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Medicaid prescription limits: policy trends and comparative impact on utilization

BACKGROUND: Medicaid programs face growing pressure to control spending. Despite evidence of clinical harms, states continue to impose policies limiting the number of reimbursable prescriptions (caps). We examined the recent use of prescription caps by Medicaid programs and the impact of policy impl...

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Autores principales: Lieberman, Daniel A., Polinski, Jennifer M., Choudhry, Niteesh K., Avorn, Jerry, Fischer, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714442/
https://www.ncbi.nlm.nih.gov/pubmed/26772962
http://dx.doi.org/10.1186/s12913-016-1258-0
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author Lieberman, Daniel A.
Polinski, Jennifer M.
Choudhry, Niteesh K.
Avorn, Jerry
Fischer, Michael A.
author_facet Lieberman, Daniel A.
Polinski, Jennifer M.
Choudhry, Niteesh K.
Avorn, Jerry
Fischer, Michael A.
author_sort Lieberman, Daniel A.
collection PubMed
description BACKGROUND: Medicaid programs face growing pressure to control spending. Despite evidence of clinical harms, states continue to impose policies limiting the number of reimbursable prescriptions (caps). We examined the recent use of prescription caps by Medicaid programs and the impact of policy implementation on prescription utilization. METHODS: We identified Medicaid cap policies from 2001–2010. We classified caps as applying to all prescriptions (overall caps) or only branded prescriptions (brand caps). Using state-level, aggregate prescription data, we developed interrupted time-series analyses to evaluate the impact of implementing overall caps and brand caps in a subset of states with data available before and after cap initiation. For overall caps, we examined the use of essential medications, which were classified as preventive or as providing symptomatic benefit. For brand caps, we examined the use of all branded drugs as well as branded and generic medications among classes with available generic replacements. RESULTS: The number of states with caps increased from 12 in 2001 to 20 in 2010. Overall cap implementation (n = 3) led to a 0.52 % (p < 0.001) annual decrease in the proportion of essential prescriptions but no change in cost. For preventive essential medications, overall caps led to a 1.12 % (p = 0.001) annual decrease in prescriptions (246,000 prescriptions annually) and a 1.20 % (p < 0.001) decrease in spending (−$12.2 million annually), but no decrease in symptomatic essential medication use. Brand cap implementation (n = 6) led to an immediate 2.29 % (p = 0.16) decrease in branded prescriptions and 1.26 % (p = 0.025) decrease in spending. For medication classes with generic replacements, the decrease in branded prescriptions (0.74 %, p = 0.003) approximately equaled the increase in generics (0.79 %, p = 0.009), with estimated savings of $17.4 million. CONCLUSIONS: An increasing number of states are using prescription caps, with mixed results. Overall caps decreased the use of preventive but not symptomatic essential medications, suggesting that patients assign higher priority to agents providing symptomatic benefit when faced with reimbursement limits. Among medications with generic replacements, brand caps shifted usage from branded drugs to generics, with considerable savings. Future research should analyze the patient-level impact of these policies to measure clinical outcomes associated with these changes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-016-1258-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-47144422016-01-16 Medicaid prescription limits: policy trends and comparative impact on utilization Lieberman, Daniel A. Polinski, Jennifer M. Choudhry, Niteesh K. Avorn, Jerry Fischer, Michael A. BMC Health Serv Res Research Article BACKGROUND: Medicaid programs face growing pressure to control spending. Despite evidence of clinical harms, states continue to impose policies limiting the number of reimbursable prescriptions (caps). We examined the recent use of prescription caps by Medicaid programs and the impact of policy implementation on prescription utilization. METHODS: We identified Medicaid cap policies from 2001–2010. We classified caps as applying to all prescriptions (overall caps) or only branded prescriptions (brand caps). Using state-level, aggregate prescription data, we developed interrupted time-series analyses to evaluate the impact of implementing overall caps and brand caps in a subset of states with data available before and after cap initiation. For overall caps, we examined the use of essential medications, which were classified as preventive or as providing symptomatic benefit. For brand caps, we examined the use of all branded drugs as well as branded and generic medications among classes with available generic replacements. RESULTS: The number of states with caps increased from 12 in 2001 to 20 in 2010. Overall cap implementation (n = 3) led to a 0.52 % (p < 0.001) annual decrease in the proportion of essential prescriptions but no change in cost. For preventive essential medications, overall caps led to a 1.12 % (p = 0.001) annual decrease in prescriptions (246,000 prescriptions annually) and a 1.20 % (p < 0.001) decrease in spending (−$12.2 million annually), but no decrease in symptomatic essential medication use. Brand cap implementation (n = 6) led to an immediate 2.29 % (p = 0.16) decrease in branded prescriptions and 1.26 % (p = 0.025) decrease in spending. For medication classes with generic replacements, the decrease in branded prescriptions (0.74 %, p = 0.003) approximately equaled the increase in generics (0.79 %, p = 0.009), with estimated savings of $17.4 million. CONCLUSIONS: An increasing number of states are using prescription caps, with mixed results. Overall caps decreased the use of preventive but not symptomatic essential medications, suggesting that patients assign higher priority to agents providing symptomatic benefit when faced with reimbursement limits. Among medications with generic replacements, brand caps shifted usage from branded drugs to generics, with considerable savings. Future research should analyze the patient-level impact of these policies to measure clinical outcomes associated with these changes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-016-1258-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-15 /pmc/articles/PMC4714442/ /pubmed/26772962 http://dx.doi.org/10.1186/s12913-016-1258-0 Text en © Lieberman et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lieberman, Daniel A.
Polinski, Jennifer M.
Choudhry, Niteesh K.
Avorn, Jerry
Fischer, Michael A.
Medicaid prescription limits: policy trends and comparative impact on utilization
title Medicaid prescription limits: policy trends and comparative impact on utilization
title_full Medicaid prescription limits: policy trends and comparative impact on utilization
title_fullStr Medicaid prescription limits: policy trends and comparative impact on utilization
title_full_unstemmed Medicaid prescription limits: policy trends and comparative impact on utilization
title_short Medicaid prescription limits: policy trends and comparative impact on utilization
title_sort medicaid prescription limits: policy trends and comparative impact on utilization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714442/
https://www.ncbi.nlm.nih.gov/pubmed/26772962
http://dx.doi.org/10.1186/s12913-016-1258-0
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