Predicting patient use of general practice services in Australia: models developed using national cross-sectional survey data
| Autor: | Graeme Miller, Christopher Harrison, Helena Britt, Joan Henderson |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Population ageing Chronic condition Adolescent Cross-sectional study General Practice MEDLINE Young Adult 03 medical and health sciences Sex Factors 0302 clinical medicine Statistical significance Health care Humans Medicine Health Workforce Multiple Chronic Conditions 030212 general & internal medicine Child Aged Aged 80 and over lcsh:R5-920 business.industry 030503 health policy & services Age Factors Australia Infant Newborn Attendance Infant Health Services Middle Aged Health Planning Cross-Sectional Studies Child Preschool Workforce planning Female lcsh:Medicine (General) 0305 other medical science Family Practice business Facilities and Services Utilization Research Article Demography |
| Zdroj: | BMC Family Practice, Vol 20, Iss 1, Pp 1-10 (2019) BMC Family Practice |
| ISSN: | 1471-2296 |
| DOI: | 10.1186/s12875-019-0914-y |
| Popis: | Background The ageing population and increasing prevalence of multimorbidity place greater resource demands on the health systems internationally. Accurate prediction of general practice (GP) services is important for health workforce planning. The aim of this research was to develop a parsimonious model that predicts patient visit rates to general practice. Methods Between 2012 and 2016, 1449 randomly selected Australian GPs recorded GP-patient encounter details for 43,501 patients in sub-studies of the Bettering the Evaluation and Care of Health (BEACH) program. Details included patient characteristics, all diagnosed chronic conditions per patient and the number of GP visits for each patient in previous 12 months. BEACH has a single stage cluster design. Survey procedures in SAS version 9.3 (SAS Inc., Cary, NC, USA) were used to account for the effect of this clustering. Models predicting patient GP visit rates were tested. R-square value was used to measure how well each model predicts GP attendance. An adjusted R-square was calculated for all models with more than one explanatory variable. Statistically insignificant variables were removed through backwards elimination. Due to the large sample size, p |
| Databáze: | OpenAIRE |
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