Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child.

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability.

Social media dominate today's information ecosystem and provide valuable information for social research. Market researchers, social scientists, policymakers, government entities, public health researchers, and practitioners recognize the potential for social data to inspire innovation, support products and services, characterize public opinion, and guide decisions. The appeal of mining these rich datasets is clear. However, there is potential risk of data misuse, underscoring an equally huge and fundamental flaw in the research: there are no procedural standards and little transparency. Transparency across the processes of collecting and analyzing social media data is often limited due to proprietary algorithms. Spurious findings and biases introduced by artificial intelligence (AI) demonstrate the challenges this lack of transparency poses for research. Social media research remains a virtual "wild west," with no clear standards for reporting regarding data retrieval, preprocessing steps, analytic methods, or interpretation. Use of emerging generative AI technologies to augment social media analytics can undermine validity and replicability of findings, potentially turning this research into a "black box" enterprise. Clear guidance for social media analyses and reporting is needed to assure the quality of the resulting research. In this article, we propose criteria for evaluating the quality of studies using social media data, grounded in established scientific practice. We offer clear documentation guidelines to ensure that social data are used properly and transparently in research and applications. A checklist of disclosure elements to meet minimal reporting standards is proposed. These criteria will make it possible for scholars and practitioners to assess the quality, credibility, and comparability of research findings using digital data.

Constructing a toolkit to evaluate quality of state and local administrative data.

In the United States, state and local agencies administering government assistance programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical for agencies to conduct such analyses and to improve their data for future use. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality. We describe our efforts in developing tools that can be used to assess data quality as well as the challenges encountered in constructing these tools. The toolkit focuses on critical dimensions of quality for analyzing an administrative dataset, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest. State and local administrative databases often include a longitudinal component which our toolkit also aims to exploit to help evaluate data quality. In addition, we incorporate data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern. While we seek to develop general tools for common data quality analyses, most administrative datasets have particularities that can benefit from a customized analysis building on our toolkit.

Breakfast Skipping, Extreme Commutes, and the Sex Composition at Birth.

A growing body of literature has shown that environmental exposures in the period around conception can affect the sex ratio at birth through selective attrition that favors the survival of female conceptuses. Glucose availability is considered a key indicator of the fetal environment, and its absence as a result of meal skipping may inhibit male survival. We hypothesize that breakfast skipping during pregnancy may lead to a reduction in the fraction of male births. Using time use data from the United States we show that women with commute times of 90 minutes or longer are 20 percentage points more likely to skip breakfast. Using U.S. census data we show that women with commute times of 90 minutes or longer are 1.2 percentage points less likely to have a male child under the age of 2. Under some assumptions, this implies that routinely skipping breakfast around the time of conception leads to a 6 percentage point reduction in the probability of a male child. Skipping breakfast during pregnancy may therefore constitute a poor environment for fetal health more generally.

Seasonal variation and predictors of epistaxis.

OBJECTIVES/HYPOTHESIS: To examine the incidence of epistaxis as a function of season and age and to determine predictors of episodes within the epistaxis patient population presenting to a tertiary hospital system. STUDY DESIGN: Retrospective cohort study. METHODS: Electronic medical record charts of patients presenting to the Northwestern Emergency Department, admitted to an inpatient ward, or seen in an outpatient setting between 2008 and 2012 were reviewed and selected for an International Classifications of Disease-Ninth Revision epistaxis code of 784.7. Season of presentation, demographic factors (age, race, gender, insurance status), medication use (including anticoagulants and topical nasal steroid administration), and several comorbidities were analyzed as potential predictors of episodes. RESULTS: A total of 2,405 patients were identified with a total of 3,666 individual epistaxis episodes over 5 years. Multivariate analysis identified allergic rhinitis (AR), chronic sinusitis (CRS), coagulopathy, hereditary hemorrhagic telangiectasia (HHT), hematologic malignancy, and hypertension (HTN) as predictors of a higher number of cases. Epistaxis occurred more frequently during colder months and in older patients. CONCLUSIONS: Epistaxis occurs more commonly during the winter and in older patients. AR, CRS, coagulopathy, HHT, hematologic malignancy, and HTN are associated with increased epistaxis incidence.