studies if they did not restrict the population under study to nonsmokers. If multiple published reports from the same study participants were available, we included only the one with the most detailed information for both outcome and exposure.
Search Strategy and Terms Used to Identify Studies on Smoking and TB
MeSH term search
3. “air pollution, indoor”
5. “fuel oils”
6. “(1) AND (2)” OR “(1) AND (3)” OR “(1) AND (4)” OR “(1) AND (5)”
Direct keyword search:
9. “indoor air pollution”
10. “cooking fuel”
12. “(7) AND (8)” OR “(7) AND (9)” OR “(7) AND (10)” OR “(7) AND (11)”
13. (6) OR (12)
Data Extraction and Quality Assessment
For every eligible study, we collected detailed information on year and country of study, study design, study population, sample size, choice of controls, definition and measurement of tobacco smoking or IAP, type of TB outcome, confounders adjusted for, effect sizes and 95% confidence intervals (CIs), and dose-response relationships. Since TB disease and death are relatively rare events, even in high-incidence areas, we assumed that odds ratios (ORs), risk ratios, and rate ratios all provided an equivalent estimate of risk and therefore reported them as ORs . Although latent TB infection is not a rare event, each of the studies of latent TB infection estimated ORs and we therefore reported ORs for this outcome as well. Data were extracted independently by two of the investigators (HL and MM), and differences were resolved by discussion with a third (ME).
We performed separate analyses for each exposure-outcome association that had been studied. Within each subanalysis we further stratified on different study designs. When more than one study used a specific study design, we assessed heterogeneity using the I2 statistic described by Higgins et al. . Because of the significant heterogeneity and different study designs within subgroups, we did not compute pooled effect measures . Instead, we graphically presented each of the weighted point estimates and 95% CIs of effect estimates for individual 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 abstrac