Excess mortality in low-and lower-middle-income countries: A systematic review and meta-analysis

Abstract Background The COVID-19 pandemic caused a massive death toll, but its effect on mortality remains uncertain in low- and lower-middle-income countries (LLMICs). This review summarized the available literature on excess mortality in LLMICs, including methods, data sources, and drivers of excess mortality. Methods A protocol was registered in PROSPERO (ID: CRD42022378267). We searched PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Scopus for studies conducted in LLMICs on excess mortality which included at least a one-year non-COVID-19 period as the comparator, and with publication date from 2019 to date. The meta-analysis included studies with extractable data on excess mortality, methods, population size, and observed and expected deaths. We used the Mantel-Haenszel method to estimate the pooled risk ratio with 95% confidence intervals. Results The review included 21 studies (5 from Africa), of which 11 were included in the meta-analysis (2 from Africa). Of 1,405,128,717 individuals, 2,161,846 deaths were expected, and 3,633,661 deaths were reported. The pooled excess mortality was 104.7 deaths per 100,000 population. The risk of excess mortality was 1.68 (95% CI: 1.67, 1.68 p < 0.001). Data sources included public cemeteries, civil registration systems, obituary notifications, surveys, funeral counts, burial site imaging, and demographic surveillance systems. Techniques used to estimate excess mortality were mainly statistical modelling and geospatial analysis. Of the 21 studies, only one reported on drivers of excess mortality and found higher excess mortality in urban settings. Conclusions Our results show that excess mortality in LLMICs during the pandemic was substantial even if it was considerably lower than in high-income countries. There is uncertainty around excess mortality estimates given comparatively weak data. Further studies are needed to identify the drivers of excess mortality by exploring different methods and data sources.