Background: Areas of low socio-economic status (SES) have a disproportionate number of emergency medical admissions; we quantitate the profile of multi-morbidity related to SES.

Methods: We developed a logistic multiple variable regression model, based on over 15 years of hospital data, to examine the effect of socio-demography on hospital outcomes.

Results: Admissions from low SES cohort were a decade younger, and had a shorter hospital stay, and lower 30-day episode mortality outcome. The number of morbidities was equivalent between groups, but the more disadvantaged were more likely to have a respiratory diagnosis or diabetes.

Conclusion: Low SES emergency admissions present > 10 yr. earlier than the high SES population; their equivalent multimorbidity, despite a lower age, could reflect accelerated disease progression.

[download-attachments container="div" style="list" display_icon="1" display_count="1" display__size="0"]

References

1. Merriam-Webster. Morbidity [cited 2018 8 May]. Available from: www.merriam-webster.com/dictionary/morbidity.
2. Cookson R, Laudicella M. Do the poor still cost more? The relationship between small area income deprivation and length of stay for elective hip replacement in the English NHS from 2001/2 to 2006/7 University of York: Health, Econometrics and Data Group; 2009 [Available from: http://www.york.ac.uk/res/herc/ research/hedg/wp.htm.
3. Barnett K, Mercer S, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37-43.
4. Walsh JB, Coakley D, Murphy C, Coakley JD, Boyle E, Johnson H. Demographic profile of the elderly population in Dublin accident and emergency hospital catchment areas. Ir Med J. 2004;97(3):84-6.
5. Tonelli M, Wiebe N, Fortin M, Guthrie B, Hemmelgarn BR, James MT, et al. Methods for identifying 30 chronic conditions: application to administrative data. (1472-6947 (Electronic)).
6. Diederichs C, Berger K, Bartels D. The measurement of multiple chronic diseases–a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci. 2011;66(3):301-11.
7. Rooney T, Moloney ED, Bennett K, O’Riordan D, Silke B. Impact of an acute medical admission unit on hospital mortality: a 5-year prospective study. QJM. 2008;101(6):457-65.
8. Conway R, O’Riordan D, Silke B. Long-term outcome of an AMAU--a decade’s experience. Quarterly Journal of Medicine. 2014;107(1):43-9.
9. Beeknoo N, Jones R. Factors Influencing A & E Attendance, Admissions and Waiting Times at Two London Hospitals. British Journal of Medicine & Medical Research. 2016;17(10):1-29.
10. O’Loughlin R, Allwright S, Barry J, Kelly A, Teljeur C. Using HIPE data as a research and planning tool: limitations and opportunities. Ir J Med Sci. 2005;174(2):40-5; discussion 52-7.
11. O’Callaghan A, Colgan MP, McGuigan C, Smyth F, Haider N, O’Neill S, et al. A critical evaluation of HIPE data. Ir Med J. 2012;105(1):21-3.
12. Quan H, Li B Fau - Saunders LD, Saunders Ld Fau - Parsons GA, Parsons Ga Fau - Nilsson CI, Nilsson Ci Fau - Alibhai A, Alibhai A Fau - Ghali WA, et al. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. (1475-6773 (Electronic)).
13. Quan H, Sundararajan V Fau - Halfon P, Halfon P Fau - Fong A, Fong A Fau - Burnand B, Burnand B Fau - Luthi J-C, Luthi Jc Fau - Saunders LD, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. (0025-7079 (Print)).
14. Ronald J. Ozminkowski PD MWS, Ph.D., Rosanna M. Coffey, Ph.D., Tami L. Mark, Ph.D., Cheryl A. Neslusan, Ph.D., and John Drabek, Ph.D. Private Payers Serving Individuals with Disabilities and Chronic Conditions2000.
15. Courtney D, Conway R, Kavanagh J, O’Riordan D, Silke B. High-sensitivity troponin as an outcome predictor in acute medical admissions. Postgrad Med J. 2014:1-7.
16. Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. (1539-3704 (Electronic)).
17. Townsend P. Deprivation. Journal of Social Policy. 1987;16:25-46.
18. Carstairs V, Morris R. Deprivation and mortality: an alternative to social class? Community medicine. 1989;11(3):210-9.
19. Kelly A TC. The National Deprivation Index for Health and Health Services Research - Update 2013. Small Area Health Research Unit, Department of Health and Primary Care: Trinity College Dublin; 2013.
20. Conway R, Galvin S, Coveney S, O’Riordan D, Silke B. Deprivation as an outcome determinant in emergency medical admissions. QJM. 2013;106(3):245-51.
21. Cournane S, Byrne D, Conway R, O’Riordan D, Coveney S, Silke B. Social deprivation and hospital admission rates, length of stay and readmissions in emergency medical admissions. European Journal of Internal Medicine. 2015;26(10):766-71.
22. 22. McCabe JJ, McElroy K, Cournane S, Byrne D, O’Riordan D, Fitzgerald B, et al. Deprivation status and the hospital costs of an emergency medical admission. Eur J Intern Med. 2017.
23. Shirmat M. Algorithm 112: position of point relative to polygon. ACM Comm. 1962;5:434.
24. Cameron A, Trivedi P. Microeconometrics Using Stata. College Station, TX: Stata Press; 2009.
25. Silke B, Kellett J, Rooney T, Bennett K, O’Riordan D. An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling. Quarterly Journal of Medicine. 2010;103(1):23-32.
26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83.
27. Chotirmall SH, Callaly E, Lyons J, O’Connell B, Kelleher M, Byrne D, et al. Blood cultures in emergency medical admissions: a key patient cohort. Eur J Emerg Med. 2014.
28. Boyd C, Fortin M. Future of Multimorbidity Research: How Should Understanding of Multimorbidity Inform Health System Design? : Public Health Reviews 32: 451–474; 2010 [Available from: http://core.kmi.open.ac.uk/display/964118.
29. Powell H, Lim Ll Fau - Heller RF, Heller RF. Accuracy of administrative data to assess comorbidity in patients with heart disease. an Australian perspective. (0895-4356 (Print)).
30. Orueta JF, García-Álvarez A, García-Goñi M, Paolucci F, NuñoSolinís R. Prevalence and Costs of Multimorbidity by Deprivation Levels in the Basque Country: A Population Based Study Using Health Administrative Databases. PloS one. 2014;9(2):e89787.
31. Agborsangaya CB, Lau D Fau - Lahtinen M, Lahtinen M Fau - Cooke T, Cooke T Fau - Johnson JA, Johnson JA. Multimorbidity prevalence and patterns across socioeconomic determinants: a cross-sectional survey. (1471-2458 (Electronic)).