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

doi:10.1371/journal.pone.0230376

. 2020 Mar 17;15(3):e0230376. doi: 10.1371/journal.pone.0230376

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 ^1,^2,^*, Limakatso Lebina ³, Kegaugetswe Motsomi ³, Zama Bosch ³, Minja Milovanovic ³, Andrew Ratsela ⁴, Sanjay Lala ⁵, Ebrahim Variava ⁶, Jonathan E Golub ⁷, Emily L Webb ⁸, Neil A Martinson ^5,⁷

Editor: Richard John Lessells⁹

¹Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, England, United Kingdom

²Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi

³SA MRC Soweto Matlosana Collaborating Centre for HIV/AIDS and TB, Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa

⁴Department of Internal Medicine, University of Limpopo, and Limpopo Department of Health, Polokwane, South Africa

⁵Department of Paediatrics and Child Health, Chris Hani Baragwanath Academic Hospital and University of the Witwatersrand, Johannesburg, South Africa

⁶Department of Internal Medicine, Klerksdorp Tshepong Hospital Complex, North West Department of Health and University of the Witwatersrand, Polokwane, South Africa

⁷Johns Hopkins University Center for TB Research, Baltimore, Maryland, United States of America

⁸MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, England, United Kingdom

⁹University of KwaZulu-Natal, SOUTH AFRICA

Competing Interests: The authors have declared that no competing interests exist

^✉

* E-mail: peter.macpherson@lstmed.ac.uk

Roles

Peter MacPherson: Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

Limakatso Lebina: Investigation, Project administration, Writing – review & editing

Kegaugetswe Motsomi: Investigation, Project administration, Writing – review & editing

Zama Bosch: Investigation, Project administration, Writing – review & editing

Minja Milovanovic: Investigation, Project administration, Writing – original draft, Writing – review & editing

Andrew Ratsela: Conceptualization, Funding acquisition, Writing – review & editing

Sanjay Lala: Conceptualization, Writing – review & editing

Ebrahim Variava: Conceptualization, Funding acquisition, Writing – review & editing

Jonathan E Golub: Conceptualization, Writing – review & editing

Emily L Webb: Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

Neil A Martinson: Conceptualization, Funding acquisition, Writing – original draft, Writing – review & editing

Richard John Lessells: Editor

PMCID: PMC7077873 PMID: 32182274

Abstract

Introduction

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.

Materials and 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). 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.

Results

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.

Conclusions

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.

Introduction

An estimated one-quarter of the world’s population are latently-infected with Mycobacterium tuberculosis,[1] and are at risk of progression to active disease.[2,3] To achieve the End-TB strategy, the World Health Organization (WHO) has prioritised the identification and delivery of tuberculosis (TB) preventive interventions to people at high-risk of infection.[4] In sub-Saharan 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] TB preventive therapy significantly reduces the risk of progression to active disease for both HIV-positive and HIV-negative individuals,[7–9] and newer more tolerable and shorter regimens are becoming available through national HIV and TB programmes.[10] However, coverage of TB preventive therapy remains unacceptably low and innovative approaches to prioritise groups at the highest risk of disease are urgently needed.[11]

People exposed to a person with infectious pulmonary TB within the home (household contacts) are likely to share risk factors for TB with index cases, such as poverty, living and environmental conditions and health determinants, including HIV status, nutrition and access to healthcare.[12] Indeed, data from several settings demonstrates high prevalence and incidence of TB disease in household contacts following diagnosis of an index patients with TB.[13–15] The evidence for the effectiveness and cost-effectiveness of household contact screening and treatment in sub-Saharan Africa is limited [16] because of the uncertainty over the role of community transmission outside the household.[17] However, in high-income settings public health approaches to TB contact-tracing have prioritised the most susceptible individuals and those with the greatest cumulative exposure for preventive interventions.[18] In high HIV prevalence settings, there remains considerable uncertainty around which index case-, household contact-, and community-level factors that are most important in determining risk of TB infection.

Using data collected prospectively from participants in the intervention arm of a household cluster-randomised trial being conducted in South Africa, we set out to describe the prevalence of latent TB infection among household contacts of pulmonary TB index cases, and thereby identify population groups that may receive the greatest benefit and could be targeted in efforts to expand access to TB preventive therapy, or for roll-out of novel TB vaccines. Using a Bayesian multilevel modelling analysis approach, we additionally aimed to provide evidence for how shared household risk factors between index cases (particularly HIV status) and household contacts interact to determine household contact prevalence of latent TB infection within provinces with differing TB and HIV epidemics.

Materials and methods

Study setting and participants

We conducted an epidemiological analysis of data collected during a household cluster randomised trial (ISRCTN16006202) conducted in two provinces of South Africa investigating the effect of intensified home screening and linkage to treatment for TB and HIV on TB-free survival.[19] We purposefully selected two study sites with very different annual TB incidence. The study sites were Mangaung Municipality in the Free State (2016 population: 787,803); and Capricorn District in Limpopo Province (2016 population: 1,330,436).[20] Antenatal HIV seroprevalence and the annual TB incidence for 2015 in Mangaung and in Capricorn were 31.7% and 616/100,000, and 21.6% and 328/100,000, respectively.[21]

At both sites index TB cases were identified by visiting hospital wards and clinics, reviewing routine reporting of TB cases in public-sector health facilities (TB registers and notifications), and from lists of specimens that were either smear-positive or culture-positive for M.tuberculosis generated by public sector laboratories. Index TB cases of all ages who had been diagnosed with TB within the preceding six-weeks were eligible to participate, providing they were a permanent resident of one of the study districts, and with no plans to relocate during the study. We excluded TB cases older than seven years if they did not have a microbiologically-confirmed diagnosis of pulmonary TB; children seven years-or-younger were eligible if they had either microbiologically-confirmed or microbiologically-unconfirmed TB, diagnosed by a doctor. Severely-ill and deceased patients were included if consent to participate was obtained from a close family member. We excluded TB cases who were incarcerated or receiving long-term in-patient care.

Because of the profusion of backyard dwellings, a household was defined as all rooms under a contiguous roofed area linked by doorways or windows through which air could pass. Household contacts of index TB cases were defined as individuals who slept overnight at least once or shared at least two meals in the same household as the index case in the 14 days prior to the index case’s diagnosis of TB.

For this analysis, we only included participants randomly allocated to the intervention arm of the trial. In the intervention arm, households received a home visit within 14 days of recruiting the index TB patient, where a household census was undertaken, and a socio-demographic and health questionnaire was completed for each household contact. Household contacts were screened for symptoms of TB, and sputum was collected from participants able to produce it–irrespective of the presence of symptoms–with Xpert and culture requested to be performed at the local National Health Laboratory Service (NHLS) mycobacterial laboratory, and with subsequent linkage to treatment for positive results. HIV testing and counselling with linkage to care and prevention services was offered to all household contacts who did not have previously confirmed HIV infection or who were not taking ART. Isoniazid preventive therapy (IPT) initiation in the household was offered to HIV-positive individuals and children younger than five years without TB symptoms; we also offered TB preventive treatment to HIV-negative household contacts older than five years. Initially, following National TB Guidelines, home initiation of IPT was based on the presence of a positive tuberculin skin test (TST) for HIV-infected individuals and children under five years of age. Approximately midway through the study having a positive TST result was removed from Guidelines[22] and we then offered home IPT initiation to all children under five years of age and all HIV-infected individuals irrespective of the TST result. However, we continued until study end to offer home IPT initiation for HIV-seronegative individuals with a positive TST.

Tuberculin skin testing

Tuberculin skin testing using purified protein derivative (PPD; from a variety of manufacturers, owing to limited global supply) was offered to all household contacts. TST was administered by study nurses who received study-specific training. Quality of TST testing was evaluated by supervisors to ensure standardization at both sites. An intradermal injection of 0.1 milliliter of PPD was placed in the volar aspect of the left forearm to raise a visible bleb. Household contacts were visited 48–72 hours after placement for reading. The presence of induration was recorded, and two dots were marked at each margin on the long axis of the forearm. The induration diameter was then measured using a millimeter ruler.[23]

Ethical considerations

All index TB cases (or their representative if deceased or severely unwell) and household contacts provided written informed consent to participate in the study. Consent to participate in research was obtained for children <18 years from their parent or guardian, and assent was obtained from children 7–18 years. Approvals were obtained from ethical review committees of the University of Witwatersrand and London School of Hygiene and Tropical Medicine.

Statistical methods

We summarised the characteristics of index cases and their household contacts using medians, ranges and proportions, and compared the two study sites. To account for missing values in the HIV status of index cases (4.9%) and HIV status of household contacts (5.0%), we did multiple imputation using additive regression, bootstrapping, and predictive mean matching.[24]

To investigate whether index case HIV status was associated with evidence of latent TB infection, we fitted two multilevel Bayesian regression models with Bernoulli likelihood distribution to the TST data. The first model investigated the proportion of household contacts with TST induration ≥5mm, and the second the proportion of household contacts with TST induration ≥10mm. TST thresholds were based on conventionally-used definitions for latent TB infection. We plotted a directed acyclic graph to describe the putative causal pathway between index case HIV-positivity and household contact latent TB infection, and to identify the minimal sufficient adjustment set of covariates for estimating the direct effect of index case HIV status on latent TB infection (S1 Fig). 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 $\hat{R}$ statistics.[27]

Data sharing

Code and datasets to reproduce analysis are available to download from https://github.com/petermacp/tstsa

Results

Characteristics of index patients and household contacts

A total of 924 index TB patients were included in this analysis. Overall, 60% (559/924) of index patients were male, and the median age was 37 years (interquartile range [IQR]: 28–47 years) (Table 1); 2.9% (27/924) TB patients were included after their death, and 98% (897/924) had microbiologically-confirmed TB. HIV prevalence among index TB patients was 56%, and 65% of HIV-positive TB patients were taking ART, with a median CD4 cell count of (133 cells/μl, IQR: 53–330). Index patients from Mangaung were more likely to be current tobacco smokers (26% vs. 22%), alcohol drinkers (33% vs. 26%), and have unknown HIV status (6.2% vs. 2.5%).

Table 1. Characteristics of index cases (top), dwellings (centre), and household contacts (bottom) by study site.

	Mangaung	Capricorn	Total
Index TB cases	(N = 517)	(N = 407)	(N = 924)
Sex
Female	191 (36.9%)	174 (42.8%)	365 (39.5%)
Male	326 (63.1%)	233 (57.2%)	559 (60.5%)
Age (years, median, IQR)	37 (28, 48)	37 (29, 47)	37 (28, 47)
Vital status at recruitment
Alive	504 (97.5%)	393 (96.6%)	897 (97.1%)
Deceased	13 (2.5%)	14 (3.4%)	27 (2.9%)
Microbiological TB status
Not microbiologically-confirmed TB	8 (1.5%)	14 (3.4%)	22 (2.4%)
Microbiologically-confirmed TB	509 (98.5%)	393 (96.6%)	902 (97.6%)
Tobacco smoking
Never smoked	253 (48.9%)	267 (65.6%)	520 (56.3%)
Previous smoker	127 (24.6%)	49 (12.0%)	176 (19.0%)
Current smoker	137 (26.5%)	91 (22.4%)	228 (24.7%)
HIV status
HIV-negative	207 (40.0%)	178 (43.7%)	385 (41.7%)
HIV-positive	278 (53.8%)	219 (53.8%)	497 (53.8%)
HIV-unknown	32 (6.2%)	10 (2.5%)	42 (4.5%)
Duration of cough (days, median, IQR)	30 (10, 60)	30 (0, 60)	30 (7, 60)
Dwellings	(N = 517)	(N = 407)	(N = 924)
Dwelling type
Brick/concrete house, apartment	443 (85.7%)	402 (98.8%)	845 (91.5%)
Traditional/shack/garage/1room	74 (14.3%)	5 (1.2%)	79 (8.5%)
Rooms in dwelling (median, range)	4.0 (1.0, 10.0)	6.0 (1.0, 22.0)	5.0 (1.0, 22.0)
Windows in dwelling (median, range)	4.0 (0.0, 15.0)	8.0 (0.0, 27.0)	5.0 (0.0, 27.0)
Tobacco smokers in household (median, range)	0.0 (0.0, 4.0)	0.0 (0.0, 6.0)	0.0 (0.0, 6.0)
Household contacts	(N = 1687)	(N = 1298)	(N = 2985)
Contacts per household (median, range)	4.0 (1.0, 14.0)	4.0 (1.0, 14.0)	4.0 (1.0, 14.0)
Age group of household contact
0–4 years	196 (15.1%)	249 (14.8%)	445 (14.9%)
5–9 years	206 (15.9%)	274 (16.2%)	480 (16.1%)
10–15 years	174 (13.4%)	267 (15.8%)	441 (14.8%)
15–24 years	219 (16.9%)	267 (15.8%)	486 (16.3%)
25–34 years	139 (10.7%	176 (10.4%)	315 (10.6%)
35–44 years	78 (6.0%)	133 (7.9%)	211 (7.1%)
45–54 years	84 (6.5%)	106 (6.3%)	190 (6.4%)
55+ years	202 (15.6%)	215 (12.7%)	417 (14.0%)
Household contact sex
Female	1045 (61.9%)	803 (61.9%)	1848 (61.9%)
Male	642 (38.1%)	495 (38.1%)	1137 (38.1%)
Household contact age (median, range)	16.0 (0.0, 95.0)	17.0 (0.0, 98.0)	17.0 (0.0, 98.0)
Time spent with index case
Missing	1	2	3
Every now and again	123 (7.3%)	241 (18.6%)	364 (12.2%)
Part of the day	773 (45.8%)	628 (48.5%)	1401 (47.0%)
Most of the day	790 (46.9%)	427 (32.9%)	1217 (40.8%)
Shared bedroom with index case
No	1303 (77.2%)	1184 (91.2%)	2487 (83.3%)
Yes	384 (22.8%)	114 (8.8%)	498 (16.7%)
Tobacco smoking
Never smoked	1498 (88.8%)	1200 (92.4%)	2698 (90.4%)
Current smoker	159 (9.4%)	83 (6.4%)	242 (8.1%)
Previous smoker	30 (1.8%)	15 (1.2%)	45 (1.5%)
HIV status of household contact
Missing	0	3	3
HIV negative	1349 (80.0%)	1136 (87.7%)	2485 (83.3%)
HIV positive	223 (13.2%)	126 (9.7%)	349 (11.7%)
HIV unknown	115 (6.8%)	33 (2.5%)	148 (5.0%)

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A total of 2,985 household contacts were identified from 924 intervention households, with a median of 4.0 (range: 1.0–14.0) contacts per index patient in both Mangaung and Capricorn. In both sites, 38% of household contacts were male, and the overall median age was 17 years (range: 0–98 years). Household contacts from Mangaung were more likely to share a bedroom with the index TB patient compared to contacts in Capricorn (23% vs. 9%). 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). In Mangaung, 47% of household contacts reported that they spent “most of the day” with the index case, compared to only 33% in Capricorn.

Tuberculin skin testing

Overall, 2,725/2,985 (91.3%) of household contacts underwent TST. Characteristics of household contacts who did and did not receive TST were similar, but with some minor differences (S1 Table). A greater proportion of household contacts in Capricorn (95.8%) compared to Mangaung (87.8%) received TST testing. Additionally, HIV-positive (84.0%) household contacts were less likely to receive TST compared to HIV-negative household contacts (94.0%). HIV-positive household contacts taking ART (73.4%) were less likely to receive TST compared to those not taking ART (81.4%). Sixty-four percent of household contacts had no induration. Seventeen percent (458/2,725, 16.8%) of household contacts had an induration diameter of ≥5mm; and 13% (359/2725, 13.1%) had an induration diameter of ≥10mm.

At both TST induration diameter cut-offs (≥5mm, ≥10mm), the percentage of household contacts with a positive TST reaction showed an S-shaped distribution across age groups with a higher percentage in the 0–4 year old group compared to in childhood and early adolescence, before increasing rapidly in late adolescence and early adulthood (Fig 1), although the early childhood peak was more apparent in Mangaung than in Capricorn. TST positivity was consistently higher across all contact age groups in the Mangaung site compared to the Capricorn site.

Fig 1 — 95% confidence intervals estimated using the binomial exact method.

There were substantial differences between study sites in the percentage of TST positive household contacts when examined by key index TB case and household contact characteristics (Table 2). Across all characteristics (excluding among household contacts who were previous smokers, where numbers were small), the percentage of household contacts with either a TST induration ≥5mm or ≥10mm were higher in Mangaung. In Capricorn 11.4% of household contacts of HIV-positive index cases and 9.8% of household contacts of HIV-negative index cases had TST induration of ≥5mm; however, in Mangaung 19.7% of household contacts of HIV-positive index cases and 24.8% of household contacts of HIV-negative index cases had TST duration of ≥5mm. This difference in percentage by site was apparent at the ≥10mm threshold. Whereas in Capricorn TST positivity rates were similar for male (10.9%) and female (10.5%) household contacts, in Mangaung female (23.4%) contacts were more likely to be TST positive than male contacts (19.6%) at the ≥5mm thresholds. Household contacts who were current smokers had a higher percentage of TST positivity at both the ≥5mm and ≥10mm thresholds in both Capricorn and Mangaung.

Table 2. Tuberculin skin test positivity by index TB case and household contact characteristics.

Characteristic	TST threshold	Capricorn n/N, (%, 95% CI)^*	Mangaung n/N, (%, 95% CI)^*
Index TB case female	≥5mm	64/540 (11.9%, 9.2%-14.9%)	131/599 (21.9%, 18.6%-25.4%)
Index TB case male	≥5mm	69/704 (9.8%, 7.7%-12.2%)	194/882 (22.0%, 19.3%-24.9%)
Index TB case HIV-negative	≥5mm	54/553 (9.8%, 7.4%-12.5%)	161/648 (24.8%, 21.6%-28.4%)
Index TB case HIV-positive	≥5mm	79/691 (11.4%, 9.2%-14.0%)	164/833 (19.7%, 17.0%-22.6%)
Index TB case not micro-confirmed TB	≥5mm	3/51 (5.9%, 1.2%-16.2%)	7/27 (25.9%, 11.1%-46.3%)
Index TB case micro-confirmed TB	≥5mm	130/1193 (10.9%, 9.2%-12.8%)	318/1454 (21.9%, 19.8%-24.1%)
Contact female	≥5mm	81/769 (10.5%, 8.5%-12.9%)	216/925 (23.4%, 20.7%-26.2%)
Contact male	≥5mm	52/475 (10.9%, 8.3%-14.1%)	109/556 (19.6%, 16.4%-23.2%)
Contact HIV-negative	≥5mm	113/1129 (10.0%, 8.3%-11.9%)	274/1286 (21.3%, 19.1%-23.6%)
Contact HIV-positive	≥5mm	20/115 (17.4%, 11.0%-25.6%)	51/195 (26.2%, 20.1%-32.9%)
Contact with index case most of day	≥5mm	43/406 (10.6%, 7.8%-14.0%)	142/663 (21.4%, 18.4%-24.7%)
Contact with index case part of day	≥5mm	65/609 (10.7%, 8.3%-13.4%)	154/701 (22.0%, 19.0%-25.2%)
Contact with index case rarely	≥5mm	25/229 (10.9%, 7.2%-15.7%)	29/117 (24.8%, 17.3%-33.6%)
Shares bedroom with index case	≥5mm	112/1134 (9.9%, 8.2%-11.8%)	244/1162 (21.0%, 18.7%-23.5%)
Doesn't share bedroom with index case	≥5mm	21/110 (19.1%, 12.2%-27.7%)	81/319 (25.4%, 20.7%-30.5%)
Contact never smoked	≥5mm	115/1147 (10.0%, 8.3%-11.9%)	287/1318 (21.8%, 19.6%-24.1%)
Contact current smoker	≥5mm	15/82 (18.3%, 10.6%-28.4%)	33/134 (24.6%, 17.6%-32.8%)
Contact previous smoker	≥5mm	3/15 (20.0%, 4.3%-48.1%)	5/29 (17.2%, 5.8%-35.8%)
Index TB case female	≥10mm	41/540 (7.6%, 5.5%-10.2%)	109/599 (18.2%, 15.2%-21.5%)
Index TB case male	≥10mm	41/704 (5.8%, 4.2%- 7.8%)	168/882 (19.0%, 16.5%-21.8%)
Index TB case HIV-negative	≥10mm	31/553 (5.6%, 3.8%- 7.9%)	141/648 (21.8%, 18.6%-25.1%)
Index TB case HIV-positive	≥10mm	51/691 (7.4%, 5.5%- 9.6%)	136/833 (16.3%, 13.9%-19.0%)
Index TB case not micro-confirmed TB	≥10mm	2/51 (3.9%, 0.5%-13.5%)	6/27 (22.2%, 8.6%-42.3%)
Index TB case micro-confirmed TB	≥10mm	80/1193 (6.7%, 5.4%- 8.3%)	271/1454 (18.6%, 16.7%-20.7%)
Contact female	≥10mm	51/769 (6.6%, 5.0%- 8.6%)	181/925 (19.6%, 17.1%-22.3%)
Contact male	≥10mm	31/475 (6.5%, 4.5%- 9.1%)	96/556 (17.3%, 14.2%-20.7%)
Contact HIV-negative	≥10mm	67/1129 (5.9%, 4.6%- 7.5%)	232/1286 (18.0%, 16.0%-20.3%)
Contact HIV-positive	≥10mm	15/115 (13.0%, 7.5%-20.6%)	45/195 (23.1%, 17.4%-29.6%)
Contact with index case most of day	≥10mm	24/406 (5.9%, 3.8%- 8.7%)	122/663 (18.4%, 15.5%-21.6%)
Contact with index case part of day	≥10mm	40/609 (6.6%, 4.7%- 8.8%)	133/701 (19.0%, 16.1%-22.1%)
Contact with index case rarely	≥10mm	18/229 (7.9%, 4.7%-12.1%)	22/117 (18.8%, 12.2%-27.1%)
Shares bedroom with index case	≥10mm	72/1134 (6.3%, 5.0%- 7.9%)	205/1162 (17.6%, 15.5%-20.0%)
Doesn't share bedroom with index case	≥10mm	10/110 (9.1%, 4.4%-16.1%)	72/319 (22.6%, 18.1%-27.6%)
Contact never smoked	≥10mm	72/1147 (6.3%, 4.9%- 7.8%)	244/1318 (18.5%, 16.5%-20.7%)
Contact current smoker	≥10mm	9/82 (11.0%, 5.1%-19.8%)	28/134 (20.9%, 14.4%-28.8%)
Contact previous smoker	≥10mm	1/15 (6.7%, 0.2%-31.9%)	5/29 (17.2%, 5.8%-35.8%)

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*95% confidence interval estimated using binomial exact method

Associations with TST positivity

In the directed acyclic graph (S1 Fig), study site and index case HIV status were identified as the minimal sufficient adjustment set of covariates for estimating the direct effect of index case HIV status on latent TB infection. Models fitted to data (Fig 2) showed marked differences between study sites in the proportion of household contacts with TST induration ≥5mm and ≥10mm, with contacts from Mangaung having consistently higher percentages of positivity compared to contacts from Mangaung. Both study sites showed that the probability of household contact TST positivity was highest where the index TB case was young and decreased with index case, but these trends were most pronounced among contacts from Mangaung. Across all index case ages, the mean proportion of contacts with a TST reaction ≥5mm and ≥10mm were consistently higher where the index case was HIV-negative.

Fig 2 — Estimated from fitted predictions from hierarchical multivariable Bernoulli regression model, with random intercepts for households.

In multivariable regression analysis, there was strong evidence that a greater percentage of household contacts resident in Mangaung compared to Capricorn had TST ≥5mm (odds ratio [OR]: 3.08, 95% credible interval [CI]: 2.13–4.58) and ≥10mm (OR: 4.52, 95% CI: 3.03–6.97). Where the index TB case was HIV positive, household contacts were less likely to be TST positive at the ≥5mm threshold (OR: 0.79, 95% CI: 0.56–1.14), or at the ≥10mm thresholds (0.77, 95% CI: 0.53–1.10). The posterior probability that the odds of TST induration ≥5mm were lower in household contacts of HIV-positive compared to HIV-negative index cases was 90%; for TST induration ≥10mm it was 92%.

Each year increase in the age of the index case was associated with a 2% decrease in the odds of TST positivity at the ≥5mm thresholds (OR: 0.98, 95% CI: 0.96–0.99), and a 2% decrease at the ≥10mm threshold (OR: 0.98, 95% CI: 0.97–0.99).

Discussion

In this analysis of >2,500 household contacts of index TB patients in the intervention arm of a cluster-randomised trial in two provinces of South Africa, we found a high prevalence of latent TB infection, with contacts living in Mangaung (a locality with very high annual TB burden), older contacts, and contacts of microbiologically-confirmed index TB cases having a greater prevalence of infection. In contrast, we found that household contacts of HIV-positive index TB cases were less likely to have evidence of latent TB infection compared to contacts of HIV-negative index TB cases.

Of particular concern was the extremely high prevalence of latent TB infection among children aged 0–4 years in Mangaung, with 22% and 20% having indurations of ≥5mm and ≥10mm respectively. Similar rates were reported in a township in the Western Cape [28] and in Matlosana in younger individuals.[29] Evidence increasingly shows that young children (<5 years old) are predominantly exposed to infectious TB outside their immediate household.[17] A high prevalence of latent TB infection among this group is indicative of intense community transmission and high prevalence of undiagnosed infectious TB,[30] meaning that urgent public health action is required to increase case detection and protect vulnerable groups and individuals. We are evaluating an intervention to protect household contacts and improve TB free survival in contacts of TB index patients in the parent cluster randomised trial; results will be reported once the trial has concluded.

We found that, after adjusting for important confounders, household contacts of HIV-positive index cases had substantially lower odds of latent TB infection at both the ≥5mm and ≥10mm thresholds, with differences particularly apparent when compared between study sites. In the Capricorn study site–which included many participants living in rural areas, has a lower HIV prevalence, and almost half the TB annual TB incidence of Mangaung[21]–the proportion of household contacts with immunological evidence of latent TB infection was lower than in Mangaung (which is characterised by large, densely-populated urban and peri-urban townships and high HIV prevalence) across all age bands. In both sites, the prevalence of TST positivity began to decline after approximately age 34–44 years, and may represent a survivorship effect with disproportionate population mortality among younger adults with TB infection over the past 10–20 years who have not survived into older age due to HIV-related mortality.[31]

There is uncertainty over the role of HIV in the infectiousness of pulmonary tuberculosis, particularly with immune reconstitution due to ART.[32] This is partly because of the lack of a robust test for diagnosis of recent TB infection in settings where community force of infection is high, and data suggesting that HIV infection may lead to a shorter duration of symptomatic pulmonary disease and with less cavitation and lower bacillary load in people living with HIV.[33] In a high-TB burden community, TST induration in a household contact may represent infection as a consequence of household exposure from an index case, infection from other community contacts, or both. In this study, we found that contacts of HIV-positive index cases had a lower prevalence of TST positivity compared to contacts of HIV-negative household contacts, even when stratified by study sites with markedly different epidemiological patterns of HIV and TB. Although by no means conclusive and assuming that contacts of HIV-positive and HIV-negative index cases had similar patterns of exposure outside of the household, this suggests that HIV-positive TB patients with pulmonary tuberculosis are less infectious than HIV-negative TB patients. A greater understanding of the possible reasons for this is required, including through comparison of the rate of generation of infectious quanta between HIV-positive and HIV-negative people, and although challenging, prospective cohort studies of incidence in latent TB infection (perhaps using newer, more accurate tests) combined with epidemiological and social mixing data should be considered.

From a public health perspective, a number of recommendations arise from this analysis. In high HIV prevalence settings, substantially greater evidence for the effectiveness and cost-effectiveness of household contact screening vs. current standard of care (primarily delivered in conjunction with passive detection of active TB cases, with few resources available for contact tracing activities, and occasionally through active case finding activities) is needed. Shortened, more tolerable TB preventive treatment regimens may significantly alter what is feasible and acceptable to deliver to individuals and communities,[10] and operational research to develop, refine and evaluate new models will be critical to achieving success. Importantly, as data from the current analysis demonstrates, a single delivery approach generalised across all population groups in highly-differentiated epidemiological settings are unlikely to be successful. Instead, local public health authorities need local-relevant data on the trends and patterns of infection and disease within their populations to inform the design of a suite of TB prevention interventions. This will require a renewal of local public health capacity, as well as strengthened links within and between communities.

There are a number of limitations to this analysis. There were differences in characteristics between individuals who received and did not receive TST in the study, with HIV-positive individuals less likely to complete testing. This could potentially have resulted in selection bias, although it is uncertain in which direction this could have influenced estimates. Although ART status of household contacts was recorded, we were unable to evaluate the interaction between HIV status, ART and age on TST induration diameter due to the small numbers of young children with HIV in this epidemiologically-important age group. PPD was intermittently available during the trial period due to a global shortage, and we had to purchase PPD from different suppliers. We didn’t record Bacille Calmette–Guérin (BCG) vaccination status of household contacts, although coverage of BCG vaccination is high in South Africa. Although standardised training and regular quality assessment of TST procedures was provided (including periods where study coordinators swapped between sites), it is possible that performance of TST (which is subject to operator and measurement error) may have varied between sites. We evaluated the effect of intensity of exposure to index cases by categorising into self-reported groups; although very resource-intensive, future studies may wish to measure exposure prospectively using diaries or contact sensor approaches. Our estimate of the effect of index case HIV-positive status on TST positivity in household contacts can only be interpreted as causal under the assumption of a correctly specified DAG model. Moreover, unmeasured confounding may be an issue, particularly due to latent variables identified but uncontrolled for in the DAG. Finally, in the absence of a gold-standard test for latent TB infection and the limitations of TST, our estimates of prevalence are likely to be subject to some misclassification bias.

In conclusion, we found strong evidence of a very high burden of latent TB infection among household contacts of TB cases in two provinces of South Africa, with worryingly high infection rates among young children in Mangaung, Free State. We found evidence that household contacts of HIV-positive index cases were less likely to have latent TB infection than contacts of HIV-negative index cases. In addition to urgent public health action to reduce TB transmission, new approaches are required to reach population groups at greatest risk and deliver effective TB preventive treatment.

Supporting information

S1 Table. Characteristics of household contacts who did and did not receive tuberculin skin testing.

Putative causal pathways are shown by arrowed lines. Unmeasured variables are filled in white. Using the backdoor criteria, study site, household contact sex and HIV status, and index case age, sex, HIV status and microbiological TB status were identified as the minimal sufficient adjustment set of covariates for estimating the direct effect of index case HIV status on latent TB infection.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S1 Fig. Directed acyclic graph to describe the putative causal relationship between index case HIV status and household contact latent TB infection.

(TIFF)

Click here for additional data file.^{(2.1MB, tiff)}

Data Availability

Code and datasets to reproduce analysis are available to download from https://github.com/petermacp/tstsa

Funding Statement

This study was funded by the South African Medical Research Council and the UK MRC Newton Fund (SAMRC grant number: 96783). The South African Government funded all laboratory investigations. Support and approval was obtained from the Provincial Departments of Health of the Limpopo and Free State Provinces. PM is supported by Wellcome (grant number: WT089673). The funders had no role in the analysis, writing or decision to submit the manuscript.

References

1.Houben RMGJ Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med. 2016;13: e1002152 10.1371/journal.pmed.1002152 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Getahun H, Matteelli A, Chaisson RE, Raviglione M. Latent Mycobacterium tuberculosis infection. N Engl J Med. 2015;372: 2127–2135. 10.1056/NEJMra1405427 [DOI] [PubMed] [Google Scholar]
3.Rangaka MX, Cavalcante SC, Marais BJ, Thim S, Martinson NA, Swaminathan S, et al. Controlling the seedbeds of tuberculosis: diagnosis and treatment of tuberculosis infection. Lancet. 2015;386: 2344–2353. 10.1016/S0140-6736(15)00323-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.World Health Organization. WHO End TB Strategy. In: WHO; [Internet]. 2015. [cited 13 Aug 2019]. Available: https://www.who.int/tb/post2015_strategy/en/ [Google Scholar]
5.World Health Organization. Global tuberculosis report 2018. Geneva; 2018. Available: http://www.who.int/tb/publications/global_report/en/
6.Dye C, Williams BG. Tuberculosis decline in populations affected by HIV: a retrospective study of 12 countries in the WHO African Region. Bulletin of the World Health Organization. 2019;97 10.2471/BLT.18.219980 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Akolo C, Adetifa I, Shepperd S, Volmink J. Treatment of latent tuberculosis infection in HIV infected persons. Cochrane Database Syst Rev. 2010; CD000171 10.1002/14651858.CD000171.pub3 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Zunza M, Gray DM, Young T, Cotton M, Zar HJ. Isoniazid for preventing tuberculosis in HIV-infected children. Cochrane Database Syst Rev. 2017;8: CD006418 10.1002/14651858.CD006418.pub3 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Smieja MJ, Marchetti CA, Cook DJ, Smaill FM. Isoniazid for preventing tuberculosis in non-HIV infected persons. Cochrane Database Syst Rev. 2000; CD001363 10.1002/14651858.CD001363 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Churchyard GJ, Swindells S. Controlling latent TB tuberculosis infection in high-burden countries: A neglected strategy to end TB. PLOS Medicine. 2019;16: e1002787 10.1371/journal.pmed.1002787 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Chaisson RE, Golub JE. Preventing tuberculosis in people with HIV-no more excuses. Lancet Glob Health. 2017;5: e1048–e1049. 10.1016/S2214-109X(17)30390-X [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Lönnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, et al. Tuberculosis control and elimination 2010–50: cure, care, and social development. Lancet. 2010;375: 1814–1829. 10.1016/S0140-6736(10)60483-7 [DOI] [PubMed] [Google Scholar]
13.van Schalkwyk C, Variava E, Shapiro AE, Rakgokong M, Masonoke K, Lebina L, et al. Incidence of TB and HIV in prospectively followed household contacts of TB index patients in South Africa. PLoS ONE. 2014;9: e95372 10.1371/journal.pone.0095372 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Fox GJ, Barry SE, Britton WJ, Marks GB. Contact investigation for tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2013;41: 140–156. 10.1183/09031936.00070812 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Martinez L, Shen Y, Mupere E, Kizza A, Hill PC, Whalen CC. Transmission of Mycobacterium Tuberculosis in Households and the Community: A Systematic Review and Meta-Analysis. Am J Epidemiol. 2017;185: 1327–1339. 10.1093/aje/kwx025 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Ayles H, Muyoyeta M, Du Toit E, Schaap A, Floyd S, Simwinga M, et al. Effect of household and community interventions on the burden of tuberculosis in southern Africa: the ZAMSTAR community-randomised trial. Lancet. 2013;382: 1183–94. 10.1016/S0140-6736(13)61131-9 [DOI] [PubMed] [Google Scholar]
17.Martinez L, Lo NC, Cords O, Hill PC, Khan P, Hatherill M, et al. Paediatric tuberculosis transmission outside the household: challenging historical paradigms to inform future public health strategies. Lancet Respir Med. 2019;7: 544–552. 10.1016/S2213-2600(19)30137-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Erkens CGM, Kamphorst M, Abubakar I, Bothamley GH, Chemtob D, Haas W, et al. Tuberculosis contact investigation in low prevalence countries: a European consensus. Eur Respir J. 2010;36: 925–949. 10.1183/09031936.00201609 [DOI] [PubMed] [Google Scholar]
19.ISRCTN—ISRCTN16006202: Intensified household contact tracing, prevention and treatment support versus enhanced standard of care for index tuberculosis cases in South Africa. [cited 13 Aug 2019]. 10.1186/ISRCTN16006202 [DOI] [PMC free article] [PubMed]
20.Statistics South Africa. Provincial Profile. 2016. Available: http://cs2016.statssa.gov.za
21.Health Systems Trust. District Health Barometer 2015–2016. 2016. Available: https://www.hst.org.za/publications/Pages/-District-Health-Barometer-201516.aspx
22.Southern African HIV Clinicians Society. Adult antiretroviral therapy guidelines. 2017. Available: https://sahivsoc.org/Files/Adult%20ART%20GUIDELINE_June%202017.pdf
23.Sokal JE. Editorial: Measurement of delayed skin-test responses. N Engl J Med. 1975;293: 501–502. 10.1056/NEJM197509042931013 [DOI] [PubMed] [Google Scholar]
24.Harrell FE Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer; 2015. [Google Scholar]
25.McElreath R. Statistical rethinking: a Bayesian course with examples in R and Stan. Boca Raton: CRC Press/Taylor & Francis Group; 2016. [Google Scholar]
26.Bürkner P-C. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software. 2017;80: 1–28. 10.18637/jss.v080.i01 [DOI] [Google Scholar]
27.Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis, Third Edition CRC Press; 2013. [Google Scholar]
28.Wood R, Liang H, Wu H, Middelkoop K, Oni T, Rangaka MX, et al. Changing prevalence of tuberculosis infection with increasing age in high-burden townships in South Africa. Int J Tuberc Lung Dis. 2010;14: 406–12. [PMC free article] [PubMed] [Google Scholar]
29.Lebina L, Abraham PM, Milovanovic M, Motlhaoleng K, Chaisson RE, Rakgokong M, et al. Latent tuberculous infection in schoolchildren and contact tracing in Matlosana, North West Province, South Africa. Int J Tuberc Lung Dis. 2015;19: 1290–1292. 10.5588/ijtld.15.0370 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Rieder H. Annual risk of infection with Mycobacterium tuberculosis. Eur Respir J. 2005;25: 181–185. 10.1183/09031936.04.00103804 [DOI] [PubMed] [Google Scholar]
31.Bor J, Herbst AJ, Newell M-L, Bärnighausen T. Increases in adult life expectancy in rural South Africa: valuing the scale-up of HIV treatment. Science. 2013;339: 961–965. 10.1126/science.1230413 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Khan PY, Crampin AC, Mzembe T, Koole O, Fielding KL, Kranzer K, et al. Does antiretroviral treatment increase the infectiousness of smear-positive pulmonary tuberculosis? Int J Tuberc Lung Dis. 2017;21: 1147–1154. 10.5588/ijtld.17.0162 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Yates TA, Khan PY, Knight GM, Taylor JG, McHugh TD, Lipman M, et al. The transmission of Mycobacterium tuberculosis in high burden settings. Lancet Infect Dis. 2016;16: 227–238. 10.1016/S1473-3099(15)00499-5 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0230376.r001

Decision Letter 0

Richard John Lessells

17 Dec 2019

PONE-D-19-29656

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

PLOS ONE

Dear Dr. MacPherson,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The two reviewers have made some helpful comments and suggestions on the manuscript - I would encourage you to address these in full. In particular, I do agree with the point from reviewer #1 that there could perhaps be stronger justification for analyzing TST induration as a continuous variable in your model.

In terms of the first major comment from reviewer #2, I'm not quite sure if they are just suggesting some more discussion of the likely direction of bias based on the minor differences between those with vs without TST results, or suggesting imputing outcome data for the missing TST results. I would suggest the former would be fine, but I don't think the latter is necessary.

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Reviewer #2: Yes

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Reviewer #1: This a an interesting analysis of a single arm of a clustered randomised clinical trial conducted in two study sites with different annual incidence of TB (Mangaung Municipality in the Free State and Capricorn District in Limpopo Province).

The analysis is well conducted and manuscript easy to read.

I have a number of points that need to be addressed:

1. I am unclear why the length of induration as a continuous variable was used as the primary endpoint. The diameters of 5 and 10 mm have been mentioned as standard clinical cut-off.

Thus, for example what is the clinical relevance of a difference in risk of 3.68 (95% 3.04-4.41) comparing the two sites? Is this a risk or, being the endpoint continuous, a difference in mm? I would have been more relevant to show that the proportion of people with a diameter >5 mm (or >100 mm) was higher in one site compared to the other. All other detected differences are even smaller than 3.7 (mm?). Figure 3 clearly shows means in mm.

2. In the Discussion it is stated that the induration was greater in HIV-positive compared to HIV-negative contacts after controlling for confounding factors. My understanding is that potential confounding was controlled by fitting random intercepts for each household to allow partial pooling of estimates and to account for clustering of characteristics within households. Correct? Going back for example to the comparison by sites, there were clear differences in terms of prevalence of HIV, frequency of contact with the index case as well as modality of sleeping (in the same bed as index case or not). All these are causes of diameter induration but consequences of residency so are more mediators than confounders. How does the multilevel Bayesian zero-inflated Poisson mixture model account for mediation?

3. Lines 236- 249. Four factors are mentioned as associated with diameter induration from fitting a multivariable model in this section: study site, HIV-status of contacts, HIV-status of index case and proportion of the day spent with index case. It is unclear whether these were ‘independent’ associations. Following from point #2 it makes sense to control for study site when evaluating the effect of HIV-positive status (as study site is a confounder). Viceversa, it seems inappropriate to control for HIV-status in the model focusing on study site as the exposure as HIV-status is a consequence of residency and not a cause. Authors should consider fitting different models according to the exposure of interest and control only for confounding factors, not mediator or possible colliders. Finally, what about age? There is not mention of age (strongly associated in univariable analysis) in the multivariable model (Figure 2).

4. There is no discussion of the fact that proportion of day spent with index case showed a significant association with diameter induration but sleeping in the same bed with the index case was not. What are the possible speculations for these findings?

5. Identifying predictors of larger diameter seems the main aim of the analysis. Nevertheless, message appear to be contradictory. Lines 241-243 implies that HIV-status of index case is indeed a predictor. In contrast in the Discussion it is stated that ‘In this study, we found that contacts of HIV-positive index cases had similar median posterior TST induration diameters to contacts of HIV-negative household contacts, even when stratified by study sites with markedly different epidemiological patterns of HIV and TB’. In general, there is a lack of standardisation in the presentation of results (HIV-status and age seem to be the only important predictor from reading the Conclusions of the abstract; HIV-status and duration of contacts with index case in the last paragraph of the Discussion). If the aim of the paper is direct public health efforts to specific target populations the identification of this population is rather confusing

6. Title of Table 1 should be. Characteristics of index cases (top) and household contacts (bottom) by Study site

7. Lines 224-227. From the introducing text a reader would expect to see the comparison of percentage of positivity in the 0-4 years old vs. >4 years old stratified by site, not the difference in proportion of positivity in the 0-4 years old by site alone.

8. Line 255. What was the p-value for this 3-way interaction? Wonder whether instead of describing the effect of age by HIV-status/study would have been more informative to give the differences according to HIV-status by age/study strata?

9. Abstract line 60. Data were collected prospectively as part 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).

I would rephrase making it clear from the start that this analysis only include the data from the intervention arm. Essentially is a cohort study using a single arm of a trial. This only becomes clear from reading the full paper.

10. Page 16, four lines form the end. Typo mortality instead of morality

11. Page 19. what does BCG stand for? Spell out

12. Figure 1. Add a footnote to clarify that top of the figure is for cut off 5 mm and bottom for 10 mm?

13. Figure 2. Are these mean differences in diameter or RR? RR of what?

14. Figure 3 How can age of house contact be -1? Why is only the range [-1; +3] shown?

Reviewer #2: I commend the authors for the important study on the major public health problem in South Africa. Although study results are compelling, there are few methodological issues which need to be addressed/further explained in order to improve this manuscript.

Major Issue

• Page 18, paragraph 5, limitations: Authors have acknowledged that there were differences between individuals who received and did not TST in the study and these differences could have resulted in selection bias. I would suggest they use the data on those who did not receive TST to investigate how it could have influenced the results.

Minor Issue

• Page 6 line 151: Authors need to describe cut-offs of skin induration for LTBI under Tuberculin skin testing sub-section of Material and Methods section and which guidelines the cut-offs are based on.

In addition, the manuscript is clearly written in a professional manner. Conclusions are presented in an appropriate fashion and are supported by the data If there is a weakness, it is minor methodological issues (as I have noted above).

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Reviewer #1: No

Reviewer #2: Yes: Jabulani Ronnie Ncayiyana

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PLoS One. 2020 Mar 17;15(3):e0230376. doi: 10.1371/journal.pone.0230376.r002

Author response to Decision Letter 0

29 Jan 2020

Wednesday, 29th January 2020

Dear Prof Lessells,

Re: PONE-D-19-29656. 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

I write on behalf of my co-authors to thank the reviewers for their careful review, and appreciate the opportunity to respond with a revised submission. Please see a point-by-point response below.

We are particularly grateful for the Reviewers’ suggestions for additional analyses and interpretation; these have undoubtedly strengthened the manuscript. In particular, we have: formulated a more specific hypothesis to be tested in the analysis; constructed a directed acyclic graph to specify the putative causal pathway between index TB case HIV status and household contact latent TB infection, accounting for mediating factors and confounders to identify the minimal sufficient adjustment set of covariates for estimating the direct causal effect; and refined statistical modelling approached to estimate posterior distributions against clinically meaningful outcomes (the proportion of household contacts with tuberculin skin test induration ≥5mm and ≥5mm). Together, we hope that these revised analyses are now methodologically rigorous and will provide strong evidence of clinical and public health relevance.

We look forward to your response.

Yours Sincerely,

Peter MacPherson

(On behalf of the authors)

RESPONSE TO REVIEWER 1’S COMMENTS

1. This an interesting analysis of a single arm of a clustered randomised clinical trial conducted in two study sites with different annual incidence of TB (Mangaung Municipality in the Free State and Capricorn District in Limpopo Province). The analysis is well conducted and manuscript easy to read.

We thank the Reviewer for their detailed comments, which are extremely helpful, and have undoubtedly helped strengthen the manuscript.

2. I am unclear why the length of induration as a continuous variable was used as the primary endpoint. The diameters of 5 and 10 mm have been mentioned as standard clinical cut-off.

Thank you for this comment. Having carefully reviewed the Reviewer’s extremely helpful suggestions here and below, we have revised our analysis strategy to model the proportion of household contacts with TST induration ≥5mm and ≥10mm. We had previously attempted to model TST induration as a continuous mixture distribution, but appreciate that this approach may have been difficult to interpret and have had less immediate public health relevance. We believe that our revised analyse provides stronger evidence of greater clinical and public health relevance (see Statistical Methods section – line 188-288 for revised description of analytic approach.).

3. In the Discussion it is stated that the induration was greater in HIV-positive compared to HIV-negative contacts after controlling for confounding factors. My understanding is that potential confounding was controlled by fitting random intercepts for each household to allow partial pooling of estimates and to account for clustering of characteristics within households. Correct? Going back for example to the comparison by sites, there were clear differences in terms of prevalence of HIV, frequency of contact with the index case as well as modality of sleeping (in the same bed as index case or not). All these are causes of diameter induration but consequences of residency so are more mediators than confounders. How does the multilevel Bayesian zero-inflated Poisson mixture model account for mediation?

Again, we thank the Reviewer for this insightful comment. The Reviewer rightly notes that that some variable previously identified as confounders may have in fact been mediators. To address this comment, before conducting our revised statistical analysis, we undertook two actions. Firstly, we formulated a more-specific testable hypothesis to be addressed by the analysis, namely: “are household contacts of HIV-positive index TB patients less likely to have latent TB infection?”. Secondly, we constructed a directed acyclic graph based on our knowledge and literature of the epidemiology of TB transmission to specify the putative causal pathway between index TB case HIV status and household contact latent TB infection, accounting for mediating factors and confounders and to identify the minimal sufficient adjustment set of covariates for estimating the direct causal effect. This graph can now be seen in Supplemental Figure 1. By explicitly describing the proposed causal pathway between index case HIV status and household contact latent TB infection, we hope that this will have resulted in a more rational approach to variable inclusion in models. Indeed, variables that the Reviewer identified as potential mediators in the relationship were no longer required to be adjusted for in the fully specified model.

To answer the question about partial pooling – yes, including a random intercept term for households allows information to be shared between households, reducing uncertainty whilst accounting for the clustering of characteristics between households.

4. Lines 236- 249. Four factors are mentioned as associated with diameter induration from fitting a multivariable model in this section: study site, HIV-status of contacts, HIV-status of index case and proportion of the day spent with index case. It is unclear whether these were ‘independent’ associations. Following from point #2 it makes sense to control for study site when evaluating the effect of HIV-positive status (as study site is a confounder). Viceversa, it seems inappropriate to control for HIV-status in the model focusing on study site as the exposure as HIV-status is a consequence of residency and not a cause. Authors should consider fitting different models according to the exposure of interest and control only for confounding factors, not mediator or possible colliders. Finally, what about age? There is not mention of age (strongly associated in univariable analysis) in the multivariable model (Figure 2).

Thank you. As described in response to point 3 above, we have revised our modelling approach to be hypothesis driven, and adapted variable inclusion strategy to be based upon the causal pathways inferred by the directed acyclic graph. We hope that this principled approach provides strong rationale for the model specification approach taken.

We have now added additional text to describe the effect of index case age on household contact latent TB prevalence (lines 328-329), and have plotted new figures that we believe better convey the importance of household contact age on the risk of TST induration (Figure 1).

5. There is no discussion of the fact that proportion of day spent with index case showed a significant association with diameter induration but sleeping in the same bed with the index case was not. What are the possible speculations for these findings?

In our revised model, neither proportion of day spent with index case nor sleeping in the same bed as the index case were identified as variables necessary to include in the final models. Thus the inconsistent results described above may have bene an artifact of the previous modelling approach.

6. Identifying predictors of larger diameter seems the main aim of the analysis. Nevertheless, message appear to be contradictory. Lines 241-243 implies that HIV-status of index case is indeed a predictor. In contrast in the Discussion it is stated that ‘In this study, we found that contacts of HIV-positive index cases had similar median posterior TST induration diameters to contacts of HIV-negative household contacts, even when stratified by study sites with markedly different epidemiological patterns of HIV and TB’. In general, there is a lack of standardisation in the presentation of results (HIV-status and age seem to be the only important predictor from reading the Conclusions of the abstract; HIV-status and duration of contacts with index case in the last paragraph of the Discussion). If the aim of the paper is direct public health efforts to specific target populations the identification of this population is rather confusing.

Thank you for these helpful comments. We hope that the revised modelling approach described in response to points 1-5 above have addressed these concerns. In addition, we have made substantial revisions to the Discussion and Conclusion to provide greater clarity of the clinical and public health implications of the research findings.

7. Title of Table 1 should be. Characteristics of index cases (top) and household contacts (bottom) by Study site

Thank you, we have made this change.

8. Lines 224-227. From the introducing text a reader would expect to see the comparison of percentage of positivity in the 0-4 years old vs. >4 years old stratified by site, not the difference in proportion of positivity in the 0-4 years old by site alone.

Thank you – we have revised this paragraph to add clarity.

9. Line 255. What was the p-value for this 3-way interaction? Wonder whether instead of describing the effect of age by HIV-status/study would have been more informative to give the differences according to HIV-status by age/study strata?

In the revised models, we have removed analysis of the three-way interactions. Instead, we have presented fitted posterior distributions of TST positivity for index case age groups stratified by index case HIV status and microbiological status, study site, and household HIV status.

10. Abstract line 60. Data were collected prospectively as part 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).

Thank you. We have added this to the Abstract

11. Page 16, four lines form the end. Typo mortality instead of morality

We have made this correction.

12. Page 19. what does BCG stand for? Spell out

BCG stands for Bacille Calmette–Guérin, the vaccine against tuberculosis. We have spelt this out.

13. Figure 1. Add a footnote to clarify that top of the figure is for cut off 5 mm and bottom for 10 mm?

We have removed this figure in the revised version of the manuscript.

14. Figure 2. Are these mean differences in diameter or RR? RR of what?

We have removed this figure in the revised version of the manuscript.

15. Figure 3 How can age of house contact be -1? Why is only the range [-1; +3] shown?

We have removed this figure in the revised version of the manuscript.

RESPONSE TO REVIEWER 2’S COMMENTS

1. I commend the authors for the important study on the major public health problem in South Africa. Although study results are compelling, there are few methodological issues which need to be addressed/further explained in order to improve this manuscript.

We thank the Reviewer for these comments.

2. Page 18, paragraph 5, limitations: Authors have acknowledged that there were differences between individuals who received and did not TST in the study and these differences could have resulted in selection bias. I would suggest they use the data on those who did not receive TST to investigate how it could have influenced the results.

Thank you. In Supplemental Table 1, we have presented characteristics of participants, stratified by whether TST testing was done or not. We hope that this provides the clarity required.

3. Page 6 line 151: Authors need to describe cut-offs of skin induration for LTBI under Tuberculin skin testing sub-section of Material and Methods section and which guidelines the cut-offs are based on.

Thank you. We have added text to describe the two thresholds selected for analysis (≥5mm and ≥10mm) – Lines 205-208. In particular, we note that the updated 2018 latent TB guidelines emphasise that there is no gold-standard test for latent tuberculosis infection and that there is no consensus about optimal thresholds for a) use in predicting subsequent progression to active tuberculosis, or b) to inform treatment decisions, either for individual people, or groups of patients. Therefore, we elected to select two conventionally-used thresholds in our analysis: ≥5mm and ≥10mm.

PLoS One. doi: 10.1371/journal.pone.0230376.r003

Decision Letter 1

Richard John Lessells

25 Feb 2020

PONE-D-19-29656R1

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

PLOS ONE

Dear Dr. MacPherson,

==============================

Thank you for engaging so thoroughly and thoughtfully with the initial round of comments from the two reviewers. There are just a few remaining minor comments from reviewer #1. If these can be addressed I would be happy to accept the paper.

==============================

We would appreciate receiving your revised manuscript by Apr 10 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Richard John Lessells, BSc, MBChB, MRCP, DTM&H, DipHIVMed, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: This a much improved version of the analysis in which the authors have formulated a more specific hypothesis to be tested and constructed a directed acyclic graph (DAG) to specify the putative causal pathway between index TB case HIV status and household contact latent TB infection, accounting for mediating factors and confounders to identify the minimal sufficient adjustment set of covariates for estimating the direct causal effect. They also refined the statistical modelling by estimating a posterior distributions against more clinically meaningful outcomes (the proportion of household contacts with tuberculin skin test induration ≥5mm and ≥5mm).

I have a few extra requests of clarification.

1. From the described DAG there are only two identified confounders: index case age and province (labelled with a pink rectangular in the Figure). Therefore if the authors did use the minimal sufficient adjustment set of covariates for estimating the direct causal effect I would expect in Table 2 the models to be controlled for only these two factors and nothing else.

2. At page 20 of the revised Discussion the sentence that the results were obtained after controlling for both confounders and mediators is retained. This is worrying. For clarity to control for a mediator would be a mistake because the indirect effect is part of the causal effect that needs to be estimated so it does not need to be adjusted away.

3. It is ok to keep the DAG Figure as supplementary material but the text in the Methods need to be expanded to clarify the strategy used to construct the multivariable binomial regression. In other words, it has to be said that under the assumption described in the DAG age and province are the only two confounders and that controlling for these factors would remove all backdoor pathways of measured confounding. This appears later in results (lines 235-237) but the set of confounders listed (apart from index case age which is an ancestor of exposure and outcome) none of the others (study site, household contact sex and HIV status, sex, HIV status and microbiological TB status) seem to match the DAG.

4. I would also add a sentence in the limitations at page 22 of the Discussion to say that because only the intervention arm of the trial were used, unmeasured confounding is an issue. There are indeed latent variables described in the DAG which could be additional confounders that cannot be controlled for and that, more generally, the estimate can be interpreted as causal only under the assumption of a correctly specified DAG model.

Reviewer #2: (No Response)

**********

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Reviewer #2: No

PLoS One. 2020 Mar 17;15(3):e0230376. doi: 10.1371/journal.pone.0230376.r004

Author response to Decision Letter 1

26 Feb 2020

Wednesday, 26 February 2020

Dear Prof Lessells,

I write on behalf of my co-authors to thank the reviewers for their careful second review, and appreciate the opportunity to respond with a revised submission. Please see a point-by-point response below.

We are particularly grateful for the Reviewers’ careful and consider suggestions for refinement to our analysis strategy, and again feel that they have provide substantial and positive input to this analysis, for which we are very grateful.

We look forward to your response.

Yours Sincerely,

Peter MacPherson

(On behalf of the authors)

RESPONSE TO REVIEWER 1’S COMMENTS

1. This a much improved version of the analysis in which the authors have formulated a more specific hypothesis to be tested and constructed a directed acyclic graph (DAG) to specify the putative causal pathway between index TB case HIV status and household contact latent TB infection, accounting for mediating factors and confounders to identify the minimal sufficient adjustment set of covariates for estimating the direct causal effect. They also refined the statistical modelling by estimating a posterior distributions against more clinically meaningful outcomes (the proportion of household contacts with tuberculin skin test induration ≥5mm and ≥5mm).

Thank you for these comments.

2. From the described DAG there are only two identified confounders: index case age and province (labelled with a pink rectangular in the Figure). Therefore if the authors did use the minimal sufficient adjustment set of covariates for estimating the direct causal effect I would expect in Table 2 the models to be controlled for only these two factors and nothing else.

Thank you for this comment and recommendation. We have revised the analysis to adjust for only index case age and province in the regression models now. Results are reporting in lines 274-302, and displayed visually in the new Figure 2. To allow readers to interpret data on TST positivity by other key characteristics (including age group of household contact), we have added a new Table 2 and Figure 1 reporting crude results.

3. At page 20 of the revised Discussion the sentence that the results were obtained after controlling for both confounders and mediators is retained. This is worrying. For clarity to control for a mediator would be a mistake because the indirect effect is part of the causal effect that needs to be estimated so it does not need to be adjusted away.

Thank you. We have corrected this sentence to note that only confounders were adjusted for in the analysis.

4. It is ok to keep the DAG Figure as supplementary material but the text in the Methods need to be expanded to clarify the strategy used to construct the multivariable binomial regression. In other words, it has to be said that under the assumption described in the DAG age and province are the only two confounders and that controlling for these factors would remove all backdoor pathways of measured confounding. This appears later in results (lines 235-237) but the set of confounders listed (apart from index case age which is an ancestor of exposure and outcome) none of the others (study site, household contact sex and HIV status, sex, HIV status and microbiological TB status) seem to match the DAG.

Thank you. We have corrected this description of the modelling strategy to reflect the adjustment for the minimally sufficient set of confounders (site and index case age) and to match the DAG.

5. I would also add a sentence in the limitations at page 22 of the Discussion to say that because only the intervention arm of the trial were used, unmeasured confounding is an issue. There are indeed latent variables described in the DAG which could be additional confounders that cannot be controlled for and that, more generally, the estimate can be interpreted as causal only under the assumption of a correctly specified DAG model.

We have added a sentence to the limitations to highlight the issue of potential unadjusted confounders, and the fact that the estimate can be interpreted as causal only under the assumption of a correctly specified DAG:

“Our estimate of the effect of index case HIV-positive status on TST positivity in household contacts can only be interpreted as causal under the assumption of a correctly specified DAG model. Moreover, unmeasured confounding may be an issue, particularly due to latent variables identified but uncontrolled for in the DAG.”

PLoS One. doi: 10.1371/journal.pone.0230376.r005

Decision Letter 2

Richard John Lessells

28 Feb 2020

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

PONE-D-19-29656R2

Dear Dr. MacPherson,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Richard John Lessells, BSc, MBChB, MRCP, DTM&H, DipHIVMed, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0230376.r006

Acceptance letter

Richard John Lessells

5 Mar 2020

PONE-D-19-29656R2

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

Dear Dr. MacPherson:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Characteristics of household contacts who did and did not receive tuberculin skin testing.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S1 Fig. Directed acyclic graph to describe the putative causal relationship between index case HIV status and household contact latent TB infection.

(TIFF)

Click here for additional data file.^{(2.1MB, tiff)}

Data Availability Statement

Code and datasets to reproduce analysis are available to download from https://github.com/petermacp/tstsa

[pone.0230376.ref001] 1.Houben RMGJ Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med. 2016;13: e1002152 10.1371/journal.pmed.1002152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref002] 2.Getahun H, Matteelli A, Chaisson RE, Raviglione M. Latent Mycobacterium tuberculosis infection. N Engl J Med. 2015;372: 2127–2135. 10.1056/NEJMra1405427 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref003] 3.Rangaka MX, Cavalcante SC, Marais BJ, Thim S, Martinson NA, Swaminathan S, et al. Controlling the seedbeds of tuberculosis: diagnosis and treatment of tuberculosis infection. Lancet. 2015;386: 2344–2353. 10.1016/S0140-6736(15)00323-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref004] 4.World Health Organization. WHO End TB Strategy. In: WHO; [Internet]. 2015. [cited 13 Aug 2019]. Available: https://www.who.int/tb/post2015_strategy/en/ [Google Scholar]

[pone.0230376.ref005] 5.World Health Organization. Global tuberculosis report 2018. Geneva; 2018. Available: http://www.who.int/tb/publications/global_report/en/

[pone.0230376.ref006] 6.Dye C, Williams BG. Tuberculosis decline in populations affected by HIV: a retrospective study of 12 countries in the WHO African Region. Bulletin of the World Health Organization. 2019;97 10.2471/BLT.18.219980 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref007] 7.Akolo C, Adetifa I, Shepperd S, Volmink J. Treatment of latent tuberculosis infection in HIV infected persons. Cochrane Database Syst Rev. 2010; CD000171 10.1002/14651858.CD000171.pub3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref008] 8.Zunza M, Gray DM, Young T, Cotton M, Zar HJ. Isoniazid for preventing tuberculosis in HIV-infected children. Cochrane Database Syst Rev. 2017;8: CD006418 10.1002/14651858.CD006418.pub3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref009] 9.Smieja MJ, Marchetti CA, Cook DJ, Smaill FM. Isoniazid for preventing tuberculosis in non-HIV infected persons. Cochrane Database Syst Rev. 2000; CD001363 10.1002/14651858.CD001363 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref010] 10.Churchyard GJ, Swindells S. Controlling latent TB tuberculosis infection in high-burden countries: A neglected strategy to end TB. PLOS Medicine. 2019;16: e1002787 10.1371/journal.pmed.1002787 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref011] 11.Chaisson RE, Golub JE. Preventing tuberculosis in people with HIV-no more excuses. Lancet Glob Health. 2017;5: e1048–e1049. 10.1016/S2214-109X(17)30390-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref012] 12.Lönnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, et al. Tuberculosis control and elimination 2010–50: cure, care, and social development. Lancet. 2010;375: 1814–1829. 10.1016/S0140-6736(10)60483-7 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref013] 13.van Schalkwyk C, Variava E, Shapiro AE, Rakgokong M, Masonoke K, Lebina L, et al. Incidence of TB and HIV in prospectively followed household contacts of TB index patients in South Africa. PLoS ONE. 2014;9: e95372 10.1371/journal.pone.0095372 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref014] 14.Fox GJ, Barry SE, Britton WJ, Marks GB. Contact investigation for tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2013;41: 140–156. 10.1183/09031936.00070812 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref015] 15.Martinez L, Shen Y, Mupere E, Kizza A, Hill PC, Whalen CC. Transmission of Mycobacterium Tuberculosis in Households and the Community: A Systematic Review and Meta-Analysis. Am J Epidemiol. 2017;185: 1327–1339. 10.1093/aje/kwx025 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref016] 16.Ayles H, Muyoyeta M, Du Toit E, Schaap A, Floyd S, Simwinga M, et al. Effect of household and community interventions on the burden of tuberculosis in southern Africa: the ZAMSTAR community-randomised trial. Lancet. 2013;382: 1183–94. 10.1016/S0140-6736(13)61131-9 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref017] 17.Martinez L, Lo NC, Cords O, Hill PC, Khan P, Hatherill M, et al. Paediatric tuberculosis transmission outside the household: challenging historical paradigms to inform future public health strategies. Lancet Respir Med. 2019;7: 544–552. 10.1016/S2213-2600(19)30137-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref018] 18.Erkens CGM, Kamphorst M, Abubakar I, Bothamley GH, Chemtob D, Haas W, et al. Tuberculosis contact investigation in low prevalence countries: a European consensus. Eur Respir J. 2010;36: 925–949. 10.1183/09031936.00201609 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref019] 19.ISRCTN—ISRCTN16006202: Intensified household contact tracing, prevention and treatment support versus enhanced standard of care for index tuberculosis cases in South Africa. [cited 13 Aug 2019]. 10.1186/ISRCTN16006202 [DOI] [PMC free article] [PubMed]

[pone.0230376.ref020] 20.Statistics South Africa. Provincial Profile. 2016. Available: http://cs2016.statssa.gov.za

[pone.0230376.ref021] 21.Health Systems Trust. District Health Barometer 2015–2016. 2016. Available: https://www.hst.org.za/publications/Pages/-District-Health-Barometer-201516.aspx

[pone.0230376.ref022] 22.Southern African HIV Clinicians Society. Adult antiretroviral therapy guidelines. 2017. Available: https://sahivsoc.org/Files/Adult%20ART%20GUIDELINE_June%202017.pdf

[pone.0230376.ref023] 23.Sokal JE. Editorial: Measurement of delayed skin-test responses. N Engl J Med. 1975;293: 501–502. 10.1056/NEJM197509042931013 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref024] 24.Harrell FE Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer; 2015. [Google Scholar]

[pone.0230376.ref025] 25.McElreath R. Statistical rethinking: a Bayesian course with examples in R and Stan. Boca Raton: CRC Press/Taylor & Francis Group; 2016. [Google Scholar]

[pone.0230376.ref026] 26.Bürkner P-C. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software. 2017;80: 1–28. 10.18637/jss.v080.i01 [DOI] [Google Scholar]

[pone.0230376.ref027] 27.Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis, Third Edition CRC Press; 2013. [Google Scholar]

[pone.0230376.ref028] 28.Wood R, Liang H, Wu H, Middelkoop K, Oni T, Rangaka MX, et al. Changing prevalence of tuberculosis infection with increasing age in high-burden townships in South Africa. Int J Tuberc Lung Dis. 2010;14: 406–12. [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref029] 29.Lebina L, Abraham PM, Milovanovic M, Motlhaoleng K, Chaisson RE, Rakgokong M, et al. Latent tuberculous infection in schoolchildren and contact tracing in Matlosana, North West Province, South Africa. Int J Tuberc Lung Dis. 2015;19: 1290–1292. 10.5588/ijtld.15.0370 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref030] 30.Rieder H. Annual risk of infection with Mycobacterium tuberculosis. Eur Respir J. 2005;25: 181–185. 10.1183/09031936.04.00103804 [DOI] [PubMed] [Google Scholar]

[pone.0230376.ref031] 31.Bor J, Herbst AJ, Newell M-L, Bärnighausen T. Increases in adult life expectancy in rural South Africa: valuing the scale-up of HIV treatment. Science. 2013;339: 961–965. 10.1126/science.1230413 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref032] 32.Khan PY, Crampin AC, Mzembe T, Koole O, Fielding KL, Kranzer K, et al. Does antiretroviral treatment increase the infectiousness of smear-positive pulmonary tuberculosis? Int J Tuberc Lung Dis. 2017;21: 1147–1154. 10.5588/ijtld.17.0162 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230376.ref033] 33.Yates TA, Khan PY, Knight GM, Taylor JG, McHugh TD, Lipman M, et al. The transmission of Mycobacterium tuberculosis in high burden settings. Lancet Infect Dis. 2016;16: 227–238. 10.1016/S1473-3099(15)00499-5 [DOI] [PubMed] [Google Scholar]

PERMALINK

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

Limakatso Lebina

Kegaugetswe Motsomi

Zama Bosch

Minja Milovanovic

Andrew Ratsela

Sanjay Lala

Ebrahim Variava

Jonathan E Golub

Emily L Webb

Neil A Martinson

Roles

Abstract

Introduction

Materials and methods

Results

Conclusions

Introduction

Materials and methods

Study setting and participants

Tuberculin skin testing

Ethical considerations

Statistical methods

Data sharing

Results

Characteristics of index patients and household contacts

Table 1. Characteristics of index cases (top), dwellings (centre), and household contacts (bottom) by study site.

Tuberculin skin testing

Fig 1. Tuberculin skin test positivity (≥5mm, ≥10mm) by household contact age group in Capricorn and Mangaung, South Africa.

Table 2. Tuberculin skin test positivity by index TB case and household contact characteristics.

Associations with TST positivity

Fig 2. Model-fitted percentage of household contacts with tuberculin skin test induration ≥5mm and ≥10mm by index TB case age and study site.

Discussion

Supporting information

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Richard John Lessells

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