Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa

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.

Fig 1.. Case-Control Decision Tree.

Study participants were categorized as cases or controls based on medical record information and self-reported data. All participants were GeneXpert tested for active TB infection at the time of enrollment. Past TB episodes were self-reported and cross-referenced with medical records when available.

Fig 2.. Case-Control status shifts across Age groups.

A) Overlapping density plots of age distribution stratified by TB status (n= 878). At the oldest and youngest ages, most of our study participants are cases whilst at middle-age groups, the majority are controls. B) Empirical odds of active TB by age group. The x-axis bins our participants into 7 age groups and the y-axis: the empirical odds of active TB. Empirical odds are calculated by dividing the number of controls divided by the number of cases in each age bin. The size of the dots corresponds to the sample size of the age group. Our data reveal a signal of survivor bias. Since age is a cumulative measure of exposure, the empirical odds of TB should increase with age. This pattern is observed from our youngest age group up to age 58. The empirical odds of TB progressively decrease after age 58. Older age groups are biased towards controls due to the mortality of TB.

Fig 3.. Khoe-San Ancestry is the Primary Genetic Ancestry in Clinics from the Northern Cape, South Africa.

A subset of participants (n=159) was genotyped for preliminary ancestry analysis. A) The study population is admixed with 5 distinct ancestries with the Southern African indigenous Khoe-San ancestry being the largest proportion of ancestry across all study sites. (B) Although Khoe-San ancestry is the largest proportion of ancestry in our sample, it varies significantly across study sites.

Figure 4.. Effect Plots demonstrating the relationship between Active TB Status and A) Gender, B) Current Residence and C) Smoking.

These plots are reported from the best-performing logistic regression model (SES model). Y-axes for all panels show the odds of active TB. We find that the odds of active TB are 3 times higher in Males. Individuals currently residing in Towns have about 2.5 times higher odds of active TB as compared to individuals currently residing in rural areas. Smoking slightly increases the odds of active TB but is not statistically significant.

Fig 5.. Logistic regression interaction plots.

A) The odds of active TB by education level vary across age groups (shown above by the different color lines). More years of education decreases the odds of active TB in younger age groups, but this pattern reverses in the oldest age groups. In middle-aged individuals, there is no relationship between age and years of education. B) Effect plot from the residence model visualizing an interaction term between birthplace residence and current residence. Regardless of birthplace, the odds of active TB is highest in individuals who currently reside in towns. Individuals born in towns and currently residing in rural areas have the lowest odds of active TB.