Predicting psychiatric readmission: sex-specific models to predict 30-day readmission following acute psychiatric hospitalization
- 452 Downloads
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.
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.
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).
Certain key predictors of psychiatric readmission differ by sex. This knowledge may help to reduce psychiatric hospital readmission rates by focusing interventions.
KeywordsPsychiatric readmission Psychiatric epidemiology Sex-based analysis Sex differences
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
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.
- 2.Canadian Institute for Health Information (2011) Health indicators 2011. Canadian Institute for Health Information, OttawaGoogle Scholar
- 3.Heslin KC, Weiss AJ (2015) Hospital readmissions involving psychiatric disorders, 2012. HCUP Statistical Brief #189Google Scholar
- 6.Vigod SN, Kurdyak PA, Seitz D, Herrmann N, Fung K, Lin E, Perlman C, Taylor VH, Rochon PA, Gruneir A (2015) READMIT: a clinical risk index to predict 30-day readmission after discharge from acute psychiatric units. J Psychiatr Res 61:205–213. doi: 10.1016/j.jpsychires.2014.12.003 CrossRefPubMedGoogle Scholar
- 8.Vigod SN, Kurdyak P, Fung K, Gruneir A, Herrmann N, Hussain-Shamsy N, Isen M, Lin E, Rochon P, Taylor VH, Seitz D (2016) Psychiatric hospitalizations: a comparison by gender, sociodemographics, clinical profile, and postdischarge outcomes. Psychiatr Serv 67:1376–1379. doi: 10.1176/appi.ps.201500547 CrossRefPubMedGoogle Scholar
- 13.Wizemann TM (2012) Sex-specific reporting of scientific research: a workshop summary. Institute of Medicine of the National Academies, Washington, DCGoogle Scholar
- 17.American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (4th edn., text rev.). doi: 10.1176/appi.books.9780890423349
- 19.John Hopkins Bloomberg School of Public Health (2009) The Johns Hopkins ACG System Technical Reference Guide, Version 9.0. The John Hopkins University, BaltimoreGoogle Scholar
- 23.Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. L. Erlbaum Associates, HillsideGoogle Scholar
- 25.Allison P (2013) Why I don’t trust the Hosmer-Lemeshow test for logistics regression. Stat Horizons. https://statisticalhorizons.com/hosmer-lemeshow
- 27.Donisi V, Tedeschi F, Percudani M, Fiorillo A, Confalonieri L, De Rosa C, Salazzari D, Tansella M, Thornicroft G, Amaddeo F (2013) Prediction of community mental health service utilization by individual and ecological level socio-economic factors. Psychiatry Res 209:691–698. doi: 10.1016/j.psychres.2013.02.031 CrossRefPubMedGoogle Scholar
- 30.Nawka A, Kalisova L, Raboch J, Giacco D, Cihal L, Onchev G, Karastergiou A, Solomon Z, Fiorillo A, Del Vecchio V, Dembinskas A, Kiejna A, Nawka P, Torres-Gonzales F, Priebe S, Kjellin L, Kallert TW (2013) Gender differences in coerced patients with schizophrenia. BMC Psychiatry 13:257. doi: 10.1186/1471-244x-13-257 CrossRefPubMedPubMedCentralGoogle Scholar