ABSTRACT
AIMS: To assess whether glycaemic control is associated with a lifelong increased risk of fracture in people with newly diagnosed Type 1 diabetes. METHODS: People with newly diagnosed Type 1 diabetes between 1 January 1995 and 10 May 2016 were identified in The Health Improvement Network database. Longitudinal HbA1c measurements from diagnosis to fracture or study end or loss to follow-up were collected. A Cox proportional hazards model with HbA1c included as a time-dependent variable was fitted to these data. RESULTS: Some 5368 people with newly diagnosed Type 1 diabetes were included. The estimated adjusted hazard ratio (aHR) for HbA1c was statistically significant [aHR 1.007; 95% confidence interval (CI) 1.002-1.011 (mmol/mol) and aHR 1.07; 95% CI 1.03-1.12 (%)]. An incremental higher risk of fracture was observed with increasing levels of HbA1c . CONCLUSIONS: In people with newly diagnosed Type 1 diabetes, higher HbA1c is associated with an increased risk for fractures.
Subject(s)
Diabetes Mellitus, Type 1/prevention & control , Fractures, Bone/etiology , Adolescent , Adult , Blood Glucose/metabolism , Child , Diabetes Mellitus, Type 1/blood , Diabetes Mellitus, Type 1/complications , Female , Glycated Hemoglobin/metabolism , Humans , Hypoglycemic Agents/therapeutic use , Male , Retrospective Studies , Risk Factors , Treatment Outcome , United Kingdom/epidemiology , Young AdultABSTRACT
Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.
