Stata-Based Epidemiological Analysis of Age Disparities in Homelessness, Substance Use Patterns, and Buprenorphine Access

This project executed a comprehensive quantitative data analysis and multivariable regression pipeline in Stata 15 SE to investigate age-related disparities in housing stability, substance use behaviors, and medication-for-opioid-use-disorder (MOUD) access among individuals experiencing homelessness ($N = 144$).The analytical methodology followed a rigorous data-cleaning, screening, and statistical modeling workflow: Data Management & Quality Control: Imported Qualtrics survey data, applied inclusion criteria (valid age 18–90, complete survey records), converted string fields using destring, and categorized respondents into a dichotomous age variable ($0 = 18\text{--}49$ years, $n = 112$; $1 = 50+$ years, $n = 32$). Descriptive & Bivariate Inference: Computed means, standard deviations, medians, and ranges for continuous measures, and proportions for categorical variables. Evaluated group differences using independent-samples t-tests, Pearson chi-square tests, and Fisher's exact tests. To control family-wise error across multiple hypothesis testing, a Bonferroni correction ($\alpha = 0.05 / 8 = 0.00625$) was applied. Multivariable Logistic Regression: Estimated sequential logistic regression models predicting the primary binary outcome—ever accessed buprenorphine (bupe_ever)—adjusting for age group, recent opioid and stimulant use, chronic pain, and past-month depression/anxiety days. Evaluated model performance using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Variance Inflation Factors (VIF) for multicollinearity, and the Hosmer-Lemeshow goodness-of-fit test. Effect Modification Analysis: Tested interaction terms between age group and substance use categories to explore moderated pathways in treatment access.