ABSTRACT
INTRODUCTION: For older men who undergo bone mineral density (BMD) testing, the optimal osteoporosis screening schedule is unknown. Time-to-disease estimates are necessary to inform screening intervals. METHODS: A prospective cohort study of 5,415 community-dwelling men aged ≥65 years without hip or clinical vertebral fracture or antifracture treatment at baseline was conducted. Participants had concurrent BMD and fracture follow-up between 2000 and 2009, and additional fracture follow-up through 2014. Data were analyzed in 2015. Time to incident osteoporosis (lowest T-score ≤ -2.50) for men without baseline osteoporosis, and time to hip or clinical vertebral fracture or major osteoporotic fracture for men without or with baseline osteoporosis, were estimated. RESULTS: Nine men (0.2%) with BMD T-scores >-1.50 at baseline developed osteoporosis during follow-up. The adjusted estimated time for 10% to develop osteoporosis was 8.5 (95% CI=6.7, 10.9) years for those with moderate osteopenia (lowest T-score, -1.50 to -1.99) and 2.7 (95% CI=2.1, 3.4) years for those with advanced osteopenia (lowest T-score, -2.00 to -2.49) at baseline. The adjusted times for 3% to develop a first hip or clinical vertebral fracture ranged from 7.1 (95% CI=6.0, 8.3) years in men with baseline T-scores > -1.50 to 1.7 (95% CI=1.0, 3.1) years in men with baseline osteoporosis. CONCLUSIONS: Men aged 65 years and older with femoral neck, total hip, and lumbar spine BMD T-scores >-1.50 on a first BMD test were very unlikely to develop osteoporosis during follow-up. Additional BMD testing may be most informative in older men with T-scores ≤-1.50.
Subject(s)
Models, Statistical , Osteoporosis/complications , Osteoporotic Fractures/physiopathology , Aged , Bone Density/physiology , Humans , Male , Osteoporosis/diagnosis , Osteoporotic Fractures/etiology , Prospective Studies , Risk Factors , Time FactorsABSTRACT
Mixture structural equation model with regime switching (MSEM-RS) provides one possible way of representing over-time heterogeneities in dynamic processes by allowing a system to manifest qualitatively or quantitatively distinct change processes conditional on the latent "regime" the system is in at a particular time point. Unlike standard mixture structural equation models such as growth mixture models, MSEM-RS allows individuals to transition between latent classes over time. This class of models, often referred to as regime-switching models in the time series and econometric applications, can be specified as regime-switching mixture structural equation models when the number of repeated measures involved is not large. We illustrate the empirical utility of such models using one special case-a regime-switching bivariate dual change score model in which two growth processes are allowed to manifest regime-dependent coupling relations with one another. The proposed model is illustrated using a set of longitudinal reading and arithmetic performance data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 study (ECLS-K; U.S. Department of Education, National Center for Education Statistics, 2010).
