Evidence map›Paper›PMID 37097718›Full record

ArticleJMIR research protocols2023

A Computational Platform to Support the Detection, Follow-up, and Epidemiological Surveillance of Mental Health and Substance Use Disorders: Protocol for a Development and Evaluation Study.

Juan Martínez-Miranda, Martha Janet Meza Magallanes, Cándido Silva-Peña, Martha Xitlali Mercado Rivas, María Del Rocío Figueroa-Varela, Magda Lidiana Sánchez Aranda

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Juan Martínez-Miranda *Unidad de Transferencia Tecnológica Tepic, Centro de Investigación Científica y de Educación Superior de Ensenada, Tepic, Mexico.ORCID https://orcid.org/0000-0001-5081-8212
Martha Janet Meza Magallanes *Centro de Atención Primaria en Adicciones, Servicios de Salud de Nayarit, Tepic, Mexico.ORCID https://orcid.org/0009-0001-9383-0980
Cándido Silva-Peña *Unidad Académica de Ciencias Sociales, Universidad Autónoma de Nayarit, Tepic, Mexico.ORCID https://orcid.org/0009-0005-6606-4482
Martha Xitlali Mercado Rivas *Unidad Académica de Medicina, Universidad Autónoma de Nayarit, Tepic, Mexico.ORCID https://orcid.org/0000-0003-4571-9433
María Del Rocío Figueroa-Varela *Unidad Académica de Ciencias Sociales, Universidad Autónoma de Nayarit, Tepic, Mexico.ORCID https://orcid.org/0000-0003-0858-383X
Magda Lidiana Sánchez Aranda *Unidad Académica de Ciencias Sociales, Universidad Autónoma de Nayarit, Tepic, Mexico.ORCID https://orcid.org/0000-0002-3064-9105
Universidad Autónoma de Nayarit · MXCentro de Investigación Científica y de Educación Superior de Ensenada · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccording to the World Health Organization, approximately 15% of the global population is affected by mental health or substance use disorders. These conditions contribute significantly to the global disease burden, which has worsened because of the direct and indirect effects of COVID-19. In Mexico, a quarter of the population between the ages of 18 and 65 years who reside in urban areas present a mental health condition. The presence of a mental or substance abuse disorder is behind a significant percentage of suicidal behaviors in Mexico, where only 1 in 5 of those who have these disorders receive any treatment.

objectiveThis study aims to develop, deploy, and evaluate a computational platform to support the early detection and intervention of mental and substance use disorders in secondary and high schools as well as primary care units. The platform also aims to facilitate monitoring, treatment, and epidemiological surveillance ultimately helping specialized health units at the secondary level of care.

methodsThe development and evaluation of the proposed computational platform will run during 3 stages. In stage 1, the identification of the functional and user requirements and the implementation of the modules to support the screening, follow-up, treatment, and epidemiological surveillance will be performed. In stage 2, the initial deployment of the screening module will be carried out in a set of secondary and high schools, as well as the deployment of the modules to support the follow-up, treatment, and epidemiological surveillance processes in primary and secondary care health units. In parallel, during stage 2, patient applications to support early interventions and continuous monitoring will also be developed. Finally, during stage 3, the deployment of the complete platform will be performed jointly with a quantitative and qualitative evaluation.

resultsThe screening process has started, and 6 schools have been currently enrolled. As of February 2023, a total of 1501 students have undergone screening, and the referral of those students presenting a risk in mental health or substance use to primary care units has also started. The development, deployment, and evaluation of all the modules of the proposed platform are expected to be completed by late 2024.

conclusionsThe expected results of this study are to impact a better integration between the different levels of health care, from early detection to follow-up and epidemiological surveillance of mental and substance use disorders contributing to reducing the gap in the attention to these problems in the community. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44607.

Indexed as

addictaddictioncomputational platformdigital screeningepidemiological surveillanceintravenous drugIVDUlevel of health caremental healthmental health and addictionmHealthopioidsubstance abusesubstance usesubstance use disordersuicidal behavior

Identifiers

PMID37097718
PMCPMC10170360
OpenAlexW4366985211

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.