Burke Lauralyn K, Brown C Perry, Johnson Tammie M
Division of Health Informatics and Information Management at Florida A&M University in Tallahassee, FL.
Public health in the Institute of Public Health at the College of Pharmacy and Pharmaceutical Sciences at Florida A&M University in Tallahassee, FL.
Perspect Health Inf Manag. 2016 Oct 1;13(Fall):1c. eCollection 2016 fall.
Interrupted time-series analysis (ITSA) can be used to identify, quantify, and evaluate the magnitude and direction of an event on the basis of time-series data. This study evaluates the impact of the bioterrorist anthrax attacks ("Amerithrax") on hospital inpatient discharges in the metropolitan statistical area of Palm Beach, Broward, and Miami-Dade counties in the fourth quarter of 2001. Three statistical methods-standardized incidence ratio (SIR), segmented regression, and an autoregressive integrated moving average (ARIMA)-were used to determine whether Amerithrax influenced inpatient utilization. The SIR found a non-statistically significant 2 percent decrease in hospital discharges. Although the segmented regression test found a slight increase in the discharge rate during the fourth quarter, it was also not statistically significant; therefore, it could not be attributed to Amerithrax. Segmented regression diagnostics preparing for ARIMA indicated that the quarterly data time frame was not serially correlated and violated one of the assumptions for the use of the ARIMA method and therefore could not properly evaluate the impact on the time-series data. Lack of data granularity of the time frames hindered the successful evaluation of the impact by the three analytic methods. This study demonstrates that the granularity of the data points is as important as the number of data points in a time series. ITSA is important for the ability to evaluate the impact that any hazard may have on inpatient utilization. Knowledge of hospital utilization patterns during disasters offer healthcare and civic professionals valuable information to plan, respond, mitigate, and evaluate any outcomes stemming from biothreats.
中断时间序列分析(ITSA)可用于根据时间序列数据识别、量化和评估事件的规模及方向。本研究评估了2001年第四季度生物恐怖主义炭疽袭击(“美国炭疽事件”)对棕榈滩、布劳沃德和迈阿密-戴德县大都市统计区医院住院患者出院情况的影响。使用了三种统计方法——标准化发病率(SIR)、分段回归和自回归积分移动平均(ARIMA)——来确定美国炭疽事件是否影响住院患者的利用率。SIR发现医院出院人数有2%的下降,但无统计学意义。尽管分段回归测试发现第四季度出院率略有上升,但也无统计学意义;因此,不能将其归因于美国炭疽事件。为ARIMA准备的分段回归诊断表明,季度数据时间框架不存在序列相关性,违反了使用ARIMA方法的假设之一,因此无法正确评估对时间序列数据的影响。时间框架的数据粒度不足阻碍了这三种分析方法对影响的成功评估。本研究表明,数据点的粒度与时间序列中数据点的数量同样重要。ITSA对于评估任何危害可能对住院患者利用率产生的影响至关重要。了解灾难期间的医院利用模式可为医疗保健和市政专业人员提供有价值的信息,以便规划、应对、减轻和评估生物威胁产生的任何结果。