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

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 factors that might have influenced excess mortality. Methods: The 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. These included studies with at least a one-year non-COVID-19 period as the comparator in estimating excess mortality and with publication dates 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 of excess mortality with 95% confidence intervals. Results: The review included studies from 29 countries, of which 10 were included in the meta-analysis. Of 1,405,128,717 individuals, 2,152,474 deaths were expected, and 3,555,880 deaths were reported. The pooled excess mortality was 100.3 deaths per 100,000 population per pandemic period. The excess risk of death was 1.65 (95% CI: 1.649, 1.655 p<0.001). Data sources included civil registration systems, obituary notifications, surveys, public cemeteries, funeral counts, burial site imaging, and demographic surveillance systems. Techniques used to estimate excess mortality were mainly statistical forecast modelling and geospatial analysis. Of the 24 studies, only one found higher excess mortality in urban settings. Conclusion: Our results show that excess mortality in LLMICs during the pandemic was substantial. 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.