Spatial disparities in impoverishing effects of out-of-pocket health payments in Malawi.

BACKGROUND: Out-of-pocket health payments as a means of financing health services are a cause of concern among households in low and middle-income countries. They prevent households from accessing health care services, can disrupt households' living standards by reducing consumption of other basic needs and push households into poverty. Previous studies have reported geographical variations in impoverishing effects of out-of-pocket health payments. Yet, we know relatively little about spatial effects on impoverishing effects of health payments. OBJECTIVE: This paper assesses the factors associated with impoverishing effects of health payments and quantifies the role of districts spatial effects on impoverishment in Malawi. METHODS: The paper uses a cross sectional integrated household survey data collected from April 2016 to April 2017 among 12447 households in Malawi. Impoverishing effect of out-of-pocket health payments was calculated as the difference between poverty head count ratio before and after subtracting health payments from total household consumption expenditures. We assessed the factors associated with impoverishment and quantified the role of spatial effects using a spatial multilevel model. RESULTS: About 1.6% and 1.2% of the Malawian population were pushed below the national and international poverty line of US$1.90 respectively due health payments. We found significant spatial variations in impoverishment across districts with higher spatial residual effects clustering in central region districts. Higher socio-economic status (AOR=0.34, 95% CI=0.22-0.52) decreased the risk of impoverishment whereas hospitalizations (AOR=3.63, 95% CI 2.54-5.15), chronic illness (AOR=1.56, 95% CI=1.10-1.22), residency in rural area (AOR=2.03, 95% CI=1.07-4.26) increased the risk of impoverishment. CONCLUSIONS: Our study suggests the need to plan financial protection programs according to district specific needs and target the poor, residents of rural areas and those with chronic illnesses. Policy makers need to pay attention to the importance of spatial and neighborhood effects when designing financial protection programs and policies.

Decomposing socio-economic inequality in catastrophic out-of-pocket health expenditures in Malawi.

Reducing health inequalities and inequities is one of the key goals that health systems aspire to achieve as it ensures improvement in health outcomes among all population groups. Addressing the factors contributing to inequality in catastrophic health expenditures is important to reducing inequality in the burden of health expenditures. However, there are limited studies to explain the factors contributing to inequalities in catastrophic health expenditures. The study aimed to measure and decompose socio-economic inequality in catastrophic health into its determinants. Data for the analysis come from the fourth integrated household survey. Data for 12447 households in Malawi were collected from April 2016 to April 2017 by the National Statistical Office. The secondary analysis was conducted from June 2021 to October 2021. Catastrophic health expenditure was estimated as a proportion of households whose out-of-pocket health expenditures as a ratio of non-food consumption expenditures exceeds 40% threshold level. We estimated the magnitude of socio-economic inequality using the Erreygers corrected concentration index and used decomposition analysis to assess the contribution of inequality in each determinant of catastrophic health expenditure to the overall socio-economic inequality. The magnitude of the Erreygers corrected concentration index of catastrophic health expenditure (CI = 0.004) is small and positive which indicates that inequality is concentrated among the better-off. Inequality in catastrophic health expenditure is largely due to inequalities in rural residency (127%), socio-economic status (-40%), household size (14%), presence of a child under five years old (10%) and region of the household (10%). The findings indicate that socio-economic inequality in catastrophic health expenditures is concentrated among the better-off in Malawi. The results imply that policies that aim to reduce inequalities in catastrophic health expenditures should simultaneously address urban-rural and income inequalities.

Correction: Examining the incidence of catastrophic health expenditures and its determinants using multilevel logistic regression in Malawi.

[This corrects the article DOI: 10.1371/journal.pone.0248752.].

Examining the incidence of catastrophic health expenditures and its determinants using multilevel logistic regression in Malawi.

BACKGROUND: Despite a free access to public health services policy in most sub-Saharan African countries, households still contribute to total health expenditures through out-of-pocket expenditures. This reliance on out-of-pocket expenditures places households at a risk of catastrophic health expenditures and impoverishment. This study examined the incidence of catastrophic health expenditures, impoverishing effects of out-of-pocket expenditures on households and factors associated with catastrophic expenditures in Malawi. METHODS: We conducted a secondary analysis of the most recent nationally representative integrated household survey conducted by the National Statistical Office between April 2016 to 2017 in Malawi with a sample size of 12447 households. Catastrophic health expenditures were estimated based on household annual nonfood expenditures and total household annual expenditures. We estimated incidence of catastrophic health expenditures as the proportion of households whose out-of-pocket expenditures exceed 40% threshold level of non-food expenditures and 10% of total annual expenditures. Impoverishing effect of out-of-pocket health expenditures on households was estimated as the difference between poverty head count before and after accounting for household health payments. We used a multilevel binary logistic regression model to assess factors associated with catastrophic health expenditures. RESULTS: A total of 167 households (1.37%) incurred catastrophic health expenditures. These households on average spend over 52% of household nonfood expenditures on health care. 1.6% of Malawians are impoverished due to out-of-pocket health expenditures. Visiting a religious health facility (AOR = 2.27,95% CI:1.24-4.15), hospitalization (AOR = 6.03,95% CI:4.08-8.90), larger household size (AOR = 1.20,95% CI:1.24-1.34), higher socioeconomic status (AOR = 2.94,95% CI:1.39-6.19), living in central region (AOR = 3.54,95% CI:1.79-6.97) and rural areas (AOR = 5.13,95% CI:2.14-12.29) increased the odds of incurring catastrophic expenditures. CONCLUSION: The risk of catastrophic health expenditures and impoverishment persists in Malawi. This calls for government to improve the challenges faced by the free public health services and design better prepayment mechanisms to protect more vulnerable groups of the population from the burden of out-of-pocket payments.

Hierarchical spatial modelling of pneumonia prevalence when response outcome has misclassification error: Applications to household data from Malawi.

Pneumonia remains a major cause of child mortality in less developed countries. However, the accuracy of its prevalence and burden remains a challenge because disease data is often based on self-reports, resulting in measurement error in a form of under- and over-reporting. We propose hierarchical disease mapping approaches that permit measurement error, through different prior distributions of sensitivity and specificity. Proposed models were used to evaluate spatial variation of risk of pneumonia in children in Malawi. Results show that the true prevalence was 0.50 (95 CI: 0.4-0.66), however, estimates were dependent on sensitivity and specificity parameters. The estimated sensitivity was 0.76 (95% CI: 0.68-0.95), whereas specificity was 0.84 (95% CI: 0.72-0.93). A lower specificity underestimated the true prevalence, while sensitivity and specificity of greater or equal to 0.75 provided reliable and stable prevalence estimates. The spatial variation in disease risk changed little; however, misclassification of areas as high risk was visible.