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- Title
Multilevel modeling of geographic variation in general practice consultations.
- Authors
Astell-Burt, Thomas; Navakatikyan, Michael A.; Arnolda, Leonard F.; Xiaoqi Feng
- Abstract
Objective: To test relatively simple and complex models for examining model fit, higher-level variation in, and correlates of, GP consultations, where known nonhier-archical data structures are present. Setting: New South Wales (NSW), Australia. Design: Association between socioeconomic circumstances and geographic remoteness with GP consultation frequencies per participant was assessed using single-level, hierarchical, and multiple membership cross-classified (MMCC) models. Models were adjusted for age, gender, and a range of socioeconomic and demographic confounds. Data Collection/Extraction Methods: A total of 261,930 participants in the Sax Institute's 45 and Up Study were linked to all GP consultation records (Medicare Benefits Schedule; Department of Human Services) within 12 months of baseline (2006-2009). Principal Findings: Deviance information criterion values indicated the MMCC negative binomial regression was the best fitting model, relative to an MMCC Poisson equivalent and simpler hierarchical and single-level models. Between-area variances were relatively consistent across models, even when between GP variation was estimated. Lower rates of GP consultation outside of major cities were only observed once between-GP variation was assessed simultaneously with between-area variation in the MMCC models. Conclusions: Application of the MMCC model is necessary for estimation of variances and effect sizes in sources of big data on primary care in which complex nonhi-erarchical clustering by geographical area and GP is present.
- Publication
Health Services Research, 2021, Vol 56, Issue 6, p1252
- ISSN
0017-9124
- Publication type
Article
- DOI
10.1111/1475-6773.13644