Prevalence and risk factors for latent tuberculosis infection among household contacts of index cases in two South African provinces: Analysis of baseline data from a cluster-randomised trial.

Peter Macpherson,Kegaugetswe Motsomi,Andrew Ratsela,Zama Bosch,Emily L Webb,Limakatso Lebina,Sanjay Lala,Minja Milovanovic,Ebrahim Variava,Neil A Martinson,Jonathan E Golub

doi:10.1371/journal.pone.0230376

Abstract

Household contacts of patients with active pulmonary tuberculosis (TB) often have latent TB infection, and are at risk of progression to disease. We set out to investigate whether index TB case HIV status was linked to a higher probability of latent TB infection among household contacts. Data were collected prospectively from participants in the intervention arm of a household cluster-randomised trial in two South Africa provinces (Mangaung, Free State, and Capricorn, Limpopo). In intervention group households, TB contacts underwent HIV testing and tuberculin skin testing (TST). TST induration was estimated at two cut-offs (≥5mm, ≥10mm). Multilevel Bayesian regression models estimated posterior distributions of the percentage of household contacts with TST induration ≥5mm and ≥10mm by age group, and compared the odds of latent TB infection by key risk factors including HIV status index case age and study province. A total of 2,985 household contacts of 924 index cases were assessed, with most 2,725 (91.3%) undergoing TST. HIV prevalence in household contacts was 14% and 10% in Mangaung and Capricorn respectively. Overall, 16.8% (458/2,725) had TST induration of ≥5mm and 13.1% (359/2,725) ≥10mm. In Mangaung, children aged 0-4 years had a high TST positivity prevalence compared to their peers in Capricorn (22.0% vs. 7.6%, and 20.5% vs. 2.3%, using TST thresholds of ≥5mm and ≥10mm respectively). Compared to contacts from Capricorn, household contacts living in Mangaung were more likely to have TST induration ≥5mm (odds ratio [OR]: 3.08, 95% credibility interval [CI]: 2.13-4.58) and ≥10mm (OR: 4.52, 95% CI: 3.03-6.97). There was a 90% and 92% posterior probability that the odds of TST induration ≥5mm (OR: 0.79, 95% CI: 0.56-1.14) and ≥10mm (OR: 0.77, 95% CI: 0.53-1.10) respectively were lower in household contacts of HIV-positive compared to HIV-negative index cases. High TST induration positivity, especially among young children and people living in Mangaung indicates considerable TB transmission despite high antiretroviral therapy coverage. Household contact of HIV-positive index TB cases were less likely to have evidence of latent TB infection than contacts of HIV-negative index cases.

Highlights

Household contacts of patients with active pulmonary tuberculosis (TB) often have latent TB infection, and are at risk of progression to disease
In subSaharan Africa, people living with human immunodeficiency virus (HIV) infection continue to have substantially-higher incidence of active TB disease than people without HIV, despite high levels of population coverage of antiretroviral therapy (ART) in many settings.[5,6]
HIV prevalence was higher in Mangaung compared to in Capricorn (14% vs. 10%), and ART coverage among HIV-positive household contacts was high in both sites (86% and 89% respectively)

Summary

Methods

Data were collected prospectively from participants in the intervention arm of a household cluster-randomised trial in two South Africa provinces (Mangaung, Free State, and Capricorn, Limpopo). Random intercepts for each household were included to allow partial pooling of estimates and to account for clustering of characteristics within households.[25] We placed weakly informative priors on intercepts (normal[mean = 0, sd = 2]), beta coefficients (normal[mean = 0, sd = 2]), and on the standard deviation of random intercepts (half-Cauchy[0,1]) Parameters and their credible intervals were estimated using Hamiltonian Markov chain Monte Carlo procedures implemented in Stan, interfaced through the ‘brms 2.9.0‘package in R.[26] The percentage and 95% uncertainty intervals of household contacts with TST induration reactions 5mm and 10mm were estimated by drawing samples from model posterior distributions, and fitting to index case age distributions, stratified by study site and index case HIV status. Model checking included inspection of prior predictive distributions, inspection of trace plots, estimation of the number of effective samples per iteration, and estimation of R^ statistics.[27]

Results

Discussion

Conclusion