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Open Access Research article

Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey

Rosa Gini12*, Paolo Francesconi2, Giampiero Mazzaglia3, Iacopo Cricelli4, Alessandro Pasqua3, Pietro Gallina5, Salvatore Brugaletta6, Daniele Donato5, Andrea Donatini7, Alessandro Marini8, Carlo Zocchetti9, Claudio Cricelli3, Gianfranco Damiani10, Mariadonata Bellentani11, Miriam CJM Sturkenboom2 and Martijn J Schuemie2

Author affiliations

1 , Agenzia regionale di sanità della Toscana, Via Pietro Dazzi 1, 50141 Florence, Italy

2 , Department of Medical Informatics, Erasmus Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, The Netherlands

3 , Società italiana di medicina generale, Via del Pignoncino, 9-11, 50142 Florence, Italy

4 , Genomedics, Via Sestese 61, 50141 Florence, Italy

5 , ULSS 16 Padova, Via Enrico Degli Scrovegni 14, 35131 Padua, Italy

6 , ASP 7 Ragusa, Piazza Igea 1, 97100 Ragusa, Italy

7 , Assessorato Politiche per la Salute, Viale Aldo Moro 21, 40127 Bologna, Italy

8 , Zona Territoriale Senigallia, Via Piero della Francesca 14

9 , Regione Lombardia, Piazza Città di Lombardia 1, 20124 Milan, Italy

10 , Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00198 Rome, Italy

11 , Agenzia Nazionale per il Servizi Sanitari Regionali, Via Puglie 23, 00187 Rome, Italy

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Citation and License

BMC Public Health 2013, 13:15  doi:10.1186/1471-2458-13-15

Published: 9 January 2013

Abstract

Background

Administrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources.

Methods

Data from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment.

Results

Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs’ estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources.

Conclusion

This study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement.

Keywords:
Prevalence; Chronic disease; Validation studies; Data reuse