Plasmodium falciparum resistance to sulfadoxine-pyrimethamine in Africa: a systematic analysis of national trends

Abstract

Introduction: The rising burden of drug resistance is a major challenge to the global fight against malaria. We estimated national Plasmodium falciparum resistance to sulfadoxine-pyrimethamine (SP) across Africa, from 2000 to 2020.

Methods: We assembled molecular, clinical and endemicity data covering malaria-endemic African countries up to December 2018. Subsequently, we reconstructed georeferenced patient data, using pfdhps540E and pfdhps581G to measure mid-level and high-level SP resistance. Gaussian process regression was applied to model spatiotemporal standardised prevalence.

Results: In eastern Africa, mid-level SP resistance increased by 64.0% (95% uncertainty interval, 30.7%-69.8%) in Tanzania, 55.4% (31.3%-65.2%) in Sudan, 45.7% (16.8%-54.3%) in Mozambique, 29.7% (10.0%-45.2%) in Kenya and 8.7% (1.4%-36.8%) in Malawi from 2000 to 2010. This was followed by a steady decline of 76.0% (39.6%-92.6%) in Sudan, 65.7% (25.5%-85.6%) in Kenya and 17.4% (2.6%-37.5%) in Tanzania from 2010 to 2020. In central Africa, the levels increased by 28.9% (7.2%-62.5%) in Equatorial Guinea and 85.3% (54.0%-95.9%) in the Congo from 2000 to 2020, while in the other countries remained largely unchanged. In western Africa, the levels have remained low from 2000 to 2020, except for Nigeria, with a reduction of 14.4% (0.7%-67.5%) and Mali, with an increase of 7.0% (0.8%-25.6%). High-level SP resistance increased by 5.5% (1.0%-20.0%) in Malawi, 4.7% (0.5%-25.4%) in Kenya and 2.0% (0.1%-39.2%) in Tanzania, from 2000 to 2020.

Conclusion: Under the WHO protocols, SP is no longer effective for intermittent preventive treatment in pregnancy and infancy in most of eastern Africa and parts of central Africa. Strengthening health systems capacity to monitor drug resistance at subnational levels across the endemicity spectrum is critical to achieve the global target to end the epidemic.

Keywords: child health; epidemiology; health policy; malaria; maternal health.

Figure 1

Evidence gathering flowchart. The full description of the search algorithm and the eligibility criteria considered for each outcome cluster is provided in online supplemental files 1.1–1.2.

Figure 2

Patient data coverage. The circle sizes are proportional to the number of surveys reporting patient data in each country. The shading depicts the number of clinical samples tested in each country. The intervals are left-opened and right-closed. (A) pfdhps540E patient data. (B) pfdhps581G patient data.

Figure 3

National scale temporal trends in, and projections of, Plasmodium falciparum resistance to sulfadoxine-pyrimethamine. The upper and lower lines denote upper and lower bounds of the 95% uncertainty interval, respectively, and the middle, the median of the posterior distribution. The estimates are population-level resistance levels per respective geography. The points and vertical bars indicate point estimates from each survey with respective uncertainty interval, whereas the colours denote the administrative level one of the sites where the patients were recruited, and clinical samples collected. National trends and projections are shown as graphs for selected countries. Countries with the smallest, largest and/or typical changes in resistance in each region (eastern, central and western Africa) are shown, to illustrate the regional trends and cross-country heterogeneity across the continent. Figures for all countries analysed are provided in online supplemental file 3.4. The full list of site-years is summarised in online supplemental file 1.3. Posterior probability distribution of prevalence per survey is given in online supplemental file 3.5. (A) Mid-level P. falciparum resistance to sulfadoxine-pyrimethamine. (B) High-level P. falciparum resistance to sulfadoxine-pyrimethamine.