Evaluating Health Inequality in Five Caribbean Basin Countries Using EQ-5D-5L

Abstract

Background EQ-5D-5L is a standardized health outcomes instrument that can be added to national surveys to measure inequality in health outcomes. The aim of this study was to produce baseline values of health inequality using EQ-5D-5L for five countries in the Caribbean Basin region based on national surveys in 2012-2014. Methods The EQ-5D-5L questionnaire was included in adult population surveys of Barbados, Belize, Colombia, Jamaica and Trinidad and Tobago. EQ-5D-5L measures were calculated for demographic groups using stratifiers from the World Health Organization's PROGRESS-Plus framework, and generalized linear models were used to test for association between EQ-5D-5L and the PROGRESS-Plus variables. Ordered logit models were used to obtain odds ratios for the effect of the PROGRESS-Plus variables on reporting problems on the EQ-5D-5L dimensions. The Kakwani index was calculated for each country. Results Data were obtained for representative samples in each country, giving a combined total of 11,284 respondents. Different patterns of inequality were observed among the five countries. The biggest drivers of inequality were age and gender, and the biggest EQ-5D factors were self-care in Belize and pain/discomfort in the other four countries. Conclusion This study demonstrated that the EQ-5D-5L instrument can easily be added to national surveys. Inequality measures from this study can be used as baseline values for comparisons with future similar surveys in these five countries to infer changes in health inequality as measured by EQ-5D outcomes. These can be used to track the performance of policy initiatives aimed at specific demographic groups. Key Points for Decision MakersEQ-5D-5L is a standardized health outcomes instrument that can be added to national surveys to measure inequality in health outcomes.This study provides baseline values of inequality measures using EQ-5D-5L for five countries against which comparisons can be made with data from future studies.These can be used to track the performance of policy initiatives aimed at specific demographic groups.

Socio-demographic indicators of self-reported health based on EQ-5D-3L: A cross-country analysis of population surveys from 18 countries

“…Our results are in line with previous findings using the one-dimension self-rated health question (4) showing variation in inequalities in health associated with socioeconomic status across European countries. The successful application of the EQ-5D (both 3L or 5L) in socio-demographic analyses in selected countries and regions were also confirmed by several other recent studies (36)(37)(38)(39), and in vulnerable groups like homeless persons (40). Specifically, Sronk et al showed that among three European countries the EQ-5D-5L was able to detect education level related inequalities in health, however, differences varied across countries and some of the impact was canceled out when controlling for chronic conditions and ability to work.…”

“…This was the case even after taking into account health related behaviors and conditions (37). A study from five Caribbean countries used odds ratios and concentration indices to detect education and income related inequalities with the EQ-5D-5L and also found significant inequalities that varied across countries (38). Studies using the EQ-5D-3L provided similar evidence of detecting income related health inequalities in low income communities (39), and high reported problems among homeless people (40).…”

“…Recent studies using single datasets have indeed explored a broader list of socio-demogrpahic and disease related variables in inequality analyses. Important social stratifyer variables included employment and occupational status, region and type of location (rural and urban), ethnicity, family and living status, loss in health insurance and access to care (38,41,42). Teni et al also pointed out that the inclusion of variables on health related behaviors, diseases diagnosed by a physician, selfreported conditions, and BMI levels can also be important in inequality analyses as they can help to show the extent to which diferences between socioeconomic groups are explained by diferences in health-related behaviors (37).…”

Both religious and secular ethics to achieve both happiness and health: Panel data results based on a dynamic theoretical model

“…There are many cross-country papers that focus on the relationship between income inequality and average health status. For example, the negative effect of income inequality on average health status is shown to be larger in countries characterised by market-based more than community-based health-care strategies [ 67 ], a larger range of age and gender inequality [ 68 , 69 ], a larger range of relative poverty [ 70 ], a larger range of absolute poverty [ 71 ] or a less efficient health-care system [ 72 ].…”

Metodología para la estimación del umbral de costo-efectividad: el caso de República Dominicana

“…Pero como dichos puntajes fueron calculados para el Reino Unido y tienen más de una década de antigüedad, decidimos ajustarlos de manera que reflejen mejor la realidad actual de República Dominicana. Para ello utilizamosBailey et al (2022), que calcula los pesos de calidad de vida para cinco países del Caribe: Barbados, Belice, Colombia, Jamaica y Trinidad y Tobago. Lamentablemente, las ponderaciones resultantes se encuentran demasiado agregadas para poder hacer buenos ajustes.…”