Adherence to Treatment and Outcomes in Older Adults with Substance Use Disorders
Description
The dataset comprises anonymized clinical and sociodemographic data of patients aged 50 years and older who were evaluated at the Alcohol and Drug Addiction Research, Treatment, and Education Center (AMATEM) between June 1, 2021, and June 30, 2025, in both outpatient and inpatient settings. A structured sociodemographic and clinical data collection form was developed and completed using information obtained from patient medical records and the hospital information system, and the dataset was compiled in SPSS format. The dataset includes variables related to sociodemographic characteristics (e.g., age, sex, marital status, education, employment, and housing conditions), substance use history (age at first use, primary and additional substances, route of administration, duration and recency of use, and intravenous use), treatment characteristics (outpatient and inpatient treatment history, number and duration of hospitalizations, and pharmacological treatments including agonist and antagonist therapies), psychiatric and medical comorbidities, and risk-related behaviors (self-harm and suicide attempts). Patients who had completed a minimum follow-up period of 6 months by the end of the data collection period were eligible for follow-up analyses. Based on follow-up duration, evaluations were conducted at two time points: (1) the first 6 months following the initial presentation and (2) the 6–12 months post-presentation period. At each follow-up interval, patients were classified as adherent if they attended three or more outpatient visits, and as non-adherent if they attended fewer than three visits. Demographic and clinical characteristics were subsequently compared between adherent and non-adherent patients within each follow-up period. It was hypothesized that treatment adherence among older adults with substance use disorders would be higher during the early follow-up period and would vary according to time-dependent clinical and substance-related factors. All variables have been translated into English, and a codebook is provided describing variable names, definitions, value labels, and measurement levels. The dataset contains no direct or indirect personal identifiers and complies with ethical standards for research involving human participants. Ethical approval was obtained from the relevant institutional review board, and data sharing is restricted to research and educational purposes.
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Steps to reproduce
The dataset can be reproduced by importing the SPSS (.sav) file into IBM SPSS Statistics. Variable definitions, coding, and measurement levels are provided in the accompanying codebook. Treatment adherence is operationalized based on outpatient follow-up attendance (three or more visits vs. fewer than three visits) and evaluated separately for the 0–6 month and 6–12 month follow-up periods. Descriptive statistics and group comparisons can be conducted in line with the analytical approach below and also reported in the main manuscript: Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as mean ± standard deviation or median (25th–75th percentiles), depending on their distribution, while categorical variables were presented as frequencies and percentages. The normality of continuous variables was tested with the Shapiro–Wilk and Kolmogorov–Smirnov tests. For comparisons between two independent groups, Student’s t-test was used when data were normally distributed, and the Mann–Whitney U test was applied for non-normally distributed data. Categorical variables were compared using the Pearson chi-square test; when expected cell counts were low, either Yates continuity correction or Fisher’s exact chi-square test was used. To identify factors related to the dependent variable, binary logistic regression analysis was performed. Variables for the regression model were selected based on clinical expertise and their appropriateness given the sample size, and they were entered using the enter method. Results are presented as adjusted odds ratios (AORs) with 95% confidence intervals. The model’s ability to discriminate was assessed using accuracy and the area under the receiver operating characteristic curve (Area Under the Receiver Operating Characteristic Curve, AUC). All statistical tests were two-tailed, and a p-value <0.05 was considered statistically significant.