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1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis

BACKGROUND: Vaccine hesitancy in low vaccine coverage “hot spots” has led to recent outbreaks of vaccine-preventable diseases across the United States. State policies to improve vaccine coverage by restricting non-medical (personal belief) exemptions are heavily debated and their effectiveness is un...

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Autores principales: Nyathi, Sindiso, Karpel, Hannah, Sainani, Kristin L, Maldonado, Yvonne, Hotez, Peter J, Bendavid, Eran, Lo, Nathan C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810639/
http://dx.doi.org/10.1093/ofid/ofz360.1484
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author Nyathi, Sindiso
Karpel, Hannah
Sainani, Kristin L
Maldonado, Yvonne
Hotez, Peter J
Bendavid, Eran
Lo, Nathan C
author_facet Nyathi, Sindiso
Karpel, Hannah
Sainani, Kristin L
Maldonado, Yvonne
Hotez, Peter J
Bendavid, Eran
Lo, Nathan C
author_sort Nyathi, Sindiso
collection PubMed
description BACKGROUND: Vaccine hesitancy in low vaccine coverage “hot spots” has led to recent outbreaks of vaccine-preventable diseases across the United States. State policies to improve vaccine coverage by restricting non-medical (personal belief) exemptions are heavily debated and their effectiveness is unclear due to limited rigorous policy analysis. In 2016, a California policy (SB 277) eliminated non-medical exemptions from kindergarten requirements. To address the ongoing debate on such policies, we performed a quasi-experimental, controlled analysis of the policy’s impact on vaccine and exemption outcomes. METHODS: We used state vaccine coverage and exemption data (2011–2017) from the CDC and health data from public sources. We prespecified a primary outcome of MMR coverage (%) and secondary outcomes of medical and non-medical exemptions (%). We included covariates related to socioeconomic and health measures (e.g., insurance, well child visits) and pre-2016 mean coverage. Using the synthetic control method, with 2016 as the treatment year and a 2-year post-policy period, we constructed a “control” California, from a weighted sum of states. We used permutation testing to repeat the process for each of the other states and their unique synthetic control, to determine whether there was a meaningful difference in California (i.e., a change in California’s coverage relative to its control in the top 5th percentile of states). We tested the model’s sensitivity to various analytical assumptions. RESULTS: Of 43 control states, synthetic California was predominantly comprised of Idaho, Mississippi, and Arkansas, and had a good pre-policy match on outcomes. MMR coverage in California increased by 3.2% relative to synthetic California in the post period (Top 1 of 44 states, Figure 1). Medical exemptions increased by 0.4%, while non-medical exemptions decreased by 2.2% in the post-period (Top 1 of 43 states). The model was robust to changes in covariates and control states. CONCLUSION: The policy resulted in a meaningful increase in MMR coverage and reduction in non-medical exemptions. We measured a modest increase in medical exemptions, but this was offset by the larger reduction in non-medical exemptions. State policies removing non-medical exemptions can be effective in increasing vaccination coverage. [Image: see text] DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-68106392019-10-28 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis Nyathi, Sindiso Karpel, Hannah Sainani, Kristin L Maldonado, Yvonne Hotez, Peter J Bendavid, Eran Lo, Nathan C Open Forum Infect Dis Abstracts BACKGROUND: Vaccine hesitancy in low vaccine coverage “hot spots” has led to recent outbreaks of vaccine-preventable diseases across the United States. State policies to improve vaccine coverage by restricting non-medical (personal belief) exemptions are heavily debated and their effectiveness is unclear due to limited rigorous policy analysis. In 2016, a California policy (SB 277) eliminated non-medical exemptions from kindergarten requirements. To address the ongoing debate on such policies, we performed a quasi-experimental, controlled analysis of the policy’s impact on vaccine and exemption outcomes. METHODS: We used state vaccine coverage and exemption data (2011–2017) from the CDC and health data from public sources. We prespecified a primary outcome of MMR coverage (%) and secondary outcomes of medical and non-medical exemptions (%). We included covariates related to socioeconomic and health measures (e.g., insurance, well child visits) and pre-2016 mean coverage. Using the synthetic control method, with 2016 as the treatment year and a 2-year post-policy period, we constructed a “control” California, from a weighted sum of states. We used permutation testing to repeat the process for each of the other states and their unique synthetic control, to determine whether there was a meaningful difference in California (i.e., a change in California’s coverage relative to its control in the top 5th percentile of states). We tested the model’s sensitivity to various analytical assumptions. RESULTS: Of 43 control states, synthetic California was predominantly comprised of Idaho, Mississippi, and Arkansas, and had a good pre-policy match on outcomes. MMR coverage in California increased by 3.2% relative to synthetic California in the post period (Top 1 of 44 states, Figure 1). Medical exemptions increased by 0.4%, while non-medical exemptions decreased by 2.2% in the post-period (Top 1 of 43 states). The model was robust to changes in covariates and control states. CONCLUSION: The policy resulted in a meaningful increase in MMR coverage and reduction in non-medical exemptions. We measured a modest increase in medical exemptions, but this was offset by the larger reduction in non-medical exemptions. State policies removing non-medical exemptions can be effective in increasing vaccination coverage. [Image: see text] DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6810639/ http://dx.doi.org/10.1093/ofid/ofz360.1484 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. 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-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Nyathi, Sindiso
Karpel, Hannah
Sainani, Kristin L
Maldonado, Yvonne
Hotez, Peter J
Bendavid, Eran
Lo, Nathan C
1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title_full 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title_fullStr 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title_full_unstemmed 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title_short 1620. Effectiveness of the 2016 California Policy Eliminating Non-Medical Exemptions on Vaccine Coverage: A Synthetic Control Analysis
title_sort 1620. effectiveness of the 2016 california policy eliminating non-medical exemptions on vaccine coverage: a synthetic control analysis
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810639/
http://dx.doi.org/10.1093/ofid/ofz360.1484
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