Advertisement

A case-mix classification system for explaining healthcare costs using administrative data in Italy

Published:March 04, 2018DOI:https://doi.org/10.1016/j.ejim.2018.02.035

      Highlights

      • Progressive aging of the population represents a challenge for health care systems.
      • Diagnoses and drugs variables explain a relevant percentage of total costs variance.
      • ACG System predicts healthcare costs, improving the equity of the capitation system.

      Abstract

      Background

      The Italian National Health Service (NHS) provides universal coverage to all citizens, granting primary and hospital care with a copayment system for outpatient and drug services. Financing of Local Health Trusts (LHTs) is based on a capitation system adjusted only for age, gender and area of residence. We applied a risk-adjustment system (Johns Hopkins Adjusted Clinical Groups System, ACG® System) in order to explain health care costs using routinely collected administrative data in the Veneto Region (North-eastern Italy).

      Methods

      All residents in the Veneto Region were included in the study. The ACG system was applied to classify the regional population based on the following information sources for the year 2015: Hospital Discharges, Emergency Room visits, Chronic disease registry for copayment exemptions, ambulatory visits, medications, the Home care database, and drug prescriptions. Simple linear regressions were used to contrast an age-gender model to models incorporating more comprehensive risk measures aimed at predicting health care costs.

      Results

      A simple age-gender model explained only 8% of the variance of 2015 total costs. Adding diagnoses-related variables provided a 23% increase, while pharmacy based variables provided an additional 17% increase in explained variance. The adjusted R-squared of the comprehensive model was 6 times that of the simple age-gender model.

      Conclusions

      ACG System provides substantial improvement in predicting health care costs when compared to simple age-gender adjustments. Aging itself is not the main determinant of the increase of health care costs, which is better explained by the accumulation of chronic conditions and the resulting multimorbidity.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to European Journal of Internal Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. Toniolo F, Mantoan D, Maresso A. Health System in Transition, Veneto Region, Italy. ©World Health Organization 2012, on behalf of the European Observatory on Health Systems and Policies, ISSN 1817–6127 vol. 14 N. 01.

        • Wolff J.
        • Starfield B.
        • Anderson G.
        Prevalence, expenditures, and complications of multiple chronic conditions in the elderly.
        Arch Intern Med. 2002; 162: 2269-2276
        • Fabbri E.
        • Zoli M.
        • Gonzales-Freire M.
        • et al.
        Aging and multimorbidity: new tasks, priorities and frontiers for integrated Gerontological and clinical research.
        JAMDA. 2015; 16: 640-647
        • Barnett K.
        • Mercer S.W.
        • Norbury M.
        • et al.
        Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.
        Lancet. 2012; 380: 37-43
        • Pefoyo A.J.
        • Bronskill S.E.
        • Gruneir A.
        • et al.
        The increasing burden and complexity of multimorbidity.
        BMC Public Health. 2015; 15: 415
        • Starfield B.
        • Kinder K.
        Multimorbidity and its measurement.
        Health Policy. 2011; 103: 3-8
        • Charlson M.E.
        • Charlson R.E.
        • Peterson J.C.
        • et al.
        The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients.
        J Clin Epidemiol. 2008; 61: 1234-1240
        • Huntley A.L.
        • Johnson R.
        • Purdy S.
        • et al.
        Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide.
        Ann Fam Med. 2012; 10: 134-141
      2. The Johns Hopkins ACG®System web site.http://acg.jhsph.org/index.php/the-acg-system-advantage.Last visit on 18 October 2016.

        • Starfield B.
        • Kinder K.
        Multimorbidity and its measurement.
        . 2011 Nov; 103: 3-8
        • Shadmi E.
        • Balicer R.D.
        • Kinder K.
        • Abrams C.
        • Weiner J.P.
        Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment.
        BMC Public Health. 2011; 11: 609
        • Zielinski A.
        • Kronogard M.
        • Lenhoff H.
        • Halling A.
        Validation of ACG case-mix for equitable resource allocation in Swedish primary health care.
        BMC Public Health. 2009; 9: 347
        • Reid R.J.
        Making Sense out of the Case-mix “Al phabet Soup”: ACGs, ADGs, RUBs & EDCs.
        Centre for Health Services and Policy Research Working Seminar, Vancouver, BC2002/11/20
        • Reid R.J.
        • Roos N.P.
        • MacWilliam L.
        • Frohlich N.
        • Black C.
        Assessing population health care need using a claims-based ACG morbidity measure: a validation analysis in the province of Manitoba.
        Health Serv Res. 2002; 37: 1345-1364
        • Orueta J.F.
        • Lopez-De-Munain J.
        • Báez K.
        • et al.
        Application of the ambulatory care groups in the primary care of a European national health care system: does it work?.
        Med Care. 1999; 37: 238-248
        • Halling A.
        • Fridh G.
        • Ovhed I.
        Validating the Johns Hopkins ACG case-mix system of the elderly in Swedish primary health care.
        BMC Public Health. 2006; 6: 171
        • Sicras-Mainar A.
        • Navarro-Artieda R et Grupo de estudio ACG-BSA
        Validating the adjusted clinical groups [ACG] case-mix system in a Spanish population setting: a multicenter study.
        Gac Sanit. 2009; 23: 228-231
        • Sicras-Mainar A.
        • Navarro-Artieda R.
        • Blanca-Tamayo M.
        • et al.
        The relationship between effectiveness and costs measured by a risk-adjusted case-mix system: multicentre study of Catalonian population data bases.
        BMC Public Health. 2009; 9: 202
        • Alonso-Morán E.
        • Nuño-Solinis R.
        • Onder G.
        • Tonnara G.
        Multimorbidity in risk stratification tools to predict negative outcomes in adult population.
        Eur J Intern Med. 2015 Apr; 26: 182-189
        • Demurtas J.
        • Alba N.
        • Rigon G.
        • et al.
        Epidemiological trends and direct costs of diabetes in a northern Italy area: 2012 health administrative records analysis LHT n. 20 Verona.
        Prim Care Diabetes. 2017 Dec; 11: 570-576
      3. National Center for Health Statistics, CDC. ICD-9-CM Guidelines, Conversion Table, and Addenda. Classification of Diseases, Functioning, and Disability. (Retrieved 2010-01-24).

        • World Health Organization
        International statistical classification of diseases and related health problems. - 10th revision, edition.
        2010
      4. Lamberts H. Wood M. ICPC, international classification of primary care. Oxford University Press, Oxford1987
        • Kuo R.N.
        • Lai M.S.
        Comparison of Rx-defined morbidity groups and diagnosis-based risk adjusters for predicting healthcare costs in Taiwan.
        BMC Health Serv Res. 2010; 10: 126
      5. http://demo.istat.it/ (accessed December 12th 2015).

        • Gini R.
        • Francesconi P.
        • Mazzaglia G.
        • et al.
        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.
        BMC Public Health. 2013; 13: 15
        • Lapi F.
        • Bianchini E.
        • Cricelli I.
        • et al.
        Development and validation of a score for adjusting health care costs in general practice.
        Value Health. 2015 Sep; 18: 884-895
        • Reid J.R.
        • MacWilliam L.
        • Verhulst L.
        • Roos N.
        • Atkinson M.
        Performance of the ACG case-mix system in two Canadian provinces.
        Med Care. 2001; 39: 86-99
        • Weiner J.P.
        • Yeh S.
        • Blumenthal D.
        The impact of health information technology and e-health on the future demand for physician services.
        HealthAff (Millwood). 2013; 32: 1998-2004
        • Lee W.C.
        Quantifying morbidities by adjusted clinical group system for a Taiwan population: a nationwide analysis.
        BMC Health Serv Res. 2008; 8: 153
        • Epstein A.M.
        • Stern R.S.
        • Tognetti J.
        • et al.
        The association of patients' socioeconomic characteristic with the length of hospital stay and hospital charges within diagnosis-related groups.
        N Engl J Med. 1988; 318: 1579-1585
        • Thomson S.
        • Jowett M.
        • Evetovits T.
        • et al.
        Health, health systems and economic crisis in Europe: Impact and policy implications.
        in: World Health Organization. 2013
        • Conklin A.
        • Nolte E.
        • Vrijhoef H.
        Approaches to chronic disease management evaluation in use in Europe: a review of current methods and performance measures.
        Int J Technol Assess Health Care. 2012; 29: 61-70
        • Tsiachristas A.
        • Cramm J.M.
        • Nieboer A.P.
        • Rutten-van Mölken M.P.
        Changes in costs and effects after the implementation of disease management programs in the Netherlands: variability and determinants.
        Cost Eff ResourAlloc. 2014; 12: 17
        • Seow H.S.
        • Lyn M.
        • Sibley L.M.
        Developing a dashboard to help measure and achieve the triple aim: a population-based cohort study.
        BMC Health Serv Res. 2014; 14: 363