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Fan M, Stephan AJ, Emmert-Fees K, Peters A, Laxy M. Health and economic impact of improved glucose, blood pressure and lipid control among German adults with type 2 diabetes: a modelling study. Diabetologia 2023; 66:1693-1704. [PMID: 37391625 PMCID: PMC10390361 DOI: 10.1007/s00125-023-05950-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/18/2023] [Indexed: 07/02/2023]
Abstract
AIMS/HYPOTHESIS The aim of this study was to estimate the long-term health and economic consequences of improved risk factor control in German adults with type 2 diabetes. METHODS We used the UK Prospective Diabetes Study Outcomes Model 2 to project the patient-level health outcomes and healthcare costs of people with type 2 diabetes in Germany over 5, 10 and 30 years. We parameterised the model using the best available data on population characteristics, healthcare costs and health-related quality of life from German studies. The modelled scenarios were: (1) a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), of systolic BP (SBP) by 10 mmHg, or of LDL-cholesterol by 0.26 mmol/l in all patients, and (2) achievement of guideline care recommendations for HbA1c (≤53 mmol/mol [7%]), SBP (≤140 mmHg) or LDL-cholesterol (≤2.6 mmol/l) in patients who do not meet the recommendations. We calculated nationwide estimates using age- and sex-specific quality-adjusted life year (QALY) and cost estimates, type 2 diabetes prevalence and population size. RESULTS Over 10 years, a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), SBP by 10 mmHg or LDL-cholesterol by 0.26 mmol/l led to per-person savings in healthcare expenditures of €121, €238 and €34, and 0.01, 0.02 and 0.015 QALYs gained, respectively. Achieving guideline care recommendations for HbA1c, SBP or LDL-cholesterol could reduce healthcare expenditure by €451, €507 and €327 and gained 0.03, 0.05 and 0.06 additional QALYs in individuals who did not meet the recommendations. Nationally, achieving guideline care recommendations for HbA1c, SBP and LDL-cholesterol could reduce healthcare costs by over €1.9 billion. CONCLUSIONS/INTERPRETATION Sustained improvements in HbA1c, SBP and LDL-cholesterol control among diabetes patients in Germany can lead to substantial health benefits and reduce healthcare expenditures.
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Affiliation(s)
- Min Fan
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
- German Center for Diabetes Research (DZD), Munich, Germany.
- Institute of Health Economics and Health Care Management, Helmholtz Munich, Munich, Germany.
| | - Anna-Janina Stephan
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
- Institute of Health Economics and Health Care Management, Helmholtz Munich, Munich, Germany
| | - Karl Emmert-Fees
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
- Institute of Health Economics and Health Care Management, Helmholtz Munich, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Munich, Munich, Germany
| | - Michael Laxy
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
- Institute of Health Economics and Health Care Management, Helmholtz Munich, Munich, Germany
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Li X, Li F, Wang J, van Giessen A, Feenstra TL. Prediction of complications in health economic models of type 2 diabetes: a review of methods used. Acta Diabetol 2023; 60:861-879. [PMID: 36867279 PMCID: PMC10198865 DOI: 10.1007/s00592-023-02045-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 03/04/2023]
Abstract
AIM Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. METHODS PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. RESULTS The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. CONCLUSIONS The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.
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Affiliation(s)
- Xinyu Li
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, A. Deusinglaan1, 9713AV, Groningen, The Netherlands.
| | - Fang Li
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, A. Deusinglaan1, 9713AV, Groningen, The Netherlands
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Anoukh van Giessen
- Expertise Center for Methodology and Information Services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Talitha L Feenstra
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, A. Deusinglaan1, 9713AV, Groningen, The Netherlands
- Center for Nutrition, Prevention and Health Services Research, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
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Hatipoglu BA. Rekindling Hope for Remission: Current Impact of Diabetes for Our World's Future Health and Economy. Endocrinol Metab Clin North Am 2023; 52:1-12. [PMID: 36754486 DOI: 10.1016/j.ecl.2022.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The individual and societal burdens of living with a chronic disease are a global issue. Diabetes directly increases health care costs to manage the disease and the associated complications and indirectly increases the economic burden through long-term complications that hinder the productivity of humans worldwide. Thus, it is crucial to have accurate information on diabetes-related costs and the geographic and global economic impact when planning interventions and future strategies. Health care systems must work with government agencies to plan national-level pre diabetes and diabetes strategies and policies. Public health services must focus on diabetes screening prevention and remission.
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Affiliation(s)
- Betul A Hatipoglu
- Case Western Reserve University, School of Medicine, Department of Medicine University Hospitals Cleveland Medical Center, Department of Medicine, Adult Endocrinology, 11100 Euclid Avenue, Cleveland, OH 44106, USA.
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Twumwaa TE, Justice N, Robert VDM, Itamar M. Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review. BMC Health Serv Res 2022; 22:1397. [PMID: 36419101 PMCID: PMC9684986 DOI: 10.1186/s12913-022-08820-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 11/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Decision analytical models (DAMs) are used to develop an evidence base for impact and health economic evaluations, including evaluating interventions to improve diabetes care and health services-an increasingly important area in low- and middle-income countries (LMICs), where the disease burden is high, health systems are weak, and resources are constrained. This study examines how DAMs-in particular, Markov, system dynamic, agent-based, discrete event simulation, and hybrid models-have been applied to investigate non-pharmacological population-based (NP) interventions and how to advance their adoption in diabetes research in LMICs. METHODS We systematically searched peer-reviewed articles published in English from inception to 8th August 2022 in PubMed, Cochrane, and the reference list of reviewed articles. Articles were summarised and appraised based on publication details, model design and processes, modelled interventions, and model limitations using the Health Economic Evaluation Reporting Standards (CHEERs) checklist. RESULTS Twenty-three articles were fully screened, and 17 met the inclusion criteria of this qualitative review. The majority of the included studies were Markov cohort (7, 41%) and microsimulation models (7, 41%) simulating non-pharmacological population-based diabetes interventions among Asian sub-populations (9, 53%). Eleven (65%) of the reviewed studies evaluated the cost-effectiveness of interventions, reporting the evaluation perspective and the time horizon used to track cost and effect. Few studies (6,35%) reported how they validated models against local data. CONCLUSIONS Although DAMs have been increasingly applied in LMICs to evaluate interventions to control diabetes, there is a need to advance the use of DAMs to evaluate NP diabetes policy interventions in LMICs, particularly DAMs that use local research data. Moreover, the reporting of input data, calibration and validation that underlies DAMs of diabetes in LMICs needs to be more transparent and credible.
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Affiliation(s)
- Tagoe Eunice Twumwaa
- grid.11984.350000000121138138Department of Management Science, University of Strathclyde, Glasgow, UK
| | - Nonvignon Justice
- grid.8652.90000 0004 1937 1485School of Public Health, University of Ghana, Legon, Ghana
| | - van Der Meer Robert
- grid.11984.350000000121138138Department of Management Science, University of Strathclyde, Glasgow, UK
| | - Megiddo Itamar
- grid.11984.350000000121138138Department of Management Science, University of Strathclyde, Glasgow, UK
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Tew M, Willis M, Asseburg C, Bennett H, Brennan A, Feenstra T, Gahn J, Gray A, Heathcote L, Herman WH, Isaman D, Kuo S, Lamotte M, Leal J, McEwan P, Nilsson A, Palmer AJ, Patel R, Pollard D, Ramos M, Sailer F, Schramm W, Shao H, Shi L, Si L, Smolen HJ, Thomas C, Tran-Duy A, Yang C, Ye W, Yu X, Zhang P, Clarke P. Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge. Med Decis Making 2022; 42:599-611. [PMID: 34911405 PMCID: PMC9329757 DOI: 10.1177/0272989x211065479] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
BACKGROUND Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. METHODS Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. RESULTS Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049). CONCLUSIONS Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.
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Affiliation(s)
- Michelle Tew
- Centre for Health Policy, Melbourne School of
Population and Global Health, The University of Melbourne, Melbourne,
Victoria, Australia
| | - Michael Willis
- The Swedish Institute for Health Economics,
Lund, Sweden
| | | | | | - Alan Brennan
- School of Health and Related Research,
University of Sheffield, Sheffield, UK
| | - Talitha Feenstra
- Groningen University, Faculty of Science and
Engineering, GRIP, Groningen, The Netherlands,Groningen University, UMCG, Groningen, The
Netherlands,Netherlands Institute for Public Health and the
Environment (RIVM), Bilthoven, The Netherlands
| | - James Gahn
- Medical Decision Modeling Inc., Indianapolis,
IN, USA
| | - Alastair Gray
- Health Economics Research Centre, Nuffield
Department of Population Health, University of Oxford, Oxford, UK
| | - Laura Heathcote
- School of Health and Related Research,
University of Sheffield, Sheffield, UK
| | - William H. Herman
- Department of Internal Medicine, University of
Michigan, Ann Arbor, MI, USA
| | - Deanna Isaman
- Department of Biostatistics, University of
Michigan, Ann Arbor, MI, USA
| | - Shihchen Kuo
- Department of Internal Medicine, University of
Michigan, Ann Arbor, MI, USA
| | - Mark Lamotte
- Global Health Economics and Outcomes Research,
Real World Solutions, IQVIA, Zaventem, Belgium
| | - José Leal
- Health Economics Research Centre, Nuffield
Department of Population Health, University of Oxford, Oxford, UK
| | - Phil McEwan
- Health Economics and Outcomes Research Ltd,
Cardiff, UK
| | | | - Andrew J. Palmer
- Centre for Health Policy, Melbourne School of
Population and Global Health, The University of Melbourne, Melbourne,
Victoria, Australia,Menzies Institute for Medical Research, The
University of Tasmania, Hobart, Tasmania, Australia
| | - Rishi Patel
- Health Economics Research Centre, Nuffield
Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Pollard
- School of Health and Related Research,
University of Sheffield, Sheffield, UK
| | - Mafalda Ramos
- Global Health Economics and Outcomes Research,
Real World Solutions, IQVIA, Porto Salvo, Portugal
| | - Fabian Sailer
- GECKO Institute for Medicine, Informatics and
Economics, Heilbronn University, Heilbronn, Germany
| | - Wendelin Schramm
- GECKO Institute for Medicine, Informatics and
Economics, Heilbronn University, Heilbronn, Germany
| | - Hui Shao
- Department of Pharmaceutical Outcomes and
Policy. University of Florida College of Pharmacy. Gainesville, FL,
USA
| | - Lizheng Shi
- Department of Health Policy and Management;
Tulane University School of Public Health and Tropical Medicine
| | - Lei Si
- Menzies Institute for Medical Research, The
University of Tasmania, Hobart, Tasmania, Australia,The George Institute for Global Health, UNSW
Sydney, Kensington, Australia
| | | | - Chloe Thomas
- School of Health and Related Research,
University of Sheffield, Sheffield, UK
| | - An Tran-Duy
- Centre for Health Policy, Melbourne School of
Population and Global Health, The University of Melbourne, Melbourne,
Victoria, Australia
| | - Chunting Yang
- Department of Biostatistics, University of
Michigan, Ann Arbor, MI, USA
| | - Wen Ye
- Department of Biostatistics, University of
Michigan, Ann Arbor, MI, USA
| | - Xueting Yu
- Medical Decision Modeling Inc., Indianapolis,
IN, USA
| | - Ping Zhang
- Division of Diabetes Translation, Centres for
Disease Control and Prevention, Atlanta, GA, USA
| | - Philip Clarke
- Philip Clarke, Health Economics Research
Centre, Nuffield Department of Population Health, University of Oxford, Oxford,
UK; ()
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Pöhlmann J, Bergenheim K, Garcia Sanchez JJ, Rao N, Briggs A, Pollock RF. Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts. Diabetes Ther 2022; 13:651-677. [PMID: 35290625 PMCID: PMC8991383 DOI: 10.1007/s13300-022-01208-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/20/2022] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTION As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development. METHODS This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources. RESULTS The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials. CONCLUSION Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.
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Affiliation(s)
| | - Klas Bergenheim
- Global Market Access and Pricing, BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | | | - Naveen Rao
- Global Market Access and Pricing, BioPharmaceuticals, AstraZeneca, Cambridge, UK
| | - Andrew Briggs
- London School of Hygiene and Tropical Medicine, London, UK
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Mukonda E, Cleary S, Lesosky M. A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries. BMC Health Serv Res 2021; 21:1313. [PMID: 34872555 PMCID: PMC8650231 DOI: 10.1186/s12913-021-07324-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 11/18/2021] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION The burden of type 2 diabetes is steadily increasing in low-and-middle-income countries, thereby posing a major threat from both a treatment, and funding standpoint. Although simulation modelling is generally relied upon for evaluating long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the characteristics and capabilities of available models used in low-and-middle-income countries. We review the use of computer simulation modelling for the management of type 2 diabetes in low-and-middle-income countries. METHODS A search for studies reporting computer simulation models of the natural history of individuals with type 2 diabetes and/or decision models to evaluate the impact of treatment strategies on these populations was conducted in PubMed. Data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed using modelling checklists. Publications before the year 2000, from high-income countries, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and the model structure was extracted. RESULTS Of the 79 articles suitable for full text review, 44 studies met the inclusion criteria. All were cost-effectiveness/utility studies with the majority being from the East Asia and Pacific region (n = 29). Of the included studies, 34 (77.3%) evaluated the cost-effectiveness of pharmacological interventions and approximately 75% of all included studies used HbA1c as one of the treatment effects of the intervention. 32 (73%) of the publications were microsimulation models, and 29 (66%) were state-transition models. Most of the studies utilised annual cycles (n = 29, 71%), and accounted for costs and outcomes over 20 years or more (n = 38, 86.4%). CONCLUSIONS While the use of simulation modelling in the management of type 2 diabetes has been steadily increasing in low-and-middle-income countries, there is an urgent need to invest in evaluating therapeutic and policy interventions related to type 2 diabetes in low-and-middle-income countries through simulation modelling, especially with local research data. Moreover, it is important to improve transparency and credibility in the reporting of input data underlying model-based economic analyses, and studies.
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Affiliation(s)
- Elton Mukonda
- Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, 7925, South Africa.
| | - Susan Cleary
- Health Economics Unit, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Maia Lesosky
- Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, 7925, South Africa
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Hernández-Jiménez S, García-Ulloa AC, Anaya P, Gasca-Pineda R, Sánchez-Trujillo LA, Peña Baca H, González-Pier E, Graue-Hernández EO, Aguilar-Salinas CA, Gómez-Pérez FJ, Kershenobich-Stalnikowitz D. Cost-effectiveness of a self-management and comprehensive training intervention in patients with type 2 diabetes up to 5 years of diagnosis in a specialized hospital in Mexico City. BMJ Open Diabetes Res Care 2021; 9:9/1/e002097. [PMID: 34167953 PMCID: PMC8230997 DOI: 10.1136/bmjdrc-2020-002097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 06/05/2021] [Indexed: 11/15/2022] Open
Abstract
INTRODUCTION To assess the cost-effectiveness of a multidisciplinary and comprehensive innovative diabetes care program (CAIPaDi) versus usual treatment in public health institutions. RESEARCH DESIGN AND METHODS Using a cost-effectiveness analysis, we compared the CAIPaDi program versus usual treatment given in Mexican public health institutions. The analysis was based on the IQVIA Core Diabetes Model, a validated simulation model used to estimate long-term clinical outcomes. Data were prospectively obtained from the CAIPaDi program and from public databases and published papers. Health outcomes were expressed in terms of life-years gained and quality-adjusted life years (QALYs). Health and economic outcomes were estimated from a public perspective and discounted at 5% per year over a 20-year horizon. Costs are reported in US dollars (US$) of 2019. A probabilistic sensitivity analysis was performed using life-years gained and QALYs. RESULTS The CAIPaDi costs on average US$559 (95% CI: -$879 to -$239) less than the usual treatment (95% CI: -$879 to -$239) and produced a difference in mean life-years gained (0.48, 95% CI: 0.45 to 0.52) and mean QALYs (1.43, 95% CI: 1.40 to 1.46). The cost-effectiveness ratio resulted in a saving per life-year gained of -US$1155 (95% CI: -$1962 to -$460). Mean differences in QALYs resulted in a saving per QALY of -US$735 (95% CI: -$1193 to -$305). Probabilistic sensitivity analysis proved the results are robust on both life-years gained and QALYs. CONCLUSIONS CAIPaDi has a better cost-effectiveness ratio than the usual therapy in Mexican public health institutions.
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Affiliation(s)
| | | | | | | | | | | | | | - Enrique O Graue-Hernández
- Department of Cornea and Refractive Surgery, Institute of Ophthalmology Conde de Valenciana Foundation IAP, Mexico City, Mexico
| | - Carlos Alberto Aguilar-Salinas
- Endocrinology and Metabolism, National Institute of Medical Sciences and Nutrition Salvador Zubiran, Tlalpan, Mexico
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Franciso Javier Gómez-Pérez
- Endocrinology and Metabolism, National Institute of Medical Sciences and Nutrition Salvador Zubiran, Tlalpan, Mexico
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