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Exploring the predictors of early readmission to psychiatric hospital

Published online by Cambridge University Press:  23 February 2015

A. D. Tulloch Open the ORCID record for A. D. Tulloch [Opens in a new window] ,
A. S. David Open the ORCID record for A. S. David [Opens in a new window]  and
G. Thornicroft
A. D. Tulloch*
Affiliation:
King's College London, King's Health Partners, Institute of Psychiatry, PO29, De Crespigny Park, London SE5 8AF, UK
A. S. David
Affiliation:
King's College London, King's Health Partners, Institute of Psychiatry, PO29, De Crespigny Park, London SE5 8AF, UK
G. Thornicroft
Affiliation:
King's College London, King's Health Partners, Institute of Psychiatry, PO29, De Crespigny Park, London SE5 8AF, UK
*
*Address for correspondence: Dr A. D. Tulloch, King's College London, King's Health Partners, Institute of Psychiatry, PO29, De Crespigny Park, London SE5 8AF, UK. (Email: [email protected])
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Abstract

Background.

Aims of this study are to explore the associations of readmission to psychiatric hospital over time, to develop a statistical model for early readmission to psychiatric hospital and to assess the feasibility of predicting early readmission.

Method.

The sample comprised 7891 general psychiatric discharges in South London, taken from a large anonymised repository of electronic patient records. We initially explored time to readmission using Cox regression – this included investigation of time-dependent effects. Subsequently, we used logistic regression to create a predictive model for 90-day readmission. We investigated the effect on readmission of a set of variables that included demographic variables, diagnosis and legal status during the index admission, previous service use, housing variables and individual item scores on the Health of the Nation Outcome Scales (HoNOS) at admission and at discharge.

Results.

Fifteen per cent of those discharged were readmitted within 90 days. Cox regression demonstrated that the estimated baseline hazard of readmission declined steeply after discharge and that the effects of several predictors, especially diagnosis, changed over time – most notably, personality disorder was associated with increased readmission relative to schizophrenia at the time of discharge, but did not significantly differ by 1-year postdischarge. In the logistic regression, increased readmission was associated with personality disorder diagnosis; shorter length of the index admission (excepting zero length admissions); number of discharges in the preceding 2 years; and having a high score at discharge on the HoNOS overactive and aggressive behaviour item, cognitive problems item or hallucinations and delusions items. Detention under Section 3 or a forensic section of the Mental Health Act during the index admission was associated with reduced readmission. The coefficient of discrimination for the logistic regression, which is equivalent to r2, was 0.04 and the estimated area under the receiver operating curve was 0.65.

Conclusions.

The association found between early readmission and personality disorder diagnosis merits further investigation, as does the possible trade-off between reduction in length of stay and increased readmission. Other novel findings such as the associations found with HoNOS item scores also merit replication. As with previous studies, we found that the rate of readmission declines steeply after hospital discharge, so that the period immediately subsequent to discharge is a period of comparatively high risk. However, prediction of early readmission within this high-risk group remains challenging – it seems most likely that many unmeasured influences operate subsequent to the time of discharge.

Keywords

Hospitalpatient readmissionpsychiatricPsychiatric Department
Type
Original Articles
Information
Epidemiology and Psychiatric Sciences , Volume 25 , Issue 2 , April 2016 , pp. 181 - 193
Copyright
Copyright © Cambridge University Press 2015 

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References

van Buuren, S, Boshuizen, HC, Knook, DL (1999). Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 18, 681–694.Google Scholar
Byrne, SL, Hooke, GR, Page, AC (2010). Readmission: a useful indicator of the quality of inpatient psychiatric care. Journal of Affective Disorders 126, 206–213.Google Scholar
Callaly, T, Trauer, T, Hyland, M, Coombs, T, Berk, M (2011). An examination of risk factors for readmission to acute adult mental health services within 28 days of discharge in the Australian setting. Australasian Psychiatry 19, 221–225.Google Scholar
Carr, VJ, Lewin, TJ, Sly, KA, Conrad, AM, Tirupati, S, Cohen, M, Ward, PB, Coombs, T (2008). Adverse incidents in acute psychiatric inpatient units: rates, correlates and pressures. The Australian and New Zealand Journal of Psychiatry 42, 267–282.Google Scholar
Clements, KM, Murphy, JM, Eisen, SV, Normand, S-LT (2006). Comparison of self-report and clinician-rated measures of psychiatric symptoms and functioning in predicting 1-year hospital readmission. Administration and Policy in Mental Health and Mental Health Services Research 33, 568–577.CrossRefGoogle ScholarPubMed
Durbin, J, Lin, E, Layne, C, Teed, M (2007). Is readmission a valid indicator of the quality of inpatient psychiatric care? The Journal of Behavioral Health Services & Research 34, 137–150.Google Scholar
Figueroa, R, Harman, J, Engberg, J (2004). Use of claims data to examine the impact of length of inpatient psychiatric stay on readmission rate. Psychiatric Services 55, 560–565.Google Scholar
Heggestad, T (2001). Operating conditions of psychiatric hospitals and early readmission — effects of high patient turnover. Acta Psychiatrica Scandinavica 103, 196–202.Google Scholar
Heggestad, T, Lilleeng, SE, Ruud, T (2011). Patterns of mental health care utilisation: distribution of services and its predictability from routine data. Social Psychiatry and Psychiatric Epidemiology 46, 1275–1282.Google Scholar
Hendryx, MS, Moore, R, Leeper, T, Reynolds, M, Davis, S (2001). An examination of methods for risk-adjustment of rehospitalization rates. Mental Health Services Research 3, 15–24.Google Scholar
Hendryx, MS, Russo, JE, Stegner, B, Dyck, DG, Ries, RK, Roy-Byrne, P (2003). Predicting rehospitalization and outpatient services from administration and clinical databases. The Journal of Behavioral Health Services & Research 30, 342–351.Google Scholar
Hodgson, RE, Lewis, M, Boardman, AP (2001). Prediction of readmission to acute psychiatric units. Social Psychiatry and Psychiatric Epidemiology 36, 304–309.Google Scholar
Hosmer, DW, Lemeshow, S (1980). Goodness of fit tests for the multiple logistic regression model. Communications in Statistics – Theory and Methods 9, 1043–1069.Google Scholar
Hosmer, DW, Lemeshow, S, May, S (2008). Applied Survival Analysis: Regression Modeling of Time-to-Event Data. John Wiley & Sons: Hoboken, New Jersey.CrossRefGoogle Scholar
Kind, AJH, Smith, MA, Frytak, JR, Finch, MD (2007 a). Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke. Journal of the American Geriatrics Society 55, 365–373.CrossRefGoogle ScholarPubMed
Kind, AJH, Smith, MA, Pandhi, N, Frytak, JR, Finch, MD (2007 b). Bouncing-back: rehospitalization in patients with complicated transitions in the first thirty days after hospital discharge for acute stroke. Home Health Care Services Quarterly 26, 37–55.CrossRefGoogle ScholarPubMed
Kind, AJH, Smith, MA, Liou, J-I, Pandhi, N, Frytak, JR, Finch, MD (2008). The price of bouncing back: one-year mortality and payments for acute stroke patients with 30-day bounce-backs. Journal of the American Geriatrics Society 56, 999–1005.CrossRefGoogle Scholar
Kind, AJH, Smith, MA, Liou, J-I, Pandhi, N, Frytak, JR, Finch, MD (2010). Discharge destination's effect on bounce-back risk in Black, White, and Hispanic acute ischemic stroke patients. Archives of Physical Medicine and Rehabilitation 91, 189–195.CrossRefGoogle ScholarPubMed
Klinkenberg, WD, Calsyn, RJ (1996). Predictors of receipt of aftercare and recidivism among persons with severe mental illness: a review. Psychiatric Services 47, 487–496.Google ScholarPubMed
Korkeila, JA, Lehtinen, V, Tuori, T, Helenius, H (1998). Frequently hospitalised psychiatric patients: a study of predictive factors. Social Psychiatry and Psychiatric Epidemiology 33, 528–534.Google Scholar
Mangalore, R, Knapp, M (2007). Cost of schizophrenia in England. The Journal of Mental Health Policy and Economics 10, 23–41.Google Scholar
Mellesdal, L, Mehlum, L, Wentzel-Larsen, T, Kroken, R, Arild Jorgensen, H (2010). Suicide risk and acute psychiatric readmissions: a Prospective Cohort Study. Psychiatric Services 61, 25–31.CrossRefGoogle ScholarPubMed
Mojtabai, R, Nicholson, RA, Neesmith, DH (1997). Factors affecting relapse in patients discharged from a public hospital: results from survival analysis. The Psychiatric Quarterly 68, 117–129.Google Scholar
Moran, PW, Doerfler, LA, Scherz, J, Lish, JD (2000). Rehospitalization of psychiatric patients in a managed care environment. Mental Health Services Research 2, 191–198.CrossRefGoogle Scholar
NHS National Services Scotland (2009). SPARRA Mental Disorder: Scottish Patients at Risk of Readmission and Admission (to psychiatric hospitals or units). A report on the development of SPARRA MD and recommendations for its use. January. Information Services Division.Google Scholar
Nuffield Trust (2011). Predictive Risk and Health Care: An Overview. Nuffield Trust: London.Google Scholar
OECD (2011). Health at a Glance 2011: OECD Indicators. OECD Publishing: Paris.Google Scholar
Parker, G, O'Donnell, M, Hadzi-Pavlovic, D, Proberts, M (2002). Assessing outcome in community mental health patients: a comparative analysis of measures. International Journal of Social Psychiatry 48, 11–19.Google Scholar
Paul, P, Pennell, ML, Lemeshow, S (2013). Standardizing the power of the Hosmer–Lemeshow goodness of fit test in large data sets. Statistics in Medicine 32, 67–80.CrossRefGoogle ScholarPubMed
Royston, P (2004). Multiple imputation of missing values. Stata Journal 4, 227–241.Google Scholar
Royston, P, Sauerbrei, W (2008). Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. John Wiley & Sons: Chichester.CrossRefGoogle Scholar
Rubin, DB (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons: New York.Google Scholar
Russo, J, Roy-Byrne, P, Jaffe, C, Ries, R, Dagadakis, C, Avery, D (1997). Psychiatric status, quality of life, and level of care as predictors of outcomes of acute inpatient treatment. Psychiatric Services 48, 1427–1434.Google Scholar
Schoenbaum, SC, Cookson, D, Stelovich, S (1995). Postdischarge follow-up of psychiatric inpatients and readmission in an HMO setting. Psychiatric Services 46, 943–945.Google Scholar
Sherman, FT (2009). Rehospitalizations: packaging discharge and transition services to prevent ‘bounce backs’. Geriatrics 64, 8–9.Google Scholar
Stewart, R, Soremekun, M, Perera, G, Broadbent, M, Callard, F, Denis, M, Hotopf, M, Thornicroft, G, Lovestone, S (2009). The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry 9, 51.Google Scholar
Sytema, S, Burgess, P (1999). Continuity of care and readmission in two service systems: a comparative Victorian and Groningen case-register study. Acta Psychiatrica Scandinavica 100, 212–219.Google Scholar
Thompson, EE, Neighbors, HW, Munday, C, Trierweiler, S (2003). Length of stay, referral to aftercare, and rehospitalization among psychiatric inpatients. Psychiatric Services 54, 1271–1276.Google Scholar
Tjur, T (2009). Coefficients of determination in logistic regression models—a new proposal: the coefficient of discrimination. The American Statistician 63, 366–372.Google Scholar
Tulloch, AD, Khondoker, MR, Thornicroft, G, David, AS (2014). Home treatment teams and facilitated discharge from psychiatric hospital. Epidemiology and Psychiatric Sciences 1–13.Google Scholar
Valevski, A, Olfson, M, Weizman, A, Shiloh, R (2007). Risk of readmission in compulsorily and voluntarily admitted patients. Social Psychiatry and Psychiatric Epidemiology 42, 916–922.Google Scholar
Vigod, S, Kurdyak, P, Dennis, C-L, Leszcz, T, Taylor, V, Blumberger, D, Seitz, D (2013). Transitional interventions to reduce early psychiatric readmissions in adults: systematic review. British Journal of Psychiatry 202, 187–194.CrossRefGoogle ScholarPubMed
Wheeler, A, Moyle, S, Jansen, C, Robinson, E, Vanderpyl, J (2011). Five-year follow-up of an acute psychiatric admission cohort in Auckland, New Zealand. New Zealand Medical Journal 124, 30–8.Google Scholar
Wing, JK, Beevor, A, Curtis, R, Park, S, Hadden, S, Burns, A (1998). Health of the Nation Outcome Scales (HoNOS). Research and development. British Journal of Psychiatry 172, 11–18.Google Scholar
Zilber, N, Hornik-Lurie, T, Lerner, Y (2011). Predictors of early psychiatric rehospitalization: a national case register study. Israel Journal of Psychiatry and Related Sciences 48, 49–53.Google Scholar