ArticleBMC health services research2009
Is there much variation in variation? Revisiting statistics of small area variation in health services research.
Article in BMC health services research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Sociodemographic and Environmental Determinants of Regional Prevalence of Psoriasis in Germany: A Spatiotemporal Study of Ambulatory Claims Data.Acta dermato-venereologica · 2024Article
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- Geographic variation in the utilisation of specialist healthcare for patients with severe mental illness in Norway: a population-based registry study.Research in health services & regions · 2023Article
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- Is there variation between hospitals within each region in postoperative mortality for lung cancer surgery in France? A nationwide study from 2013 to 2020.Frontiers in medicine · 2023Article
- Comparing healthcare quality: A common framework for both ordinal and cardinal data with an application to primary care variation in England.Health economics · 2022Article
- Geographic variation in hip fracture surgery rate, care quality and outcomes: a comparison between national registries in Ireland and Denmark.Archives of osteoporosis · 2022Article
- [Regional and gender variations in appendicectomy : Nationwide small-area development of operation rates in the time series].Chirurgie (Heidelberg, Germany) · 2022Article
- Atlas VPM: two decades informing on unwarranted variations in health care in Spain.Research in health services & regions · 2022Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe importance of Small Area Variation Analysis for policy-making contrasts with the scarcity of work on the validity of the statistics used in these studies. Our study aims at 1) determining whether variation in utilization rates between health areas is higher than would be expected by chance, 2) estimating the statistical power of the variation statistics; and 3) evaluating the ability of different statistics to compare the variability among different procedures regardless of their rates.
methodsParametric bootstrap techniques were used to derive the empirical distribution for each statistic under the hypothesis of homogeneity across areas. Non-parametric procedures were used to analyze the empirical distribution for the observed statistics and compare the results in six situations (low/medium/high utilization rates and low/high variability). A small scale simulation study was conducted to assess the capacity of each statistic to discriminate between different scenarios with different degrees of variation.
resultsBootstrap techniques proved to be good at quantifying the difference between the null hypothesis and the variation observed in each situation, and to construct reliable tests and confidence intervals for each of the variation statistics analyzed. Although the good performance of Systematic Component of Variation (SCV), Empirical Bayes (EB) statistic shows better behaviour under the null hypothesis, it is able to detect variability if present, it is not influenced by the procedure rate and it is best able to discriminate between different degrees of heterogeneity.
conclusionThe EB statistics seems to be a good alternative to more conventional statistics used in small-area variation analysis in health service research because of its robustness.
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