TY - JOUR

T1 - Comparing variation across European countries

T2 - Building geographical areas to provide sounder estimates

AU - ECHO Consortium

AU - Thygesen, Lau C.

AU - Baixauli-Pérez, Cristobal

AU - Librero-López, Julián

AU - Martínez, N.

AU - Ridao-López, Manuel

AU - Bernal-Delgado, E.

AU - Bernal-Delgado, E.

AU - García-Armesto, S.

AU - Martínez, N.

AU - Seral, M.

AU - Estupiñán, F.

AU - Comendeiro, M.

AU - Angulo-Pueyo, E.

AU - Ridao, M.

AU - Baixaulí, C.

AU - Librero, J.

AU - Christiansen, T.

AU - Thygesen, L. C.

AU - Bloor, K.

AU - Cookson, R.

AU - Gutacker, N.

AU - Mateus, C.

AU - Nunes, C.

AU - Joaquim, I.

AU - Yazbeck, A. M.

AU - Galsworthy, M.

AU - Albreht, T.

AU - Munck, J.

AU - Güntert, B.

AU - Bremner, J.

AU - Giepmans, P.

AU - Dix, O.

PY - 2015/2/12

Y1 - 2015/2/12

N2 - Background: In geographical studies, population distribution is a key issue. An unequal distribution across units of analysis might entail extra-variation and produce misleading conclusions on healthcare performance variations. This article aims at assessing the impact of building more homogeneous units of analysis in the estimation of systematic variation in three countries. Methods: Hospital discharges for six conditions (congestive heart failure, short-term complications of diabetes, hip fracture, knee replacement, prostatectomy in prostate cancer and percutaneous coronary intervention) produced in Denmark, England and Portugal in 2008 and 2009 were allocated to both original geographical units and new ad hoc areas. New areas were built using Ward's minimum variance methods. The impact of the new areas on variability was assessed using Kernel distribution curves and different statistic of variation such as Extremal Quotient, Interquartile Interval ratio, Systematic Component of Variation and Empirical Bayes statistic. Results: Ward's method reduced the number of areas, allowing a more homogeneous population distribution, yet 20% of the areas in Portugal exhibited less than 100 000 inhabitants vs. 7% in Denmark and 5% in England. Point estimates for Extremal Quotient and Interquartile Interval Ratio were lower in the three countries, particularly in less prevalent conditions. In turn, the Systematic Component of Variation and Empirical Bayes statistic were slightly lower in more prevalent conditions. Conclusions: Building new geographical areas produced a reduction of the variation in hospitalization rates in several prevalent conditions mitigating random noise, particularly in the smallest areas and allowing a sounder interpretation of the variation across countries.

AB - Background: In geographical studies, population distribution is a key issue. An unequal distribution across units of analysis might entail extra-variation and produce misleading conclusions on healthcare performance variations. This article aims at assessing the impact of building more homogeneous units of analysis in the estimation of systematic variation in three countries. Methods: Hospital discharges for six conditions (congestive heart failure, short-term complications of diabetes, hip fracture, knee replacement, prostatectomy in prostate cancer and percutaneous coronary intervention) produced in Denmark, England and Portugal in 2008 and 2009 were allocated to both original geographical units and new ad hoc areas. New areas were built using Ward's minimum variance methods. The impact of the new areas on variability was assessed using Kernel distribution curves and different statistic of variation such as Extremal Quotient, Interquartile Interval ratio, Systematic Component of Variation and Empirical Bayes statistic. Results: Ward's method reduced the number of areas, allowing a more homogeneous population distribution, yet 20% of the areas in Portugal exhibited less than 100 000 inhabitants vs. 7% in Denmark and 5% in England. Point estimates for Extremal Quotient and Interquartile Interval Ratio were lower in the three countries, particularly in less prevalent conditions. In turn, the Systematic Component of Variation and Empirical Bayes statistic were slightly lower in more prevalent conditions. Conclusions: Building new geographical areas produced a reduction of the variation in hospitalization rates in several prevalent conditions mitigating random noise, particularly in the smallest areas and allowing a sounder interpretation of the variation across countries.

U2 - 10.1093/eurpub/cku229

DO - 10.1093/eurpub/cku229

M3 - Journal article

C2 - 25690124

AN - SCOPUS:84929055179

VL - 25

SP - 8

EP - 14

JO - European Journal of Public Health

JF - European Journal of Public Health

SN - 1101-1262

IS - Suppl. 1

ER -