Chronic obstructive pulmonary disease (COPD) was the third leading cause of death among Americans in 2014. Multiple factors, ranging from proximal (i.e. biological characteristics and behaviors) to distal (i.e. environmental characteristics and policies), have been shown to predict COPD outcomes. The Social Ecological Model (SEM), which specifies that five levels of influence (i.e. intrapersonal, interpersonal, institutional, community, and public policy factors) are implicated in the development of ill health, has not been used in epidemiological studies of COPD prevalence. A better understanding of the community-level correlates of COPD prevalence may improve community health practice.
Methods:
Using several sources of county-level secondary data (n = 646), changes in COPD prevalence from 2012 to 2014 were predicted from the five levels of the SEM using ordinary least squares (OLS) hierarchical linear regression. The geographic distribution of county-level changes in COPD prevalence was analyzed with a choropleth map.
Results:
County-level COPD prevalence increased by 4.76% between 2012 and 2014 overall; however, the greatest increases were observed among counties within the Appalachian region (i.e. counties in West Virginia). Results showed that the SEM, with five levels of influence, was a statistically significant framework for examining changes in county-level COPD prevalence, F = 10.21, p < .001, R2 = 0.28. Statistically significant county-level predictors of changes in COPD prevalence included racial composition, ethnic composition, poverty, altitude, air pollution, and smoking policy.
Conclusion:
COPD prevention may be assisted by the implementation of community-based programs rooted in a multilevel approach.
National Center for Health Statistics. Health, United States 2015 with special feature on racial and ethnic health disparities. Hyattsville, MD: US Department of Health and Human Services. Available online at: http://www.cdc.gov/nchs/hus/ (last accessed 5 January 2018).
2.
WheatonAGCunninghamTJFordESet al. Employment and activity limitations among adults with chronic obstructive pulmonary disease – United States, 2013. MMWR2015; 64(11): 290–5.
3.
ManninoDMGagnonRCPettyTLet al. Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med2000; 160: 1683–9.
4.
WheatonAGFordESCunninghamTJet al. Chronic obstructive pulmonary disease, hospital visits, and comorbidities – National Survey of Residential Care Facilities, 2010. J Aging Health2015; 27: 480–99.
5.
GreenlundKJLiuYDeokarAJet al. Association of chronic obstructive pulmonary disease with increased confusion or memory loss and functional limitations among adults in 21 states, 2011 Behavioral Risk Factor Surveillance System. Prev Chronic Dis2016; 13: 150428.
6.
IsraelBASchulzAJParkerEAet al. Review of community based research: assessing partnership approaches to improve public health. Ann Rev Pub Health1998; 19: 173–202.
McLeroyKRBibeauDStecklerAet al. An ecological perspective on health promotion programs. Health Educ Q1988; 15(4): 351–77.
9.
SallisJFOwenN. Ecological models of health behavior. In GlanzKRimerBKViswanathK (eds). Health behavior: theory, research, and practice. 5th ed.San Francisco, CA: Jossey-Bass; 2015, pp. 465–85.
10.
FukuchuY. The aging lung and chronic obstructive pulmonary disease similarity and difference. Proceed Am Thoracic Society2009; 6(7): 570–2.
11.
Centers for Disease Control Prevention. Chronic obstructive pulmonary disease among adults – United States, 2011. MMWR2012; 61(46): 938–43.
12.
GhattasCDaiAGemmelDJet al. Over diagnosis of chronic obstructive pulmonary disease in an underserved patient population. Int J Chron Obstruct Pulmon Dis2013; 8: 545–9.
13.
ManninoDMBuistAS. Global burden of COPD: risk factors, prevalence, and future trends. Lancet2007; 370: 765–73.
14.
UchinoBN. Social support and health: a review of physiological processes potentially underlying links to disease outcomes. J Behav Med2006; 29(4): 377–87.
15.
CohenS. Psychosocial models of the role of social support in the etiology of physical disease. Health Psych1988; 7(3): 269–97.
16.
Centers for Disease Control and Prevention. Social Ecological Model. Atlanta, GA: US Department of Health and Human Services. Available online at: https://www.cdc.gov/cancer/crccp/sem.htm (last accessed 16 December 2017).
17.
KellyCHulmeCFarragherTet al. Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes: a systematic review. BMJ Open2016; 6(11): e013059.
18.
StarfieldBShiLMacinkoJ. Contribution of primary care to health systems and health. Milbank Q2005; 83(3): 457–502.
PumaJEBelanskyESGarciaRet al. A community engaged approach to collecting rural health surveillance data. J Rural Health2017; 33(3): 257–65.
21.
CrightonEJRagetlieRLuoJet al. A spatial analysis of COPD prevalence, incidence, mortality, and health service use in Ontario. Health Rep2015; 26(3): 10–18.
22.
HayangaAJZeliadtSBBackhusLM. Lung cancer mortality and residential segregation in the United States. J Am Coll Surg2011; 213(3): S117.
23.
CoultasDSametJWigginsC. Altitude and mortality from chronic obstructive lung disease in New Mexico. Arch Environ Health1984; 39: 355–9.
24.
RegensteinerJMooreL. Migration of the elderly from high altitudes in Colorado. JAMA1985; 253: 3124–8.
25.
MirabelliMCVaidyanathanAFlandersWDet al. Outdoor PM2.5, ambient air temperature, and asthma symptoms in the past 14 days among adults with active asthma. Environ Health Persp2016; 124(12): 1882–90.
26.
EzzatiMHorwitzMEMThomasDSKet al. Altitude, life expectancy and mortality from ischaemic heart disease, stroke, COPD, and cancers: national population-based analysis of US counties. J Epidemiol Community Health2012; 66: e17.
27.
HornerASorianoJBPuhanMAet al. Altitude and COPD prevalence: analysis of the PREPOCOL-PLATINO-BOLD-EPI-SCAN study. Respir Res2017; 18: 162.
28.
NowakDJHirabayashiSBodineAet al. Tree and forest effects on air quality and human health in the United States. Environ Pollution2014; 193: 119–29.
29.
HermanPMWalshME. Hospital admissions for acute myocardial infarction, angina, stroke, and asthma after implementation of Arizona’s comprehensive statewide smoking ban. Am J Public Health2011; 101(3): 491–6.
30.
EdwardsRThomsonGWilsonNet al. After the smoke has cleared: evaluation of the impact of a new national smoke-free law in New Zealand. Tob Control2008; 17(1): e2.
31.
LinH-HMurrayMCohenTet al. Effects of smoking and solid fuel use on COPD, lung cancer, and tuberculosis in China: a time-based, multiple risk factor, modelling study. The Lancet2008; 372(9648): 1473–83.
32.
American Lung Association. State of the air, 2014. Chicago, IL: American Lung Association; 2014.
33.
American Lung Association. State of the air, 2016. Chicago, IL: American Lung Association; 2016.
ZhangXHoltJBLuHet al. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. Am J Epidemiol2014; 179(8): 1025–33.
36.
ZhangXHoltJBYunSet al. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. Am J Epidemiol2015; 182(2): 127–37.
BarrecaAIShimshackJP. Absolute humidity, temperature, and influenza mortality: 30 years of county-level evidence from the United States. Am J Epidemiol2012; 176(7): 114–22.
BerrocalVJGelfandAEHollandDM. Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics2012; 68: 837–48.
45.
United States Environmental Protection Agency. Air quality system. Available online at: https://www.epa.gov/aqs (last accessed 20 December 2017).
46.
Centers for Disease Control and Prevention. State tobacco activities tracking and evaluation (STATE) system. Available online at: https://www.cdc.gov/statesystem/ (last accessed 20 December 2017).
47.
Quantum Geographic Information System. QGIS version 2.18.15. Available online at: http://qgis.com/ (accessed last 20 December 2017).
48.
RaudenbushSWBrykAS. Hierarchical linear models: applications and data analysis methods. Thousand Oaks, CA: Sage; 2002.
49.
AllisonPD. Change scores as dependent variables in regression analysis. Sociol Method1990; 20: 93–114.
50.
SeberGAFLeeAJ. Linear regression analysis. Hoboken, NJ: John Wiley & Sons; 2012.
51.
NeterJWassermanWKutnerMH. Applied linear regression models. Homewood, IL: Irwin; 1989.
52.
van den BergRAHoefslootHJCWesterhuisJAet al. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics2006; 7: 142–56.
53.
WhiteH. A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica1980; 48(4): 817–38.
54.
HayesAFCaiL. Using heteroscedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation. Behav Res Methods2007; 39(4): 709–22.
55.
GilkesAAshworthMSchofieldPet al. Does COPD risk vary by ethnicity: a retrospective cross-sectional study. Int J Cron Obstruct Pulmon Dis2016; 11: 739–46.
BurneyPJithooAKatoBet al. Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty – a BOLD analysis. Thorax2014; 69: 645–73.
58.
LamprechtBSorianoJBStudnickaMet al. Determinants of underdiagnosis of COPD in national and international surveys. Chest2015; 148: 971–85.
59.
LopezADMathersCDEzzatiMet al. Global burden of disease and risk factors. Washington, DC: The World Bank; 2006.
60.
MarmotM. Social determinants of health inequalities. Lancet2005; 365: 1099–104.
61.
KentBDSulaimanINicholsonTTet al. Acute pulmonary admissions following implementation of a national workplace smoking ban. Chest2012; 142(3): 673–9.
62.
NaimanAGlazierRHMoineddinR. Association of anti-smoking legislation with rates of hospital admission for cardiovascular and respiratory conditions. CMAJ2010; 182(8): 761–7.
