The effects of rurality on substance use disorder diagnosis: A multiple-groups latent class analysis

Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities.

Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership.

Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P value<0.0001). Overall the rural class structure exhibited less diversity in class structure and lower prevalence of SUD in multiple drug categories (e.g. cocaine, hallucinogens, and stimulants).

This result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities.