Burden of Disease Attributable to Secondhand ... - SAGE Journals

667779 research-article2016

APHXXX10.1177/1010539516667779Asia Pacific Journal of Public HealthZahra et al

Original Article

Burden of Disease Attributable to Secondhand Smoking in Korea

Asia Pacific Journal of Public Health 2016, Vol. 28(8) 737–750 © 2016 APJPH Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1010539516667779 aph.sagepub.com

Aqeela Zahra, PhD1, Hae-Kwan Cheong, PhD1, Eun-Whan Lee, PhD2, and Jae-Hyun Park, PhD1

Abstract This study aims to estimate the burden of disease (BOD) due to secondhand smoking (SHS) in Korea. SHS-related diseases were selected via systematic review. Population attributable fraction (PAF) was calculated by using standard formula. Disability adjusted life years (DALYs) were estimated using Statistical Office and Health Insurance data. SHS burden was calculated by multiplying nonsmoker’s BOD with the PAF of SHS. Total BOD due to SHS was 44 143 DALYs with 57% from males and 43% from females. The highest percentage of SHS burden was due to stroke. BOD was highest in the 50s age group in both genders. Years of life lost contributed major part of BOD due to all diseases. SHS burden in Korea in 2013 was the highest among the high-income Asia Pacific group countries. Effective intervention policies with more focus on vulnerable groups like adults in their 50s should be implemented to control SHS-related burden. Keywords Burden of disease, disability adjusted life years, population attributable fraction, relative risk, secondhand smoking

Introduction Tobacco is one of the biggest health hazards and a major cause of mortality and morbidity around the world.1-3 Secondhand smoke (SHS) released from burning tobacco products is a common indoor air pollutant. Almost 250 chemicals in SHS are toxic to humans3 and cause cancer, heart disease, and respiratory diseases. Therefore, the effects of SHS on people’s health should be evaluated. According to global burden of disease (GBD) calculation, SHS contributed 9 316 121 disability adjusted life years (DALYs) globally in 2013.4 Korea has one of the highest smoking rates,5 resulting in high exposure to SHS among nonsmokers. The prevalence of exposure to SHS in Korea is higher than many other developed countries, and every 1 in 3 Korean adults is exposed to SHS every day.6 SHS exposure in Korean men and women in 2005 was greater than the worldwide prevalence of SHS exposure in 2004.7 Tobacco smoke, including SHS, was the third leading cause of DALY in Korea in 2013.8

1Sungkyunkwan 2Gyeonggi

University, Jangan-gu, Suwon, South Korea Research Institute, Jangan-gu, Suwon, South Korea

Corresponding Author: Jae-Hyun Park, Department of Social and Preventive Medicine, Samsung Biomedical Research Institute, Sungkyunkwan University School of Medicine, 2066 Seobu-ro, Jangan-gu, Suwon 440-746, South Korea. Email: [email protected]

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As SHS is a considerable health problem in Korea, quantifying the related disease burden is a powerful tool for evidence-based policies and priority settings. Previously, disease burden was assessed either as mortality or morbidity, lacking the use of a single measure of health that reflects the quality of life in a comprehensive way. Based on these issues, the World Health Organization and GBD group introduced DALY, a modeling technique that combines and compares the total fatal and nonfatal health losses from diseases and injuries in a population.9,10 Before year 2000, the World Health Organization calculated DALY by an old methodology. Years lived with disability (YLD) calculation was based on disease incidence and age weighting and discounting was applied. GBD reports released in 2010 and 2013 calculated smoking-related BOD by an updated methodology.10,11 For population attributable fraction (PAF) calculation of active smoking, the new term “smoking impact ratio” (SIR) was introduced, instead of smoking prevalence.11 SIR uses lung cancer mortality as an indirect indicator of cumulative hazards of smoking, taking into account duration, intensity, and lag time between smoking exposure and outcome.12 SIR is defined as Korean lung cancer mortality in excess of never smoker’s lung cancer mortality, relative to excess lung cancer mortality of a known group of smokers. Details of the SIR method have been described elsewhere.12-14 SHS is a major health issue not only worldwide but also in Korea, which is a country with one of the highest SHS prevalence. However, scientific studies in this field have been less satisfactory in Korea. Most of the previous studies in Korea have concentrated on active smoking,15-17 with little research available on SHS. The studies related to SHS mostly focused on estimating the prevalence of SHS and its associated factors.6,18,19 Few studies calculated the relative risk (RR) of diseases related to SHS,20,21 while some calculated the burden in terms of PAF and attributable deaths.7,22 Furthermore, most of these studies are outdated and lack the YLD estimates and recent advances in burden of disease (BOD) calculation. Estimation of both fatal and nonfatal burden in terms of DALY provides comprehensive information about the magnitude of health loss caused by a specific risk factor. Recent developments in methods for calculating DALY are also deficient in previous Korean studies. Although GBD calculated BOD due to SHS in Korea, it also has several limitations. The GBD study lacks precise consideration of country-specific data, for example, country-specific RR and disability weights (DW)23 were not used, and disease prevalence was modeled using DISMOD MR rather than actual statistics. Due to these reasons, several countries like New Zealand24 and Australia25 conducted their own BOD studies applying the methodology of the GBD study to country-specific data. Also, people can be exposed to SHS at home, work, or in public places. The GBD report considered exposure to SHS at home only for the BOD calculation. For SHS exposure at workplace, only BOD for lung cancer was calculated, with no estimates for other related diseases. Many previous studies have proven a strong link between workplace exposure to SHS and cardiovascular diseases.26-28 Therefore, this study will calculate BOD due to SHS exposure at any place, home or work, for all 3 related diseases based on the latest methods developed by GBD, and will use the most recent and reliable national statistics available for Korea. We will also apply RR (for active smoking) and DW from Korean studies to get nationally representative results. This study can provide a means of estimating the present magnitude of adverse effects of SHS on the health of the Korean population and can be helpful for policy makers to implement interventions to control health hazards due to SHS.

Materials and Methods This study followed the GBD methodology for calculating BOD. GBD is a project of the Institute of Health Metrics and Evaluation (IHME) and is considered as a gold standard for the calculation of BOD worldwide.5,10,11

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Zahra et al The following steps were used for calculating BOD due to SHS.

Selection of SHS and Active Smoking-Related Health Outcomes and Relative Risk A systematic review of previous studies was performed to select diseases related to SHS. The final list of health outcomes was selected on the basis of level of evidence for relation with SHS and quality of study design. Epidemiological studies assessing the association between SHS and risk of disease were searched using PubMed, Ovid, and Korea Med Database. Key words (which mean SHS) were selected using MeSH terms. Environmental tobacco smoke, secondhand smoking, passive smoking, ETS, and tobacco smoke pollution were used as key words. We found 19 524 articles related to SHS initially, which reduced to 3299 after limiting the search to cohort studies, meta-analysis, and systematic reviews. Only 14 studies were related to Korea. Finally, 3 diseases, lung cancer, ischemic heart disease (IHD), and stroke, were found to have strong evidence of association with SHS in adults. The GBD study also calculated BOD for these 3 diseases. Health outcomes for BOD calculation along with ICD-10 codes are given in Table 1. As high-quality Korean studies related to SHS are not available, RR was taken from the GBD 2013 study, which generated age-specific RR using integrated exposure curves based on previously published articles for RR.10 RR for active smoking was derived from Korean studies. Systematic review of Korean literature was performed using the same procedure and search engines as for SHS. Active smoking, tobacco smoking, relative risk, RR, odds ratio, OR, and Korea were used as key words. A total of 566 studies were identified. If multiple studies were identified for the same disease, the publication with the largest sample size and longest follow-up period was selected for RR. RRs for each disease along with reference articles and ICD-10 codes are mentioned in Table 1.

Calculation of Population Attributable Fraction PAF Calculation of SHS. PAF is the proportion of reduction in disease morbidity or mortality if exposure to a specific risk factor is reduced to zero. Age- and sex-specific PAF for SHS was calculated using the following standard formula9: PAFSHS = PSHS (RR SHS − 1) / [PSHS (RR SHS − 1)] + 1, where PSHS is the proportion of adult nonsmokers (25 years and above) who are exposed to SHS at work or home, and RRSHS is the RR of disease among people who are exposed to SHS to the risk among people who are unexposed. We did not assess BOD separately for home and workplace because a separate evaluation of SHS burden at work and home and adding them together can result in double counting and overestimation of the total BOD. Furthermore, public place exposure was not included because of the limitation of RR to work or home in previous studies. Following the GBD study, no lag time was used for the prevalence of SHS.10 PAF Calculation of Active Smoking. Prevalence-based formula of PAF calculation was used for diseases with short lag (IHD and stroke)9: PAFCS = PCS (RR CS − 1) / [PCS (RR CS − 1)] + 1, where PCS is the proportion of adults who are current active smokers and RRCS is the RR of disease due to active smoking. Five-year lagged smoking prevalence was used following the GBD study.10

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Table 1. Health Outcomes (With Strong Evidence of Associations With SHS) With ICD-10 Codes and Relative Risk. Relative Risk Disease Trachea, bronchus, and lung cancer IHD

Stroke

ICD-10 Codes C33-C34

SHS (From GBD 2013)

1.506 for both sexes and all age groups I20-I25.9, Z82.4-Z82.49 25-29 years = 1.468 30-34 years =1.433 35-39 years =1.4 40-44 years =1.368 45-49 years =1.336 50-54 years = 1.305 55-59 years = 1.276 60-64 years = 1.247 65-69 years =1.219 70-74 years =1.191 75-79 years = 1.165 80+ years = 1.139 I60, I61, I62.0, I63, I64, 25-29 years = 1.59 I65, I66, I67.0, I67.1, I67.2, 30-34 years =1.541 I67.3, I67.5, I67.6, I67.7, 35-39 years =1.493 I69.0, I69.1, I69.2, I69.3, 40-44 years =1.448 G45, G46 45-49 years =1.405 50-54 years = 1.362 55-59 years = 1.322 60-64 years = 1.283 65-69 years =1.246 70-74 years =1.211 75-79 years = 1.177 80+ years = 1.145

Active Smoking SIR-based method does not require RRa 2.2 for males; 1.7 for females (data from Jee et al29,30)

1.6 for both sexes and all age groups (data from Jee et al29,30)

Abbreviations: SHS, secondhand smoke; SIR, smoking impact ratio; IHD, ischemic heard disease; PAF, population attributable fraction. aPAF for lung cancer (SIR method) = Total lung cancer mortality rate − Never smoker lung cancer mortality rate/ Total lung cancer mortality rate.14

For diseases with long lag period (eg, cancer), a new methodology for PAF has been introduced in the GBD study in which current smoking prevalence is replaced with SIR.11 Using this updated SIR-based method, PAF for lung cancer was calculated directly by the absolute difference between the total lung cancer mortality rate in Korea and an estimated rate among nonsmokers.14 Data Source. Prevalence of SHS was taken from the Korean National Health and Nutrition Examination Survey 2013 (KNHANES), while prevalence of current active smoking was derived from KNHANES 2008 (5-year lag). KNHANES are successive cross-sectional surveys conducted by the Korean Ministry of Health and Welfare.32 For PAF calculation of lung cancer due to active smoking, data for the total lung cancer mortality rate in Korea was taken from a garbage code amended report31 based on mortality data from the National Statistical Office Database.32,33 Lung cancer mortality rate for neversmokers was taken from the previously published Korean Cancer Prevention Study (KCPS).

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KCPS is a cohort study including more than 603 549 Korean nonsmokers and 6 years of follow-up.34

Calculation of Disability Adjusted Life Years Method. One DALY can be defined as loss of 1 year of healthy life. DALYs for a disease are calculated by the sum of the years of life lost (YLL) due to premature mortality and the years lost due to disability (YLD).35 DALY = YLL + YLD YLL was calculated by multiplying the number of deaths due to a specific disease by the standard life expectancy for the age at which death occurred. The basic formula for YLL is35 YLL = N × L where N is the number of deaths and L is the standard life expectancy at age of death in years. YLD for a specific disease was calculated by multiplying the number of prevalent cases (updated GBD method) by the DW. DW indicates the severity of the disease on a scale from 0 (perfect health) to 1 (dead). The formula for YLD is35 YLD = P × DW where P is the number of prevalent cases and DW is the disability weight Data Source. To calculate BOD for SHS among Korean people, we used several databases described below. For YLL calculation, age- and sex-specific deaths were derived from a garbage code amended data based on the National Statistical Office Database 2013 following the garbage code amending method of a recent Korean study.32 These data cover almost all national deaths based on death certificates.33 Standard life expectancy by age and gender was also taken from the National Statistical Office Database.33 For YLD estimation, age- and sex-specific prevalence was taken from the Health Insurance Review and Assessment Service (HIRA) data 2013. HIRA is a government-affiliated organization built for an accurate claims review and quality assessment system for the National Health Insurance. Korean Cancer Center Registry Database (KCCR) 2012 (latest) was used for obtaining the prevalence of lung cancer. Started in 1980, KCCR collects data from nationwide hospitals that are equipped with histopathology laboratories. In contrast to the GBD study, we used DW from a recent Korean study.36 Final DW for each disease was calculated by using severity distribution and modeling of prevalence data. For IHD and stroke, severity level was distributed according to a recently published Korean study.36 For lung cancer, New Zealand BOD study modeling was used due to the absence of a Korean study. This study was released in 2012 and adopted the methodology of GBD and the Australian BOD study.24

Calculation of BOD Due to SHS Among Nonsmokers First, the BOD among nonsmokers was calculated by subtracting the current smoking burden from the total BOD by the following equation9:

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Table 2. Prevalence of Active Smoking and SHS by Gender and Age Group. Active Smoking Prevalence, NHNES 2008a (%)

SHS Prevalence, NHNES 2013 (%)

Age Group (Years)

Male

Female

Male

Female

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total

59.7 61.0 52.8 53.7 44.8 44.1 37.4 33.8 33.6 28.6 32.8 16.3 47.8

12.3 8.3 6.2 4.6 7.4 4.5 2.1 4.2 4.7 6.5 10.8 8.2 6.6

41.7 59.0 56.4 66.0 59.0 53.3 53.5 37.4 27.5 16.3 17.9 5.2 46.9

42.5 23.3 30.0 30.0 34.2 34.3 33.1 23.2 14.9 16.2 8.5 4.4 27.7

Abbreviations: SHS, secondhand smoke; NHNES, National Health and Nutrition Examination Survey; BOD, burden of disease. aFive years lagged active smoking prevalence used for BOD calculation following GBD 2013.

BNS = (B − (B × PAFCS )) × (1 − PCS ) where BNS is BOD among nonsmokers, B is the total BOD for both nonsmokers and smokers, PAFCS is the population attributable fraction of active smoking, and PCS is the active smoking prevalence. Finally, BOD attributable to SHS among nonsmokers was calculated as9 BSHS = PAFSHS × B NS

Results Prevalence of Active Smoking and SHS The prevalence of active smokers (PCS) and SHS exposure (PSHS) are shown in Table 2. Our results show that PCS was higher in males (47.8%) than in females (6.6%) in 2008. PSHS was also higher in men (46.9%) than in women (27.7%), and was more predominant among younger age groups in both sexes in 2013.

PAF of Active Smoking and SHS PAF of SHS and active smoking by disease, age, and gender is shown in Table 3. Active smoking PAF for males was higher than females in all age groups and all diseases. PAF was higher in younger age groups and decreased with age. Overall lung cancer PAF was highest in both genders and all age groups. SHS PAF for males was higher than females in all age groups except 25 to 29, 70 to 74, and 80+ age groups, in which PAF of both genders was equal in almost all diseases. PAF was higher in younger age groups followed by a decreasing trend in older ages. Lung cancer PAF was highest in both genders and all age groups, except 25 to 34 years of age, in

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Zahra et al Table 3. PAF of SHS and Active Smoking by Age and Gender in Korea. SHS Lung Cancer

Active Smoking

IHD

Age group

Males

Females

Males

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+

0.17 0.23 0.22 0.25 0.23 0.21 0.21 0.16 0.12 0.08 0.08 0.03

0.18 0.11 0.13 0.13 0.15 0.15 0.14 0.11 0.07 0.08 0.04 0.02

0.16 0.20 0.18 0.20 0.17 0.14 0.13 0.08 0.06 0.03 0.03 0.01

Stroke

Lung Cancer

IHD

Stroke

Females Males Females Males Females Males Females Males Females 0.17 0.09 0.11 0.10 0.10 0.09 0.08 0.05 0.03 0.03 0.01 0.01

0.20 0.24 0.22 0.23 0.19 0.16 0.15 0.10 0.06 0.03 0.03 0.01

0.20 0.11 0.13 0.12 0.12 0.11 0.10 0.06 0.04 0.03 0.01 0.01

0.00 1.00 0.09 0.53 0.18 0.57 0.64 0.74 0.71 0.74 0.76 0.79

0.00 0.00 0.00 0.03 0.20 0.28 0.34 0.30 0.30 0.49 0.58 0.59

0.42 0.42 0.39 0.39 0.35 0.35 0.31 0.29 0.29 0.26 0.28 0.16

0.08 0.05 0.04 0.03 0.05 0.03 0.01 0.03 0.03 0.04 0.07 0.05

0.26 0.27 0.24 0.24 0.21 0.21 0.18 0.17 0.17 0.15 0.16 0.09

0.07 0.05 0.04 0.03 0.04 0.03 0.01 0.02 0.03 0.04 0.06 0.05

Abbreviations: PAF, population attributable fraction; SHS, secondhand smoke; IHD, ischemic heart disease.

Table 4. BOD of SHS by Gender and Disease in Korea (Unit: Total DALY). Lung Cancer

IHD

Stroke

Total

Age

Males

Females

Males

Females

Males

Females

Males

Females

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total

17 0 124 263 618 851 1082 801 797 636 528 84 5803

30 45 180 419 624 1052 991 790 493 556 239 119 5537

84 120 356 701 1064 1597 1437 959 640 458 409 111 7936

20 37 104 187 336 489 626 537 367 733 383 314 4133

127 239 522 1040 1632 2036 1820 1245 940 788 743 184 11 316

163 284 392 893 1228 1741 1292 802 568 1053 590 413 9419

228 359 1002 2004 3314 4484 4340 3005 2378 1881 1679 379 25 054

212 365 676 1499 2188 3282 2908 2130 1428 2342 1212 846 19 089

Abbreviations: BOD, burden of disease; SHS, secondhand smoke; DALY, disability adjusted life year; IHD, ischemic heart disease.

which stroke PAF was the highest, and 35 to 39 age group, in which stroke and lung cancer PAF were equal.

Disease Burden Due to SHS by Gender BOD due to SHS by gender, age, and disease is presented in Table 4 in terms of total DALY and in Table 5 in terms of age- and sex-specific rate per 100 000. Total BOD attributable to SHS was 44 143 DALYs (122 per 100 000) with 57% (25 054 DALYs, 141/100 000) from males and 43% (19 089 DALYs, 104/100 000) from females. The highest percentage of SHS burden was due to stroke in both genders (total 57 DALYs per 100 000).

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Table 5. BOD by Each Disease, Gender, and Age (Unit: DALY Age- and Sex-Specific Rate per 100 000). Lung Cancer

IHD

Stroke

Total

Age

Males

Females

Males

Females

Males

Females

Males

Females

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Total

1.0 0.0 6.2 11.4 29.0 38.9 62.9 67.5 87.8 81.6 110.1 25.6 32.6

1.9 2.3 9.2 18.7 30.2 49.0 57.3 63.9 48.7 55.9 32.2 15.0 30.0

5.0 5.8 17.6 30.2 49.9 73.0 83.5 80.8 70.5 58.8 85.2 34.1 44.6

1.3 1.9 5.3 8.3 16.3 22.8 36.2 43.4 36.3 73.8 51.6 39.8 22.4

7.6 11.6 25.8 44.8 76.5 93.1 105.8 104.9 103.5 101.1 154.9 56.2 63.6

10.4 14.3 20.1 39.9 59.5 81.0 74.7 64.9 56.1 106.1 79.5 52.4 51.1

13.7 17.4 49.6 86.4 155.4 205.0 252.1 253.2 261.9 241.5 350.2 115.8 140.8

13.6 18.5 34.6 66.9 106.0 152.8 168.3 172.3 141.1 235.8 163.2 107.3 103.5

Abbreviations: BOD, burden of disease; DALY, disability adjusted life year; IHD, ischemic heart disease.

Disease Burden Due to SHS by Age Group Total YLL and YLD by age and gender are presented in Figure 1, and rate per 100 000 is shown in Figure 2. SHS burden was correlated with age in both genders. BOD due to SHS increased with age and peaked in the 50s age group in both genders, followed by a decreasing trend in older ages. YLL contributed a major part of BOD (89%) in all age groups and both genders as compared to YLD (11%). Total DALY was highest among those aged 50 to 59 years and lowest in those 25 to 34 years in both genders. DALY rate per 100 000 was highest in older ages (70s age group) in both genders.

Disease Burden Due to SHS by Each Disease Total SHS burden by each disease, age, and gender is shown in Table 4, and rate per 100 000 is presented in Table 5. Stroke contributed the highest part of BOD (47%), followed by IHD (27%) and lung cancer (26%). BOD was higher in males than females for all 3 diseases. Lung cancer and stroke burden increased with age, with the highest level in the 50s age group for both males and females. For IHD, the peak level of BOD was in the 50s age group among males and 70 to 74 years age group among females. YLL contributed most of the BOD in all diseases (lung cancer 91%, IHD 85%, stroke 91%) followed by YLD (lung cancer 9%, IHD 15%, stroke 9%). For stroke, 71% of total tobacco BOD was contributed by active smoking and 29% (highest) by SHS. Eighty-six percent of IHD burden was due to active smoking and 14% due to SHS. For lung cancer, 94% of total tobacco BOD was contributed by active smoking and 6% by SHS.

Discussion According to our findings, the total BOD due to SHS among nonsmokers in Korean adults is 44 143 DALYs (122 per 100 000), which is higher than the estimates by the GBD 2013,10 that is, 35 276 DALYs (100 per 100 000). This disparity can be due to several reasons. First, we used RR

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Figure 1. SHS burden presented as YLD and YLL by gender and age.

Figure 2. SHS burden presented as YLD and YLL (Unit: age- and sex-specific rate per 100 000).

for active smoking from Korean studies, which are quite lower from IHME. This low RR results in less active smoking BOD, which in turn increases the BOD among nonsmokers exposed to SHS. Several previous studies in Korea,22 as well as Japan,37 have proven that there is a marked difference of lung cancer RR due to active smoking between the Asian population and Western countries. This difference can be due to various genetic, dietary, and ethnic factors,37-39 which make Asians less susceptible to the effects of smoking, as well as different age of onset, quality, and intensity of smoking compared to Western countries.22 Second, this study used “exposure to SHS at work or home” for the calculation of BOD. IHME calculated BOD for “SHS at home” for all 3 diseases, while only lung cancer burden was calculated for SHS exposure at the workplace. Several previous studies suggest a strong link between workplace exposure to SHS and cardiovascular disease (CVD).26-28 Ignoring this considerable part of disease burden due to CVD from workplace exposure can result in a significant underestimation of results. Third, we used the Korean DW for YLD calculation, which is higher than IHME DW, for example, DW of severe angina for Korea was 0.47 while IHME DW was 0.17. Last, IHME used DISMOD MR to model

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disease prevalence, while we used actual statistics from precise national database, which may have resulted in a more accurate estimation of BOD. Total active smoking burden was 921 670 DALYs in Korea in 20134 and SHS contributed 4.6% (44 143 DALYs) of the total tobacco burden, which is quite high. SHS burden was higher than many environmental factors, including ozone pollution (20 347 DALYs), radon exposure (18 734 DALYs), and occupational risks like occupational asthma (9963 DALYs), carcinogens (24 732 DALYs), and particulate matter (19 057 DALYs).4 These findings suggest that government and policy makers need to pay considerable attention to priority settings and resource allocation for health risks due to SHS. According to our findings, SHS burden in Korean adults (122 per 100 000) was highest among the high-income Asia Pacific group, including Japan (78 per 100 000), Singapore (58 per 100 000), and Brunei (87 per 100 000). Korean BOD was also higher than many other developed countries, including the United States and the United Kingdom, but lower than China.4 This might be due to the different calculation method than IHME. This can also be explained by high active smoking prevalence in Korea. Korea is among those countries that have the highest prevalence as well as intensity of smoking, resulting in high exposure to SHS.5 Many previous studies have also shown that SHS prevalence is higher in Korea than many developed countries,6,18 which may be due to a more strict restriction of smoking at workplace and high implementation of home smoking ban policies in these countries. BOD due to SHS was higher in males (57%) than in females (43%). These results are due to higher SHS prevalence among males (46%) than females (27%), resulting in high PAF value. SHS prevalence among males is specifically high due to a higher level of workplace exposure (57.2%). Moreover, according to our study as well as many previous studies, the prevalence of all 3 diseases (lung cancer, IHD, stroke) is higher among males than females,40,41 which makes the total BOD higher for males. However, it was observed that although SHS prevalence is quite low in females compared to males, this gap between genders is narrowed in SHS-related BOD. These findings show that in spite of low SHS prevalence, disease burden is significantly high in females as well. Many previous studies have suggested that females are more vulnerable to tobacco-related health hazards42,43 and many other cultural differences put them at high risk of health threats. Smoking ban policies at public and indoor workplaces were implemented in Korea in 1995 and have been continuously expanded and updated. Health warnings were issued in 2007, and the tax on tobacco products was increased in 2015. Smoking ban policies are known to affect the prevalence trend of SHS. Our results show that active smoking and SHS prevalence followed almost the same pattern by age group (higher in younger age groups followed by decreasing trend). This suggests that policies like increased tax and health warnings are needed not only for active but also SHS prevalence control. Additionally, direct interventions like smoking ban are also of significance for controlling SHS prevalence. Although smoking ban policies are implemented in Korea, they are focused on public places and indoor workplaces only. We suggest that more attention should be paid to make gender sensitive policies for reducing SHS exposure among females, especially at home. Home smoking ban policies cannot be effective without behavior change and appropriate awareness of the adverse effects of smoking and SHS among lay people. Ban policies should be implemented in outdoor workplaces also to protect labor workers from health damage. Due to lack of SHS exposure data, we did not calculate BOD among children. However, SHS prevalence and burden in children should be calculated in future studies and specific policies for reducing SHS exposure among children should also be introduced accordingly. Most of the BOD (total DALYs) was observed after 40 years of age, peaking in the 50s age group. This finding is consistent with previous studies.7 According to our results, active smoking prevalence decreases with age in both genders, and the number of nonsmoker increases, resulting in high nonsmoker DALYs and high SHS burden in these age groups. Also, our target diseases

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are ailments of old age with longer latency periods causing more effects on elderly population. Increase in number of deaths from these diseases in the elderly might also contribute to the increasing pattern of BOD with increasing age. Moreover, like SHS, total BOD due to all risk factors in Korea was second highest in the 50s age group (highest in the 80+ age group) in 2013.4 This high burden in the 50s age group indicates that health interventions and services need to concentrate on this population in terms of early diagnosis and treatment of SHS-related health threats. BOD, in terms of rate per 100 000, was highest in older age (70s age group) in both genders. This may be due to the demographic structure of the population. Total population is lower in the older age groups, while disease prevalence and BOD is high, which in turn makes the BOD rate higher in the elderly. The major part of disease burden was contributed by YLL (89%). As mentioned earlier, prevalence of these diseases is higher in older age groups with less life expectancy, causing a high mortality rate, and in turn high YLL. BOD contributed by stroke was highest (47%) among all 3 diseases in all age groups and both genders. Prevalence and mortality of stroke is quite high in Korea and it ranked 10th for stroke mortality among OECD countries in 2013.44 Stroke was the second leading cause of DALY and the first leading cause of YLL consistently between 1990 and 2013 in Korea.8 Additionally, RR and PAF of stroke are also considerably high, causing an increased BOD of stroke. Our results also suggest that IHD was the second leading cause of DALY due to SHS. So, it can be inferred that vigorous interventions and screening programs should be instigated to control health hazards related to stroke and IHD. Our study has a few limitations. First, we did not assess the amount and time period of exposure to SHS. Occurrence of diseases may be linked with the duration of exposure, but studies about RR according to intensity of exposure are also very limited. Future studies related to dose-response relationship might be helpful in a more accurate measurement of BOD. Second, we estimated BOD in adults only. Children are also affected by SHS. However, there is no database representing SHS prevalence in utero and in infancy in Korea. According to GBD 2013 estimates, SHS burden due to children was only 1.7% of the total burden in Korea.4 Thus, we expect that this would not have significant effects on our results. Third, due to lack of data on SHS-related RR in Korea, we used international studies for our calculation, which may not represent the exact situation in Korea. Therefore, to estimate the SHS burden more accurately in Korea, long-term follow-up studies based on Korean data are required. Fourth, this study did not calculate BOD for the 19 to 24 years age group because diseases related to SHS have long latency period and RR calculation for these diseases requires long-term follow-up studies. Due to these reasons, previous studies are deficient in RR of younger age groups. The GBD 2013 study also did not calculate RR for this age group. Further studies are needed to explore burden in this age group. Despite these limitations, our study is unique in many ways. To our knowledge, this was the first study to calculate the SHS burden including both YLL and YLD estimates using the most updated methods by IHME. We derived RR for active smoking and DW from Korean studies, which made the results more nationally representative. This study also used actual disease prevalence statistics from HIRA data instead of modeled statistics used by IHME. In contrast to the GBD study, which did not calculate the burden of IHD and stroke for workplace exposure to SHS, we calculated the burden for all 3 diseases for exposure at work or home. This study calculated both total BOD and rate per 100 000. Total BOD provides actual numeration and can be helpful for policy makers to set priorities to allocate health resources. BOD rate can be used for comparison of burden among different countries according to population size.

Conclusion and Suggestions In conclusion, we posit that BOD due to SHS in Korea is considerably higher than many other developed countries, including the United Kingdom, the United States, and all countries of the

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high-income Asia Pacific group.4 Smoking ban policies in Korea are focused on public places and indoor workplaces only. Although public place exposure to SHS is high, its duration is very trivial compared to home and workplace exposure where people spend most of their daily hours. So specific tobacco ban policies need to be implemented at outdoor workplaces and home. Rewards should be offered to companies for strict restriction on smoking at workplaces. In order to make smoke-free laws effective at homes, complementary educational plans about the hazards of tobacco smoke should be made to induce behavioral changes, especially among males. Specific gender sensitive policies and interventions are needed to protect females from the hazards of SHS. Integrated health care programs focused on the population in the 50s age group should be implemented for not only SHS but also other health risks in this age. In order to estimate a more accurate and nationally representative BOD, further epidemiological studies about RR of SHS in Korea are needed. Collaboration of national researchers with international investigators is needed to review and compare the results and develop methodologies for BOD calculations. Continuous monitoring of SHS-related burden according to the latest statistics is suggested for priority settings and evaluation of policy effectiveness for decline in SHS-related burden. Authors’ Note This study involves human subjects but consent was not needed from subjects as we used already collected data from Korean surveys. However, this study protocol was approved by the Institutional Review Board of Sungkyunkwan University (IRB No. 2014-06-008).

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Korea Ministry of Environment as “The Environmental Health Action Program” (2014001360001).

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