We evaluated a Veterans Health Administration (VHA) care coordination/ home-telehealth (CC/HT) program on the utilization of health care services and health-related quality of life (HRQL) in veterans with diabetes. Administrative records of 445 veterans with diabetes were reviewed to compare health care service utilization in the 1-year period before and 1-year period post-enrollment and also examined self-reported HRQL at enrollment and 1 year later. Multivariate analyses indicated a statistically significant reduction in the proportion of patients who were hospitalized (50% reduction), emergency room use (11% reduction), reduction in the average number of bed days of care (decreased an average of 3.0 days), and improvement in the HRQL role-physical functioning, bodily pain, and social functioning. The results need to be interpreted with caution because we used a single-group study design that may be influenced by regression to the mean. Ideally, future research should use a randomized controlled trial design.

We assessed the utilization of health-care services and clinical outcomes in veterans with diabetes who were enrolled in two care coordination/home telehealth programmes. One group of patients was monitored weekly (n = 197), with more intensive evaluations, while the other was monitored daily (n = 100), but less intensively. Although patients in the two groups were fairly similar in demographic terms and in their clinical characteristics at baseline, they had different service utilization patterns during the 12-month pre-enrollment period. Over the 12-month study period, the proportion of one or more hospital admissions and number of bed days of care decreased in the daily monitoring group, and increased in the weekly monitoring group, more or less doubling in the former and being halved in the latter. Unscheduled primary care clinic visits were lower in the daily monitored group than in the weekly monitored group. The differences between the two groups were significant (P < 0.01). There were no significant differences between the groups in the clinical outcomes. Future research should employ randomized controlled trial designs to determine if intensities of home monitoring lead to differences in service utilization and health outcomes.

Generalized linear models with random effects are often used to explain the serial dependence of longitudinal categorical data. Marginalized random effects models (MREMs) permit likelihood-based estimations of marginal mean parameters and also explain the serial dependence of longitudinal data. In this paper, we extend the MREM to accommodate multivariate longitudinal binary data using a new covariance matrix with a Kronecker decomposition, which easily explains both the serial dependence and time-specific response correlation. A maximum marginal likelihood estimation is proposed utilizing a quasi-Newton algorithm with quasi-Monte Carlo integration of the random effects. Our approach is applied to analyze metabolic syndrome data from the Korean Genomic Epidemiology Study for Korean adults.

The SMS differentiated between passing and failing drivers and can be used to inform clinical decision making.