ArticleHealth services research2021
Multilevel modeling of geographic variation in general practice consultations.
Article in Health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Comparing Parkinson's Disease Medications Categorised by Location in New South Wales, Australia: Linking the 45 and Up Study and the Pharmaceutical Benefits Scheme (PBS) Data.The Australian journal of rural health · 2025Article
- Article
- Multilevel modeling of geographic variation in general practice consultations.Health services research · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo test relatively simple and complex models for examining model fit, higher-level variation in, and correlates of, GP consultations, where known nonhierarchical data structures are present.
settingNew South Wales (NSW), Australia.
designAssociation 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
methodsA 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
findingsDeviance 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.
conclusionsApplication of the MMCC model is necessary for estimation of variances and effect sizes in sources of big data on primary care in which complex nonhierarchical clustering by geographical area and GP is present.
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Registered trials
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