Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

Abstract

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6-36.9) in Kenya, 10.6% (3.4-39.2) in mainland Tanzania, and 9.5% (4.0-48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.

Fig 1. Assembled parasite rate surveys.

(A) Distribution of all assembled survey data (n = 18940) between 2010–2020; (B) the distribution of age-corrected and microscopy-standard parasite prevalence (PfPR_2-10) estimates among samples ≥10 individuals with the highest values on top when multiple surveys conducted at the same location. Base shapefiles used in all figures downloaded from: Kenya– https://data.humdata.org/dataset/ken-administrative-boundaries; Uganda– https://data.humdata.org/dataset/uganda-administrative-boundaries-admin-1-admin-3 and Tanzania–https://data.humdata.org/dataset/tanzania-administrative-boundaries-level-1-to-3-regions-districts-and-wards-with-2012-population https://gadm.org/.

Fig 2. Assembled surveys by year and data source.

(A) Temporal distribution of surveys 2010–2020 by country (B) Temporal distribution of surveys 2010–2020 according to the data source

Fig 3. Predicted mean PAPfPR_2-10 at 1 × 1 km spatial resolution maps in 2019.

(A) mean prevalence (continuous stretched scale), and (B) Classified mean of the endemicity. The white represents the climatic unsuitability for transmission (TSI = 0). PAPfPR_2–10 predictions are shown for areas within the stable limits of transmission.

Fig 4. East Africa PfPR_2-10 stratification.

Stratification of health decision-making units based on the level of PAPfPR_2-10 (aggregated mean) for 2020. These comprised 47 counties in Kenya, 184 councils in mainland Tanzania, and 135 districts in Uganda (see S1 Table).

Fig 5. Non-exceedance probability (NEP) maps for 2019.

PAPfPR_2-10 predictions are 90% certain to be < 1%, shown in green. Derived from the fitted spatiotemporal model, formally expressed as: NEP = (Prob PAPfPR_2–10 (x, t) < l|Data); where l is the prevalence threshold. A NEP close to 100% indicates that PAPfPR_2–10 is highly likely to be below the threshold l; if close to 0%, PAPfPR_2–10, is highly likely to be above the threshold l; if close to 50%, PAfPR_2–10, is equally likely to be above or below the threshold l, hence corresponding to a high level of uncertainty.