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Figure.  Smoking Prevalence Inequities and Their Association With Inequities in Chronic Diseases
Smoking Prevalence Inequities and Their Association With Inequities in Chronic Diseases

A, All cities have at least some inequity (Gini coefficient ≥0.03) and 4 cities (Washington, DC; Atlanta, Georgia; Fort Lauderdale, Florida; and Miami, Florida) have particularly high inequity (Gini ≥0.20). B, The prevalence of current self-reported asthma, chronic obstructive pulmonary disease (COPD), and coronary heart disease (CHD) is higher in tracts with higher prevalence of current smoking. Estimates shown in Figure 1B result from multivariable linear mixed models that adjust for percent non-Hispanic white population, median household income, total population size, and a random effect for city. We calculated the expected percent increase in prevalence of each chronic disease of changing from the 10th to 90th percentile of smoking prevalence (from 10.7% to 27.6%) and the corresponding 95% confidence intervals using these multivariable linear mixed models and by using 1000 draws from the multivariate normal distribution with the mean equal to the maximum likelihood point estimate and the variance equal to the coefficient covariance matrix. C, Differences in tract-level smoking prevalence for the city with the greatest inequity in smoking prevalence: Washington, DC (Gini = 0.23). The color in the image uses Jenks natural breaks (9 classes) based on the data for the Washington, DC, census tracts. One Washington, DC, census tract had insufficient data to make a smoking prevalence estimate and is illustrated in gray. Data sources and analytical methods are further detailed in the supplementary appendix.

Table.  Fixed Effects and Random Effects Resulting From the Linear Mixed Models for Smoking Prevalence Among 27 204 Census Tracts in the 500 Cities Projecta
Fixed Effects and Random Effects Resulting From the Linear Mixed Models for Smoking Prevalence Among 27 204 Census Tracts in the 500 Cities Projecta
1.
Chetty  R, Stepner  M, Abraham  S,  et al.  The Association Between Income and Life Expectancy in the United States, 2001-2014.  JAMA. 2016;315(16):1750-1766. doi:10.1001/jama.2016.4226PubMedGoogle ScholarCrossref
2.
500 Cities: Local Data for Better Health, 2017 release | Chronic Disease and Health Promotion Data & Indicators. https://chronicdata.cdc.gov/500-Cities/500-Cities-Local-Data-for-Better-Health-2017-relea/6vp6-wxuq. Accessed June 21, 2018.
3.
Kaufman  TK, Sheehan  DM, Rundle  A,  et al.  Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set.  BMC Res Notes. 2015;8:507. doi:10.1186/s13104-015-1482-4PubMedGoogle ScholarCrossref
4.
Cowell  FA. Measurement of inequality. In: Atkinson A, Bourguignon F, eds.  Handbook of Income Distribution. Vol 1. Elsevier; 2000:87-166, doi:10.1016/S1574-0056(00)80005-6.
5.
Brown  T, Platt  S, Amos  A.  Equity impact of population-level interventions and policies to reduce smoking in adults: a systematic review.  Drug Alcohol Depend. 2014;138:7-16. doi:10.1016/j.drugalcdep.2014.03.001PubMedGoogle ScholarCrossref
6.
Luke  DA, Hammond  RA, Combs  T,  et al.  Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density.  Am J Public Health. 2017;107(5):740-746. doi:10.2105/AJPH.2017.303685PubMedGoogle ScholarCrossref
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    Research Letter
    January 7, 2019

    Place-Based Inequity in Smoking Prevalence in the Largest Cities in the United States

    Author Affiliations
    • 1Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, California
    JAMA Intern Med. 2019;179(3):442-444. doi:10.1001/jamainternmed.2018.5990

    Achieving universal health and well-being for all Americans is the ideal goal for US public health efforts, but inequities in chronic disease and life expectancy present a persistent challenge, particularly in large cities.1 In 2016, the Robert Wood Johnson Foundation and the US Centers for Disease Control and Prevention launched the 500 Cities Project, providing small-area estimates of modifiable risk factors for chronic disease in the 500 largest US cities.2 To guide prevention efforts, we used these data to characterize inequities in cigarette smoking both between and within cities and in relation to sociodemographic factors and chronic diseases.

    Methods

    The 500 Cities Project provides model-estimated health indicators at the census-tract level from the 2014 Behavioral Risk Factor Surveillance Survey.2 A census tract is generally smaller than a city, larger than a block group, and a fairly permanent subdivision of a county. Our analysis used the prevalence of adult (≥18 years) self-reported current smoking, asthma, chronic obstructive pulmonary disease (COPD), and coronary heart disease (CHD). Complete data from the 500 Cities Project were available for 27 204 tracts. We combined the 500 Cities Project estimates with tract-level sociodemographic data from the American Community Survey (2012-2016) and counts of likely tobacco retailers from 10 North American Industrial Classification System codes in the National Establishment Time Series Data for 2012 (120 470 tobacco retailers in the 27 204 tracts).3

    We used linear mixed models to characterize smoking prevalence inequities within and between cities; assess tract-level smoking prevalence as a function of tract-level sociodemographic characteristics and tobacco retailer counts; and assess tract-level asthma, COPD, and CHD prevalence as a function of tract-level smoking prevalence. We also computed Gini coefficients to quantify the dispersion of smoking prevalence between census tracts within each of the 500 cities, where 0 = perfect equality and 1 = maximal inequality.4 Data sources and analytical methods are further detailed in the Supplement.

    Results

    Smoking prevalence inequities were greater between tracts within cities (56.1% of the total variation) than between cities (43.9% of the total variation) (Table). Tracts with higher smoking prevalence had more tobacco retailers (5-store increase, β = 0.11; 95% CI, 0.07-0.16; P < .001), lower median household income ($10 000 increase, β = −0.92; 95% CI, −0.94 to −0.90; P < .001), and a smaller percentage of non-Hispanic white residents (10% increase, β = −0.84; 95% CI, −0.86 to −0.82; P < .001).

    Although all cities had some smoking prevalence inequity (Figure, A) (Gini coefficients ≥0.03), inequity was greatest in Washington, DC (Gini = 0.23); Atlanta, Georgia (Gini = 0.22); Fort Lauderdale, Florida (Gini = 0.21); and Miami, Florida (Gini = 0.20). Figure, C illustrates smoking prevalence in the city with the greatest inequity, Washington, DC.

    At the tract level, higher smoking prevalence was associated with higher prevalence of asthma, COPD, and CHD (Figure, B). For instance, a change from the 10th to the 90th percentile of smoking prevalence (from 10.7% to 27.6%) was associated with a 38.9% (95% CI, 38.1%-39.5%) increase in the prevalence of asthma, a 120.2% (95% CI, 116.6%-124.0%) increase in the prevalence of COPD, and a 26.6% (95% CI, 24.5%-29.0%) increase in the prevalence of CHD.

    Discussion

    Smoking prevalence was unevenly distributed both within and between America’s largest cities, and was associated with inequities in income, race, exposure to tobacco retailers, and smoking-related diseases. Strengthening existing tobacco control interventions, such as raising excise taxes and implementing cessation programs targeted to resource-poor communities, may aid in counteracting these inequities in smoking.5 In addition, novel policies that restrict the retail environment (eg, by limiting the quantity, location, and type of tobacco retailers) show promise for reducing the unequal distribution of tobacco retailers and warrant further investigation.6

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    Article Information

    Corresponding Author: Eric C. Leas, PhD, MPH, Stanford Prevention Research Center, Stanford University School of Medicine, 3300 Hillview Rd, Ste 114, Palo Alto, CA 94304-1334 (ecleas@stanford.edu).

    Accepted for Publication: September 8, 2018.

    Published Online: January 7, 2019. doi:10.1001/jamainternmed.2018.5990

    Author Contributions: Dr Leas had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: Leas, Schleicher, Henriksen.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Leas, Henriksen.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Leas, Schleicher.

    Obtained funding: Henriksen.

    Administrative, technical, or material support: Leas, Henriksen.

    Study supervision: Prochaska, Henriksen.

    Conflict of Interest Disclosures: Dr. Prochaska has provided consultation to pharmaceutical and technology companies that make medications and other treatments for smoking cessation. She has also served as an expert witness in lawsuits against the tobacco companies. No other conflicts of interest were reported.

    Funding/Support: This work is supported by the National Institutes of Health (NIH) grant P01-CA225597 and grant U01CA854281, both from the National Cancer Institute. Dr Leas was supported by NIH grant T32-HL007034 from the National Heart, Lung and Blood Institute.

    Role of the Funder/Sponsor: The National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Chetty  R, Stepner  M, Abraham  S,  et al.  The Association Between Income and Life Expectancy in the United States, 2001-2014.  JAMA. 2016;315(16):1750-1766. doi:10.1001/jama.2016.4226PubMedGoogle ScholarCrossref
    2.
    500 Cities: Local Data for Better Health, 2017 release | Chronic Disease and Health Promotion Data & Indicators. https://chronicdata.cdc.gov/500-Cities/500-Cities-Local-Data-for-Better-Health-2017-relea/6vp6-wxuq. Accessed June 21, 2018.
    3.
    Kaufman  TK, Sheehan  DM, Rundle  A,  et al.  Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set.  BMC Res Notes. 2015;8:507. doi:10.1186/s13104-015-1482-4PubMedGoogle ScholarCrossref
    4.
    Cowell  FA. Measurement of inequality. In: Atkinson A, Bourguignon F, eds.  Handbook of Income Distribution. Vol 1. Elsevier; 2000:87-166, doi:10.1016/S1574-0056(00)80005-6.
    5.
    Brown  T, Platt  S, Amos  A.  Equity impact of population-level interventions and policies to reduce smoking in adults: a systematic review.  Drug Alcohol Depend. 2014;138:7-16. doi:10.1016/j.drugalcdep.2014.03.001PubMedGoogle ScholarCrossref
    6.
    Luke  DA, Hammond  RA, Combs  T,  et al.  Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density.  Am J Public Health. 2017;107(5):740-746. doi:10.2105/AJPH.2017.303685PubMedGoogle ScholarCrossref
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