Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48479
DC FieldValueLanguage
dc.contributor.authorIbá̃ez, Bertaen_US
dc.contributor.authorLibrero, Juliánen_US
dc.contributor.authorBernal-Delgado, Enriqueen_US
dc.contributor.authorPeiró, Salvadoren_US
dc.contributor.authorGonzalez Lopez-Valcarcel, Beatrizen_US
dc.contributor.authorMartínez, Nataliaen_US
dc.contributor.authorAizpuru, Felipeen_US
dc.contributor.otherLibrero, Julian-
dc.contributor.otherPeiro, Salvador-
dc.contributor.otherIbanez, Berta-
dc.contributor.otherValcarcel, Bea-
dc.date.accessioned2018-11-23T22:10:30Z-
dc.date.available2018-11-23T22:10:30Z-
dc.date.issued2009en_US
dc.identifier.issn1472-6963en_US
dc.identifier.urihttp://hdl.handle.net/10553/48479-
dc.description.abstractBackground: The 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.Methods: Parametric 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.Results: Bootstrap 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.Conclusion: The 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.
dc.languageengen_US
dc.relation.ispartofBMC Health Services Researchen_US
dc.sourceBmc Health Services Research[ISSN 1472-6963],v. 9en_US
dc.subject531207 Sanidaden_US
dc.subject.otherEstadísticas sanitariasen_US
dc.subject.otherSistema sanitario españolen_US
dc.titleIs there much variation in variation?: revisiting statistics of small area variation in health services researchen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/1472-6963-9-60
dc.identifier.scopus65149091234-
dc.identifier.isi000265766500001-
dcterms.isPartOfBmc Health Services Research
dcterms.sourceBmc Health Services Research[ISSN 1472-6963],v. 9
dc.contributor.authorscopusid26530669300-
dc.contributor.authorscopusid6603600917-
dc.contributor.authorscopusid55331996100
dc.contributor.authorscopusid16244273200-
dc.contributor.authorscopusid7004791738-
dc.contributor.authorscopusid6507677112-
dc.contributor.authorscopusid26530619300-
dc.contributor.authorscopusid57193917963-
dc.identifier.issue60-
dc.relation.volume9-
dc.investigacionCiencias Sociales y Jurídicasen_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000265766500001-
dc.contributor.daisngid461865-
dc.contributor.daisngid963402-
dc.contributor.daisngid996919-
dc.contributor.daisngid211348-
dc.contributor.daisngid1229412-
dc.contributor.daisngid7008994
dc.contributor.daisngid2598196
dc.contributor.daisngid4641140-
dc.contributor.daisngid637456-
dc.identifier.investigatorRIDF-4947-2017-
dc.identifier.investigatorRIDH-5778-2017-
dc.identifier.investigatorRIDA-6924-2017-
dc.identifier.investigatorRIDA-9891-2010-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Ibanez, B
dc.contributor.wosstandardWOS:Librero, J
dc.contributor.wosstandardWOS:Bernal-Delgado, E
dc.contributor.wosstandardWOS:Peiro, S
dc.contributor.wosstandardWOS:Lopez-Valcarcel, BG
dc.contributor.wosstandardWOS:Martinez, N
dc.contributor.wosstandardWOS:Aizpuru, F
dc.date.coverdateEnero 2009
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.deptEconomía de la salud y políticas públicas-
crisitem.author.orcid0000-0002-5571-3257-
crisitem.author.parentorgDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.fullNameGonzález Lopez-Valcarcel, Beatriz-
crisitem.author.departamentoMétodos Cuantitativos en Economía y Gestión-
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