Trends in malaria prevalence among school-age children in Mainland Tanzania, 2015-2023: A multilevel survey analysis

Abstract

In high-transmission areas, school-aged children have higher malaria prevalence and contribute significantly to the transmission reservoir. Malaria infections can be asymptomatic or present with symptoms which may contribute to anaemia, severe illness and fatal malaria. This analysis provides trends of malaria prevalence and associated risk factors among school-aged children in mainland Tanzania. Data for this analysis were obtained from nationwide school malaria surveillance conducted every other year from 2015 to 2023. A total of 307,999 school children aged 5-16 years old from 850 public primary schools were tested for malaria infection using rapid diagnostic tests, assessed for malaria control intervention coverage and other malaria-related parameters. A multilevel mixed-effects logistic regression model was used to assess associated risk factors. Overall malaria prevalence was 21.6% (95%CI: 21.3-22.0) in 2015 which progressively decreased to 11.8% (95%CI: 11.5-12.0 p <0.001) in 2021 with no significant change in the overall malaria risk between 2021 and 2023 (AOR 1.32, CI: 0.92-1.81, p=0.08). School children aged between 9-12 years and 13-16 years had 20% higher risk of malaria (95% CI: 1.15-1.25) and 21% higher risk of malaria (95% CI: 1.16-1.27), respectively, compared to those aged between 5-8 years. Geographically, children from the Lake zone had the highest odds of prevalence (AOR: 18.75; 95% CI: 12.91-27.23) compared to the Central zone, and sleeping under an insecticide-treated net demonstrated a protective effect (AOR=0.68, 95%CI: 0.64-0.72, p < 0.001). There was a significant decline in the prevalence of malaria infection across the study period. We presented a countrywide active surveillance data, collected over time and in different settings which are unique and seldom presented. We believe various stakeholders will use our findings and join force to combat malaria not just in Tanzania but, in all malaria endemic countries.

Fig 1. A map of Tanzania indicating surveyed schools along with regional and council boundaries was created.

The base map was generated using a shapefile for Tanzania that included regional and council boundaries, obtained from NBS Microdata Catalog. Geographic coordinates (latitude and longitude) of the surveyed schools were recorded during data collection. QGIS software was used to plot and visualize the data on the map.

Fig 2. Malaria infection prevalence across different age groups and survey years is presented.

Fig 2A shows trends in malaria prevalence over the years (2015–2023) among all surveyed SAC in the country, while Fig 2B illustrates age-disaggregated malaria prevalence among SAC during the same period. Error bars represent 95% confidence intervals, unadjusted for school clustering.

Fig 3. Malaria prevalence over the years is presented by age (Fig 3A), sex (Fig 3B), history of fever (Fig 3C), geographical zones (Fig 3D), ITN use (Fig 3E), and elevation (Fig 3F).

History of fever and ITN use were self-reported by SAC during the interviews. Error bars indicate 95% confidence intervals, unadjusted for school clustering.