ual studies within subanalyses. For the subanalysis in which we found no significant heterogeneity, effect estimates were given a weight equal to the inverse variance of the study (fixed effects model). For those subanalyses in which we noted significant heterogeneity, we used a random effects model to assign the weight of each study according to the method described by DerSimonian and Laird . In order to assess the effect of study quality on the reported effect estimates, we conducted sensitivity analyses in which we compared pooled effect estimates for subgroups stratified on quality-associated study characteristics including study design (cohort, case-control or cross-sectional), type of control selection (population based or other), adjustment for important potential confounder (alcohol and socioeconomic status), and outcome classification (microbiological or other). We considered studies to be of higher quality if they (1) were cohort studies, (2) were case-control studies using population-based controls, (3) adjusted for important confounders, (4) classified the outcome on the basis of microbiological findings, and (5) restricted the outcome to pulmonary TB. As above, pooled estimates were calculated using a fixed effects model if there was no significant heterogeneity and a random effects models for those subanalyses in which we found heterogeneity.
We tested for possible publication bias using Begg's and Egger's tests and by visual inspection for asymmetry of a plot of the natural logarithms of the effect estimates against their standard errors according to method described by Begg [9,10]. Several large studies on smoking and TB mortality had highly variable results and thus fell outside the lines of the funnel plot. Therefore, we conducted a sensitivity analysis in which we repeated the funnel plot excluding all of the mortality studies. All statistical procedures were carried out in Intercooled Stata Version 8.2 (Stata, http://www.stata.com).
We identified and screened 1,397 papers by titles and abstracts. We excluded 1,340 papers because they were judged not to be related to smoking, IAP, and TB. The remaining 57 articles were obtained for detailed review; 19 of these were excluded because the same studies were published in different journals [11,12], the effect sizes and CIs of interest were not reported or could not be estimated [13–24], there were severe flaws in study design [25–27], or the article was not original [28,29]. Thirty-eight papers were included in the final analysis. Figure 1 delineates the exclusion process and Table 1 summarizes the studies that were included in the final analysis.
Tobacco Smoking and Latent TB Infection
Figure 2 shows the risk of latent TB among smokers compared with nonsmokers in six studies [30–35] on tobacco smoking and latent infection. The studies were conducted in five countries: the US, Spain, South Africa, Pakistan, and Vietnam. Although the timing of smoking (current, former, and ever) in relation to the study varied, we did not differentiate between these reported exposures, because the actual time of TB infection was unknown. There was only one case-control study; for the five cross-sectional studies that were included, we found minimal heterogeneity (I2 = 0%). We also stratified studies that used different cutoffs for the TST; among those analyses that used induration size of 5 mm as the cutoff for a positive test [32,33], the pooled OR for latent TB was 2.08 (95% CI, 1.53–2.83), while among those that used a 10 mm cutoff [30,31,34,35], the pooled OR was 1.83 (95% CI, 1.49–2.23). When we stratified on other quality-associated study characteristics, we found that ORs for TB infection were lower among studies that adjusted for alcohol (Table 2), but that a positive effect of smoking on latent TB remained.
Quality Assessment and Subgroup Analysis: Tobacco Smoking and Latent TB Infection
Tobacco Smoking and Clinical TB Disease
The 23 studies that we identified on the association between tobacco smoking and clinical TB disease were conducted in 12 countries: China/Ho