Social deprivation, population dependency ratio and an extended hospital episode — Insights from acute medicine

Published:September 11, 2015DOI:


      • We evaluated predictors of admission for episodes with length of stay (LOS) >30 days.
      • Extended LOS was investigated by deprivation, dependency and illness severity.
      • Predictors of extended LOS included deprivation and dependency in our catchment area.
      • High deprivation and dependency majorly influenced admissions with extended LOS.



      Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; this study aimed to investigate the extent to which Deprivation status and the population Dependency Ratio influenced extended hospital episodes.


      All Emergency Medical admissions (75,018 episodes of 41,728 patients) over 12 years (2002–2013) categorized by quintile of Deprivation Index and Population Dependency Rates (proportion of non-working/working) were evaluated against length of stay (LOS). Patients with an Extended LOS (ELOS), >30 days, were investigated, by Deprivation status, Illness Severity and Co-morbidity status. Univariate and multi-variable risk estimates (Odds Rates or Incidence Rate Ratios) were calculated, using truncated Poisson regression.


      Hospital episodes with ELOS had a frequency of 11.5%; their median LOS (IQR) was 55.0 (38.8, 97.6) days utilizing 57.6% of all bed days by all 75,018 emergency medical admissions. The Deprivation Index independently predicted the rate of such ELOS admissions; these increased approximately five-fold (rate/1000 population) over the Deprivation Quintiles with model adjusted predicted admission rates of for Q1 0.93 (95% CI: 0.86, 0.99), Q22.63 (95% CI: 2.55, 2.71), Q3 3.84 (95% CI: 3.77, 3.91), Q4 3.42 (95% CI: 3.37, 3.48) and Q5 4.38 (95% CI: 4.22, 4.54). Similarly the Population Dependency Ratio Quintiles (dependent to working structure of the population by small area units) independently predicted extended LOS admissions.


      The admission of patients with an ELOS is strongly influenced by the Deprivation status and the population Dependency Ratio of the catchment area. These factors interact, with both high deprivation and Dependency cohorts having a major influence on the numbers of emergency medical admission patients with an extended hospital episode.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to European Journal of Internal Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Lynch C.
        • Holman C.
        • Arcy J.
        • Moorin R.E.
        Use of Western Australian linked hospital morbidity and mortality data to explore theories of compression, expansion and dynamic equilibrium.
        Aust Health Rev. 2007; 31: 571-581
        • Cournane S.
        • Byrne D.
        • O'Riordan D.
        • Silke B.
        Factors associated with length of stay following an emergency medical admission.
        Eur J Intern Med. 2015; 4 ([1113-25])
        • McMullan R.
        • Silke B.
        • Bennett K.
        • Callachand S.
        Resource utilisation, length of hospital stay, and pattern of investigation during acute medical hospital admission.
        Postgrad Med J. 2004; 80: 23-26
        • Townsend P.
        J Soc Policy. 1987; 16: 25-46
        • Morgan O.
        • Baken A.
        Measuring deprivation in England and Wales using 2001 Carstairs scores.
        Health Stat Q. 2006; 31: 28-33
        • Benach J.
        • Yasui Y.
        Geographical patterns of excess mortality in Spain explained by two indices of deprivation.
        J Epidemiol Community Health. 1999; 53: 423-431
        • Carstairs V.
        • Morris R.
        Deprivation: explaining differences in mortality between Scotland and England and Wales.
        BMJ. 1989; 299: 886-889
        • Fukuda Y.
        • Nakamura K.
        • Takano T.
        Higher mortality in areas of lower socioeconomic position measured by a single index of deprivation in Japan.
        Public Health. 2007; 121: 163-173
        • Hanlon P.
        • Lawder R.S.
        • Buchanan D.
        • Redpath A.
        • Walsh D.
        • Wood R.
        • et al.
        Why is mortality higher in Scotland than in England and Wales? Decreasing influence of socioeconomic deprivation between 1981 and 2001 supports the existence of a Scottish Effect.
        J Public Health (Oxf). 2005; 27: 199-204
        • Zhao Y.
        • You J.
        • Guthridge S.L.
        • Lee A.H.
        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
        • Conway R.
        • Galvin S.
        • Coveney S.
        • O'Riordan D.
        • Silke B.
        Deprivation as an outcome determinant in emergency medical admissions.
        QJM. 2013; 106: 245-251
        • Walsh J.B.
        • Coakley D.
        • Murphy C.
        • Coakley J.D.
        • Boyle E.
        • Johnson H.
        Demographic profile of the elderly population in Dublin accident and emergency hospital catchment areas.
        Ir Med J. 2004; 97: 84-86
        • 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 ([discussion 52–7]): 40-45
        • O'Callaghan A.
        • Colgan M.P.
        • McGuigan C.
        • Smyth F.
        • Haider N.
        • O'Neill S.
        • et al.
        A critical evaluation of HIPE data.
        Ir Med J. 2012; 105: 21-23
        • Kelly A.
        • Teljeur C.
        SAHRU National Deprivation Index Trinity College, Dublin.
        (Available from)
        • Shimrat M.
        Algorithm 112: position of point relative to polygon.
        Commun ACM. 1962; 5: 434
        • Cameron A.
        • Trivedi P.
        Microeconometrics using Stata.
        Stata Press, College Station, TX2009
        • 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: 23-32
        • 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 Med. 2012; 2: 59-65
        • Charlson M.E.
        • Pompei P.
        • Ales K.L.
        • MacKenzie C.R.
        A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
        J Chronic Dis. 1987; 40: 373-383
        • Chotirmall S.H.
        • 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: 546-553
        • Cunningham C.J.
        • Walsh J.B.
        • Coakley D.
        • Walsh C.
        • Connolly C.
        • Murphy M.
        • et al.
        Survival of patients discharged to long term care.
        Ir Med J. 2008; 101: 305-307
        • Barnett K.
        • Mercer S.W.
        • 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: 37-43
        • Kone Pefoyo A.J.
        • Bronskill S.E.
        • Gruneir A.
        • Calzavara A.
        • Thavorn K.
        • Petrosyan Y.
        • et al.
        The increasing burden and complexity of multimorbidity.
        BMC Public Health. 2015; 15: 415
        • Jayadevappa R.
        • Chhatre S.
        • Weiner M.
        • Raziano D.B.
        Health resource utilization and medical care cost of acute care elderly unit patients.
        Value Health. 2006; 9: 186-192
        • Purdy S.
        Avoiding hospital admissions: what does the research evidence say?.
        The King's Fund. 2010
        • Starfield B.
        • Shi L.
        • Macinko J.
        Contribution of primary care to health systems and health.
        Milbank Q. 2005; 83: 457-502
        • Nishino Y.
        • Gilmour S.
        • Shibuya K.
        Inequality in diabetes-related hospital admissions in England by socioeconomic deprivation and ethnicity: facility-based cross-sectional analysis.
        PLoS One. 2015; 10e0116689
        • Aljuburi G.
        • Majeed A.
        Trends in hospital admissions for sickle cell disease in England.
        J Public Health. 2013; 35: 179
        • Epstein A.M.
        • Stern R.S.
        • Weissman J.S.
        Do the poor cost more? A multihospital study of patients' socioeconomic status and use of hospital resources.
        N Engl J Med. 1990; 322: 1122-1128
      1. McCormick B, Hill P-S, Emmi P. Are hospital services used differently in deprived areas?

      2. Evidence to identify commissioning challenges Oxford: Centre for Health Service Economics & Organisation (CHSEO).
        (Available from)
        • 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:])