Admission and readmission rate incidences from deprived areas—impact of a classical or multi-dimensional model
Classical deprivation instruments use a factor analytical approach relying on a smaller number of dimensions, factors or components. Multi-dimensional deprivation models attempt classification in fine detail—even down to street level.
Single-centre retrospective cohort study using routinely collected aggregated and anonymised data on emergency medical admissions (96,526 episodes in 50,731 patients; 2002–2016). We calculated admission/readmission rate incidences for the 74 small areas within the hospital catchment area. We compared a classical Small Area Health Research Unit (SAHRU) to the multi-dimensional POBAL Haase and Pratschke Deprivation Index for Small Areas (POBAL) deprivation instrument and their deprivation ranks for two Irish censuses (2006/ 2011).
There was poor agreement between the instruments of the Deprivation Ranks by Quintile—with agreement in 46 and 42% of small areas for the respective 2006 and 2011 censuses. The classical model (SAHRU) suggested more areas with severe deprivation (Q5 66 and 55%) compared with POBAL (Q5 32 and 24%) from the respective censuses. SAHRU classical instrument had a higher prediction level incidence rate ratio (IRR) 1.48 (95% CI 1.47, 1.49)) compared with POBAL IRR 1.28 (95% CI 1.27, 1.28) and systematically lower estimates of hospital admission and readmission rate incidences. Earlier Census data modelled more powerfully, suggesting a long latency between social circumstances and the ultimate expression of the emergency medical admission.
Deprivation influences hospital incidence rates for emergency medical admissions and readmissions; instruments focusing at the very small area (individual or street level) have a utility but appear inferior in terms of representing the population risk of environmental/socio-economic factors which seem best approximated at a larger scale.
KeywordsDeprivation model Emergency medical admission Incidence rates
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 3.Yang T, Dixon A, Gao H (2012) Emergency hospital admissions for ambulatory care-sensitive conditions: identifying the potential for reductions 2012. The King’s Fund, LondonGoogle Scholar
- 9.Walsh JB, Coakley D, Murphy C, Coakley JD, Boyle E, Johnson H (2004) Demographic profile of the elderly population in Dublin accident and emergency hospital catchment areas. Ir Med J 97(3):84–86Google Scholar
- 13.Kelly A, Teljeur C (2007) SAHRU National Deprivation Index Trinity College, Dublin. Available from: http://www.sahru.tcd.ie/services/deprivation/Deprivation Files/WebReport07.pdf
- 15.Kelly A TC (2013) 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 DublinGoogle Scholar
- 16.Morgan O, Baker A (2006) Measuring deprivation in England and Wales using 2001 Carstairs scores. Health Stat Q/Office for National Statistics (31):28–33Google Scholar
- 17.Haase T, Pratschke J (2017) The 2016 Pobal HP Deprivation Index for small areas (SA). Available from: https://www.pobal.ie/Publications/Documents/The%202016%20Pobal%20HP%20Deprivation%20Index%20-%20Introduction%2007.pdf
- 21.O'Callaghan A, Colgan MP, McGuigan C, Smyth F, Haider N, O'Neill S, Moore D, Madhavan P (2012) A critical evaluation of HIPE data. Ir Med J 105(1):21–23Google Scholar
- 22.Carstairs V, Morris R (1989) Deprivation and mortality: an alternative to social class? Commun Med 11(3):210–219Google Scholar
- 23.Shirmat M (1962) Algorithm 112: position of point relative to polygon. ACM Comm 5:434Google Scholar
- 25.O’Sullivan E, Callely E, O'Riordan D, Bennett K, Silke B (2012) Predicting outcomes in emergency medical admissions—role of laboratory data and co-morbidity. Acute Med 2:59–65Google Scholar
- 28.Chotirmall SH, Callaly E, Lyons J, O'Connell B, Kelleher M, Byrne D et al (2016) Blood cultures in emergency medical admissions: a key patient cohort. Eur J Emerg Med 23(1):38–43Google Scholar
- 29.Cameron A, Trivedi P (2009) Microeconometrics using Stata. Stata Press, College StationGoogle Scholar
- 31.Cournane S, Conway R, Byrne D, O’Riordan D, Coveney S, Silke B (2016) Social deprivation and the rate of emergency medical admission for older persons. QJM (in press):hcw029Google Scholar
- 33.Tian Y, Dixon A, Gao H (2012) Emergency hospital admissions for ambulatory care-sensitive conditions: identifying the potential for reductions. Data briefing London [12 June 2012]. Available from: www.kingsfund.org.uk/publications/data_briefing.html
- 37.Sheppard S Why is gentrification a problem ? Centre for Creative Community Development. Available from: http://web.williams.edu/Economics/ArtsEcon/library/pdfs/WhyIsGentrificationAProbREFORM.pdf
- 38.Ding L, Hwang J, Divringi E (2016) Gentrification and residential mobility in Philadelphia. Federal Reserve Bank of Philadelphia / Princeton University. Available from: https://www.philadelphiafed.org/-/media/community-development/publications/discussion-papers/discussion-paper_gentrification-and-residential-mobility.pdf?la=en