Dataset Information

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: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: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.

Project description:IntroductionReliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents.MethodsWe conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF<0.90 or VF>1.10 indicated over- and under-reporting in HMIS, respectively.ResultsHigh proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits.ConclusionThere was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

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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