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

Abstract

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.

Examining the state of energy poverty in Rwanda: An inter-indicator analysis

“…Following Mulaga et al. (2021) , Crowson (2020) , and Sommet and Morselli (2017) , suppose is the energy poverty status class for the household in district , is the probability of falling in a particular energy poverty class and represents household-level characteristics.…”

“…Following Mulaga et al (2021), Crowson (2020), and Sommet and Morselli (2017), suppose HEP ij is the energy poverty status class for the i th household in district j th , P ij is the probability of falling in a particular energy poverty class and X ij represents household-level characteristics. Now, also that HEP ij follows a binomial distribution (HEP ij ∽ Bin (1, P ij ) based on some of the statistical tests (using qnorm command and symplot Stata statistical software commands).…”

Analysis of the Effect of Health Insurance on Health Care Utilization in Rwanda

“…From the analytical framework of the previous studies on the effect of health insurance on health care utilization in developing countries with the same similarities as Rwanda and the existing literature (Agatuba dependents' health services. We also include household expenditure quintiles [35], sex of the head of household, households size, region, household insurance status, district, occupation status of the head of household, marital status of the head of household, head of household insurance status, out of pocket health expenditures of the household, household per capta expenditures. Finally, in this study we performed first the decision tree model for the purpose of classifying the districts based on the use of health services at the household level.…”

“…Previous research in Rwanda has talked about various aspects of the effect of health insurance on health care utilization in Rwanda focusing much on mutual health insurance or Community Based Health Insurance(CBHI), including helping to understand the determinants of enrolment for mutual health insurance [28]. However, some studies also reveal that out-of-pocket health payment is still powerful and penetratingly in Rwanda despite the presence of Mutual health insurance (Lu C et al2012 [22,29,30,34,35,36]. To our knowledge no study on the effect of health insurance on health care utilization in Rwanda has used decision tree models to classify districts of Rwanda on the use of health services by households based on the same characteristics of the households.…”

“…et.al, (2012); G.Ewmmanuel Gwindon, (2014); Thuong NTT., (2020), Nakovics et.al, (2020); Mulaga AN.et.al, (2021))[25,26,27,29,35] we selected covariates based on socioeconomics status, demographics, education level, region. Ages were divided into five groups, education level of the head of households supposing that better educated head of households are more likely to make their own decisions on theirPreprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 10 June 2021 doi:10.20944/preprints202106.0277.v1…”