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Multimorbidity and its measurement

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  • Starfield, Barbara
  • Kinder, Karen

Abstract

Multimorbidity is increasing in frequency. It can be quantitatively measured and is a major correlate of high use of health services resources of all types, especially over time. The ACG System for characterizing multimorbidity is the only widely used method that is based on combinations of different TYPES of diagnoses over time, rather than the presence or absence of particular conditions or numbers of conditions. It incorporates administrative data (as from claims forms or medical records) on all types of encounters and is not limited to diagnoses captured during hospitalizations or other places of encounter. It can be employed in any one or combination of analytic models, and can incorporate medication use if desired. It is being used in clinical care, management of health services resources, in health services research to control for degree of morbidity, and in understanding morbidity patterns over time. In addition to its research uses, it is being employed in many countries in various applications as a policy to better understand health needs of populations and tailor health services resources to health needs.

Suggested Citation

  • Starfield, Barbara & Kinder, Karen, 2011. "Multimorbidity and its measurement," Health Policy, Elsevier, vol. 103(1), pages 3-8.
    • Handle: RePEc:eee:hepoli:v:103:y:2011:i:1:p:3-8
    DOI: 10.1016/j.healthpol.2011.09.004
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    Citations

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    Cited by:

    1. Juan F Orueta & Arturo García-Álvarez & Manuel García-Goñi & Francesco Paolucci & Roberto Nuño-Solinís, 2014. "Prevalence and Costs of Multimorbidity by Deprivation Levels in the Basque Country: A Population Based Study Using Health Administrative Databases," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    2. Kristensen, Troels & Rose Olsen, Kim & Sortsø, Camilla & Ejersted, Charlotte & Thomsen, Janus Laust & Halling, Anders, 2013. "Resources allocation and health care needs in diabetes care in Danish GP clinics," Health Policy, Elsevier, vol. 113(1), pages 206-215.
    3. Esperanza Diaz & Beatriz Poblador-Pou & Luis-Andrés Gimeno-Feliu & Amaia Calderón-Larrañaga & Bernadette N Kumar & Alexandra Prados-Torres, 2015. "Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-18, December.
    4. Yang, Lianping & Liu, Chaojie & Ferrier, J. Adamm & Zhang, Xinping, 2015. "Organizational barriers associated with the implementation of national essential medicines policy: A cross-sectional study of township hospitals in China," Social Science & Medicine, Elsevier, vol. 145(C), pages 201-208.
    5. Troels Kristensen & Kim Olsen & Henrik Schroll & Janus Thomsen & Anders Halling, 2014. "Association between fee-for-service expenditures and morbidity burden in primary care," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(6), pages 599-610, July.
    6. Groenewegen, Peter P. & Dourgnon, Paul & Greß, Stefan & Jurgutis, Arnoldas & Willems, Sara, 2013. "Strengthening weak primary care systems: Steps towards stronger primary care in selected Western and Eastern European countries," Health Policy, Elsevier, vol. 113(1), pages 170-179.
    7. Ahmad Alkhatib & Lawrence Achilles Nnyanzi & Brian Mujuni & Geofrey Amanya & Charles Ibingira, 2021. "Preventing Multimorbidity with Lifestyle Interventions in Sub-Saharan Africa: A New Challenge for Public Health in Low and Middle-Income Countries," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    8. Bache, Stefan Holst Milton & Kristensen, Troels, 2013. "A simple but efficient approach to the analysis of multilevel data," DaCHE discussion papers 2013:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.

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