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Spatial overlap links seemingly unconnected genotype-matched TB cases in rural Uganda.

  • Author(s): Chamie, Gabriel;
  • Kato-Maeda, Midori;
  • Emperador, Devy M;
  • Wandera, Bonnie;
  • Mugagga, Olive;
  • Crandall, John;
  • Janes, Michael;
  • Marquez, Carina;
  • Kamya, Moses R;
  • Charlebois, Edwin D;
  • Havlir, Diane V
  • et al.
Abstract

Introduction

Incomplete understanding of TB transmission dynamics in high HIV prevalence settings remains an obstacle for prevention. Understanding where transmission occurs could provide a platform for case finding and interrupting transmission.

Methods

From 2012-2015, we sought to recruit all adults starting TB treatment in a Ugandan community. Participants underwent household (HH) contact investigation, and provided names of social contacts, sites of work, healthcare and socializing, and two sputum samples. Mycobacterium tuberculosis culture-positive specimens underwent 24-loci MIRU-VNTR and spoligotyping. We sought to identify epidemiologic links between genotype-matched cases by analyzing social networks and mapping locations where cases reported spending ≥12 hours over the one-month pre-treatment. Sites of spatial overlap (≤100m) between genotype-matched cases were considered potential transmission sites. We analyzed social networks stratified by genotype clustering status, with cases linked by shared locations, and compared network density by location type between clustered vs. non-clustered cases.

Results

Of 173 adults with TB, 131 (76%) were enrolled, 108 provided sputum, and 84/131 (78%) were MTB culture-positive: 52% (66/131) tested HIV-positive. Of 118 adult HH contacts, 105 (89%) were screened and 3 (2.5%) diagnosed with active TB. Overall, 33 TB cases (39%) belonged to 15 distinct MTB genotype-matched clusters. Within each cluster, no cases shared a HH or reported shared non-HH contacts. In 6/15 (40%) clusters, potential epidemiologic links were identified by spatial overlap at specific locations: 5/6 involved health care settings. Genotype-clustered TB social networks had significantly greater network density based on shared clinics (p<0.001) and decreased density based on shared marketplaces (p<0.001), compared to non-clustered networks.

Conclusions

In this molecular epidemiologic study, links between MTB genotype-matched cases were only identifiable via shared locations, healthcare locations in particular, rather than named contacts. This suggests most transmission is occurring between casual contacts, and emphasizes the need for improved infection control in healthcare settings in rural Africa.

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