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Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis
BACKGROUND: Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517644/ https://www.ncbi.nlm.nih.gov/pubmed/32972408 http://dx.doi.org/10.1186/s12913-020-05742-0 |
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author | Huang, Lihong Sullivan, Lindsay Yang, Jingzhen |
author_facet | Huang, Lihong Sullivan, Lindsay Yang, Jingzhen |
author_sort | Huang, Lihong |
collection | PubMed |
description | BACKGROUND: Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis. METHODS: We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0–18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results. RESULTS: A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1–13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. CONCLUSIONS: Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance. |
format | Online Article Text |
id | pubmed-7517644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75176442020-09-25 Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis Huang, Lihong Sullivan, Lindsay Yang, Jingzhen BMC Health Serv Res Research Article BACKGROUND: Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis. METHODS: We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0–18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results. RESULTS: A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1–13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. CONCLUSIONS: Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance. BioMed Central 2020-09-24 /pmc/articles/PMC7517644/ /pubmed/32972408 http://dx.doi.org/10.1186/s12913-020-05742-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Article Huang, Lihong Sullivan, Lindsay Yang, Jingzhen Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title | Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title_full | Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title_fullStr | Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title_full_unstemmed | Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title_short | Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
title_sort | analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517644/ https://www.ncbi.nlm.nih.gov/pubmed/32972408 http://dx.doi.org/10.1186/s12913-020-05742-0 |
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