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The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States., Competing Interests: Competing interest statement: P.L.D. has received research funding from Merck for an unrelated project.

Objectives: Kenya has a significant refugee population, including large numbers of Somali migrants. This study examines the vaccination status of Kenyan children and sociodemographic predictors of vaccination, including Somali ethnicity., Methods: Using the 2014 Kenyan Demographic and Health Survey, we calculated the proportion of non-vaccinated, under-vaccinated, and fully vaccinated children, defining full vaccination as one dose Bacille Calmette-Guerin, three doses polio, three doses pentavalent, and one dose measles. We assessed associations among various factors and vaccination status using multinomial logistic regression and explored the effect of Somali ethnicity through interaction analysis., Results: The study sample comprised 4052 children aged 12-23 months, with 79.4% fully, 19.0% under-, and 1.6% non-vaccinated. Among Somalis, 61.9% were fully, 28.7% under-, and 9.4% non-vaccinated. Somalis had significantly greater odds of under- and non-vaccination than the Kikuyu ethnic group. Wealth and birth setting were associated with immunization status for Somalis and non-Somalis., Conclusions: Disparities persist in pediatric vaccinations in Kenya, with Somali children more likely than non-Somalis to be under-vaccinated. Health inequalities among migrants and ethnic communities in Kenya should be addressed.

Introduction: Vaccines are vital to reducing childhood mortality, and prevent an estimated 2 to 3 million deaths annually which disproportionately occur in the developing world. Overall vaccine coverage is typically used as a metric to evaluate the adequacy of vaccine program performance, though it does not account for untimely administration, which may unnecessarily prolong children's susceptibility to disease. This study explored a hypothesized positive association between increasing vaccine hesitancy and untimeliness of immunizations administered under the Expanded Program on Immunization (EPI) in Addis Ababa, Ethiopia., Methods: This cross-sectional survey employed a multistage sampling design, randomly selecting one health center within five sub-cities of Addis Ababa. Caregivers of 3 to 12-month-old infants completed a questionnaire on vaccine hesitancy, and their infants' vaccination cards were examined to assess timeliness of received vaccinations., Results: The sample comprised 350 caregivers. Overall, 82.3% of the surveyed children received all recommended vaccines, although only 55.9% of these vaccinations were timely. Few caregivers (3.4%) reported ever hesitating and 3.7% reported ever refusing a vaccine for their child. Vaccine hesitancy significantly increased the odds of untimely vaccination (AOR 1.94, 95% CI: 1.02, 3.71) in the adjusted analysis., Conclusions: This study found high vaccine coverage among a sample of 350 young children in Addis Ababa, though only half received all recommended vaccines on time. High vaccine hesitancy was strongly associated with infants' untimely vaccination, indicating that increased efforts to educate community members and providers about vaccines may have a beneficial impact on vaccine timeliness in Addis Ababa.

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical vaccine exemptions (NMEs) and low vaccination coverage compared with the rest of the United States. There is a critical need to better understand the landscape of nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008–2018 using Moran's I , the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% to assess the bias incurred when aggregating vaccination data. We found that aggregating school-level data to levels commonly used for public reporting can lead to large biases in identifying the number and location of at-risk students and that different clustering metrics yielded variable interpretations of the nonvaccination landscape in Michigan. This study shows the importance of choosing clustering metrics with their mechanistic interpretations in mind, be it large- or fine-scale heterogeneity or between- and within-group contributions to spatial variation. [ABSTRACT FROM AUTHOR]