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      The extra cost of comorbidity: multiple illnesses and the economic burden of non-communicable diseases

      1 , 2 , , 2 , 3
      BMC Medicine
      BioMed Central
      Cost of illness, Comorbidity, Chronic diseases, Prevention policies

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          The literature offers competing estimates of disease costs, with each study having its own data and methods. In 2007, the Dutch Center for Public Health Forecasting of the National Institute for Public Health and the Environment provided guidelines that can be used to set up cost-of-illness (COI) studies, emphasising that most COI analyses have trouble accounting for comorbidity in their cost estimations. When a patient has more than one chronic condition, the conditions may interact such that the patient’s healthcare costs are greater than the sum of the costs for the individual diseases. The main objective of this work was to estimate the costs of 10 non-communicable diseases when their co-occurrence is acknowledged and properly assessed.


          The French Echantillon Généraliste de Bénéficiaires (EGB) database was used to assign all healthcare expenses for a representative sample of the population covered by the National Health Insurance. COIs were estimated in a bottom-up approach, through regressions on individuals’ healthcare expenditure. Two-way interactions between the 10 chronic disease variables were included in the expenditure model to account for possible effect modification in the presence of comorbidity(ies).


          The costs of the 10 selected chronic diseases were substantially higher for individuals with comorbidity, demonstrating the pattern of super-additive costs in cases of diseases interaction. For instance, the cost associated with diabetes for people without comorbidity was estimated at 1776 €, whereas this was 2634 € for people with heart disease as a comorbidity. Overall, we detected 41 cases of super-additivity over 45 possible comorbidities. When simulating a preventive action on diabetes, our results showed that significant monetary savings could be achieved not only for diabetes itself, but also for the chronic diseases frequently associated with diabetes.


          When comorbidity exists and where super-additivity is involved, a given preventive policy leads to greater monetary savings than the costs associated with the single diagnosis, meaning that the returns from the action are generally underestimated.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12916-017-0978-2) contains supplementary material, which is available to authorized users.

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          Most cited references23

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          Annual medical spending attributable to obesity: payer-and service-specific estimates.

          In 1998 the medical costs of obesity were estimated to be as high as $78.5 billion, with roughly half financed by Medicare and Medicaid. This analysis presents updated estimates of the costs of obesity for the United States across payers (Medicare, Medicaid, and private insurers), in separate categories for inpatient, non-inpatient, and prescription drug spending. We found that the increased prevalence of obesity is responsible for almost $40 billion of increased medical spending through 2006, including $7 billion in Medicare prescription drug costs. We estimate that the medical costs of obesity could have risen to $147 billion per year by 2008.
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            Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures.

            Many methods for modeling skewed health care cost and use data have been suggested in the literature. This paper compares the performance of eight alternative estimators, including OLS and GLM estimators and one- and two-part models, in predicting Medicare costs. It finds that four of the alternatives produce very similar results in practice. It then suggests an efficient method for researchers to use when selecting estimators of health care costs. Copyright 2004 Elsevier B.V.
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              Cost-of-illness analysis. What room in health economics?

              Cost-of-illness (COI) was the first economic evaluation technique used in the health field. The principal aim was to measure the economic burden of illness to society. Its usefulness as a decision-making tool has however been questioned since its inception. The main criticism came from welfare economists who rejected COIs because they were not grounded in welfare economics theory. Other attacks related to the use of the human capital approach (HCA) to evaluate morbidity and mortality costs since it was said that the HCA had nothing to do with the value people attach to their lives. Finally, objections were made that COI could not be of any help to decision makers and that other forms of economic evaluation (e.g. cost-effectiveness, cost-benefit analysis) would be much more useful to those taking decisions and ranking priorities. Conversely, it is here suggested that COI can be a good economic tool to inform decision makers if it is considered from another perspective. COI is a descriptive study that can provide information to support the political process as well as the management functions at different levels of the healthcare organisations. To do that, the design of the study must be innovative, capable of measuring the true cost to society; to estimate the main cost components and their incidence over total costs; to envisage the different subjects who bear the costs; to identify the actual clinical management of illness; and to explain cost variability. In order to reach these goals, COI need to be designed as observational bottom-up studies.

                Author and article information

                +334 13 73 22 86 , sebastien.cortaredona@inserm.fr
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                8 December 2017
                8 December 2017
                : 15
                [1 ]ISNI 0000 0004 0467 0503, GRID grid.464064.4, Aix-Marseille University, INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l’Information Médicale, ; 19-21 boulevard Jean Moulin, 13005 Marseille, France
                [2 ]ORS PACA, Observatoire régional de la santé Provence-Alpes-Côte d’Azur, Marseille, France
                [3 ]ISNI 0000 0001 2176 4817, GRID grid.5399.6, Aix-Marseille Univ., CNRS, EHESS, Centrale Marseille, Aix-Marseille School of Economics, ; Marseille, France
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funded by: FundRef http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 643576
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                Research Article
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                © The Author(s) 2017

                cost of illness,comorbidity,chronic diseases,prevention policies
                cost of illness, comorbidity, chronic diseases, prevention policies


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