Weather Variability on All-Cause And Malaria-Specific Mortality in Three Geographical Zones in Ghana

ABSTRACT

Background: Climate change with its associated weather variability affects health leading to mortality. It is estimated that climate change contributed 0.2% of the world’s annual mortality. Mortality in Ghana is mainly due to malaria, diarrhoea, acute respiratory infection, cardiovascular diseases, maternal, neonatal, and road traffic accident, most of which are weather-related. Also, a relatively high clustering of deaths in the population can hinder efforts to reduce mortality if these clusters are not identified for targeted intervention. This study, therefore, was to examine the relationship between temperature, rainfall and mortality (all-cause and malaria-specific) and also the clustering of deaths across three geographical zones in Ghana. Methods: The study utilized longitudinal data (2006-2012) from three Health and Demographic Surveillance Systems (HDSSs) located in three geographical zones (Dodowa-coastal belt, Kintampo-middle belt and Navrongo-northern savannah belt) in Ghana. Data points or variables such as individual identification number, sex of individual, date of birth, date of death or exit, place of death and household assets were extracted from the HDSS sites. Georeferenced data was also collected from the sites. Weather data for the study were obtained from Ghana Meteorological Agency (GMA). Monthly mortality and weather data were generated from the daily data. A Bayesian probability model for interpreting VA, InterVA-4 was used to determine malaria deaths for this analysis. Generalized additive models were fitted with quasi-Poisson link functions to assess the association between monthly mortality and weather variables (temperature, rainfall) allowing for over-dispersion. Trend and seasonal variables were used to adjust for covariates and also model the expected mortality at all-time points. Natural cubic splines were used to adjust for nonlinear time varying covariates. The analysis was stratified by sex, age and socioeconomic status. SaTScan software version 8 was used to determine spatiotemporal distribution of deaths in HDSS sites. This was done for all ages and for children under-five. ix Results: Descriptive analysis reveals seasonal patterns of all-cause and malaria-specific mortality. Higher rates of mortality were observed during the rainy season. Association of weather variables (temperature, rainfall) and monthly mortality was evident. The findings indicate that the effect of weather variable on health and mortality varies with location. For children under-five years of age, all-cause mortality was associated with mean temperature in the month of death in Dodowa with relative risk (RR) of 1.315(95% CI, 1.022, 1.692). The RR associated with rainfall below 34.1mm in the previous one month was 1.294 (95% CI, 1.014, 1.651). In Kintampo, the RR associated with rainfall below 22.8mm in the month of death was 0.7666 (0.5961, 0.9858) and that of the previous two months was 0.6940(0.5639, 0.8542). In Navrongo, temperature above 29.3°C was associated with mortality RR=1.549 (95% CI, 1.219, 1.969). For malaria-specific mortality, the RR with higher temperature in the previous three months in Kintampo was 0.6921(0.5174, 0.9260). In Navrongo, the RR with low mean temperature in the previous three months was 1.554 (95% CI, 1.024, 2.359). For under-five malaria-specific mortality, temperature and rainfall in the previous one month significantly associated with mortality in Dodowa. In Kintampo and Navrongo, temperature in the previous three months significantly associated with mortality. The spatiotemporal analysis revealed significant clustering of high mortality in all HDSS areas. Clusters were observed in deprived areas and those close to water bodies. Conclusion: The findings from this study demonstrate that temperature and rainfall are associated with all-cause and malaria-specific mortality. There is also clustering of mortality in the HDSS sites. This provides information that can assist in interventions for climate change adaptive measures. The results will also assist health managers in the study districts to deliver targeted health services.