Secondhand smoke (SHS) remains a major global health concern, exposing individuals to toxic compounds that significantly increase the risk of cardiovascular diseases, metabolic disorders, and respiratory illnesses. This study aims to assess the global burden of disease attributable to SHS by analyzing Disability-Adjusted Life Years (DALYs) across Socio-Demographic Index (SDI) regions. This study utilized secondary data from the Global Burden of Disease (GBD 2021) database, analyzing epidemiological metrics across 204 countries. The SDI was applied to assess disparities, and SHS exposure was defined based on household and workplace factors. Statistical analyses estimated the burden of SHS-associated diseases, stratified by demographic and socioeconomic categories, with results mapped globally using ArcGIS. Globally, ischemic heart disease posed a greater burden on males (131.31 DALY) compared to females (96.42 DALY), while diabetes mellitus and stroke affected females more (46.99 and 84.05 DALY, respectively). COPD exhibited the highest DALY rates in low-middle SDI regions (males: 129.85; females: 124.42), whereas high-income regions had the lowest burden. Diabetes mellitus showed a rising trend across SDI regions, with females in middle and high-middle SDI regions experiencing the highest YLDs. The analysis reveals significant disparities in disease burden from secondhand smoke exposure across regions and sexes. The findings highlight the sex-specific and regional variations in disease burden, underscoring the need for targeted health interventions and tobacco control strategies.
Keywords: Secondhand smoke, Cardiovascular diseases, Disability-adjusted life years, Socio-demographic index, Public health impact
Clinical trial number
Not applicable.
Introduction
Second-hand smoke (SHS) remains a major global public health concern, contributing substantially to morbidity and mortality [1]. Cigarette smoke contains more than 7,000 chemicals, hundreds of which are toxic, and at least 70 are recognized carcinogens [2]. Compared to mainstream smoke, SHS exposes non-smokers to higher concentrations of certain harmful compounds, and evidence indicates that SHS increases the risk of cardiovascular diseases (CVD) by approximately 30% and is significantly associated with impaired glycemic control in household environments [3]. Even short-term exposure has adverse effects on respiratory and cardiovascular systems; nevertheless, an estimated 37% of the global population remains exposed, with women and children disproportionately affected [4].
Several recent studies have examined the disease burden attributable to SHS using Global Burden of Disease (GBD) data. Su et al. reported a comprehensive analysis across 204 countries using GBD 2021, presenting Disability-Adjusted Life Years (DALYs) for multiple SHS-associated diseases and detailed years of life lost (YLL) and years lost due to disability (YLD) estimates [5]. However, their emphasis was primarily on global aggregates, with limited interpretation of age-standardized rates and only modest attention to disparities across Socio-Demographic Index (SDI) regions. Similarly, Liu et al. reported SHS-attributable cardiometabolic and respiratory disease burden by sex and SDI. Our study advances this line of research by foregrounding age-standardized rates and providing a comparative interpretation of SDI disparities, thereby offering a more nuanced understanding of regional heterogeneity [6]. Other investigations have addressed specific outcomes such as cardiovascular disease [1], lung cancer [4], and cardiometabolic conditions [3], yet the comparative interpretation of SHS burden across SDI strata remains underexplored.
The present study builds upon this literature by explicitly stratifying SHS-attributable burden across SDI regions and emphasizing age-standardized rates to enable meaningful comparisons between populations with differing demographic structures. We focus on five major SHS-associated diseases—chronic obstructive pulmonary disease (COPD), diabetes mellitus, ischemic heart disease, lower respiratory infections, and stroke—that together account for the largest share of attributable DALYs and represent conditions with consistent epidemiological evidence and policy relevance [4–8]. Although other outcomes such as intracerebral hemorrhage contribute substantially to the burden, our selection reflects diseases with the most robust causal associations and highest public health impact. By analyzing trends from 1990 to 2021 and presenting detailed results for 2021, this study provides updated and regionally disaggregated insights that complement and extend prior global analyses, offering evidence to inform targeted tobacco control and prevention strategies.
Methods
Data Source
This study draws upon secondary data from the GBD 2021 database, which provides comprehensive epidemiological metrics for 204 countries and territories, covering 369 diseases and 87 risk factors from 1990 to 2021. The primary objective of the GBD study is to quantify health losses attributed to a broad spectrum of diseases, injuries, and risk factors worldwide and regionally over time, facilitating evidence-based policymaking and informed resource allocation decisions. (Source: GBD Results Tool-http://ghdx.healthdata.org/gbdresults-tool)
SDI
The SDI is a composite measure of regional development, calculated as the geometric mean of three standardized components: income, education, and fertility. Income is represented by gross domestic product (GDP) per capita, adjusted for purchasing power parity, providing an economic perspective on development levels. Education is assessed based on the average years of schooling among individuals aged 15 and older, reflecting the overall educational attainment of a population. Fertility is measured by the total fertility rate, defined as the average number of live births per woman, which serves as an indicator of demographic trends. SDI values range from 0, indicating the lowest level of development, to 1, signifying the highest. The GBD study utilizes SDI to facilitate comparisons of disease burden across populations with varying socioeconomic conditions, allowing for a more nuanced understanding of health disparities worldwide [9, 10].
Definition of SHS Exposure
Exposure to SHS is defined as the current exposure of non-smokers to tobacco smoke in residential settings, workplaces, or public areas. Household exposure is assessed based on household composition, with the assumption that individuals residing with at least one daily smoker are exposed to SHS. Workplace exposure estimates are derived from nationally representative surveys, providing a broader perspective on occupational SHS exposure [11].
Statistical analysis
The extracted data included age-standardized rates (per 100,000 populations) for disability-adjusted life years (DALYs; DALY = YLL + YLD), years lived with disability (YLDs; YLD = Prevalence × Disability Weight × Duration), and years of life lost (YLLs; YLL = Number of Deaths × Life Expectancy Lost) associated with five major SHS-associated diseases: Stroke, Lower respiratory infections, Ischemic heart disease, COPD, and Diabetes mellitus. The attributable burden for each of these five SHS-associated diseases was estimated separately using the corresponding population attributable fraction (PAF) for second-hand smoke exposure. Analyses were stratified by sex, age group, and five categories of the SDI: high, high-middle, middle, low-middle, and low [12, 13].
The analysis was carried out in two main stages. First, DALYs for all five SHS-associated diseases were computed and aggregated for 204 countries to estimate the total national burden attributable to SHS. Second, these aggregated DALY values were mapped globally using ArcGIS, employing a gradient color scale where lighter shades represented lower disease burden and darker shades indicated higher burden. This visualization enabled both qualitative interpretation of global patterns and precise quantitative comparisons between countries. Statistical analysis was conducted using Microsoft Excel (2019) for data organization and management, and ArcGIS (v10.7.1) for geospatial visualization.
Results
Table 1 illustrate globally, males exhibited higher DALY rates for ischemic heart disease (131.31, 95% UI: 96.56–166.48) compared to females (96.42, 95% UI: 73.08–122.25). In contrast, females had higher DALY rates for diabetes mellitus (46.99, 95% UI: 17.02–80.25) and stroke (84.05, 95% UI: 56.59–111.41) than males (37.84 and 78.21, respectively).
Table 1.
Disease burden attributable to SHS exposure by SDI region and Sex, 2021 (Age-Standardized rates per 100,000 Population)
| COPD | Diabetes mellitus | Ischemic heart disease | Lower respiratory infections | Stroke | |||
|---|---|---|---|---|---|---|---|
| Global | DALY | Male | 71.48(28.68-115.43) | 37.84(13.46–65.80) | 131.31(96.56-166.48) | 94.86(30.81-163.32) | 78.21(52.65-104.65) |
| Female | 63.54(24.49–102.60) | 46.99(17.02–80.25) | 96.42(73.08-122.25) | 82.07(27.18-138.79) | 84.05(56.59-111.41) | ||
| YLDs | Male | 9.49(3.63–15.88) | 20.35(6.77–36.67) | 2.50(1.55–3.82) | 0.48(0.15–0.88) | 6.35(3.97–9.30) | |
| Female | 14.89(5.85–24.58) | 25.97(8.77–47.51) | 2.31(1.44–3.49) | 0.46(0.15–0.83) | 10.02(6.29–14.74) | ||
| YLLs | Male | 61.99(24.44-101.77) | 17.49(6.20-29.48) | 128.82(94.54-163.31) | 94.38(30.65-162.59) | 71.87(48.27–96.22) | |
| Female | 48.65(18.86–79.52) | 21.01(7.77–35.04) | 94.11(71.36-119.61) | 81.61(27.03-138.19) | 74.03(49.61–99.02) | ||
| High SDI | DALY | Male | 21.60(8.63–35.56) | 27.53(9.63–48.49) | 67.79(50.17–87.54) | 16.11(5.32–27.67) | 24.43(16.48–32.59) |
| Female | 14.36(5.57–23.61) | 20.57(7.06–36.43) | 25.89(19.50-33.94) | 8.62(2.85–14.66) | 17.89(12.13–24.21) | ||
| YLDs | Male | 5.96(2.23–10.27) | 19.72(6.55–35.91) | 1.99(1.23–3.01) | 0.09(0.03–0.17) | 4.85(3.05–7.16) | |
| Female | 5.30(2.04–8.78) | 16.03(5.31–29.58) | 1.10(0.68–1.65) | 0.08(0.03–0.14) | 4.91(3.10–7.25) | ||
| YLLs | Male | 15.64(6.15–25.59) | 7.80(2.80-13.16) | 65.81(48.48–84.37) | 16.01(5.28–27.50) | 19.58(13.32–25.77) | |
| Female | 9.06(3.56–15.15) | 4.54(1.64–7.60) | 24.78(18.63–32.65) | 8.55(2.82–14.51) | 12.98(8.76–17.74) | ||
| High-middle SDI | DALY | Male | 63.04(24.32-103.81) | 33.22(11.82–57.12) | 160.40(115.96-206.17) | 41.83(14.20-69.28) | 93.18(60.06-128.63) |
| Female | 52.75(20.85–86.09) | 44.88(16.13–76.44) | 117.60(87.31-154.66) | 30.10(10.35–50.69) | 90.03(61.82-121.87) | ||
| YLDs | Male | 8.53(3.22–14.26) | 21.22(7.20-38.51) | 3.30(2.05–4.96) | 0.41(0.14–0.75) | 8.46(5.25–12.42) | |
| Female | 16.32(6.38–26.74) | 30.44(10.37–56.21) | 3.43(2.16–5.19) | 0.46(0.16–0.83) | 13.62(8.59–19.91) | ||
| YLLs | Male | 54.52(20.64–91.32) | 12.00(4.08–20.02) | 157.10(113.24-202.67) | 41.42(14.02–68.58) | 84.72(54.13-117.54) | |
| Female | 36.43(14.44–60.38) | 14.44(5.25–24.18) | 114.17(84.67-149.41) | 29.64(10.20-49.93) | 76.42(52.19-105.69) | ||
| Middle SDI | DALY | Male | 89.66(34.15-150.41) | 40.83(14.50-70.07) | 144.27(106.93-187.97) | 75.41(24.46–126.60) | 101.69(66.31-138.07) |
| Female | 85.01(32.51-139.01) | 58.40(21.27–98.95) | 117.16(86.95-153.97) | 62.79(21.32–106.20) | 117.17(78.44-158.45) | ||
| YLDs | Male | 10.23(3.87–17.16) | 20.39(6.73–36.90) | 2.69(1.65–4.17) | 0.41(0.13–0.75) | 7.05(4.34–10.39) | |
| Female | 19.17(7.48–31.15) | 30.90(10.49–56.05) | 2.87(1.77–4.39) | 0.41(0.14–0.75) | 13.33(8.30-19.45) | ||
| YLLs | Male | 79.42(29.98-133.97) | 20.44(7.23–34.05) | 141.58(104.78-184.99) | 75.00(24.33-125.92) | 94.64(61.57-128.63) | |
| Female | 65.84(25.68-110.38) | 27.50(10.06–45.48) | 114.29(84.55-150.82) | 62.38(21.16–105.50) | 103.84(69.89-141.04) | ||
| Low-middle SDI | DALY | Male | 129.85(50.89-211.24) | 53.68(18.50-92.34) | 168.91(124.49-217.12) | 138.84(44.19-239.29) | 91.54(62.10-123.64) |
| Female | 124.42(47.86-210.98) | 68.52(25.57-115.37) | 132.52(96.76-170.18) | 135.57(45.08-229.96) | 109.59(74.17-148.04) | ||
| YLDs | Male | 13.95(5.54–23.49) | 23.12(7.63–42.63) | 2.34(1.40–3.54) | 0.90(0.29–1.66) | 5.32(3.27–7.78) | |
| Female | 20.25(7.89–33.38) | 28.64(9.82–52.43) | 1.96(1.17–2.99) | 0.89(0.29–1.63) | 7.99(5.03–11.67) | ||
| YLLs | Male | 115.90(45.52-189.24) | 30.56(10.25–51.56) | 166.57(122.59–214.10) | 137.94(43.90-237.43) | 86.22(58.41-116.48) | |
| Female | 104.17(39.22-179.64) | 39.88(14.64–67.56) | 130.56(95.33-167.49) | 134.68(44.77-228.45) | 101.60(68.36-137.69) | ||
| Low SDI | DALY | Male | 93.75(36.48-154.89) | 41.43(13.90–71.30) | 97.58(69.78–130.30) | 156.43(49.77-270.42) | 73.67(48.98–99.31) |
| Female | 82.24(30.13-141.94) | 40.44(14.35–69.40) | 65.81(46.79–89.49) | 133.09(41.03-229.19) | 69.26(45.56–94.11) | ||
| YLDs | Male | 11.44(4.38–19.23) | 15.64(5.20-28.82) | 1.51(0.92–2.29) | 0.81(0.25–1.50) | 4.09(2.54–5.97) | |
| Female | 12.95(4.87–21.81) | 15.57(5.32–28.98) | 0.95(0.57–1.45) | 0.68(0.22–1.26) | 4.87(3.05–7.12) | ||
| YLLs | Male | 82.30(31.86-136.31) | 25.79(8.50-44.39) | 96.07(68.69-128.62) | 155.63(49.50-269.25) | 69.58(46.19–93.50) | |
| Female | 69.29(25.44-122.23) | 24.87(8.75–42.05) | 64.87(46.00-88.21) | 132.41(40.81–227.90) | 64.38(42.08–87.68) | ||
Across SDI regions, the highest DALY rates for COPD were observed in low-middle SDI regions (males: 129.85; females: 124.42), while high SDI regions reported the lowest (males: 21.60; females: 14.36). For ischemic heart disease, high-middle SDI regions had the highest burden (males: 160.40; females: 117.60), whereas high SDI regions recorded the lowest (males: 67.79; females: 25.89).
YLDs were consistently higher for diabetes mellitus across all regions, with the highest values in middle SDI females (30.90, 95% UI: 10.49–56.05) and high-middle SDI females (30.44, 95% UI: 10.37–56.21). YLLs dominated the burden for ischemic heart disease, particularly in males from high-middle SDI regions (157.10, 95% UI: 113.24–202.67). These findings highlight the differential impact of SHS exposure by disease, sex, and SDI region.
Figure 1 illustrate, the majority of SDI regions showed a decline in diseases like stroke, lower respiratory infections, ischemic heart disease, and COPD. In middle SDI regions, for example, the age-standardized rate of COPD dropped dramatically from about 229 cases per 100,000 in 1990 to 86 cases per 100,000 in 2021. Diabetes mellitus, on the other hand, was an exception, exhibiting an increasing trend across the board, especially in low-middle SDI regions. In low-middle SDI regions, the age-standardized rate of diabetes mellitus rose from 48 cases per 100,000 in 1990 to 61 cases per 100,000 in 2021. Furthermore, compared to other regions, areas with a lower SDI typically had a higher burden of disease.
Fig. 1.
Age-Standardized Trends in DALYs (Per 100,000 Populations) Attributable to SHS-associated diseases, Disaggregated by SDI Regions, for Both Sexes (1990–2021)
Figure 2 shows that in regions with a low SDI, lower respiratory infections account for the highest proportion of DALYs, with an estimated value of approximately 33.8% for men and 34% for women. In contrast, ischemic heart disease dominates in high SDI regions, contributing around 43% of DALYs among men. Gender-specific patterns indicate that, overall, men tend to bear a higher proportion of DALYs across most diseases and regions. However, an important exception is observed in the case of diabetes mellitus, where women in high SDI regions experience a significantly higher burden, with approximately 23.5% of DALYs attributed to this condition.
Fig. 2.
Age-Standardized DALY Rates for Five Diseases Attributable to SHS, by SDI Region and Sex, 2021
This GIS map (Fig. 3) utilizes a color gradient to visualize the severity of health impacts from SHS exposure, where darker hues indicate higher disease burden (measured by DALY rates) and lighter shades represent lower risk levels. The findings reveal a distinct geographical pattern: low-income nations in South Asia (Afghanistan, Pakistan) and North Africa (Egypt, Libya) exhibit the most severe health consequences, while high-income countries in North America (United States, Canada), Western Europe (Germany, Switzerland), and Oceania (Australia, New Zealand) demonstrate the lowest disease burden.
Fig. 3.
GIS map- Global Burden of Disease Attributable to SHS Exposure, 2021 (Age-Standardized DALY Rates per 100,000 Population in 204 Countries)
Discussion
The study underscores the significant health burden of SHS exposure, particularly its role in cardiovascular and metabolic diseases. Sex-specific disparities reveal men face higher risks of ischemic heart disease, while women are more affected by SHS-related metabolic disorders like diabetes. These variations may stem from differences in endocrine function and indoor exposure patterns. One intriguing aspect of smoking is the heightened vulnerability of the female gender. The mortality rate from CVD is higher among women than male smokers. Additionally, female smokers exhibit a 25% greater risk of developing coronary heart disease (CHD) compared to men with the same exposure to tobacco smoke [14]. Furthermore, findings from the systematic review and meta-analysis conducted by Zhang and colleagues indicated that an estimated 6.77% (95% CI: 5.31%–8.46%) of all CVD cases in men and 7.15% (95% CI: 5.62%–8.93%) in women may be attributed to exposure to SHS worldwide [15]. Evidence also suggests that although more women than men die from CVD, the age-standardized rates of complications and mortality are higher in men [16]. To reduce health risks from SHS, a comprehensive approach is crucial. Education should raise awareness about SHS’s disproportionate impact on men’s cardiovascular health and women’s metabolic systems. Policies should enforce smoke-free environments, strengthen smoking cessation programs, and integrate gender-sensitive health interventions. Stricter regulations and public health initiatives can protect vulnerable groups and promote healthier living spaces. The findings highlight the need for targeted interventions, stricter regulations, and increased public awareness to mitigate the global health impact of SHS exposure. While prior studies have reported global and SDI-stratified estimates of SHS-attributable burden, their emphasis has largely been on overall trends. In contrast, our analysis has placed greater focus on age-standardized rates and provided a comparative interpretation across SDI strata. This approach has extended earlier findings and offered clearer insight into regional inequities, thereby highlighting the unique contribution of the present study.
Over the past three decades, the global health landscape has witnessed a significant transformation. While stroke, lower respiratory infections, ischemic heart disease, and COPD have steadily declined across most SDI regions, diabetes mellitus has surged—particularly in low-middle SDI areas. This alarming trend points to shifting dietary habits and increasingly sedentary lifestyles as key contributors to metabolic disorders [17–19]. The notable reduction in COPD cases in middle SDI regions—from 229 per 100,000 in 1990 to just 86 per 100,000 in 2021—highlights the power of strengthened tobacco control and enhanced healthcare access. However, as diabetes rates continue to climb, further research is needed to explore regional disparities, particularly in relation to diet, socioeconomic conditions, and healthcare infrastructure. Policymakers must act swiftly to counter this rise. Governments should prioritize targeted nutritional education, promote physical activity programs, and implement proactive health interventions to curb diabetes growth. At the same time, successful COPD reduction strategies should be scaled up to lower SDI regions, ensuring widespread access to effective prevention and treatment measures. A comprehensive, data-driven approach is essential to shaping a healthier global future.
The burden of SHS exposure varies drastically across the globe, with low-income countries in South Asia—such as Afghanistan and Pakistan—and North Africa, including Egypt and Libya, facing the highest health impacts. A combination of widespread smoking habits, fragile healthcare systems, and weak enforcement of smoke-free policies exacerbates the problem, leaving vulnerable populations at greater risk. Conversely, high-income nations like the United States, Canada, Germany, Switzerland, and Australia have successfully minimized SHS-related health burdens, thanks to stringent tobacco control measures, extensive public health education, and the widespread adoption of smoke-free environments [3, 4]. These contrasting realities underscore the urgent need for targeted policy interventions. Strengthening smoke-free regulations, enhancing healthcare accessibility, and advancing comprehensive tobacco control strategies will be critical to reducing SHS-related risks, particularly in regions where the disease burden remains disproportionately high. By emphasizing age-standardized rates and SDI-stratified comparisons, our study has provided clearer evidence of these inequities, reinforcing the need for region-specific strategies to effectively reduce SHS exposure worldwide.
Conclusion
This study has emphasized the substantial global disease burden associated with SHS exposure and has revealed disparities by sex, disease type, and SDI classification. While concerted tobacco control efforts have successfully mitigated SHS-associated diseases over time, emerging trends—such as increasing diabetes mellitus prevalence and persistent exposure in low-income regions—have highlighted the need for renewed policy interventions. By building on prior studies that reported global and SDI-stratified estimates, our analysis has placed greater emphasis on age-standardized rates and has provided a comparative interpretation of SDI disparities. In doing so, it has extended earlier findings and has offered clearer insight into regional inequities in SHS-attributable burden. Future research should focus on gender-sensitive analyses, novel intervention strategies, and sustainable health policies to further reduce SHS exposure and its long-term consequences.
Acknowledgements
We would like to express our sincere gratitude to the dedicated staff at the Institute for Health Metrics and Evaluation for their invaluable work in producing the publicly accessible data used in the GBD study. Additionally, we extend our deepest appreciation to the Research Vice-Chancellor at Kermanshah University of Medical Sciences (KUMS) for their unwavering support and contributions to this manuscript.
Author contributions
M.M.A. and F.J. contributed to the idea of study interpretation. R.G.V., and M.M.N., contributed to the data analysis. M.M.A. and F.J. contributed to the set-out of the first draft of the manuscript and data collection. F.F, and M.T. contributed to the edit of the manuscript. All authors participate in the final approval of the revised manuscript for publication.
Funding
This manuscript is support by Research Vice-Chancellor at KUMS.
Data availability
For this study, we utilized publicly available data from the GBD website. To cite the data included in this download, please use the following reference: Global Burden of Disease Collaborative Network. GBD 2021 Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2022. Available from: https://vizhub.healthdata.org/gbd-results.
Declarations
Ethics approval and consent to participate
The research protocol received approval from the ethics committee at KUMS (IR.KUMS.REC.1404.236). This study utilized publicly available data from the GBD website. As such, no direct participant consent was required for this research. The data used were sourced from public databases, and there was no direct interaction with or collection of personal information from individuals.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
For this study, we utilized publicly available data from the GBD website. To cite the data included in this download, please use the following reference: Global Burden of Disease Collaborative Network. GBD 2021 Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2022. Available from: https://vizhub.healthdata.org/gbd-results.