to those scholars’ work, Eldemire[ 11 ] finds that 34.8% of new cases of diabetes and 39.6% of hypertension were associated to senior citizens (i.e. ages 60 and over). A national study of 958 Jamaicans found that 18% of women had hypertension compared to 8% of men; 4.8% of women with diabetes compared to 3.3% of men[ 4 ]; and an earlier study by Forrester et al[ 8 ] had found that 19.3% of African-Jamaican females reported hypertension compared to 13.0% of African-Jamaican males.

When the WHO[ 1 ] argued that some deaths are premature, a part of this answer lies in health care-seeking behaviour; time of treatment; identification of illness; poverty; inaccessibility; unhealthy lifestyle practices; and physical inactivity. According to WHO[ 1 ], one-half of all chronic diseases occur prematurely in people who are below the age of 70 years compared to one quarter of those younger than 60 years. The organization also reported that 80% of premature heart disease, stroke and diabetes mellitus could have been prevented from happening. Can premature deaths be prevented from happening?

Embedded in WHO publication is the relationship between poverty and illness, poverty and chronic diseases and poverty and premature death. Marmot[ 12 ] explained that income is positively associated with better health, and that poverty means poor nutrition; inadequate physical milieu, and poor water and food supply which account for increased ill-health in this cohort. Like Marmot[ 12 ], Sen[ 13 , 14 ] argued that poverty denotes reduced capability as this retard choices; freedom; educational access; proper nutrition; and therefore justifies not only chronic diseases but also employability; health insurance coverage; and medical care-seeking behaviour. Statistics from the Planning Institute of Jamaica and the Statistical Institute of Jamaica[ 5 ] revealed that those below the poverty line sought the least medical care: 51.7% for those below the poverty line; 52.7% for those just above the poverty line; 61.2% for those in the middle income categorization; 61.8% in the wealthy income category and 67.6% of those in the wealthiest income cohort. Concomitantly, the poorest income category had the highest reported illness (85.4%) compared to 85.1%; 79.6%: 67.5%; and 74.3% for poor, middle class, wealth and wealthiest income category respectively[ 5 ].

The poor not only seek less medical care; and this offers some more explanation for their increased probability of contracting chronic illness and other mortality causing morbidities; but they are least likely to purchase health insurance coverage. Poverty means in measurable terms inaffordability from material and other social resources, which explains the low likeliness to purchase food and other vital non-food items. In 2007, statistics on Jamaica revealed that 2.2% of those below the poverty line had health insurance coverage compared to 10.1% of those just above the poverty line; 15.9% of the middle class; 20.9% of the wealthy and 37.7% of the wealthiest income category[ 5 ]. This finding highlights the reality of the poor; that in order for them to access health care, this is substantially an out of pocket payment or that it has to state funded. With the probability that they are least likely to find out of pocket money to utilize on health care, premature mortality indeed will be greater for this cohort than other income cohorts.

Poverty therefore erodes good health status of a populace and further deepens individual and national poverty while creating a public health concern for the society. Inflation is a persistent upward movement in prices. It erodes the socio-economic choices of people within a society. Inflation increases the prices of goods and services and a part of this consequence is the cost of health care. In 2007, the annual rate of inflation on food and non-alcoholic beverages was 24.7% compared to 3.4% on health care cost ( Table 1 ), while it was 16.8% for the nation. The rate of the increase of inflation for 2007 over 2006 was 194.7%. With increases in food prices comes the upward price movement in other goods and services prices and such reality removes the willingness of people from seeking medical care as their priority would be to spend on food rather choosing to spend on medical care. The information above highlights the interconnectedness between poverty, unemployment, ill-health; not seeking medical care; health insurance coverage and mortality. In spite of this reality, extensive review of the literature has not found a study that has examined the aforementioned variables in a single research. The current study will bridge the gap by providing an investigation of poverty; not seeking medical care; illness; health insurance coverage; inflation and mortality in Jamaica.

Annual Inflation in Food and Non-Alcoholic beverages and Health Care Cost, 2003-2007

Using two decades of data (1988-2007), the current work will examine 10 hypotheses and provide an extensive account for mortality; not seeking medical care; illness; health insurance coverage and unemployment patterns in Jamaica in an attempt to provide research literature for future public health planning and a better understanding of mortality and premature mortality in Jamaica. The hypotheses are 1) there is a statistical correlation between not seeking medical care and poverty; 2) there is a statistical association between not seeking medical care and unemployment; 3) there is a statistical association between poverty and unemployment; 4) there is a statistical relationship between poverty and inflation; 5) there is a statistical association between not seeking medical care and illness; 6) there is a statistical association between not seeking medical care and health insurance coverage; 7) there is a statistical association between mortality and poverty; 8) there is a statistical relationship between mortality and unemployment, 9) there is a statistical relationship between mortality and not seeking medical care, and 10) there is a significant statistical association between not seeking medical care and inflation.

The aim of this study was to examine the impact of poverty, not seeking medical care, unemployment, inflation, self-reported illness, health insurance coverage on mortality in Jamaica in order to provide public health practitioners and health promotion specialists with research findings on those matters in Jamaica.

The currents findings revealed significant statistical correlation between not seeking medical-care and 1) prevalence of poverty(r = 0.759, p< 0.05); 2) unemployment; 3) inflation (r = 0.752); 4) illness; 5) health insurance coverage; and mortality. There is a positive correlation between prevalence of poverty and unemployment (r = 0.69), with 48% of poverty able to be explained by unemployment. A strong positive statistical correlation was found between poverty and inflation (r = 0.856), as 73.2% of poverty can be explained by inflation. A strong negative statistical correlation was found between mortality and prevalence of poverty (r=0.717), with 51.4% of the variance in mortality can be explained by poverty. The relationship between mortality and unemployment was an unsure one, with there being no clear linear or non-linear correlation. Linear associations were found between most of the aforementioned variable; however, non linear correlations were found between 1) mortality and not seeking-medical care; 2) mortality and unemployment; 3) not seeking medical-care and health insurance coverage; not seeking medical-care and illness; and 4) not seeking-medical care and unemployment.

Materials and Methods

Using two decades (1988-2007), the current study used three sets of secondary data published by the 1) Planning Institute of Jamaica and the Statistical Institute of Jamaica (Jamaica Survey of Living Conditions); 2) the Statistical Institute of Jamaica (Demographic Statistics); and 3) the Bank of Jamaica (Economic Report). The years selected for this paper is due to the availability of data on health care seeking behaviour; and illness.

Health care-seeking behaviour, poverty and illness data were taken from the Jamaica Survey of Living Conditions. The Jamaica Survey of Living Conditions (JSLC) is conducted jointly by the Planning Institute of Jamaica and the Statistical Institute of Jamaica. Its purpose is to collect data on living standards of Jamaicans. The JSLC used a detailed questionnaire to collect data from respondents between April and October each year. A self-administered questionnaire was used to collect the data which were stored and analyzed using SPSS for Windows 16.0 (SPSS Inc; Chicago, IL, USA). The questionnaire was modelled from the World Bank's Living Standards Measurement Study (LSMS) household survey. There are some modifications to the LSMS, as JSLC is more focused on policy impacts. The questionnaire covered areas such as socio-demographic, economic and health variables. The non-response rate for the survey was 26.2%.

The survey was drawn using stratified random sampling. This design was a two-stage stratified random sampling design where there was a Primary Sampling Unit (PSU) and a selection of dwellings from the primary units. The PSU is an Enumeration District (ED), which constitutes of a minimum of 100 dwellings in rural areas and 150 in urban areas. An ED is an independent geographic unit that shares a common boundary. This means that the country was grouped into strata of equal size based on dwellings (EDs). Based on the PSUs, a listing of all the dwellings was made, and this became the sampling frame from which a Master Sample of dwelling was compiled, which in turn provided the sampling frame for the labour force. One third of the Labour Force Survey (i.e. LFS) was selected for the survey. The sample was weighted to reflect the population of the nation. Furthermore, the instrument is posted on the World Bank's site to provide information on the typologies of question ( http://www.worldbank.org/html/prdph/lsms/country/jm/docs/JAM04.pdf ).

Unemployment data were taken from the publication of the Labour Force Survey of Jamaica (conducted by the STATIN).

Mortality data were taken from the publication of the demographic statistics. Although a medical certificate of death is used to indicate mortality, data from the Registrar General Department (RGD) were cleaned, modified and validated by the Statistical Institute of Jamaica[ 15 ]. Using a study that was conducted in 1999 which showed that there was under-registration of deaths in RGD's figures, the STATIN developed a methodology that accounted for complete mortality.

For the period 1998-2001, STATIN subtracted the number of deaths as reported by the police (deaths from external causes) from the RGD's record on external deaths. The difference was added to the mortality data set. Secondly, on investigation of the infant mortality (ages below 1 year), STATIN found that 80.25 percent of the deaths occurs in the year in question and 19.75 years in the previous year. This was taken into consideration with the RGD's figures in order to account for all deaths occurring in the year in question. For a more detailed explanation of this methodology, readers can consult Demographic Statistics[ 15 ].

Inflation data were taken from Economic Statistics (published by the Bank of Jamaica).

Information is not available on those who are ill but not seeking medical care. As a result this information was computed by subtracting the percentage reported seeking medical care from 100 each year.

The aforementioned data will be used to provide background information on the study. Descriptive statistics and percentage will be presented on mortality; seeking medical care for the population, and males and females.

Scatter diagrams were used to examine correlations between the particular dependent and independent variables. For the current study, a number of hypotheses were tested to provide explanation morality in Jamaica. Four hypotheses will be tested in this study: (1) there is a statistical correlation between not seeking medical care and poverty; (2) there is a statistical association between not seeking medical care and unemployment; (3) there is a statistical association between poverty and unemployment; (4) there is a statistical relationship between poverty and inflation; (5) there is a statistical association between not seeking medical care and illness; (6) there is a statistical association between not seeking medical care and health insurance coverage; (7) there is a statistical association between mortality and poverty; (8) there is a statistical relationship between mortality and unemployment, (9) there is a statistical relationship between mortality and not seeking medical care, and (10) there is a significant statistical association between not seeking medical care and inflation.

Inflation: This is measured as the per cent increase in prices from December to December of each year.

Not seeking medical care: This variable is the difference between those who reported seeking medical care owing to illness/injury which is expressed as a percent and 100 percent.

Medical care-seeking behaviour: This is the total number of people who reported seeking medical care (i.e. health care practitioner; healer; pharmacist; nurse) (expressed in percent).

Poverty is categorized in two major headings: (1) absolute and (2) relative poverty[ 13 ]. Absolute poverty denotes the lack of particular social necessities that is caused by ‘limited material resource’ in which to function – affordability of meeting basic needs, such as adequate nutrition, clothing and housing. Relative poverty, on the other hand, speaks to the individuals’ low financial resources (money or income) or other material resources relative to other people. The Senate says that “relative poverty is defined not in terms of a lack of sufficient resources to meet basic needs, but rather as lacking the resources required to participate in the lifestyle and consumption patterns enjoyed by others in the society”[ 16 ].

The Senate Community Affairs Reference Committee (SCARC) ascribes Professor Ronald Henderson the developer of the ‘poverty line’. “…he developed his ‘poverty line’ which was originally set equal to the minimum wage plus child endowment in Melbourne in 1966”[ 16 ]. Within this measurement approach, poverty becomes a relative phenomenon instead of an absolutism technique. The SCARC[ 16 ] says that, “the aggregate money value of the poverty gap indicates the minimum financial cost of raising all poor families to the poverty line”[ 16 ]. The concept of the poverty line is used in Jamaica to evaluate poverty. In 2007, the poverty line for a household of five was $302,696.07 compared to $281,009.93 in 2006[ 5 ].

On average over the period, the percent of Jamaicans not seeking medical care was 41.9%. The number of Jamaicans not seeking medical care has been steadily declining, which indicates that health care-seekers have been increasing over the past 2 decades ( Figure 1 ; Table 2 ). In 1990, the most Jamaicans who did not seek medical care were 61.4% and this fell to 52.3% in 1991; 49.1% in 1992 and 48.2% the proceeding year. Based on the percentages, in the early 1990s (1990-1994), the percent of Jamaicans not seeking medical care was close to 50% and in the latter part of the decade, the figure was in the region of 30% and the low as 31.6% in 1999. In 2006, the percent of Jamaicans not seeking medical care despite being ill was 30% and this increased by 4% the following year.

Not seeking medical care (%) by Year. There is a linear pattern in percent of Jamaicans not seeking medical care.

Inflation, Public-Private Health Care Service Utilization, Incidence of Poverty, Illness and Prevalence of Population with Health

Figure 1 showed that not seeking medical care (which is derived by subtracting medical care-seeking behaviour from 100%) can be fitted with a straight line. Furthermore, not seeking medical care has been steadily declining. However, mortality is best fitted with a non-linear curve. It was found that mortality was falling up to 1990 then it reached the minimum then began rising at an increasing rate up to 2002, then an ever- growing declining set in post 2005 ( Fig. 2 ).

Annual Mortality in Years. The annual number of Jamaicans who die is best fitted with a non-linear diagram.

Based on findings ( Table 2 ), Jamaicans have a preference for private health care utilization. During the 1990s (1994-1995), the disparity between private and public health care utilization was approximately 40%; which continues to narrow post that period. In 2007, the disparity was 11%, which represents a 28% narrowing of the gap between both utilizations.

Concomitantly, during the latter part of the 1980s to early 1990s, inflation began mounting so much so that it peaked at 80.2% in 1991 ( Table 2 ). While inflation was rising, there were fluctuations between poverty and self-reported illness/injury. Continuing, when inflation was at it highest (80.2%), poverty was also at its peak (44.6%), unemployment was close to the peak (15.3%) ( Table 3 ) and so was the percent of not seeking medical care (52.3%). Inflation increased by 194% in 2007 over 2006 and during that period, health insurance coverage was at its highest (21.2%); medical care-seeking behaviour fell by 4% and self-reported illness increased by 3% (to 15.5%) and 4% more Jamaicans did not seek medical care.

Seeking medical care, self-reported illness, and gender composition of those who report illness and seek medical care in Jamaica (in percentage), 1988-2007.

Table 3 revealed that average mortality over the 2 decade period was 15,966 people, which in 1999; the figure was 18,200 people and a low of 13,200 people in 1992. Correspondingly, over the 2 decades it was on one occasion that men sought more medical care than women (2006), with the general trend in the data that men are less likely to report illness/injury. In 2007, the findings revealed that the mean number of days spent in medical care by men was marginally more (10.6 days) compared to women (9.3 days); but that generally the difference is minimal ( Table 3 ).

Not seeking medical-care

There is a significant statistical correlation between not seeking medical-care and prevalence of poverty (r=0.759, p<0.05). The association therefore is a strong positive one, with 57.6% of the variance in not seeking medical care can be explained by 1% change poverty ( Fig. 3 ).

Not Seeking Medical Care (%) by Prevalence of poverty rate (in %). There is a linear association between not seeking medical care (%) and prevalence of poverty (%) in Jamaica ( Fig. 3 ). Furthermore, 58% of the variability in not seeking medical care (%) can be explained by a 1% change in prevalence of poverty (%).

There is a statistical correlation between not seeking medical care and unemployment; but the association is a non-linear one ( Fig. 4 ). The findings revealed that there is a direct correlation between not seeking medical care and unemployment between 7.5% and 15% after which it begins to fall. At 15% of unemployment (not clear) not seeking medical care is at its maximum; then post that rate, the rate of not seeking medical care precipitously fall.

Not Seeking Medical Care (%) by Unemployment rate (%). The statistical correlation between not seeking medical care (%) and unemployment rate (%) is not a linear one. Based on Figure 4 , it is best fitted with a non-linear cure.

The findings revealed that there is a strong direct association between not seeking medical care and inflation rate (r=0.752). Continuing, 56.5% of the variance in not seeking medical care can be explained by a 1% change in inflation rate.

There is a non-linear statistical association between not seeking medical care and illness/injury ( Fig. 5 ). The findings revealed that when the rate of illness/injury is more than 9% and less than 14%, the rate of not seeking medical care falls at a decreasing rate and after 15% the rate rises significantly.

Not Seeking Medical Care (%) by Illness/Injury (%). Statistical correlation between not seeking medical care (%) and illness/injury (%) is a non-linear one.

Figure 6 revealed a statistical association between not seeking medical care and health insurance coverage; but that the relationship is a non-linear one. It was found that between 8 to 18%, the correlation is an inverse one and after 18% it becomes a direct one. Hence, the more people have health insurance coverage; the less likely that they will not seek medical care and this correlation reverses beyond 18% of coverage.

Mortality (No of people) by Not Seeking Medical Care (%). The association between mortality (number of people that died) and not seeking medical care (%) can be best fitted with a non-linear curve.

There is a statistical relationship between mortality and not seeking medical care. Based on Figure 6 , the correlation is best fitted with a non-linear curve than a linear one. Hence, the association does not have the same gradient throughout the curve. It follows that after 35% of not seeking medical care, the rate of change in mortality was decreasing and after 55% of not seeking medical care, the rate begins to mounting at an increasing rate.

Poverty, Unemployment, Inflation and Mortality

There is a positive correlation between prevalence of poverty and unemployment (r=0.69), with 48% of poverty able to be explained by unemployment ( Fig. 7 ).

Prevalence of poverty rate (%) and unemployment rate (%).

A strong positive statistical correlation was found between poverty and inflation (r=0.856), as 73.2% of poverty can be explained by inflation ( Fig. 8 ).

Not Seeking Medical Care (%) by Health Insurance Coverage (%). A non-linear relationship existed between not seeking medical care (%) and health insurance coverage (%).

Mortality (No. of people) by Prevalence of Poverty (%). Mortality (annual number of deaths) and prevalence of poverty (%) is a linear one.

A strong negative statistical correlation was found between mortality and prevalence of poverty (r=0.717), with 51.4% of the variance in mortality can be explained by poverty.

The relationship between mortality and unemployment was an unsure one, with there being no clear linear or non-linear correlation ( Fig. 10 ).

Mortality by unemployment rate (in%). There is no clear pattern between mortality (number of people who die, annual) and unemployment rate (%) in Jamaica.

Murray[ 18 ] found that there is a clear interrelation between poverty and health. She noted that financial inadequacy prevents an individual from accessing – food and good nutrition, potable water, proper sanitation, medicinal care, preventative care, adequate housing, knowledge of health practices - and attendance at particular educational institutions among other things, which was in agreement to Marmot and Sen's perspectives. Marmot[ 12 ] opined that poverty reduced an individual's socio-economic and political choices and like Sen[ 13 ], he saw this phenomenon as a retardation of human capabilities. They believed that poverty accounted for much of the low educational outcome of those that are therein as well as the poor nutrition, low water quality; poor physical environment and that this is not surprising when the poor experience increased health conditions. Marmot[ 12 ] argued that money can buy health as those who have it are able to afford medical care treatment; purchase particular goods; create a good physical milieu and by extension experience a better health status than the poor. This argument is not entirely correct as income cannot buy health, as health is not a commodity that can be purchased. However, income can buy the treatment which is a precursor to better health status; and this is what the wealthy has over the poor and not necessarily better health status. Easterlin[ 17 ] argued that material resources have the capacity to improve ones choices, comfort level, state of happiness and leisure; and not that money can buy health or happiness.

Poverty undoubtedly incapacitates those that are therein, which explains why the WHO[ 1 ] argued that some of the mortality in this group will be prematurely caused death. The current study found that there is a strong direct correlation between not seeking medical care and poverty. With 57% of reasons Jamaicans do not seek medical care being accounted for by poverty, it follows that some of the morbidities that require medical care will be attended to with home remedy and non-medical healers, and by extension will result in premature deaths. This is concurring with Murray's work which showed that poverty also leads to increased dangers to health: working environments of poorer people often hold more environmental risks for illness and disability; other environmental factors, such as lack of access to clean water, disproportionately affect poor families[ 18 ].

The studies clearly show a relationship between persistent and elongated poverty and health and even mortality[ 18 – 20 ]. If poverty is an undisputable a primary cause of malnutrition[ 21 ], then access to money plays a pivotal role in the well-being. In order to grasp the severity of the issue of money, we need to be brought into the recognition of poverty and health status. According to Bloom and Canning[ 22 ], ‘ill-health’ significantly affects poor people. This postulate further goes on to explain the higher probability (5 times) of mortality of the poor than the rich[ 23 ].

A survey conducted by Diener, Sandvik, Seidlitz and Diener[ 24 ], stated that correlation between income and subjective well-being was small in most countries. According to Diener[ 25 ], “…, there is a mixed pattern of evidence regarding the effects of income on SWB [subjective well-being]”. Benzeval, Judge and Shouls[ 26 ] study concurred with Diener that income is associated with health status. Benzeval et al went further as their research revealed that a strong negative correlation exists between increasing income and poor health. Furthermore, from a study, it was found that people from the bottom 25 percent of the income distribution self-reported poorer subjective health by 2.4 times than people in the to fifth quintile[ 26 ].

The poor like the wealthy or middle class also want long life and a life full of satisfaction; but the reality is, in order for them to spend on education and health care, they must first cover food and non-alcoholic beverage costs. In 2007, inflation on non-alcoholic beverages was 24.7% which means that the poor must now face the addition cost of survivalability before venturing into health care treatment. In 2003 and 2006, health care cost was close to double digits and in the latter year, the price increase was greater than that for food and non-alcoholic beverages. With the poor experiencing material and income inadequacies, inflation does not only create an economic hardship but a treatment care hindrance. This study revealed that there is a strong positive statistical relationship between not seeking medical care and inflation, which means that when inflation increased by 194% in 2007 over 2006, many poor Jamaicans delayed medical care treatment to their very detriment. It should be noted here that during the aforementioned period, the percentage of Jamaicans reporting health conditions increased to 15.5% (from 12.2% in 2006), suggesting that many poor people were not being treated for some of the chronic diseases that they were experiencing on a daily basis.

One of the ways that is used by many people to afford health care is health insurance coverage. Health insurance coverage reduces out of pocket payment, and makes medical care more affordable for countless non-wealthy people. To address the exponential increase in prices that took place in 2007 over 2006, many Jamaicans purchased health insurance as percentage of people holding health insurance coverage stood at 21.2%, the highest in the nation's 20 year history. Concomitantly, only 2.2% of those in the poorest income categorization were holders of health insurance coverage and 10.1% of those just above the poverty line, suggesting that health care treatment would be an out-of pocket payment for those individuals. With the typologies of diseases reported by Jamaicans being hypertension; diabetes mellitus; asthma; and arthritis; health insurance coverage increases the probability of medical care utilization and non-out of pocket expenditure on medication and health care treatment. The current research revealed that health insurance coverage is positively correlated with not seeking medical care. However, the association is not a linear one and so, beyond 18% of Jamaicans holding health insurance coverage, more of them see it as switching to not seeking medical care. Embedded in this finding is the fact that buying more health insurance coverage does not indicate a willingness to seek medical care treatment as beyond a certain percentage health insurance ownership does not encourage more health care-seeking behaviour.

The WHO[ 1 ] opined that poverty is associated with increased chronic diseases and premature death, and this is cemented by this work. The findings herein revealed that poverty is positively correlated with lowered medical care seeking behaviour; and it was also found that there is a negative relationship between mortality and poverty. This denotes that more poverty does not equate to increased death; instead the converse is true. The study showed that when mortality is high, poverty is less than 18% and that when poverty increased beyond 20%, mortality begins to decline and that it reaches it least when poverty is in excess of 40%. If poverty is not directly correlated with mortality, then is it possible that there are premature deaths of the poor?

Studies on morality have shown that there is a high correlation between patterns of death and health and/or life expectancy[ 27 , 28 ], indicating that not unattended health conditions could cause death. According to Kimmel[ 29 ], 80% of deaths post 65 years is attributed to cardiovascular diseases, blindness, hearing impairment, diabetes, heart conditions, high blood pressure, arthritis, and rheumatism. While this study was on Jamaicans and not of a particular age cohort, the poor reported the greatest percentage of health conditions and within the context of their inaffordability and low response to seek medical treatment compared to the other social classes, there should be some cases of premature mortality associated with low health care-seeking behaviour.

An interesting finding of the current study was observed as an association was found between mortality and not seeking medical care and that it was a non-linear one. Hence, when not seeking medical care is less than 35%, as not seeking medical care increase to this point the association between the two phenomena was positive and after it passes this threshold, increases in not seeking medical care begins to fall to approximately 55%. Beyond 55%, the association between the two variables was a positive one. It was found that an exponential increase in mortality was found when not seeking medical care surpassed 55%, suggesting that when people avoidance of health care is less than 45%, a case of premature mortality must be occurring to cause this increase in deaths. There is a direct correlation between poverty and not seeking medical care and so is not seeking medical care and inflation, which accounts for not only increased diseases; but a case of premature mortality. It is not just of premature deaths as the findings revealed that men sought less health care than women, and this account for more mortality of this group and a part of this would be premature deaths. Statistics for Jamaica in 2005 showed that there was 117 males to every 100 females that died, and this increased from 115 males to every 100 females in 1998 (Statistical Institute of Jamaica, 2008:56). Embedded in those mortality data are the fact that marginal disparity in figures could not be justifying that the drastic mortality increase could be premature deaths for only males.

Conclusions

Not seeking medical care is influenced by inflation, poverty and unemployment. With the low probability that the impoverished is likely to be holders of health insurance coverage in Jamaica, their out of pocket payment for health care treatment will be higher and therefore the high likeliness of medical care visits will be to the detriment of their health. Not seeking medical care is not a good indicator of premature mortality; but that this percentage must be excess of 55%. While this study cannot confirm a clear rate of premature mortality, there are some indications that this occurs beyond a certain level of not seeking care for illness.

Acknowledgement

The author would like to extend sincere gratitude to Ms. Neva South-Bourne who offered invaluable assistance in editing the final draft of this manuscript.