Open Access

Malaria is an environmental disease, influenced not only by physical and biological environmental factors but also by socio-cultural ones. These factors affect each other, and, in turn, cause the disease in endemic areas. Some factors that cause the high morbidity rate associated with the disease include climate change, physical environment that varies geographically, socio-economic circumstances, and human behaviour in the affected areas. Other risk factors include housing conditions and poor sanitation, lack of hygiene practices, and inadequate health services in endemic areas. Efforts to eliminate malaria have been a topic at various public health meetings for decades. However, in Indonesia, malaria continues to be one of the leading causes of morbidity and mortality. The research aimed to analyse and model the critical variables associated with malaria in endemic areas of Indonesia. So, this included relationships between malaria and both socio-demographic variables and physical environments. The research is in three parts, adding value to a model that determines malaria in Indonesia. This dissertation follows a cross-sectional design survey. The research data in this PhD dissertation is drawn from four sources: routine reporting of malaria from provincial health departments in South Sumatra; the national basic health research data (IDN acronym: Riskesdas); climate data from the Meteorology, Climatology, and Geophysics Climatological Agency (IDN acronym: BMKG); spatial data from Geospatial Information Agency (IDN acronym: BIG). This study takes a holistic approach, integrating the following univariate, bivariate, and multivariable logistic regressions, to establish a modelling determinant of malaria. Additionally, the researchers compared the performance of both Geographically Weighted Regression (GWR) and Ordinary Least Square (OLS). It also used some statistical analysis software tools for data processing, analysis, visualisation, and the development of the model as follows: Statistical Package for the Social Sciences (SPSS), Stata, Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS) 10.3, and GWR 4.0 version 4.0.90 for Windows. The prevalence of malaria varied according to the local area, which, in turn, was related to the local physical environment that varied geographically. The determinants for malaria cases varied locally and regionally as well. Rural areas with a high percentage of households keeping livestock/pets showed a higher proportion of malaria prevalence than the national average. Other socio-demographic risk factors included gender, age, occupation, knowledge about healthcare, protection against mosquito bites, and condition of dwellings. This study reveals that the independent variables - "rainfall", "altitude", and "distance from mosquito resting sites in the forest," in global OLS analysis- are significantly associated with malaria cases in South Sumatra, Indonesia. On the other hand, in the GWR analysis, the determinants of malaria cases at the village level vary geographically. Therefore, it is essential for the decision maker, the government, to acquire a more in-depth understanding of region-specific, ecological factors that influence confirmed malaria cases. The findings lead to the recommendation for developing sustainable regional malaria control programs and incentivising malaria elimination efforts, particularly at the village level. In another setting, the research led to the conclusion that the presence of mid-sized livestock comprised a significant risk factor for contracting malaria in rural Indonesia. The recommendation, especially for the study area, is to employ integrated vector management (IVM), for example, the simultaneous implementation of insecticide-treated bed nets (ITNs) and insecticide-treated livestock (ITL). Other factors such as socio-demographic and use of health care facilities were also crucial as they related to malaria prevalence. Further, the research leads to the recommendation for increased education and increased promotion and utilisation of the health care framework to promote knowledge and awareness of villagers on how to protect themselves from Anopheles bites. Finally, improving information concerning the availability of health care services and access to various health facilities in endemic areas is essential.