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  • The Health Effects of Arsenic Longitudinal Study (HEALS) is an ongoing prospective, population-based cohort study in Araihazar, Bangladesh [8]. Between October 2000 and May 2002, from a well-defined 25 km2 geographical area, we recruited 11,746 men and women (original cohort) who were 1) married (to reduce loss to follow-up) and aged 18–75 years, 2) residents of the study area for at least 5 years prior to recruitment, and 3) primarily drinking water from a local well. During 2006–2008, HEALS was expanded to include an additional 8,287 participants (expansion cohort) following the same methodologies. The overall participation rate was 97%. The participation rate was estimated as the proportion of subjects who agreed to participate among the potential participants we invited to the study [8]. Demographic and lifestyle data for both the original and expansion cohort participants were collected using a standardized questionnaire. Trained study physicians measured height and weight using a locally manufactured tape measure and a Misaki (Okaka, Japan) scale (calibrated weekly), respectively. Both height and weight were measured three times at baseline and averaged [9]. Trained clinicians measured blood pressure with an automatic sphygmomanometer [10], [11]. Although the original aim of the study was to investigate health effects of arsenic exposure, population-based studies can be used to provide epidemiologic data on health effects of smoking that may be more generalizable. Follow-up in-person interviews at two-year intervals were conducted by trained physicians following the same procedures used in the baseline interview. Between the biennial follow-up visits, passive follow-up is conducted, as participants with health conditions would visit the field clinic which was established exclusively for the cohort participants to receive medical diagnoses and treatments [8], and the relevant data are collected. Informed consent was obtained from study participants and the study procedures were approved by the Ethical Committee of the Bangladesh Medical Research Council and the Institutional Review Boards of Columbia University and the University of Chicago. The present study included follow up data from baseline to October 9, 2011. Details of the methods for the assessment of causes of death are described elsewhere [9], [12], [13]. Briefly, we adapted a validated verbal autopsy procedure, developed by the International Centre for Diarrhea Disease Research, Bangladesh, in collaboration with the WHO, to ascertain the causes of death. During the follow-up, upon receipt of a death reported by family or neighbors, a study physician and a trained social worker administered the verbal autopsy form to the next of kin. Medical records and death certificates were collected and reviewed monthly by an outcome assessment committee, consisting of physicians and consulting medical specialists. Causes of deaths were coded according to the WHO classification [14] and the International Classification of Diseases, 10th Revision (ICD-10) [15]. The International Centre for Diarrhea Disease Research, Bangladesh has used this method to ascertain causes of deaths since 1971 [16], [17] and documented an overall 95% specificity, with a 85% sensitivity for cancer deaths and 85% sensitivity for deaths due to CVD [18]. Assessment of Tobacco Smoking Variables: At baseline, detailed information on smoking of tobacco products was collected. Details of smoking cigarettes and bidis (filterless locally produced cigarettes) were asked together, including information on past or current use, duration of use, age at start, number of sticks per day, and age at quitting. A separate set of questions were asked for hookah smoking (tobacco smoking using waterpipes). We observed a high correlation between hookah and cigarette/bidi smoking, such that 98% of the 2,005 ever smokers of hookah were ever smokers of cigarette/bidi. Although cigarettes and bidis frequently are sold individually in Bangladesh, we calculated “pack-years” (product of sticks of cigarette/bidi per day and years of smoking, divides by 20) for ease in comparison with other studies. Similarly, a “time-years” index (product of times per day and years of smoking) was calculated for hookah smoking. Person years of follow-up were calculated from baseline to the date of death from any cause (for those who died) or to October 9, 2011, the date of last death observed in the follow-up at the time of data analyses (for those who were alive). Cox proportional hazards models were used to estimate hazard ratios (HRs) and their 95% confidence intervals (CIs) for deaths from all-cause, cancer, CVD, ischemic heart disease (IHD), and stroke, in relation to status, duration, and intensity of cigarette/bidi and hookah smoking. Survival curves for overall survival were graphed by cigarette/bidi smoking status among men and women. We evaluated the effect of age at starting cigarette/bidi smoking and time since quitting smoking on mortality from all-cause, cancer, and IHD among men only, as the data were limited in women. All the analyses were stratified by sex, as the prevalence of smoking is different in men and women. HRs were adjusted for baseline age (years), body mass index (BMI; kg/m2), and educational attainment (years). Analyses were done with additional adjustments of other potential confounders such as betel quid chewing and arsenic exposure or potential intermediate factors such as systolic blood pressure and diabetes. Results were similar and therefore are not shown. Additional analyses were conducted to control for cigarette/bidi smoking in the analyses of hookah smoking, and vice versa. However, almost all the hookah smokers (99.1%) were also cigarette/bidi smokers in our study population, and therefore it was not possible to estimate HRs associated with hookah smoking among nonsmokers of cigarette/bidi or to assess additive interaction between hookah and cigarette/bidi smoking. Additional stratification by age, education, BMI, and betel quid chewing status did not suggest any subgroup-specific associations (all P for interaction were >0.10) and therefore results are not shown. We calculated the population attributable fraction (PAF) for all-cause mortality associated with ever cigarette/bidi or hookah smoking and smoking intensity using adjusted HRs estimated from Cox proportional hazards models. PAF was calculated as follows [19]: Where Pi is the proportion of all-cause deaths in the ith smoking category and HRi is the adjusted hazard ratio associated with the ith smoking category (relative to nonsmokers). All analyses were done with the SPSS 19.0 software (SPSS Inc., Chicago, IL).
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