ArticleBMJ (Clinical research ed.)1999
Explaining variation in hospital admission rates between general practices: cross sectional study.
Article in BMJ (Clinical research ed.), 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Primary care visits and ambulatory care sensitive diabetes hospitalizations among adult Alabama Medicaid beneficiaries.Primary care diabetes · 2022Article
- Primary care doctors' understandings of and strategies to tackle health inequalities: a qualitative study.Primary health care research & development · 2019Article
- Active listing and more consultations in primary care are associated with shorter mean hospitalisation and interacting with psychiatric disorders when adjusting for multimorbidity, age and sex.Scandinavian journal of primary health care · 2018Article
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- Association between the 2012 Health and Social Care Act and specialist visits and hospitalisations in England: A controlled interrupted time series analysis.PLoS medicine · 2017Article
- Risk factors for admission at three urban emergency departments in England: a cross-sectional analysis of attendances over 1 month.BMJ open · 2017Article
- Incentivised chronic disease management and the inverse equity hypothesis: findings from a longitudinal analysis of Scottish primary care practice-level data.BMC medicine · 2017Article
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- A system-wide approach to explaining variation in potentially avoidable emergency admissions: national ecological study.BMJ quality & safety · 2014Article
- How can socioeconomic inequalities in hospital admissions be explained? A cohort study.BMJ open · 2013Article
- The choice of alternatives to acute hospitalization: a descriptive study from Hallingdal, Norway.BMC family practice · 2013Article
- Methodological challenges of measuring primary care delivery to pediatric medicaid beneficiaries who use community health centers.American journal of public health · 2013Article
- What aspects of primary care predict emergency admission rates? A cross sectional study.BMC health services research · 2013Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo quantify the extent of the variation in hospital admission rates between general practices, and to investigate whether this variation can be explained by factors relating to the patient, the hospital, and the general practice.
designCross sectional analysis of routine data.
settingMerton, Sutton, and Wandsworth Health Authority, which includes areas of inner and outer London. SUBJECTS: 209 136 hospital admissions in 1995-6 in patients registered with 120 general practices in the study area.
main outcome measuresHospital admission rates for general practices for overall, emergency, and elective admissions.
resultsCrude admission rates for general practices displayed a twofold difference between the 10th and the 90th centile for all, emergency, and elective admissions. This difference was only minimally reduced by standardising for age and sex. Sociodemographic patient factors derived from census data accounted for 42% of the variation in overall admission rates; 45% in emergency admission rates; and 25% in elective admission rates. There was a strong positive correlation between factors related to deprivation and emergency, but not elective, admission rates, raising questions about equity of provision of health care. The percentage of each practice's admissions to different local hospitals added significantly to the explanation of variation, while the general practice characteristics considered added very little.
conclusionsHospital admission rates varied greatly between general practices; this was largely explained by differences in patient populations. The lack of significant factors related to general practice is of little help for the direct management of admission rates, although the effect of sociological rather than organisational practice variables should be explored further. Admission rates should routinely be standardised for differences in patient populations and hospitals used.
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