American Journal of Respiratory and Critical Care Medicine

Rationale: Previous case-control studies and a small number of cohort studies in high-risk populations have found an association between tobacco and active tuberculosis, but no cohort studies have been conducted in the general population on this association to date.

Objectives: To investigate the association between tobacco smoking and active tuberculosis in a cohort of a general population.

Methods: 17,699 participants (≥12 y of age) in Taiwan National Health Interview Survey were followed up from 2001 to 2004. Smoking status and other covariates were measured by an in-person interview at baseline. Incident cases of active tuberculosis were identified from the National Health Insurance database. Multivariate logistic regression was used to estimate the association between smoking status and active tuberculosis, with adjustment for age, sex, alcohol consumption, socioeconomic status, and other covariates.

Measurements and Main Results: Fifty-seven new cases of active tuberculosis occurred during the 3.3 years of follow-up. Current smoking was associated with an increased risk of active tuberculosis (adjusted odds ratio [OR], 1.94; 95% confidence interval, 1.01–3.73). The association was stronger among those less than 65 years of age (adjusted OR, 3.04) than those greater than 65 years of age (adjusted OR, 0.78; Pinteraction = 0.036). We found significant dose–response relations for cigarettes per day (Ptrend = 0.0036), years of smoking (Ptrend = 0.023), and pack-years (Ptrend = 0.0023).

Conclusions: Tobacco smoking was associated with a twofold increased risk of active tuberculosis in a representative cohort of Taiwan's population.

Scientific Knowledge on the Subject

Previous case-control studies and a small number of cohort studies in high-risk populations have found an association between tobacco and active tuberculosis, but no cohort studies have been conducted in the general population on this association to date.

What This Study Adds to the Field

Tobacco smoking was associated with a twofold increased risk of active tuberculosis in a representative cohort of Taiwan's population. The finding that smoking increases the risk of tuberculosis suggests that tobacco control be considered as an important component in the global effort to eliminate tuberculosis.

Tuberculosis (TB) is among the leading causes of death from infectious diseases in the world, with 9.3 million new cases and 1.8 million deaths reported in 2007 (1). The World Health Organization has set a goal to lower annual TB incidence to less than one case per million by 2050 (1). Although the current approach to TB control focuses on case detection and treatment, recent studies suggest that this strategy might not be sufficient to achieve this goal and it may also be necessary to reduce risk factors that contribute to the occurrence of tuberculosis infection and/or disease (2, 3). Such risk factors may act at one of several steps in the natural history of the disease. Susceptible individuals become infected with Mycobacterium tuberculosis after exposure to a person with active pulmonary tuberculosis. Once infected, individuals either develop disease within the first 1 to 2 years of infection, referred to as primary disease, or go on to develop a latent asymptomatic infection, which can progress to active disease sometime during that individual's life. A first step toward this goal is to better understand the effects of modifiable risk factors, including tobacco smoking, on any of these steps that could ultimately increase the occurrence of TB.

An estimated 1.3 billion people smoke tobacco products, the majority of whom live in low- or middle-income countries where the burden of TB is also concentrated (4). Several systematic reviews and meta-analyses of observational studies have pointed to a positive association between tobacco smoking and active TB disease (57). However, the design of previous studies has usually been case-control and cross-sectional, and their results might be biased by the limitations of these types of studies. For example, case-control studies can suffer from selection bias if the control group does not represent the study population. Cross-sectional studies obscure the temporal relation between smoking and active TB and may therefore underestimate the association if patients quit smoking after being diagnosed with active TB. A few cohort studies have reported a positive association between smoking and active TB (811). However, these cohort studies might not have yielded generalizable results because they were conducted in high-risk populations such as gold miners (8), patients with silicosis (9, 10), and the elderly (11). Several large observational studies also reported an increased mortality from TB among smokers, but these studies did not provide direct evidence on smoking and the incidence of active TB (1214). Furthermore, many observational studies failed to address confounding factors that are related both to smoking and TB (e.g., alcohol use and socioeconomic status) and may have resulted in spurious associations between smoking and TB (1521). Taiwan is a country where the prevalence of current smoking is 47 and 4% among males and females (22), respectively, and the annual incidence of TB is 94 per 100,000 (23). We conducted a prospective cohort study on the association between tobacco smoking and incident active TB, with adjustment for potential confounders, in the general population of Taiwan.

We used a prospective cohort whose subjects were the participants in Taiwan's 2001 National Health Interview Survey (NHIS), which measured baseline information on participants' smoking status and sociodemographic and behavioral factors using in-person interviews. We identified incident cases of active TB from the cohort by linking the NHIS database to the National Health Insurance (NHI) database through December 2004.

The National Health Interview Survey

The NHIS in Taiwan is a cross-sectional nationally representative survey jointly conducted by the National Health Research Institute and Bureau of Health Promotion between August 2001 and February 2002 (24). A total of 17,699 participants of NHIS were included in this cohort study (see the online supplement for details on NHIS).

Measurement of Smoking Status

We retrieved information from the individual NHIS records on smoking status (never, current, and former) at baseline. Data were available on the number of cigarettes smoked per day, years of smoking, age of smoking onset, and exposure to second-hand smoke at home. Ever-smokers were defined as those who had smoked more than 100 cigarettes, current-smokers as ever-smokers who had smoked in the month before the interview, former-smokers as ever-smokers who had not smoked in the month before interview (see the online supplement for further details).

Measurement of Covariates

From the NHIS database, we chose potential confounders based on reported risk factors for active TB in the literature (25, 26). The covariates considered included sex, age, residing in a crowded home, household income, receiving government subsidy, marriage status, education, alcohol use, residence in an indigenous community, and employment status (see the online supplement).

Measurement of TB Status

The primary outcome for the analysis was active TB. We identified incident cases of active TB from the NHI database. All residents in Taiwan are required to participate in the NHI, and the enrollment rate in 2001 was 97% (27). We defined active TB by compatible ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) codes of TB (010–018) plus the prescription of more than two antituberculosis medications for more than 28 days (see online supplement for detail).

Statistical Analysis

We assessed the crude associations between the exposures (smoking status and other covariates) and the outcome (active TB) by computing odds ratios [OR] and corresponding 95% confidence intervals [CI]. Multivariate logistic regression was used to estimate the association between smoking and active TB, adjusting for potential confounders (see Measurement of Covariates). Effect modification by age, sex, and living in an indigenous community was tested. Dose–response analyses were conducted for smoking intensity, smoking duration, and pack-years. The potential clustering of TB cases within household was accounted for by using generalized estimating equations (28).

We conducted sensitivity analyses to address the effect of unmeasured confounding by socioeconomic status and the influence of competing risk from smoking-associated mortality (see online supplement for further details). The data management and the statistical analyses were performed using SAS 9.0 (SAS Institute Inc., Cary, NC). This study was exempted from Institutional Review Board review under the Statistics Act of Taiwan and the Human Subject Committee of Harvard School of Public Health.

Among the 17,699 participants, 3,893 were current-smokers, 552 were former-smokers, and 13,254 had never smoked. Forty percent of men and 4.2% of women smoked at baseline. Table 1 shows the baseline characteristics of current-, former-, and never-smokers. Active TB occurred in 24 current-smokers, 3 former-smokers, and 30 never-smokers. Because of the small number of TB cases, we did not separately report the association for former-smokers. Compared with never smoking, the crude OR for ever-smoking and current-smoking were 2.69 (95% CI, 1.60, 4.54) and 2.73 (95% CI, 1.60, 4.68) (Table 2). Among the 57 cases of TB, 54 had pulmonary and other respiratory TB (ICD-9 codes: 011–012). As a result, when we restricted the outcome to pulmonary and other respiratory TB, the OR for ever- and current-smoking changed only slightly (2.88 for ever-smoking and 2.91 for current-smoking).


Current-smoker (n = 3,893)

Former-smoker (n = 552)

Never-smoker (n = 13,254)

Overall (n = 17,699)

P Value
Age in years, median (IQR)39.0 (29.9-50.4)57.0 (42.3-70.8)36.6 (22.8-50.8)37.9 (24.9-51.4)<0.0001
Residing in a crowded home*
Low-income household22.129.120.321.0<0.0001
Receiving government subsidy19.830.119.319.7<0.0001
Marriage status<0.0001
 Never married27.69.837.034.1
 Married or co-habitating64.180.854.257.2
 High school46.241.248.347.6
 Elementary school46.144.840.141.5
 Less than elementary school7.714.011.610.8
Alcohol use<0.0001
Residence in an indigenous community9.<0.0001
Employment status (yes/no)70.448.047.152.2<0.0001
BMI kg/m2, mean (SD)
23.6 (3.5)
24.3 (3.4)
22.6 (3.7)
22.9 (3.7)

Definition of abbreviations: BMI = body mass index; IQR = interquartile range.

Values represent percentages unless otherwise noted.

*Residing in a crowded home: more than or equal to eight persons per household.

Others: widowed, divorced, separated, or serving as a single parent.

P value is for the association between baseline characteristic and smoking status.



Multivariate, Current-smoking

Multivariate, Ever-smoking
Smoking status
 Current2.73 (1.60, 4.68)1.94 (1.01, 3.73)
 Ever2.69 (1.60, 4.54)1.71 (0.90, 3.26)
Male1.62 (0.95, 2.76)1.18 (0.58, 2.40)1.18 (0.58, 2.38)
Age (per 10-year increase)1.63 (1.42, 1.86)1.69 (1.34, 2.13)1.67 (1.33, 2.09)
Residing in a crowded home1.65 (0.79, 3.45)1.40 (0.66, 2.99)1.34 (0.63, 2.85)
Low-income household2.10 (1.22, 3.62)1.01 (0.55, 1.86)0.97 (0.53, 1.79)
Receiving government subsidy3.20 (1.90, 5.38)1.33 (0.69, 2.53)1.43 (0.76, 2.67)
Marriage status
 Never married1.001.001.00
 Married or cohabitating2.09 (1.04, 4.23)0.49 (0.18, 1.32)0.48 (0.18, 1.29)
 Others*4.73 (2.04, 10.99)0.43 (0.11, 1.73)0.41 (0.10, 1.64)
 High school1.001.001.00
 Elementary school1.61 (0.83, 3.12)0.80 (0.39, 1.62)0.85 (0.42, 1.69)
 Less than elementary school6.23 (3.21, 12.09)1.27 (0.47, 3.40)1.38 (0.53, 3.56)
Alcohol use
 Social1.10 (0.46, 2.65)1.16 (0.50, 2.70)1.13 (0.49, 2.62)
 Regular2.83 (1.39, 5.77)1.66 (0.70, 3.91)1.63 (0.70, 3.76)
 Heavy5.53 (2.71, 11.28)3.51 (1.53, 8.02)3.72 (1.67, 8.27)
Residence in an indigenous community4.98 (2.81, 8.82)3.03 (1.57, 5.86)2.95 (1.55, 5.59)
Employment status (yes/no)
0.53 (0.31, 0.91)
0.82 (0.43, 1.56)
0.83 (0.44, 1.55)

Associations are presented as odds ratios and 95% confidence intervals.

*Others: widowed, divorced, separated, or serving as a single parent.

After adjusting for potential confounders (Table 2), current smoking was still significantly associated with TB (OR, 1.94 [1.01, 3.73]). The association between ever-smoking and TB was not significant (OR, 1.71 [0.90, 3.26]) in the multivariate model. We did not adjust for diabetes and body mass index (BMI) as confounders in the main analysis because they might be affected by smoking (2931). When diabetes and BMI were further included in the multivariate model, the adjusted OR for current-smoking and TB became 2.16. We found evidence of effect modification by age (P for interaction, 0.036) with an OR of 0.78 (0.26, 2.32) for current smoking for those aged 65 and older and an OR of 3.04 (1.36, 6.82) for those less than 65. We found no effect modification by sex (P for interaction, 0.94) or living in an indigenous community (P for interaction, 0.77). Table 3 presents the predicted cumulative incidence of active TB by smoking status and age group during the study period. The ORs by different categories of smoking intensity, duration and pack-years are shown in Table 4. We observed significant linear dose–response relations for cigarettes per day (Ptrend = 0.0036), years of smoking (Ptrend = 0.023), and pack-years (Ptrend = 0.0023). Because smoking intensity and duration were highly correlated (r = 0.78, P < 0.001), we did not find an independent dose–response relation for either when mutually adjusting for each other in the same model.




<65 years259 (134, 501)85 (47, 155)
≥65 years
648 (173, 2,420)
832 (399, 1,737)

Cumulative incidence is presented as cases per 100,000 and 95% confidence intervals.

*Adjusted for sex, residing in a crowded home, household income, receiving government subsidy, marriage status, education, alcohol use, residence in an indigenous community, and employment status.

The median age in this group (34.9 y) was used for prediction.

The median age in this group (72.0 y) was used for prediction.


Cases, n

Participants, n

OR (95% CI)
 ≤535811.87 (0.55, 6.38)
 >5 and ≤1551,3501.16 (0.41, 3.27)
 >15 and ≤25101,4732.14 (0.96, 4.77)
 >2563835.15 (1.74, 15.18)
Years of smoking
 Never smoker3013,0331.00
 ≤1021,0491.06 (0.23, 4.80)
 >10 and ≤2029780.91 (0.21, 3.99)
 >20201,8012.38 (1.15, 4.94)
 Never smoker3013,0331.00
 ≤1031,6460.77 (0.23, 2.61)
 >10 and ≤2071,0102.06 (0.80, 5.31)
 >20141,1283.19 (1.42, 7.16)


Definition of abbreviations: CI = confidence interval; OR = odds ratio.

*Adjusted for sex, age, residing in a crowded home, household income, receiving government subsidy, marriage status, education, alcohol use, residence in an indigenous community, and employment status.

We also examined the association between second-hand smoke and active TB in 13,033 never-smokers, among whom 30 TB cases occurred. The crude and adjusted OR were 1.32 (0.63, 2.73) and 1.25 (0.57, 2.73), respectively. We did not find a significant dose–response relation for frequency (days of a week) of second-hand smoke exposure and active TB (adjusted OR for everyday increase, 1.02 (0.91, 1.14); P = 0.74).

In this prospective cohort study, we found a twofold increase in the risk of active TB in current smokers compared with never-smokers after adjusting for potential confounders. We also observed significant dose–response relations for cigarettes per day, years of smoking, and pack-years. Based on our analysis, 17% of incident TB cases in this population were attributable to smoking. When extrapolated to the national population, this translated into 2,841 cases among the 16,580 reported in Taiwan in 2,005 (23) (see the online supplement).

Three meta-analyses of observational studies have found a positive association between current-smoking and active TB, with substantial heterogeneity across studies (57). The observed magnitude of association ranged from null to OR as high as 4.6. Most studies in these meta-analyses were case-control or cross-sectional studies that are subject to a number of sources of biases, and many did not adequately account for confounding by alcohol consumption or any measures of socioeconomic status. In one meta-analysis, the pooled relative risk from studies that adjusted for alcohol consumption was 1.62 (1.15, 2.29), and that from studies that adjusted for socioeconomic status was 1.95 (1.45, 2.61) (7). The results from our study are consistent with the positive association found in other studies, and provide evidence from a prospective cohort with adequate adjustment for potential confounders.

We observed a smaller and insignificant association for ever-smoking compared with our finding on current-smoking. This suggests that the hazard of TB might be lower among former compared with current-smokers, although the small number of cases among former-smokers (n = 3) made it impossible to investigate quantitatively whether risk after quitting is lower relative to those who continue to smoke. Results from previous studies that examined the effect of former-smoking on TB have been inconsistent; in a large cohort study among the Hong Kong elderly, Leung et al. also reported a stronger association between active TB and current smoking (hazard ratio [HR], 2.87 [2.00, 4.11]) than former smoking (hazard ratio [HR], 1.39 [0.98, 1.97]) (11). However, previous case-control studies have reported inconsistent results when the effects of both current and former smoking were examined, possibly because TB cases cease smoking when they develop symptoms of TB, i.e., reverse causation (7).

The association between secondhand-smoke exposure at home and active TB, although elevated, was not statistically significant. The small sample size (30 cases of active TB) and small magnitude of effect (adjusted OR, 1.25 [0.57, 2.73]) limited the statistical power of this analysis. Although several previous case-control studies reported a positive association between secondhand smoke and active TB, with the effect sizes ranging from 2 to approximately 2.5 for adults (20, 32) and 5 to approximately 9 for children (33, 34), we were not able to estimate this association among children because there were no TB cases in children in the NHIS survey.

Our analysis suggests that the association between smoking and TB might differ by age, with a higher OR for those less than 65 years of age. Other studies on the associations between smoking and other diseases, e.g., coronary heart disease (35, 36), and between diabetes and active TB (37), have reported decreasing relative risks by age. We were not able to examine the potential age-specific effect at a finer age scale because of the small case number (n = 57). Nonetheless, we propose two hypotheses if the age gradient for smoking and TB is confirmed in future studies. First, in areas where the prevalence of active TB has been declining, long-term smokers who are susceptible to the effect of smoking may have developed TB early in their lives, and the susceptible population may therefore have been depleted among elderly smokers. Second, if TB in the elderly results from reactivation of remote infection, whereas TB in the young population is more often due to recent infection (38, 39), the age gradient could be interpreted to suggest that smoking may be a stronger risk factor for recent infection than for reactivation disease.

In a cohort study of the elderly in Hong Kong, current-smokers had a nearly threefold increased risk of active TB (adjusted HR, 2.87 [2.00–4.11]) (11), in contrast to the observed OR of 0.78 (0.26, 2.32) among the elderly in this study. It is unknown whether the differential association by age observed in our cohort also occurred in Hong Kong, because the Hong Kong cohort only included the elderly. The observed difference in risk between Taiwan and Hong Kong elderly might be due to either a generally greater risk for TB among smokers in Hong Kong across all age groups or a particularly increased risk among elderly smokers in Hong Kong. Because the average number of cigarettes smoked by the Hong Kong elderly (11.0 cig/d) was smaller than that smoked by the Taiwan elderly (15.1 cig/d), the observed difference in risk between these two cohorts cannot be explained by differences in smoking intensity.

Laboratory studies on humans and animals have shed light on potential biological mechanisms through which smoking might affect the risk of TB. Smoking was found to impair clearance of secretions on the tracheobronchial mucosal surface (40), reduce phagocytic function of pulmonary alveolar macrophages (41), decrease intracellular production of tumor necrosis factor-α (42), and cause iron overload (43) in macrophages. When these normal defense mechanisms were compromised, the development of TB might ensue upon exposure to the TB pathogen.

It has long been noted that the prevalence and notification rates of active TB are higher among men (4447), but it is unclear whether this association is due to biological or behavioral factors. In our study population the increased risk among men was substantially attenuated when smoking, with alcohol use, and when other sociodemographic factors were accounted for; the OR for male compared with female was 1.62 (0.95, 2.76) in the crude analysis, 1.06 (0.54, 2.11) when current smoking was adjusted, and 1.18 (0.58, 2.40) when other covariates were further adjusted. This suggests that smoking, and not biological factors, may be the underlying cause of the sex difference in TB.

One limitation of this study is that deaths were not recorded in the NHI database. Because smokers have a higher mortality rate than never-smokers (48), the differential loss to follow-up would bias the results toward a smaller association between smoking and TB. Given the short duration of follow-up, the impact from this bias would be small (<1%, see online supplement). Another limitation is that we did not have results from bacteriological studies for the diagnosis of TB. The definition of active TB relied on ICD-9-CM codes and prescription history, and the outcome of TB may have been misclassified. This nondifferential misclassification of outcome would most likely have biased our estimates toward a null association. In addition, TB is usually diagnosed when symptomatic patients present to their health care providers (49, 50). Patients with acute symptoms of TB might have quit smoking before the diagnosis. If this “ill-quitter” phenomenon occurred before baseline smoking status was measured in the NHIS, it would underestimate the association between current smoking and TB. To assess the impact of this phenomenon, we repeated the analysis excluding TB cases that occurred during the first year of follow-up. The OR for current-smoking increased slightly from 1.94 to 1.95. Although we did not have information on HIV status in our study population, the prevalence of HIV infection in the general Taiwanese population was less than 0.02% at the end of 2003 (51) and we therefore did not expect that confounding by HIV would affect our results.

A major strength of this study is the prospective cohort design, which avoided the problems of control selection in case-control studies and obscured temporality in cross-sectional studies. Unlike previous cohort studies that included only high-risk populations, our study was conducted in a general population from a nationally representative sample and has greater generalizability. The NHIS was designed and executed by an experienced national survey team with quality control of the interviews (52). Availability of thorough smoking metrics made it possible to demonstrate a dose–response relationship between smoking and active TB. The detailed information on demographic, socioeconomic, and behavioral factors allowed us to adjust for major potential confounders.

To our knowledge this is the first cohort study from a general population that provides evidence on the positive association between tobacco smoking and active TB. Based on the results from our and other observational studies, policy makers and public health personnel should consider addressing tobacco cessation as part of TB control (53). Recent studies suggest that introducing brief tobacco cessation advice may be feasible among TB patients (54, 55), and an integrated approach has been proposed to monitor smoking cessation in TB care (56). From the perspective of prevention, the target of smoking cessation should aim beyond TB patients to reach high-risk populations who are likely to benefit most from cessation.

The authors thank Yi-Li Chuang and Jimmy Tsai for technical assistance on the National Health Interview Survey and National Health Insurance databases. We are grateful to Ted Cohen, Marc Lipsitch, and James Robins for their valuable advice.

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Correspondence and requests for reprints should be addressed to Hsien-Ho Lin, M.D., Sc.D., Department of Epidemiology, Harvard School of Public Health, 667 Huntington Avenue, Kresge Building Room 801, Boston, MA 02115. E-mail: .


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