AI Article Synopsis

  • - Reactive case detection (RCD) in Zambia aims to improve malaria control by testing and treating individuals living near confirmed cases, but a small screening radius (140m) may not be effective in low-endemic areas, prompting a study to assess whether expanding the radius to 250m could enhance detection.
  • - From 2015 to 2017, the study enrolled 4,170 individuals from 158 index and 531 secondary households, using rapid diagnostic tests and quantitative PCR to measure malaria prevalence and analyze environmental risk factors using satellite imagery.
  • - Results showed low parasite prevalence (0.7% by RDT and 1.8% by qPCR) in secondary households, with 8.5% of

Article Abstract

Background: Reactive case detection (RCD) seeks to enhance malaria surveillance and control by identifying and treating parasitaemic individuals residing near index cases. In Zambia, this strategy starts with passive detection of symptomatic incident malaria cases at local health facilities or by community health workers, with subsequent home visits to screen-and-treat residents in the index case and neighbouring (secondary) households within a 140-m radius using rapid diagnostic tests (RDTs). However, a small circular radius may not be the most efficient strategy to identify parasitaemic individuals in low-endemic areas with hotspots of malaria transmission. To evaluate if RCD efficiency could be improved by increasing the probability of identifying parasitaemic residents, environmental risk factors and a larger screening radius (250 m) were assessed in a region of low malaria endemicity.

Methods: Between January 12, 2015 and July 26, 2017, 4170 individuals residing in 158 index and 531 secondary households were enrolled and completed a baseline questionnaire in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia. Plasmodium falciparum prevalence was measured using PfHRP2 RDTs and quantitative PCR (qPCR). A Quickbird™ high-resolution satellite image of the catchment area was used to create environmental risk factors in ArcGIS, and generalized estimating equations were used to evaluate associations between risk factors and secondary households with parasitaemic individuals.

Results: The parasite prevalence in secondary (non-index case) households was 0.7% by RDT and 1.8% by qPCR. Overall, 8.5% (n = 45) of secondary households had at least one resident with parasitaemia by qPCR or RDT. The risk of a secondary household having a parasitaemic resident was significantly increased in proximity to higher order streams and marginally with increasing distance from index households. The adjusted OR for proximity to third- and fifth-order streams were 2.97 (95% CI 1.04-8.42) and 2.30 (95% CI 1.04-5.09), respectively, and that for distance to index households for each 50 m was 1.24 (95% CI 0.98-1.58).

Conclusion: Applying proximity to streams as a screening tool, 16% (n = 3) more malaria-positive secondary households were identified compared to using a 140-m circular screening radius. This analysis highlights the potential use of environmental risk factors as a screening strategy to increase RCD efficiency.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206707PMC
http://dx.doi.org/10.1186/s12936-020-03245-1DOI Listing

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