To provide a reliable instrument for use in large population surveys, we developed a short questionnaire based on existing International Headache Society diagnostic criteria and administered the questionnaire to 50 consecutive patients seeking evaluation at a university-based headache clinic. A single neurologist subsequently examined all patients. Based only on the questionnaires, reviewers scored each patient as having migraine with aura, migraine without aura, or nonmigrainous headache. High predictive validity and low interobserver variability between the examining neurologist and the independent reviewers suggest that the questionnaire may be quite useful as a survey instrument.

Rollouts of COVID-19 vaccines in the USA were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large US cities over 3 weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large US cities.

Rollouts of COVID-19 vaccines in the U.S. were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large U.S. cities over three weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large U.S. cities.

No abstract

While there is much research on income segregation, we know less about the factors that contribute to the uneven distribution of households across neighborhoods by educational attainment. Although globalization is thought to influence segregation, its association with socioeconomic segregation is debated. Using data from the 2016-2020 American Community Survey, the Globalization and World Cities Research Network, and the MIT Election Data + Science Lab, we investigate the correlates of educational segregation within large core-based statistical areas in the United States, focusing on globalization, income inequality, and political preferences in the 2016 presidential election. Multivariate results reveal that globalization and income inequality are the most significant correlates of educational segregation. Political preferences are only significantly associated with residential dissimilarity between those with a master’s degree or higher and those with some college. We discuss the implications of these results for understanding residential inequality on the basis of education in metropolitan America.