Spatial distribution of mortality: evidence from DIMAMO HDSS, Limpopo Province, South Africa.

BACKGROUND: There has been a general decrease in mortality globally due to improved healthcare and modernization. However, rural areas of African countries remain unexplored on this subject. It is against this background that the study aimed to determine the spatial distribution of mortality in Limpopo province, South Africa, from 2014 to 2023. METHODS: This descriptive paper sampled from a cohort of 115,000 participants surveillance data. Conducted in DIMAMO HDSS located approximately 35 km northeast of Polokwane in Limpopo Province. The University of Limpopo Research Ethics Committee has approved this study. Mortality data were collected through Computer-Assisted Personal Interviews (CAPI). To examine spatial patterns of mortality, the study employed descriptive statistics and spatial analysis techniques to identify geographical variations in mortality rates. RESULTS: The study included about 92,430 (males, 40,609 and females, 51,821). The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage (74.8%). Under-5 mortality was elevated in all clusters (Cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92 per 1000 person years). Adult mortality is stated to have peaked in the age category 50-54, with those aged above 80 having the highest level of mortality. The year 2020 had the highest mortality rates compared to other years. CONCLUSION: The present study found an overall decrease in mortality rates in the area. However, there were variations among the village clusters, with some experiencing high mortality rates. Regarding age and mortality, the present study found that under-5 mortality was elevated in all clusters. Factors such as unemployment, marital status, and education level were associated with mortality. Mortality was highest in 2020 during the COVID-19 pandemic. We recommend for the integration of spatial analysis with demographic surveillance data, and incorporation of the same into primary healthcare to provide insight for epidemiologists, the Department of Health, and policymakers to create interventions that are community-specific to reduce preventable deaths in DIMAMO HDSS.