NSSP Emergency Department Visit Trajectories by State and Sub State Regions- COVID-19, Flu, RSV, Combined

NSSP Emergency Department (ED) Visit Trajectories by State and Sub-State Regions- COVID-19, Flu, RSV, Combined. This dataset provides the percentage of emergency department patient visits for the specified pathogen of all ED patient visits for the specified geographic part of the country that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP). In addition, the trend over time is characterized as increasing, decreasing or no change, with exceptions for when there are no data available, the data are too sparse, or there are not enough data to compute a trend. These data are to provide awareness of how the weekly trend is changing for the given geographic region. 

Note that the reported sub-state trends are from Health Service Areas (HSA) and the data reported from the health care facilities located within the given HSA. Health Service Areas are regions of one or more counties that align to patterns of care seeking. The HSA level data are reported for each county in the HSA.

More information on HSAs is available <a href="https://seer.cancer.gov/seerstat/variables/countyattribs/hsa.html"><b>here</b></a>.

For the emergency department time series, trajectory classifications reported on for sub-state (HSA) emergency department time series, trajectory classifications are based on approximations of the first derivative (slope) of trends that are smoothed using generalized additive models (GAMs). To determine time intervals in which the slope is sufficiently changing (i.e., rate of change distinguishable from 0), 95% confidence intervals for the slope approximations are calculated and assessed. Weeks with a 95% confidence interval not containing 0 are classified as increasing if the slope estimate is positive and decreasing if the slope estimate is negative. Weeks with a 95% confidence interval containing 0 are classified as stable. In the scenario that an HSA's time series is determined to be too sparse (i.e., many weeks with percentages of 0%), a model is not fit, and the HSA is classified as “sparse”.

For additional information, please see: <a href="https://www.cdc.gov/ncird/surveillance/respiratory-illnesses/index.html#companion-guide"><b>Companion Guide: NSSP Emergency Department Data on Respiratory Illness</b></a>

Updated once per week on Fridays.

国家综合征监测计划（NSSP）紧急救治部门（ED）按州及次级州域划分的就诊轨迹- COVID-19、流感、RSV、综合。该数据集提供了特定地理区域内所有紧急救治部门就诊患者中，针对指定病原体就诊的患者就诊百分比。此外，时间序列趋势被描述为增加、减少或无变化，但以下情况除外：数据不可用、数据过于稀疏或数据不足以计算趋势。这些数据旨在提供特定地理区域内每周趋势变化的认识。请注意，所报告的次级州域趋势来自医疗服务区域（HSA）及位于给定HSA内的医疗设施报告的数据。医疗服务区域是指一个或多个县，这些县与寻求医疗服务的模式相一致。HSA级别的数据报告了HSA内每个县的数据。

更多关于HSAs的信息请参阅<a href="https://seer.cancer.gov/seerstat/variables/countyattribs/hsa.html">此处</a>。

对于紧急救治部门的时间序列数据，次级州域（HSA）紧急救治部门时间序列的轨迹分类基于趋势一阶导数（斜率）的近似值，这些近似值通过广义加性模型（GAMs）进行平滑处理。为了确定斜率变化充分的时间间隔（即变化率与0可区分），计算并评估斜率近似值的95%置信区间。当95%置信区间不包含0时，如果斜率估计值为正，则将周归类为增加；如果斜率估计值为负，则将周归类为减少。当95%置信区间包含0时，将周归类为稳定。在医疗服务区域的时间序列被认为过于稀疏（即许多周的数据百分比为0%）的情况下，不拟合模型，并将HSA归类为“稀疏”。

有关更多信息，请参阅<a href="https://www.cdc.gov/ncird/surveillance/respiratory-illnesses/index.html#companion-guide">配套指南：NSSP紧急救治部门呼吸道疾病数据</a>。

每周五更新一次。