Trends, Socio-Demographic Characteristics and Risk Factors for Malaria Incidence and Mortality in uMkhanyakude District Kwa-Zulu Natal, 2014-2022

Abstract

Introduction Malaria is still a notifiable disease in South Africa (SA) and needs to be continuously monitored. Therefore, ongoing systematic surveillance becomes necessary. Malaria incidence and mortality numbers vary from year to year in the provinces that are regarded as malaria-endemic in SA. South Africa has committed itself to eliminating malaria by 2030, as stipulated in the Sustainable Development Goals. Methods This retrospective cohort study was conducted at uMkhanyakude District Municipality in KwaZulu-Natal province. This study was used to explore the trends in malaria incidence and mortality and identify risk factors associated with malaria deaths and cases of all reported cases and deaths from 2014 to 2022. The study utilized secondary data extracted from the Malaria Information System database using District Health Information System 2. Incidence and mortality were assessed using graphs, case fatality rate and incidence per 100,000. Risk factors for case classification(imported/local) were estimated by fitting Binary Logistic regression models. Poisson regression was used to estimate rates and trends for malaria incidence. The other analyses were descriptive, summarizing the socio-demographic characteristics of the malaria cases and investigating the association of factors with malaria mortality using univariable analyses (Fisher’s exact test) and case classification. Results Overall, no consistent annual trends identified for malaria incidence and mortality over the 9 years. The municipality contributing the highest number of cases over all the years was the uMhlabuyalingana Local Municipality. The summer season has been found to contribute to the majority of cases over the years. There was a higher number of imported cases compared to local cases. The Poisson regression results for all three models using case classification as an outcome indicated that local municipality and year were associated with an increase in malaria incidence rate per unit population; this was statistically significant (p<0.001). As there were only 10 deaths in the study period, we were unable to find factors associated with mortality. Binary Logistic regression showed lower odds (OR: 0.0) of imported cases in Jozini Municipality. iii Conclusion This study emphasised the importance of monitoring trends, as this also shows whether implemented interventions are effective or whether additional planning or actions are required to meet the elimination goals. One of the limitations of this study was missing data, which limited analysis for mortality. Efforts should be made to improve the quality and completeness of routine data captured on the DHIS2. This study will help to identify gaps in malaria interventions and also help in allocating resources effectively for municipalities under the district.