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Review 5: "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 onJan 18, 2024
Review 5: "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

Abstract 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:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.

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Review:

The study examined key risk factors for active tuberculosis (TB) within a population with high TB incidence in Northern Cape Province, South Africa. It revealed that specific demographic shifts, such as changes in socioeconomic status and residential mobility, exerted a significant influence on TB risk. 

Dr. Oyageshio and the authors assessed the risk factors for active TB using logistic regression and random forest models in a case-control study design in Northern Cape, South Africa. The manuscript is well written, and all data are well analyzed and presented. The conclusions are supported by the data. 

Here are some minor comments: 

  1. Why would the case group include an individual with a TB history? It was self-reported, and the duration between the last episode and the current enrollment varied in this group. What about the same analyses without the individual with TB history in the case group?

  2. The host genetic information is not necessary to be part of this manuscript nor in the risk factor analysis. 

The results pertaining to demographic dynamics underscore the significance of individual factor heterogeneity in influencing the risk of TB epidemics. This insight can guide the development of targeted and efficient interventions tailored to local TB control efforts. 

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