Socio-economic and environmental deteminants of malaria in four malaria endemic provinces of Zambia.

Abstract

A large fraction of the global malaria burden occurs in sub-Saharan Africa and its endemicity depends on the interaction of environmental factors, vectors, parasites and the host. In Zambia, the negative effect of the break in interventions experienced in the late 2000s varied by regions. Therefore, it was necessary to determine the malaria determinants through the study of: statistical models that have been employed; knowledge of the community in malaria management and control; prevalence of malaria and presence of social and community-related factors influencing malaria control in selected communities; contribution of other social and environmental determinants of malaria from the household point of view; and also socio-economic and climatic determinants of malaria at provincial level, in Zambia. This work was achieved through a number of methods beginning with a systematic review of studies that have identified socio-economic and eco-environmental determinants of malaria through the use of statistical models in malaria burden determination and prediction in southern Africa. We also conducted a cross-sectional survey employing a simple random sampling technique to administer questionnaires to 584 household heads from selected communities, on the following components: knowledge, attitude and practices in malaria control; the role of social and community-related structures in malaria burden and control; and water sources and practices as well as housing structures in relation to self-reported malaria infections. Malaria testing was also performed using a rapid diagnostic test (RDT) in 756 individuals sampled from the 584 households. The household-level data was analysed in STATA and WinBUGS whereas the provincial-level malaria cases, government socio-economic and remotely-sensed climatic data were analysed in STATA, WinBUGS and also in R- integrated Nested Laplace (R-INLA) The focus of the studies conducted in southern Africa reviewed, has mainly been on malaria determinants related to intervention strategies and climatic factors. Additionally, the use of Bayesian statistical modelling was quite low (29.2%) in the studies reviewed. The community knowledge study showed that although knowledge levels in malaria were high they were not interrelated with attitudes and practices. In the malaria testing survey, a higher infection rate was seen in children and the highest RDT malaria prevalence was recorded in communities of Luapula province. Relating malaria burden with the role of community health workers (CHWs) in malaria control, malaria prevalence was inversely related with CHWs presence in Western Province. On the other hand, relating malaria burden with water practices and housing structures, “river” as a water source was the main predictor. The Bayesian hierarchical (or Generalised Linear mixed model) and R-INLA based models showed that region on one hand and region, time and precipitation on the other, were strong predictors of malaria incidence. More research in the area of statistical modeling as well as in other areas such as behaviour change, strengthening of existing CHW and exploring new avenues with regards to community social structures and ecological and climatic factors by locality is a great need.