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Review 2: "Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa"

Reviewer concerns include insufficient motivation for interaction terms, limited covariates of interest, and insufficient details given for how the genomic data was used.

Published onDec 12, 2023
Review 2: "Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa"
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Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa
Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa
Description

South Africa is among the world’s top eight TB burden countries, and despite a focus on HIV-TB co-infection, most of the population living with TB are not HIV co-infected. The disease is endemic across the country with 80-90% exposure by adulthood. We investigated epidemiological risk factors for tuberculosis (TB) in the Northern Cape Province, South Africa: an understudied TB endemic region with extreme TB incidence (645/100,000) and the lowest provincial population density. We leveraged the population’s high TB incidence and community transmission to design a case-control study with population-based controls, reflecting similar mechanisms of exposure between the groups. We recruited 1,126 participants with suspected TB from 12 community health clinics, and generated a cohort of 878 individuals (cases =374, controls =504) after implementing our enrollment criteria. All participants were GeneXpert Ultra tested for active TB by a local clinic. We assessed important risk factors for active TB using logistic regression and random forest modeling. Additionally, a subset of individuals were genotyped to determine genome-wide ancestry components. Male gender had the strongest effect on TB risk (OR: 2.87 [95% CI: 2.1-3.8]); smoking and alcohol consumption did not significantly increase TB risk. We identified two interactions: age by socioeconomic status (SES) and birthplace by residence ­­locality on TB risk (OR = 3.05, p = 0.016) – where rural birthplace but town residence was the highest risk category. Finally, participants had a majority Khoe-San ancestry, typically greater than 50%.  Epidemiological risk factors for this cohort differ from other global populations. The significant interaction effects reflect rapid changes in SES and mobility over recent generations and strongly impact TB risk in the Northern Cape of South Africa. Our models show that such risk factors combined explain 16% of the variance (r2) in case/control status.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

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Review:

The authors present a case control study that aims to identify factors associated with TB in a high-burden but understudied population in South Africa. They identify gender as an important risk factor for TB, after adjusting for age, smoking, alcohol use, and location of residence. The authors also identify an interaction between years of education and age as risk factors for TB, such that higher levels of education are associated with reduced odds of TB among younger adults, but increased odds of TB among older adults. 

The study is of potential importance, but the design of the study needs further clarification to allow for interpretation of the results. The rationale for the definition of “cases” is not clear – the authors combined individuals with active confirmed TB and those with a self-reported history of TB, which are two very different groups. In particular, the self-report group is problematic as 1. Previous studies have documented that self-report of TB is often inaccurate (especially the distinction between active TB and latent infection) and 2. The exposures (age, smoking, residence, etc.) are being ascertained at a different timepoint than the outcome (i.e., exposures are measured at study enrollment, not retrospectively). Additionally, the exclusion of HIV-infected individuals only from the cases may lead to bias, as some of the controls may have HIV infection and that is not accounted for in the model. Finally, the authors assume that the controls are TB infected, given the high prevalence of TB infection in the population. However, that is a strong assumption (especially for the younger age group) and the authors may be better off comparing those with active TB to those with active TB ruled out. It would also be helpful to clarify what the authors mean by using “population-based controls” – as it seems that the recruitment of both cases and controls was done at clinics providing Xpert testing.

The authors also overstate the impact of socioeconomic status – the only measure of SES used in the study is years of education. In drawing conclusions around the association of SES and TB, it would be better to emphasize that education is one piece of SES. If the authors have no other SES data available, that should be mentioned as a limitation.

The covariates of interest are rather limited and have been identified as relevant in other contexts. Thus, the additional contribution of this analysis to scientific evidence is unclear. It may strengthen the analysis to look at additional stratifications and interactions to understand why well-known factors associated with TB such as smoking and diabetes may not be relevant in this population. Additionally, please consider describing how diabetes was diagnosed or measured – was it self-report or a blood test?

The inclusion of genomic ancestry data is potentially interesting, but the authors make little use of it. They describe the genetic ancestry results and the self-reported ethnicity data, but do not make comparisons across these two measures or investigate the potential contribution of genetic factors to TB risk.

Overall, this study addresses an important issue in a population that is understudied and underserved, but the methodological challenges combined with limited risk factors of interest limit the conclusions that can be drawn from the study. 

The importance of well-known risk factors for TB may vary in different populations. This analysis confirms other research that men are at increased risk for TB, but also finds that education and location of residence may play a bigger role than smoking, alcohol use, and diabetes in this South African population. 

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