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Zhao T, Tew M, Feenstra T, van Baal P, Willis M, Valentine WJ, Clarke PM, Hunt B, Altunkaya J, Tran-Duy A, Pollock RF, Malkin SJP, Nilsson A, McEwan P, Foos V, Leal J, Huang ES, Laiteerapong N, Lamotte M, Smolen H, Quan J, Martins L, Ramos M, Palmer AJ. The Impact of Unrelated Future Medical Costs on Economic Evaluation Outcomes for Different Models of Diabetes. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2024; 22:861-869. [PMID: 39283475 PMCID: PMC11470878 DOI: 10.1007/s40258-024-00914-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/20/2024] [Indexed: 10/13/2024]
Abstract
OBJECTIVE This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus. METHODS Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes. RESULTS The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations. CONCLUSIONS For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.
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Affiliation(s)
- Ting Zhao
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia
| | - Michelle Tew
- Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Talitha Feenstra
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Pieter van Baal
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Michael Willis
- The Swedish Institute for Health Economics, Lund, Sweden
| | | | - Philip M Clarke
- Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK
- Australian Centre for Accelerating Diabetes Innovations (ACADI), Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Barnaby Hunt
- Ossian Health Economics and Communications, Basel, Switzerland
| | - James Altunkaya
- Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK
| | - An Tran-Duy
- Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Australian Centre for Accelerating Diabetes Innovations (ACADI), Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | | | | | | | - Phil McEwan
- Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Volker Foos
- Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Jose Leal
- Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK
| | - Elbert S Huang
- Center for Chronic Disease Research and Policy (CDRP), The University of Chicago, Chicago, IL, USA
| | - Neda Laiteerapong
- Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Harry Smolen
- Medical Decision Modeling Inc., Indianapolis, IN, USA
| | - Jianchao Quan
- School of Public Health, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
- HKU Business School, University of Hong Kong, Hong Kong, Hong Kong
| | | | | | - Andrew J Palmer
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia.
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Su HY, Nguyen TTD, Lin WH, Ou HT, Kuo S. External validation and calibration of risk equations for prediction of diabetic kidney diseases among patients with type 2 diabetes in Taiwan. Cardiovasc Diabetol 2024; 23:357. [PMID: 39385193 PMCID: PMC11465834 DOI: 10.1186/s12933-024-02443-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Accepted: 09/17/2024] [Indexed: 10/12/2024] Open
Abstract
BACKGROUND Most existing risk equations for predicting/stratifying individual diabetic kidney disease (DKD) risks were developed using relatively dated data from selective and homogeneous trial populations comprising predominately Caucasian type 2 diabetes (T2D) patients. We seek to adapt risk equations for prediction of DKD progression (microalbuminuria, macroalbuminuria, and renal failure) using empiric data from a real-world population with T2D in Taiwan. METHODS Risk equations from three well-known simulation models: UKPDS-OM2, RECODe, and CHIME models, were adapted. Discrimination and calibration were determined using the area under the receiver operating characteristic curve (AUROC), a calibration plot (slope and intercept), and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was performed for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of risk equations to address risk variations among patients. RESULTS The RECODe equations for microalbuminuria and macroalbuminuria showed moderate discrimination (AUROC: 0.62 and 0.76) but underestimated the event risks (calibration slope > 1). The CHIME equation had the best discrimination for renal failure (AUROCs from CHIME, UKPDS-OM2 and RECODe: 0.77, 0.60 and 0.64, respectively). All three equations overestimated renal failure risk (calibration slope < 1). After rigorous updating, the calibration slope/intercept of the recalibrated RECODe for predicting microalbuminuria (0.87/0.0459) and macroalbuminuria (1.10/0.0004) risks as well as the recalibrated CHIME equation for predicting renal failure risk (0.95/-0.0014) were improved. CONCLUSIONS Risk equations for prediction of DKD progression in real-world Taiwanese T2D patients were established, which can be incorporated into a multi-state simulation model to project and differentiate individual DKD risks for supporting timely interventions and health economic research.
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Affiliation(s)
- Hsuan-Yu Su
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan
| | - Thi Thuy Dung Nguyen
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan
| | - Wei-Hung Lin
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Nephrology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Huang-Tz Ou
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan.
- Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Shihchen Kuo
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
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Altunkaya J, Li X, Adler A, Feenstra T, Fridhammar A, Keng MJ, Lamotte M, McEwan P, Nilsson A, Palmer AJ, Quan J, Smolen H, Tran-Duy A, Valentine W, Willis M, Leal J, Clarke P. Examining the Impact of Structural Uncertainty Across 10 Type 2 Diabetes Models: Results From the 2022 Mount Hood Challenge. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:1338-1347. [PMID: 38986899 DOI: 10.1016/j.jval.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes. METHODS Ten independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences. RESULTS Overall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially-by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB. CONCLUSIONS This exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling.
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Affiliation(s)
- James Altunkaya
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, England, UK.
| | - Xinyu Li
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
| | - Amanda Adler
- Diabetes Trial Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, England, UK
| | - Talitha Feenstra
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | | | - Mi Jun Keng
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, England, UK
| | - Mark Lamotte
- IQVIA, Zaventem, Belgium; Th(is)(2)Modeling, Asse, Belgium
| | - Phil McEwan
- Health Economics and Outcomes Research Ltd, Cardiff, Wales, UK
| | | | - Andrew J Palmer
- Menzies Institute for Medical Research, The University of Tasmania, Hobart, Tasmania, Australia
| | - Jianchao Quan
- School of Public Health, LKS Faculty of Medicine, University of Hong Kong, Hong Kong
| | - Harry Smolen
- Medical Decision Modeling Inc, Indianapolis, IN, USA
| | - An Tran-Duy
- Centre for Health Policy, Melbourne School of Population and Global Health, the University of Melbourne, Melbourne, VIC, Australia; Australian Centre for Accelerating Diabetes Innovations (ACADI), Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
| | | | - Michael Willis
- The Swedish Institute for Health Economics, Lund, Sweden
| | - José Leal
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, England, UK
| | - Philip Clarke
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, England, UK
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Shen X, Zhang XH, Yang L, Wang PF, Zhang JF, Song SZ, Jiang L. Development and validation of a nomogram of all-cause mortality in adult Americans with diabetes. Sci Rep 2024; 14:19148. [PMID: 39160223 PMCID: PMC11333764 DOI: 10.1038/s41598-024-69581-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 08/06/2024] [Indexed: 08/21/2024] Open
Abstract
This study aimed to develop and validate a predictive model of all-cause mortality risk in American adults aged ≥ 18 years with diabetes. 7918 participants with diabetes were enrolled from the National Health and Nutrition Examination Survey (NHANES) 1999-2016 and followed for a median of 96 months. The primary study endpoint was the all-cause mortality. Predictors of all-cause mortality included age, Monocytes, Erythrocyte, creatinine, Nutrition Risk Index (NRI), neutrophils/lymphocytes (NLR), smoking habits, alcohol consumption, cardiovascular disease (CVD), urinary albumin excretion rate (UAE), and insulin use. The c-index was 0.790 (95% CI 0.779-0.801, P < 0.001) and 0.792 (95% CI: 0.776-0.808, P < 0.001) for the training and validation sets, respectively. The area under the ROC curve was 0.815, 0.814, 0.827 and 0.812, 0.818 and 0.829 for the training and validation sets at 3, 5, and 10 years of follow-up, respectively. Both calibration plots and DCA curves performed well. The model provides accurate predictions of the risk of death for American persons with diabetes and its scores can effectively determine the risk of death in outpatients, providing guidance for clinical decision-making and predicting prognosis for patients.
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Affiliation(s)
- Xia Shen
- Department of Nursing, School of Health and Nursing, Wuxi Taihu University, 68 Qian Rong Rode, Bin Hu District, Wuxi, China
| | - Xiao Hua Zhang
- Cardiac Catheter Room, Wuxi People's Hospital, Jiangsu, No.299 Qing Yang Road, Wuxi, 214000, China
| | - Long Yang
- Department of Pediatric Cardiothoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University, 137 Li Yu Shan Road, Urumqi, 830054, China
| | - Peng Fei Wang
- Department of Traditional Chinese Medicine, Fuzhou University Affiliated Provincial Hospital, 134 East Street, Gu Lou District, Fuzhou, 350001, China
| | - Jian Feng Zhang
- Research and Teaching Department, Taizhou Hospital of Integrative Medicine, Jiangsu Province, No. 111, Jiang Zhou South Road, Taizhou City, Jiangsu, China
| | - Shao Zheng Song
- Department of Basci, School of Health and Nursing, Wuxi Taihu University, 68 Qian Rong Rode, Bin Hu District, Wuxi, China.
| | - Lei Jiang
- Department of Radiology, The Convalescent Hospital of East China, No.67 Da Ji Shan, Wuxi, 214065, China.
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Yang CT, Chong KS, Wang CC, Ou HT, Kuo S. Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach. Cardiovasc Diabetol 2024; 23:244. [PMID: 38987773 PMCID: PMC11238483 DOI: 10.1186/s12933-024-02320-0] [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: 03/01/2024] [Accepted: 06/16/2024] [Indexed: 07/12/2024] Open
Abstract
OBJECTIVE To adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan. METHODS The EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients' cardiovascular risks across institutions. RESULTS The CHIME risk equations had acceptable discrimination (AUROC: 0.71-0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/- 0.0008, 1.1003/- 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/- 0.0011, 0.8714/0.0030, and 1.0476/- 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05). CONCLUSIONS We provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.
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Affiliation(s)
- Chun-Ting Yang
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kah Suan Chong
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chi-Chuan Wang
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan
| | - Huang-Tz Ou
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Shihchen Kuo
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
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Li W, Lai Z, Tang N, Tang F, Huang G, Lu P, Jiang L, Lei D, Xu F. Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES. Diabetes Res Clin Pract 2024; 207:111081. [PMID: 38160736 DOI: 10.1016/j.diabres.2023.111081] [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: 10/07/2023] [Revised: 12/17/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
AIMS To develop a metric termed the diabetic retinopathy-related homeostatic dysregulation (DRHD) value, and estimate its association with future risk of mortality in individuals with type 2 diabetes. METHODS With the data of the NHANES, the biomarkers associated with DR were identified from 40 clinical parameters using LASSO regression. Subsequently, the DRHD value was constructed utilizing the Mahalanobis distance approach. In the retrospective cohortof 6420 type 2 diabetes patients, we estimated the associations between DRHD values and mortality related to all-cause, cardiovascular disease (CVD) and diabetes-specific causes using Cox proportional hazards regression models. RESULTS A set of 14 biomarkers associated with DR was identified for the construction of DRHD value. During an average of 8 years of follow-up, the multivariable-adjusted HRs and corresponding 95 % CIs for the highest quartiles of DRHD values were 2.04 (1.76, 2.37), 2.32 (1.78, 3.01), and 2.29 (1.72, 3.04) for all-cause, CVD and diabetes-specific mortality, respectively. Furthermore, we developed a web-based calculator for the DRHD value to enhance its accessibility and usability (https://dzwxl-drhd.streamlit.app/). CONCLUSIONS Our study constructed the DRHD value as a measure to assess homeostatic dysregulation among individuals with type 2 diabetes. The DRHD values exhibited potential as a prognostic indicator for retinopathy and for mortality in patients affected by type 2 diabetes.
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Affiliation(s)
- Wenxiang Li
- Nanjing Medical University, Nanjing 210000, China
| | - Zhaoguang Lai
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Ningning Tang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Fen Tang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Guangyi Huang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Peng Lu
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Li Jiang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Daizai Lei
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China.
| | - Fan Xu
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China.
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7
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Yuan S, Wu Y. Effectiveness and cost-effectiveness of six GLP-1RAs for treatment of Chinese type 2 diabetes mellitus patients that inadequately controlled on metformin: a micro-simulation model. Front Public Health 2023; 11:1201818. [PMID: 37744474 PMCID: PMC10513082 DOI: 10.3389/fpubh.2023.1201818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To systematically estimate and compare the effectiveness and cost-effectiveness of the glucagon-like peptide-1 receptor agonists (GLP-1RAs) approved in China and to quantify the relationship between the burden of diabetic comorbidities and glycosylated hemoglobin (HbA1c) or body mass index (BMI). Methods To estimate the costs (US dollars, USD) and quality-adjusted life years (QALY) for six GLP-1RAs (exenatide, loxenatide, lixisenatide, dulaglutide, semaglutide, and liraglutide) combined with metformin in the treatment of patients with type 2 diabetes mellitus (T2DM) which is inadequately controlled on metformin from the Chinese healthcare system perspective, a discrete event microsimulation cost-effectiveness model based on the Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model was developed. A cohort of 30,000 Chinese patients was established, and one-way sensitivity analysis and probabilistic sensitivity analysis (PSA) with 50,000 iterations were conducted considering parameter uncertainty. Scenario analysis was conducted considering the impacts of research time limits. A network meta-analysis was conducted to compare the effects of six GLP-1RAs on HbA1c, BMI, systolic blood pressure, and diastolic blood pressure. The incremental net monetary benefit (INMB) between therapies was used to evaluate the cost-effectiveness. China's per capita GDP in 2021 was used as the willingness-to-pay threshold. A generalized linear model was used to quantify the relationship between the burden of diabetic comorbidities and HbA1c or BMI. Results During a lifetime, the cost for a patient ranged from USD 42,092 with loxenatide to USD 47,026 with liraglutide, while the QALY gained ranged from 12.50 with dulaglutide to 12.65 with loxenatide. Compared to exenatide, the INMB of each drug from highest to lowest were: loxenatide (USD 1,124), dulaglutide (USD -1,418), lixisenatide (USD -1,713), semaglutide (USD -4,298), and liraglutide (USD -4,672). Loxenatide was better than the other GLP-1RAs in the base-case analysis. Sensitivity and scenario analysis results were consistent with the base-case analysis. Overall, the price of GLP-1RAs most affected the results. Medications with effective control of HbA1c or BMI were associated with a significantly smaller disease burden (p < 0.05). Conclusion Loxenatide combined with metformin was identified as the most economical choice, while the long-term health benefits of patients taking the six GLP-1RAs are approximate.
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Affiliation(s)
| | - Yingyu Wu
- Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China
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8
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Wang C, Wang J, Wan R, Yuan T, Yang L, Zhang D, Li X, Wang M, Liu H, Lei Y, Wei H, Li J, Liu M, Hua Y, Sun L, Zhang L. Relationship between baseline and changed serum uric acid and the incidence of type 2 diabetes mellitus: a national cohort study. Front Public Health 2023; 11:1170792. [PMID: 37483942 PMCID: PMC10357007 DOI: 10.3389/fpubh.2023.1170792] [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: 02/21/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023] Open
Abstract
Objective To explore the correlation between baseline serum uric acid (SUA) and SUA changes with the incidence of type 2 diabetes mellitus (T2DM) among middle-aged and older individuals. Method Binary logistic regression was used to calculate the odds ratio (ORs) and 95% confidence intervals (CIs) of the effects of baseline and changes in SUA on the incidence of T2DM. Stratified analysis was conducted based on sex, and the SUA levels were classified into four quartiles to assess the effect of baseline and relative changes in SUA on the incidence of T2DM. Furthermore, interaction analysis was performed between body mass index (BMI) and SUA, age and SUA, and sex and SUA. Results In the cohort study, the highest quartiles of SUA were significantly correlated with an increased incidence of T2DM among females in model 1 [OR = 2.231 (1.631, 3.050)], model 2 [OR = 2.090 (1.523, 2.867)], model 3 [OR = 2.075 (1.511, 2.849)], and model 4 [OR = 1.707 (1.234, 2.362)]. The highest quartiles of SUA had a statistically significant effect on the incidence of T2DM among all participants in model 1 [OR = 1.601 (1.277, 2.008)], model 2 [OR = 1.519 (1.204, 1.915)], model 3 [OR = 1.597 (1.257, 2.027)], and model 4 [OR = 1.380 (1.083, 1.760)]. Regarding the relative change of SUA, the highest quantiles of SUA were significantly correlated with an increased incidence of T2DM among females in model 1 [OR = 1.409 (1.050, 1.890)], model 2 [OR = 1.433 (1.067, 1.926)], and model 3 [OR = 1.420 (1.056, 1.910)], and there was a statistically significant correlation with incident T2DM among all participants in model 4 [OR = 1.346 (1.079, 1.680)] after adjusting for all covariates. However, there was no significant correlation between baseline, relative, and absolute changes in SUA and the incidence of T2DM among males. The interaction analysis demonstrated that sex, BMI, and the relative changes in SUA had a combined effect on the incidence of T2DM, while age and the changes in SUA had a joint effect on the incidence of T2DM only in females. Conclusion There was a positive association between SUA and the incidence of T2DM for all participants. However, significant sex differences in incidence were observed only in women, not men.
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Affiliation(s)
- Congzhi Wang
- Department of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Jiazhi Wang
- Sports Institute, Chi Zhou College, Education Park, Chi Zhou, China
| | - Rui Wan
- Business School, Yunnan University of Finance and Economics, Kunming, China
| | - Ting Yuan
- Obstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Liu Yang
- Department of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Dongmei Zhang
- Department of Pediatric Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Xiaoping Li
- Department of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Min Wang
- Department of Pharmacy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Haiyang Liu
- Student Health Center, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Yunxiao Lei
- Obstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Huanhuan Wei
- Obstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Jing Li
- Department of Surgical Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Mingming Liu
- Department of Surgical Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Ying Hua
- Rehabilitation Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Lu Sun
- Department of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
| | - Lin Zhang
- Department of Internal Medicine Nursing, School of Nursing, Wannan Medical College, Higher Education Park, Wuhu, China
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9
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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: 2] [Impact Index Per Article: 2.0] [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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10
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Quan J, Zhao Z, Wang L, Ng CS, Kwok HH, Zhang M, Zhou S, Ye J, Ong XJ, Ma R, Leung GM, Eggleston K, Zhou M. Potential health and economic impact associated with achieving risk factor control in Chinese adults with diabetes: a microsimulation modelling study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 33:100690. [PMID: 37181534 PMCID: PMC10166995 DOI: 10.1016/j.lanwpc.2023.100690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/30/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023]
Abstract
Background The prevalence of diabetes has risen sharply in China. Improving modifiable risk factors such as glycaemia and blood pressure could substantially reduce disease burden and treatment costs to achieve a healthier China by 2030. Methods We used a nationally representative population-based survey of adults with diabetes in 31 provinces in mainland China to assess the prevalence of risk factor control. We adopted a microsimulation approach to estimate the impact of improved control of blood pressure and glycaemia on mortality, quality-adjusted life-years (QALYs), and healthcare cost. We applied the validated CHIME diabetes outcomes model over a 10-year time horizon. Baseline scenario of status quo was evaluated against alternative strategies based on World Health Organization and Chinese Diabetes Society guidelines. Findings Among 24,319 survey participants with diabetes (age 30-70), 69.1% (95% CI: 67.7-70.5) achieved optimal diabetes control (HbA1c <7% [53 mmol/mol]), 27.7% [26.1-29.3] achieved blood pressure control (<130/80 mmHg) and 20.1% (18.6-21.6) achieved both targets. Achieving 70% control rate for people with diabetes could reduce deaths before age 70 by 7.1% (5.7-8.7), reduce medical costs by 14.9% (12.3-18.0), and gain 50.4 QALYs (44.8-56.0) per 1000 people over 10 years compared to the baseline status quo. The largest health gains were for strategies including strict blood pressure control of 130/80 mmHg, particularly in rural areas. Interpretation Based on a nationally representative survey, few adults with diabetes in China achieved optimal control of glycaemia and blood pressure. Substantial health gains and economic savings are potentially achievable with better risk factor control especially in rural settings. Funding Chinese Central Government, Research Grants Council of the Hong Kong Special Administrative Region, China [27112518].
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Affiliation(s)
- Jianchao Quan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Zhenping Zhao
- The National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Xicheng District, 100050, Beijing, China
| | - Limin Wang
- The National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Xicheng District, 100050, Beijing, China
| | - Carmen S. Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Harley H.Y. Kwok
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mei Zhang
- The National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Xicheng District, 100050, Beijing, China
| | - Sunyue Zhou
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jiaxi Ye
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Xin Jiong Ong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Robyn Ma
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gabriel M. Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong SAR, China
| | - Karen Eggleston
- Stanford University, Stanford, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Maigeng Zhou
- The National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Xicheng District, 100050, Beijing, China
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11
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Quan J, Ng CS. Incremental healthcare costs attributable to type 2 diabetes in Hong Kong: A population-based cost of illness study. Diabet Med 2023; 40:e14970. [PMID: 36209369 DOI: 10.1111/dme.14970] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 01/17/2023]
Affiliation(s)
- Jianchao Quan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Carmen S Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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12
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Zhou Y, Li X, Sun Q, Wei J, Liu H, Wang K, Luo J. Adherence to Annual Fundus Exams among Chinese Population with Diagnosed Diabetes. J Clin Med 2022; 11:jcm11226859. [PMID: 36431336 PMCID: PMC9697630 DOI: 10.3390/jcm11226859] [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: 10/08/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022] Open
Abstract
Adherence to annual fundus examinations in the Chinese population with diabetes and its correlates have not been investigated. The present study obtained data for the first nationally representative survey in China, China Health and Retirement Longitudinal Survey (CHARLS), which collected a wide range of data every 2 years, including demographic, socioeconomic, medical and lifestyle-related information. The adherence rates to annual fundus exams across four waves (2011−2018) were assessed. Univariate and multivariable logistic regressions were used to determine factors associated with adherence. The adherence rates to annual fundus examinations of ou study population were 23.6% in 2011, 15.3% in 2013, 17.5% in 2015 and 21.5% in 2018, respectively. Consistent results over four waves showed that non-adherent patients had a relatively lower educational level, insufficient diabetes medication use, fewer non-medication treatments and irregular physical examination compared to those who were adherent to the annual fundus exam (all p values < 0.05). These variables were further identified as factors associated with adherence according to univariate and multivariate logistic regression analyses (all p values < 0.05). The present study provides explicit evidence that the adherence rate to annual fundus examinations among Chinese population with diabetes is worryingly low. Insufficient educational attainment, especially specific diabetes education, has a negative impact on patients’ adherence to clinical guideline for eye health.
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Affiliation(s)
- Yifan Zhou
- Department of Ophthalmology, Putuo People’s Hospital, Tongji University, Shanghai 200060, China
| | - Xiaowen Li
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - Qinglei Sun
- Department of Ophthalmology, Shanghai East Hospital, Shanghai 200120, China
| | - Jin Wei
- Department of Ophthalmology, Shanghai General Hospital (Shanghai First People’s Hospital), School of Medicine, Shanghai JiaoTong University, Shanghai 200080, China
- National Clinical Research Center for Eye Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
| | - Haiyun Liu
- Department of Ophthalmology, Shanghai General Hospital (Shanghai First People’s Hospital), School of Medicine, Shanghai JiaoTong University, Shanghai 200080, China
- National Clinical Research Center for Eye Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
| | - Keyan Wang
- Department of Ophthalmology, Eye and ENT Hospital of Fudan University, Shanghai 200031, China
- Correspondence: (K.W.); (J.L.)
| | - Jianfeng Luo
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
- Correspondence: (K.W.); (J.L.)
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13
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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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14
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Yin Y, Tu Y, Zhao M, Tang W. Effectiveness and Cost-Effectiveness of Non-Pharmacological Interventions among Chinese Adults with Prediabetes: A Protocol for Network Meta-Analysis and CHIME-Modeled Cost-Effectiveness Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031622. [PMID: 35162645 PMCID: PMC8835234 DOI: 10.3390/ijerph19031622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 01/28/2022] [Accepted: 01/29/2022] [Indexed: 11/16/2022]
Abstract
Patients with prediabetes who are at a high risk of progressing to diabetes are recommended early-stage intervention, according to guidelines. Non-pharmacological interventions are effective and cost-effective for glycemic control compared with medicines. We aim to explore which non-pharmacological interventions have the greatest potential effectiveness, cost-effectiveness, and feasibility in community-based diabetes management in China. We will perform a systematic review and network meta-analysis to compare the effectiveness of included non-pharmacological interventions, then use Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) to model the yearly incidence of complications, costs, and health utility for the lifetime. Published studies (only randomized controlled trials (RCTs) and cluster RCTs with at least one study arm of any non-pharmacological intervention) will be retrieved and screened using several databases. Primary outcomes included blood glucose, glycated hemoglobin, incidence of type 2 diabetes mellitus, and achievement of normoglycemia. Health utilities and cost parameters are to be calculated using a societal perspective and integrated into the modified CHIME model to achieve quality-adjusted life-year (QALY) estimates and lifetime costs. QALYs and incremental cost-effectiveness ratio will then be used to determine effectiveness and cost-effectiveness, respectively. Our study findings can inform improved diabetes management in countries with no intervention programs for these patients.
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Affiliation(s)
- Yue Yin
- Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China; (Y.Y.); (Y.T.); (M.Z.)
- Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing 211198, China
| | - Yusi Tu
- Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China; (Y.Y.); (Y.T.); (M.Z.)
- Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing 211198, China
| | - Mingye Zhao
- Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China; (Y.Y.); (Y.T.); (M.Z.)
- Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing 211198, China
| | - Wenxi Tang
- Department of Pharmacoeconomics, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China; (Y.Y.); (Y.T.); (M.Z.)
- Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing 211198, China
- Correspondence:
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