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

Using a Syndromic Surveillance System to Evaluate the Impact of a Change in Alcohol Law

In 2011, Washington State voters passed an initiative which closed state liquor stores and opened private sector liquor sales. We examined trends in alcohol-related emergency department (ED) visits associated with this law change. Data were from the King County syndromic surveillance system. Alcohol-related ED visits were identified using chief complaint search strings and diagnosis codes. We used a linear regression model with a spline at the date of law change and controlled for other factors. Significant increases in alcohol-related ED visits were observed associated with the law change among minors (age <21) and adults ages 40 and older