Achieving an Optimal Childhood Vaccine Policy

Policies to remove parents' ability to opt-out from school immunization requirements on the basis of religious or personal beliefs (ie, nonmedical exemptions) may be a useful strategy to increase immunization rates and prevent outbreaks of vaccine-preventable disease. However, there is uncertainty about the effectiveness of this strategy and the range of possible outcomes. We advocate for a more deliberative process through which a broad range of outcomes is scrutinized and the balance of values underlying the policy decision to eliminate nonmedical exemptions is clearly articulated. We identify 3 outcomes that require particular consideration before policies to eliminate nonmedical exemptions are implemented widely and outline a process for making the values underlying such policies more explicit

A simulation study comparing aberration detection algorithms for syndromic surveillance

BACKGROUND: The usefulness of syndromic surveillance for early outbreak detection depends in part on effective statistical aberration detection. However, few published studies have compared different detection algorithms on identical data. In the largest simulation study conducted to date, we compared the performance of six aberration detection algorithms on simulated outbreaks superimposed on authentic syndromic surveillance data. METHODS: We compared three control-chart-based statistics, two exponential weighted moving averages, and a generalized linear model. We simulated 310 unique outbreak signals, and added these to actual daily counts of four syndromes monitored by Public Health – Seattle and King County's syndromic surveillance system. We compared the sensitivity of the six algorithms at detecting these simulated outbreaks at a fixed alert rate of 0.01. RESULTS: Stratified by baseline or by outbreak distribution, duration, or size, the generalized linear model was more sensitive than the other algorithms and detected 54% (95% CI = 52%–56%) of the simulated epidemics when run at an alert rate of 0.01. However, all of the algorithms had poor sensitivity, particularly for outbreaks that did not begin with a surge of cases. CONCLUSION: When tested on county-level data aggregated across age groups, these algorithms often did not perform well in detecting signals other than large, rapid increases in case counts relative to baseline levels

Accuracy of Medical Examiner’s Assessment for Near–Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017–February 2018

Objectives Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near–real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring. Methods We analyzed data on deaths in the King County (Washington) Medical Examiner’s Office (KCMEO) jurisdiction that occurred during March 1, 2017–February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs’ classification by using the final death certificate as the gold standard. Results KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event. Conclusions King County MEs’ probable overdose classification provides a near–real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic. </jats:sec

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Associated With an Indoor Music Event That Required Proof of Full Vaccination Against Coronavirus Disease 2019 (COVID-19) Prior to Entry—Seattle, July 2021

Abstract In July 2021, Public Health–Seattle &amp; King County investigated a coronavirus disease 2019 (COVID-19) outbreak at an indoor event intended for fully vaccinated individuals, revealing unvaccinated staff, limited masking, poor ventilation, and overcrowding. Supporting businesses to develop and implement comprehensive COVID-19 prevention plans is essential for reducing spread in these settings.</jats:p