Family based spatial correlation model

In spatial data analysis, linear, count or binary responses are collected from a large sequence of (spatial) locations. This type of responses from the (spatial) locations may be influenced by certain fixed covariates associated to the location itself as well as certain invisible random effects from the members of the neighboring locations. Also the responses may be subject to certain model errors. In familial/ clustered setup, responses are collected from the members of a large number of independent families, where the pairwise responses within the family are correlated. In a spatial set up, the pairwise responses within a family of locations are correlated similar to the familial setup, but unlike in the familial setup, the responses from neighboring families will also be correlated. In this thesis, unlike in the existing studies, we develop a moving or band correlation structure that reflects the correlations for within and between families. This is done first for linear (continuous) data and then for binary responses. As far as the inference are concerned, we discuss method of moments (MM) and maximum likelihood (ML) approach for the estimation of parameters in linear mixed model setup. Because the exact likelihood estimation approach for the spatial binary models is complicated, we demonstrate how to use the generalized quasi-likelihood ( GQL) approach for such models

Six-year time-trend analysis of dyslipidemia among adults in Newfoundland and Labrador: findings from the laboratory information system between 2009 and 2014

Background: Dyslipidemia, an increased level of total cholesterol (TC), triglycerides (TG), low-density-lipoprotein cholesterol (LDL-C) and decreased level of high-density-lipoprotein cholesterol (HDL-C), is one of the most important risk factors for cardiovascular disease. We examined the six-year trend of dyslipidemia in Newfoundland and Labrador (NL), a Canadian province with a historically high prevalence of dyslipidemia. Methods: A serial cross-sectional study on all of the laboratory lipid tests available from 2009 to 2014 was performed. Dyslipidemia for every lipid component was defined using the Canadian Guidelines for the Diagnosis and Treatment of Dyslipidemia. The annual dyslipidemia rates for each component of serum lipid was examined. A fixed and random effect model was applied to adjust for confounding variables (sex and age) and random effects (residual variation in dyslipidemia over the years and redundancies caused by individuals being tested multiple times during the study period). Results: Between 2009 and 2014, a total of 875,208 records (mean age: 56.9 ± 14.1, 47.6% males) containing a lipid profile were identified. The prevalence of HDL-C and LDL-C dyslipidemia significantly decreased during this period (HDL-C: 35.8% in 2009 [95% CI 35.5-36.1], to 29.0% in 2014 [95% CI: 28.8-29.2], P = 0.03, and LDL-C: 35.2% in 2009 [95% CI: 34.9-35.4] to 32.1% in 2014 [95% CI: 31.9-32.3], P = 0.02). A stratification by sex, revealed no significant trend for any lipid element in females; however, in men, the previously observed trends were intensified and a new decreasing trend in dyslipidemia of TC was appeared (TC: 34.1% [95% CI 33.7-34.5] to 32.3% [95%CI: 32.0-32.6], p < 0.02, HDL-C: 33.8% (95%CI: 33.3-34.2) to 24.0% (95% CI: 23.7-24.3)], P < 0.01, LDL-C: 32.9% (95%CI:32.5-33.3) to 28.6 (95%CI: 28.3-28.9), P < 0.001). Adjustment for confounding factors and removing the residual noise by modeling the random effects did not change the significance. Conclusion: This study demonstrates a significant downward trend in the prevalence of LDL-C, HDL-C, and TC dyslipidemia, exclusively in men. These trends could be the result of males being the primary target for cardiovascular risk management