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DOI: 10.1055/a-1005-6792
Comparability in Cross-National Health Research Using Insurance Claims Data: The Cases of Germany and The Netherlands
Publikationsverlauf
Publikationsdatum:
19. November 2019 (online)
Abstract
Objective Comparison is a key method in learning about what works in health and healthcare. We discuss the importance of comparability in cross-national health research using health insurance claims data, develop a framework to systematically asses these threats and apply it to the German (DaTraV) and Dutch (Vektis) national-level insurance claims datasets.
Methods We propose a framework of threats to the comparability of health insurance claims databases, which includes three domains: (1) representation of populations compared, (2) data sources and data processing and (3) database contents and availability for research purposes. We apply the framework to analyze the comparability of DaTraV and Vektis databases using publicly available information (organization’s websites, scientific publications) and our experiences from an interregional project on rare diseases (EMRaDi).
Results Both databases were created for the same purpose (morbidity-based risk adjustment) and use the same underlying sources of data. Differences in population representation and uncertainty about data processing procedures represent potential sources of incomparability. Access for research purposes is feasible in both databases but may be subject to long processing time.
Conclusions We find important threats to the comparability of the Dutch and German national insurance claims databases and by extension to validity of any comparative health studies that rely on them. Standard adjustment techniques, making more information available about data collection and processing procedures and adding more diagnosis-related descriptors offer ways to overcome the identified threats to comparability.
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References
- 1 Marmor T, Freeman R, Okma K. Comparative perspectives and policy learning in the World of Health Care. J Comp Policy Anal Res Pract 2005; 7: 331-348
- 2 Groenewegen PP. Analyzing European health systems: Europe as a research laboratory. Eur J Public Health 2013; 23: 185-186
- 3 Burgun A, Bernal-Delgado E, Kuchinke W. et al. Health data for public health: Towards new ways of combining data sources to support research efforts in Europe. Yearb Med Inform 2017; 26: 235-240
- 4 Powell AE, Davies HTO, Thomson RG. Using routine comparative data to assess the quality of health care: Understanding and avoiding common pitfalls. Qual Saf Health Care 2003; 12: 122-128
- 5 Bates DW, Saria S, Ohno-Machado L. et al. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 2014; 33: 1123-1131
- 6 Kreis K, Neubauer S, Klora M. et al. Status and perspectives of claims data analyses in Germany—A systematic review. Health Policy 2016; 120: 213-226
- 7 van Walraven C, Austin P. Administrative database research has unique characteristics that can risk biased results. J Clin Epidemiol 2012; 65: 126-131
- 8 Virnig BA, McBean M. Administrative data for public health surveillance and planning. Annu Rev Public Health 2001; 22: 213-230
- 9 Davidov E, Meuleman B, Cieciuch J. et al. Measurement equivalence in cross-national research. Annu Rev Sociol 2014; 40: 55-75
- 10 Fehr A, Tijhuis MJ, Hense S. et al. European Core Health Indicators - status and perspectives. Arch Public Health 2018; 76 Im Internet: https://archpublichealth.biomedcentral.com/articles/ DOI: 10.1186/s13690-018-0298-9.
- 11 WHO Europe. Defining the vision for harmonized and interoperable information systems for health for Europe. In European Health Report. More Than Numbers - Evidence for All. Copenhagen: WHO Regional Office for Europe; 2018. Im Internet: http://www.euro.who.int/__data/assets/pdf_file/0007/379879/who-ehr-2018-04-eng.pdf?ua=1
- 12 Mattke S, Epstein AM, Leatherman S. The OECD Health Care Quality Indicators Project: History and background. Int J Qual Health Care 2006; 18: 1-4
- 13 Ekholm O, Brønnum-Hansen H. Cross-national comparisons of non-harmonized indicators may lead to more confusion than clarification. Scand J Public Health 2009; 37: 661-663
- 14 Kroneman M, Boerma W, van den Berg M. et al. Netherlands health system review. Health Syst Transit 2016; 18: 1-239
- 15 Busse R, Blümel M. Germany: Health system review; Health Syst Transit 2014: 16: 1–296 xxi
- 16 Reinhardt UE, Hussey PS, Anderson GF. Cross-national comparisons of health systems using OECD Data, 1999. Health Aff (Millwood) 2002; 21: 169-181
- 17 Spector PE, Liu C, Sanchez JI. Methodological and substantive issues in conducting multinational and cross-cultural research. Annu Rev Organ Psychol Organ Behav 2015; 2: 101-131
- 18 BMJV. DaTraV. 2018; Im Internet http://www.gesetze-im-internet.de/datrav/
- 19 DIMDI. Informationen zur Nutzung. 2018; Im Internet: https://www.dimdi.de/dynamic/de/weitere-fachdienste/versorgungsdaten/antragsverfahren/
- 20 DIMDI. Antragsregister. Im Internet: https://www.dimdi.de/dynamic/de/weitere-fachdienste/versorgungsdaten/wissenswertes/antragsregister/
- 21 DIMDI. Informationssystem Versorgungsdaten (Datentransparenz): Datensatzbeschreibung. 2018; Im Internet: https://www.dimdi.de/static/.downloads/deutsch/versorgungsdaten-datensatzbeschreibung.pdf
- 22 GKV-Spitzenverband. Statutory health insurance - GKV-Spitzenverband. Im Internet: https://www.gkv-spitzenverband.de/english/statutory_health_insurance/statutory_health_insurance.jsp
- 23 Hoffmann F, Icks A. Unterschiede in der Versichertenstruktur von Krankenkassen und deren Auswirkungen für die Versorgungsforschung: Ergebnisse des Bertelsmann-Gesundheitsmonitors. Gesundheitswesen 2012; 74: 291-297
- 24 DIMDI. FAQ Versorgungsdaten. 2019; Im Internet: https://www.dimdi.de/dynamic/de/weitere-fachdienste/versorgungsdaten/faq-versorgungsdaten/
- 25 DIMDI. Versorgungsdaten - Beispieldatensatz. 2018; Im Internet: https://dimdi.de/static/de/versorgungsdaten/datensatzbeschreibung/beispieldatensatz.htm
- 26 BMJV. DaTraGebV. 2018; Im Internet: http://www.gesetze-im-internet.de/datragebv/index.html
- 27 Smeets HM, de Wit NJ, Hoes AW. Routine health insurance data for scientific research: Potential and limitations of the Agis Health Database. J Clin Epidemiol 2011; 64: 424-430
- 28 Struijs J, Mohnen S, Molema C et al. Effects of bundled payment on curative health care costs in the Netherlands. Bilthoven: RIVM 2012
- 29 de Boer WIJ, Buskens E, Koning RH. et al. Neighborhood socioeconomic status and health care costs: A population-wide study in the Netherlands. Am J Public Health 2019; 109: 927-933
- 30 Vektis. Voorwaarden maatwerkverzoek. Vektis Im Internet. https://www.vektis.nl/intelligence/inzichten-op-maat/voorwaarden-maatwerkverzoek
- 31 Centraal Bureau voor de Statistiek. Diensten en kosten. Cent Bur Voor Stat. 2019; Im Internet: https://www.cbs.nl/nl-nl/onze-diensten/maatwerk-en-microdata/microdata-zelf-onderzoek-doen/diensten-en-kosten
- 32 Centraal Bureau voor de Statistiek. Microdata: Zelf onderzoek doen. Cent Bur Voor Stat. Im Internet: https://www.cbs.nl/nl-nl/onze-diensten/maatwerk-en-microdata/microdata-zelf-onderzoek-doen
- 33 Donnelly DW, Hegarty A, Sharp L. et al. The impact of adjustment for socioeconomic status on comparisons of cancer incidence between Two European Countries. J Cancer Epidemiol 2013; 2013: 612514
- 34 European Union. Real-world data. 2018; Im Internet: https://ec.europa.eu/research/health/pdf/factsheets/real_world_data_factsheet.pdf