Evidence map›Paper›PMID 28595642›Full record

ArticleSubstance abuse treatment, prevention, and policy2017

Adherence to methadone maintenance treatment and associated factors among patients in Vietnamese mountainside areas.

Long Hoang Nguyen, Huong Thu Thi Nguyen, Huong Lan Thi Nguyen, Bach Xuan Tran, Carl A Latkin

Abstract read
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Article in Substance abuse treatment, prevention, and policy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

31 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Long Hoang NguyenSchool of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam.
Huong Thu Thi NguyenInstitute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
Huong Lan Thi NguyenInstitute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam. lanhuong.hmu@gmail.com.
Bach Xuan TranInstitute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
Carl A LatkinJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedication adherence is essential to achieve successful methadone maintenance treatment (MMT). However, treatment adherence among MMT patients in the mountainous setting in Vietnam has not been yet investigated. This study aimed to explore the medication adherence and associated factors in MMT patients in Tuyen Quang, a mountainous province.

methodsA cross-sectional survey was conducted in two MMT clinics namely Tuyen Quang and Son Duong. Convenience sampling method was used to recruit patients. Adherence to MMT was assessed by using three questions: 1) number of days that they missed doses in the last 4 days; 2) whether they missed doses during the last weekend and 3) when they missed a dose within the last 3 months. Adherence was considered optimal if patients reported 'no' to three questions. Socioeconomic status, health status (measured by EuroQol-5 Dimensions - 5 Levels - EQ5D5L and Visual analogue scale - VAS), substance use and abuse and methods to support adherence were also collected.

resultsAmong 241 patients, 34.4% reported optimal adherence. Self-help was the most popular (89.2%) method used to support adherence. Risk factors of missing doses and suboptimal adherence included higher education and economic status; being a worker/farmer; longer duration of treatment; and suffering pain/discomfort and anxiety/depression. Protective factors were older age, having problems in usual activities/self-care, higher EQ-VAS and EQ-5D index; and reminded by mobile phone and family members.

conclusionsThis study found a high sub-optimal adherence rate among MMT patients in a mountainous setting in Vietnam. Measuring adherence by using several simple items could be used periodically to monitor the treatment adherence in the clinical setting. Family and mobile phone support would have a potential role in supporting patients to adhere treatment.

Indexed as

Rural PopulationAdultAnalgesics, OpioidCross-Sectional StudiesFemaleHumansMaleMedication AdherenceMethadoneMiddle AgedOpiate Substitution TreatmentRisk FactorsSubstance-Related DisordersUrban PopulationVietnamYoung AdultAnalgesics, OpioidMethadoneAdherenceMethadoneMMTMountainousVietnam

Identifiers

PMID28595642
PMCPMC5465686

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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.