Evidence map›Paper›PMID 28076648›Full record

ArticleRevista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)

Latent class profile of psychiatric symptoms and treatment utilization in a sample of patients with co-occurring disorders.

Luis Villalobos-Gallegos, Rodrigo Marín-Navarrete, Calos Roncero, Hugo González-Cantú

Abstract read
In one paragraph

Article in Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Luis Villalobos-GallegosUnidad de Ensayos Clínicos en Adicciones y Salud Mental, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Mexico City, Mexico.
Rodrigo Marín-NavarreteUnidad de Ensayos Clínicos en Adicciones y Salud Mental, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Mexico City, Mexico.
Calos RonceroUnidad de Adicciones y Patología Dual, Servicio de Psiquiatría, Hospital Universitario Vall d'Hebrón-Agencia de Salud Pública de Barcelona (ASPB), Centro de Investigación Biomédica en Red-Área Temática de Salud Mental (CIBERSAM).
Hugo González-CantúClínica de Trastornos Adictivos, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Mexico City, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify symptom-based subgroups within a sample of patients with co-occurring disorders (CODs) and to analyze intersubgroup differences in mental health services utilization.

methodsTwo hundred and fifteen patients with COD from an addiction clinic completed the Symptom Checklist 90-Revised. Subgroups were determined using latent class profile analysis. Services utilization data were collected from electronic records during a 3-year span.

resultsThe five-class model obtained the best fit (Bayesian information criteria [BIC] = 3,546.95; adjusted BIC = 3,363.14; bootstrapped likelihood ratio test p < 0.0001). Differences between classes were quantitative, and groups were labeled according to severity: mild (26%), mild-moderate (28.8%), moderate (18.6%), moderate-severe (17.2%), and severe (9.3%). A significant time by class interaction was obtained (chi-square [χ2[15]] = 30.05, p = 0.012); mild (χ2[1] = 243.90, p < 0.05), mild-moderate (χ2[1] = 198.03, p < 0.05), and moderate (χ2[1] = 526.77, p < 0.05) classes displayed significantly higher treatment utilization.

conclusionThe classes with more symptom severity (moderate-severe and severe) displayed lower utilization of services across time when compared to participants belonging to less severe groups. However, as pairwise differences in treatment utilization between classes were not significant between every subgroup, future studies should determine whether subgroup membership predicts other treatment outcomes.

Indexed as

AdolescentAdultBayes TheoremDiagnostic and Statistical Manual of Mental DisordersFemaleHumansMaleMental DisordersMental Health ServicesMiddle AgedModels, PsychologicalSeverity of Illness IndexSocioeconomic FactorsSubstance-Related DisordersSymptom AssessmentYoung Adult

Identifiers

PMID28076648
PMCPMC7111413

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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.