Smoke-Free Policies and 30-Day Mortality Rates for Chronic Obstructive Pulmonary Disease

Introduction

Chronic obstructive pulmonary disease (COPD) is a slowly progressive, non-reversible lung disease that includes chronic bronchitis, emphysema, and refractory asthma.¹ A confirmed diagnosis of COPD is when airflow obstruction FEV1/FVC ratio is less than 0.70.² COPD affects an individual’s physical, social, and mental well-being, and results in an increased incidence of comorbidities such as cardiovascular disease, diabetes, mental disorders, and physical disabilities.^3-5 In the United States, COPD affects 6.2% (age-adjusted) of the adult population and is the third leading cause of death.^6,7 Prior research shows that COPD has a direct national economic burden that continues to increase annually and was estimated to reach $49 billion by the end of 2020.^8-12 Therefore, the Centers for Medicare and Medicaid Services (CMS) began using 30-day mortality COPD as a marker of hospital performance and a driver for hospital reimbursement.¹³

While other risk factors, including indoor air pollution, occupational exposures, and genetics play a role in the development of COPD, cigarette smoking contributes to nearly 80% of COPD mortality.^8,14 A meta-analysis of 129 studies determined a causal relationship among persons who ever smoked and COPD.¹⁵ Secondhand smoke exposure is also a relevant risk factor for COPD development, exacerbations, and mortality. For example, a study has observed an association between secondhand smoke exposure in childhood and increased COPD mortality (hazard ratio = 1.31; 95% CI: 1.05-1.65) among never smokers.¹⁶ Another study identified similar COPD prevalence among active and non-smokers within the same geographic area, suggesting a relationship between regions, secondhand smoke, and smoke-free policies.⁷ Although the association between secondhand smoke exposure and COPD mortality has not been extensively examined, a study conducted in the Republic of Ireland demonstrated an immediate 38% reduction in COPD mortality following the implementation of a national smoke-free policy with the most predominant effects occurring among females and those aged ≥65 years.¹⁷ A follow-up study further determined that these all-cause and COPD-related mortality reductions were concentrated among those with the lowest socioeconomic status.¹⁸ Since active smoking prevalence did not appreciably change during this period, it was determined that the observed mortality reductions were attributable to decreased exposure to secondhand smoke. Further, these two studies were deemed to have a low risk of bias in a meta-analysis of 50 articles regarding smoke-free legislation and respiratory disorders.¹⁹

Comprehensive smoke-free policies that prohibit smoking completely in public spaces are one public health countermeasure aimed at minimizing secondhand smoke exposure among non-smokers. Numerous studies have observed protective associations between the implementation of comprehensive smoke-free policies and COPD-related hospitalizations and mortality.^17,20-26 Recently, a study found that county-level smoke-free policies reduce 30-day hospital readmissions due to COPD.²⁷ In the United States, each state or county has the choice and responsibility for implementing smoke-free policies. As of April 1, 2020, 23 states had not adopted comprehensive smoke-free policies, leaving smoke-free policies to be determined at the county-level, and creating a wide variance in the implementation of these policies throughout the state.²⁸

The importance of determining the relationship between smoke-free policies and the risk of 30-day COPD mortality is multifaceted. For example, healthcare and public health professionals seek information about how to better protect the population’s health and save lives. At the policy level, it is paramount that enough evidence is available to support decision-making. In addition, 30-day COPD mortality rates are used as a hospital performance metric, prompting acute care providers and hospital administrators to seek opportunities to reduce COPD associated mortalities.²⁹ Therefore, this study aims to investigate the relationship between smoke-free policies and 30-day mortality rates following COPD hospitalization in the United States.

Methods

Data Source and Study Population

The 2019 CMS Hospital Value-Based Purchasing (HVBP) Program dataset publicly reports aggregated hospital-specific 30-day risk-standardized mortality measures. The report includes condition-specific and procedure-specific mortality measures for inpatient discharges from July 1, 2015, to June 30, 2018. The five condition-specific measures are acute myocardial infarction, COPD, heart failure, pneumonia, and stroke; the one procedure-specific measure is coronary artery bypass graft. For this study, we utilized the CMS’s HVBP program to identify hospital COPD 30-day mortality rates throughout the United Stattes. Utilizing the 2017 County Federal Information Processing Standards (FIPS), we linked hospitals’ COPD mortality rates to the U.S. Tobacco Control Laws Database maintained by the American Nonsmokers’ Rights Foundation (ANRF).

The ANRF was used to identify all state and county smoke-free policies from April 1936 to November 2018. The database reports on 1763 counties across all 50 states and the District of Columbia. ANRF collects policies through the review of public records including official websites, news services, and elected officials. A senior ANRF analyst indexes all reviewed policies and policy changes in accordance with established guidelines.³⁰ Again utilizing the 2017 County FIPS, we linked hospitals to the counties in which they reside using the 2015-2018 American Hospital Association (AHA) Annual surveys, and the US Census Bureau Current Population Surveys. For this study, the AHA annual surveys were utilized to determine hospital census data and hospital characteristics, while the US Census Bureau Current Population Surveys were utilized to determine 1- and 5-year estimates for household adults ≥18 years of age.

Variables

The study outcome was in-hospital 30-day mortalityfollowing admission for an acute exacerbation of COPD in patients ≥65 years of age enrolled in Medicare fee-for-service for the 12 months before admission. The diagnosis codes of COPD used by CMS were validated and reported by Lindenauer, Grosso, Wang, Wang, Krishnan, Lee, Au, Mularski, Bernheim, Drye³¹ using International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] and revalidated by CMS QualityNet³² for ICD‐10‐CM. CMS risk-adjusted rates of 30-day mortality to determine a hospital’s case-mix (disease severity),³³ and further calculate the rate for each hospital through fitting a hierarchical logistic regression to adjust for differences in hospital case mix index and to account for clustering of patients within a hospital. The measure calculates a 3-year average risk-standardized ratio as the number of COPD predicted deaths to the number of expected deaths, multiplied by the observed national mortality rate from July 1, 2015 to June 30, 2018.³⁴ However, the measure does not adjust for socioeconomic status or race as outlined in National Quality Forum guidelines.

The independent variable was county smoke-free law coverage. We based the variable on county or state smoke-free laws covering both public and private workplaces, restaurants, and bars through June 30, 2018. For this study, we excluded counties that implemented smoke-free policies during the study period (2015-2018) and we included the District of Columbia as a county. Consistent with the classification in prior literature, we defined 100% smoke-free as the implementation of a comprehensive smoke-free law prohibiting smoking indoors with no exceptions.^35-37 We operationalized smoke-free law into three mutually exclusive categories: full coverage (FC = smoke-free law in workplaces, restaurants, and bars), partial coverage (PC = smoke-free in one to two locations), and no coverage (NC).

To account for differences across counties, we included variables previously identified in the literature with the potential to impact respiratory outcomes in analyses. Prior studies have shown that there are sex-specific and race/ethnic-related differences in COPD manifestation,³⁸ risks,³⁹ and access to quality health services.⁴⁰ Therefore, for the study period (2015-2018), we included a measure of the spatial distribution of multiple racial/ethnic groups simultaneously by calculating the average diversity score.⁴¹ We calculated the diversity score for each county using the sum of the log proportions of each of the 6 racial/ethnic groups represented in the United States, with a maximum value of 1.77 where equal proportions of all groups (33%) are present within the county.⁴¹ We also included the average total population covered by the smoke-free policy, percent of females ≥65 years of age, and population with ≥4 years college who are ≥65 years of age. In addition, we accounted for the percent of the county designated as a rural area, the average percent of the county living in poverty, and the average total number of hospitals within a county and counties within a state to account for disease burden.⁴²

Previous studies have shown that there are differences in hospital quality of care,⁴³ safety performance,⁴⁴ resource allocation,⁴⁵ and community outreach^46,47 based on hospital characteristics. Therefore, we included the average number of hospital beds within the study period (2015-2018), number of teaching hospitals, number of hospitals part of a system, percent of not-for-profit hospitals, and number of hospitals offering tobacco services as independent variables. We also included average Medicare and Medicaid percentages calculated by dividing total Medicare and Medicaid discharges by total inpatient admissions.⁴⁸

Statistical Methods

We designed the study to investigate the association between county-level smoke-free policies and average hospital COPD 30-day mortality rates within the study period. We based county inclusion on having at least one hospital present in the county that was participating in CMS’s HVBP program. Bivariate relationships were assessed between all hospital and county-level variables including mortality using t tests for dichotomous variables and analysis of variance for ordinal and continuous variables. Three hierarchical Poisson regression models were developed in accordance with previous work²⁷ to estimate incidence rate ratios (IRRs) between county smoke-free law and hospital COPD mortality rate to account for nesting effects within states. The total count of individuals covered by the policy was included as an exposure term. The first partial model estimated county-level variables, the second partial model estimated hospital-level variables, and the third full model fitted both county- and hospital-level variables. All statistical testing was 2-sided, at a significance level of 0.05. We conducted all analyses using Stata version 14SE. Hierarchical models were fit using the MEPOISSON procedure in Stata.⁴⁹ In accordance with the policy of the University, the research was categorized as exempt by the Institutional Review Board since the study analyzed secondary data that is publicly available.

Results

Of the 1171 counties included in this study period (2015-2018), 596 (51%) were FC, 385 (33%) were PC, and 190 (16%) were NC (Table 1). The NC counties tended to have a greater average COPD mortality (7.42 NC, 7.24 PC, 7.06 FC), greater percent of the county designated as rural (45.72 NC, 38.99 PC, 37.99 FC), higher average percent poverty (17.78 NC, 15.63 PC, 15.39 FC), higher average diversity index, ie, more heterogeneous population (0.71 NC, 0.62 PC, 0.60 FC), lower total population (49 864 NC, 76 169 PC, 85 014 FC), lower Medicaid population (0.20 NC, 0.22 PC, 0.26, FC), and lower percent of the population ≥65 with ≥4 years of college education (18.92 NC, 22.40 PC, 22.42 FC).

Concerning hospital characteristics, NC counties had fewer hospitals (2.24 NC, 3.42 PC, 3.91 FC), a lower number of beds (315.84 NC, 591.67 PC, 686.07, FC), fewer system hospitals (2.21 NC, 3.39 PC, 3.88 FC), fewer teaching hospitals (0.84 NC, 1.44 PC, 1.78 FC), a lower percentage of not-for-profit hospitals (46.48 NC, 59.91 PC, 68.59 FC), and fewer tobacco services offered by hospitals (0.84 NC, 1.39 PC, 1.50 FC).

The hierarchical Poisson regression models (Table 2) revealed a consistent relationship between stronger smoke-free policies and a reduction in COPD mortality rates across all three models. Model 1 evaluated county characteristics associated with COPD mortality. Results showed that PC and FC counties experienced a reduced IRR of 0.80 (95% CI: 0.73, 0.89) and 0.75 (95% CI: 0.68, 0.83), respectively, compared to NC counties.

Model 2 evaluated hospital characteristics and COPD mortality rates (Table 3). Our results associated PC and FC counties with reduced incidence rates of 0.86 (95% CI: 0.77, 0.94) and 0.71 (95% CI: 0.65, 0.79) compared to NC counties.

Model 3 evaluated both county and hospital characteristics associated with COPD mortality rates (Table 4). After adjusting for each (Model 3), only FC counties had a reduced rate of COPD mortality (IRR: 0.87, 95% CI: 0.79, 0.96) compared to NC counties. The percent of the county designated as rural (IRR: 1.02, 95% CI: 1.02, 1.02), average percent poverty (IRR: 1.03, 95% CI: 1.02, 1.03), total number of hospital tobacco services (IRR: 1.06, 95% CI: 1.03, 1.09) and total number of hospitals (IRR: 1.26, 95% CI: 1.18, 1.33) were all associated with increased risk of COPD mortality. The average percent population ≥65 years of age with ≥4 years of college (IRR 1.02, 95% CI: 1.02, 1.02), greater average percent female ≥65 years of age (IRR 0.97, 95% CI: 0.95, 0.99), greater average diversity index (IRR 0.54, 95% CI: 0.47, 0.62), greater average Medicaid percentage (IRR 0.42, 95% CI: 0.33, 0.52), greater average Medicare percentage (IRR: 0.63, 95% CI: 0.51, 0.79), greater total number of system hospitals (IRR: 0.80, 95% CI:0.75, 0.85), and greater number of teaching hospitals (IRR: 0.90, 95% CI: 0.88, 0.93) were all associated with decreases in COPD mortality rate.

Discussion

Our findings show that comprehensive smoke-free policies are associated with a reduced risk of 30-day COPD mortality among hospitalized patients. Our data indicate that the risk of in-hospital mortality was the lowest for counties that had more extensive smoke-free policies than PC and even lower risk than NCs, indicating a dose-response relationship. Previous research on COPD re-admission rates and smoke-free policies in the United States reported a similar trend.²⁷ For example, a 2020 quasi-experimental Brazilian-based study found that both partial and comprehensive smoke-free legislation was associated with a reduction in infant mortality.⁵⁰ Another study that examined the association of smoke-free policies and incidence rates of cardiovascular disease showed that there is a long-term impact of these policies on lowering the risk of incident cardiovascular disease.⁵¹ Additionally, these findings are further supported by research conducted in Ireland,¹⁷ and Switzerland⁵² which found smoking bans to be associated with a reduction in mortality. Further, a systematic review of legislative smoking bans identified associations with reduced hospital admissions as well as reduced hospital mortality.⁵³ As such, the current study’s findings provide incremental evidence that more comprehensive smoking bans within the United States are also associated with a reduction in COPD hospital mortality.

Demographic and other study variables relating to the social determinants of health were important covariates in our models. Rurality, poverty rate, hospital tobacco services and the number of hospitals were each associated with worsened COPD outcomes, supporting findings from previous research.^45,54 For example, a population-based study found an increased prevalence of COPD among older individuals and those with lower educational attainment, suggesting that these factors contribute to the onset of the disease which may explain the positive association indicated in this study.⁵⁵ In contrast, counties with a greater percentage of older populations with higher educational attainment and a greater percentage of Medicare and Medicaid coverage were associated with a reduced risk of COPD mortality.

Our findings also revealed that counties with a greater percentage of females were associated with lower COPD in-hospital mortality rates. This result also corresponds with past research indicating the association between counties with a greater percentage of females ≥65 years and decreased 30-day readmissions for COPD.²⁷ However our findings as well as this previous study conflicts with the increasing trend of COPD mortality among women in the United States.⁵⁶ In fact, previous COPD and gender studies have identified both an increased prevalence of COPD among women compared to men, as well as a higher mortality rate.^57,58 Further, previous research shows that females are more likely to have reduced lung function and worse forms of COPD compared to males diagnosed with COPD.⁵⁹ These findings support the importance of considering gender when addressing disparities in COPD outcomes, as well as a need to further investigate the causal mechanisms attributing to these gender differences.^59,60

Regarding hospital characteristics, we observed a relationship that the total number of system hospitals and teaching hospitals were associated with a decreased rate of 30-day COPD mortality. These findings are consistent with previous evidence indicating that system hospitals may have increased market power, translating into higher resource availability.⁶¹ Additionally, previous research among Medicare beneficiaries has shown that teaching hospital status has lower 30-day mortality rates for all hospitalizations when compared to non-teaching hospitals.⁶² Further, the association with both teaching and system hospitals suggest that counties with larger and integrated healthcare systems might be able to coordinate care more effectively for this patient population. Additionally, in this study, we observed greater COPD mortality for counties that had a greater number of hospitals. With respect to the number of hospitals, we could link this to counties with fragmented hospitals that are not part of a healthcare system and greater market competition.⁶¹

In total, these results suggest that population-level interventions focused on more strict smoke-free policies as well as interventions aimed at reducing poverty, supporting education, and increased Medicare and Medicaid coverage may improve COPD mortality rates. This study is the first to investigate the association between county-level smoke-free policies and 30-day mortality following hospital admission for COPD. Strengths of the study include the extensive data sources allowing for precise estimates of the exposure and outcome as well as the ability to account for numerous potentially confounding factors, thus minimizing study bias. The multilevel hierarchical Poisson regression analysis ensured rigorous estimates of 30-day mortality risk by intrinsically addressing the nesting structure of the data, specifically counties within states and hospitals within counties.

The limitations of this study include inability to adjust for potential confounding factors at the individual patient level and, in some cases, at the county level. Specifically, data limitations prevented adjustment for active or secondhand smoking prevalence, and air pollution measures as well as their corresponding changes over time. We were unable to adjust for air pollution due to inconsistent data availability at the county level for the years of the study. To address these limitations, we included the total population in each county covered by a smoke-free policy as an independent variable in statistical analyses. Moreover, prior evidence has shown a 2-fold hazard increase for hospitalization due to COPD exacerbations in winter compared to summer.⁶³ However, in this study, we are unable to determine the seasonality of initial hospital admission or 30-day mortality or because the outcome measure for 30-day mortality is an overall hospital-level calculation.

Conclusion

The findings of this study are clear. Comprehensive, smoke-free policies with full coverage for workplaces, bars, and restaurants are associated with saving lives. It is also clear that partial smoke-free legislation is less protective than comprehensive legislation in reducing 30-day mortality following COPD hospital admission. For this reason, US counties and states should prioritize the implementation of smoke-free policies. Support for these policies benefits overarching public health programs, hospitals, and individual patients.

Ethical issues

In accordance with the policy of the University of North Florida the research was categorized as exempt by the University of North Florida’s Institutional Review Board since the study analyzed secondary data that is publicly available.

Competing interests

Authors declare that they have no competing interests.

Authors’ contributions

HH assisted in conceiving and designing the work, analyzing and interpreting the data, and writing part of the manuscript. SSS assisted in conceiving and designing the work, acquisition of data, literature search, writing part of the manuscript, and critical revision of manuscript for important intellectual content. EA assisted in writing part of the manuscript and critical revision of manuscript for important intellectual content. BP assisted in drafting part of the manuscript and literature search. AS assisted in conceiving and designing the work, interpreting the data, and drafting part of the manuscript.

Funding

The University of North Florida: Academic Affairs Faculty Scholarship Development Grant.

References

1. Caraballo RS, Giovino GA, Pechacek TF, Mowery PD. Factors associated with discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons aged 17 years or older: Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol 2001; 153(8):807-814. doi: 10.1093/aje/153.8.807 [Crossref] [ Google Scholar]
2. Qaseem A, Wilt TJ, Weinberger SE. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011; 155(3):179-191. doi: 10.7326/0003-4819-155-3-201108020-00008 [Crossref] [ Google Scholar]
3. Cazzola M, Bettoncelli G, Sessa E, Cricelli C, Biscione G. Prevalence of comorbidities in patients with chronic obstructive pulmonary disease. Respiration 2010; 80(2):112-119. doi: 10.1159/000281880 [Crossref] [ Google Scholar]
4. Eisner MD, Wang Y, Haight TJ, Balmes J, Hammond SK, Tager IB. Secondhand smoke exposure, pulmonary function, and cardiovascular mortality. Ann Epidemiol 2007; 17(5):364-373. doi: 10.1016/j.annepidem.2006.10.008 [Crossref] [ Google Scholar]
5. Djibo DA, Goldstein J, Ford JG. Prevalence of disability among adults with chronic obstructive pulmonary disease, Behavioral Risk Factor Surveillance System 2016-2017. PLoS One 2020; 15(2):e0229404. doi: 10.1371/journal.pone.0229404 [Crossref] [ Google Scholar]
6. Koru-Sengul T, Clark JD 3rd, Ocasio MA, Wanner A, Fleming LE, Lee DJ. Utilization of the National Health and Nutrition Examination (NHANES) Survey for symptoms, tests, and diagnosis of chronic respiratory diseases and assessment of second hand smoke exposure. Epidemiology (Sunnyvale) 2011; 1(2):104. doi: 10.4172/2161-1165.1000104 [Crossref] [ Google Scholar]
7. Wheaton AG, Liu Y, Croft JB. Chronic obstructive pulmonary disease and smoking status - United States, 2017. MMWR Morb Mortal Wkly Rep 2019; 68(24):533-538. doi: 10.15585/mmwr.mm6824a1 [Crossref] [ Google Scholar]
8. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance--United States, 1999-2011. Chest 2013; 144(1):284-305. doi: 10.1378/chest.13-0809 [Crossref] [ Google Scholar]
9. Criner RN, Han MK. COPD care in the 21st century: a public health priority. Respir Care 2018; 63(5):591-600. doi: 10.4187/respcare.06276 [Crossref] [ Google Scholar]
10. Zamosky LI. COPD: exploring the value of care. Med Econ 2013; 90(7):49-54. [ Google Scholar]
11. Jain RB, Bernert JT. Effect of body mass index and total blood volume on serum cotinine levels among cigarette smokers: NHANES 1999-2008. Clin Chim Acta 2010; 411(15-16):1063-1068. doi: 10.1016/j.cca.2010.03.040 [Crossref] [ Google Scholar]
12. Sullivan J, Pravosud V, Mannino DM, Siegel K, Choate R, Sullivan T. National and state estimates of COPD morbidity and mortality - United States, 2014-2015. Chronic Obstr Pulm Dis 2018; 5(4):324-333. doi: 10.15326/jcopdf.5.4.2018.0157 [Crossref] [ Google Scholar]
13. Xia Y, Bernert JT, Jain RB, Ashley DL, Pirkle JL. Tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) in smokers in the United States: NHANES 2007-2008. Biomarkers 2011; 16(2):112-119. doi: 10.3109/1354750x.2010.533288 [Crossref] [ Google Scholar]
14. Centers for Disease Control and Prevention (CDC). Smoking-attributable mortality, years of potential life lost, and productivity losses--United States, 2000-2004. MMWR Morb Mortal Wkly Rep. 2008;57(45):1226-1228.
15. Forey BA, Thornton AJ, Lee PN. Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema. BMC Pulm Med 2011; 11:36. doi: 10.1186/1471-2466-11-36 [Crossref] [ Google Scholar]
16. Diver WR, Jacobs EJ, Gapstur SM. Secondhand smoke exposure in childhood and adulthood in relation to adult mortality among never smokers. Am J Prev Med 2018; 55(3):345-352. doi: 10.1016/j.amepre.2018.05.005 [Crossref] [ Google Scholar]
17. Stallings-Smith S, Zeka A, Goodman P, Kabir Z, Clancy L. Reductions in cardiovascular, cerebrovascular, and respiratory mortality following the national Irish smoking ban: interrupted time-series analysis. PLoS One 2013; 8(4):e62063. doi: 10.1371/journal.pone.0062063 [Crossref] [ Google Scholar]
18. Stallings-Smith S, Goodman P, Kabir Z, Clancy L, Zeka A. Socioeconomic differentials in the immediate mortality effects of the national Irish smoking ban. PLoS One 2014; 9(6):e98617. doi: 10.1371/journal.pone.0098617 [Crossref] [ Google Scholar]
19. Rando-Matos Y, Pons-Vigués M, López MJ. Smokefree legislation effects on respiratory and sensory disorders: a systematic review and meta-analysis. PLoS One 2017; 12(7):e0181035. doi: 10.1371/journal.pone.0181035 [Crossref] [ Google Scholar]
20. Galán I, Simón L, Boldo E. Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain. PLoS One 2017; 12(5):e0177979. doi: 10.1371/journal.pone.0177979 [Crossref] [ Google Scholar]
21. Naiman A, Glazier RH, Moineddin R. Association of anti-smoking legislation with rates of hospital admission for cardiovascular and respiratory conditions. CMAJ 2010; 182(8):761-767. doi: 10.1503/cmaj.091130 [Crossref] [ Google Scholar]
22. Tan CE, Glantz SA. Association between smoke-free legislation and hospitalizations for cardiac, cerebrovascular, and respiratory diseases: a meta-analysis. Circulation 2012; 126(18):2177-2183. doi: 10.1161/circulationaha.112.121301 [Crossref] [ Google Scholar]
23. Kent BD, Sulaiman I, Nicholson TT, Lane SJ, Moloney ED. Acute pulmonary admissions following implementation of a national workplace smoking ban. Chest 2012; 142(3):673-679. doi: 10.1378/chest.11-2757 [Crossref] [ Google Scholar]
24. Vander Weg MW, Rosenthal GE, Vaughan Sarrazin M. Smoking bans linked to lower hospitalizations for heart attacks and lung disease among Medicare beneficiaries. Health Aff (Millwood) 2012; 31(12):2699-2707. doi: 10.1377/hlthaff.2011.0385 [Crossref] [ Google Scholar]
25. Dusemund F, Baty F, Brutsche MH. Significant reduction of AECOPD hospitalisations after implementation of a public smoking ban in Graubünden, Switzerland. Tob Control 2015; 24(4):404-407. doi: 10.1136/tobaccocontrol-2013-051290 [Crossref] [ Google Scholar]
26. Hahn EJ, Rayens MK, Adkins S, Simpson N, Frazier S, Mannino DM. Fewer hospitalizations for chronic obstructive pulmonary disease in communities with smoke-free public policies. Am J Public Health 2014; 104(6):1059-1065. doi: 10.2105/ajph.2014.301887 [Crossref] [ Google Scholar]
27. Stallings-Smith S, Hamadi HY, Peterson BN, Apatu EJI, Spaulding AC. Smoke-free policies and 30-day readmission rates for chronic obstructive pulmonary disease. Am J Prev Med 2019; 57(5):621-628. doi: 10.1016/j.amepre.2019.06.008 [Crossref] [ Google Scholar]
28. American Nonsmokers’ Rights Foundation. Overview List – Number of Smokefree and Other Tobacco-Related Laws. https://no-smoke.org/wp-content/uploads/pdf/mediaordlist.pdf. Accessed January 31, 2020.
29. U.S. Centers for Medicare and Medicaid Services. Hospital Compare. 30-Day Death (Mortality) Rates. https://www.medicare.gov/HospitalCompare/Data/Death-rates.html. Accessed November 14, 2019.
30. American Nonsmokers’ Rights Foundation. US Tobacco Control Laws Database. Berkeley, CA: ANRF; 2017.
31. Lindenauer PK, Grosso LM, Wang C. Development, validation, and results of a risk-standardized measure of hospital 30-day mortality for patients with exacerbation of chronic obstructive pulmonary disease. J Hosp Med 2013; 8(8):428-435. doi: 10.1002/jhm.2066 [Crossref] [ Google Scholar]
32. QualityNet. Archived Measure Methodology. Centers for Medicare & Medicaid Services. https://qualitynet.cms.gov/inpatient/measures/readmission/resources#tab3. Accessed October 4, 2019. Published 2020.
33. Mendez CM, Harrington DW, Christenson P, Spellberg B. Impact of hospital variables on case mix index as a marker of disease severity. Popul Health Manag 2014; 17(1):28-34. doi: 10.1089/pop.2013.0002 [Crossref] [ Google Scholar]
34. Drye EE, Normand SL, Wang Y. Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling. Ann Intern Med 2012; 156(1 Pt 1):19-26. doi: 10.7326/0003-4819-156-1-201201030-00004 [Crossref] [ Google Scholar]
35. Juster HR, Loomis BR, Hinman TM. Declines in hospital admissions for acute myocardial infarction in New York state after implementation of a comprehensive smoking ban. Am J Public Health 2007; 97(11):2035-2039. doi: 10.2105/ajph.2006.099994 [Crossref] [ Google Scholar]
36. Herman PM, Walsh ME. Hospital admissions for acute myocardial infarction, angina, stroke, and asthma after implementation of Arizona’s comprehensive statewide smoking ban. Am J Public Health 2011; 101(3):491-496. doi: 10.2105/ajph.2009.179572 [Crossref] [ Google Scholar]
37. Barr CD, Diez DM, Wang Y, Dominici F, Samet JM. Comprehensive smoking bans and acute myocardial infarction among Medicare enrollees in 387 US counties: 1999-2008. Am J Epidemiol 2012; 176(7):642-648. doi: 10.1093/aje/kws267 [Crossref] [ Google Scholar]
38. Kamil F, Pinzon I, Foreman MG. Sex and race factors in early-onset COPD. Curr Opin Pulm Med 2013; 19(2):140-144. doi: 10.1097/MCP.0b013e32835d903b [Crossref] [ Google Scholar]
39. Gilkes A, Ashworth M, Schofield P. Does COPD risk vary by ethnicity? a retrospective cross-sectional study. Int J Chron Obstruct Pulmon Dis 2016; 11:739-746. doi: 10.2147/copd.s96391 [Crossref] [ Google Scholar]
40. Mamary AJ, Stewart JI, Kinney GL. Race and gender disparities are evident in COPD underdiagnoses across all severities of measured airflow obstruction. Chronic Obstr Pulm Dis 2018; 5(3):177-184. doi: 10.15326/jcopdf.5.3.2017.0145 [Crossref] [ Google Scholar]
41. Iceland J. The multigroup entropy index (also known as Theil’s H or the information theory index). US Census Bureau; 2004. https://www2.census.gov/programs-surveys/demo/about/housing-patterns/multigroup_entropy.pdf.
42. Pleasants RA, Riley IL, Mannino DM. Defining and targeting health disparities in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2016; 11:2475-2496. doi: 10.2147/copd.s79077 [Crossref] [ Google Scholar]
43. Sosunov EA, Egorova NN, Lin HM. The impact of hospital size on CMS hospital profiling. Med Care 2016; 54(4):373-379. doi: 10.1097/mlr.0000000000000476 [Crossref] [ Google Scholar]
44. Chen AS, Revere L, Ratanatawan A, Beck CL, Allo JA. A comparative analysis of academic and nonacademic hospitals on outcome measures and patient satisfaction. Am J Med Qual 2019; 34(4):367-375. doi: 10.1177/1062860618800586 [Crossref] [ Google Scholar]
45. Haley DR, Zhao M, Spaulding A, Hamadi H, Xu J, Yeomans K. The influence of hospital market competition on patient mortality and total performance score. Health Care Manag (Frederick) 2016; 35(3):266-276. doi: 10.1097/hcm.0000000000000117 [Crossref] [ Google Scholar]
46. Park S, Hamadi H, Apatu E, Spaulding AC. Hospital partnerships in population health initiatives. Popul Health Manag 2020; 23(3):226-233. doi: 10.1089/pop.2019.0074 [Crossref] [ Google Scholar]
47. Apatu E, Hamadi H, Abrams JA, Metzger IW, Spaulding A. Association between hospital community services and county population health in the USA. J Public Health (Oxf) 2019; 41(1):71-79. doi: 10.1093/pubmed/fdy007 [Crossref] [ Google Scholar]
48. Bazzoli GJ, Chen HF, Zhao M, Lindrooth RC. Hospital financial condition and the quality of patient care. Health Econ 2008; 17(8):977-995. doi: 10.1002/hec.1311 [Crossref] [ Google Scholar]
49. StataCorp LLC. Stata Statistical Software (Version Release 14). College Station, TX: StataCorp LLC; 2015.
50. Hone T, Szklo AS, Filippidis FT. Smoke-free legislation and neonatal and infant mortality in Brazil: longitudinal quasi-experimental study. Tob Control 2020; 29(3):312-319. doi: 10.1136/tobaccocontrol-2019-054923 [Crossref] [ Google Scholar]
51. Mayne SL, Widome R, Carroll AJ. Longitudinal associations of smoke-free policies and incident cardiovascular disease: CARDIA study. Circulation 2018; 138(6):557-566. doi: 10.1161/circulationaha.117.032302 [Crossref] [ Google Scholar]
52. Vicedo-Cabrera AM, Röösli M, Radovanovic D. Cardiorespiratory hospitalisation and mortality reductions after smoking bans in Switzerland. Swiss Med Wkly 2016; 146:w14381. doi: 10.4414/smw.2016.14381 [Crossref] [ Google Scholar]
53. Frazer K, Callinan JE, McHugh J. Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Rev 2016; 2(2):CD005992. doi: 10.1002/14651858.CD005992.pub3 [Crossref] [ Google Scholar]
54. Raju S, Keet CA, Paulin LM. Rural residence and poverty are independent risk factors for chronic obstructive pulmonary disease in the United States. Am J Respir Crit Care Med 2019; 199(8):961-969. doi: 10.1164/rccm.201807-1374OC [Crossref] [ Google Scholar]
55. Tan WC, Sin DD, Bourbeau J. Characteristics of COPD in never-smokers and ever-smokers in the general population: results from the CanCOLD study. Thorax 2015; 70(9):822-829. doi: 10.1136/thoraxjnl-2015-206938 [Crossref] [ Google Scholar]
56. Hong Y, Ji W, An S, Han SS, Lee SJ, Kim WJ. Sex differences of COPD phenotypes in nonsmoking patients. Int J Chron Obstruct Pulmon Dis 2016; 11:1657-1662. doi: 10.2147/copd.s108343 [Crossref] [ Google Scholar]
57. Aryal S, Diaz-Guzman E, Mannino DM. COPD and gender differences: an update. Transl Res 2013; 162(4):208-218. doi: 10.1016/j.trsl.2013.04.003 [Crossref] [ Google Scholar]
58. Akinbami OJ, Liu X. Chronic obstuctive pulmonary disease among adults aged 18 and over in the United States, 1998-2009. NCHS Data Brief 2011(63):1-8.
59. Sørheim IC, Johannessen A, Gulsvik A, Bakke PS, Silverman EK, DeMeo DL. Gender differences in COPD: are women more susceptible to smoking effects than men?. Thorax 2010; 65(6):480-485. doi: 10.1136/thx.2009.122002 [Crossref] [ Google Scholar]
60. de Torres JP, Casanova C, Hernández C, Abreu J, Aguirre-Jaime A, Celli BR. Gender and COPD in patients attending a pulmonary clinic. Chest 2005; 128(4):2012-2016. doi: 10.1378/chest.128.4.2012 [Crossref] [ Google Scholar]
61. Cuellar AE, Gertler PJ. How the expansion of hospital systems has affected consumers. Health Aff (Millwood) 2005; 24(1):213-219. doi: 10.1377/hlthaff.24.1.213 [Crossref] [ Google Scholar]
62. Burke LG, Frakt AB, Khullar D, Orav EJ, Jha AK. Association between teaching status and mortality in US hospitals. JAMA 2017; 317(20):2105-2113. doi: 10.1001/jama.2017.5702 [Crossref] [ Google Scholar]
63. Wise RA, Calverley PM, Carter K, Clerisme-Beaty E, Metzdorf N, Anzueto A. Seasonal variations in exacerbations and deaths in patients with COPD during the TIOSPIR(®) trial. Int J Chron Obstruct Pulmon Dis 2018; 13:605-616. doi: 10.2147/copd.s148393 [Crossref] [ Google Scholar]