BackgroundOut of pocket (OOP) health spending can potentially expose households to risk of incurring large medical bills, and this may impact on their welfare. This work investigates the effect of catastrophic OOP on the incidence and depth of poverty in Malawi.MethodsThe paper is based on data that was collected from 12,271 households that were interviewed during the third Malawi integrated household survey (IHS-3). The paper considered a household to have incurred a catastrophic health expenditure if the share of health expenditure in the household’s non-food expenditure was greater than a given threshold ranging between 10 and 40%.ResultsAs we increase the threshold from 10 to 40%, we found that OOP drives between 9.37 and 0.73% of households into catastrophic health expenditure. The extent by which households exceed a given threshold (mean overshoot) drops from 1.01% of expenditure to 0.08%, as the threshold increased. When OOP is accounted for in poverty estimation, additional 0.93% of the population is considered poor and the poverty gap rises by almost 2.54%. Our analysis suggests that people in rural areas and middle income households are at higher risk of facing catastrophic health expenditure.ConclusionWe conclude that catastrophic health expenditure increases the incidence and depth of poverty in Malawi. This calls for the introduction of social insurance system to minimize the incidence of catastrophic health expenditure especially to the rural and middle income population.

Background A variety of antenatal care models have been implemented in low and middle-income countries over the past decades, as proposed by the World Health Organisation (WHO). One such model is the 2001 Focused Antenatal Care (FANC) programme. FANC recommended a minimum of four visits for women with uncomplicated pregnancies and emphasised quality of care to improve both maternal and neonatal outcomes. Malawi adopted FANC in 2003, however, up to now no study has been done to analyse the model’s performance with regards to antenatal care service quality and utilisation patterns. Methods The paper is based on data pooled from three comparable nationally representative Malawi Demographic and Health Survey (MDHS) datasets (2000, 2004 and 2010). The DHS collects data on demographics, socio-economic indicators, antenatal care, and the fertility history of reproductive women aged between 15 and 49. We pooled a sample of 8545 women who had a live birth in the last 5 years prior to each survey. We measure the impact of FANC on early access to care, underutilisation of care and quality of care with interrupted time series analysis. This method enables us to track changes in both levels and the trends of our outcome variables. Results We find that FANC is associated with earlier access to care. However, it has also been associated with unintended increases in underutilisation. We see no change in the quality of ANC services. Conclusion In light of the WHO 2016 ANC guidelines, which recommend an increase of visits to eight, these results are important. Given that we find underutilisation when the benchmark is set at four visits, eight visits are unlikely to be feasible in low-resource settings.

The inclusion of Universal Health Coverage (UHC) in the Sustainable Development Goals (target 3.8) cemented its position as a key global health priority and highlighted the need to measure it, and to track progress over time. In this study, we aimed to develop a summary measure of UHC for Malawi which will act as a baseline for tracking UHC index between 2020 and 2030. We developed a summary index for UHC by computing the geometric mean of indicators for the two dimensions of UHC; service coverage (SC) and financial risk protection (FRP). The indicators included for both the SC and FRP were based on the Government of Malawi's essential health package (EHP) and data availability. The SC indicator was computed as the geometric mean of preventive and treatment indicators, whereas the FRP indicator was computed as a geometric mean of the incidence of catastrophic healthcare expenditure, and the impoverishing effect of healthcare payments indicators. Data were obtained from various sources including the 2015/2016 Malawi Demographic and Health Survey (MDHS); the 2016/2017 fourth integrated household survey (IHS4); 2018/2019 Malawi Harmonized Health Facility Assessment (HHFA); the MoH HIV and TB data, and the WHO. We also conducted various combinations of input indicators and weights as part of sensitivity analysis to validate the results. The overall summary measure of UHC index was 69.68% after adjusting for inequality and unadjusted measure was 75.03%. As regards the two UHC components, the inequality adjusted summary indicator for SC was estimated to be 51.59% and unadjusted measure was 57.77%, whereas the inequality adjusted summary indicator for FRP was 94.10% and unweighted 97.45%. Overall, with the UHC index of 69.68%, Malawi is doing relatively well in comparison to other low income countries, however, significant gaps and inequalities still exist in Malawi's quest to achieve UHC especially in the SC indicators. It is imperative that targeted health financing and other health sector reforms are made to achieve this goal. Such reforms should be focused on both SC and FRP rather than on only either, of the dimensions of UHC.

Out-of-pocket (OOP) expenditures on health remain high in many low- and middle-income countries despite policy efforts aiming to reduce these health costs by targeting their hotspots. Hotspot targeting remains inadequate, particularly where the OOP expenditures are related across geographic regions due to unequal demand, supply and prices of healthcare services. In this paper, we investigate the existence of geographical correlations in OOP health expenditures by employing a spatial Durbin model on data from 778 clusters obtained from the 2016 Malawi’s Integrated Household Survey. Results reveal that Malawian communities face geographical spillovers of OOP health expenditures. Furthermore, we find that factors including household size, education and geographical location are important drivers of the OOP health expenditure’s spatial dependency. The paper calls for policy in low-income countries to improve the quality and quantity of healthcare services in both OOP hotspots and their neighbouring communities.

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