Literature DB >> 17202004

Predicting costs of mental health care: a critical literature review.

Julia Jones1, Francesco Amaddeo, Corrado Barbui, Michele Tansella.   

Abstract

BACKGROUND: Cost evaluation research in the mental health field is being increasingly recognized as a way to achieve a more effective deployment of scarce resources. However, there is a paucity of studies that seek to identify predictors of psychiatric service utilization and costs. This paper aims to critically review the published research in the field of psychiatric service utilization and costs, and discusses current methodological developments in this field.
METHOD: Sixteen studies were identified and are critically reviewed.
RESULTS: No single variable alone can explain variations in costs between patients; instead, a range of different clinical and non-clinical variables provides a greater explanation of cost variations. Having a history of previous psychiatric service use is the most consistent predictor of higher psychiatric costs. Only one study considers indirect costs incurred by users, their families and friends and society as a whole, with the remaining 15 studies focusing on direct mental health care costs. There is a lack of studies that consider the future psychiatric service utilization and costs of care of children and older people. The cross-validation of predictive models is not yet routine, with only four of the studies including a cross-validation procedure.
CONCLUSIONS: The predictive approach in mental health cost evaluation has relevance for both mental health policy and practice. However, there is a paucity of studies that focus on children, older people and indirect costs. Furthermore, there remain a number of methodological challenges to address.

Entities:  

Mesh:

Year:  2007        PMID: 17202004     DOI: 10.1017/S0033291706009676

Source DB:  PubMed          Journal:  Psychol Med        ISSN: 0033-2917            Impact factor:   7.723


  8 in total

1.  Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

Authors:  Akritee Shrestha; Savannah Bergquist; Ellen Montz; Sherri Rose
Journal:  Health Serv Res       Date:  2017-12-15       Impact factor: 3.402

2.  Computational health economics for identification of unprofitable health care enrollees.

Authors:  Sherri Rose; Savannah L Bergquist; Timothy J Layton
Journal:  Biostatistics       Date:  2017-10-01       Impact factor: 5.899

3.  The impact of nonclinical factors on care use for patients with depression: a STAR*D report.

Authors:  T Michael Kashner; Madhukar H Trivedi; Annie Wicker; Maurizio Fava; Stephen R Wisniewski; A John Rush
Journal:  CNS Neurosci Ther       Date:  2009-08-27       Impact factor: 5.243

4.  Design of a quasi-experiment on the effectiveness and cost-effectiveness of using the child-interview intervention during the investigation following a report of child abuse and/or neglect.

Authors:  Froukje Snoeren; Cees Hoefnagels; Francien Lamers-Winkelman; Paul Baeten; Silvia M A A Evers
Journal:  BMC Public Health       Date:  2013-12-11       Impact factor: 3.295

5.  Predicting psychiatric inpatient costs.

Authors:  Ramon Sabes-Figuera; Paul McCrone; Emese Csipke; Tom K J Craig; Diana Rose; Bina Sharma; Til Wykes
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2015-12-18       Impact factor: 4.328

6.  Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis.

Authors:  Vincent Bremer; Dennis Becker; Spyros Kolovos; Burkhardt Funk; Ward van Breda; Mark Hoogendoorn; Heleen Riper
Journal:  J Med Internet Res       Date:  2018-08-21       Impact factor: 5.428

Review 7.  Case-Mix Classification for Mental Health Care in Community Settings: A Scoping Review.

Authors:  Nam Tran; Jeffrey W Poss; Christopher Perlman; John P Hirdes
Journal:  Health Serv Insights       Date:  2019-08-05

8.  Supply factors as determinants of treatment costs: clinicians' assessments of a given set of referrals to community mental health centers in Norway.

Authors:  Knut Reidar Wangen; Sverre Grepperud
Journal:  BMC Health Serv Res       Date:  2018-01-30       Impact factor: 2.655

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.