Evidence map›Paper›PMID 32188440›Full record

ArticleBMC health services research2020

Identification of psychiatric patients with high mortality and low medical utilization: a population-based propensity score-matched analysis.

Jong-Yi Wang, Cheng-Chen Chang, Meng-Chen Lee, Yi-Jhen Li

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Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Jong-Yi WangDepartment of Health Services Administration, China Medical University, No. 91, Xueshi Rd., North Dist, Taichung City, 40402, Taiwan.
Cheng-Chen ChangDepartment of Psychiatry, Changhua Christian Hospital, 135 Nanxiao St., Changhua City, Changhua County, 50006, Taiwan.
Meng-Chen LeeDepartment of Medical Affairs, Landseed International Hospital, No. 77, Guangtai Rd., Pingzhen Dist, Taoyuan City, 32449, Taiwan.
Yi-Jhen LiDepartment of Health Services Policy and Management, University of South Carolina, 915 Greene St, Columbia, SC, 29208, USA. yjwendyli@gmail.com.

Funding

China Medical University, Taiwan CMU107-S-18China Medical University, Taiwan CMU108-MF-103Ministry of Science and Technology, Taiwan MOST105-2410-H-039-008-MY2Ministry of Science and Technology, Taiwan MOST108-2410-H-039-001
6 · The paper itself

Abstract

backgroundThe decreased life expectancy and care costs of mental disorders could be enormous. However, research that compares mortality and utilization concurrently across the major category of mental disorders is absent. This study investigated all-cause mortality and medical utilization among patients with and without mental disorders, with an emphasis on identifying the psychiatric category of high mortality and low medical utilization.

methodsA total of 570,250 individuals identified from the 2002-2013 Taiwan National Health Insurance Reearch Database consistuted 285,125 psychiatric patients and 285,125 non-psychiatric peers through 1:1 dual propensity score matching (PSM). The expenditure survival ratio (ESR) was proposed to indicate potential utilization shortage. The category of mental disorders and 13 covariates were analyzed using the Cox proportional hazard model and general linear model (GLM) through SAS 9.4.

resultsPSM analyses indicated that mortality and total medical expenditures per capita were both significantly higher in psychiatric patients than those in non-psychiatric patients (all P <.0.0001). Patients with substance use disorders were reported having the youngest ages at diagnosis and at death, with the highest 25.64 of potential years of life loss (YPLL) and relevant 2904.89 of ESR. Adjusted Cox model and GLM results indicated that, compared with anxiety disorders, affective disorders and substance use disorders were significantly associated with higher mortality (HR = 1.246 and 1.064, respectively; all P < 0.05); schizophrenia was significantly associated with higher total medical expenditures per capita (P < 0.0001). Thirteen additional factors were significantly associated with mortality or utilization (all P < 0.05).

conclusionSubstance use disorders are the category of highest YPLL but notably in insufficient utilization. Health care utilization in patients with substance use disorders should be augmented timely after the diagnosis, especially toward home and community care. The factors related to mortality and utilization identified by this study merit clinical attention.

Indexed as

Patient Acceptance of Health CareAdolescentAdultAgedDatabases, FactualFemaleHealth ExpendituresHumansMaleMental DisordersMiddle AgedNational Health ProgramsPropensity ScoreProportional Hazards ModelsRetrospective StudiesSchizophreniaCategory of mental disordersExpenditure survival ratioMedical utilizationMortalityPotential years of life loss

Identifiers

PMID32188440
PMCPMC7081626

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.