Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-04T13:04:38.432Z Has data issue: false hasContentIssue false
  • English
  • Français

Diagnosis Prevalence and Comorbidity in a Population of Mobile Integrated Community Health Care Patients

Published online by Cambridge University Press:  27 December 2018

Becca M. Scharf ,
Rick A. Bissell ,
Jamie L. Trevitt  and
J. Lee Jenkins
Becca M. Scharf
Affiliation:
University of Maryland Baltimore County, Baltimore, MarylandUSA
Rick A. Bissell
Affiliation:
University of Maryland Baltimore County, Baltimore, MarylandUSA
Jamie L. Trevitt
Affiliation:
University of Maryland Baltimore County, Baltimore, MarylandUSA
J. Lee Jenkins*
Affiliation:
University of Maryland Baltimore County, Baltimore, MarylandUSA Johns Hopkins University School of Medicine, Baltimore, MarylandUSA
*
Correspondence:J. Lee Jenkins, MD, MS 1000 Hilltop Circle Baltimore, Maryland 21250 USA E-mail: Jleejenkins@umbc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

Frequent calls to 911 and requests for emergency services by individuals place a costly burden on emergency response systems and emergency departments (EDs) in the United States. Many of the calls by these individuals are non-emergent exacerbations of chronic conditions and could be treated more effectively and cost efficiently through another health care service. Mobile integrated community health (MICH) programs present a possible partial solution to the over-utilization of emergency services by addressing factors which contribute to a patient’s likelihood of frequent Emergency Medical Services (EMS) use. To provide effective care to eligible individuals, MICH providers must have a working understanding of the common conditions they will encounter.

Objective

The purpose of this descriptive study was to evaluate the diagnosis prevalence and comorbidity among participants in the Queen Anne’s County (Maryland USA) MICH Program. This fundamental knowledge of the most common medical conditions within the MICH Program will inform future mobile integrated health programs and providers.

Methods

This study examined preliminary data from the MICH Program, as well as 2017 Maryland census data. It involved secondary analysis of de-identified patient records and descriptive statistical analysis of the disease prevalence, degree of comorbidity, insurance coverage, and demographic characteristics among 97 program participants. Diagnoses were grouped by their ICD-9 classification codes to determine the most common categories of medical conditions. Multiple linear regression models and chi-squared tests were used to assess the association between age, sex, race, ICD-9 diagnosis groups, and comorbidity among program enrollees.

Results

Results indicated the most prevalent diagnoses included hypertension, high cholesterol, esophageal reflux, and diabetes mellitus. Additionally, 94.85% of MICH patients were comorbid; the number of comorbidities per patient ranged from one to 13 conditions, with a mean of 5.88 diagnoses per patient (SD=2.74).

Conclusion

Overall, patients in the MICH Program are decidedly medically complex and may be well-suited to additional community intervention to better manage their many conditions. The potential for MICH programs to simultaneously improve patient outcomes and reduce health care costs by expanding into larger public health and addressing the needs of the most vulnerable citizens warrants further study.

ScharfBM, BissellRA, TrevittJL, JenkinsJL.Diagnosis Prevalence and Comorbidity in a Population of Mobile Integrated Community Health Care PatientsPrehosp Disaster Med. 2019;34(1):46–55.

Keywords

community healthcomorbidityEmergency Medical Servicesmobile healthpublic health
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 34 , Issue 1 , February 2019 , pp. 46 - 55
Copyright
© World Association for Disaster and Emergency Medicine 2018 

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.)

Footnotes

Conflicts of interest: none

References

1. Michelen, W, Martinez, J, Lee, A, Wheeler, DP. Reducing frequent flyer emergency department visits. J Health Care Poor Underserved. 2006;17(1 Suppl):59-69.Google Scholar
2. Beck, E, Craig, A, Beeson, J, et al. Mobile integrated healthcare practice: a healthcare delivery strategy to improve access, outcomes, and value. www.acep.org/uploadedFiles/ ACEP/Practice_Resources/disater_and_EMS/MIHP_white paper%20FINAL1.pdf. Accessed October 15, 2015.Google Scholar
3. Smith, J. Queen Anne’s county mobile integrated community health pilot program: program overview. Maryland Chronic Disease Conference. 2015. http://healthystmarys.com/wp-content/uploads/2015/06/MICHPP-HSMP-Inaugural-Meeting-2015.pdf. Accessed October 15, 2015.Google Scholar
4. Gindi, RM, Black, LI, Cohen, RA. Reasons for Emergency Room Use Among US Adults Aged 18-64: National Health Interview Survey, 2013 and 2014. Atlanta, Georgia USA: US Department of Health and Human Services - National Center for Health Statistics; 2016. https://www.cdc.gov/nchs/data/nhsr/nhsr090.pdf. Published February 18, 2016. Accessed September 7, 2016.Google Scholar
5. Smulowitz, PB, Honigman, L, Landon, BE. A novel approach to identifying targets for cost reduction in the emergency department. Ann of Emerg Med. 2013;61(3):293-300.Google Scholar
6. LaCalle, E, Rabin, E. Frequent users of emergency departments: the myths, the data, and the policy implications. Ann of Emerg Med. 2010;56(1):42-48.Google Scholar
7. Hunt, KA, Weber, EJ, Showstack, JA, Colby, DC, Callaham, ML. Characteristics of frequent users of emergency departments. Ann of Emerg Med. 2006;48(1):1-8.Google Scholar
8. Hansagi, H, Olsson, M, Sjoberg, S, Thomson, Y, Goransson, S. Frequent use of the hospital emergency department is indicative of high use of other health care services. Ann of Emerg Med. 2001;37(6):561-567.Google Scholar
9. Tangherlini, N, Pletcher, MJ, Covec, , Brown, JF. Frequent use of emergency medical services by the elderly: a case-control study using paramedic records. Prehosp Disaster Med. 2010;25(3):258-264.Google Scholar
10. Norman, C, Mello, M, Choi, B. Identifying frequent users of an urban emergency medical service using descriptive statistics and regression analysis. West J Emerg Med. 2016;17(1):39-45.Google Scholar
11. Uscher-Pines, L, Pines, J, Kellermann, A, Gillen, E, Mehrotra, A. Deciding to visit the emergency department for non-urgent conditions: a systematic review of the literature. Am J Manag Care. 2013;19(1):47-59.Google Scholar
12. Bodenheimer, T, Pham, HH. Primary care: current problems and proposed solutions. Health Aff (Millwood). 2010;29(5):799-805.Google Scholar
13. Bodenheimer, T. Primary care- will it survive? N Engl J Med. 2006;355(9):861-864.Google Scholar
14. The Network for Excellence in Health Innovation. A matter of urgency: reducing emergency department overuse. https://www.nehi.net/writable/publication_files/file/nehi_ed_overuse_issue_brief_032610finaledits.pdf. Published March 2010. Accessed September 7, 2016.Google Scholar
15. Richman, IB, Clark, S, Sullivan, AF, Camargo, CA Jr. National study of the relation of primary care shortages to emergency department utilization. Acad Emerg Med. 2007;14(3):279-282.Google Scholar
16. Scott, J, Strickland, AP, Warner, K, Dawson, P. Describing and predicting frequent callers to an ambulance service: analysis of 1-year data. Emerg Med J. 2014;31(5):408-414.Google Scholar
17. Hoot, NR, Aronsky, D. Systematic review of emergency department crowding: causes, effects and solutions. Ann of Emerg Med. 2008;52(2):126-136.Google Scholar
18. Sun, BC, Burstin, HR, Brennan, TA. Predictors and outcomes of frequent emergency department use. Acad Emerg Med. 2003;10(4):320-328.Google Scholar
19. Emergency Medicine Statistical Profile. American College of Emergency Physicians. https://www.acep.org/content.aspx?id=25234. Updated January 2017. Accessed February 20, 2017.Google Scholar
20. Weinik, RM, Burns, RM, Mehrotra, A. Many emergency department visits could be managed at urgent care centers and retail clinics. Health Aff (Millwood). 2010;29(9):1630-1636.Google Scholar
21. Pines, JM, Asplin, BR, Kaji, AH, et al. Frequent users of emergency department services: gaps in knowledge and a proposed research agenda. Acad Emerg Med. 2011;18(6):e64-69.Google Scholar
22. IRCP Mission and Vision Statements. International Roundtable on Community Paramedicine Web Site. http://ircp.info/About-Us. Published 2005. Accessed December 4, 2015.Google Scholar
23. Dixon, S, Mason, S, Knowles, E, et al. Is it cost effective to introduce paramedic practitioners for older people in the ambulance service? Results of a cluster randomized controlled trial. Emerg Med J. 2009;26(6):446-451.Google Scholar
24. Machen, I, Dickinson, A, Williams, J, Widiatmoko, D, Kendall, S. Nurses and paramedics in partnership: perceptions of a new response to low-priority ambulance calls. Accid Emerg Nurs. 2007;15(4):185-192.Google Scholar
25. Choi, BY, Blumberg, C, Williams, K. Mobile integrated health care and community paramedicine: an emerging emergency medical services concept. Ann of Emerg Med. 2016;67(3):361-366.Google Scholar
26. Mobile Integrated Community Health (MICH). Maryland Department of Health Web Site. https://health.maryland.gov/qahealth/community-health/Pages/mich.aspx. Published January 2015. Accessed December 4, 2015.Google Scholar
27. DeVoe, JE, Fryer, GE, Phillips, R, Green, L. Receipt of preventive care among adults: insurance status and usual source of care. Am J Public Health. 2003;93(5):786-791.Google Scholar
28. Hall, MK, Raven, MC, Hall, J, et al. EMS-STARS: emergency medical services “superuser” transport associations: an adult retrospective study. Prehosp Emerg Care. 2015;19(1):61-67.Google Scholar
29. Tadros, AS, Castillo, EM, Chan, TC, et al. Effects of an emergency medical services-based resource access program on frequent users of health services. Prehosp Emerg Care. 2012;16(4):541-547.Google Scholar
30. Mandelberg, JH, Kuhn, RE, Kohn, MA. Epidemiologic analysis of an urban, public emergency department’s frequent users. Acad Emerg Med. 2000;7(6):637-646.Google Scholar
31. US Census Bureau. Quick Facts Maryland. https://www.census.gov/quickfacts/table/PST045215/24,24035. Updated 2017. Accessed February 20, 2017.Google Scholar
32. Centers for Disease Control and Prevention. The state of aging and health in America 2013. https://www.cdc.gov/aging/pdf/State-Aging-Health-in-America-2013.pdf. Published 2013. Accessed February 20, 2017.Google Scholar
33. University of Maryland Shore Regional Health. Community health needs assessment & implementation plan executive summary FY 2017-2019. https://umshoreregional.org/-/media/systemhospitals/shore/pdfs/about/chna-2016-board-approved.pdf?la=en&hash=6A2546CBBE3EBA5A572C0AE9E1A6383B39A8878B. Published May 25, 2016. Accessed February 20, 2017.Google Scholar
34. Maryland Department of Health. A public health needs assessment. https://pophealth.health.maryland.gov/Documents/Maryland%20Public%20Health%20Needs%20Assessment%202014.pdf. Published September 2014. Accessed March 3, 2017.Google Scholar
35. Stata Statistical Software. Version 14.0. College Station, Texas USA: StataCorp LP; 2015.Google Scholar
36. Centers for Medicare and Medicaid Services. ICD-9-CM diagnosis and procedure codes: abbreviated and full code titles. https://www.cms.gov/medicare/coding/ICD9providerdiagnosticcodes/codes.html. Published October 1, 2005. Updated March 20, 2014. Accessed March 3, 2017.Google Scholar
37. Centers for Disease Control and Prevention. Multiple chronic conditions. http://www.cdc.gov/chronicdisease/about/multiple-chronic.htm. Published February 13, 2015. Updated January 20, 2016. Accessed February 20, 2017.Google Scholar
38. US Department of Health and Human Services. Multiple chronic conditions–a strategic framework: optimum health and quality of life for individuals with chronic conditions. https://www.hhs.gov/sites/default/files/ash/initiatives/mcc/mcc_framework.pdf. Published December 2010. Accessed February 20, 2017.Google Scholar
39. Gerteis, J, Izrael, D, Deitz, D, et al. Agency for Healthcare Research and Quality. Multiple chronic conditions chartbook. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/prevention-chronic-care/decision/mcc/mccchartbook.pdf. Published April 2014. Accessed February 20, 2017.Google Scholar
40. Centers for Disease Control and Prevention. High blood pressure. https://www.cdc.gov/bloodpressure/index.htm. Published January 2014. Updated March 3, 2017. Accessed March 20, 2017.Google Scholar
41. American Heart Association. Changes you can make to manage high blood pressure. http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/MakeChangesThatMatter/Changes-You-Can-Make-to-Manage-High-BloodPressure_UCM_002054_Article.jsp#.WNpqEW_yvcs. Published November 2017. Updated January 11, 2018. Accessed February 8, 2018.Google Scholar
42. Bergen, G, Stevens, MR, Burns, ER. Falls and fall injuries among adults aged ≥65 years – MMWR Morb Mortal Wkly Rep. 2016;65:993-998.Google Scholar