193P
Older Adults Who Drive Under the Influence of Alcohol or Drugs: Risk Profiles

Schedule:
Friday, January 16, 2015
Bissonet, Third Floor (New Orleans Marriott)
* noted as presenting author
Namkee G. Choi, PhD, Louis and Ann Wolens Centennial Chair in Gerontology, University of Texas at Austin, Austin, TX
Diana M. DiNitto, PhD, Cullen Trust Centennial Professor in Alcohol Studies and Education, University of Texas at Austin, Austin, TX
C. Nathan Marti, PhD, Lecturer, University of Texas at Austin, Austin, TX
Background and Purpose: Previous studies of driving safety among older adults have focused primarily on age-related declines in cognitive, functional, and visual capacities. Few have considered one of the major sources of preventable accidents—driving under the influence of alcohol and/or drugs (DUI). More than half of individuals aged 50+ use alcohol and/or illicit drugs, and the rates of substance use and misuse are projected to substantially increase in the future when the baby boomers swell the ranks of older adults. Based on theories of alcohol (drug) expectancy and psychosocial vulnerability, the purpose of this study was to identify subgroups of older adults at risk of DUI, with alcohol and illicit drug use patterns and co-occurring mental disorders as indicators.

Methods: Data for this study came from the public use files of the 2008 to 2012 National Survey on Drug Use and Health. The study sample (age 50+) from the multi-year data set was 29,634. Descriptive analyses were conducted with Stata/MP 13’s svy function to take into account NSDUH’s multi-stage, stratified sampling design. Latent class analysis (LCA) implemented in Mplus 7.11 was used to identify unobserved subgroups in the sample. We evaluated models with one to six latent classes using average class probabilities, entropy, the Lo-Mendell-Rubin adjusted likelihood ratio test, and visual inspection of the data to determine the most parsimonious LCA model, which was determined to be the three class model. Multinomial logistic regression was used to further evaluate risk profiles in the three-class model.

Results: The three classes that emerged were (1) high DUI-risk group (13.29%) with high substance use but low mental health problems; (2) medium DUI-risk group (6.11%) with medium substance use but high mental health problems; and (3) no/low-risk group (80.60%). Analysis found that 33.04% of the high-risk group and 12.45% of the medium risk group met criteria for DSM-IVdiagnoses of past-year alcohol dependence or abuse, and 43.32% and 26.57% in each group used illicit drugs. Although 34.47% of the high-risk group and 11.40% of the medium-risk group perceived the need for treatment, only 3.46% and 5.90% in each group received any substance abuse treatment during the preceding year. Regarding mental health problems, 3.36% and 2.66% of the high-risk group, compared to 32.80% and 21.20% of the medium-risk group had an anxiety disorder and serious suicidal ideation, respectively. Sociodemographic data show that the low-risk group was the most advantaged in education and income and the medium-risk group was the most disadvantaged of all.

Conclusions and Implications: The implications of the findings are: (1) Access to evidence-based treatment/interventions should be facilitated for older substance abusers in general and DUI risk groups in particular; (2) Addressing mental health problems may also reduce substance use and promote driving safety among those at risk of DUI; (3) Protocols for assessing older adults’ driving safety need to include screening for substance abuse and comorbid mental health conditions in addition to assessing motor, visuo-perceptual, and cognitive functioning; and (4) Future DUI research should be expanded to include at-risk older adults.