With 9 million incident cases and 2 millions deaths reported in 2009, Tuberculosis (TB) continues to cause significant morbidity and mortality, especially in the low and middle income countries (LMIC) identified by the World Health Organization (WHO) as the 22 “high burden” nations [1]. While TB incidence has declined in most regions of the world, the slow pace of progress has prompted a search for new targets for interventions [2]. Recent work suggests that, on a national level, TB trends track more closely with social and economic indicators than with measures of TB control activities [3], [4]. These data imply that the targeting of interventions to the most vulnerable groups may be necessary to speed progress toward elimination of this scourge.

There is substantial evidence that poverty is a determinant of TB, both at the macro-scale and in individual and hierarchical analyses. Janssens and Rieder documented a linear association between per capita GDP [5] and TB incidence, and Dye found that the country level human development index was a strong predictor of changes in TB incidence over time [4]. Although several studies report discrepant findings [6]–[8], most analyses of data have confirmed the positive association between household and area poverty indicators and TB in such diverse settings as South Africa [9], Brazil [10], Vietnam [11] and Zambia [12]. Among the social, environmental and biological determinants of TB, many are more prevalent among the poor than in wealthier groups and these determinants likely contribute to a complex web of poverty-based risk factors that is difficult to tease apart.

With the recognition of poverty as a root cause of TB [13]–[16], the need to intervene not only on economic status, but also on the proximal risk factors that put the poor at risk is increasingly clear. Several groups have described frameworks that suggest how and when common proximate risk factors act on the TB pathogenetic pathway that includes exposure, infection, active disease and eventual disease outcomes [14], [17]. Although some epidemiologic studies have sought to measure the impacts of these determinants, only a few have addressed this question in the context of understanding the routes by which poverty leads to TB [12]. The objective of our study was to investigate the mechanisms by which poverty increases the risk of TB, using data from a large population based survey in India.

The prevalence of self reported TB in India was 545 per 100,000 (95% CI 493–597 per 100,000). Figure 1 shows that TB prevalence increases linearly with wealth quintile, with estimates ranging from 201 per 100,000 population (95% CI 142–260 per 100,000) among the wealthiest to 1105 per 100,000 population (95% CI 919–1291 per 100,000) in the poorest. Figure 2A–F shows that most risk factors for TB are more prevalent among the poor than in the wealthier strata, with the exceptions of DM, which is highest in the wealthiest population, and HIV, which is evenly distributed among the low and middle strata but is less common in the wealthiest quintile.

Table 1 compares the summary effect estimates reported from published sources with the adjusted odds ratios for proximal determinants of TB in the DHS data set. Published effect estimates fall within the 95% confidence intervals of the adjusted estimates for DHS for all variables except cigarette and alcohol use and BMI. As above, alcohol use/abuse was differently classified in the DHS than in the meta-analyses. Although the impact of HIV on TB prevalence was less than reported estimates of its impact on incidence, previous studies have noted the marked discrepancy between the relative risk of HIV for TB incidence and prevalence [12], and our estimates were consistent with other reports on prevalence [12], [31]–[33], which have not, to date, been summarized in a systematic review or meta-analysis.

Low BMI was calculated to have the highest PAF at 34.2% in the lowest wealth category, followed by IAP with a PAF of 28.5%, and cigarette smoking with a PAF of 16.0% (Table 2). Low BMI also had the largest PAF at 20.3% among the wealthiest category. Diabetes was the only risk factor that was predicted to have a greater impact in the wealthier strata, with a PAF of 4.0%.

PCA transformed 20 out of 21 variables into the following six uncorrelated components (Table 3); 1) protein intake; 2) educational achievement; 3) tobacco use, alcohol use and male gender; 4) rural setting, exposure to indoor air pollution and health insurance; 5) intake of fresh produce, and 6) BMI, anemia, milk intake and DM. (For this component, anemia was negatively but strongly correlated with BMI, milk intake and DM and that this component therefore likely reflects dietary fat/protein intake). A full list of variables initially included in the PCA, the rotated factor pattern, and the percent of explained variance accounted for by the retained components are included in Table S1A–C in Appendix S1.

Table 4 shows that principal component 6 (BMI, anemia, milk intake and DM) has the greatest relative mediation effect (RME = 11.9%, p = 0.019) on the impact of poverty on TB prevalence. Although Component 2 (educational achievement) also had an RME value of 6.6%, this effect was not statistically significant.

DHS data shows that members of the poorest quintile in India are at a 5.5-fold higher risk for self- reported prevalent TB than those in the wealthiest quintile. The TB prevalence rate of 1105 per 100,000 in this group exceeds those estimated in high HIV burden populations like South Africa where the national prevalence of TB was reported to be 782/100,000 in 2009 [1] and the prevalence of HIV is 18% [34]. In contrast, the HIV prevalence among the poorest quintile in India was only 0.4%. Almost all known TB risk factors were more common among the Indian poor, with the notable exceptions of DM and HIV. Those in the poorest strata frequently share multiple risk factors for active TB, substantially increasing their risk. Among the 49% of the poorest quintile who share the two risk factors, low BMI and indoor air pollution, TB risk is increased almost 5 fold compared to the less than 1% in the same strata without either risk factor (results not shown). Low BMI was found to have be the strongest mediator of the association between TB and poverty, and was responsible for the largest PAF for TB in all income levels with biomass fuel exposure a close second in all but the wealthiest sector of the population.

The finding that the poor have many overlapping risk factors for TB is supported by various theoretical models described in the literature [15], [16]. Changes to structural determinants of TB epidemiology at a global level (including population mobility, rapid urbanization and population growth) have given rise to an unequal distribution of many of the social determinants considered in this study [15]. People with low SES typically live in poor housing and environmental conditions, have greater food insecurity and have less access to quality health care relative to those from higher SES groups [16]. All of these social determinants are also related to TB, and often work together to put the poor at greater risk of disease by acting on different stages in the pathogenetic pathway.

Although the strong association between per capita GDP and TB incidence has been well established both on a global and regional level [3], [4] and both ecological and hierarchical analyses within countries have identified low SES as a strong risk factor for TB [35], [36], few studies have attempted to quantitatively assess how poverty creates this risk. In one recent study, Boccia et al. used mediation analysis to identify the proximal causes that resulted in more than a six fold increase in TB risk in the poorest sectors of a Zambian population [6]. These investigators then evaluated the impact of blocks of risk factors for TB from their conceptual framework and found that reduced food intake variables were the only ones that emerged as mechanisms mediating the causal relationship between poverty and TB. Like Boccia and others, we recognized that the risk factors that may mediate the association between poverty and TB are often jointly distributed. We also addressed this issue by grouping these risk factors into clusters or blocks but did so agnostically, by performing a PCA to identify determinants that co-vary. The resulting clusters included several discrete types of risk factors such educational achievement, tobacco and alcohol, and exposure to biomass cooking fuel. Interestingly, however, nutritional variables were grouped into three different uncorrelated clusters, two of which reflected intake of protein and fresh produce respectively and a third which reflected a strong positive correlation between underweight and anemia, and a strong negative correlation between these two markers of poor general nutritional status and diabetes and milk intake. These data suggest that the nutritional determinants of TB are complex and multifactorial.

Another study by Boccia et al. considered PAFs for risk factors, but included only those determinants and effect estimates that were identified as risk factors in the study data, which was limited by a very small sample size [12]. In contrast to this approach, we relied on previously reported effect estimates and estimated PAFs for different socio-economic sectors based on the prevalence of these risk factors in the India DHS data. We assumed that the observed association would also exist in India and confirmed that most of these summary effects were consistent with those in our data set. Nonetheless, we recognized that some risk factors for incident TB might reduce survival and thereby decrease disease prevalence. This is particularly true for HIV which is a strong risk factor not only for TB but also for death. Previous studies have shown that HIV increases the TB prevalence 2–3 fold in contrast to its 20 fold impact on TB incidence [25]. Some behavioral risk factors such as smoking and alcohol use might be altered by TB disease and would be better assessed in cohort studies in which these exposures were assessed prior to onset of TB [37]. Lastly, we recognized that since TB was classified on the basis of self-report, this might lead to bias if wealthier individuals are more likely to know or report their TB status. Because of these methodological issues, we used published estimates even when these deviated from the estimated association in the DHS data.

Our study has several limitations. First, DHS data relies on self-report for most variables with the exception of HIV status, anemia and anthropomorphic measures. Most notably, DM is self-reported and the prevalence of 3.5% reported in our study was much lower than the 11% recently reported by Danaei et al [38] who used measures of hyperglycemia to confirm the diagnosis. When we used this higher prevalence estimate for DM to estimate the PAF for India, we found that it increased to almost 20%. However, since DM prevalence was not stratified by SES status in the Danaei study, it is not possible to estimate the PAF for rich and poor communities respectively. TB status was also determined by self-report and we found that the prevalence of self reported TB (545 per 100,000) is substantially higher than the estimated prevalence of TB reported by WHO in 2006 (299 per 100,000) [39]. However, no recent prevalence survey has been conducted in India and in the absence of actual prevalence assessments, WHO prevalence estimates are derived from case notifications, with correction factors for the expected case detection rate and the mean duration of disease. Thus, the discrepancy between these estimates may reflect the fact that a large portion of TB patients who are diagnosed and treated in the private sector in India and who are thus not notified as cases. Importantly, one of the last prevalence surveys done nationally in 2000 estimated a prevalence of 846 per 100,000 in marked contrast to the 458 per 100,000 estimate reported by the WHO in this same period [40].

Secondly, although the data are cross-sectional, we assumed that SES is a determinant of prevalent TB rather than that prevalent TB affects economic status. Although there is clear evidence that out-of-pocket TB treatment expenses can result in catastrophic spending in India and elsewhere [41]–[44], we chose an asset based socio-economic assessment that we believe is less likely to vary in relation to prevalent health shocks than consumption based measures.

Here, we reported PAFs in order to provide an estimate of how much disease in an exposed population might be prevented by removing a specific exposure. This measure is meant to capture the fraction of the disease burden that could be prevented by eliminating a specific risk factor. In our analysis, the sum of the individual PAFs were found to approach 100% in the poorest stratum while the sum of individual PAFs was less than 50% in the rich strata. These findings suggest that it will be necessary to target multiple proximal risk factors to achieve a lasting impact among this vulnerable population.

Our finding that the prevalence of TB was substantially higher in the poorest stratum in India is one that can likely be replicated in any of the 22 high TB burden countries. That the poor are also the sector that are least likely to access TB diagnosis and care emphasizes the need to improve and focus TB services on this group. While the alleviation of poverty globally may be slow to be realized in the current economy, specific measures designed to reduce TB among the poor can be implemented. For example, new but more costly diagnostics that enable earlier detection and treatment may be unlikely to reach poor populations without specific policies that target these interventions to those most in need. The targeting of active case finding to the poor who share multiple risk factors may be a more successful strategy than the focusing on those with individual risk factors. On a more global scale, the existing TB control targets set by the World Health Assembly in 1991, to detect 70% of TB cases and successfully treat 85% of those detected, allow for neglect of the most vulnerable populations; revision of these goals to explicitly address equity could enable the development and financing of TB control strategies aimed at the poor. In addition to diagnosis and care, TB prevention should be tailored to address the most important determinants contributing to TB among the poor - in this setting, specifically low BMI and indoor air pollution. Given the complexity of the interactions between multiple risk factors, it will also be essential to consider the root causes of these proximal determinants. Our findings support the approach recently endorsed by the Stop TB Department at the WHO [13], [16] that a comprehensive and integrated approach to disease control that simultaneously targets specific risk factors and poverty reduction more widely should be supported and prioritized in order to reduce TB mortality and morbidity.