quantitative research in healthcare administration

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Research Article

Recent quantitative research on determinants of health in high income countries: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

• Vladimira Varbanova, 
• Philippe Beutels

• Published: September 17, 2020
• https://doi.org/10.1371/journal.pone.0239031
• Peer Review
• Reader Comments

Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.

We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013—July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.

Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.

Conclusions

We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants’ influence.

Citation: Varbanova V, Beutels P (2020) Recent quantitative research on determinants of health in high income countries: A scoping review. PLoS ONE 15(9): e0239031. https://doi.org/10.1371/journal.pone.0239031

Editor: Amir Radfar, University of Central Florida, UNITED STATES

Received: November 14, 2019; Accepted: August 28, 2020; Published: September 17, 2020

Copyright: © 2020 Varbanova, Beutels. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study (and VV) is funded by the Research Foundation Flanders ( https://www.fwo.be/en/ ), FWO project number G0D5917N, award obtained by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Identifying the key drivers of population health is a core subject in public health and health economics research. Between-country comparative research on the topic is challenging. In order to be relevant for policy, it requires disentangling different interrelated drivers of “good health”, each having different degrees of importance in different contexts.

“Good health”–physical and psychological, subjective and objective–can be defined and measured using a variety of approaches, depending on which aspect of health is the focus. A major distinction can be made between health measurements at the individual level or some aggregate level, such as a neighborhood, a region or a country. In view of this, a great diversity of specific research topics exists on the drivers of what constitutes individual or aggregate “good health”, including those focusing on health inequalities, the gender gap in longevity, and regional mortality and longevity differences.

The current scoping review focuses on determinants of population health. Stated as such, this topic is quite broad. Indeed, we are interested in the very general question of what methods have been used to make the most of increasingly available region or country-specific databases to understand the drivers of population health through inter-country comparisons. Existing reviews indicate that researchers thus far tend to adopt a narrower focus. Usually, attention is given to only one health outcome at a time, with further geographical and/or population [ 1 , 2 ] restrictions. In some cases, the impact of one or more interventions is at the core of the review [ 3 – 7 ], while in others it is the relationship between health and just one particular predictor, e.g., income inequality, access to healthcare, government mechanisms [ 8 – 13 ]. Some relatively recent reviews on the subject of social determinants of health [ 4 – 6 , 14 – 17 ] have considered a number of indicators potentially influencing health as opposed to a single one. One review defines “social determinants” as “the social, economic, and political conditions that influence the health of individuals and populations” [ 17 ] while another refers even more broadly to “the factors apart from medical care” [ 15 ].

In the present work, we aimed to be more inclusive, setting no limitations on the nature of possible health correlates, as well as making use of a multitude of commonly accepted measures of general population health. The goal of this scoping review was to document the state of the art in the recent published literature on determinants of population health, with a particular focus on the types of determinants selected and the methodology used. In doing so, we also report the main characteristics of the results these studies found. The materials collected in this review are intended to inform our (and potentially other researchers’) future analyses on this topic. Since the production of health is subject to the law of diminishing marginal returns, we focused our review on those studies that included countries where a high standard of wealth has been achieved for some time, i.e., high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD) or Europe. Adding similar reviews for other country income groups is of limited interest to the research we plan to do in this area.

In view of its focus on data and methods, rather than results, a formal protocol was not registered prior to undertaking this review, but the procedure followed the guidelines of the PRISMA statement for scoping reviews [ 18 ].

We focused on multi-country studies investigating the potential associations between any aggregate level (region/city/country) determinant and general measures of population health (e.g., life expectancy, mortality rate).

Within the query itself, we listed well-established population health indicators as well as the six world regions, as defined by the World Health Organization (WHO). We searched only in the publications’ titles in order to keep the number of hits manageable, and the ratio of broadly relevant abstracts over all abstracts in the order of magnitude of 10% (based on a series of time-focused trial runs). The search strategy was developed iteratively between the two authors and is presented in S1 Appendix . The search was performed by VV in PubMed and Web of Science on the 16 th of July, 2019, without any language restrictions, and with a start date set to the 1 st of January, 2013, as we were interested in the latest developments in this area of research.

Eligibility criteria

Records obtained via the search methods described above were screened independently by the two authors. Consistency between inclusion/exclusion decisions was approximately 90% and the 43 instances where uncertainty existed were judged through discussion. Articles were included subject to meeting the following requirements: (a) the paper was a full published report of an original empirical study investigating the impact of at least one aggregate level (city/region/country) factor on at least one health indicator (or self-reported health) of the general population (the only admissible “sub-populations” were those based on gender and/or age); (b) the study employed statistical techniques (calculating correlations, at the very least) and was not purely descriptive or theoretical in nature; (c) the analysis involved at least two countries or at least two regions or cities (or another aggregate level) in at least two different countries; (d) the health outcome was not differentiated according to some socio-economic factor and thus studied in terms of inequality (with the exception of gender and age differentiations); (e) mortality, in case it was one of the health indicators under investigation, was strictly “total” or “all-cause” (no cause-specific or determinant-attributable mortality).

Data extraction

The following pieces of information were extracted in an Excel table from the full text of each eligible study (primarily by VV, consulting with PB in case of doubt): health outcome(s), determinants, statistical methodology, level of analysis, results, type of data, data sources, time period, countries. The evidence is synthesized according to these extracted data (often directly reflected in the section headings), using a narrative form accompanied by a “summary-of-findings” table and a graph.

Search and selection

The initial yield contained 4583 records, reduced to 3686 after removal of duplicates ( Fig 1 ). Based on title and abstract screening, 3271 records were excluded because they focused on specific medical condition(s) or specific populations (based on morbidity or some other factor), dealt with intervention effectiveness, with theoretical or non-health related issues, or with animals or plants. Of the remaining 415 papers, roughly half were disqualified upon full-text consideration, mostly due to using an outcome not of interest to us (e.g., health inequality), measuring and analyzing determinants and outcomes exclusively at the individual level, performing analyses one country at a time, employing indices that are a mixture of both health indicators and health determinants, or not utilizing potential health determinants at all. After this second stage of the screening process, 202 papers were deemed eligible for inclusion. This group was further dichotomized according to level of economic development of the countries or regions under study, using membership of the OECD or Europe as a reference “cut-off” point. Sixty papers were judged to include high-income countries, and the remaining 142 included either low- or middle-income countries or a mix of both these levels of development. The rest of this report outlines findings in relation to high-income countries only, reflecting our own primary research interests. Nonetheless, we chose to report our search yield for the other income groups for two reasons. First, to gauge the relative interest in applied published research for these different income levels; and second, to enable other researchers with a focus on determinants of health in other countries to use the extraction we made here.

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https://doi.org/10.1371/journal.pone.0239031.g001

Health outcomes

The most frequent population health indicator, life expectancy (LE), was present in 24 of the 60 studies. Apart from “life expectancy at birth” (representing the average life-span a newborn is expected to have if current mortality rates remain constant), also called “period LE” by some [ 19 , 20 ], we encountered as well LE at 40 years of age [ 21 ], at 60 [ 22 ], and at 65 [ 21 , 23 , 24 ]. In two papers, the age-specificity of life expectancy (be it at birth or another age) was not stated [ 25 , 26 ].

Some studies considered male and female LE separately [ 21 , 24 , 25 , 27 – 33 ]. This consideration was also often observed with the second most commonly used health index [ 28 – 30 , 34 – 38 ]–termed “total”, or “overall”, or “all-cause”, mortality rate (MR)–included in 22 of the 60 studies. In addition to gender, this index was also sometimes broken down according to age group [ 30 , 39 , 40 ], as well as gender-age group [ 38 ].

While the majority of studies under review here focused on a single health indicator, 23 out of the 60 studies made use of multiple outcomes, although these outcomes were always considered one at a time, and sometimes not all of them fell within the scope of our review. An easily discernable group of indices that typically went together [ 25 , 37 , 41 ] was that of neonatal (deaths occurring within 28 days postpartum), perinatal (fetal or early neonatal / first-7-days deaths), and post-neonatal (deaths between the 29 th day and completion of one year of life) mortality. More often than not, these indices were also accompanied by “stand-alone” indicators, such as infant mortality (deaths within the first year of life; our third most common index found in 16 of the 60 studies), maternal mortality (deaths during pregnancy or within 42 days of termination of pregnancy), and child mortality rates. Child mortality has conventionally been defined as mortality within the first 5 years of life, thus often also called “under-5 mortality”. Nonetheless, Pritchard & Wallace used the term “child mortality” to denote deaths of children younger than 14 years [ 42 ].

As previously stated, inclusion criteria did allow for self-reported health status to be used as a general measure of population health. Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [ 25 , 43 – 48 ]. Additionally, the Health Human Development Index [ 49 ], healthy life expectancy [ 50 ], old-age survival [ 51 ], potential years of life lost [ 52 ], and disability-adjusted life expectancy [ 25 ] were also used.

We note that while in most cases the indicators mentioned above (and/or the covariates considered, see below) were taken in their absolute or logarithmic form, as a—typically annual—number, sometimes they were used in the form of differences, change rates, averages over a given time period, or even z-scores of rankings [ 19 , 22 , 40 , 42 , 44 , 53 – 57 ].

Regions, countries, and populations

Despite our decision to confine this review to high-income countries, some variation in the countries and regions studied was still present. Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations–based on the instances where these were also explicitly listed—were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [ 30 , 45 , 54 ] or inconsistency [ 20 , 58 ], Gross Domestic Product (GDP) too low [ 40 ], differences in economic development and political stability with the rest of the sampled countries [ 59 ], and national population too small [ 24 , 40 ]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [ 60 ], similar healthcare systems [ 23 ], and being randomly drawn from a social spending category [ 61 ]. Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [ 50 ], Central and Eastern Europe [ 48 , 60 ], the Visegrad (V4) group [ 62 ], or the Asia/Pacific area [ 32 ]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [ 31 , 51 , 56 , 62 – 66 ]. In two particular cases, a mix of countries and cities was used [ 35 , 57 ]. In another two [ 28 , 29 ], due to the long time periods under study, some of the included countries no longer exist. Finally, besides “European” and “OECD”, the terms “developed”, “Western”, and “industrialized” were also used to describe the group of selected nations [ 30 , 42 , 52 , 53 , 67 ].

As stated above, it was the health status of the general population that we were interested in, and during screening we made a concerted effort to exclude research using data based on a more narrowly defined group of individuals. All studies included in this review adhere to this general rule, albeit with two caveats. First, as cities (even neighborhoods) were the unit of analysis in three of the studies that made the selection [ 56 , 64 , 65 ], the populations under investigation there can be more accurately described as general urban , instead of just general. Second, oftentimes health indicators were stratified based on gender and/or age, therefore we also admitted one study that, due to its specific research question, focused on men and women of early retirement age [ 35 ] and another that considered adult males only [ 68 ].

Data types and sources

A great diversity of sources was utilized for data collection purposes. The accessible reference databases of the OECD ( https://www.oecd.org/ ), WHO ( https://www.who.int/ ), World Bank ( https://www.worldbank.org/ ), United Nations ( https://www.un.org/en/ ), and Eurostat ( https://ec.europa.eu/eurostat ) were among the top choices. The other international databases included Human Mortality [ 30 , 39 , 50 ], Transparency International [ 40 , 48 , 50 ], Quality of Government [ 28 , 69 ], World Income Inequality [ 30 ], International Labor Organization [ 41 ], International Monetary Fund [ 70 ]. A number of national databases were referred to as well, for example the US Bureau of Statistics [ 42 , 53 ], Korean Statistical Information Services [ 67 ], Statistics Canada [ 67 ], Australian Bureau of Statistics [ 67 ], and Health New Zealand Tobacco control and Health New Zealand Food and Nutrition [ 19 ]. Well-known surveys, such as the World Values Survey [ 25 , 55 ], the European Social Survey [ 25 , 39 , 44 ], the Eurobarometer [ 46 , 56 ], the European Value Survey [ 25 ], and the European Statistics of Income and Living Condition Survey [ 43 , 47 , 70 ] were used as data sources, too. Finally, in some cases [ 25 , 28 , 29 , 35 , 36 , 41 , 69 ], built-for-purpose datasets from previous studies were re-used.

In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [ 31 , 62 , 63 , 66 ], otherwise defined areas [ 51 ] or cities [ 56 ], and seven others that were multilevel designs and utilized both country- and region-level data [ 57 ], individual- and city- or country-level [ 35 ], individual- and country-level [ 44 , 45 , 48 ], individual- and neighborhood-level [ 64 ], and city-region- (NUTS3) and country-level data [ 65 ]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [ 25 , 33 , 43 , 45 – 48 , 50 , 62 , 67 , 68 , 71 , 72 ], albeit in four of those [ 43 , 48 , 68 , 72 ] two separate points in time were taken (thus resulting in a kind of “double cross-section”), while in another the averages across survey waves were used [ 56 ].

In studies using longitudinal data, the length of the covered time periods varied greatly. Although this was almost always less than 40 years, in one study it covered the entire 20 th century [ 29 ]. Longitudinal data, typically in the form of annual records, was sometimes transformed before usage. For example, some researchers considered data points at 5- [ 34 , 36 , 49 ] or 10-year [ 27 , 29 , 35 ] intervals instead of the traditional 1, or took averages over 3-year periods [ 42 , 53 , 73 ]. In one study concerned with the effect of the Great Recession all data were in a “recession minus expansion change in trends”-form [ 57 ]. Furthermore, there were a few instances where two different time periods were compared to each other [ 42 , 53 ] or when data was divided into 2 to 4 (possibly overlapping) periods which were then analyzed separately [ 24 , 26 , 28 , 29 , 31 , 65 ]. Lastly, owing to data availability issues, discrepancies between the time points or periods of data on the different variables were occasionally observed [ 22 , 35 , 42 , 53 – 55 , 63 ].

Health determinants

Together with other essential details, Table 1 lists the health correlates considered in the selected studies. Several general categories for these correlates can be discerned, including health care, political stability, socio-economics, demographics, psychology, environment, fertility, life-style, culture, labor. All of these, directly or implicitly, have been recognized as holding importance for population health by existing theoretical models of (social) determinants of health [ 74 – 77 ].

https://doi.org/10.1371/journal.pone.0239031.t001

It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [ 44 ], in another–welfare system typology [ 45 ], but also gender inequality [ 33 ], austerity level [ 70 , 78 ], and deprivation [ 51 ]. Most often though, attention went exclusively to GDP [ 27 , 29 , 46 , 57 , 65 , 71 ]. It was often the case that research had a more particular focus. Among others, minimum wages [ 79 ], hospital payment schemes [ 23 ], cigarette prices [ 63 ], social expenditure [ 20 ], residents’ dissatisfaction [ 56 ], income inequality [ 30 , 69 ], and work leave [ 41 , 58 ] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators.

We visualized the combinations in which the different determinants have been studied in Fig 2 , which was obtained via multidimensional scaling and a subsequent cluster analysis (details outlined in S2 Appendix ). It depicts the spatial positioning of each determinant relative to all others, based on the number of times the effects of each pair of determinants have been studied simultaneously. When interpreting Fig 2 , one should keep in mind that determinants marked with an asterisk represent, in fact, collectives of variables.

Groups of determinants are marked by asterisks (see S1 Table in S1 Appendix ). Diminishing color intensity reflects a decrease in the total number of “connections” for a given determinant. Noteworthy pairwise “connections” are emphasized via lines (solid-dashed-dotted indicates decreasing frequency). Grey contour lines encircle groups of variables that were identified via cluster analysis. Abbreviations: age = population age distribution, associations = membership in associations, AT-index = atherogenic-thrombogenic index, BR = birth rate, CAPB = Cyclically Adjusted Primary Balance, civilian-labor = civilian labor force, C-section = Cesarean delivery rate, credit-info = depth of credit information, dissatisf = residents’ dissatisfaction, distrib.orient = distributional orientation, EDU = education, eHealth = eHealth index at GP-level, exch.rate = exchange rate, fat = fat consumption, GDP = gross domestic product, GFCF = Gross Fixed Capital Formation/Creation, GH-gas = greenhouse gas, GII = gender inequality index, gov = governance index, gov.revenue = government revenues, HC-coverage = healthcare coverage, HE = health(care) expenditure, HHconsump = household consumption, hosp.beds = hospital beds, hosp.payment = hospital payment scheme, hosp.stay = length of hospital stay, IDI = ICT development index, inc.ineq = income inequality, industry-labor = industrial labor force, infant-sex = infant sex ratio, labor-product = labor production, LBW = low birth weight, leave = work leave, life-satisf = life satisfaction, M-age = maternal age, marginal-tax = marginal tax rate, MDs = physicians, mult.preg = multiple pregnancy, NHS = Nation Health System, NO = nitrous oxide emissions, PM10 = particulate matter (PM10) emissions, pop = population size, pop.density = population density, pre-term = pre-term birth rate, prison = prison population, researchE = research&development expenditure, school.ref = compulsory schooling reform, smoke-free = smoke-free places, SO = sulfur oxide emissions, soc.E = social expenditure, soc.workers = social workers, sugar = sugar consumption, terror = terrorism, union = union density, UR = unemployment rate, urban = urbanization, veg-fr = vegetable-and-fruit consumption, welfare = welfare regime, Wwater = wastewater treatment.

https://doi.org/10.1371/journal.pone.0239031.g002

Distances between determinants in Fig 2 are indicative of determinants’ “connectedness” with each other. While the statistical procedure called for higher dimensionality of the model, for demonstration purposes we show here a two-dimensional solution. This simplification unfortunately comes with a caveat. To use the factor smoking as an example, it would appear it stands at a much greater distance from GDP than it does from alcohol. In reality however, smoking was considered together with alcohol consumption [ 21 , 25 , 26 , 52 , 68 ] in just as many studies as it was with GDP [ 21 , 25 , 26 , 52 , 59 ], five. To aid with respect to this apparent shortcoming, we have emphasized the strongest pairwise links. Solid lines connect GDP with health expenditure (HE), unemployment rate (UR), and education (EDU), indicating that the effect of GDP on health, taking into account the effects of the other three determinants as well, was evaluated in between 12 to 16 studies of the 60 included in this review. Tracing the dashed lines, we can also tell that GDP appeared jointly with income inequality, and HE together with either EDU or UR, in anywhere between 8 to 10 of our selected studies. Finally, some weaker but still worth-mentioning “connections” between variables are displayed as well via the dotted lines.

The fact that all notable pairwise “connections” are concentrated within a relatively small region of the plot may be interpreted as low overall “connectedness” among the health indicators studied. GDP is the most widely investigated determinant in relation to general population health. Its total number of “connections” is disproportionately high (159) compared to its runner-up–HE (with 113 “connections”), and then subsequently EDU (with 90) and UR (with 86). In fact, all of these determinants could be thought of as outliers, given that none of the remaining factors have a total count of pairings above 52. This decrease in individual determinants’ overall “connectedness” can be tracked on the graph via the change of color intensity as we move outwards from the symbolic center of GDP and its closest “co-determinants”, to finally reach the other extreme of the ten indicators (welfare regime, household consumption, compulsory school reform, life satisfaction, government revenues, literacy, research expenditure, multiple pregnancy, Cyclically Adjusted Primary Balance, and residents’ dissatisfaction; in white) the effects on health of which were only studied in isolation.

Lastly, we point to the few small but stable clusters of covariates encircled by the grey bubbles on Fig 2 . These groups of determinants were identified as “close” by both statistical procedures used for the production of the graph (see details in S2 Appendix ).

Statistical methodology

There was great variation in the level of statistical detail reported. Some authors provided too vague a description of their analytical approach, necessitating some inference in this section.

The issue of missing data is a challenging reality in this field of research, but few of the studies under review (12/60) explain how they dealt with it. Among the ones that do, three general approaches to handling missingness can be identified, listed in increasing level of sophistication: case-wise deletion, i.e., removal of countries from the sample [ 20 , 45 , 48 , 58 , 59 ], (linear) interpolation [ 28 , 30 , 34 , 58 , 59 , 63 ], and multiple imputation [ 26 , 41 , 52 ].

Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [ 33 , 42 – 44 , 46 , 53 , 57 , 61 ]. Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, “unique” within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [ 39 ]; difference-in-difference (DiD) analysis was applied in one case [ 23 ]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [ 80 ]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [ 70 , 78 ]; hierarchical Bayesian spatial models [ 51 ] and special autoregressive regression [ 62 ] were also implemented.

Purely cross-sectional data analyses were performed in eight studies [ 25 , 45 , 47 , 50 , 55 , 56 , 67 , 71 ]. These consisted of linear regression (assumed ordinary least squares (OLS)), generalized least squares (GLS) regression, and multilevel analyses. However, six other studies that used longitudinal data in fact had a cross-sectional design, through which they applied regression at multiple time-points separately [ 27 , 29 , 36 , 48 , 68 , 72 ].

Apart from these “multi-point cross-sectional studies”, some other simplistic approaches to longitudinal data analysis were found, involving calculating and regressing 3-year averages of both the response and the predictor variables [ 54 ], taking the average of a few data-points (i.e., survey waves) [ 56 ] or using difference scores over 10-year [ 19 , 29 ] or unspecified time intervals [ 40 , 55 ].

Moving further in the direction of more sensible longitudinal data usage, we turn to the methods widely known among (health) economists as “panel data analysis” or “panel regression”. Most often seen were models with fixed effects for country/region and sometimes also time-point (occasionally including a country-specific trend as well), with robust standard errors for the parameter estimates to take into account correlations among clustered observations [ 20 , 21 , 24 , 28 , 30 , 32 , 34 , 37 , 38 , 41 , 52 , 59 , 60 , 63 , 66 , 69 , 73 , 79 , 81 , 82 ]. The Hausman test [ 83 ] was sometimes mentioned as the tool used to decide between fixed and random effects [ 26 , 49 , 63 , 66 , 73 , 82 ]. A few studies considered the latter more appropriate for their particular analyses, with some further specifying that (feasible) GLS estimation was employed [ 26 , 34 , 49 , 58 , 60 , 73 ]. Apart from these two types of models, the first differences method was encountered once as well [ 31 ]. Across all, the error terms were sometimes assumed to come from a first-order autoregressive process (AR(1)), i.e., they were allowed to be serially correlated [ 20 , 30 , 38 , 58 – 60 , 73 ], and lags of (typically) predictor variables were included in the model specification, too [ 20 , 21 , 37 , 38 , 48 , 69 , 81 ]. Lastly, a somewhat different approach to longitudinal data analysis was undertaken in four studies [ 22 , 35 , 48 , 65 ] in which multilevel–linear or Poisson–models were developed.

Regardless of the exact techniques used, most studies included in this review presented multiple model applications within their main analysis. None attempted to formally compare models in order to identify the “best”, even if goodness-of-fit statistics were occasionally reported. As indicated above, many studies investigated women’s and men’s health separately [ 19 , 21 , 22 , 27 – 29 , 31 , 33 , 35 , 36 , 38 , 39 , 45 , 50 , 51 , 64 , 65 , 69 , 82 ], and covariates were often tested one at a time, including other covariates only incrementally [ 20 , 25 , 28 , 36 , 40 , 50 , 55 , 67 , 73 ]. Furthermore, there were a few instances where analyses within countries were performed as well [ 32 , 39 , 51 ] or where the full time period of interest was divided into a few sub-periods [ 24 , 26 , 28 , 31 ]. There were also cases where different statistical techniques were applied in parallel [ 29 , 55 , 60 , 66 , 69 , 73 , 82 ], sometimes as a form of sensitivity analysis [ 24 , 26 , 30 , 58 , 73 ]. However, the most common approach to sensitivity analysis was to re-run models with somewhat different samples [ 39 , 50 , 59 , 67 , 69 , 80 , 82 ]. Other strategies included different categorization of variables or adding (more/other) controls [ 21 , 23 , 25 , 28 , 37 , 50 , 63 , 69 ], using an alternative main covariate measure [ 59 , 82 ], including lags for predictors or outcomes [ 28 , 30 , 58 , 63 , 65 , 79 ], using weights [ 24 , 67 ] or alternative data sources [ 37 , 69 ], or using non-imputed data [ 41 ].

As the methods and not the findings are the main focus of the current review, and because generic checklists cannot discern the underlying quality in this application field (see also below), we opted to pool all reported findings together, regardless of individual study characteristics or particular outcome(s) used, and speak generally of positive and negative effects on health. For this summary we have adopted the 0.05-significance level and only considered results from multivariate analyses. Strictly birth-related factors are omitted since these potentially only relate to the group of infant mortality indicators and not to any of the other general population health measures.

Starting with the determinants most often studied, higher GDP levels [ 21 , 26 , 27 , 29 , 30 , 32 , 43 , 48 , 52 , 58 , 60 , 66 , 67 , 73 , 79 , 81 , 82 ], higher health [ 21 , 37 , 47 , 49 , 52 , 58 , 59 , 68 , 72 , 82 ] and social [ 20 , 21 , 26 , 38 , 79 ] expenditures, higher education [ 26 , 39 , 52 , 62 , 72 , 73 ], lower unemployment [ 60 , 61 , 66 ], and lower income inequality [ 30 , 42 , 53 , 55 , 73 ] were found to be significantly associated with better population health on a number of occasions. In addition to that, there was also some evidence that democracy [ 36 ] and freedom [ 50 ], higher work compensation [ 43 , 79 ], distributional orientation [ 54 ], cigarette prices [ 63 ], gross national income [ 22 , 72 ], labor productivity [ 26 ], exchange rates [ 32 ], marginal tax rates [ 79 ], vaccination rates [ 52 ], total fertility [ 59 , 66 ], fruit and vegetable [ 68 ], fat [ 52 ] and sugar consumption [ 52 ], as well as bigger depth of credit information [ 22 ] and percentage of civilian labor force [ 79 ], longer work leaves [ 41 , 58 ], more physicians [ 37 , 52 , 72 ], nurses [ 72 ], and hospital beds [ 79 , 82 ], and also membership in associations, perceived corruption and societal trust [ 48 ] were beneficial to health. Higher nitrous oxide (NO) levels [ 52 ], longer average hospital stay [ 48 ], deprivation [ 51 ], dissatisfaction with healthcare and the social environment [ 56 ], corruption [ 40 , 50 ], smoking [ 19 , 26 , 52 , 68 ], alcohol consumption [ 26 , 52 , 68 ] and illegal drug use [ 68 ], poverty [ 64 ], higher percentage of industrial workers [ 26 ], Gross Fixed Capital creation [ 66 ] and older population [ 38 , 66 , 79 ], gender inequality [ 22 ], and fertility [ 26 , 66 ] were detrimental.

It is important to point out that the above-mentioned effects could not be considered stable either across or within studies. Very often, statistical significance of a given covariate fluctuated between the different model specifications tried out within the same study [ 20 , 49 , 59 , 66 , 68 , 69 , 73 , 80 , 82 ], testifying to the importance of control variables and multivariate research (i.e., analyzing multiple independent variables simultaneously) in general. Furthermore, conflicting results were observed even with regards to the “core” determinants given special attention, so to speak, throughout this text. Thus, some studies reported negative effects of health expenditure [ 32 , 82 ], social expenditure [ 58 ], GDP [ 49 , 66 ], and education [ 82 ], and positive effects of income inequality [ 82 ] and unemployment [ 24 , 31 , 32 , 52 , 66 , 68 ]. Interestingly, one study [ 34 ] differentiated between temporary and long-term effects of GDP and unemployment, alluding to possibly much greater complexity of the association with health. It is also worth noting that some gender differences were found, with determinants being more influential for males than for females, or only having statistically significant effects for male health [ 19 , 21 , 28 , 34 , 36 , 37 , 39 , 64 , 65 , 69 ].

The purpose of this scoping review was to examine recent quantitative work on the topic of multi-country analyses of determinants of population health in high-income countries.

Measuring population health via relatively simple mortality-based indicators still seems to be the state of the art. What is more, these indicators are routinely considered one at a time, instead of, for example, employing existing statistical procedures to devise a more general, composite, index of population health, or using some of the established indices, such as disability-adjusted life expectancy (DALE) or quality-adjusted life expectancy (QALE). Although strong arguments for their wider use were already voiced decades ago [ 84 ], such summary measures surface only rarely in this research field.

On a related note, the greater data availability and accessibility that we enjoy today does not automatically equate to data quality. Nonetheless, this is routinely assumed in aggregate level studies. We almost never encountered a discussion on the topic. The non-mundane issue of data missingness, too, goes largely underappreciated. With all recent methodological advancements in this area [ 85 – 88 ], there is no excuse for ignorance; and still, too few of the reviewed studies tackled the matter in any adequate fashion.

Much optimism can be gained considering the abundance of different determinants that have attracted researchers’ attention in relation to population health. We took on a visual approach with regards to these determinants and presented a graph that links spatial distances between determinants with frequencies of being studies together. To facilitate interpretation, we grouped some variables, which resulted in some loss of finer detail. Nevertheless, the graph is helpful in exemplifying how many effects continue to be studied in a very limited context, if any. Since in reality no factor acts in isolation, this oversimplification practice threatens to render the whole exercise meaningless from the outset. The importance of multivariate analysis cannot be stressed enough. While there is no “best method” to be recommended and appropriate techniques vary according to the specifics of the research question and the characteristics of the data at hand [ 89 – 93 ], in the future, in addition to abandoning simplistic univariate approaches, we hope to see a shift from the currently dominating fixed effects to the more flexible random/mixed effects models [ 94 ], as well as wider application of more sophisticated methods, such as principle component regression, partial least squares, covariance structure models (e.g., structural equations), canonical correlations, time-series, and generalized estimating equations.

Finally, there are some limitations of the current scoping review. We searched the two main databases for published research in medical and non-medical sciences (PubMed and Web of Science) since 2013, thus potentially excluding publications and reports that are not indexed in these databases, as well as older indexed publications. These choices were guided by our interest in the most recent (i.e., the current state-of-the-art) and arguably the highest-quality research (i.e., peer-reviewed articles, primarily in indexed non-predatory journals). Furthermore, despite holding a critical stance with regards to some aspects of how determinants-of-health research is currently conducted, we opted out of formally assessing the quality of the individual studies included. The reason for that is two-fold. On the one hand, we are unaware of the existence of a formal and standard tool for quality assessment of ecological designs. And on the other, we consider trying to score the quality of these diverse studies (in terms of regional setting, specific topic, outcome indices, and methodology) undesirable and misleading, particularly since we would sometimes have been rating the quality of only a (small) part of the original studies—the part that was relevant to our review’s goal.

Our aim was to investigate the current state of research on the very broad and general topic of population health, specifically, the way it has been examined in a multi-country context. We learned that data treatment and analytical approach were, in the majority of these recent studies, ill-equipped or insufficiently transparent to provide clarity regarding the underlying mechanisms of population health in high-income countries. Whether due to methodological shortcomings or the inherent complexity of the topic, research so far fails to provide any definitive answers. It is our sincere belief that with the application of more advanced analytical techniques this continuous quest could come to fruition sooner.

Supporting information

S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0239031.s001

S1 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s002

S2 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s003

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How Has Quantitative Analysis Changed Health Care?

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In health care, groundbreaking solutions often follow a new capacity for measurement and pattern finding. For example, developing the ability to measure blood glucose levels led to better treatments of diabetes. Florence Nightingale changed nursing forever with her careful measurements of hospital care outcomes. Today, we’re in the midst of an even more significant change in the health care industry: Troves of data are mixing with technologies newly powerful enough to adequately analyze them. As a result, the unprecedented pattern-finding power of quantitative analysis is remaking the health care industry.

Quantitative analysis refers to the process of using complex mathematical or statistical modeling to make sense of data and potentially to predict behavior. Though quantitative analysis is well-established in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. Some experts insist that the unfurling of QA in health care will radically change the industry—and how all of us maintain our health and are treated when we’re sick.

It will be up to professionals in the transforming field of health care information technology to make the most of the opportunities borne from these expanding data sets. But what are a few of the specific ways in which quantitative analysis could improve health care?

Stronger Research

Dr. Richard Biehl, former education coordinator of the online Master of Science in Health Care Systems Engineering program at the University of Central Florida, explains that QA stands to change the face of research in the health care field, because, suddenly, it may become very easy to test the strength of correlations between thousands of variables with the touch of a button. In other words, no researcher will need to make the concerted decision to build a study around a question such as “Is this particular allele driving lipid metabolism?” Powerful analytical tools driven by QA will be able to point researchers in the direction of promising correlations between variables they might not have realized were linked.

“We used to get the data to support our research; now we’re getting the data to suggest our research,” Dr. Biehl says. “That’s very, very different.”

The upshot? The field of health care research will become a much more targeted and efficient space—and more likely to regularly uncover lifesaving treatments.

Saving Time, Money, and Lives Through Efficiency and Safety

New QA tools will decrease wait times and call patients into doctors’ offices only when a visit is necessary. As more and more data is crunched to determine, for example, what bodily indicators tend to precede a heart attack, the provider (who will be monitoring the patient’s vital signs via wearable devices) will be able to alert the patient when his or her indicators are trending in a worrisome direction. That means paying for fewer checkup appointments when one is healthy.

Even more importantly, QA tools will allow health care professionals to decrease the impact of human error in prescribing medication and invasive health care procedures. More data can save lives by uncovering complicated patterns (in physiology, DNA, diet, or lifestyle) that help explain why certain medications can prove dangerous for some.

Making Sure Supply Meets Demand

Certain geographic locations and clinical specialties are already facing doctor shortages as mergers and acquisitions reform the health care landscape and financial difficulties force providers to close their doors. But by filling in the picture of oversupply and undersupply around the country, QA can help providers plug holes where they need to.

“Making sure there’s an adequate supply of health care in the right places, in the right specialties, and at the right times, is a health care systems engineering challenge,” Dr. Biehl says.

Amid all the exciting possibilities, QA’s application to health care is still newer than other industries and faces challenges. This type of analysis requires that variables be recorded as numerical data so that they can be analyzed with statistical tools—a format that health care has struggled to conform with, as much of its outcome data is recorded as “positive” or “negative.”

Additionally, QA statistical tools work best when fed with huge amounts of data, as more data makes for clearer patterns and stronger conclusions. Big data analysts at corporations such as Amazon and Google—where every click is tracked and measured—have been collecting unprecedented amounts of data to feed into complex statistical tools for years. Health care has yet to catch up, but it likely will once more wearable technology options, such as expanded versions of Fitbit devices and trackers embedded in up-and-coming “internet of things” appliances, track more and more users’ every move, bite, and night of sleep.

“Once we start collecting all this personal, wearable data from people, health care will start to look more like the Googles and Amazons of the world,” Dr. Biehl says. “We’ll have hundreds of millions of people collecting tens of thousands of data points a day. We’ll finally have big data; we’re heading in that direction.”

Additional Resources

https://www.worldcat.org/wcpa/servlet/DCARead?standardNo=0787971642&standardNoType=1&excerpt=true https://bizfluent.com/info-8168865-benefits-quantitative-research-health-care.html https://www.ruralhealthinfo.org/community-health/rural-toolkit/4/quantitative-qualitative https://www.dotmed.com/news/story/37262

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Using data for improvement

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• Amar Shah , chief quality officer and consultant forensic psychiatrist, national improvement lead for the Mental Health Safety Improvement Programme
• East London NHS Foundation Trust, London, E1 8DE, UK
• amarshah{at}nhs.net @DrAmarShah

What you need to know

Both qualitative and quantitative data are critical for evaluating and guiding improvement

A family of measures, incorporating outcome, process, and balancing measures, should be used to track improvement work

Time series analysis, using small amounts of data collected and displayed frequently, is the gold standard for using data for improvement

We all need a way to understand the quality of care we are providing, or receiving, and how our service is performing. We use a range of data in order to fulfil this need, both quantitative and qualitative. Data are defined as “information, especially facts and numbers, collected to be examined and considered and used to help decision-making.” 1 Data are used to make judgements, to answer questions, and to monitor and support improvement in healthcare ( box 1 ). The same data can be used in different ways, depending on what we want to know or learn.

Defining quality improvement 2

Quality improvement aims to make a difference to patients by improving safety, effectiveness, and experience of care by:

Using understanding of our complex healthcare environment

Applying a systematic approach

Designing, testing, and implementing changes using real-time measurement for improvement

Within healthcare, we use a range of data at different levels of the system:

Patient level—such as blood sugar, temperature, blood test results, or expressed wishes for care)

Service level—such as waiting times, outcomes, complaint themes, or collated feedback of patient experience

Organisation level—such as staff experience or financial performance

Population level—such as mortality, quality of life, employment, and air quality.

This article outlines the data we need to understand the quality of care we are providing, what we need to capture to see if care is improving, how to interpret the data, and some tips for doing this more effectively.

Sources and selection criteria

This article is based on my experience of using data for improvement at East London NHS Foundation Trust, which is seen as one of the world leaders in healthcare quality improvement. Our use of data, from trust board to clinical team, has transformed over the past six years in line with the learning shared in this article. This article is also based on my experience of teaching with the Institute for Healthcare Improvement, which guides and supports quality improvement efforts across the globe.

What data do we need?

Healthcare is a complex system, with multiple interdependencies and an array of factors influencing outcomes. Complex systems are open, unpredictable, and continually adapting to their environment. 3 No single source of data can help us understand how a complex system behaves, so we need several data sources to see how a complex system in healthcare is performing.

Avedis Donabedian, a doctor born in Lebanon in 1919, studied quality in healthcare and contributed to our understanding of using outcomes. 4 He described the importance of focusing on structures and processes in order to improve outcomes. 5 When trying to understand quality within a complex system, we need to look at a mix of outcomes (what matters to patients), processes (the way we do our work), and structures (resources, equipment, governance, etc).

Therefore, when we are trying to improve something, we need a small number of measures (ideally 5-8) to help us monitor whether we are moving towards our goal. Any improvement effort should include one or two outcome measures linked explicitly to the aim of the work, a small number of process measures that show how we are doing with the things we are actually working on to help us achieve our aim, and one or two balancing measures ( box 2 ). Balancing measures help us spot unintended consequences of the changes we are making. As complex systems are unpredictable, our new changes may result in an unexpected adverse effect. Balancing measures help us stay alert to these, and ought to be things that are already collected, so that we do not waste extra resource on collecting these.

Different types of measures of quality of care

Outcome measures (linked explicitly to the aim of the project).

Aim— To reduce waiting times from referral to appointment in a clinic

Outcome measure— Length of time from referral being made to being seen in clinic

Data collection— Date when each referral was made, and date when each referral was seen in clinic, in order to calculate the time in days from referral to being seen

Process measures (linked to the things you are going to work on to achieve the aim)

Change idea— Use of a new referral form (to reduce numbers of inappropriate referrals and re-work in obtaining necessary information)

Process measure— Percentage of referrals received that are inappropriate or require further information

Data collection— Number of referrals received that are inappropriate or require further information each week divided by total number of referrals received each week

Change idea— Text messaging patients two days before the appointment (to reduce non-attendance and wasted appointment slots)

Process measure— Percentage of patients receiving a text message two days before appointment

Data collection— Number of patients each week receiving a text message two days before their appointment divided by the total number of patients seen each week

Process measure— Percentage of patients attending their appointment

Data collection— Number of patients attending their appointment each week divided by the total number of patients booked in each week

Balancing measures (to spot unintended consequences)

Measure— Percentage of referrers who are satisfied or very satisfied with the referral process (to spot whether all these changes are having a detrimental effect on the experience of those referring to us)

Data collection— A monthly survey to referrers to assess their satisfaction with the referral process

Measure— Percentage of staff who are satisfied or very satisfied at work (to spot whether the changes are increasing burden on staff and reducing their satisfaction at work)

Data collection— A monthly survey for staff to assess their satisfaction at work

How should we look at the data?

This depends on the question we are trying to answer. If we ask whether an intervention was efficacious, as we might in a research study, we would need to be able to compare data before and after the intervention and remove all potential confounders and bias. For example, to understand whether a new treatment is better than the status quo, we might design a research study to compare the effect of the two interventions and ensure that all other characteristics are kept constant across both groups. This study might take several months, or possibly years, to complete, and would compare the average of both groups to identify whether there is a statistically significant difference.

This approach is unlikely to be possible in most contexts where we are trying to improve quality. Most of the time when we are improving a service, we are making multiple changes and assessing impact in real-time, without being able to remove all confounding factors and potential bias. When we ask whether an outcome has improved, as we do when trying to improve something, we need to be able to look at data over time to see how the system changes as we intervene, with multiple tests of change over a period. For example, if we were trying to improve the time from a patient presenting in the emergency department to being admitted to a ward, we would likely be testing several different changes at different places in the pathway. We would want to be able to look at the outcome measure of total time from presentation to admission on the ward, over time, on a daily basis, to be able to see whether the changes made lead to a reduction in the overall outcome. So, when looking at a quality issue from an improvement perspective, we view smaller amounts of data but more frequently to see if we are improving over time. 2

What is best practice in using data to support improvement?

Best practice would be for each team to have a small number of measures that are collectively agreed with patients and service users as being the most important ways of understanding the quality of the service being provided. These measures would be displayed transparently so that all staff, service users, and patients and families or carers can access them and understand how the service is performing. The data would be shown as time series analysis, to provide a visual display of whether the service is improving over time. The data should be available as close to real-time as possible, ideally on a daily or weekly basis. The data should prompt discussion and action, with the team reviewing the data regularly, identifying any signals that suggest something unusual in the data, and taking action as necessary.

The main tools used for this purpose are the run chart and the Shewhart (or control) chart. The run chart ( fig 1 ) is a graphical display of data in time order, with a median value, and uses probability-based rules to help identify whether the variation seen is random or non-random. 2 The Shewhart (control) chart ( fig 2 ) also displays data in time order, but with a mean as the centre line instead of a median, and upper and lower control limits (UCL and LCL) defining the boundaries within which you would predict the data to be. 6 Shewhart charts use the terms “common cause variation” and “special cause variation,” with a different set of rules to identify special causes.

A typical run chart

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A typical Shewhart (or control) chart

Is it just about numbers?

We need to incorporate both qualitative and quantitative data to help us learn about how the system is performing and to see if we improve over time. Quantitative data express quantity, amount, or range and can be measured numerically—such as waiting times, mortality, haemoglobin level, cash flow. Quantitative data are often visualised over time as time series analyses (run charts or control charts) to see whether we are improving.

However, we should also be capturing, analysing, and learning from qualitative data throughout our improvement work. Qualitative data are virtually any type of information that can be observed and recorded that is not numerical in nature. Qualitative data are particularly useful in helping us to gain deeper insight into an issue, and to understand meaning, opinion, and feelings. This is vital in supporting us to develop theories about what to focus on and what might make a difference. 7 Examples of qualitative data include waiting room observation, feedback about experience of care, free-text responses to a survey.

Using qualitative data for improvement

One key point in an improvement journey when qualitative data are critical is at the start, when trying to identify “What matters most?” and what the team’s biggest opportunity for improvement is. The other key time to use qualitative data is during “Plan, Do, Study, Act” (PDSA) cycles. Most PDSA cycles, when done well, rely on qualitative data as well as quantitative data to help learn about how the test fared compared with our original theory and prediction.

Table 1 shows four different ways to collect qualitative data, with advantages and disadvantages of each, and how we might use them within our improvement work.

Different ways to collect qualitative data for improvement

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Tips to overcome common challenges in using data for improvement?

One of the key challenges faced by healthcare teams across the globe is being able to access data that is routinely collected, in order to use it for improvement. Large volumes of data are collected in healthcare, but often little is available to staff or service users in a timescale or in a form that allows it to be useful for improvement. One way to work around this is to have a simple form of measurement on the unit, clinic, or ward that the team own and update. This could be in the form of a safety cross 8 or tally chart. A safety cross ( fig 3 ) is a simple visual monthly calendar on the wall which allows teams to identify when a safety event (such as a fall) occurred on the ward. The team simply colours in each day green when no fall occurred, or colours in red the days when a fall occurred. It allows the team to own the data related to a safety event that they care about and easily see how many events are occurring over a month. Being able to see such data transparently on a ward allows teams to update data in real time and be able to respond to it effectively.

Example of a safety cross in use

A common challenge in using qualitative data is being able to analyse large quantities of written word. There are formal approaches to qualitative data analyses, but most healthcare staff are not trained in these methods. Key tips in avoiding this difficulty are ( a ) to be intentional with your search and sampling strategy so that you collect only the minimum amount of data that is likely to be useful for learning and ( b ) to use simple ways to read and theme the data in order to extract useful information to guide your improvement work. 9 If you want to try this, see if you can find someone in your organisation with qualitative data analysis skills, such as clinical psychologists or the patient experience or informatics teams.

Education into practice

What are the key measures for the service that you work in?

Are these measures available, transparently displayed, and viewed over time?

What qualitative data do you use in helping guide your improvement efforts?

How patients were involved in the creation of this article

Service users are deeply involved in all quality improvement work at East London NHS Foundation Trust, including within the training programmes we deliver. Shared learning over many years has contributed to our understanding of how best to use all types of data to support improvement. No patients have had input specifically into this article.

This article is part of a series commissioned by The BMJ based on ideas generated by a joint editorial group with members from the Health Foundation and The BMJ , including a patient/carer. The BMJ retained full editorial control over external peer review, editing, and publication. Open access fees and The BMJ ’s quality improvement editor post are funded by the Health Foundation.

Competing interests: I have read and understood the BMJ Group policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• ↵ Cambridge University Press. Cambridge online dictionary , 2008. https://dictionary.cambridge.org/ .
• Provost LP ,
• Braithwaite J
• Neuhauser D
• Donabedian A
• Mohammed MA
• Davidoff F ,
• Dixon-Woods M ,
• Leviton L ,
• ↵ Flynn M. Quality & Safety—The safety cross system: simple and effective. https://www.inmo.ie/MagazineArticle/PrintArticle/11155 .

Quality in health care: possibilities and limitations of quantitative research instruments among health care users

• Published: 07 October 2011
• Volume 47 , pages 1703–1716, ( 2013 )

Cite this article

• Mirna Macur 1  

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Quality in health care has traditionally been dominated by medical profession, where patients’ opinions were labelled as lay evaluation. Patients’ views and opinions are important because they give us an insight into dimensions of quality that are not evaluated by medical profession and often seem to be more important. In health care quantitative methodology is often used to address these quality dimensions and introduce patients’ views and opinions. There are various benefits using quantitative research instruments, such as a detailed analysis of the importance of various quality dimensions for patients and an analysis of factors influencing patients’ satisfaction. On the other hand serious deficiencies can be tackled too, that are usually dealt with qualitative research instruments, because they go deeper into people’s motives and feelings. However, health care service is specific—it is very important to patients (health is one of the most important values), but their participation in health care service is rather low. They also don’t always say and do what they mean. In such a context combination of quantitative and qualitative research instrument does not give satisfactory answers. The importance of complaints is stressed and rewards for taking them seriously and acting upon them is discussed.

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Macur, M. Quality in health care: possibilities and limitations of quantitative research instruments among health care users. Qual Quant 47 , 1703–1716 (2013). https://doi.org/10.1007/s11135-011-9621-z

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Published : 07 October 2011

Issue Date : April 2013

DOI : https://doi.org/10.1007/s11135-011-9621-z

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Common Data Types in Public Health Research

Quantitative data.

• Quantitative data is measurable, often used for comparisons, and involves counting of people, behaviors, conditions, or other discrete events (Wang, 2013).
• Quantitative data uses numbers to determine the what, who, when, and where of health-related events (Wang, 2013).
• Examples of quantitative data include: age, weight, temperature, or the number of people suffering from diabetes.

Qualitative Data

• Qualitative data is a broad category of data that can include almost any non-numerical data.
• Qualitative data uses words to describe a particular health-related event (Romano).
• This data can be observed, but not measured.
• Involves observing people in selected places and listening to discover how they feel and why they might feel that way (Wang, 2013).
• Examples of qualitative data include: male/female, smoker/non-smoker, or questionnaire response (agree, disagree, neutral).
• Measuring organizational change.
• Measures of clinical leadership in implementing evidence-based guidelines.
• Patient perceptions of quality of care.

Data Sources

Primary data sources.

• Primary data analysis in which the same individual or team of researchers designs, collects, and analyzes the data, for the purpose of answering a research question (Koziol & Arthur, nd).

Advantages to Using Primary Data

• You collect exactly the data elements that you need to answer your research question (Romano).
• You can test an intervention, such as an experimental drug or an educational program, in the purest way (a double-blind randomized controlled trial (Romano).
• You control the data collection process, so you can ensure data quality, minimize the number of missing values, and assess the reliability of your instruments (Romano).

Secondary Data Sources

• Existing data collected for another purposes, that you use to answer your research question (Romano).

Advantages of Working with Secondary Data

• Large samples
• Can provide population estimates : for example state data can be combined across states to get national estimates (Shaheen, Pan, & Mukherjee).
• Less expensive to collect than primary data (Romano)
• It takes less time to collect secondary data (Romano).
• You may not need to worry about informed consent, human subjects restriction (Romano).

Issues in Using Secondary Data

• Study design and data collection already completed (Koziol & Arthur, nd).
• Data may not facilitate particular research question o Information regarding study design and data collection procedures may be scarce.
• Data may potentially lack depth (the greater the breadth the harder it is to measure any one construct in depth) (Koziol & Arthur, nd).
• Certain fields or departments (e.g., experimental programs) may place less value on secondary data analysis (Koziol & Arthur, nd).
• Often requires special techniques to analyze statistically the data.

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Quantitative Results of a National Intervention to Prevent Hospital-Acquired Catheter-Associated Urinary Tract Infection: A Pre-Post Observational Study

Affiliations.

• 1 University of Michigan Medical School and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (J.M., M.T.G., S.S.).
• 2 University of Michigan School of Nursing, Ann Arbor, Michigan (M.M.).
• 3 University of Michigan Medical School, Ann Arbor, Michigan (J.M.A., A.S.).
• 4 Integrated Clinical Services Team, Trinity Health, Livonia, Michigan (R.N.O.).
• 5 Health Research & Educational Trust, American Hospital Association, Chicago, Illinois (A.J.R.).
• 6 Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (D.R.).
• PMID: 31569231
• DOI: 10.7326/M18-3534

Background: Many hospitals struggle to prevent catheter-associated urinary tract infection (CAUTI).

Objective: To evaluate the effect of a multimodal initiative on CAUTI in hospitals with high burden of health care-associated infection (HAI).

Design: Prospective, national, nonrandomized, clustered, externally facilitated, pre-post observational quality improvement initiative, for 3 cohorts active between November 2016 and May 2018.

Setting: Acute care, long-term acute care, and critical access hospitals, including intensive care and non-intensive care wards.

Participants: Target hospitals had a high burden of Clostridioides difficile infection plus central line-associated bloodstream infection, CAUTI, or hospital-onset methicillin-resistant Staphylococcus aureus bloodstream infection, defined as cumulative attributable differences above the first tertile in the Targeted Assessment for Prevention (TAP) strategy. Some additional nonrecruited hospitals also joined.

Intervention: Multimodal intervention, including Practice Change Assessment tool to identify infection prevention and control (IPC) and HAI prevention gaps; Web-based, on-demand modules involving onboarding, foundational IPC practices, HAI-specific 2-tiered approach to prioritize and implement interventions, and TAP resources; monthly webinars; state partner-led in-person meetings; and feedback. State partners made site visits to at least 50% of their enrolled hospitals, to support self-assessments and coach.

Measurements: Rates of CAUTI and urinary catheter device utilization ratio.

Results: Of 387 participating hospitals from 23 states and the District of Columbia, 361 provided CAUTI data. Over the study period, the unadjusted CAUTI rate was low and relatively stable, decreasing slightly from 1.12 to 1.04 CAUTIs per 1000 catheter-days. Catheter utilization decreased from 21.46 to 19.83 catheter-days per 100 patient-days from the pre- to the postintervention period.

Limitations: The intervention period was brief, with no assessment of fidelity. Baseline CAUTI rates were low. Patient characteristics were not assessed.

Conclusion: This multimodal intervention yielded no substantial improvements in CAUTI or urinary catheter utilization.

Primary funding source: Centers for Disease Control and Prevention.

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This course provides students with a foundation of the quantitative methods and techniques needed for the new or mid-level healthcare administrator. The course will cover the practical methods of the tactical, operational, strategic decision-making and analysis required for healthcare managers and administrators. This course will utilize Excel-based examples and techniques.

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The use of this tool enables the project team to outline the important steps which increases the project success rate. DMAIC is an acronym for five interconnected phases: Define, Measure, Analyze, Improve, and Control. The student will prepare a table using the template provided in the course to summarize each stage in approximately 150 words each in current APA format.

Quantitative Inventory Analysis Assignment

This assignment will provide practical knowledge in determining safety stock calculations so that healthcare stock items can be available when needed at the point of service. The student will use an Excel spreadsheet template to answer a prompt regarding ordering hospital stock.

Supply Chain Management Assignment

The focus of this assignment is to determine why supply chain management is much more than merely cost reduction in the healthcare environment. The student will write a 500-word paper in current APA format concerning supply chain management and cost reduction. This assignment will require 2 peer-reviewed scholarly references published within the last five years and one instance of biblical integration.

Project Management Charter Assignment

This assignment will prepare the student to utilize a Project Management Institute (PMI) Project Charter to create a plan for addressing an identified issue. The student will write a 1-page paper exploring a topic related to issues within a healthcare organization using current APA formatting. This assignment requires at least two peer-reviewed articles and one instance of biblical integration.

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The student will complete the Hospital Improvement Excel spreadsheets provided to apply the financial model discussed in the readings for financial improvement of a healthcare organization.

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What are the benefits of quantitative research in health care.

Most scientific research will follow one of two approaches - it can be either qualitative or quantitative. Health care research is often based on quantitative methods in which, by definition, information is quantifiable. That is, the variables used in research are measured and recorded as numerical data that can be analyzed by means of statistical tools. The use of quantitative research in health care has several benefits.

The main strength of quantitative methods is in their usefulness in producing factual and reliable outcome data. After the effects of a given drug or treatment have been tested on a sample population, the statistic record of the observed outcomes will provide objective results generalizable to larger populations. The statistical methods associated with quantitative research are well suited for figuring out ways to maximize dependent variables on the basis of independents, which translates into a capability for identifying and applying the interventions that can maximize the quality and quantity of life for a patient.

Reductionism

Quantitative researchers are often accused of reductionism; they take a complex phenomena and reduce them to a few essential numbers, loosing every nuance in the process. However, this reductionism is a two-edged sword with a very significant benefit. By reducing health cases to their essentials, a very large number of them can be taken into consideration for any given study. Large, statistically representative samples that would be unfeasible in qualitative studies can be easily analyzed using quantitative methods.

Evidence-Based Health Research

Given the benefits of quantitative methods in health care, evidence-based medicine seeks to use scientific methods to determine which drugs and procedures are best for treating diseases. At the core of evidence-based practice is the systematic and predominantly quantitative review of randomized controlled trials. Because quantitative researchers tend to use similar statistical methods, experiments and trials performed in different institutions and at different times and places can be aggregated together in large meta-analysis. Thus, quantitative research on health care can build on previous studies, accumulating a body of evidence regarding the effectiveness of different treatments.

Mixed Methods

Evidence-based medicine, and quantitative methods overall, are sometimes accused of leading to "cookbook" medicine. Some of the phenomena of interest to health researchers are of a qualitative nature and, almost by definition, inaccessible to quantitative tools -- for example, the lived experiences of the patient, his social interactions or his perspective of the doctor-patient interaction. However, judicious researchers can find a combination of qualitative and quantitative approaches so the strengths of each method reinforce those of the other. For instance, qualitative methods can be used for the creative generation of hypotheses or research questions, adding a human touch to the rigorous quantitative approach.

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• "Research Design: Qualitative, Quantitative, and Mixed Methods Approaches" ; John W. Creswell; 2009
• Health Promotion Practice; "Appraising Quantitative Research in Health Education: Guidelines for Public Health Educators"; Leonard Jack Jr et al; 2010
• British Medical Journal; "Evidence Based Medicine: What it is and What it isn't"; David L Sackett et al; 1996

Alan Valdez started his career reviewing video games for an obscure California retailer in 2003 and has been writing weekly articles on science and technology for Grupo Reforma since 2006. He got his Bachelor of Science in engineering from Monterrey Tech in 2003 and moved to the U.K., where he is currently doing research on competitive intelligence applied to the diffusion of innovations.

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Health Services Research addresses important issues about access to health care, quality and cost, and utilization of health services. This multidisciplinary field of research studies how social factors, financing systems, technologies, organizational structures and processes, and personal behaviors contribute to health and well-being. Research studies often include patients, clinicians, policymakers and the community to innovate and inform health care. Many researchers explore why-if guidelines exist, or interventions were implemented—the problem still exists. Health services research fills the key gap between cures to care.

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Research Topics & Ideas: Healthcare

F inding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

• Allopathic medicine
• Alternative /complementary medicine
• Veterinary medicine
• Physical therapy/ rehab
• Optometry and ophthalmology
• Pharmacy and pharmacology
• Public health
• Examples of healthcare-related dissertations

Allopathic (Conventional) Medicine

• The effectiveness of telemedicine in remote elderly patient care
• The impact of stress on the immune system of cancer patients
• The effects of a plant-based diet on chronic diseases such as diabetes
• The use of AI in early cancer diagnosis and treatment
• The role of the gut microbiome in mental health conditions such as depression and anxiety
• The efficacy of mindfulness meditation in reducing chronic pain: A systematic review
• The benefits and drawbacks of electronic health records in a developing country
• The effects of environmental pollution on breast milk quality
• The use of personalized medicine in treating genetic disorders
• The impact of social determinants of health on chronic diseases in Asia
• The role of high-intensity interval training in improving cardiovascular health
• The efficacy of using probiotics for gut health in pregnant women
• The impact of poor sleep on the treatment of chronic illnesses
• The role of inflammation in the development of chronic diseases such as lupus
• The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

• The benefits of herbal medicine in treating young asthma patients
• The use of acupuncture in treating infertility in women over 40 years of age
• The effectiveness of homoeopathy in treating mental health disorders: A systematic review
• The role of aromatherapy in reducing stress and anxiety post-surgery
• The impact of mindfulness meditation on reducing high blood pressure
• The use of chiropractic therapy in treating back pain of pregnant women
• The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
• The impact of yoga on physical and mental health in adolescents
• The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
• The role of Reiki in promoting healing and relaxation post birth
• The effectiveness of naturopathy in treating skin conditions such as eczema
• The use of deep tissue massage therapy in reducing chronic pain in amputees
• The impact of tai chi on the treatment of anxiety and depression
• The benefits of reflexology in treating stress, anxiety and chronic fatigue
• The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

• The impact of sugar consumption on the oral health of infants
• The use of digital dentistry in improving patient care: A systematic review
• The efficacy of orthodontic treatments in correcting bite problems in adults
• The role of dental hygiene in preventing gum disease in patients with dental bridges
• The impact of smoking on oral health and tobacco cessation support from UK dentists
• The benefits of dental implants in restoring missing teeth in adolescents
• The use of lasers in dental procedures such as root canals
• The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
• The role of fluoride in promoting remineralization and slowing down demineralization
• The impact of stress-induced reflux on oral health
• The benefits of dental crowns in restoring damaged teeth in elderly patients
• The use of sedation dentistry in managing dental anxiety in children
• The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
• The role of orthodontic appliances in improving well-being
• The impact of periodontal disease on overall health and chronic illnesses

Topics & Ideas: Veterinary Medicine

• The impact of nutrition on broiler chicken production
• The role of vaccines in disease prevention in horses
• The importance of parasite control in animal health in piggeries
• The impact of animal behaviour on welfare in the dairy industry
• The effects of environmental pollution on the health of cattle
• The role of veterinary technology such as MRI in animal care
• The importance of pain management in post-surgery health outcomes
• The impact of genetics on animal health and disease in layer chickens
• The effectiveness of alternative therapies in veterinary medicine: A systematic review
• The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
• The impact of climate change on animal health and infectious diseases in animals
• The importance of animal welfare in veterinary medicine and sustainable agriculture
• The effects of the human-animal bond on canine health
• The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
• The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

• The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
• The impact of telerehabilitation on patient outcomes in Germany
• The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
• A comparison of manual therapy and yoga exercise therapy in the management of low back pain
• The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
• The impact of mindfulness-based interventions in physical therapy in adolescents
• The effects of resistance training on individuals with Parkinson’s disease
• The role of hydrotherapy in the management of fibromyalgia
• The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
• The use of virtual reality in physical rehabilitation of sports injuries
• The effects of electrical stimulation on muscle function and strength in athletes
• The role of physical therapy in the management of stroke recovery: A systematic review
• The impact of pilates on mental health in individuals with depression
• The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
• The effect of strength training on balance and gait in elderly patients

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Topics & Ideas: Optometry & Opthalmology

• The impact of screen time on the vision and ocular health of children under the age of 5
• The effects of blue light exposure from digital devices on ocular health
• The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
• The use of telemedicine in optometry and ophthalmology in the UK
• The impact of myopia control interventions on African American children’s vision
• The use of contact lenses in the management of dry eye syndrome: different treatment options
• The effects of visual rehabilitation in individuals with traumatic brain injury
• The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
• The impact of environmental air pollution on ocular health
• The effectiveness of orthokeratology in myopia control compared to contact lenses
• The role of dietary supplements, such as omega-3 fatty acids, in ocular health
• The effects of ultraviolet radiation exposure from tanning beds on ocular health
• The impact of computer vision syndrome on long-term visual function
• The use of novel diagnostic tools in optometry and ophthalmology in developing countries
• The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

• The impact of medication adherence on patient outcomes in cystic fibrosis
• The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
• The effects of pharmacogenomics on drug response and toxicity in cancer patients
• The role of pharmacists in the management of chronic pain in primary care
• The impact of drug-drug interactions on patient mental health outcomes
• The use of telepharmacy in healthcare: Present status and future potential
• The effects of herbal and dietary supplements on drug efficacy and toxicity
• The role of pharmacists in the management of type 1 diabetes
• The impact of medication errors on patient outcomes and satisfaction
• The use of technology in medication management in the USA
• The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
• Leveraging the role of pharmacists in preventing and managing opioid use disorder
• The impact of the opioid epidemic on public health in a developing country
• The use of biosimilars in the management of the skin condition psoriasis
• The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

• The impact of the built environment and urbanisation on physical activity and obesity
• The effects of food insecurity on health outcomes in Zimbabwe
• The role of community-based participatory research in addressing health disparities
• The impact of social determinants of health, such as racism, on population health
• The effects of heat waves on public health
• The role of telehealth in addressing healthcare access and equity in South America
• The impact of gun violence on public health in South Africa
• The effects of chlorofluorocarbons air pollution on respiratory health
• The role of public health interventions in reducing health disparities in the USA
• The impact of the United States Affordable Care Act on access to healthcare and health outcomes
• The effects of water insecurity on health outcomes in the Middle East
• The role of community health workers in addressing healthcare access and equity in low-income countries
• The impact of mass incarceration on public health and behavioural health of a community
• The effects of floods on public health and healthcare systems
• The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
• On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
• Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
• Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
• Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
• When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
• Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
• Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
• Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
• Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
• Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
• The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
• Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
• Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

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18 Comments

I need topics that will match the Msc program am running in healthcare research please

Hello Mabel,

I can help you with a good topic, kindly provide your email let’s have a good discussion on this.

Can you provide some research topics and ideas on Immunology?

Thank you to create new knowledge on research problem verse research topic

Help on problem statement on teen pregnancy

This post might be useful: https://gradcoach.com/research-problem-statement/

can you give me research titles that i can conduct as a school nurse

can you provide me with a research topic on healthcare related topics to a qqi level 5 student

Please can someone help me with research topics in public health ?

Hello I have requirement of Health related latest research issue/topics for my social media speeches. If possible pls share health issues , diagnosis, treatment.

I would like a topic thought around first-line support for Gender-Based Violence for survivors or one related to prevention of Gender-Based Violence

Please can I be helped with a master’s research topic in either chemical pathology or hematology or immunology? thanks

Can u please provide me with a research topic on occupational health and safety at the health sector

Good day kindly help provide me with Ph.D. Public health topics on Reproductive and Maternal Health, interventional studies on Health Education

may you assist me with a good easy healthcare administration study topic

May you assist me in finding a research topic on nutrition,physical activity and obesity. On the impact on children

I have been racking my brain for a while on what topic will be suitable for my PhD in health informatics. I want a qualitative topic as this is my strong area.

Hi, may I please be assisted with research topics in the medical laboratory sciences

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Methods for Quantitative Research in Psychology

• Conducting Research

Psychological Research

August 2023

This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.

The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.

A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.

Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.

The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.

Learning objectives

• Describe the steps of the scientific method.
• Specify how variables are defined.
• Compare and contrast the major research designs.
• Explain how to judge the quality of a source for a literature review.
• Compare and contrast the kinds of research questions scientists ask.
• Explain what it means for an observation to be reliable.
• Compare and contrast forms of validity as they apply to the major research designs.

This program does not offer CE credit.

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• Implement Sci

Quantitative measures of health policy implementation determinants and outcomes: a systematic review

1 Prevention Research Center, Brown School, Washington University in St. Louis, One Brookings Drive, Campus Box 1196, St. Louis, MO 63130 USA

Meagan Pilar

Callie walsh-bailey, cole hooley.

2 School of Social Work, Brigham Young University, 2190 FJSB, Provo, UT 84602 USA

Stephanie Mazzucca

Cara c. lewis.

3 Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA

Kayne D. Mettert

Caitlin n. dorsey, jonathan purtle.

4 Department of Health Management & Policy, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market St, Philadelphia, PA 19104 USA

Maura M. Kepper

Ana a. baumann.

5 Brown School, Washington University in St. Louis, One Brookings Drive, Campus Box 1196, St. Louis, MO 63130 USA

Ross C. Brownson

6 Department of Surgery (Division of Public Health Sciences) and Alvin J. Siteman Cancer Center, Washington University School of Medicine, 4921 Parkview Place, Saint Louis, MO 63110 USA

Associated Data

A compendium of identified measures is available for dissemination at https://www.health-policy-measures.org/ . A link will be provided on the website of the Prevention Research Center, Brown School, Washington University in St. Louis, at https://prcstl.wustl.edu/ . The authors invite interested organizations to provide a link to the compendium. Citations and abstracts of excluded policy-specific measures are available on request.

Public policy has tremendous impacts on population health. While policy development has been extensively studied, policy implementation research is newer and relies largely on qualitative methods. Quantitative measures are needed to disentangle differential impacts of policy implementation determinants (i.e., barriers and facilitators) and outcomes to ensure intended benefits are realized. Implementation outcomes include acceptability, adoption, appropriateness, compliance/fidelity, feasibility, penetration, sustainability, and costs. This systematic review identified quantitative measures that are used to assess health policy implementation determinants and outcomes and evaluated the quality of these measures.

Three frameworks guided the review: Implementation Outcomes Framework (Proctor et al.), Consolidated Framework for Implementation Research (Damschroder et al.), and Policy Implementation Determinants Framework (Bullock et al.). Six databases were searched: Medline, CINAHL Plus, PsycInfo, PAIS, ERIC, and Worldwide Political. Searches were limited to English language, peer-reviewed journal articles published January 1995 to April 2019. Search terms addressed four levels: health, public policy, implementation, and measurement. Empirical studies of public policies addressing physical or behavioral health with quantitative self-report or archival measures of policy implementation with at least two items assessing implementation outcomes or determinants were included. Consensus scoring of the Psychometric and Pragmatic Evidence Rating Scale assessed the quality of measures.

Database searches yielded 8417 non-duplicate studies, with 870 (10.3%) undergoing full-text screening, yielding 66 studies. From the included studies, 70 unique measures were identified to quantitatively assess implementation outcomes and/or determinants. Acceptability, feasibility, appropriateness, and compliance were the most commonly measured implementation outcomes. Common determinants in the identified measures were organizational culture, implementation climate, and readiness for implementation, each aspects of the internal setting. Pragmatic quality ranged from adequate to good, with most measures freely available, brief, and at high school reading level. Few psychometric properties were reported.

Conclusions

Well-tested quantitative measures of implementation internal settings were under-utilized in policy studies. Further development and testing of external context measures are warranted. This review is intended to stimulate measure development and high-quality assessment of health policy implementation outcomes and determinants to help practitioners and researchers spread evidence-informed policies to improve population health.

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Contributions to the literature

• This systematic review identified 70 quantitative measures of implementation outcomes or determinants in health policy studies.
• Readiness to implement and organizational climate and culture were commonly assessed determinants, but fewer studies assessed policy actor relationships or implementation outcomes of acceptability, fidelity/compliance, appropriateness, feasibility, or implementation costs.
• Study team members rated most identified measures’ pragmatic properties as good, meaning they are straightforward to use, but few studies documented pilot or psychometric testing of measures.
• Further development and dissemination of valid and reliable measures of policy implementation outcomes and determinants can facilitate identification, use, and spread of effective policy implementation strategies.

Despite major impacts of policy on population health [ 1 – 7 ], there have been relatively few policy studies in dissemination and implementation (D&I) science to inform implementation strategies and evaluate implementation efforts [ 8 ]. While health outcomes of policies are commonly studied, fewer policy studies assess implementation processes and outcomes. Of 146 D&I studies funded by the National Institutes of Health (NIH) through D&I funding announcements from 2007 to 2014, 12 (8.2%) were policy studies that assessed policy content, policy development processes, or health outcomes of policies, representing 10.5% of NIH D&I funding [ 8 ]. Eight of the 12 studies (66.7%) assessed health outcomes, while only five (41.6%) assessed implementation [ 8 ].

Our ability to explore the differential impact of policy implementation determinants and outcomes and disentangle these from health benefits and other societal outcomes requires high quality quantitative measures [ 9 ]. While systematic reviews of measures of implementation of evidence-based interventions (in clinical and community settings) have been conducted in recent years [ 10 – 13 ], to our knowledge, no reviews have explored the quality of quantitative measures of determinants and outcomes of policy implementation.

Policy implementation research in political science and the social sciences has been active since at least the 1970s and has much to contribute to the newer field of D&I research [ 1 , 14 ]. Historically, theoretical frameworks and policy research largely emphasized policy development or analysis of the content of policy documents themselves [ 15 ]. For example, Kingdon’s Multiple Streams Framework and its expansions have been widely used in political science and the social sciences more broadly to describe how factors related to sociopolitical climate, attributes of a proposed policy, and policy actors (e.g., organizations, sectors, individuals) contribute to policy change [ 16 – 18 ]. Policy frameworks can also inform implementation planning and evaluation in D&I research. Although authors have named policy stages since the 1950s [ 19 , 20 ], Sabatier and Mazmanian’s Policy Implementation Process Framework was one of the first such frameworks that gained widespread use in policy implementation research [ 21 ] and later in health promotion [ 22 ]. Yet, available implementation frameworks are not often used to guide implementation strategies or inform why a policy worked in one setting but not another [ 23 ]. Without explicit focus on implementation, the intended benefits of health policies may go unrealized, and the ability may be lost to move the field forward to understand policy implementation (i.e., our collective knowledge building is dampened) [ 24 ].

Differences in perspectives and terminology between D&I and policy research in political science are noteworthy to interpret the present review. For example, Proctor et al. use the term implementation outcomes for what policy researchers call policy outputs [ 14 , 20 , 25 ]. To non-D&I policy researchers, policy implementation outcomes refer to the health outcomes in the target population [ 20 ]. D&I science uses the term fidelity [ 26 ]; policy researchers write about compliance [ 20 ]. While D&I science uses the terms outer setting, outer context, or external context to point to influences outside the implementing organization [ 26 – 28 ], non-D&I policy research refers to policy fields [ 24 ] which are networks of agencies that carry out policies and programs.

Identification of valid and reliable quantitative measures of health policy implementation processes is needed. These measures are needed to advance from classifying constructs to understanding causality in policy implementation research [ 29 ]. Given limited resources, policy implementers also need to know which aspects of implementation are key to improve policy acceptance, compliance, and sustainability to reap the intended health benefits [ 30 ]. Both pragmatic and psychometrically sound measures are needed to accomplish these objectives [ 10 , 11 , 31 , 32 ], so the field can explore the influence of nuanced determinants and generate reliable and valid findings.

To fill this void in the literature, this systematic review of health policy implementation measures aimed to (1) identify quantitative measures used to assess health policy implementation outcomes (IOF outcomes commonly called policy outputs in policy research) and inner and outer setting determinants, (2) describe and assess pragmatic quality of policy implementation measures, (3) describe and assess the quality of psychometric properties of identified instruments, and (4) elucidate health policy implementation measurement gaps.

The study team used systematic review procedures developed by Lewis and colleagues for reviews of D&I research measures and received detailed guidance from the Lewis team coauthors for each step [ 10 , 11 ]. We followed the PRISMA reporting guidelines as shown in the checklist (Supplemental Table 1 ). We have also provided a publicly available website of measures identified in this review ( https://www.health-policy-measures.org/ ).

For the purposes of this review, policy and policy implementation are defined as follows. We deemed public policy to include legislation at the federal, state/province/regional unit, or local levels; and governmental regulations, whether mandated by national, state/province, or local level governmental agencies or boards of elected officials (e.g., state boards of education in the USA) [ 4 , 20 ]. Here, public policy implementation is defined as the carrying out of a governmental mandate by public or private organizations and groups of organizations [ 20 ].

Two widely used frameworks from the D&I field guide the present review, and a third recently developed framework that bridges policy and D&I research. In the Implementation Outcomes Framework (IOF), Proctor and colleagues identify and define eight implementation outcomes that are differentiated from health outcomes: acceptability, adoption, appropriateness, cost, feasibility, fidelity, penetration, and sustainability [ 25 ]. In the Consolidated Framework for Implementation Research (CFIR), Damschroder and colleagues articulate determinants of implementation including the domains of intervention characteristics, outer setting, inner setting of an organization, characteristics of individuals within organizations, and process [ 33 ]. Finally, Bullock developed the Policy Implementation Determinants Framework to present a balanced framework that emphasizes both internal setting constructs and external setting constructs including policy actor relationships and networks, political will for implementation, and visibility of policy actors [ 34 ]. The constructs identified in these frameworks were used to guide our list of implementation determinants and outcomes.

Through EBSCO, we searched MEDLINE, PsycInfo, and CINAHL Plus. Through ProQuest, we searched PAIS, Worldwide Political, and ERIC. Due to limited time and staff in the 12-month study, we did not search the grey literature. We used multiple search terms in each of four required levels: health, public policy, implementation, and measurement (Table ​ (Table1). 1 ). Table ​ Table1 1 shows search terms for each string. Supplemental Tables 2 and 3 show the final search syntax applied in EBSCO and ProQuest.

Search terms and strings

The authors developed the search strings and terms based on policy implementation framework reviews [ 34 , 35 ], additional policy implementation frameworks [ 21 , 22 ], labels and definitions of the eight implementation outcomes identified by Proctor et al. [ 25 ], CFIR construct labels and definitions [ 9 , 33 ], and additional D&I research and search term sources [ 28 , 36 – 38 ] (Table ​ (Table1). 1 ). The full study team provided three rounds of feedback on draft terms, and a library scientist provided additional synonyms and search terms. For each test search, we calculated the percentage of 18 benchmark articles the search captured. We determined a priori 80% as an acceptable level of precision.

Inclusion and exclusion criteria

This review addressed only measures of implementation by organizations mandated to act by governmental units or legislation. Measures of behavior changes by individuals in target populations as a result of legislation or governmental regulations and health status changes were outside the realm of this review.

There were several inclusion criteria: (1) empirical studies of the implementation of public policies already passed or approved that addressed physical or behavioral health, (2) quantitative self-report or archival measurement methods utilized, (3) published in peer-reviewed journals from January 1995 through April 2019, (4) published in the English language, (5) public policy implementation studies from any continent or international governing body, and (6) at least two transferable quantitative self-report or archival items that assessed implementation determinants [ 33 , 34 ] and/or IOF implementation outcomes [ 25 ]. This study sought to identify transferable measures that could be used to assess multiple policies and contexts. Here, a transferable item is defined as one that needed no wording changes or only a change in the referent (e.g., policy title or topic such as tobacco or malaria) to make the item applicable to other policies or settings [ 11 ]. The year 1995 was chosen as a starting year because that is about when web-based quantitative surveying began [ 39 ]. Table ​ Table2 2 provides definitions of the IOF implementation outcomes and the selected determinants of implementation. Broader constructs, such as readiness for implementation, contained multiple categories.

Health policy implementation outcomes and determinants assessed in included measures ( N = 70 unique measures in 66 health policy implementation studies)

Exclusion criteria in the searches included (1) non-empiric health policy journal articles (e.g., conceptual articles, editorials); (2) narrative and systematic reviews; (3) studies with only qualitative assessment of health policy implementation; (4) empiric studies reported in theses and books; (5) health policy studies that only assessed health outcomes (i.e., target population changes in health behavior or status); (6) bill analyses, stakeholder perceptions assessed to inform policy development, and policy content analyses without implementation assessment; (7) studies of changes made in a private business not encouraged by public policy; and (8) countries with authoritarian regimes. We electronically programmed the searches to exclude policy implementation studies from countries that are not democratically governed due to vast differences in policy environments and implementation factors.

Screening procedures

Citations were downloaded into EndNote version 7.8 and de-duplicated electronically. We conducted dual independent screening of titles and abstracts after two group pilot screening sessions in which we clarified inclusion and exclusion criteria and screening procedures. Abstract screeners used Covidence systematic review software [ 40 ] to code inclusion as yes or no. Articles were included in full-text review if one screener coded it as meeting the inclusion criteria. Full-text screening via dual independent screening was coded in Covidence [ 40 ], with weekly meetings to reach consensus on inclusion/exclusion discrepancies. Screeners also coded one of the pre-identified reasons for exclusion.

Data extraction strategy

Extraction elements included information about (1) measure meta-data (e.g., measure name, total number of items, number of transferable items) and studies (e.g., policy topic, country, setting), (2) development and testing of the measure, (3) implementation outcomes and determinants assessed (Table ​ (Table2), 2 ), (4) pragmatic characteristics, and (5) psychometric properties. Where needed, authors were emailed to obtain the full measure and measure development information. Two coauthors (MP, CWB) reached consensus on extraction elements. For each included measure, a primary extractor conducted initial entries and coding. Due to time and staff limitations in the 12-month study, we did not search for each empirical use of the measure. A secondary extractor checked the entries, noting any discrepancies for discussion in consensus meetings. Multiple measures in a study were extracted separately.

Quality assessment of measures

To assess the quality of measures, we applied the Psychometric and Pragmatic Evidence Rating Scales (PAPERS) developed by Lewis et al. [ 10 , 11 , 41 , 42 ]. PAPERS includes assessment of five pragmatic instrument characteristics that affect the level of ease or difficulty to use the instrument: brevity (number of items), simplicity of language (readability level), cost (whether it is freely available), training burden (extent of data collection training needed), and analysis burden (ease or difficulty of interpretation of scoring and results). Lewis and colleagues developed the pragmatic domains and rating scales with stakeholder and D&I researchers input [ 11 , 41 , 42 ] and developed the psychometric rating scales in collaboration with D&I researchers [ 10 , 11 , 43 ]. The psychometric rating scale has nine properties (Table ​ (Table3): 3 ): internal consistency; norms; responsiveness; convergent, discriminant, and known-groups construct validity; predictive and concurrent criterion validity; and structural validity. In both the pragmatic and psychometric scales, reported evidence for each domain is scored from poor (− 1), none/not reported (0), minimal/emerging (1), adequate (2), good (3), or excellent (4). Higher values are indicative of more desirable pragmatic characteristics (e.g., fewer items, freely available, scoring instructions, and interpretations provided) and stronger evidence of psychometric properties (e.g., adequate to excellent reliability and validity) (Supplemental Tables 4 and 5 ).

Psychometric and Pragmatic Evidence Rating Scale (PAPERS) domains and definitions

Lewis et al. [ 11 ], Stanick et al. [ 42 ]

Each domain is scored from poor (− 1), none/not reported (0), minimal/emerging (1), adequate (2), good (3), or excellent (4). Specific rating scales for each domain are provided in Supplemental Tables 4 and 5

Data synthesis and presentation

This section describes the synthesis of measure transferability, empiric use study settings and policy topics, and PAPERS scoring. Two coauthors (MP, CWB) consensus coded measures into three categories of item transferability based on quartile item transferability percentages: mostly transferable (≥ 75% of items deemed transferable), partially transferable (25–74% of items deemed transferable), and setting-specific (< 25% of items deemed transferable). Items were deemed transferable if no wording changes or only a change in the referent (e.g., policy title or topic) was needed to make the item applicable to the implementation of other policies or in other settings. Abstractors coded study settings into one of five categories: hospital or outpatient clinics; mental or behavioral health facilities; healthcare cost, access, or quality; schools; community; and multiple. Abstractors also coded policy topics to healthcare cost, access, or quality; mental or behavioral health; infectious or chronic diseases; and other, while retaining documentation of subtopics such as tobacco, physical activity, and nutrition. Pragmatic scores were totaled for the five properties, with possible total scores of − 5 to 20, with higher values indicating greater ease to use the instrument. Psychometric property total scores for the nine properties were also calculated, with possible scores of − 9 to 36, with higher values indicating evidence of multiple types of validity.

The database searches yielded 11,684 articles, of which 3267 were duplicates (Fig. ​ (Fig.1). 1 ). Titles and abstracts of the 8417 articles were independently screened by two team members; 870 (10.3%) were selected for full-text screening by at least one screener. Of the 870 studies, 804 were excluded at full-text screening or during extraction attempts with the consensus of two coauthors; 66 studies were included. Two coauthors (MP, CWB) reached consensus on extraction and coding of information on 70 unique quantitative eligible measures identified in the 66 included studies plus measure development articles where obtained. Nine measures were used in more than one included study. Detailed information on identified measures is publicly available at https://www.health-policy-measures.org/ .

PRISMA flow diagram

The most common exclusion reason was lack of transferable items in quantitative measures of policy implementation ( n = 597) (Fig. ​ (Fig.1). 1 ). While this review focused on transferable measures across any health issue or setting, researchers addressing specific health policies or settings may find the excluded studies of interest. The frequencies of the remaining exclusion reasons are listed in Fig. ​ Fig.1 1 .

A variety of health policy topics and settings from over two dozen countries were found in the database searches. For example, the searches identified quantitative and mixed methods implementation studies of legislation (such as tobacco smoking bans), regulations (such as food/menu labeling requirements), governmental policies that mandated specific clinical practices (such as vaccination or access to HIV antiretroviral treatment), school-based interventions (such as government-mandated nutritional content and physical activity), and other public policies.

Among the 70 unique quantitative implementation measures, 15 measures were deemed mostly transferable (at least 75% transferable, Table ​ Table4). 4 ). Twenty-three measures were categorized as partially transferable (25 to 74% of items deemed transferable, Table ​ Table5); 5 ); 32 measures were setting-specific (< 25% of items deemed transferable, data not shown).

Mostly transferable measures identified in studies of health policy implementation ( n = 15)

Mostly transferable measures are defined here as those in which ≥ 75% of items can readily be used in multiple settings without change or by changing only the referent (i.e., policy name, setting)

a Pragmatic PAPERS score—Psychometric and Pragmatic Evidence Rating Scale [ 11 , 41 , 42 ], five domains assessed: brevity (score based on number of items), language simplicity, burden/ease of interpretation of scoring, and training burden, total possible score 20, higher numbers indicate greater ease to use the measure

b Additional subscale level psychometric properties were reported

Partially transferable measures identified in studies of health policy implementation ( n = 23)

Partially transferable measures are defined here as those in which 25 to < 75% of items can readily be used in multiple settings without change or by changing only the referent (i.e., policy name, setting)

QI quality improvement

Implementation outcomes

Among the 70 measures, the most commonly assessed implementation outcomes were fidelity/compliance of the policy implementation to the government mandate (26%), acceptability of the policy to implementers (24%), perceived appropriateness of the policy (17%), and feasibility of implementation (17%) (Table ​ (Table2). 2 ). Fidelity/compliance was sometimes assessed by asking implementers the extent to which they had modified a mandated practice [ 45 ]. Sometimes, detailed checklists were used to assess the extent of compliance with the many mandated policy components, such as school nutrition policies [ 83 ]. Acceptability was assessed by asking staff or healthcare providers in implementing agencies their level of agreement with the provided statements about the policy mandate, scored in Likert scales. Only eight (11%) of the included measures used multiple transferable items to assess adoption, and only eight (11%) assessed penetration.

Twenty-six measures of implementation costs were found during full-text screening (10 in included studies and 14 in excluded studies, data not shown). The cost time horizon varied from 12 months to 21 years, with most cost measures assessed at multiple time points. Ten of the 26 measures addressed direct implementation costs. Nine studies reported cost modeling findings. The implementation cost survey developed by Vogler et al. was extensive [ 53 ]. It asked implementing organizations to note policy impacts in medication pricing, margins, reimbursement rates, and insurance co-pays.

Determinants of implementation

Within the 70 included measures, the most commonly assessed implementation determinants were readiness for implementation (61% assessed any readiness component) and the general organizational culture and climate (39%), followed by the specific policy implementation climate within the implementation organization/s (23%), actor relationships and networks (17%), political will for policy implementation (11%), and visibility of the policy role and policy actors (10%) (Table ​ (Table2). 2 ). Each component of readiness for implementation was commonly assessed: communication of the policy (31%, 22 of 70 measures), policy awareness and knowledge (26%), resources for policy implementation (non-training resources 27%, training 20%), and leadership commitment to implement the policy (19%).

Only two studies assessed organizational structure as a determinant of health policy implementation. Lavinghouze and colleagues assessed the stability of the organization, defined as whether re-organization happens often or not, within a set of 9-point Likert items on multiple implementation determinants designed for use with state-level public health practitioners, and assessed whether public health departments were stand-alone agencies or embedded within agencies addressing additional services, such as social services [ 69 ]. Schneider and colleagues assessed coalition structure as an implementation determinant, including items on the number of organizations and individuals on the coalition roster, number that regularly attend coalition meetings, and so forth [ 72 ].

Tables of measures

Tables ​ Tables4 4 and ​ and5 5 present the 38 measures of implementation outcomes and/or determinants identified out of the 70 included measures with at least 25% of items transferable (useable in other studies without wording changes or by changing only the policy name or other referent). Table ​ Table4 4 shows 15 mostly transferable measures (at least 75% transferable). Table ​ Table5 5 shows 23 partially transferable measures (25–74% of items deemed transferable). Separate measure development articles were found for 20 of the 38 measures; the remaining measures seemed to be developed for one-time, study-specific use by the empirical study authors cited in the tables. Studies listed in Tables ​ Tables4 4 and ​ and5 5 were conducted most commonly in the USA ( n = 19) or Europe ( n = 11). A few measures were used elsewhere: Africa ( n = 3), Australia ( n = 1), Canada ( n = 1), Middle East ( n = 1), Southeast Asia ( n = 1), or across multiple continents ( n = 1).

Quality of identified measures

Figure ​ Figure2 2 shows the median pragmatic quality ratings across the 38 measures with at least 25% transferable items shown in Tables ​ Tables4 4 and ​ and5. 5 . Higher scores are desirable and indicate the measures are easier to use (Table ​ (Table3). 3 ). Overall, the measures were freely available in the public domain (median score = 4), brief with a median of 11–50 items (median score = 3), and had good readability, with a median reading level between 8th and 12th grade (median score = 3). However, instructions on how to score and interpret item scores were lacking, with a median score of 1, indicating the measures did not include suggestions for interpreting score ranges, clear cutoff scores, and instructions for handling missing data. In general, information on training requirements or availability of self-training manuals on how to use the measures was not reported in the included study or measure development article/s (median score = 0, not reported). Total pragmatic rating scores among the 38 measures with at least 25% of items transferable ranged from 7 to 17 (Tables ​ (Tables4 4 and ​ and5), 5 ), with a median total score of 12 out of a possible total score of 20. Median scores for each pragmatic characteristic were the same across all measures as for the 38 mostly or partially transferable measures, with a median total score of 11 across all measures.

Pragmatic rating scale results across identified measures. Footnote: pragmatic criteria scores from Psychometric and Pragmatic Evidence Rating Scale (PAPERS) (Lewis et al. [ 11 ], Stanick et al. [ 42 ]). Total possible score = 20, total median score across 38 measures = 11. Scores ranged from 0 to 18. Rating scales for each domain are provided in Supplemental Table 4

Few psychometric properties were reported. The study team found few reports of pilot testing and measure refinement as well. Among the 38 measures with at least 25% transferable items, the psychometric properties from the PAPERS rating scale total scores ranged from − 1 to 17 (Tables ​ (Tables4 4 and ​ and5), 5 ), with a median total score of 5 out of a possible total score of 36. Higher scores indicate more types of validity and reliability were reported with high quality. The 32 measures with calculable norms had a median norms PAPERS score of 3 (good), indicating appropriate sample size and distribution. The nine measures with reported internal consistency mostly showed Cronbach’s alphas in the adequate (0.70 to 0.79) to excellent (≥ 90) range, with a median of 0.78 (PAPERS score of 2, adequate) indicating adequate internal consistency. The five measures with reported structural validity had a median PAPERS score of 2, adequate (range 1 to 3, poor to good), indicating the sample size was sufficient and the factor analysis goodness of fit was reasonable. Among the 38 measures, no reports were found for responsiveness, convergent validity, discriminant validity, known-groups construct validity, or predictive or concurrent criterion validity.

In this systematic review, we sought to identify quantitative measures used to assess health policy implementation outcomes and determinants, rate the pragmatic and psychometric quality of identified measures, and point to future directions to address measurement gaps. In general, the identified measures are easy to use and freely available, but we found little data on validity and reliability. We found more quantitative measures of intra-organizational determinants of policy implementation than measures of the relationships and interactions between organizations that influence policy implementation. We found a limited number of measures that had been developed for or used to assess one of the eight IOF policy implementation outcomes that can be applied to other policies or settings, which may speak more to differences in terms used by policy researchers and D&I researchers than to differences in conceptualizations of policy implementation. Authors used a variety of terms and rarely provided definitions of the constructs the items assessed. Input from experts in policy implementation is needed to better understand and define policy implementation constructs for use across multiple fields involved in policy-related research.

We found several researchers had used well-tested measures of implementation determinants from D&I research or from organizational behavior and management literature (Tables ​ (Tables4 4 and ​ and5). 5 ). For internal setting of implementing organizations, whether mandated through public policy or not, well-developed and tested measures are available. However, a number of authors crafted their own items, with or without pilot testing, and used a variety of terms to describe what the items assessed. Further dissemination of the availability of well-tested measures to policy researchers is warranted [ 9 , 13 ].

What appears to be a larger gap involves the availability of well-developed and tested quantitative measures of the external context affecting policy implementation that can be used across multiple policy settings and topics [ 9 ]. Lack of attention to how a policy initiative fits with the external implementation context during policymaking and lack of policymaker commitment of adequate resources for implementation contribute to this gap [ 23 , 93 ]. Recent calls and initiatives to integrate health policies during policymaking and implementation planning will bring more attention to external contexts affecting not only policy development but implementation as well [ 93 – 99 ]. At the present time, it is not well-known which internal and external determinants are most essential to guide and achieve sustainable policy implementation [ 100 ]. Identification and dissemination of measures that assess factors that facilitate the spread of evidence-informed policy implementation (e.g., relative advantage, flexibility) will also help move policy implementation research forward [ 1 , 9 ].

Given the high potential population health impact of evidence-informed policies, much more attention to policy implementation is needed in D&I research. Few studies from non-D&I researchers reported policy implementation measure development procedures, pilot testing, scoring procedures and interpretation, training of data collectors, or data analysis procedures. Policy implementation research could benefit from the rigor of D&I quantitative research methods. And D&I researchers have much to learn about the contexts and practical aspects of policy implementation and can look to the rich depth of information in qualitative and mixed methods studies from other fields to inform quantitative measure development and testing [ 101 – 103 ].

Limitations

This systematic review has several limitations. First, the four levels of the search string and multiple search terms in each level were applied only to the title, abstract, and subject headings, due to limitations of the search engines, so we likely missed pertinent studies. Second, a systematic approach with stakeholder input is needed to expand the definitions of IOF implementation outcomes for policy implementation. Third, although the authors value intra-organizational policymaking and implementation, the study team restricted the search to governmental policies due to limited time and staffing in the 12-month study. Fourth, by excluding tools with only policy-specific implementation measures, we excluded some well-developed and tested instruments in abstract and full-text screening. Since only 12 measures had 100% transferable items, researchers may need to pilot test wording modifications of other items. And finally, due to limited time and staffing, we only searched online for measures and measures development articles and may have missed separately developed pragmatic information, such as training and scoring materials not reported in a manuscript.

Despite the limitations, several recommendations for measure development follow from the findings and related literature [ 1 , 11 , 20 , 35 , 41 , 104 ], including the need to (1) conduct systematic, mixed-methods procedures (concept mapping, expert panels) to refine policy implementation outcomes, (2) expand and more fully specify external context domains for policy implementation research and evaluation, (3) identify and disseminate well-developed measures for specific policy topics and settings, (4) ensure that policy implementation improves equity rather than exacerbating disparities [ 105 ], and (5) develop evidence-informed policy implementation guidelines.

Easy-to-use, reliable, and valid quantitative measures of policy implementation can further our understanding of policy implementation processes, determinants, and outcomes. Due to the wide array of health policy topics and implementation settings, sound quantitative measures that can be applied across topics and settings will help speed learnings from individual studies and aid in the transfer from research to practice. Quantitative measures can inform the implementation of evidence-informed policies to further the spread and effective implementation of policies to ultimately reap greater population health benefit. This systematic review of measures is intended to stimulate measure development and high-quality assessment of health policy implementation outcomes and predictors to help practitioners and researchers spread evidence-informed policies to improve population health and reduce inequities.

Supplementary information

Acknowledgements.

The authors are grateful for the policy expertise and guidance of Alexandra Morshed and the administrative support of Mary Adams, Linda Dix, and Cheryl Valko at the Prevention Research Center, Brown School, Washington University in St. Louis. We thank Lori Siegel, librarian, Brown School, Washington University in St. Louis, for assistance with search terms and procedures. We appreciate the D&I contributions of Enola Proctor and Byron Powell at the Brown School, Washington University in St. Louis, that informed this review. We thank Russell Glasgow, University of Colorado Denver, for guidance on the overall review and pragmatic measure criteria.

Abbreviations

Authors’ contributions

Review methodology and quality assessment scale: CCL, KDM, CND. Eligibility criteria: PA, RCB, CND, KDM, SM, MP, JP. Search strings and terms: CH, PA, MP with review by AB, RCB, CND, CCL, MMK, SM, KDM. Framework selection: PA, AB, CH, MP. Abstract screening: PA, CH, MMK, SM, MP. Full-text screening: PA, CH, MP. Pilot extraction: PA, DNC, CH, KDM, SM, MP. Data extraction: MP, CWB. Data aggregation: MP, CWB. Writing: PA, RCB, JP. Editing: RCB, JP, SM, AB, CD, CH, MMK, CCL, KM, MP, CWB. The authors read and approved the final manuscript.

This project was funded March 2019 through February 2020 by the Foundation for Barnes-Jewish Hospital, with support from the Washington University in St. Louis Institute of Clinical and Translational Science Pilot Program, NIH/National Center for Advancing Translational Sciences (NCATS) grant UL1 TR002345. The project was also supported by the National Cancer Institute P50CA244431, Cooperative Agreement number U48DP006395-01-00 from the Centers for Disease Control and Prevention, R01MH106510 from the National Institute of Mental Health, and the National Institute of Diabetes and Digestive and Kidney Diseases award number P30DK020579. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official positions of the Foundation for Barnes-Jewish Hospital, Washington University in St. Louis Institute of Clinical and Translational Science, National Institutes of Health, or the Centers for Disease Control and Prevention.

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The authors declare they have no conflicting interests.

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Supplementary information accompanies this paper at 10.1186/s13012-020-01007-w.

• Open access
• Published: 30 August 2024

Exploring medical and dental practitioner perspectives and developing a knowledge attitude and practice (KAP) evaluation tool for the common risk factor approach in managing non-communicable and periodontal diseases

• Lakshmi Puzhankara   ORCID: orcid.org/0000-0002-5559-5887 1 ,
• Vineetha Karuveettil   ORCID: orcid.org/0000-0002-5358-4391 2 ,
• Chandrashekar Janakiram   ORCID: orcid.org/0000-0003-1907-8708 2 ,
• Ramprasad Vasthare   ORCID: orcid.org/0000-0002-0181-7069 3 ,
• Sowmya Srinivasan   ORCID: orcid.org/0000-0001-8236-0103 4 &
• Angel Fenol   ORCID: orcid.org/0000-0001-5088-8368 5  

BMC Oral Health volume  24 , Article number:  1017 ( 2024 ) Cite this article

Metrics details

The Common Risk Factor Approach (CRFA) is one of the methods to achieve medical-dental integration. CRFA addresses shared risk factors among major Non-communicable Diseases (NCDs). This study aimed to explore the perspectives of dental and medical practitioners concerning CRFA for managing NCDs and periodontal diseases and to create and validate a tool to evaluate the Knowledge, Attitude, and Practice (KAP) of medical and dental practitioners in relation to utilization of CRFA for management of NCDs and Periodontal diseases.

This research employed a concurrent mixed-method model and was carried out from January 2021 to February 2022, focusing on medical and dental practitioners in South India. In the qualitative phase, online interviews were conducted with dental and medical practitioners, recorded, and transcribed. Thematic analysis was applied after achieving data saturation. In the quantitative phase, a KAP questionnaire was developed. The sample size was determined by using the G power statistical power analysis program. A sample size of 220 in each group (dentists and medical practitioners) was estimated. Systematic random sampling was used to recruit the potential participants. The data obtained through the online dissemination of KAP tool was analysed and scores were standardized to categorize the KAP.

Qualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. In addition, thematic analysis revealed seventeen subthemes. For the quantitative phase, standardization was applied to a 14-item KAP questionnaire for medical practitioners and a 19-item KAP questionnaire for dental practitioners. The total KAP score for medical practitioners in the study was 21.84 ± 2.87, while dental practitioners scored 22.82 ± 3.21, which indicated a high level of KAP regarding CRFA. Meta integration of qualitative and quantitative data identified eight overarching themes: four were concordant, three were discordant, and one theme provided the explanatory component.

The study’s structured, validated questionnaire showed that both medical and dental professionals had a high knowledge of CRFA. However, they were not appreciably aware of the risk factors that are shared between NCDs and periodontal disease. Both groups were interested in the idea of using CRFA in integrated medical and dental care.

Peer Review reports

Introduction

Non-communicable diseases (NCDs) account for more than 41 million deaths globally each year [ 1 ]. These diseases are influenced by both non-modifiable and modifiable risk factors [ 2 ]. Periodontal disease, another multifactorial non-communicable ailment, shares several risk factors with NCDs. Individuals with periodontal diseases, particularly periodontitis, face a heightened risk of losing multiple teeth, leading to compromised masticatory function and altered dietary habits [ 3 ]. This not only affects the quality of life and self-esteem of affected individuals but also imposes significant socio-economic burdens and healthcare costs [ 4 ]. Despite the evident connections between periodontal disease and NCDs [ 5 , 6 ], there persists a historical divide between oral and general healthcare [ 7 ], further reinforced by the establishment of medical insurance [ 8 ]. This separation has contributed to out-of-pocket expenditures (OOPE) on dental care, accounting for approximately 14% of OOPE in Organisation for Economic Co-operation and Development (OECD) countries. [ 9 ] A recent study in South India revealed that 15.4% of sanitary workers experienced Catastrophic Dental Health Expenditure (CDHE) [ 10 ]. Additionally, a global study involving 41 low- and middle-income countries found that 7% of households faced CDHE [ 11 ].

The integration of dental and medical care would bring substantial benefits to the general population. Oral health has a significant impact on general health. Simple, non-invasive periodontal therapy was found to result in a remarkable (40–70%) reduction in medical costs and hospitalizations for individuals with conditions such as diabetes, coronary artery disease, or during pregnancy [ 12 ]. This underscores the potential advantages of addressing oral health within the broader spectrum of healthcare, leading to improved overall health outcomes and reduced healthcare costs.

Several methods of integrating medical and dental care have been explored, [ 13 , 14 , 15 ] and one such strategy is risk reduction for disease prevention. Common risk factors such as smoking, obesity, poor nutrition, low socioeconomic status, stress, and inadequate oral hygiene are shared by both periodontitis and NCDs [ 5 ]. Traditional health promotion tends to focus on specific diseases, potentially contributing to the separation of oral health from general health. An alternative approach, the Common Risk Factor Approach (CRFA), addresses shared risk factors among major NCDs, including oral diseases. CRFA emphasizes managing contributing elements to enhance overall population health.

The approaches within CRFA aim to mitigate the impact of common chronic diseases [ 13 ] and include integrated action against shared risk factors and altering one risk factor that may influence others, leading to a cascade effect. For instance, changing smoking behavior could impact related behaviors like alcohol consumption and diet. Collaborative efforts across sectors, concentrating upstream on basic etiological factors, can lead to progress in oral health improvement and decreased oral health inequalities [ 16 ]. Given the clustering of both modifiable and non-modifiable risk factors in patients with NCDs and periodontal diseases, CRFA emerges as a cost-effective and rational approach [ 13 ]. Of these risk factors, modifiable risk factors can be controlled or changed. The control or modification of a few key risk factors can have a substantial impact on managing numerous chronic conditions.

The World Health Organization (WHO) advocates a global strategy for enhancing oral health alongside overall health, emphasizing shared risk factors [ 17 ]. Implementing CRFA for overall health, including oral health, presents opportunities to integrate oral health promotion into broader health policies, such as those related to food [ 15 ]. However, successful implementation requires appropriate evidence, guidelines, and policies due to perceived challenges in applying CRFA for oral health promotion [ 15 ].

To comprehensively assess the potential initiation of the CRFA for NCDs, including periodontal disease, it is crucial to understand the knowledge, attitudes, and practices of medical and dental practitioners regarding shared risk factors. While previous studies have explored knowledge about periodontitis risk factors among medical practitioners and the general population, [ 18 , 19 ] there is a notable gap in understanding the KAP of both medical and dental practitioners regarding shared risk factors between NCDs and periodontitis and the integration of CRFA into medical and dental practices.

Capacity-building measures are essential for implementing CRFA-based programs [ 15 ], and assessing the baseline KAP of the target population will bridge the evidence gap for integrations. Despite the pivotal role of CRFA in addressing health issues, there is currently no standardized instrument tailored to assess practitioners’ KAP in this context. Questionnaires are commonly used for KAP assessment [ 20 ], and a structured, validated questionnaire is essential for obtaining clear information on practitioners’ understanding and application of CRFA in managing NCDs and periodontal diseases.

The objectives of this mixed-method study are to address these gaps by understanding practitioners’ opinions on CRFA and developing a validated structured instrument to assess the Knowledge, Attitude, and Practice of medical and dental practitioners toward the use of CRFA for managing NCDs and periodontal diseases. The study will employ both quantitative and qualitative methods, utilizing a structured questionnaire to capture practitioners’ perspectives and incorporating open-ended communication to gain insights into the reasons behind their opinions, support, and potential hurdles in implementing CRFA in the Indian context.

The mixed-method study received ethical approval from the institutional ethics committee and institutional review board, and informed consent was obtained from the participants during the conduct of the study.

Research design

The study employed a concurrent mixed-methods model, incorporating both qualitative and quantitative arms, to holistically investigate the research questions. This approach combines the advantages of qualitative and quantitative data, allowing for a comprehensive exploration of the CRFA. The qualitative arm provides in-depth insights into the complex phenomena associated with CRFA, offering a contextual richness that complements the quantitative results. The lists of potential participants were obtained from the list of dentists and medical practitioners of Kerala, Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, and Goa available through the regional Indian Dental Association (IDA), Indian Medical Association (IMA), and directories of medical and dental practitioners. Based on the data obtained from the directories, a state-wise distribution of samples was done. Systematic random sampling was used to select the possible participants for the study from January 2021 to February 2022.

Qualitative arm

Study context and population.

The qualitative segment of the study sought to delve into the viewpoints of experts in medicine and general dental practice, particularly those possessing relevant expertise related to the CRFA. Participants were selected from specialties such as endocrinology, gynaecology, otorhinolaryngology, periodontology, general medicine, and general dentistry, based on their relevance to the shared risk factors between periodontal disease and various medical conditions. Purposive sampling was employed to recruit a diverse group of medical and dental practitioners, and the sampling units were identified from the directories of professional associations like the Indian Dental Association (IDA) and the Indian Medical Association (IMA). Participation in the online interviews using the ‘Zoom Meetings’ online platform was voluntary. After obtaining their consent, the link for the Zoom meeting was shared with the participants. Participants received acknowledgment certificates as an incentive. No explicit exclusion criteria were set, ensuring a broad representation of perspectives across the selected fields.

In-depth interviews

The qualitative phase of the study utilized in-depth interview guides that covered similar topics for both dental and medical practitioners. These guides included components related to the understanding of common risk factors, risk factor reduction, and disease burden, suggested methods for integrating CRFA into clinical practice, and barriers to CRFA. The semi-structured questions were developed a priori, drawing from existing literature. The interviews were conducted with consent, and a note-keeper recorded the proceedings, while in-depth interviews were recorded for transcription. The recordings were transformed into verbatim transcripts at the end of each day.

The number of participants for in-depth interviews was determined based on achieving data saturation, ensuring that the sample size was sufficient to capture a diverse range of perspectives until no new information or themes emerged. Data saturation enhances the credibility and trustworthiness of study findings, signifying theoretical sufficiency. The analysis methodology involved progressive analysis throughout the study, allowing for the incremental identification and incorporation of themes and sub-themes after each interview. This iterative process facilitated the continual refinement of emerging data patterns.

The decision to conclude interviews was guided by the observation of the ceased emergence of new themes, indicating data saturation. Close monitoring of interview data helped identify a point where further sessions yielded no novel insights or themes. After achieving data saturation, a comprehensive final thematic analysis was conducted following guidelines by Braun and Clark [ 21 ] and reiterated by Kiger et al [ 22 ]. This analysis involved data review, coding, categorization, and synthesis to derive conclusive themes and sub-themes. Each transcript underwent review by two researchers, and emerging themes were developed, involving a third author in cases of disagreement. Consensus on codes, categories, and themes was reached through regular discussions. The data was organized and managed using computer-assisted qualitative research software, QDA Miner Lite (Version 2.0.7; Provalis Research).

Quantitative arm

The quantitative segment of the mixed-method study focused on developing and validating a KAP questionnaire on the CRFA for the integration of medical and dental care. Distinct questionnaires were created for medical and dental practitioners. The development of the questionnaire occurred in two stages.

In the first stage, item and domain development took place, involving a deductive approach to form initial questions, followed by content validation and test-retest reliability. The second stage involved the validation of the questionnaire through item response theory, exploratory factor analysis, and internal consistency reliability assessment. This two-stage process ensured the robustness and appropriateness of the questionnaire for assessing the KAP of medical and dental practitioners regarding CRFA in the context of managing NCDs and Periodontal diseases.

Study population

The study included both medical practitioners and dental practitioners, encompassing those with and without a postgraduate degree or specialization. This diverse inclusion aimed to capture perspectives from practitioners with varying levels of education and expertise, providing a comprehensive understanding of the knowledge, attitudes, and practices related to the CRFA among professionals in both fields.

Sample size

The sample size was determined by using the G power statistical power analysis program. Based on the findings from a previous study [ 23 ] a sample size of 220 dentists and medical practitioners was estimated. This was done by taking into account the Chi-square test’s effect size of 0.30, the study’s power of 0.95, and the number of groups of medical and dental practitioners that could be used to compare mean knowledge, attitude, and practice scores.

Data collection

The study utilized a systematic approach for sampling dental and medical practitioners from Kerala, Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, and Goa. The directories of the regional Indian Dental Association (IDA) and Indian Medical Association (IMA) were consulted to compile a list of practitioners (both specialists and general practitioners). To ensure a representative sample, the distribution of participants was organized by state (Table  1 ).

Systematic random sampling was employed to select potential participants, minimizing bias in participant selection. Contact details were then used to send a web-based questionnaire via Google Forms, accompanied by an invitation to participate. Anticipating a 50% non-response rate, the questionnaires were distributed to twice the required number of participants. The final analysis included responses from 225 medical practitioners and 307 dental practitioners across South India.

Questionnaire development

The development and validation of the KAP questionnaire occurred in two distinct stages. In the first stage, item and domain development were undertaken through a three-step process: (i) Deductive approach, (ii) Content validation, (iii) Test-retest reliability. The second stage involved the validation of the questionnaire using: (i) Item response theory, (ii) Exploratory factor analysis, (iii) Internal consistency reliability assessment. Subsequently, scores were standardized to categorize the KAP of the population into low, medium, and high categories. This multi-stage process ensured the reliability and validity of the questionnaire for assessing participants’ knowledge, attitude, and practice regarding the CRFA.

Stage one: item and domain development

The deductive approach was employed to develop items for the questionnaire based on existing literature related to the CRFA in the management of periodontal disease and NCDs. Eight referenced articles contributed to the conceptual definition of knowledge, attitude, and practice regarding CRFA [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. The definition of CRFA emphasized its role in creating cross-disciplinary health promotion programs that address common risk factors for diseases. Knowledge, attitude, and practice were defined in terms of awareness, thoughts, behaviors, and understanding of shared risk factors and etiology related to periodontal disease and NCDs, as well as CRFA.

The initial questionnaire, developed in English, consisted of 28 items for the dental questionnaire and 24 items for the medical questionnaire, distributed across four domains: (1) Demography of participants; (2) Knowledge towards CRFA for NCDs and oral health; (3) Attitude towards CRFA for NCDs and oral health; and (4) Practice towards implementing CRFA for NCDs and oral health. To ensure content validity, the initial questionnaire underwent review by an expert panel comprising dental and medical practitioners. The test-retest reliability of the questionnaire was assessed by administering it twice to 30 participants within a one-month timeframe.

Stage two: questionnaire validation

The study included responses from 225 medical practitioners and 307 dental practitioners across six states in South India to evaluate the additional psychometric properties of the questionnaire. Data analysis was conducted using JMETRIK software.

Item response theory (IRT)

In the knowledge domain, a two-parameter logistic item response theory (2-PL IRT) analysis was conducted using responses categorized as either correct or incorrect. The analysis was performed in JMETRIK (version 4.0.0, Charlottesville, Virginia, USA) using the RASCH (log odds ratio) limited package. The analysis considered the range of difficulty (-4 to + 4) and discrimination (0.20 to infinity) as the cut-off values for evaluating psychometric properties. Item fit was assessed using chi-square goodness-of-fit per item, and p values were reported. The modified parallel analysis was employed to evaluate one-dimensionality.

Exploratory factor analysis

The adequacy of sampling was assessed using the Kaiser–Meyer–Olkin measure (KMO) and Bartlett’s test of sphericity [ 20 ]. A KMO value above 0.5 and a significant result in Bartlett’s test ( p  < 0.001) were considered indicative of a sufficient sample.

Internal consistency reliability

The internal consistency (IC) of the items was calculated using the coefficient of Cronbach’s alpha [ 31 ] and correlation between items.

Standardization of scores

The responses to the questions in the Knowledge, Attitude, and Practice groups were coded, and scores were calculated for each group. The scores were then split into percentiles for standardization. The total KAP score was also calculated and interpreted as low KAP (0 to 24th percentile), medium KAP (25th to 75th percentile), and high KAP (76th to 100th percentile) based on the percentile scores [ 32 ].

In-depth interviews involved five medical practitioners specializing in endocrinology, gynaecology, otorhinolaryngology, and general medicine, along with five general dental practitioners and five periodontists. The qualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. Subsequently, seventeen subthemes emerged, encompassing topics such as enumerating risk factors, transitioning from disease-specific to risk factor approaches, diagnosing systemic NCDs through identifying risk factors and oral signs, controlling risk factors and NCD burden, the impact of periodontal therapy on NCD burden, the influence of medical practitioners over periodontists, measures for integrating CRFA, barriers to integration, and more.

Theme 1: understanding shared common risk factors

The study revealed that medical and dental practitioners, including periodontists, demonstrated awareness of the association between diabetes and periodontal disease, as well as the shared risk factor of smoking. However, their knowledge regarding risk factors common to other major NCDs and periodontal disease was limited. Many practitioners were unable to identify shared risk factors such as obesity, the presence of oral pathogens, and nutritional deficiency [ 5 ]. This knowledge gap may be attributed to the prevailing practice of treating patients based on specific diseases rather than targeting shared risk factors. Although there is a gradual shift toward a risk factor-based approach in certain specialties, there remains a general scepticism about patient compliance with long-term risk factor reduction strategies. The subthemes that emerged under this major theme are: (i) Enumeration of the risk factors (ii) Transition from disease specific to risk factor approach (iii) Diagnosis of a systemic NCD through identification of presence of risk factors and oral signs .

‘There are many risk factors, ranging from smoking to genetics. Very common ones are smoking, alcohol, lifestyle. Each and every factor has a specific role. Genetics has a significant role. If a parent is diabetic by his or her 50s then the next generation will become diabetic by 30s’. (MP1)

There was a consensus regarding the need for a change from a disease specific approach to a risk factor approach.

MP1 had supported CRFA. ‘This is a very good approach. Common risk factors are present for many diseases. So, if we can create an awareness regarding smoking, alcohol, and sedentary lifestyle, it can significantly reduce the development of many diseases.’

The identification of clustering of risk factors for periodontal disease and NCDs in patients, in addition to the occurrence of oral signs, can sometimes lead to the diagnosis of systemic diseases.

PR5 ‘In diabetes we have noticed. They come with multiple abscesses, then we advise them to check the blood glucose level and they are diagnosed with diabetes. They are not aware of the condition before. So, once we treat the patient and with the consultation with the diabetologist, we have noticed an improvement in the status.’

Theme 2: risk factor reduction and disease burden

All practitioners concurred on the potential positive impact of early identification of risk factors, counselling, and reducing risk factors to mitigate disease burden. Nevertheless, medical practitioners acknowledged that a significant portion of them tend to overlook oral health, possibly due to a lack of awareness regarding its association with systemic conditions. The thematic analysis revealed subthemes such as (i) Control of Risk Factors and Impact on NCD Burden (ii) The Role of Periodontal Therapy in Alleviating the NCD Burden (iii) Reciprocal Impact of Other NCD Therapies on Periodontal Disease Burden (iv) Influence of Medical Practitioners in Shaping Patient Decisions Over Periodontists. These subthemes underscored the interconnectedness of risk factors, diverse therapies, and the collaborative role of medical and dental practitioners in addressing both oral and systemic health.

PR1 stated that ‘Lifestyle modification…I have been following the periodontal patients in my clinic. There are patients whom I have been following for last 6 to 7 years. Patients who have been motivated to maintain the oral hygiene, their rate of progression (of periodontal disease) and diabetic control is much better than patients who are not maintaining their oral hygiene properly.’

The dental practitioners have observed that periodontal therapy can result in improving the NCD status and that a better compliance is observed when the advice is given by a medical practitioner.

‘Yes, after periodontal treatment, sugar level often reduces as noticed in diabetes. Diabetics with uncontrolled sugar levels, fluctuating sugar levels, after periodontal therapy usually have better controlled sugar levels’, GP1 said.

PR3 said, ‘Yes definitely, when a physician refers the patient to us, they are more willing to listen to us and adapt to whatever changes we say.’

Theme 3: methods suggested for integrating CRFA into clinical practice

Various approaches have been proposed to integrate the CRFA into clinical practice. These strategies encompass capacity building initiatives to promote medical-dental integration, such as establishing NCD clinics; raising awareness among the medical community regarding the interconnectedness of medical and dental health; advocating for policies that underscore the significance of CRFA integration in clinical settings; developing effective healthcare referral systems and cross-disciplinary health promotion strategies, including oral health care; and encouraging patient education and motivation. The subthemes within this overarching theme are: (i) Capacity Building (ii) Advocacy and Policy Implications (iii) Healthcare Partnerships Involving Referrals and Cross-Disciplinary Health Promotion Strategies (iv) Patient Education and Motivation.

The interviews highlighted diverse strategies for capacity building, including the implementation of regular check-ups and screening camps as integral components of healthcare services. Furthermore, suggestions encompassed the use of awareness posters and videos, adoption of evidence-based practices, and the establishment of NCD clinicsx [ 33 ]. NCD clinics, as proposed, would serve as essential hubs for screening, diagnosing, and managing NCDs. These clinics would offer comprehensive examinations, including diet counselling, lifestyle management, and home-based care. Patients could be referred to these clinics by other healthcare centres, health workers, or they could directly report to the clinic, enabling the identification and management of complications or advanced stages of NCDs. MP1 stated, ‘In government clinics, there are NCD clinic. Along with the NCD clinic, if a dental clinic can be set up, a lot of cases with oral manifestations will be diagnosed. So integrated clinics with NCD and dental will be very useful.’

Advocacy enables stakeholders and government decision-makers to have discussions and bring out suggestions and recommendations to a prevailing policy that is of interest to them.

MP1 Suggested that ‘Even for a job opportunity, basic examination is physical examination and evaluation for systemic diseases. Oral examination may be included in the basic fitness requirement for the job.’

Interdisciplinary collaboration is also essential for medical dental integration as stated by MP3, ‘There should be a rapport between the medical and dental practitioner so that there is communication regarding the cases and there is a follow-up of the cases.’

Communication through mass media and other visual aids, generating social and cultural awareness for patient education, and motivation for holistic health care have also been suggested to facilitate the implementation of integrated care delivery.

PR2 has mentioned, ‘When this gets published, apart from journals, this should reach the common population also. The common population rarely see the journal articles. So, it should be brought forth in mass media so that it reaches the population.’

Theme 4: barriers for implementation of CRFA

CRFA is considered a relatively novel approach, as the comprehensive exploration of shared risk factors and risk reduction strategies for common NCDs and periodontal disease is a recent development. The lack of awareness regarding this concept has been identified as a significant barrier to its implementation, coupled with challenges such as time constraints, concerns about the sustainability of long-term risk reduction strategies, and the need for extended resources. Moreover, the existing strict specialization within healthcare disciplines and the lack of interdisciplinary coordination pose additional obstacles to the effective execution of CRFA. The subthemes encompass: (i) Lack of awareness (ii) Time constraints (iii) Sustainability (iv) Long-term outcomes or no outcomes (v) Lack of resources (vi) Lack of interdisciplinary coordination and strict specialization.MP2 said ‘One is that among us practitioners, we do not give due significance to the link between oral health and systemic health. There are no awareness programs as far as I know. The emphasis is less’.

GP5 said, ‘They (medical practitioners) don’t have time to peep into the oral cavity to say you have caries, go to a dentist or say you have diabetes and there is a chance to develop periodontal disease. Such opportunities are less.’

The results of following the risk reduction strategies may take a long time to manifest, and sometimes the outcomes are not as significant as what the patient would have expected. This results in a spiralling of the patient’s attitude and a failure of further follow-up.

‘In long term, the patients may become uncooperative, and patients will not be willing for a follow-up, they will go for things that have cost-benefit’MP1.

The lack of resources, manpower and facilities to deliver the care act as significant barriers to implementation of CRFA.

MP3 has stated, ‘Cost is a problem, social acceptance is a problem, policy makers and political involvement are a problem, lack of communication between communities…In the western countries, like UK, they have NHS care, we don’t have that in India and patients hence don’t go for any care if they feel it is unnecessary’.

i) Content validation

The total number of questions included in the dental and medical questionnaires using the deductive approach was 28 and 24 respectively. After discussion, one question was eliminated from both the medical and dental questionnaires as it had a similar connotation to a previous question. Content validation of each scale was performed by five experts to ensure content relevance, representativeness, and technical quality. The KAP questionnaire was reduced to 26 questions for dental practitioners after content validation. Item reduction was performed to 22 for the questionnaire for medical practitioners after eliminating 1 question. A few questions were rephrased based on the suggestions given by the expert panel prior to administering the questionnaire for test-retest reliability assessment. The details of content validation are given in Table  2 .

ii) Test-re test reliability

The scoring of items was done, and the data was utilized to assess the reliability of the questionnaire. 21 questions in dental and medical questionnaires were subjected to test-retest reliability assessment. Five questions in the dental questionnaire were option questions, leading questions, or open-ended questions (Eg: Are you a periodontist) and one question in the medical questionnaire was open ended, hence they were not subjected to test-retest reliability. The unweighted Kappa coefficient was used to assess the reliability of the items with binary responses (Table  3 ). The intraclass correlation coefficient (ICC) was used for assessing the questions in the attitude category with categorical variables (Table  3 ). Based on the test-retest reliability assessment, three questions from the dental questionnaire and two questions from the medical questionnaire were eliminated.

iii)Psychometric evaluation of questionnaire

The 20-item medical and 23-item dental KAP questionnaires (including the open-ended and leading questions) were administered to 450 medical and dental practitioners, and responses were obtained from 225 samples in the medical stream and 307 in the dental stream.

In the medical KAP questionnaire, four items from the knowledge domain and one item each from the attitude and practice domain were eliminated owing to the high difficulty statistic. One item each from the knowledge and practice domain was retained considering the importance of the items, even though they had a higher difficulty range. After item reduction using item response theory, 14 items (including the open-ended question) remained in the final questionnaire for medical practitioners. The KMO sampling adequacy and test of sphericity for the domains of knowledge, attitude, and practice were found to be in an acceptable range. Internal consistency measured using Cronbach’s alpha improved from 0.471 to 0.658 for the attitude domain after item deletion. For knowledge and practice, the Cronbach’s alpha after item deletion was reported to be 0.553 and 0.727, respectively.

The 23-item questionnaire was reduced to 19 items with the elimination of 3 items from THE knowledge domain and single item from attitude domain. Two items with poor scores of difficulty were deemed to be important in the questionnaire and were not eliminated. After item reduction, a total of 14 items remained in the final questionnaire in addition to the five leading/option questions. The KMO sampling adequacy and test of sphericity for the domains of knowledge, attitude, and practice were found to be in acceptable range. Internal consistency measured using Cronbach’s alpha was found to be slightly reliable in case of the attitude domain (0.459). While for knowledge and practice domain internal consistency was within the acceptable range (Knowledge 0.634, Practice 0.513) after item deletion.

Multivariate logistic regression was attempted between the parameters such as age, gender, qualification, experience, type of service, location, and number of patients seen per day and the knowledge, attitude, and practice regarding CRFA for both medical and dental practitioners, and no significant results were obtained for both medical and dental practitioners. (The details of the psychometric evaluation of the questionnaire and the characteristics of the study population are given in supplementary file 1)

iv) standardization of scores

For the south Indian population, the 14 item questionnaire scores were standardized (Table  4 ).

The validated questionnaires for medical and dental practitioners are given in supplementary file 2. For the medical KAP questionnaire, scores below 14 indicated low KAP, scores between 15 and 18 indicated medium, and scores greater than 18 indicated good knowledge, attitude, and practice of CRFA. For dental practitioners, scores less than 16 were reported to be low KAP; scores 16 to 19 indicated medium level; and scores greater than 20 indicated a good level of knowledge, attitude, and practice regarding CRFA.

Total KAP amongst the medical practitioners who participated in the present study was 21.84 ± 2.87 and that of dental practitioners was 22.82 ± 3.21. Both values indicated a high level of KAP amongst the participants regarding CRFA.

Meta-integration

Eight overarching themes emerged in the meta integration of the qualitative and quantitative data (Fig.  1 ). The themes that had a confirmatory fit as assessed from both the quantitative and qualitative aspects of the study include (i) awareness of common risk factors for NCDs including periodontal diseases, (ii) neglect of dental status while assessing general health, (iii) awareness of effect of systemic diseases on oral health, (iv) awareness of risk factor reduction and improvement of NCD status. Contradictory observations from the quantitative and qualitative arms of the study resulted in a discordant fit in the following themes: (i) regular follow-up of periodontal health of patients with NCDs (ii) awareness regarding need for referral for periodontal examination and management in patients with NCDs (iii) awareness of perio-systemic interlink. The qualitative arm of the study explained the theme ‘Reasons for lack of referral to dental practitioners by medical practitioners’ and provided reasons such a reduced emphasis on oral health with a lack of awareness regarding the same amongst the practitioners, resource and time constraints that prevent the medical practitioners from looking into the overall health of the patient apart from the presenting complaint, overspecialization of the medical field with focus only on the specific field of specialization, to state a few.

Awareness of common risk factors for NCDs including periodontal diseases

NCDs and periodontal disease pose substantial societal burdens in terms of economic costs and years lost to ill-health, disability, or premature death [ 34 ]. Various factors, including social, demographic, environmental, behavioural, and personal elements, predispose individuals to major NCDs and oral diseases [ 5 ]. The CRFA addresses these shared risk factors, allowing the regulation of a few risk factors to exert a significant impact on controlling multiple chronic conditions [ 5 ]. This study has successfully developed and validated a questionnaire with satisfactory content validity and reliability to assess the knowledge, attitude, and behavior regarding CRFA for managing NCDs and periodontal disease.

To the best of our knowledge, this is the first study to create a suitable questionnaire for this purpose, incorporating a qualitative component to comprehend potential pathways and barriers to CRFA implementation. All retained questionnaire items demonstrated discrimination and difficulty parameters within acceptable ranges [ 20 ]. The KAP questionnaire exhibited acceptable internal consistency, validating its effectiveness for assessing CRFA-related KAP.

A crucial finding is the lack of understanding among medical and dental practitioners regarding common risk factors for NCDs and periodontal disease, hindering the implementation of CRFA. Literature that demonstrates the presence of shared risk factors between periodontal disease and other non-communicable diseases has, perchance, not been extensively explored by the health-care community. Almeida et al., in their systematic review, showed that the inflammatory mediators CRP and IL-6 had a significant association with both periodontitis and atherosclerosis [ 35 ]. A study by Arregoces et al. [ 36 ] showed an increase in ultrasensitive CRP (usCRP) in acute myocardial infarction (AMI), diabetes and periodontal disease. abdominal obesity [ 37 ] and insulin resistance [ 38 ] are proven to be contributing risk factors for metabolic syndrome and periodontal disease. The risk for CVD and periodontal disease is related to poor glycemic control, dyslipidemia, and chronic inflammatory state [ 39 , 40 , 41 ]. Smoking has been proven as a risk factor for periodontal disease, hypertension, diabetes, and metabolic syndrome through several studies [ 42 , 43 , 44 , 45 ]. Holmlund et al. have demonstrated the association between immunoglobulin G levels against P gingivalis and the risk for AMI and periodontal disease [ 46 ]. The presence of Aggregatibacter actinomycetemcomitans (Aa) is shown to be a risk factor for Coronary Artery Disease (CAD) and periodontal diseasecx [ 47 ]. The role of stress and depression as risk factors for CVD and periodontal disease has been investigated and recognized [ 48 ].

Apart from the lack of sufficient knowledge regarding the shared risk factors between periodontal disease and NCDs, there are additional barriers to the implementation of CRFA for the management of periodontal disease and NCDs. Barriers include time and resource constraints, oral health neglect in general health assessments, insufficient recognition of the need for oral health care referral for NCD patients, and limited acknowledgment of the perio-systemic interlink. However, the integration of medical and dental care is not impossible, and efforts such as creating awareness, education programs, mass media campaigns, and efficient referral systems are advocated by healthcare professionals.

The Health Resources and Services Administration (HRSA) has explained initiatives for incorporating oral health into primary medical care practice and training primary health care professionals in oral health assessment and clinical competencies [ 49 ]. The combination of preventive dental care with general health care practice can help reduce duplication of care modalities and expenses incurred. Six levels of integration, with the evolution of the key elements involved in the integration, from communication to physical proximity to practice change, have been described [ 50 ]. Communication is the key element in the first and second levels of integration in which there is minimal collaboration and basic collaboration at a distance respectively. Basic collaboration onsite and close collaboration onsite with some system integration form the third and fourth levels of integration, in which physical proximity is the key element. The fifth and sixth levels of integration include practice change, in which there is close collaboration with an integrated practice and full collaboration with a merged, integrated practice [ 50 ].

This research indicates that while the presence of shared risk factors among NCDs is acknowledged, medical practitioners often overlook the link between oral health and systemic health. Addressing this gap in healthcare practice involves providing basic oral health care training as an integral part of general health education.

In India, the checklist for early detection of NCDs, which is used in community based NCD surveillance, takes into consideration risk factors such as age of patient, smoking, alcohol consumption, measurement of waist, physical activities, and family history of NCDs [ 51 ]. These risk factors are similar to the risk factors for periodontal disease [ 13 ]. Thus, the risk factor surveillance may be extended to include periodontal disease as well. The primary healthcare teams can be trained in strategies to reduce or modify the risk factors associated with systemic diseases and oral diseases. The methods to assess the efficiency of the integrated practice in the primary health care setting include the calculation of the percentage of patients assessed using the surveillance tool, to the percentage of staff satisfied with the referral process [ 52 ]. Research conducted in Saudi Arabia showed that the availability of an appropriate source of oral health knowledge was significantly associated with increased odds of inter-disciplinary practice [ 53 ]. Regular patient reviews and examinations, along with the reinforcement of risk reduction strategies, can be achieved through the application of knowledge regarding shared risk factors, facilitating the efficient integration of medical and dental care.

This combined mixed-methods study has the limitation that the quantitative aspect was primarily conducted through online Google Forms, which were sent only to the medical and dental practitioners who are registered in the databases that were utilized in the study, and hence the representativeness of the sample may be compromised. However, given the study’s design, which provides insights into the perspectives of healthcare professionals in various fields, the results offer a valuable reflection of the KAP regarding CRFA among medical and dental practitioners.

The questionnaire derived from the quantitative segment of this study stands as a straightforward and effective tool for evaluating KAP related to the CRFA concerning both oral and general health. In alignment with the ongoing global efforts to enhance oral health strategies, CRFA emerges as a promising approach for seamlessly integrating medical and dental care. The qualitative aspect of this study showed that to foster this integration, key recommendations include raising awareness about the interconnectedness of oral and systemic conditions, addressing constraints related to time and resources, and establishing robust referral systems between medical and dental practitioners. These measures collectively aim to establish a unified and integrated medical-dental care system.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.The mixed-method study received ethical approvals from the Institutional Review Board of Amrita Institute of Medical Sciences, Kochi, with the reference IRB-AIMS-2020-165, and the Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee, under the reference IEC-664/2020 and informed consent was obtained from the participants.

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Department of Public Health Dentistry, Amrita School of Dentistry, Amrita Vishwa Vidyapeetham, Kochi, Kerala, India

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Department of Periodontics, Amrita School of Dentistry, Amrita Vishwa Vidyapeetham, Kochi, Kerala, India

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LP: conception and design, acquisition of data and interpretation of data, drafting the article, final approval of the version to be published; VK: conception and design, acquisition of data, analysis and interpretation of data, drafting the article, final approval of the version to be published; LP and VK contributed equally for the preparation of the manuscript; CJ: conception and design, analysis and interpretation of data, revising article critically, final approval of the version to be published; RV: Design, interpretation of data, revising article critically, final approval of the version to be published; SS: Design, interpretation of data, revising article critically, final approval of the version to be published; AF: Design, interpretation of data, revising article critically, final approval of the version to be published.

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Solid health care waste management practice in Ethiopia, a convergent mixed method study

• Yeshanew Ayele Tiruneh 1 ,
• L. M. Modiba 2 &
• S. M. Zuma 2  

BMC Health Services Research volume  24 , Article number:  985 ( 2024 ) Cite this article

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Introduction

Healthcare waste is any waste generated by healthcare facilities that is considered potentially hazardous to health. Solid healthcare waste is categorized into infectious and non-infectious wastes. Infectious waste is material suspected of containing pathogens and potentially causing disease. Non-infectious waste includes wastes that have not been in contact with infectious agents, hazardous chemicals, or radioactive substances, similar to household waste, i.e. plastic, papers and leftover foods.

This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia. The setting was all health facilities found in Hossaena town.

A mixed-method study design was used. For the qualitative phase of this study, eight FGDs were conducted from 4 government health facilities, one FGD from each private health facility (which is 37 in number), and forty-five FGDs were conducted. Four FGDs were executed with cleaners; another four were only health care providers because using homogeneous groups promotes discussion. The remaining 37 FGDs in private health facilities were mixed from health professionals and cleaners because of the number of workers in the private facilities. For the quantitative phase, all health facilities and health facility workers who have direct contact with healthcare waste management practice participated in this study. Both qualitative and quantitative study participants were taken from the health facilities found in Hossaena town.

Seventeen (3.1%) health facility workers have hand washing facilities. Three hundred ninety-two (72.6%) of the participants agree on the availability of one or more personal protective equipment (PPE) in the facility ‘‘ the reason for the absence of some of the PPEs, like boots and goggles, and the shortage of disposable gloves owes to cost inflation from time to time and sometimes absent from the market’’ . The observational finding shows that colour-coded waste bins are available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. Two hundred seventy-one (50.2%) of the respondents were satisfied or agreed that satisfactory procedures are available in case of an accident. Only 220 (40.8%) respondents were vaccinated for the Hepatitis B virus.

Hand washing facilities, personal protective equipment and preventive vaccinations are not readily available for health workers. Solid waste segregation practices are poor and showed that solid waste management practices (SWMP) are below the acceptable level.

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Healthcare waste (HCW) encompasses all types of waste generated while providing health-related services, spanning activities such as diagnosis, immunization, treatment, and research. It constitutes a diverse array of materials, each presenting potential hazards to health and the environment. Within the realm of HCW, one finds secretions and excretions from humans, cultures, and waste containing a stock of infectious agents. Discarded plastic materials contaminated with blood or other bodily fluids, pathological wastes, and discarded medical equipment are classified as healthcare waste. Sharps, including needles, scalpels, and other waste materials generated during any healthcare service provision, are also considered potentially hazardous to health [ 1 ].

Healthcare waste in solid form (HCW) is commonly divided into two primary groups: infectious and non-infectious. The existence of pathogens in concentrations identifies infectious waste or amounts significant enough to induce diseases in vulnerable hosts [ 1 ] If healthcare facility waste is free from any combination with infectious agents, nearly 85% is categorized as non-hazardous waste, exhibiting characteristics similar to conventional solid waste found in households [ 2 ]. World Health Organization (WHO) recommends that appropriate colour-coded waste receptacles be available in all medical and other waste-producing areas [ 3 ].

Solid waste produced in the course of healthcare activities carries a higher potential for infection and injury than any other type of waste. Improper disposal of sharps waste increases the risk of disease transmission among health facility workers and general populations [ 1 ]. Inadequate and inappropriate handling of healthcare waste may have serious public health consequences and a significant environmental impact. The World Health Organization (2014) guidelines also include the following guidance for hand washing and the use of alcohol-based hand rubs: Wash hands before starting work, before entering an operating theatre, before eating, after touching contaminated objects, after using a toilet, and in all cases where hands are visibly soiled [ 4 ].

Among the infectious waste category, sharps waste is the most hazardous waste because of its ability to puncture the skin and cause infection [ 3 ]. Accidents or occurrences, such as near misses, spills, container damage, improper waste segregation, and incidents involving sharps, must be reported promptly to the waste management officer or an assigned representative [ 5 ].

Africa is facing a growing waste management crisis. While the volumes of waste generated in Africa are relatively small compared to developed regions, the mismanagement of waste in Africa already impacts human and environmental health. Infectious waste management has always remained a neglected public health problem in developing countries, resulting in a high burden of environmental pollution affecting the general masses. In Ethiopia, there is no updated separate regulation specific to healthcare waste management in the country to enforce the proper management of solid HCW [ 6 ].

In Ethiopia, like other developing countries, healthcare waste segregation practice was not given attention and did not meet the minimum HCWM standards, and it is still not jumped from paper. Previous study reveals that healthcare waste generation rates are significantly higher than the World Health Organization threshold, which ranges from 29.5–53.12% [ 7 , 8 ]. In Meneilk II Hospital, the proportion of infectious waste was 53.73%, and in the southern and northern parts of Ethiopia, it was 34.3 and 53%, respectively. Generally, this figure shows a value 3 to 4 times greater than the threshold value recommended by the World Health Organization [ 7 ].

Except for sharp wastes, segregation practice was poor, and all solid wastes were collected without respecting the colour-coded waste disposal system [ 9 ]. The median waste generation rate was found to vary from 0.361- 0.669 kg/patient/day, comprising 58.69% non-hazardous and 41.31% hazardous wastes. The amount of waste generated increased as the number of patients flow increased. Public hospitals generated a high proportion of total healthcare waste (59.22%) in comparison with private hospitals (40.48) [ 10 ]. The primary SHCW treatment and disposal mechanism was incineration, open burning, burring into unprotected pits and open dumping on municipal dumping sites as well as in the hospital backyard. Carelessness, negligence of the health workers, patients and cleaners, and poor commitment of the facility leaders were among the major causes of poor HCWM practice in Ethiopia [ 9 ]. This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia.

The setting for this study was all health facilities found in Hossaena town, which is situated 232 kms from the capital city of Ethiopia, Addis Ababa, and 165 kms from the regional municipality of Hawasa. The health facilities found in the town were one university hospital, one private surgical centre, three government health centres, 17 medium clinics, and 19 small clinics were available in the city and; health facility workers who have direct contact with generating and disposal of HCW and those who are responsible as a manager of health facilities found in Hossaena town are the study settings. All health facilities except drug stores and health facility workers who have direct contact with healthcare waste generation participated in this study.

A mixed-method study design was used. For the quantitative part of this study, all healthcare workers who have direct contact with healthcare waste management practice participated in this study, and one focus group discussion from each health facility was used. Both of the study participants were taken from the same population. All health facility workers who have a role in healthcare waste management practice were included in the quantitative part of this study. The qualitative data collection phase used open-ended interviews, focus group discussions, and visual material analysis like posters and written materials. All FGDs were conducted by the principal investigator, one moderator, and one note-taker, and it took 50 to 75 min. 4–6 participants participated in each FGD.

According to Elizabeth (2018: 5), cited by Creswell and Plano (2007: 147), the mixed method is one of the research designs with philosophical assumptions as well as methods of inquiry. As a method, it focuses on collecting, analyzing, and mixing both quantitative and qualitative data in a single study. As a methodology, it involves philosophical assumptions guiding the direction of the collection and analysis and combining qualitative and quantitative approaches in many phases of the research project. The central premise is that using qualitative and quantitative approaches together provides a better understanding of the research problems than either approach alone.

The critical assumption of the concurrent mixed methods approach in this study is that quantitative and qualitative data provide different types of information, often detailed views of participants’ solid waste management practice qualitatively and scores on instruments quantitatively, and together, they yield results that should be the same. In this approach, the researcher collected quantitative and qualitative data almost simultaneously and analyzed them separately to cross-validate or compare whether the findings were similar or different between the qualitative and quantitative information. Concurrent approaches to the data collection process are less time-consuming than other types of mixed methods studies because both data collection processes are conducted on time and at the same visit to the field [ 11 ].

Data collection

The data collection involves collecting both quantitative and qualitative data simultaneously. The quantitative phase of this study assessed three components. Health care waste segregation practice, the availability of waste segregation equipment for HCW segregation, temporary storage facilities, transportation for final disposal, and disposal facilities data were collected using a structured questionnaire and observation of HCW generation. Recycling or re-using practice, waste treatment, the availability of the HCWM committee, and training data were collected.

Qualitative data collection

The qualitative phase of the data collection for this study was employed by using focus group discussions and semi-structured interviews about SHCWMP. Two focus group discussions (FGD) from each health facility were conducted in the government health facilities, one at the administrative level and one at the technical worker level, and one FGD was conducted for all private health facilities because of the number of available health facility workers. Each focus group has 4–6 individuals.

In this study, the qualitative and the quantitative data provide different information, and it is suitable for this study to compare and contrast the findings of the two results to obtain the best understanding of this research problem.

Quantitative data collection

The quantitative data were entered into Epi data version 3.1 to minimize the data entry mistakes and exported to the statistical package for social science SPSS window version 27.0 for analysis. A numeric value was assigned to each response in a database, cleaning the data, recoding, establishing a codebook, and visually inspecting the trends to check whether the data were typically distributed.

Data analysis

Data were analyzed quantitatively by using relevant statistical tools, such as SPSS. Descriptive statistics and the Pearson correlation test were used for the bivariate associations and analysis of variance (ANOVA) to compare the HCW generation rate between private and government health facilities and between clinics, health centres and hospitals in the town. Normality tests were performed to determine whether the sample data were drawn from a normally distributed population.

The Shapiro–Wilk normality tests were used to calculate a test statistic based on the sample data and compare it to critical values. The Shapiro–Wilk test is a statistical test used to assess whether a given sample comes from a normally distributed population. The P value greater than the significance level of 0.05 fails to reject the null hypothesis. It concludes that there is not enough evidence to suggest that the data does not follow the normal distribution. Visual inspection of a histogram, Q-Q plot, and P-P plot (probability-probability plot) was assessed.

Bivariate (correlation) analysis assessed the relationships between independent and dependent variables. Then, multiple linear regression analysis was used to establish the simple correlation matrices between different variables for investigating the strength relationships of the study variables in the analysis. In most variables, percentages and means were used to report the findings with a 95% confidence interval. Open-ended responses and focused group findings were undertaken by quantifying and coding the data to provide a thematic narrative explanation.

Appropriate and scientific care was taken to maintain the data quality before, during, and after data collection by preparing the proper data collection tools, pretesting the data collection tools, providing training for data collectors, and proper data entry practice. Data were cleaned on a daily basis during data collection practice, during data entry, and before analysis of its completeness and consistency.

Data analysis in a concurrent design consists of three phases. First, analyze the quantitative database in terms of statistical results. Second, analyze the qualitative database by coding the data and collapsing the codes into broad themes. Third comes the mixed-method data analysis. This is the analysis that consists of integrating the two databases. This integration consists of merging the results from both the qualitative and the quantitative findings.

Descriptive analysis was conducted to describe and summarise the data obtained from the samples used for this study. Reliability statistics for constructs, means and modes of each item, frequencies and percentage distributions, chi-square test of association, and correlations (Spearman rho) were used to portray the respondents’ responses.

All patient care-providing health facilities were included in this study, and the generation rate of healthcare waste and composition assessed the practice of segregation, collection, transportation, and disposal system was observed quantitatively using adopted and adapted structured questionnaires. To ensure representativeness, various levels of health facilities like hospitals, health centres, medium clinics, small clinics and surgical centres were considered from the town. All levels of health facilities are diagnosing, providing first aid services and treating patients accordingly.

The hospital and surgical centre found in the town provide advanced surgical service, inpatient service and food for the patients that other health facilities do not. The HCW generation rate was proportional to the number of patients who visited the health facilities and the type of service provided. The highest number of patients who visited the health facilities was in NEMMCSH; the service provided was diverse, and the waste generation rate was higher than that of other health facilities. About 272, 18, 15, 17, and 20 average patients visited the health facilities daily in NEMMCSH: government health centres, medium clinics, small clinics, and surgical centres. Paper and cardboard (141.65 kg), leftover food (81.71 kg), and contaminated gloves (42.96 kg) are the leading HCWs generated per day.

A total of 556 individual respondents from sampled health facilities were interviewed to complete the questionnaire. The total number of filled questionnaires was 540 (97.1) from individuals representing these 41 health facilities.

The principal investigator observed the availability of handwashing facilities near SHCW generation sites. 17(3.1%) of health facility workers had hand washing facilities near the health care waste generation and disposal site. Furthermore,10 (3.87%), 2 (2.1%), 2 (2.53%), 2 (2.1%), 1 (6.6%) of health facility workers had the facility of hand washing near the health care waste generation site in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH), government health centres, medium clinics, small clinics, and surgical centre respectively. This finding was nearly the same as the study findings conducted in Myanmar; the availability of hand washing facilities near the solid health care waste generation was absent in all service areas [ 12 ]. The observational result was convergent with the response of facility workers’ response regarding the availabilities of hand washing facilities near to the solid health care waste generation sites.

The observational result was concurrent with the response of facility workers regarding the availability of hand-washing facilities near the solid health care waste generation sites.

The availability of personal protective equipment (PPE) was checked in this study. Three hundred ninety-two (72.6%) of the respondents agree on the facility’s availability of one or more personal protective equipment (PPE). The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The analysis further shows that the availability of masks for healthcare workers was above the mean in NEMMCSH and surgical centres.

Focus group participants indicated that health facilities did not volunteer to supply Personal protective equipment (PPEs) for the cleaning staff.

“We cannot purchase PPE by ourselves because of the salary paid for the cleaning staff.”

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained about by all (41) health facility owners.

“the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves are owing to cost inflation from time to time and sometimes absent from the market is the reason why we do not supply PPE to our workers.”

Using essential personal protective equipment (PPEs) based on the risk (if the risk is a splash of blood or body fluid, use a mask and goggles; if the risk is on foot, use appropriate shoes) is recommended by the World Health Organization [ 13 ]. The mean availability of gloves in health facilities was 343 (63.5% (95% CI: 59.3–67.4). Private health institutions are better at providing gloves for their workers, 67.1%, 72.8%, and 62.5% in medium clinics, small clinics, and surgical centres, respectively, which is above the mean.

Research participants agree that.

‘‘ there is a shortage of gloves to give service in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH) and government health centres .’’

Masks are the most available personal protective equipment for health facility workers compared to others. 65.4%, 55.6%, and 38% of the staff are available with gloves, plastic aprons and boots, respectively.

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. Health facility workers were asked about the availability of different personal protective equipment, and 38% of the respondents agreed with the presence of boots in the facility. Still, the qualitative observational findings of this study show that all health facility workers have no shoes or footwear during solid health care waste management practice.

SHCW segregation practice was checked by observing the availability of SHCW collection bins in each patient care room. Only 4 (1.7%) of the room’s SHCW bins are collected segregated (non-infectious wastes segregated in black bins and infectious wastes segregated in yellow bins) based on the World Health Organization standard. Colour-coded waste bins, black for non-infectious and yellow for infectious wastes, were available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. Only 6.7% of the waste bins were pedal operated and adequately covered, and the rest were fully opened, or a tiny hole was prepared on the container’s cover. All of the healthcare waste disposal bins in each health facility and at all service areas were away from the arm’s reach distance of the waste generation places, and this is contrary to World Health Organization SHCWM guidelines [ 13 ]. The observation result reveals that the reason for the above result was that medication trolleys were not used during medication or while healthcare providers provided any health services to patients.

Most medical wastes are incinerated. Burning solid and regulated medical waste generated by health care creates many problems. Medical waste incinerators emit toxic air pollutants and ash residues that are the primary source of environmental dioxins. Public concerns about incinerator emissions and the creation of federal regulations for medical waste incinerators are causing many healthcare facilities to rethink their choices in medical waste treatment. Health Care Without Harm [ 14 ], states that non-incineration treatment technologies are a growing and developing field. The U.S. National Academy of Science 2000 argued that the emission of pollutants during incineration is a potential risk to human health, and living or working near an incineration facility can have social, economic, and psychological effects [ 15 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. Incineration of healthcare waste can produce secondary waste and pollutants if the treatment facilities are not appropriately constructed, designed, and operated. It can be one of the significant sources of toxic substances, such as polychlorinated dibenzo-dioxins/dibenzofurans (PCDD/ PCDF), polyvinyl chloride (PVC), hexachlorobenzenes and polychlorinated biphenyls, and dioxins and furans that are known as hazardous pollutants. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ].

All government health facilities (4 in number) used incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres. This finding contradicts the study findings in the United States of America and Malaysia, in which 49–60% and 59–60 were incinerated, respectively, and the rest were treated using other technologies [ 15 , 16 ].

World Health Organization (2014:45) highlighted those critical elements of the appropriate operation of incinerators include effective waste reduction and waste segregation, placing incinerators away from populated areas, satisfactory engineered design, construction following appropriate dimensional plans, proper operation, periodic maintenance, and staff training and management are mandatory.

Solid waste collection times should be fixed and appropriate to the quantity of waste produced in each area of the health care facility. General waste should not be collected simultaneously or in the same trolley as infectious or hazardous wastes. The collection should be done daily for most wastes, with collection timed to match the pattern of waste generation during the day [ 13 ].

SHCW segregation practices were observed for 240 rooms in 41 health facilities that provide health services in the town. In government health centres, medium clinics, small clinics, and surgical centres, SHCW segregation practice was not based on the World Health Organization standard. All types of solid waste were collected in a single container near the generation area, and there were no colour-coded SHCW storage dust bins. Still, in NEMMCSH, in most of the service areas, colour-coded waste bins are available, and the segregation practice was not based on the standard. Only 3 (10%) of the dust bins collected the appropriate wastes according to the World Health Organization standard, and the rest were mixed with infectious and non-infectious SHCW.

Table 1 below shows health facility managers were asked about healthcare waste segregation practices, and 9 (22%) of the facility leaders responded that there is an appropriate solid healthcare waste segregation practice in their health facilities. Still, during observation, only 4 (1.7%) of the rooms in two (4.87%) of the facilities, SHCW bins collected the segregated wastes (non-infectious wastes segregated at the black bin and infectious wastes segregated at yellow bin) based on the world health organization standard. The findings of this study show there is a poor segregation practice, and all kinds of solid wastes are collected together.

In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. During observation in one of the study health facilities, infectious wastes were not collected daily and left for days. Utility gloves, boots, and aprons are not available for cleaning staff to collect and transport solid healthcare wastes in all study health facilities. 29.26% of the facilities’ cleaning staff have a face mask, and 36.5% of the facilities remove waste bins from the service area when 3/4 full, and the rest were not removed or replaced with new ones. There is a separate container only in 2 health facilities for infectious and non-infectious waste segregation practice, and the rest were segregated and collected using single and non-colour coded containers.

At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site were transported manually by carrying the collection container and there is no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Only 2 out of 41 health facilities have temporary solid waste storage points at the facility. One of the temporary storage places was clean, and the other needed to be properly cleaned and unsightly. Two (100%) of the temporary storage areas are not fenced and have no restriction to an authorized person. Temporary storage areas are available only in two health facilities that are away from the service provision areas.

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. 95% of the facilities have no water supply for hand washing during and after solid healthcare waste generation, collection, and disposal.

The United States Agency estimated sharp injuries from medical wastes to health professionals and sanitary service personnel for toxic substances and disease registry. Most of the injuries are caused during the recapping of hypodermic needles before disposal into sharps containers [ 13 ]. Nearly half of the respondents, 245 (51.5%), are recapping needles after providing an injection to the patient. Recapping was more practised in NEMMCSH and surgical centres, which is 57.5% and 57.5%, respectively. In government health centres, medium clinics, and surgical centres, the recapping of used needles was practised below the mean, which is 47.9%, 48, and 43.8%, respectively. This finding was reasonable compared to the study findings of Doylo et al. [ 18 ] in western Ethiopia, where 91% of the health workers are recapping needles after injection [ 18 ]. The research finding shows that there is no significant association P-value of 0.82 between the training and recapping of needles after injection.

Focus group participants ’ response for appropriate SHCWMP regarding patients ’ and visitors ’ lack of knowledge on SHCW segregation practice

“The personal responsibilities of patients and visitors on solid HCW disposal should be explained to help appropriate safe waste management practice and maintain good hygiene .” “Providing waste management training and creating awareness are the two aspects of improving SHCW segregation practice.” “Training upgrades and creates awareness on hygiene for all workers.”

Sharp waste collection practices were observed in 240 rooms in the study health facilities, and 9.2% of the rooms used disposable sharp containers.

Sixty per cent (60%), 13.3%, 8.24%, and 15.71% of the sharps containers in NEMMCSH, government health centres, medium clinics, and small clinics, respectively, were using disposable sharps containers; sharps were disposed together with the sharps container, and surgical centre was using reusable sharp collection container. All disposable sharps containers in medium and small clinics used non-puncture-resistant or simple packaging carton boxes. 60% and 13.3% of the disposable sharps containers in NEMMCSH and the government health centre use purposefully manufactured disposable safety boxes.

Needle sticks injury reporting and occurrence

A total of 70 injuries were reported to the health facility manager in the last one year, and 44 of the injuries were reported by health professionals. The rest of the injuries were reported by supportive staff. These injuries were reported from 35 health facilities, and the remaining six health facilities did not report any cases of injury related to work; see Tables 2 and 3 below.

Accidents or incidents, including near misses, spillages, damaged containers, inappropriate segregation, and any incidents involving sharps, should be reported to the waste-management officer. Accidental contamination must be notified using a standard-format document. The cause of the accident or incident should be investigated by the waste-management officer (in case of waste) or another responsible officer, who should also take action to prevent a recurrence [ 13 ]. Two hundred seventy-one (50.2% (CI: 45.7–54.6) of the respondents agree that satisfactory procedures are available in case of an accident, while the remaining 269 (49.8%( CI: 45.4–54.3) of respondents do not agree on the availability of satisfactory procedures in case of an accident, see Table  4 below. The availability of satisfactory procedures in case of an accident is above the mean in medium clinics, which is 60.8%. 132(24.4%) of the staff are pricked by needle stick injury while providing health services. Nearly half of the respondents, 269 (49.8%), who have been exposed to needle stick injury do not get satisfactory procedures after being pricked by a needle, and those who have not been stung by a needle stick injury for the last year. 204 (37.8%) disagree with the presence of satisfactory procedures in the case of a needle stick injury. In NEMMCSH, 30.2% of the research participants were pricked by needle stick injury within one year of period, and 48.8% of those who were stung by needle stick injuries did not agree upon the presence of satisfactory procedures in case of needle stick injuries in the study hospital. 17.9% and 49.5%, 24.1% and 60.8%, 7.6% and 50% of the respondents are pricked by needle sticks, and they disagree on the availability of satisfactory procedures in case of accidents, respectively, in government health centres, medium clinics, small clinics, and surgical centre respectively.

One hundred seventy-seven (32.7% (CI:29.1–37) respondents were exposed to needle stick injury while working in the current health facilities. One hundred three (58.1%) and 26 (32.9%) needle stick injuries were reported from WUNEMMCSH and medium clinics, which is above the mean. One hundred thirty-two(24.7% (95%CI:20.7–28.1) of the respondents are exposed to needle stick injury within one year of the period. Seventy-eight(30.2%), 17 (17.9%), 19 (24.1%), 15 (16.3%), 3 (18.8%) of the staff are injured by needle sticks from NEMMCSH, government health centres, medium clinics, small clinics, and surgical centre staffs respectively within one year of service.

The mean availabilities of satisfactory procedures in case of accidents were 321 (59.4% (CI:55.4–63.7). Out of this, 13.7% of the staff is injured by needle sticks within one year before the survey. Except in NEMMCSH, the mean availabilities of satisfactory procedures were above the mean, which is 50%, 60%, 77.2%, 66.3%, and 81.3% in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively.

Table 5 below shows that Hepatitis B, COVID-19, and tetanus toxoid vaccinations are the responses of the research participants to an open-ended question on which vaccine they took. The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One hundred fifty-six (70.9%) of the respondents are vaccinated to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19.

Appropriate health care waste management practice was assessed by using 12 questions: availability of colour-coded waste bins, foot-operated dust bins, elbow or foot-operated hand washing basin, personal protective equipment, training, role and responsibility of the worker, the presence of satisfactory procedures in case of an accident, incinerator, vaccination, guideline, onsite treatment, and the availability of poster. The mean of appropriate healthcare waste management practice was 55.58%. The mean of solid health care waste management practice based on the level of health facilities was summed and divided into 12 variables to get each health facility’s level of waste management practice. 64.9%, 45.58%, 49%, 46.9%, and 51.8% are the mean appropriate health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively. In NEMMCSH, the practice of solid healthcare waste management shows above the mean, and the rest was below the mean of solid healthcare waste management practice.

Healthcare waste treatment and disposal practice

Solid waste treatment before disposal was not practised at all study health facilities. There is an incineration practice at all of the study health facilities, and the World Health Organization 2014 recommended three types of incineration practice for solid health care waste management: dual-chamber starved-air incinerators, multiple chamber incinerators, and rotary kilns incinerators. Single-chamber, drum, and brick incinerators do not meet the best available technique requirements of the Stockholm Convention guidelines [ 13 ]. The findings of this study show that none of the incinerators found in the study health facilities meet the minimum standards of solid healthcare waste incineration practice, and they need an air inlet to facilitate combustion. Eleven (26.82%) of the health facilities have an ash pit to dispose of burned SHCW; the majority, 30 (73.17%), dispose of the incinerated ash and burned needles in the municipal waste disposal site. In one out of 11 health facilities with an ash pit, one of the incinerators was built on the ash pit, and the incinerated ashes were disposed of in the ash pit directly. Pre-treatment of SHCW before disposal was not practised at all health facilities; see Table  6 below.

All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the solid wastes are incinerated in WUNEMMCS Hospital and government health centres, respectively. This finding was not similar to the other studies because other technologies like autoclave microwave and incineration were used for 59–60% of the waste [ 15 ]. Forty-one (100%) of the study facilities were using incinerators, and only 5 (12.19%) of the incinerators were constructed by using brick and more or less promising than others for incinerating the generated solid wastes without considering the emitting gases into the atmosphere and the residue chemicals and minerals in the ashes.

Research participants’ understanding of the environmental friendliness of health care waste management practice was assessed, and the result shows that more than half, 312(57%) of the research participants do not agree on the environmental friendliness of the waste disposal practices in the health facilities. The most disagreement regarding environmental friendliness was observed in NEMMCSH; 100 (38.8%) of the participants only agreed the practice was environmentally friendly of the service. Forty-four (46.3%), 37 (46.8%), 40 (43.5%), and 7 (43.8%) of the participants agree on the environmental friendliness of healthcare waste management practice in government health centres, medium clinics, small clinics, and surgical centres, respectively.

One hundred twenty-five (48.4%) and 39(42.4%) staff are trained in solid health care waste management practice in NEMMCSH and small clinic staff, respectively; this result shows above the mean. Twenty-seven (28.4%), 30 (38%), and 4 (25%) of the staff are trained in health care waste management practice in Government health centres, medium clinics, and surgical centres, respectively. The training has been significantly associated with needle stick injury, and the more trained staff are, the less exposed to needle stick injury. One hundred ninety-six (36.4%) of the participants answered yes to the question about the availability of trainers in the institution. 43.8% of the NEMMCSH staff agreed on the availability of trainers on solid health care waste management, which is above the mean, and 26.3%, 31.6%, 31.5%, and 25% for the government health centres, medium clinics, small clinics, and surgical centre respectively, which is below the mean.

Trained health professionals are more compliant with SHCWM standards, and the self-reported study findings of this study show that 41.7% (95%CI:37.7–46) of the research participants are trained in health care waste management practice. This finding was higher compared to the study findings of Sahiledengle in 2019 in the southeast of Ethiopia, shows 13.0% of healthcare workers received training related to HCWM in the past one year preceding the study period and significantly lower when compared to the study findings in Egypt which is 71% of the study participants were trained on SHCWM [ 8 , 19 , 20 ].

Three out of four government health facility leaders, 17 (45.94%) of private health facility leaders/owners of the clinic and 141 FGD participants complain about the absence of some PPEs like boots and aprons to protect themselves from infectious agents.

‘ ‘Masks, disposable gloves, and changing gowns are a critical shortage at all health facilities.’’

Cleaners in private health facilities are more exposed to infectious agents because of the absence of personal protective equipment. Except for the cleaning staff working in the private surgical centre, all cleaning staff 40 (97.56) of the health facilities complain about the absence of changing gowns and the fact that there are no boots in the facilities.

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained by all of (41) the health facility owners and the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves. Sometimes, absence from the market is the reason why we do not supply PPE to our workers.

Thirty-four (82.92%) of the facility leaders are forwarded, and there is a high expense and even unavailability of some of the PPEs, which are the reasons for not providing PPEs for the workers.

‘‘Medical equipment and consumables importers and whole sellers are selective for importing health supplies, and because of a small number of importers in the country and specifically, in the locality, we can’t get materials used for health care waste management practice even disposable gloves. ’’

One of the facility leaders from a private clinic forwarded that before the advent of COVID-19 -19) personal protective equipment was more or less chip-and-get without difficulty. Still, after the advent of the first Japanese COVID-19 patient in Ethiopia, people outside the health facilities collect PPEs like gloves and masks and storing privately in their homes.

‘‘PPEs were getting expensive and unavailable in the market. Incinerator construction materials cost inflation, and the ownership of the facility building are other problems for private health facilities to construct standard incinerators.’’

For all of the focus group discussion participants except in NEMMCSH and two private health facilities, covered and foot-operated dust bins were absent or in a critical shortage compared to the needed ones.

‘‘ Waste bins are open and not colour-coded. The practice attracts flies and other insects. Empty waste bins are replaced without cleaning and disinfecting by using chlorine solution.’’ “HCW containers are not colour-coded, but we are trying to label infectious and non-infectious in Amharic languages.”

Another issue raised during focus group discussions is incineration is not the final disposal method. It needs additional disposal sites, lacks technology, is costly to construct a brick incinerator, lacks knowledge for health facility workers, shortage of man powers /cleaners, absence of environmental health professionals in health centres and all private clinics, and continues exposure to the staff for needle stick injury, foully smell, human scavengers, unsightly, fire hazard, and lack of water supply in the town are the major teams that FGD participants raise and forwarded the above issue as a problem to improve SHCWMP.

Focus group participants, during the discussion, raised issues that could be more comfortable managing SHCWs properly in their institution. Two of the 37 private health facilities are working in their own compound, and the remaining 35 are rented; because of this, they have difficulty constructing incinerators and ash removal pits and are not confident about investing in SHCWM systems. Staff negligence and involuntary abiding by the rules of the facilities were raised by four of the government health facilities, and it was difficult to punish those who violated the healthcare waste management rules because the health facility leaders were not giving appropriate attention to the problem.

Focus group participants forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows:

“PPE should be available in quality and quantity for all health facility workers who have direct contact with SHCW.” “Scientific-based waste management technologies should be availed for health facilities.” “Continuous induction HCW management training should be provided to the workers. Law enforcement should be strengthened.” “Communal HCW management sites should be availed, especially for private health facilities.” “HCWM committee should be strengthened.” “Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places.” “Leaders should be knowledgeable on the SHCWM system and supervise the practice continuously.” “Patient and client should be oriented daily about HCW segregation practice.” “Regulatory bodies should supervise the health facilities before commencing and periodically between services .”

The above are the themes that FGD participants discussed and forwarded for the future improvements of SHAWMP in the study areas.

Lack of water supply in the town

Other issues raised during FGDs were health facilities’ lack of water supply. World Health Organization (2014: 89) highlights that water supply for the appropriate waste management system should be mandatory at any time in all health service delivery points.

Thirty-nine (95.12%) of the health facilities complain about the absence of water supply to improve HCW management practices and infection prevention and control practices in the facilities.

“We get water once per week, and most of the time, the water is available at night, and if we are not fetching as scheduled, we can’t get water the whole week”.

In this research, only those who have direct contact have participated in this study, and 434 (80.4%) of the respondents agree they have roles and responsibilities for appropriate solid health care waste management practice. The rest, 19.6%, do not agree with their commitment to manage health care wastes properly, even though they are responsible. Health facility workers in NEMMCSH and medium clinics know their responsibilities better than others, and their results show above the mean. 84.5%, 74.5%, 81%, 73.9% and 75% in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively.

Establishing a policy and a legal framework, training personnel, and raising public awareness are essential elements of successful healthcare waste management. A policy can be viewed as a blueprint that drives decision-making at a political level and should mobilize government effort and resources to create the conditions to make changes in healthcare facilities. Three hundred and seventy-four (69.3%) of the respondents agree with the presence of any solid healthcare waste management policy in Ethiopia. The more knowledge above the mean (72.9%) on the presence of the policy is reported from NEMMCSH.

Self-reported level of knowledge on what to do in case of an accident revealed that 438 (81.1% CI: 77.6–84.3%) of the respondents knew what to do in case of an accident. Government health centre staff and medium clinic staff’s knowledge about what to do in case of an accident was above the mean (88.4% and 82.3%), respectively, and the rest were below the mean. The action performed after an occupational accident revealed that 56 (35.7%) of the respondents did nothing after any exposure to an accident. Out of 56 respondents who have done nothing after exposure, 47 (83.92%) of the respondents answered yes to their knowledge about what to do in case of an accident. Out of 157 respondents who have been exposed to occupational accidents, only 59 (37.6%) of the respondents performed the appropriate measures, 18 (11.5%), 9 (5.7%), 26 (16.6%), 6 (3.8%) of the respondents are taking prophylaxis, linked to the incident officer, consult the available doctors near to the department, and test the status of the patient (source of infection) respectively and the rest were not performing the scientific measures, that is only practising one of the following practices washing the affected part, squeezing the affected part to remove blood, cleaning the affected part with alcohol.

Health facility workers’ understanding of solid health care waste management practices was assessed by asking whether the current SHCWM practice needs improvement. Four hundred forty-nine (83.1%) health facility workers are unsatisfied with the current solid waste management practice at the different health facility levels, and they recommend changing it to a scientific one. 82.6%, 87.4%, 89.9%, 75%, and 81.3% of the respondents are uncomfortable or need to improve solid health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively.

Lack of safety box, lack of colour-coded waste bins, lack of training, and no problems are the responses to the question problems encountered in managing SHCWMP. Two Hundred and Fifty (46.92%) and 232 (42.96%) of the respondents recommend the availability of safety boxes and training, respectively.

Four or 9.8% of the facilities have infection prevention and control (IPC) teams in the study health facilities. This finding differed from the study in Pakistan, where thirty per cent (30%) of the study hospitals had HCWM or infection control teams [ 21 ]. This study’s findings were similar to those conducted in Pakistan by Khan et al. [ 21 ], which confirmed that the teams were almost absent at the secondary and primary healthcare levels [ 20 ].

The availability of health care waste management policy report reveals that 69.3% (95% CI: 65.4–73) of the staff are aware of the presence of solid health care waste management policy in the institution. Availability of health care waste management policy was 188 (72.9%), 66 (69.5%), 53 (677.1%), 57 (62%), 10 (62.5%) in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centre respectively. Healthcare waste management policy availability was above the mean in NEMMCSH and government health centres; see Table  6 below.

Open-ended responses on the SHCWM practice of health facility workers were collected using the prepared interview guide, and the responses were analyzed using thematic analysis. All the answered questions were tallied on the paper and exported to Excel software for thematic analysis.

The study participants recommend.

“appropriate segregation practice at the point of generation” "health facility must avail all the necessary supplies that used for SHCWMP, punishment for those violating the rule of SHCWMP",

“waste management technologies should be included in solid waste management guidelines, and enforcement should be strengthened.”

The availability of written national or adopted/adapted SHCWM policies was observed at all study health facilities. Twenty eight (11.66%) of the rooms have either a poster or a written document of the national policy document. However, all staff working in the observed rooms have yet to see the inside content of the policy. The presence of the policy alone cannot bring change to SHCWMP. This finding shows that the presence of policy in the institution was reasonable compared to the study findings in Menelik II hospital in Addis Ababa, showing that HCWM regulations and any applicable facility-based policy and strategy were not found [ 22 ]. The findings of this study were less compared to the study findings in Pakistan; 41% of the health facilities had the policy document or internal rules for the HCWM [ 21 ].

Focus group participants have forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows.

‘‘Supplies should be available in quality and quantity for all health facility workers with direct contact with SHCW. Scientific-based waste management technologies should be available for health facilities. Continues and induction health care waste management training should be provided to the workers. Law enforcement should be strengthened. Community healthcare waste management sites should be available, especially for private health facilities. HCWM committee should be strengthened. Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places. Leaders should be knowledgeable about the SHCWM system and supervise the practice continuously. Patients and clients should be oriented daily about health care waste segregation practices. Regulatory bodies should supervise the health facilities before commencing and periodically in between the service are the themes those FGD participants discussed and forward for the future improvements of SHCWMP in the study areas.’’

The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The availability of PPEs in this study was nearly two-fold when compared to the study findings in Myanmar, where 37.6% of the staff have PPEs [ 12 ].

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. This finding shows masks are less available in the study health facilities compared to other studies. The availability of utility gloves, boots, and plastic aprons is good in this study compared to the study conducted by Banstola, D in Pokhara Sub-Metropolitan City [ 23 ].

The findings of this study show there is a poor segregation practice, and all kinds of solid wastes were collected together. This finding was similar to the study findings conducted in Addis Ababa, Ethiopia, by Debere et al. [ 24 ] and contrary to the study findings conducted in Nepal and India, which shows 50% and 65–75% of the surveyed health facilities were practising proper waste segregation systems at the point of generation without mixing general wastes with hazardous wastes respectively [ 9 , 17 ].

Ninety percent of private health facilities collect and transport SHCW generated in every service area and transport it to the disposal place by the collection container (no separate container to collect and transport the waste to the final disposal site). This finding was similar to the study findings of Debre Markos’s town [ 25 ]. At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site manually by carrying the collection container, and there was no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. This study was contrary to the findings of Pullishery et al. [ 26 ], conducted in Mangalore, India, which depicted pre-treatment of the waste in 46% of the hospitals [ 26 ]. 95% of the facilities have no water supply for handwashing during and after solid healthcare waste generation, collection, and disposal. This finding was contrary to the study findings in Pakistan hospitals, which show all health facilities have an adequate water supply near the health care waste management sites [ 27 ].

Questionnaire data collection tools show that 129 (23.8%) of the staff needle stick injuries have occurred on health facility workers within one year of the period before the data collection. This finding was slightly smaller than the study findings of Deress et al. [ 25 ] in Debre Markos town, North East Ethiopia, where 30.9% of the workers had been exposed to needle stick injury one year prior to the study [ 25 ]. Reported and registered needle stick injuries in health facilities are less reported, and only 70 (54.2%) of the injuries are reported to the health facilities. This finding shows an underestimation of the risk and the problem, which was supported by the study conducted in Menilik II hospitals in Addis Ababa [ 22 ]. 50%, 33.4%, 48%, 52%, and 62.5% of needle stick injuries were not reported in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively, to the health facility manager.

Nearly 1/3 (177 or 32.7%) of the staff are exposed to needle stick injuries. Needle stick injuries in health facilities are less reported, and only 73 (41.24%) of the injuries are reported to the health facilities within 12 months of the data collection. This finding is slightly higher than the study finding of Deress et al. [ 25 ] in Debere Markos, Ethiopia, in which 23.3% of the study participants had encountered needle stick/sharps injuries preceding 12 months of the data collection period [ 25 ].

Seventy-three injuries were reported to the health facility manager in the last one year, 44 of the injuries were reported by health professionals, and the rest were reported by supportive staff. These injuries were reported from 35(85.3%) health facilities; the remaining six have no report. These study findings were better than the findings of Khan et al. [ 21 ], in which one-third of the facilities had a reporting system for an incident, and almost the same percentage of the facilities had post-exposure procedures in both public and private sectors [ 21 ].

Within one year of the study period, 129 (23.88%) needle stick injuries occurred. However, needle stick injuries in health facilities are less reported, and only 70 (39.5%) of the injuries are reported to the health facilities. These findings were reasonable compared to the study findings of the southwest region of Cameroon, in which 50.9% (110/216) of all participants had at least one occupational exposure [ 28 , 29 ]. This result report shows a very high exposure to needle stick injury compared to the study findings in Brazil, which shows 6.1% of the research participants were injured [ 27 ].

The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One Hundred Fifty-six (70.9%) of the respondents are vaccinated in order to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19. This finding was nearly the same as the study findings of Deress et al. [ 7 ],in Ethiopia, 30.7% were vaccinated, and very low compared to the study findings of Qadir et al. [ 30 ] in Pakistan and Saha & Bhattacharjya India which is 66.67% and 66.17% respectively [ 25 , 30 , 31 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ]. All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres, respectively. This finding contradicts the study findings in the United States of America and Malaysia, which are 49–60% and 59–60 are incinerated, respectively, and the rest are treated using other technologies [ 15 , 16 ].

All study health facilities used a brick or barrel type of incinerator. The incinerators found in the study health facilities need to meet the minimum standards of solid health care waste incineration practice. These findings were similar to the study findings of Nepal and Pakistan [ 32 ]. The health care waste treatment system in health facilities was found to be very unsystematic and unscientific, which cannot guarantee that there is no risk to the environment and public health, as well as safety for personnel involved in health care waste treatment. Most incinerators are not properly operated and maintained, resulting in poor performance.

All government health facilities use incineration to dispose of solid waste. All the generated sharp wastes are incinerated using brick or barrel incinerators, as shown in Fig.  1 above. This finding was consistent with the findings of Veilla and Samwel [ 33 ], who depicted that sharp waste generation is the same as sharps waste incinerated [ 33 ]. All brick incinerators were constructed without appropriate air inlets to facilitate combustion except in NEMMCSH, which is built at a 4-m height. These findings were similar to the findings of Tadese and Kumie at Addis Ababa [ 34 ].

Barrel and brick incinerators used in private clinic

Strengths and limitations

This is a mixed-method study; both qualitative and quantitative study design, data collection and analysis techniques were used to understand the problem better. The setting for this study was one town, which is found in the southern part of the country. It only represents some of the country’s health facilities, and it is difficult to generalize the findings to other hospitals and health centres. Another limitation of this study was that private drug stores and private pharmacies were not incorporated.

Conclusions

In the study, health facilities’ foot-operated solid waste dust bins are not available for healthcare workers and patients to dispose of the generated wastes. Health facility managers in government and private health institutions should pay more attention to the availability of colour-coded dust bins. Most containers are opened, and insects and rodents can access them anytime. Some of them are even closed (not foot-operated), leading to contamination of hands when trying to open them.

Healthcare waste management training is mandatory for appropriate healthcare waste disposal. Healthcare-associated exposure should be appropriately managed, and infection prevention and control training should be provided to all staff working in the health facilities.

Availability of data and materials

The authors declare that data for this work are available upon request to the first author.

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Acknowledgements

The authors are grateful to the health facility leaders and ethical committees of the hospitals for their permission. The authors acknowledge the cooperation of the health facility workers who participated in this study.

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Dr. Yeshanew Ayele Tiruneh is a researcher of this study; the principal investigator does all the proposal preparation, methodology, data collection, result and discussion, and manuscript writing. Professor LM Modiba and Dr. SM Zuma are supervisors for this study. They participated in the topic selection and modification to the final manuscript preparation by commenting on and correcting the study. Finally, the three authors read and approved the final version of the manuscript and agreed to submit the manuscript for publication.

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Tiruneh, Y.A., Modiba, L.M. & Zuma, S.M. Solid health care waste management practice in Ethiopia, a convergent mixed method study. BMC Health Serv Res 24 , 985 (2024). https://doi.org/10.1186/s12913-024-11444-8

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DOI : https://doi.org/10.1186/s12913-024-11444-8

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Exploring sentiment analysis and visitor satisfaction along urban liner trails: a case of the seoul trail, south korea.

1. Introduction

2. literature review, 2.1. sustainable trail management, 2.2. trail characteristics and user behavior, 2.3. previous research on sentiment analysis, 3.1. data collection and analysis process, 3.2. sentiment analysis, 3.3. instrument development and frequency analysis, 4.1. results of sentiment analysis, 4.2. sentiment characteristic proportion, 4.3. high-satisfaction trail courses, 4.4. low-satisfaction trail courses, 5. discussions, 5.1. theoretical implications, 5.2. practical implications, 5.3. limitations and future research, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Lee, S.; Chung, W.J.; Jeong, C. Exploring Sentiment Analysis and Visitor Satisfaction along Urban Liner Trails: A Case of the Seoul Trail, South Korea. Land 2024 , 13 , 1349. https://doi.org/10.3390/land13091349

Lee S, Chung WJ, Jeong C. Exploring Sentiment Analysis and Visitor Satisfaction along Urban Liner Trails: A Case of the Seoul Trail, South Korea. Land . 2024; 13(9):1349. https://doi.org/10.3390/land13091349

Lee, Sumin, Won Ji Chung, and Chul Jeong. 2024. "Exploring Sentiment Analysis and Visitor Satisfaction along Urban Liner Trails: A Case of the Seoul Trail, South Korea" Land 13, no. 9: 1349. https://doi.org/10.3390/land13091349

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