Gender Difference In Outcomes In Acute Medicine: Women Of Lower Socio-Economic Status Have Worse Outcomes
Main Article Content
Abstract
Background: There are data that suggest that women hospitalised for a variety of medical conditions may have worse outcomes than men; there is a paucity of literature on hospital mortality outcome by gender and Socio-Economic status (SES) for unselected admissions.
Methods: Emergency medical admissions between 2002 and 2018 were examined. We assessed 30-day in-hospital mortality, by gender and SES, using logistic regression and margins statistics modelled outcomes against predictor variables.
Results: There were 113,807 episodes in 58,126 patients over the period, with known SES status. There were multiple admissions per patient; only 45.4% had a single admission with the percentage of patients with 1, 2, or 3 at 18.8%, 10.4% and 6.5%, respectively. The average per patient 30-day in-hospital mortality was 11.1% (95%CI:10.6%, 11.6%) for males and 11.0% (95%CI:10.5%, 11.6%) females (p = 0.84). Males from higher, 12.2% (95%CI:10.6%, 13.8%), or lower SES small areas, 12.6% (95%CI: 12.1%, 13.1%), had equivalent 30-day mortality outcomes. Females from higher SES had significantly better outcomes compared with females from lower SES small areas- 9.4% (95% CI:8.0%, 10.8%) versus 12.7% (95%CI:12.2%,13.2%).
Conclusion: 30-day in-hospital mortality adjusted for outcome predictors were similar for males and females; however, whereas the model-adjusted mortality for males was not different across SES, females of lower SES had significantly worse outcomes than those of higher SES.
Keywords:
Emergency Medical Admissions, 30-day Mortality, SES, Gender
Article Details
How to Cite
COURNANE, Seán et al.
Gender Difference In Outcomes In Acute Medicine: Women Of Lower Socio-Economic Status Have Worse Outcomes.
Medical Research Archives, [S.l.], v. 8, n. 4, apr. 2020.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/2077>. Date accessed: 04 dec. 2024.
doi: https://doi.org/10.18103/mra.v8i4.2077.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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29. 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.
30. Chotirmall SH, Picardo S, Lyons J, D'Alton M, O'Riordan D, Silke B. Disabling disease codes predict worse outcomes for acute medical admissions. Intern Med J. 2014;44(6):546-53.
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33. 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.
34. Conway R, Byrne D, O'Riordan D, Silke B. Patient risk profiling in acute medicine: the way forward? QJM. 2015;108(9):689-96.
35. Conway R, Byrne D, Cournane S, O'Riordan D, Silke B. Fifteen-year outcomes of an acute medical admission unit. Irish Journal of Medical Science. 2018;187(4):1097-105.
36. 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.
37. Silke B, Kellett J, Rooney T, Bennett K, O'Riordan D. An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling. QJM. 2010;103(1):23-32.
38. 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.
39. 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)).
40. 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)).
41. 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)).
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43. 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)).
44. Carstairs V, Morris R. Deprivation and mortality: an alternative to social class? Community medicine. 1989;11(3):210-9.
45. 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.
46. 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.
47. 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.
48. Shirmat M. Algorithm 112: position of point relative to polygon. ACM Comm. 1962;5:434.
49. 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.
50. Piccirillo JF, Vlahiotis A, Barrett LB, Flood KL, Spitznagel EL, Steyerberg EW. The changing prevalence of comorbidity across the age spectrum. Critical Reviews in Oncology/Hematology. 2008;67(2):124-32.
51. Conway R, Byrne D, O'Riordan D, Silke B. Outcomes in acute medicine - Evidence from extended observations on readmissions, hospital length of stay and mortality outcomes. Eur J Int Med. 2019.
52. Laudicella M, Donni PL, Smith PC. Hospital readmission rates: Signal of failure or success? J Health Econ. 2013;32(5):909-21.
53. Aragón MJ, Chalkley M. How do time trends in inhospital mortality compare? A retrospective study of England and Scotland over 17 years using administrative data. BMJ open. 2018;8(2):e017195.
54. Prytherch DR, Sirl JS, Schmidt P, Featherstone PI, Weaver PC, Smith GB. The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation. 2005;66(2):203-7.
55. Froom P, Shimoni Z. Prediction of hospital mortality rates by admission laboratory tests. Clin Chem. 2006;52(2):325-8.
56. Hucker TR, Mitchell GP, Blake LD, Cheek E, Bewick V, Grocutt M, et al. Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department. Br J Anaesth. 2005;94(6):735-41.
57. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626-33.
58. Cournane S, Byrne D, O'Riordan D, Fitzgerald B, Silke B. Chronic Disabling Disease – Impact on Outcomes and Costs in Emergency Medical Admissions. Quarterly Journal of Medicine. 2014:1-29.
59. Royston P, Reitz M, Atzpodien J. An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma. Br J Cancer. 2006;94(12):1785-8.
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. Epstein AM, Stern RS, Weissman JS. Do the poor cost more? A multihospital study of patients' socioeconomic status and use of hospital resources. N Engl J Med. 1990;322(16):1122-8.
4. Stringhini S, Carmeli C, Jokela M, Avendaà M, Muennig P, Guida F, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1•7 million men and women. The Lancet. 2017;389(10075):1229-37.
5. Wehren LE, Hawkes WG, Orwig DL, Hebel JR, Zimmerman SI, Magaziner J. Gender differences in mortality after hip fracture: the role of infection. J Bone Miner Res. 2003;18(12):2231-7.
6. Long-term trends in first hospitalization for heart failure and subsequent survival between 1986 and 2003: a population study of 5.1 million people., (2009).
7. Bharathan B, Welfare M, Borowski DW, Mills SJ, Steen IN, Kelly SB. Impact of deprivation on short- and long-term outcomes after colorectal cancer surgery. Br J Surg. 2011;98(6):854-65.
8. Lewis G, Sloggett A. Suicide, deprivation, and unemployment: record linkage study. BMJ. 1998;317(7168):1283-6.
9. Exeter D, Boyle P. Does young adult suicide cluster geo- graphically in Scotland? J Epidemiol Community Health. 2007;61:731–6.
10. Burrows S, Auger N, Gamache P, D H. Individual and area socioeconomic inequalities in cause-specific uninten- tional injury mortality: 11-year follow-up study of 2.7 million Canadians. Accid Anal Prev. 2012;45:99–106.
11. Socioeconomic deprivation, urban-rural location and alcohol-related mortality in England and Wales, (2010).
12. Belon AP, Barros MBA, Marín-León L. Mortality among adults: gender and socioeconomic differences in a Brazilian city. BMC Public Health. 2012;12(1):34.
13. Vaccarino V, Parsons L, Every NR, Barron HV, Xkrumholz HM. Sex-Based Difference In Early Mortality After Myocardial Infarction. New England Journal of Medicine. 1999;341:217-25.
14. Kim C, Redberg RF, Pavlic T, Eagle KA. A Systematic Review of Gender Differences in Mortality after Coronary Artery Bypass Graft Surgery and Percutaneous Coronary Interventions. Clinical Cardiology. 2007;30(10):491-5.
15. Pietropaoli AP, Glance LG, Oakes D, Fisher SG. Gender differences in mortality in patients with severe sepsis or septic shock. Gender Medicine. 2010;7(5):422-37.
16. Yu C, An Z, Zhao W, Wang W, Gao C, Liu S, et al. Sex Differences in Stroke Subtypes, Severity, Risk Factors, and Outcomes among Elderly Patients with Acute Ischemic Stroke. Front Aging Neurosci. 2015;7:35.
17. Panula J, Pihlajamäki H, Mattila VM, Jaatinen P, Vahlberg T, Aarnio P, et al. Mortality and cause of death in hip fracture patients aged 65 or older - a population-based study. BMC Musculoskelet Disord. 2011;12(1):1583.
18. 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.
19. Zhao Y, You J, Guthridge SL, Lee AH. A multilevel analysis on the relationship between neighbourhood poverty and public hospital utilization: is the high Indigenous morbidity avoidable? BMC Public Health. 2011;11:737.
20. Yang T DA, Gao H. Emergency hospital admissions for ambulatory care-sensitive conditions: identifying the potential for reductions 2012. London, UK: The King's Fund; 2012.
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. Sloggett A, Joshi H. Higher mortality in deprived areas: community or personal disadvantage? BMJ. 1994;309(6967):1470-4.
23. Macintyre K, Stewart S, Chalmers J, Pell J, Finlayson A, Boyd J, et al. Relation between socioeconomic deprivation and death from a first myocardial infarction in Scotland: population based analysis. Bmj. 2001;322(7295):1152-3.
24. 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.
25. Cournane S, Byrne D, Conway R, O'Riordan D, Coveney S, Silke B. Effect of social deprivation on the admission rate and outcomes of adult respiratory emergency admissions. Respiratory Medicine. 2017;125:94-101.
26. Mitnitski A, Rockwood K. Frailty in Relation to the Accumulation of Deficits. The Journals of Gerontology: Series A. 2007;62(7):722-7.
27. 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.
28. O'Sullivan E, Callely E, O'Riordan D, Bennett K, Silke B. Predicting outcomes in emergency medical admissions – role of laboratory data and co-morbidity. Acute Medicine. 2012;2:59-65.
29. 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.
30. Chotirmall SH, Picardo S, Lyons J, D'Alton M, O'Riordan D, Silke B. Disabling disease codes predict worse outcomes for acute medical admissions. Intern Med J. 2014;44(6):546-53.
31. 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.
32. Coary R, Byrne D, O'Riordan D, Conway R, Cournane S, Silke B. Does admission via an Acute Medical Unit influence hospital mortality? 12 years' experience in a large Dublin Hospital. Acute Med. 2014;13(4):152-8.
33. 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.
34. Conway R, Byrne D, O'Riordan D, Silke B. Patient risk profiling in acute medicine: the way forward? QJM. 2015;108(9):689-96.
35. Conway R, Byrne D, Cournane S, O'Riordan D, Silke B. Fifteen-year outcomes of an acute medical admission unit. Irish Journal of Medical Science. 2018;187(4):1097-105.
36. 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.
37. Silke B, Kellett J, Rooney T, Bennett K, O'Riordan D. An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling. QJM. 2010;103(1):23-32.
38. 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.
39. 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)).
40. 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)).
41. 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)).
42. 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.
43. 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)).
44. Carstairs V, Morris R. Deprivation and mortality: an alternative to social class? Community medicine. 1989;11(3):210-9.
45. 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.
46. 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.
47. 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.
48. Shirmat M. Algorithm 112: position of point relative to polygon. ACM Comm. 1962;5:434.
49. 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.
50. Piccirillo JF, Vlahiotis A, Barrett LB, Flood KL, Spitznagel EL, Steyerberg EW. The changing prevalence of comorbidity across the age spectrum. Critical Reviews in Oncology/Hematology. 2008;67(2):124-32.
51. Conway R, Byrne D, O'Riordan D, Silke B. Outcomes in acute medicine - Evidence from extended observations on readmissions, hospital length of stay and mortality outcomes. Eur J Int Med. 2019.
52. Laudicella M, Donni PL, Smith PC. Hospital readmission rates: Signal of failure or success? J Health Econ. 2013;32(5):909-21.
53. Aragón MJ, Chalkley M. How do time trends in inhospital mortality compare? A retrospective study of England and Scotland over 17 years using administrative data. BMJ open. 2018;8(2):e017195.
54. Prytherch DR, Sirl JS, Schmidt P, Featherstone PI, Weaver PC, Smith GB. The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation. 2005;66(2):203-7.
55. Froom P, Shimoni Z. Prediction of hospital mortality rates by admission laboratory tests. Clin Chem. 2006;52(2):325-8.
56. Hucker TR, Mitchell GP, Blake LD, Cheek E, Bewick V, Grocutt M, et al. Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department. Br J Anaesth. 2005;94(6):735-41.
57. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626-33.
58. Cournane S, Byrne D, O'Riordan D, Fitzgerald B, Silke B. Chronic Disabling Disease – Impact on Outcomes and Costs in Emergency Medical Admissions. Quarterly Journal of Medicine. 2014:1-29.
59. Royston P, Reitz M, Atzpodien J. An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma. Br J Cancer. 2006;94(12):1785-8.