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      Role of Area-Level Access to Primary Care on the Geographic Variation of Cardiometabolic Risk Factor Distribution: A Multilevel Analysis of the Adult Residents in the Illawarra—Shoalhaven Region of NSW, Australia

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          Abstract

          Background: Access to primary care is important for the identification, control and management of cardiometabolic risk factors (CMRFs). This study investigated whether differences in geographic access to primary care explained area-level variation in CMRFs. Methods: Multilevel logistic regression models were used to derive the association between area-level access to primary care and seven discrete CMRFs after adjusting for individual and area-level co-variates. Two-step floating catchment area method was used to calculate the geographic access to primary care for the small areas within the study region. Results: Geographic access to primary care was inversely associated with low high density lipoprotein (OR 0.94, CI 0.91–0.96) and obesity (OR 0.91, CI 0.88–0.93), after adjusting for age, sex and area-level disadvantage. The intra-cluster correlation coefficient (ICCs) of all the fully adjusted models ranged between 0.4–1.8%, indicating low general contextual effects of the areas on CMRF distribution. The area-level variation in CMRFs explained by primary care access was ≤10.5%. Conclusion: The findings of the study support proportionate universal interventions for the prevention and control of CMRFs, rather than any area specific interventions based on their primary care access, as the contextual influence of areas on all the analysed CMRFs were found to be minimal. The findings also call for future research that includes other aspects of primary care access, such as road-network access, financial affordability and individual-level acceptance of the services in order to gain an overall picture of the area-level contributing role of primary care on CMRFs in the study region.

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          Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region

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            Model selection for ecologists: the worldviews of AIC and BIC.

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              Fitting Linear Mixed-Effects Models using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                16 June 2020
                June 2020
                : 17
                : 12
                : 4297
                Affiliations
                [1 ]School of Medicine, University of Wollongong, Wollongong NSW 2522, Australia; dmay8519@ 123456uni.sydney.edu.au (D.J.M.); abonney@ 123456uow.edu.au (A.B.)
                [2 ]Illawarra Health and Medical Research Institute, Wollongong NSW 2522, Australia; xiaoqi.feng@ 123456unsw.edu.au
                [3 ]Population Wellbeing and Environment Research Lab (PowerLab), School of Health and Society, Faculty of Social Sciences, University of Wollongong, Wollongong NSW 2500, Australia
                [4 ]School of Public Health and Community Medicine, University of New South Wales, Sydney NSW 2033, Australia
                [5 ]Illawarra Shoalhaven Local Health District, Public Health Unit, Warrawong NSW 2502, Australia
                [6 ]School of Public Health, The University of Sydney, Sydney NSW 2006, Australia
                Author notes
                Author information
                https://orcid.org/0000-0002-9370-3463
                Article
                ijerph-17-04297
                10.3390/ijerph17124297
                7344656
                32560149
                6028afe3-180b-42bb-89fc-777c180c0e0a
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 April 2020
                : 14 June 2020
                Categories
                Article

                Public health
                geographic access,cardiometabolic risk factor,geographic variation,multilevel logistic regression,primary care access

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