Identification of Latent Alcohol Use Groups and Transitions over Time Using a 9-Year Follow-Up Study in the Adult General Population.

INTRODUCTION: Studies investigating latent alcohol use groups and transitions of these groups over time are scarce, while such knowledge could facilitate efficient use of screening and preventive interventions for groups with a high risk of problematic alcohol use. Therefore, the present study examines the characteristics, transitions, and long-term stability of adult alcohol use groups and explores some of the possible predictors of the transitions. METHODS: Data were used from the baseline, 3-, 6-, and 9-year follow-up waves of the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2), a representative study of Dutch adults aged 18-64 at baseline (N = 6,646; number of data points: 20,574). Alcohol consumption, alcohol use disorder (AUD), and mental disorders were assessed with the Composite International Diagnostic Interview 3.0. Latent Markov Modelling was used to identify latent groups based on high average alcohol consumption (HAAC) and AUD and to determine transition patterns of people between groups over time (stayers vs. movers). RESULTS: The best fitting model resulted in four latent groups: one nonproblematic group (91%): no HAAC, no AUD; and three problematic alcohol use groups (9%): HAAC, no AUD (5%); no HAAC, often AUD (3%); and HAAC and AUD (1%). HAAC, no AUD was associated with a high mean age (55 years) and low educational level (41%), and no HAAC, often AUD with high proportions of males (78%) and people with high educational level (46%). Eighty-seven percent of all respondents - mostly people with no HAAC, no AUD - stayed in their original group during the whole 9-year period. Among movers, people in a problematic alcohol use group (HAAC and/or AUD) mostly transitioned to another problematic alcohol use group and not to the nonproblematic alcohol use group (no HAAC, no AUD). Explorative analyses suggested that lack of physical activity possibly plays a role in transitions both from and to problematic alcohol use groups over time. CONCLUSION: The detection of three problematic alcohol use groups - with transitions mostly between the different problematic alcohol use groups and not to the group without alcohol problems - points to the need to explicitly address both alcohol consumption and alcohol-related problems (AUD criteria) in screening measures and interventions in order not to miss and to adequately treat all problematic alcohol users. Moreover, explorative findings suggest that prevention measures should also include physical activity.

Prediction of recovery in trauma patients using Latent Markov models.

PURPOSE: Patients' expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics. METHODS: The data of a cohort of adult trauma patients until the age of 75 years with a length of hospital stay of 3 days and more were derived from the Brabant Injury Outcome Surveillance (BIOS) study. The EuroQol-5D 3-level version and the Health Utilities Index were used 1 week, and 1, 3, 6, 12, and 24 months after injury. Four prediction models, for mobility, pain, self-care, and daily activity, were developed using LMMs with ordinal latent states and patient characteristics as predictors for the latent states. RESULTS: In total, 1107 patients were included. Four models with three ordinal latent states were developed, with different covariates in each model. The prediction of the (ordinal) latent states in the LMMs yielded pseudo-R2 values between 40 and 53% and between 21 and 41% (depending of the type R2 used) and classification errors between 24 and 40%. Most patients seem to recover fast as only about a quarter of the patients remain with severe problems after 1 month. CONCLUSION: The use of LMMs to model the development of physical function post-injury is a promising way to obtain a prediction of the physical recovery. The step-by-step prediction fits well with the outpatient follow-up and it can be used to inform the patients more tailor-made to manage the expectations.