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Modeling the health care costs of multiple type 2 diabetes-related complications based on patient-level real-world data in Germany. a methodological and empirical study of a large statutory sickness fund population
Modeling the health care costs of multiple type 2 diabetes-related complications based on patient-level real-world data in Germany. a methodological and empirical study of a large statutory sickness fund population
Background In the context of an ageing population and unfavorable trends in lifestyle factors, more people are living longer with type 2 diabetes and associated multimorbidity. Various micro- and macrovascular complications have been shown to contribute substantially to the morbidity, mortality and economic burden of type 2 diabetes. Mathematical models of diabetes provide a useful tool that can help to simulate the disease process, predict clinical and economic outcomes, and thereby assist decision makers in assessing the possible impact of a range of new diabetes interventions. At present, internationally available type 2 diabetes models are not well adapted to German patient level data. To achieve this, and especially to obtain detailed cost information, real-world health insurance data are one of the most powerful data sources to be used. However, methodological approaches to map these data into model parameters have to be further developed. Objectives This dissertation with its sub-studies seeks to systematically analyze routine data of a large statutory health insurance fund to inform diabetes simulation models on the direct costs of type 2 diabetes-related complications. In particular, this work has the two-fold aims, to provide new empirical evidence on diabetes-related costs for Germany, and to develop conceptual and methodological approaches that are capable of dealing with validity issues of routine data and the complexity due to multimorbidity in the diabetes population. In this context, the first study provides detailed estimates on the longitudinal costs associated with the diagnosis of various complications. The second study is more focused on pursuing the methodological depth in this research by exploring different strategies that address the economic impact of multiple type 2 diabetes-related complications and their interactions. In addition, this study describes important interaction patterns of co-occurring complications. Methods This dissertation is based on nationwide claims data of 316,220 (over 18 years-old) type 2 diabetes patients who were insured by the Techniker Krankenkasse in the baseline year 2012 and the 3-year follow-up period from 2013-2015. All diabetes-related complications that are typically included in international diabetes models were identified based on outpatient and inpatient diagnoses and procedures. Quarterly observations were available for each year and patient. Direct health care costs (in 2015 euros) include costs for outpatient and inpatient care, medication, rehabilitation, and the provision of aids and appliances. Generalized estimating equations (GEE) models are used to account for repeated observations per patient as an extension to traditional generalized linear models. As the base case, a normal distribution of the mean costs was assumed, given the large population size and small proportion of zero costs. In particular, in the first study, a GEE model predicting quarterly total costs was developed, adjusted for the age group, sex, occurrence of different (incident) complications, history of prevalent complications at baseline, and death for other reasons. In addition to distinguishing incident/prevalent complications, special emphasis was given to differentiate between fatal/nonfatal acute macrovascular events, to quantify costs at the quarter of event/onset and in subsequent quarters, and to consider interactions of complications with age or sex. Building on this, the second study explores four strategies of different granularity to assess the economic impact of diabetes-related multimorbidity, including the number of prevalent complications, co-occurrence of micro- and macrovascular complications, disease–disease interactions of prevalent complications, and interactions of incident on top of already prevalent complications. For this, different GEE models were developed and applied to the annual observations to increase the statistical power. Results The additive approach (using a GEE model with a normal distribution) showed a better model fit compared to a multiplicative approach with a gamma-based GEE model. Using the example of a 60-69 year old man, the first study estimated the following total costs in the quarter of first diagnosis of the complication: diabetic foot €1,293, amputation €14,284, retinopathy €671, blindness €2,933, nephropathy €3,353, end-stage renal disease (ESRD) €22,691, nonfatal stroke €9,769, fatal stroke €11,176, nonfatal myocardial infarction/cardiac arrest (MI/CA) €8,035, fatal MI/CA €8,700, nonfatal other ischemic heart disease (IHD) €6,548, fatal IHD €20,842, chronic heart failure €3,912, and angina pectoris €2,695. In the subsequent quarters, costs ranged from €681 for retinopathy to €6,130 for ESRD. Men and women from different age groups differed in their costs for complications. In addition, the second study showed that the increased number of complications is significantly associated with higher annual total costs per patient. Further assessment of interactions revealed that macrovascular complications (e.g., CHF) and high cost complications (e.g., ESRD, amputation) lead to significant positive interactions on annual costs, whereas early microvascular complications (e.g., retinopathy) caused negative interactions. The chronology of the onset of these complications turned out to have an additional impact on the cost estimates of interactions. Conclusions The results of this dissertation have important implications for different healthcare stakeholders. From a modeler’s or researcher’s perspective, the two studies provide comprehensive empirical estimates for the economic parametrization of type 2 diabetes models, especially for Germany, as well as methodological approaches for the claims-based analysis of large diabetes populations. These concepts will also help to further improve the accuracy of international cost-effectiveness evaluations by addressing multimorbidity, and especially interaction patterns. From a policy or SHI perspective, the studies provide valuable information to support the optimal resource allocation across different intervention programs for the prevention and management of type 2 diabetes complications. In addition, the results encourage a more integrated approach that takes better account of preexisting or co-occurring conditions. From a clinician’s perspective, the empirical findings may increase the awareness of the economic burden of complications in patients with type 2 diabetes. Further observational studies are still needed to gain a more complete understanding of the multiple shared pathogenic mechanisms of diabetes and its complications. Real world data, including health insurance claims data, can be used to successfully complement clinical data. To increase the added value of these data, remaining validation gaps need to be further examined and closed.
Type 2 Diabetes, Complications, Costs, Modeling, Health Insurance, Germany
Kähm, Katharina
2019
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Kähm, Katharina (2019): Modeling the health care costs of multiple type 2 diabetes-related complications based on patient-level real-world data in Germany: a methodological and empirical study of a large statutory sickness fund population. Dissertation, LMU München: Faculty of Medicine
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Abstract

Background In the context of an ageing population and unfavorable trends in lifestyle factors, more people are living longer with type 2 diabetes and associated multimorbidity. Various micro- and macrovascular complications have been shown to contribute substantially to the morbidity, mortality and economic burden of type 2 diabetes. Mathematical models of diabetes provide a useful tool that can help to simulate the disease process, predict clinical and economic outcomes, and thereby assist decision makers in assessing the possible impact of a range of new diabetes interventions. At present, internationally available type 2 diabetes models are not well adapted to German patient level data. To achieve this, and especially to obtain detailed cost information, real-world health insurance data are one of the most powerful data sources to be used. However, methodological approaches to map these data into model parameters have to be further developed. Objectives This dissertation with its sub-studies seeks to systematically analyze routine data of a large statutory health insurance fund to inform diabetes simulation models on the direct costs of type 2 diabetes-related complications. In particular, this work has the two-fold aims, to provide new empirical evidence on diabetes-related costs for Germany, and to develop conceptual and methodological approaches that are capable of dealing with validity issues of routine data and the complexity due to multimorbidity in the diabetes population. In this context, the first study provides detailed estimates on the longitudinal costs associated with the diagnosis of various complications. The second study is more focused on pursuing the methodological depth in this research by exploring different strategies that address the economic impact of multiple type 2 diabetes-related complications and their interactions. In addition, this study describes important interaction patterns of co-occurring complications. Methods This dissertation is based on nationwide claims data of 316,220 (over 18 years-old) type 2 diabetes patients who were insured by the Techniker Krankenkasse in the baseline year 2012 and the 3-year follow-up period from 2013-2015. All diabetes-related complications that are typically included in international diabetes models were identified based on outpatient and inpatient diagnoses and procedures. Quarterly observations were available for each year and patient. Direct health care costs (in 2015 euros) include costs for outpatient and inpatient care, medication, rehabilitation, and the provision of aids and appliances. Generalized estimating equations (GEE) models are used to account for repeated observations per patient as an extension to traditional generalized linear models. As the base case, a normal distribution of the mean costs was assumed, given the large population size and small proportion of zero costs. In particular, in the first study, a GEE model predicting quarterly total costs was developed, adjusted for the age group, sex, occurrence of different (incident) complications, history of prevalent complications at baseline, and death for other reasons. In addition to distinguishing incident/prevalent complications, special emphasis was given to differentiate between fatal/nonfatal acute macrovascular events, to quantify costs at the quarter of event/onset and in subsequent quarters, and to consider interactions of complications with age or sex. Building on this, the second study explores four strategies of different granularity to assess the economic impact of diabetes-related multimorbidity, including the number of prevalent complications, co-occurrence of micro- and macrovascular complications, disease–disease interactions of prevalent complications, and interactions of incident on top of already prevalent complications. For this, different GEE models were developed and applied to the annual observations to increase the statistical power. Results The additive approach (using a GEE model with a normal distribution) showed a better model fit compared to a multiplicative approach with a gamma-based GEE model. Using the example of a 60-69 year old man, the first study estimated the following total costs in the quarter of first diagnosis of the complication: diabetic foot €1,293, amputation €14,284, retinopathy €671, blindness €2,933, nephropathy €3,353, end-stage renal disease (ESRD) €22,691, nonfatal stroke €9,769, fatal stroke €11,176, nonfatal myocardial infarction/cardiac arrest (MI/CA) €8,035, fatal MI/CA €8,700, nonfatal other ischemic heart disease (IHD) €6,548, fatal IHD €20,842, chronic heart failure €3,912, and angina pectoris €2,695. In the subsequent quarters, costs ranged from €681 for retinopathy to €6,130 for ESRD. Men and women from different age groups differed in their costs for complications. In addition, the second study showed that the increased number of complications is significantly associated with higher annual total costs per patient. Further assessment of interactions revealed that macrovascular complications (e.g., CHF) and high cost complications (e.g., ESRD, amputation) lead to significant positive interactions on annual costs, whereas early microvascular complications (e.g., retinopathy) caused negative interactions. The chronology of the onset of these complications turned out to have an additional impact on the cost estimates of interactions. Conclusions The results of this dissertation have important implications for different healthcare stakeholders. From a modeler’s or researcher’s perspective, the two studies provide comprehensive empirical estimates for the economic parametrization of type 2 diabetes models, especially for Germany, as well as methodological approaches for the claims-based analysis of large diabetes populations. These concepts will also help to further improve the accuracy of international cost-effectiveness evaluations by addressing multimorbidity, and especially interaction patterns. From a policy or SHI perspective, the studies provide valuable information to support the optimal resource allocation across different intervention programs for the prevention and management of type 2 diabetes complications. In addition, the results encourage a more integrated approach that takes better account of preexisting or co-occurring conditions. From a clinician’s perspective, the empirical findings may increase the awareness of the economic burden of complications in patients with type 2 diabetes. Further observational studies are still needed to gain a more complete understanding of the multiple shared pathogenic mechanisms of diabetes and its complications. Real world data, including health insurance claims data, can be used to successfully complement clinical data. To increase the added value of these data, remaining validation gaps need to be further examined and closed.