Heterogeneous malaria transmission patterns in southeastern Tanzania driven by socio-economic and environmental factors.

Abstract

Background: As malaria-endemic countries progress towards elimination, distinct patterns of heterogeneous transmission are emerging. In south-eastern Tanzania, despite intensive control efforts, localized transmission shows prevalence ranging from under 1% to over 50% among nearby villages. This study investigated the socioeconomic and environmental factors driving this spatial heterogeneity.

Methods: A cross-sectional survey was conducted in the Kilombero and Ulanga districts of south-eastern Tanzania between 2022 and 2023, screening 3,249 individuals (ages 5-60) across 10 villages for malaria using rapid diagnostic tests (RDTs). Socioeconomic data was collected from all surveyed households and villages via questionnaires, while environmental data were obtained from remote sensing data sources. Associations between socioeconomic factors and malaria infection were analysed using a zero-inflated negative binomial model and employed a generalized additive model (GAM) to assess the impact of rainfall, and temperature on malaria infection.

Results: Greater elevation and higher rainfall were positively associated with malaria infection (OR = 1.68, 95% CI 1.38-2.05, p < 0.001 and OR = 1.46, 95% CI 1.14-1.87, p < 0.05 respectively), while temperature showed no significant effect (OR = 0.70, 95% CI 0.51-1.13, p = 0.117). Households in densely vegetated areas had higher malaria infections compared to those in more developed, built-up areas. At the individual level, males had a higher prevalence (355; 28.6%) and displayed significantly greater odds of infection (OR = 1.53, 95% CI 1.15-2.03, p < 0.05) than females (433; 21.6%). School-aged children (5-17 years) had a higher prevalence (36.9%) compared to adults (18-60 years) (15.9%). The probability of infection declined with increasing age (OR = 0.28, 95% CI 0.25-0.31, p < 0.001). Larger household sizes (more than four members) were positively associated with malaria infection (OR = 1.72, 95% CI 1.29-2.29, p < 0.001). Open-eave housing was associated with higher odds of malaria, whereas closed eaves (OR = 0.56, 95% CI 0.38-0.82, p < 0.05) and metal roofs (OR = 0.62, 95% CI 0.44-0.87, p < 0.05) were protective factors. Open water sources were positively associated with malaria infection compared to protected water sources (OR = 0.57, 95% CI 0.38-0.85, p < 0.05). Lack of bed net use was positively associated with malaria but this was not statistically significant (OR = 1.54, 95% CI 0.68-3.48, p = 0.299).

Conclusion: This study highlights the complex interplay between socioeconomic and environmental factors contributing to the fine-scale spatial heterogeneity of malaria in south-eastern Tanzania. Understanding these localized drivers is essential for designing targeted, effective strategies that support broader malaria elimination goals.