Commitment Strength, Alcohol Dependence and Healthcall Participation: Effects On Drinking Reduction in HIV Patients

Background: The role of three factors in drinking outcome after brief intervention among heavily drinking HIV patients were investigated: strength of commitment to change drinking, alcohol dependence, and treatment type: brief Motivational Interview (MI) only, or MI plus HealthCall, a technological extension of brief intervention. Methods: HIV primary care patients (N= 139) who drank ≥4 drinks at least once in the 30 days before study entry participated in MI-only or MI. +. HealthCall in a randomized trial to reduce drinking. Patients were 95.0% minority; 23.0% female; 46.8% alcohol dependent; mean age 46.3. Outcome at end of treatment (60 days) was drinks per drinking day (Timeline Follow-Back). Commitment strength (CS) was rated from MI session recordings. Results: Overall, stronger CS predicted end-of-treatment drinking (p\u3c. .001). After finding an interaction of treatment, CS and alcohol dependence (p= .01), we examined treatment. ×. CS interactions in alcohol dependent and non-dependent patients. In alcohol dependent patients, the treatment. ×. commitment strength interaction was significant (p= .006); patients with low commitment strength had better outcomes in MI. +. HealthCall than in MI-only (lower mean drinks per drinking day; 3.5 and 4.6 drinks, respectively). In non-dependent patients, neither treatment nor CS predicted outcome. Conclusions: Among alcohol dependent HIV patients, HealthCall was most beneficial in drinking reduction when MI ended with low commitment strength. HealthCall may not merely extend MI effects, but add effects of its own that compensate for low commitment strength. Thus, HealthCall may also be effective when paired with briefer interventions requiring less skill, training and supervision than MI. Replication is warranted

Comparing mental and physical health of U.S. veterans by VA healthcare use: implications for generalizability of research in the VA electronic health records

ObjectiveThe Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not.MethodsWe analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use.ResultsAmong veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40).ConclusionsHeterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population