Search for: All records

Abstract To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or not granular enough, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here we show how search engine logs and machine learning can help to fill these gaps, using anonymized Bing data from February to August 2021. First, we develop avaccine intent classifierthat accurately detects when a user is seeking the COVID-19 vaccine on Bing. Our classifier demonstrates strong agreement with CDC vaccination rates, while preceding CDC reporting by 1–2 weeks, and estimates more granular ZIP-level rates, revealing local heterogeneity in vaccine seeking. To study vaccine hesitancy, we use our classifier to identify two groups,vaccine early adoptersandvaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 67% likelier to click on untrusted news sites, and are much more concerned about vaccine requirements, development, and vaccine myths. Even within holdouts, clusters emerge with different concerns and openness to the vaccine. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators predict when individuals convert from holding out to seeking the vaccine. 

Abstract ObjectivesTo quantify differences between (1) stratifying patients by predicted disease onset risk alone and (2) stratifying by predicted disease onset risk and severity of downstream outcomes. We perform a case study of predicting sepsis. Materials and MethodsWe performed a retrospective analysis using observational data from Michigan Medicine at the University of Michigan (U-M) between 2016 and 2020 and the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2012. We measured the correlation between the estimated sepsis risk and the estimated effect of sepsis on mortality using Spearman’s correlation. We compared patients stratified by sepsis risk with patients stratified by sepsis risk and effect of sepsis on mortality. ResultsThe U-M and BIDMC cohorts included 7282 and 5942 ICU visits; 7.9% and 8.1% developed sepsis, respectively. Among visits with sepsis, 21.9% and 26.3% experienced mortality at U-M and BIDMC. The effect of sepsis on mortality was weakly correlated with sepsis risk (U-M: 0.35 [95% CI: 0.33-0.37], BIDMC: 0.31 [95% CI: 0.28-0.34]). High-risk patients identified by both stratification approaches overlapped by 66.8% and 52.8% at U-M and BIDMC, respectively. Accounting for risk of mortality identified an older population (U-M: age = 66.0 [interquartile range—IQR: 55.0-74.0] vs age = 63.0 [IQR: 51.0-72.0], BIDMC: age = 74.0 [IQR: 61.0-83.0] vs age = 68.0 [IQR: 59.0-78.0]). DiscussionPredictive models that guide selective interventions ignore the effect of disease on downstream outcomes. Reformulating patient stratification to account for the estimated effect of disease on downstream outcomes identifies a different population compared to stratification on disease risk alone. ConclusionModels that predict the risk of disease and ignore the effects of disease on downstream outcomes could be suboptimal for stratification. 

Abstract The COVID-19 pandemic has stimulated important changes in online information access as digital engagement became necessary to meet the demand for health, economic, and educational resources. Our analysis of 55 billion everyday web search interactions during the pandemic across 25,150 US ZIP codes reveals that the extent to which different communities of internet users enlist digital resources varies based on socioeconomic and environmental factors. For example, we find that ZIP codes with lower income intensified their access to health information to a smaller extent than ZIP codes with higher income. We show that ZIP codes with higher proportions of Black or Hispanic residents intensified their access to unemployment resources to a greater extent, while revealing patterns of unemployment site visits unseen by the claims data. Such differences frame important questions on the relationship between differential information search behaviors and the downstream real-world implications on more and less advantaged populations. 

The COVID-19 pandemic has sparked un- precedented mobilization of scientists, gener- ating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of con- nections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically ex- tracted from papers (e.g., genes, drugs, dis- eases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight1 has so far served over 15K users with over 42K page views and 13% returns