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Decomposing health inequality with population-based surveys: a case study in Rwanda.

ABSTRACT: Ensuring equal access to care and providing financial risk protection are at the center of the global health agenda. While Rwanda has made impressive progress in improving health outcomes, inequalities in medical care utilization and household catastrophic health spending (HCHS) between the impoverished and non-impoverished populations persist. Decomposing inequalities will help us understand the factors contributing to inequalities and design effective policy instruments in reducing inequalities. This study aims to decompose the inequalities in medical care utilization among those reporting illnesses and HCHS between the poverty and non-poverty groups in Rwanda.Using the 2005 and 2010 nationally representative Integrated Living Conditions Surveys, our analysis focuses on measuring contributions to inequalities from poverty status and other sources. We conducted multivariate logistic regression analysis to obtain poverty's contribution to inequalities by controlling for all observed covariates. We used multivariate nonlinear decomposition method with logistic regression models to partition the relative and absolute contributions from other sources to inequalities due to compositional or response effects.Poverty status accounted for the majority of inequalities in medical care utilization (absolute contribution 0.093 in 2005 and 0.093 in 2010) and HCHS (absolute contribution 0.070 in 2005 and 0.032 in 2010). Health insurance status (absolute contribution 0.0076 in 2005 and 0.0246 in 2010) and travel time to health centers (absolute contribution 0.0025 in 2005 and 0.0014 in 2010) were significant contributors to inequality in medical care utilization. Health insurance status (absolute contribution 0.0021 in 2005 and 0.0011 in 2010), having under-five children (absolute contribution 0.0012 in 2005 and 0.0011 in 2010), and having disabled family members (absolute contribution 0.0002 in 2005 and 0.0001 in 2010) were significant contributors to inequality in HCHS. Between 2005 and 2010, the main sources of the inequalities remained unchanged.Expanding insurance coverage and reducing travel time to health facilities for those living in poverty could be used as policy instruments to mitigate inequalities in medical care utilization and HCHS between the poverty and non-poverty groups.

SUBMITTER: Liu K

PROVIDER: S-EPMC5946429 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Decomposing health inequality with population-based surveys: a case study in Rwanda.

Liu Kai K Lu Chunling C

International journal for equity in health 20180510 1

<h4>Background</h4>Ensuring equal access to care and providing financial risk protection are at the center of the global health agenda. While Rwanda has made impressive progress in improving health outcomes, inequalities in medical care utilization and household catastrophic health spending (HCHS) between the impoverished and non-impoverished populations persist. Decomposing inequalities will help us understand the factors contributing to inequalities and design effective policy instruments in r ...[more]

PMID: 29747643

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Project description:BackgroundAbout 75.5% of women in Nepal's urban areas receive at least four ANC visits, compared to 61.7% of women in the country's rural areas. Similarly, just 34% of women in the lowest wealth quintile give birth in a medical facility compared to 90% of women in the richest group. As a result of this inequality, the poor in emerging nations suffer since those who are better off can make greater use of the healthcare than those who are less fortunate. This study aims to examine and decompose the contributions of various socioeconomic factors towards MCH service inequality in Nepal in the years 2011 and 2016.MethodsInequality in MCH services was estimated using concentration curves and their corresponding indices using data from Nepal Demographic Health Survey (NDHS) 2011 and 2016. We examined the inequality across three MCH service outcomes: less than 4 ANC visits, no postnatal checkups within 2 months of delivery and no SBA delivery and decomposed them across observed characteristics of the mothers aged between 15 and 49. Furthermore, Oaxaca-blinder decomposition approach was used to measure and decompose the inequality differential between two time periods.ResultsInequality in MCH services was prevalent for all 3 MCH outcomes in 2011 and 2016, respectively. However, the concentration indices for <4 ANC visits, no SBA delivery, and no postnatal checkups within 2 months of birth increased from -0.2184, -0.1643, and -0.1284 to -0.1871, -0.0504, and -0.0218 correspondingly, showing the decrease in MCH services inequality over two time periods. Wealth index, women's literacy, place of living, mother's employment status, and problem of distance to reach nearest health facility were the main contributors.ConclusionWe find that MCH services are clearly biased towards the women with higher living standards. National policies should focus on empowering women through education and employment, along with the creation of health facilities and improved educational institutions, in order to address inequalities in living standards, women's education levels, and the problem of distance. Leveraging these factors can reduce inequality in MCH services.

Project description:A lack of evidence exists on the association between area-level income inequality and oral health within Australia. This study examined associations between area-level income inequality and oral health outcomes (inadequate dentition (<21 teeth) and poor self-rated oral health) among Australian adults. Variations in the association between area-level income inequality and oral health outcomes according to area-level mean income were also assessed. Finally, household-income gradients in oral health outcomes according to area-level income inequality were compared.For the analyses, data on Australian dentate adults (n = 5,165 nested in 435 Local Government Areas (LGAs)) was obtained from the National Dental Telephone Interview Survey-2013. Multilevel multivariable logistic regression models with random intercept and fixed slopes were fitted to test associations between area-level income inequality and oral health outcomes, examine variations in associations according to area-level mean income, and examine variations in household-income gradients in outcomes according to area-level income inequality. Covariates included age, sex, LGA-level mean weekly household income, geographic remoteness and household income.LGA-level income inequality was not associated with poor self-rated oral health and inversely associated with inadequate dentition (OR: 0.64; 95% CI: 0.48, 0.87) after adjusting for covariates. Inverse association between income inequality and inadequate dentition at the individual level was limited to LGAs within the highest tertile of mean weekly household income. Household income gradients in both outcomes showed poorer oral health at lower levels of household income. The household income gradients for inadequate dentition varied according to the LGA-level income inequality.Findings suggest that income inequality at the LGA-level in Australia is not positively associated with poorer oral health outcomes. Inverse association between income inequality and inadequate dentition is likely due to the contextual differences between Australia and other high-income countries.

Project description:IntroductionHIV testing is the entry point to HIV prevention, care and treatment and needs continuous evaluation to understand whether all social groups have accessed services equally. Addressing disparities in HIV testing between social groups results in effective and efficient response against HIV prevention. Despite these benefits, there was no previous study on inequality and determinants over time in Ethiopia. Thus, the objective of this research was to examine socioeconomic inequality in individuals undertaking HIV testing over time, allowing for the identification of persistent and emerging determinants.MethodsData sources for the current study were the 2011 and 2016 Ethiopian Demographic Health Surveys. The 2016 population health survey is the one that Ethiopia used to set national AIDS response strategies; there was no other recent survey with HIV/AIDS-related indicators in Ethiopia. The final sample size for the current study was 28,478 for the year 2011 and 25,542 for the year 2016. The concentration curve and Erreygers' concentration index were used to estimate socioeconomic inequality in HIV testing. Subsequently, decomposition analysis was performed to identify persistent and emerging contributors of socioeconomic inequality. Generalized linear regression model with the logit link function was employed to estimate the marginal effect, elasticity, Erreygers' concentration index (ECI), and absolute and percentage contributions of each covariate.ResultsThe concentration curve was below the line of equality over time, revealing the pro-rich inequality in HIV testing. The inequality was observed in both 2011 (ECI = 0.200) and 2016 (ECI = 0.213). A household wealth rank had the highest percentage contribution (49.2%) for inequality in HIV testing in 2011, which increased to 61.1% in 2016. Additional markers include listening to the radio (13.4% in 2011 and 12.1% in 2016), education status (8.1% in 2011 and 6.8% in 2016), and resident (-2.0% in 2011 and 6.3% in 2016). Persistent determinants of individuals undertaking HIV testing were age 20-34 years, geographic region, education status, marital status, religion, income, media exposure (listening to the radio, reading newspaper, watching television), knowledge about HIV/AIDS, and attitudes towards people living with HIV. Age between 35 and 44 years and urban residence emerged as new associated factors in 2016.ConclusionsThe higher HIV testing coverage was among individuals with higher socioeconomic status in Ethiopia. Socioeconomic inequality amongst individuals undertaking HIV testing was diverging over time. Household wealth rank, mass media exposure, education status, and resident took the largest share in explaining the disparity in individuals undertaking HIV testing between the lower and higher income groups. Therefore, interventions to equalise HIV testing coverage should take account of these determinants.

Project description:We examined links between markers of social inequality and fracture risk in the Danish population, demonstrating that high income and being married are associated with a significantly lower risk. INTRODUCTION:We explored whether the risk of hip, humerus, and wrist fracture was associated with markers of inequality using data from Danish health registries. METHODS:All patients 50 years or older with a primary hip (ICD10 S720, S721, S722, and S729) humerus (ICD10 S422, S423, S424, S425, S426, and S427), or wrist (ICD10: S52) fracture were identified from 1/1/1995 to 31/12/2011. Fracture patients were matched 1:1 by age, sex, and year of fracture, to a non-fracture control. Markers of inequality were as follows: income (fifths); marital status (married, divorced, widowed, or unmarried); area of residence (remote, rural, intermediate, or urban). Conditional logistic regression was used to investigate associations between these exposures, and risk of fracture, adjusting for covariates (smoking, alcohol, and Charlson co-morbidity). Interactions were fitted between exposure and covariates where appropriate. RESULTS:A total of 189,838 fracture patients (37,500 hip, 45,602 humerus, and 106,736 wrist) and 189,838 controls were included. Mean age was 73.9 years (hip), 67.5 years (humerus), and 65.3 years (wrist). High income (5th quintile) was significantly associated with a lower odds ratio of all three fractures, compared to average income (3rd quintile). Married subjects had a significantly decreased odds ratio across all three fractures. However, no overall secular difference was observed regarding the influence of the markers of inequality. CONCLUSION:In conclusion, we have demonstrated important, stable associations between social inequality, assessed using income, marital status, and area of residence, and fracture at the population level. These findings can inform approaches to healthcare, and suggest that much thought should be given to novel interventions aimed especially at those living alone, and ideally societal measures to reduce social inequality.

Dataset Information

Decomposing health inequality with population-based surveys: a case study in Rwanda.

Publications

Decomposing health inequality with population-based surveys: a case study in Rwanda.

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