Dataset Information

Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis.

ABSTRACT: 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?

SUBMITTER: Huang L

PROVIDER: S-EPMC7517644 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis.

Huang Lihong L Sullivan Lindsay L Yang Jingzhen J

BMC health services research 20200924 1

<h4>Background</h4>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 int ...[more]

PMID: 32972408

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Project description:Influenza A virus commonly circulating in swine (IAV-S) is characterized by large genetic and antigenic diversity and, thus, improvements in different aspects of IAV-S surveillance are needed to achieve desirable goals of surveillance such as to establish the capacity to forecast with the greatest accuracy the number of influenza cases likely to arise. Advancements in modeling approaches provide the opportunity to use different models for surveillance. However, in order to make improvements in surveillance, it is necessary to assess the predictive ability of such models. This study compares the sensitivity and predictive accuracy of the autoregressive integrated moving average (ARIMA) model, the generalized linear autoregressive moving average (GLARMA) model, and the random forest (RF) model with respect to the frequency of influenza A virus (IAV) in Ontario swine. Diagnostic data on IAV submissions in Ontario swine between 2007 and 2015 were obtained from the Animal Health Laboratory (University of Guelph, Guelph, ON, Canada). Each modeling approach was examined for predictive accuracy, evaluated by the root mean square error, the normalized root mean square error, and the model's ability to anticipate increases and decreases in disease frequency. Likewise, we verified the magnitude of improvement offered by the ARIMA, GLARMA and RF models over a seasonal-naïve method. Using the diagnostic submissions, the occurrence of seasonality and the long-term trend in IAV infections were also investigated. The RF model had the smallest root mean square error in the prospective analysis and tended to predict increases in the number of diagnostic submissions and positive virological submissions at weekly and monthly intervals with a higher degree of sensitivity than the ARIMA and GLARMA models. The number of weekly positive virological submissions is significantly higher in the fall calendar season compared to the summer calendar season. Positive counts at weekly and monthly intervals demonstrated a significant increasing trend. Overall, this study shows that the RF model offers enhanced prediction ability over the ARIMA and GLARMA time series models for predicting the frequency of IAV infections in diagnostic submissions.

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Project description:BackgroundInterrupted time series (ITS) analysis is a growing method for assessing intervention impacts on diseases. However, it remains unstudied how the COVID-19 outbreak impacts gonorrhea. This study aimed to evaluate the effect of COVID-19 on gonorrhea and predict gonorrhea epidemics using the ITS-autoregressive integrated moving average (ARIMA) model.MethodsThe number of gonorrhea cases reported in China from January 2005 to September 2022 was collected. Statistical descriptions were applied to indicate the overall epidemiological characteristics of the data, and then the ITS-ARIMA was established. Additionally, we compared the forecasting abilities of ITS-ARIMA with Bayesian structural time series (BSTS), and discussed the model selection process, transfer function, check model fitting, and interpretation of results.ResultDuring 2005-2022, the total cases of gonorrhea were 2,165,048, with an annual average incidence rate of 8.99 per 100,000 people. The highest incidence rate was 14.2 per 100,000 people in 2005 and the lowest was 6.9 per 100,000 people in 2012. The optimal model was ARIMA (0,1, (1,3)) (0,1,1)12 (Akaike's information criterion = 3293.93). When predicting the gonorrhea incidence, the mean absolute percentage error under the ARIMA (16.45%) was smaller than that under the BSTS (22.48%). The study found a 62.4% reduction in gonorrhea during the first-level response, a 46.47% reduction during the second-level response, and an increase of 3.6% during the third-level response. The final model estimated a step change of - 2171 (95% confidence interval [CI] - 3698 to - 644) cases and an impulse change of - 1359 (95% CI - 2381 to - 338) cases. Using the ITS-ARIMA to evaluate the effect of COVID-19 on gonorrhea, the gonorrhea incidence showed a temporary decline before rebounding to pre-COVID-19 levels in China.ConclusionITS analysis is a valuable tool for gauging intervention effectiveness, providing flexibility in modelling various impacts. The ITS-ARIMA model can adeptly explain potential trends, autocorrelation, and seasonality. Gonorrhea, marked by periodicity and seasonality, exhibited a downward trend under the influence of COVID-19 intervention. The ITS-ARIMA outperformed the BSTS, offering superior predictive capabilities for the gonorrhea incidence trend in China.

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Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis.

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Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis.

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