Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2025-01-01T08:24:15.388Z Has data issue: false hasContentIssue false
  • English
  • Français

Emergency Medical Services Demand: An Analysis of County-Level Social Determinants

Published online by Cambridge University Press:  11 April 2022

Jackson D Déziel Open the ORCID record for Jackson D Déziel [Opens in a new window]
Jackson D Déziel*
Affiliation:
School of Health Sciences, Western Carolina University, Cullowhee, NC, USA
*
Corresponding author: Jackson Déziel, Email: jddeziel@wcu.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

Variations in the demand for Emergency Medical Services (EMS) exist when observed at a local level. This unspecified heterogeneity leads to an investigation of social factors contributing to EMS demand.

Methods:

Data for this study were collected from publicly available EMS reports from Florida and Oklahoma for 2009 - 2015. Health and social data were gathered from County health rankings and roadmap reports. Data were combined into a single dataset, and pooled ordinary-least-squares models with time-fixed effects were utilized for tests of inference. EMS call volume was log-transformed to derive a semi-elasticity function.

Results:

A total of 874 county-year observations were analyzed. Increases in poor/fair health (95% CI: 0.6% - 3.9%), binge drinking (95% CI: 1.6% - 3.5%), teen birth rate (95% CI: 1.1% - 5.2%), unemployment rate (95% CI: 0.5% - 3.9%), and violent crime rate (95% CI: 1.0% - 3.0%) were associated with an increase in the EMS demand rate.

Conclusion:

The data supports the notion that some community measures have an effect on EMS demand as counties with higher levels of poor health, binge drinking, teen births, unemployment, and violent crime saw higher EMS demand. These factors may have been treated as spurious, or overlooked by policy makers and EMS leadership.

Keywords

emergency medical servicesEMS demandambulance utilizationsocial determinants of health
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e119
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bell, C, David, E. Allen. Optimal planning of an emergency ambulance service. Socio-Econ Plan Sci. 1969; 3(2): 95-101.CrossRefGoogle Scholar
Aldrich, CA, Hisserich, JC, Lave, LB. An analysis of the demand for emergency ambulance service in an urban area. Am J Public Health. 1971;61(6):1156-1169. doi: 10.2105/ajph.61.6.1156.Google Scholar
Kamenetzky, RD, Shuman, LJ, Wolfe, H. Estimating need and demand for prehospital care. Oper Res. 1982;30(6):1148-1167. doi: 10.1287/opre.30.6.1148 Google ScholarPubMed
Setzler, H, Saydam, C, Park, S. EMS call volume predictions: a comparative study. Comput Oper Res. 2009;36(6), 1843-1851.CrossRefGoogle Scholar
Vile, JL, Gillard, JW, Harper, PR, et al. Predicting ambulance demand using singular spectrum analysis. J Oper Res Soc. 2012; 63:11, 1556-1565. doi: 10.1057/jors.2011.160 CrossRefGoogle Scholar
Steins, K, Matinrad, N, Granberg, T. Forecasting the demand for emergency medical services. In: Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019. doi:10.24251/HICSS.2019.225CrossRefGoogle Scholar
Macintyre, S, Maciver, S, Sooman, A. Area, class and health: Should we be focusing on places or people? J Soc Policy. 1993;22(2):213-234. doi: 10.1017/S0047279400019310 CrossRefGoogle Scholar
Macintyre, S, Ellaway, A, Cummins, S. Place effects on health: How can we conceptualise, operationalise and measure them? Soc Sci Med. 2002;55(1):125-139. doi: 10.1016/s0277-9536(01)00214-3 CrossRefGoogle Scholar
United States department of health and human services. Healthy people 2020: an opportunity to address social determinants of health in the U.S; 2010.Google Scholar
Finn, JC, Fatovich, DM, Arendts, G, et al. Evidence-based paramedic models of care to reduce unnecessary emergency department attendance--feasibility and safety. BMC Emerg Med. 2013;13:13. Published July 15 2013. doi: 10.1186/1471-227X-13-13 CrossRefGoogle ScholarPubMed
Macintyre, S, Maciver, S, Sooman, A. Area, class and health: Should we be focusing on places or people? J Soc Policy. 1993;22(2):213-234. doi: 10.1017/S0047279400019310 CrossRefGoogle Scholar
Macintyre, S, Ellaway, A, Cummins, S. Place effects on health: How can we conceptualise, operationalise and measure them?. Soc Sci Med. 2002;55(1):125-139. doi: 10.1016/s0277-9536(01)00214-3 CrossRefGoogle Scholar
Diez Roux, AV, Mair, C. Neighborhoods and health. Ann N Y Acad Sci. 2010;1186:125-145. doi: 10.1111/j.1749-6632.2009.05333.x CrossRefGoogle ScholarPubMed
Robert, SA. Community-level socioeconomic status effects on adult health. J Health Soc Behav. 1998;39(1):18-37.CrossRefGoogle ScholarPubMed
Robert, SA. Socioeconomic position and health: the independent contribution of community socioeconomic context. Annu Rev Sociol. 1999:25(1):489-516.CrossRefGoogle Scholar
Florida Department of Health. Emergency medical services system. 2021. Accessed July 3, 2019. https://www.floridahealth.gov/licensing-and-regulation/ems-system/index.html.Google Scholar
Oklahoma State Department of Health. Emergency systems. 2021. Accessed July 3, 2019. https://oklahoma.gov/health/protective-health/emergency-systems/ems-division.html.Google Scholar
Remington, PL, Catlin, BB, Gennuso, KP. The county health rankings: rationale and methods. Popul Health Metr. 2015;13:11. Published 2015 Apr 17. doi: 10.1186/s12963-015-0044-2 CrossRefGoogle ScholarPubMed
Vrieze, SI. Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods. 2012;17(2):228-243. doi: 10.1037/a0027127 CrossRefGoogle ScholarPubMed
Salmerón, GR, García, PJ, López, DM, García, CG. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J Appl Stat. 2016; 43(10), 1831-1849. doi: 10.1080/02664763.2015.1120712 CrossRefGoogle Scholar
Ståhl, T, Wismar, M, Ollila, E, Lahtinen, E, Leppo, K. Health in all policies. Prospects and potentials. Helsinki: Finnish Ministry of Social Affairs and Health. 2006.Google Scholar
Palmer, RC, Ismond, D, Rodriquez, EJ, et al. Social determinants of health: future directions for health disparities research. Am J Public Health. 2019;109(S1):S70-S71. doi: 10.2105/AJPH.2019.304964 CrossRefGoogle ScholarPubMed
Knowlton, A, Weir, BW, Hughes, BS, et al. Patient demographic and health factors associated with frequent use of emergency medical services in a midsized city. Acad Emerg Med. 2013;20(11):1101-1111. doi: 10.1111/acem.12253 CrossRefGoogle Scholar
Siler, KF. Predicting demand for publicly dispatched ambulances in a metropolitan area. Health Serv Res. 1975;10(3):254-263.Google Scholar
Kawakami, C, Ohshige, K, Kubota, K, et al. Influence of socioeconomic factors on medically unnecessary ambulance calls. BMC Health Serv Res. 2007;7:120. Published July 27 2007. doi: 10.1186/1472-6963-7-120 CrossRefGoogle ScholarPubMed
Kvålseth, TO, Deems, JM. Statistical models of the demand for emergency medical services in an urban area. Am J Public Health. 1979;69(3):250-255. doi: 10.2105/ajph.69.3.250 CrossRefGoogle Scholar
Veser, A, Sieber, F, Groß, S, et al. The demographic impact on the demand for emergency medical services in the urban and rural regions of Bavaria, 2012-2032. Z Gesundh Wiss. 2015;23(4):181-188. doi: 10.1007/s10389-015-0675-6 CrossRefGoogle Scholar
Newgard, CD, Schmicker, RH, Sopko, G, et al. Trauma in the neighborhood: a geospatial analysis and assessment of social determinants of major injury in North America [published correction appears in Am J Public Health. July 2011;101(7):1158]. Am J Public Health. 2011;101(4):669-677. doi: 10.2105/AJPH.2010.300063 Google ScholarPubMed
Rucker, DW, Edwards, RA, Burstin, HR, et al. Patient-specific predictors of ambulance use. Ann Emerg Med. 1997;29(4):484-491. doi: 10.1016/s0196-0644(97)70221-x CrossRefGoogle ScholarPubMed
Portz, K, Newell, R, Archibong, U. Rising ambulance life-threatening call demand in high and low socioeconomic areas. J Psychol Issues Organ. Cult. 2013;3(S1):06-320.Google Scholar
Reinier, K, Stecker, EC, Vickers, C, et al. Incidence of sudden cardiac arrest is higher in areas of low socioeconomic status: a prospective two year study in a large United States community. Resuscitation. 2006;70(2):186-192. doi: 10.1016/j.resuscitation.2005.11.018 CrossRefGoogle Scholar