ArticleBMC health services research2020
An economic evaluation of a mobile text messaging intervention to improve mental health care in resource-poor communities in China: a cost-effectiveness study.
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 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Precision Mental Healthcare: Identifying Service Preferences Through Discrete-Choice Experiments in Chinese Megacities.Administration and policy in mental health · 2025Article
- Association ofNeuropsychiatric disease and treatment · 2024Article
- Economic and Performance Evaluation of E-Health before and after the Pandemic Era: A Literature Review and Future Perspectives.International journal of environmental research and public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundSevere mental disorders, a leading cause of disability has become a major public health problem. In order to promote mental health, a series of programs have been promulgated by the Chinese government. However, economic evaluations of such programs are lacking. The purpose of this study is to develop and validate an economic model to assess the cost and health outcomes of the LEAN (Lay health supporters, E-platform, Award, and iNtegration) program, and to perform an economic evaluation of LEAN versus the nationwide community-based mental health program that provides free antipsychotic medications.
methodsA cost-effectiveness and cost-utility analysis of the LEAN intervention will be performed. A Markov model will be developed, validated and used to assess and compare the costs and outcomes for the LEAN intervention versus nationwide community-based mental health program. The calculated sample size is 258 participants for the analysis. A societal perspective will be applied with the time horizon of 1-year after the termination of the LEAN program. The cost-utility will be measured primarily using Quality Adjusted Life Years and the cost-effectiveness will be measured using number of relapses and number of re-hospitalizations avoided 6-month after the intervention. Univariate and probabilistic sensitivity analysis will be conducted for the analysis of uncertainty. DISCUSSION: If proven cost-effective, this study will contribute to the nationwide implementation of the program, not only for schizophrenia but for all kind of severe mental disorders. Markov model developed as part of the study will benefit potential researchers in analyzing cost-effectiveness of other programs. The Chinese context of the study may limit the generalizability of the study results to some extent.
trial registrationThis study was registered in a Chinese Clinical Trial Registry ( ChiCTR2000034962 ) on 25 July 2020.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.