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3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS
OBJECTIVES/SPECIFIC AIMS: Objective: Approximately 86 million people in the US have prediabetes, but only a fraction of them receive proven effective therapies to prevent diabetes. Further, the effectiveness of these therapies varies with individual risk of progression to diabetes. We estimated the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798495/ http://dx.doi.org/10.1017/cts.2019.356 |
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author | Olchanski, Natalia van Klaveren, David Cohen, Joshua T Wong, John B Ruthazer, Robin Kent, David M |
author_facet | Olchanski, Natalia van Klaveren, David Cohen, Joshua T Wong, John B Ruthazer, Robin Kent, David M |
author_sort | Olchanski, Natalia |
collection | PubMed |
description | OBJECTIVES/SPECIFIC AIMS: Objective: Approximately 86 million people in the US have prediabetes, but only a fraction of them receive proven effective therapies to prevent diabetes. Further, the effectiveness of these therapies varies with individual risk of progression to diabetes. We estimated the value of targeting those individuals at highest diabetes risk for treatment, compared to treating all individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS/STUDY POPULATION: METHODS: Using a micro-simulation model, we estimated total lifetime costs and quality-adjusted life expectancy (QALE) for individuals receiving: (1) lifestyle intervention involving an intensive program focused on healthy diet and exercise, (2) metformin administration, or (3) no intervention. The model combines several components. First a Cox proportional hazards model predicted onset of diabetes from baseline characteristics for each pre-diabetic individual and yielded a probability distribution for each alternative. We derived this risk model from the Diabetes Prevention Program (DPP) clinical trial data and the follow-up study DPP-OS. The Michigan Diabetes Research Center Model for Diabetes then estimated costs and outcomes for individuals after diabetes diagnosis using standard of care diabetes treatment. Based on individual costs and QALE, we evaluated NMB of the two interventions at population and individual levels, stratified by risk quintiles for diabetes onset at 3 years. RESULTS/ANTICIPATED RESULTS: Results: Compared to usual care, lifestyle modification conferred positive benefits for all eligible individuals. Metformin’s NMB was negative for the lowest population risk quintile. By avoiding use among individuals who would not benefit, targeted administration of metformin conferred a benefit of $500-$800 per person, depending on duration of treatment effect. When treating only 20% of the population (e.g., due to capacity constraints), targeting conferred a NMB of $14,000-$18,000 per person for lifestyle modification and $16,000-$20,000 for metformin. DISCUSSION/SIGNIFICANCE OF IMPACT: Conclusions: Metformin confers value only among higher risk individuals, so targeting its use is worthwhile. While lifestyle modification confers value for all eligible individuals, prioritizing the intervention to high risk patients when capacity is constrained substantially increases societal benefits. |
format | Online Article Text |
id | pubmed-6798495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-67984952019-10-28 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS Olchanski, Natalia van Klaveren, David Cohen, Joshua T Wong, John B Ruthazer, Robin Kent, David M J Clin Transl Sci Translational Science, Policy, & Health Outcomes Science OBJECTIVES/SPECIFIC AIMS: Objective: Approximately 86 million people in the US have prediabetes, but only a fraction of them receive proven effective therapies to prevent diabetes. Further, the effectiveness of these therapies varies with individual risk of progression to diabetes. We estimated the value of targeting those individuals at highest diabetes risk for treatment, compared to treating all individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS/STUDY POPULATION: METHODS: Using a micro-simulation model, we estimated total lifetime costs and quality-adjusted life expectancy (QALE) for individuals receiving: (1) lifestyle intervention involving an intensive program focused on healthy diet and exercise, (2) metformin administration, or (3) no intervention. The model combines several components. First a Cox proportional hazards model predicted onset of diabetes from baseline characteristics for each pre-diabetic individual and yielded a probability distribution for each alternative. We derived this risk model from the Diabetes Prevention Program (DPP) clinical trial data and the follow-up study DPP-OS. The Michigan Diabetes Research Center Model for Diabetes then estimated costs and outcomes for individuals after diabetes diagnosis using standard of care diabetes treatment. Based on individual costs and QALE, we evaluated NMB of the two interventions at population and individual levels, stratified by risk quintiles for diabetes onset at 3 years. RESULTS/ANTICIPATED RESULTS: Results: Compared to usual care, lifestyle modification conferred positive benefits for all eligible individuals. Metformin’s NMB was negative for the lowest population risk quintile. By avoiding use among individuals who would not benefit, targeted administration of metformin conferred a benefit of $500-$800 per person, depending on duration of treatment effect. When treating only 20% of the population (e.g., due to capacity constraints), targeting conferred a NMB of $14,000-$18,000 per person for lifestyle modification and $16,000-$20,000 for metformin. DISCUSSION/SIGNIFICANCE OF IMPACT: Conclusions: Metformin confers value only among higher risk individuals, so targeting its use is worthwhile. While lifestyle modification confers value for all eligible individuals, prioritizing the intervention to high risk patients when capacity is constrained substantially increases societal benefits. Cambridge University Press 2019-03-27 /pmc/articles/PMC6798495/ http://dx.doi.org/10.1017/cts.2019.356 Text en © The Association for Clinical and Translational Science 2019 http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. |
spellingShingle | Translational Science, Policy, & Health Outcomes Science Olchanski, Natalia van Klaveren, David Cohen, Joshua T Wong, John B Ruthazer, Robin Kent, David M 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title | 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title_full | 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title_fullStr | 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title_full_unstemmed | 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title_short | 3385 TARGETING DIABETES PREVENTION PROGRAMS: INDIVIDUAL RISK-BASED HEALTH ECONOMIC ANALYSIS |
title_sort | 3385 targeting diabetes prevention programs: individual risk-based health economic analysis |
topic | Translational Science, Policy, & Health Outcomes Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798495/ http://dx.doi.org/10.1017/cts.2019.356 |
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