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Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.
Masters, Nina B; Eisenberg, Marisa C; Delamater, Paul L; Kay, Matthew; Boulton, Matthew L; Zelner, Jon.
  • Masters NB; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109; mastersn@umich.edu jzelner@umich.edu.
  • Eisenberg MC; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109.
  • Delamater PL; Department of Geography, University of North Carolina, Chapel Hill, NC 27514.
  • Kay M; School of Information, University of Michigan, Ann Arbor, MI 48104.
  • Boulton ML; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109.
  • Zelner J; Department of Internal Medicine, Division of Infectious Disease, University of Michigan Medical School, Ann Arbor, MI 48109.
Proc Natl Acad Sci U S A ; 117(45): 28506-28514, 2020 11 10.
Article in English | MEDLINE | ID: covidwho-892049
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Measles Vaccine / Models, Statistical / Space-Time Clustering / Vaccination / Epidemics / Measles Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Journal: Proc Natl Acad Sci U S A Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Measles Vaccine / Models, Statistical / Space-Time Clustering / Vaccination / Epidemics / Measles Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Journal: Proc Natl Acad Sci U S A Year: 2020 Document Type: Article