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Social Psychiatry and Psychiatric Epidemiology

, Volume 53, Issue 2, pp 139–149 | Cite as

Predicting psychiatric readmission: sex-specific models to predict 30-day readmission following acute psychiatric hospitalization

  • Lucy Church Barker
  • Andrea Gruneir
  • Kinwah Fung
  • Nathan Herrmann
  • Paul Kurdyak
  • Elizabeth Lin
  • Paula A. Rochon
  • Dallas Seitz
  • Valerie H. Taylor
  • Simone N. VigodEmail author
Original Paper

Abstract

Purpose

Psychiatric readmission is a common negative outcome. Predictors of readmission may differ by sex. This study aimed to derive and internally validate sex-specific models to predict 30-day psychiatric readmission.

Methods

We used population-level health administrative data to identify predictors of 30-day psychiatric readmission among women (n = 33,353) and men (n = 32,436) discharged from all psychiatric units in Ontario, Canada (2008–2011). Predictor variables included sociodemographics, health service utilization, and clinical characteristics. Using derivation data sets, multivariable logistic regression models were fit to determine optimal predictive models for each sex separately. Results were presented as adjusted odds ratios (aORs) and 95% confidence intervals (CI). The multivariable models were then applied in the internal validation data sets.

Results

The 30-day readmission rates were 9.3% (women) and 9.1% (men). Many predictors were consistent between women and men. For women only, personality disorder (aOR 1.21, 95% CI 1.03–1.42) and positive symptom score (aOR 1.41, 95% CI 1.09–1.82 for score of 1 vs. 0; aOR 1.44, 95% CI 1.26–1.64 for ≥ 2 vs. 0) increased odds of readmission. For men only, self-care problems at admission (aOR 1.20, 95% CI 1.06–1.36) and discharge (aOR 1.44, 95% CI 1.26–1.64 for score of 1 vs. 0; aOR 1.79, 95% CI 1.17–2.74 for 2 vs. 0), and mild anxiety rating (score of 1 vs. 0: aOR 1.30, 95% CI 1.02–1.64, derivation model only) increased odds of readmission. Models had moderate discriminative ability in derivation and internal validation samples for both sexes (c-statistics 0.64–0.65).

Conclusions

Certain key predictors of psychiatric readmission differ by sex. This knowledge may help to reduce psychiatric hospital readmission rates by focusing interventions.

Keywords

Psychiatric readmission Psychiatric epidemiology Sex-based analysis Sex differences 

Notes

Acknowledgements

This study was supported by a grant from the AFP Innovation Fund of the Ontario Ministry of Health and Long Term Care. It was also supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions, and statements expressed herein are those of the author, and not necessarily those of CIHI.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interest

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. This study was approved by the institutional review board at Sunnybrook Health Sciences Centre, Toronto, Canada (ICES logged study: 2013 0904 301 000).

Availability of data and materials

Under Ontario privacy legislation, ICES is a Prescribed Entity under Sect. 45(1) of Ontario’s Personal Health Information Protection Act, 2004 (PHIPA) that is permitted to hold and use administrative, population health, clinical and other data files for the purposes of analysis, evaluation, and decision support. ICES is responsible for ensuring that necessary infrastructure (i.e., privacy office, data linkage and security measures, and data sharing agreements) is in place to comply with these policies and to maintain the data platform. Due to these privacy regulations, we are not permitted to share participant-level data.

Supplementary material

127_2017_1450_MOESM1_ESM.docx (91 kb)
Supplementary material 1 (DOCX 91 KB)
127_2017_1450_MOESM2_ESM.docx (139 kb)
Supplementary material 2 (DOCX 140 KB)
127_2017_1450_MOESM3_ESM.docx (147 kb)
Supplementary material 3 (DOCX 148 KB)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Lucy Church Barker
    • 1
  • Andrea Gruneir
    • 2
    • 3
    • 4
    • 5
  • Kinwah Fung
    • 2
    • 3
  • Nathan Herrmann
    • 1
    • 6
  • Paul Kurdyak
    • 1
    • 2
    • 4
    • 7
  • Elizabeth Lin
    • 1
    • 2
    • 4
    • 7
  • Paula A. Rochon
    • 2
    • 3
    • 4
    • 8
  • Dallas Seitz
    • 9
  • Valerie H. Taylor
    • 1
    • 3
  • Simone N. Vigod
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.Department of PsychiatryUniversity of TorontoTorontoCanada
  2. 2.Institute for Clinical Evaluative SciencesTorontoCanada
  3. 3.Women’s College Hospital and Research InstituteWomen’s College HospitalTorontoCanada
  4. 4.Institute for Health Policy Management and EvaluationUniversity of TorontoTorontoCanada
  5. 5.Department of Family MedicineUniversity of AlbertaEdmontonCanada
  6. 6.Department of PsychiatrySunnybrook Health Sciences CentreTorontoCanada
  7. 7.Centre for Addiction and Mental HealthTorontoCanada
  8. 8.Department of MedicineUniversity of TorontoTorontoCanada
  9. 9.Department of PsychiatryQueen’s UniversityKingstonCanada

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