Additional burden of asymptomatic and sub-patent malaria infections during low transmission season in forested tribal villages in Chhattisgarh, India

Malar J. 2017 Aug 8;16(1):320. doi: 10.1186/s12936-017-1968-8.

Abstract

Background: The burden of sub-patent malaria is difficult to recognize in low endemic areas due to limitation of diagnostic tools, and techniques. Polymerase chain reaction (PCR), a molecular based technique, is one of the key methods for detection of low parasite density infections. The study objective was to assess the additional burden of asymptomatic and sub-patent malaria infection among tribal populations inhabiting three endemic villages in Keshkal sub-district, Chhattisgarh, India. A cross-sectional survey was conducted in March-June 2016, during the low transmission season, to measure and compare prevalence of malaria infection using three diagnostics: rapid diagnostic test, microscopy and nested-PCR.

Results: Out of 437 individuals enrolled in the study, 103 (23.6%) were malaria positive by PCR and/or microscopy of whom 89.3% were Plasmodium falciparum cases, 77.7% were afebrile and 35.9% had sub-patent infections.

Conclusions: A substantial number of asymptomatic and sub-patent malaria infections were identified in the survey. Hence, strategies for identifying and reducing the hidden burden of asymptomatic and sub-patent infections should focus on forest rural tribal areas using more sensitive molecular diagnostic methods to curtail malaria transmission.

Keywords: Asymptomatic; Chhattisgarh; India; Malaria; PCR; Sub-patent.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Asymptomatic Infections / epidemiology*
  • Child
  • Child, Preschool
  • Cross-Sectional Studies
  • Diagnostic Tests, Routine / standards
  • Female
  • Humans
  • India / epidemiology
  • Infant
  • Infant, Newborn
  • Malaria / diagnosis*
  • Malaria / epidemiology*
  • Malaria / parasitology
  • Male
  • Microscopy
  • Polymerase Chain Reaction
  • Prevalence
  • Risk Factors
  • Rural Population / statistics & numerical data*
  • Seasons