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Socioeconomic Status and Non-Fatal Adult Injuries in Selected Atlanta (Georgia USA) Hospitals

Published online by Cambridge University Press:  31 March 2017

Erin Hulland ,
Ritam Chowdhury Open the ORCID record for Ritam Chowdhury [Opens in a new window] ,
Stefanie Sarnat ,
Howard H. Chang  and
Kyle Steenland
Erin Hulland
Affiliation:
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GeorgiaUSA
Ritam Chowdhury*
Affiliation:
Department of Epidemiology, James T. Laney School of Graduate Studies, Emory University, Atlanta, GeorgiaUSA Department of Biostatistics, Harvard School of Public Health, Boston, MassachusettsUSA
Stefanie Sarnat
Affiliation:
Department of Environmental Health, Rollins School of Public Health and James T. Laney School of Graduate Studies, Emory University, Atlanta, GeorgiaUSA
Howard H. Chang
Affiliation:
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GeorgiaUSA
Kyle Steenland
Affiliation:
Department of Epidemiology, James T. Laney School of Graduate Studies, Emory University, Atlanta, GeorgiaUSA Department of Environmental Health, Rollins School of Public Health and James T. Laney School of Graduate Studies, Emory University, Atlanta, GeorgiaUSA
*
Correspondence: Ritam Chowdhury, MBBS, MPH, PhD, SM 677 Huntington Avenue Boston, Massachusetts USA 02115 E-mail: [email protected]
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Abstract

Background

Injury mortality data for adults in the United States and other countries consistently show higher mortality for those with lower socioeconomic status (SES). Data are sparse regarding the role of SES among adult, non-fatal US injuries. The current study estimated non-fatal injury risk by household income using hospital emergency department (ED) visits.

Methods

A total of 1,308,892 ED visits at 10 Atlanta (Georgia USA) hospitals from 2001-2004 (347,866 injuries) were studied. The SES was based on US census-block group income, with subjects assigned to census blocks based on reported residence. Logistic regression was used to determine risk by SES for injuries versus all other ED visits, adjusting for demographics, hospital, and weather. Supplemental analyses using hospital data from 2010-2013, without data on SES, were conducted to determine whether earlier patterns by race, age, and gender persisted.

Results

Risk for many injury categories increased with higher income. Odds ratio by quartiles of increasing income (lowest quartile as referent, 95% confidence interval [CI] given for upper most quartile) were 1.00, 1.23, 1.34, 1.40 (95% CI 1.36-1.45) for motor vehicle accidents; 1.00, 1.03, 1.11, 1.24 (95% CI 1.20-1.29) for being struck by objects; 1.00. 0.99, 1.04, 1.12 (95% CI 1.00-1.25) for suicide; and 1.00, 1.03, 1.05, 1.12 (95% CI 1.09-1.15) for falls. In contrast, decreased injury risk with increased household income was seen for assaults (1.00, 0.83, 0.73, 0.67 [95% CI 0.63-0.72], by increasing quartiles). These trends by income did not differ markedly by race and gender. Whites generally had less risk of injuries, with the exception of assaults and motor vehicle accidents. Males had higher risk of injury than females, with the exception of falls and suicide attempts. Patterns of risk for race, age, and gender were consistent between 2001-2004 and 2010-2013.

Conclusion

For most non-fatal injuries, those with higher income had more risk of ED visits, although the opposite was true for assault.

HullandE, ChowdhuryR, SarnatS, ChangHH, SteenlandK. Socioeconomic Status and Non-Fatal Adult Injuries in Selected Atlanta (Georgia USA) Hospitals. Prehosp Disaster Med. 2017;32(4):403–413.

Keywords

accidents, trafficemergency service, hospitalincomesocial classUnited States
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 32 , Issue 4 , August 2017 , pp. 403 - 413
Copyright
© World Association for Disaster and Emergency Medicine 2017 

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Footnotes

Conflicts of interest/funding: This publication was made possible by a pilot grant to Emory University (Atlanta, Georgia USA) from the Centers for Disease Control and Prevention (Atlanta, Georgia USA)-funded Emory Center for Injury Control (1 R49 CE001494). The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

References

1. Lim, SS, Vos, T, Flaxman, AD, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study. Lancet. 2012;380(9859):2224-2260.Google Scholar
2. US Burden of Disease Collaborators. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors. JAMA. 2013;310(6):591-608.Google Scholar
3. CDC. Fatal and Non-Fatal Injury Data. CDC - WISQARS. Centers for Disease Control and Prevention Web site. 2014. www.cdc.gov/injury/wisqars/index.html. Accessed April 2016.Google Scholar
4. Hoyert, DL, Arias, E, Smith, BL, Murphy, SL, Kochanek, KD. National vital statistics reports. Deaths: Final Data for 2011. www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_06.pdf. Accessed April 2016.Google Scholar
5. Finkelstein, EA, Corso, P, Miller, TR. The Incidence and Economic Burden of Injuries in the United States. Oxford, UK: Oxford University Press; 2006.Google Scholar
6. Steenland, K, Halperin, W, Hu, S, Walker, JT. Deaths due to injuries among employed adults: the effects of socioeconomic class. Epidemiology. 2003;14(1):74-79.CrossRefGoogle ScholarPubMed
7. Cubbin, C, LeClere, FB, Smith, GS. Socioeconomic status and the occurrence of fatal and nonfatal injury in the United States. Am J Public Health. 2000;90(1):70-77.Google Scholar
8. Burrows, S, Auger, N, Gamache, P, Hamel, D. Individual and area socioeconomic inequalities in cause-specific unintentional injury mortality: 11-year follow-up study of 2.7 million Canadians. Accid Anal Prev. 2012;45:99-106.CrossRefGoogle ScholarPubMed
9. Gotsens, M, Mari-Dell’Olmo, M, Perez, K, et al. Socioeconomic inequalities in injury mortality in small areas of 15 European cities. Health Place. 2013;24:165-172.CrossRefGoogle ScholarPubMed
10. Lee, J, Lee, WY, Noh, M, Khang, YH. Does a geographical context of deprivation affect differences in injury mortality? A multilevel analysis in South Korean adults residing in metropolitan cities. J Epidemiol Community Health. 2014;68(5):457-465.Google Scholar
11. McKee, CM, Gleadhill, DN, Watson, JD. Accident and emergency attendance rates: variation among patients from different general practices. Br J Gen Pract. 1990;40(333):150-153.Google ScholarPubMed
12. Gorman, DR, Ramsay, LJ, Wilson, GS, Freeland, P. Using routine accident and emergency department data to describe local injury epidemiology. Public Health. 1999;113(6):285-289.Google Scholar
13. Alexandrescu, R, O’Brien, SJ, Lecky, FE. A review of injury epidemiology in the UK and Europe: some methodological considerations in constructing rates. BMC Public Health. 2009;9:226.Google Scholar
14. Tolbert, PE, Klein, M, Metzger, KB, et al. Interim results of the study of particulates and health in Atlanta (SOPHIA). J Expo Anal Environ Epidemiol. 2000;10(5):446-460.Google Scholar
15. Metzger, KB, Tolbert, PE, Klein, M, et al. Ambient air pollution and cardiovascular emergency department visits. Epidemiology. 2004;15(1):46-56.CrossRefGoogle ScholarPubMed
16. Sarnat, SE, Klein, M, Sarnat, JA, et al. An examination of exposure measurement error from air pollutant spatial variability in time-series studies. J Expo Sci Environ Epidemiol. 2010;20(2):135-146.Google Scholar
17. Shavers, VL. Measurement of socioeconomic status in health disparities research. J Natl Med Assoc. 2007;99(9):1013-1023.Google Scholar
18. United States Census Bureau. American FactFinder. 2016. http://factfinder.census.gov/ faces/nav/jsf/pages/index.xhtml2016. Accessed April 2016.Google Scholar
19. Pucher, J, Renne, J. Socioeconomics of Urban Travel: Evidence From the 2001 NHTS. Transportation Quarterly. 2003;57(3):49-77.Google Scholar
20. Strine, TW, Beck, LF, Bolen, J, Okoro, C, Dhingra, S, Balluz, L. Geographic and sociodemographic variation in self-reported seat belt use in the United States. Accid Anal Prev. 2010;42(4):1066-1071.CrossRefGoogle ScholarPubMed
21. Burrows, S, Laflamme, L. Socioeconomic disparities and attempted suicide: state of knowledge and implications for research and prevention. Int J Inj Contr Saf Promot. 2010;17(1):23-40.Google Scholar
22. Vyrostek, SB, Annest, JL, Ryan, GW. Surveillance for fatal and nonfatal injuries–United States, 2001. MMWR. 2004;53(SS07):1-57.Google Scholar
23. Peck, MD. Epidemiology of burns throughout the world. Part I: distribution and risk factors. Burns. 2011;37(7):1087-1100.CrossRefGoogle ScholarPubMed
24. Harrell, L, Berzofsky, M, Couzens, L, Smiley-McDonald, H. Household Poverty and Nonfatal Violent Victimization, 2008-2012. Bureau of Justice Statistics, 2012. http://www.bjs.gov/index.cfm?ty=pbdetail&iid=5137. Accessed November 25, 2014.Google Scholar
25. Gjelsvik, A, Zierler, S, Blume, J. Homicide risk across race and class: a small-area analysis in Massachusetts and Rhode Island. J Urban Health. 2004;81(4):702-718.Google Scholar