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Review 3: "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 3: "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:

  • 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 study analyzed demographic and life style risk factors for tuberculosis (TB). Patients were recruited from community health clinics and cases were defined by a diagnosis of active TB. The case control sample set included 374 cases and 504 controls. Demographic and SES risk factors were analyzed. In addition to univariate analysis, and logistic regression analysis, random forest modeling was also used. The most striking results were the strong interaction effect between SES, including age and year of education, and birth place.

While such interaction effects are interesting, the authors should elaborate more how it may affect the TB epidemiology based on the predicted population dynamics in the next 10 years or so. Or in other words, how would the authors predict the incidence of TB to go up or down in the next 10 years if the interaction effects are true?

The use of random forest model in this study is not fully elaborated. Why is it used? Did it lead to new conclusion? It appears that RF did not show the interaction effect. The authors could further address these points. If RF is not revealing or playing any role, that section could be removed to improve clarity.

Overall, this manuscript reveals interesting interaction effects of epidemiology and demographic risk factors of TB. Firstly, interactions are rarely studied and secondly, this study showed convincing interaction effects. This will lead to new insight into TB epidemiology. 

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Comments
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Charmaine Mia:

Further fnaf elaboration on the implications and limitations of the findings is necessary to increase their potential informativeness and usefulness for decision-making.