ArticleEpidemics2019
Patterns of seasonal influenza activity in U.S. core-based statistical areas, described using prescriptions of oseltamivir in Medicare claims data.
Article in Epidemics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Understanding spatiotemporal clustering of seasonal influenza in the United States.BMC infectious diseases · 2026Article
- Differences in influenza testing and treatment in micropolitan versus metropolitan areas in the U.S. using medicare claims data from 2010 to 2016.BMC public health · 2025Article
- Epidemiological and Clinical Characteristics of Influenza Outbreaks Among Children in Chongqing, China.Frontiers in public health · 2022Article
- Interpreting COVID-19 deaths among nursing home residents in the US: The changing role of facility quality over time.PloS one · 2021Article
- Differences in epidemic spread patterns of norovirus and influenza seasons of Germany: an application of optical flow analysis in epidemiology.Scientific reports · 2020Article
- Estimating the Incidence of Influenza at the State Level - Utah, 2016-17 and 2017-18 Influenza Seasons.MMWR. Morbidity and mortality weekly report · 2019Article
- Notes from the Field: Assessment of State-Level Influenza Season Severity - Minnesota and Utah, 2017-18 Influenza Season.MMWR. Morbidity and mortality weekly report · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Using Medicare claims data on prescriptions of oseltamivir dispensed to people 65 years old and older, we present a descriptive analysis of patterns of influenza activity in the United States for 579 core-based statistical areas (CBSAs) from the 2010-2011 through the 2015-2016 influenza seasons. During this time, 1,010,819 beneficiaries received a prescription of oseltamivir, ranging from 45,888 in 2011-2012 to 380,745 in 2014-2015. For each season, the peak weekly number of prescriptions correlated with the total number of prescriptions (Pearson's r ≥ 0.88). The variance in peak timing decreased with increasing severity (p < 0.0001). Among these 579 CBSAs, neither peak timing, nor relative timing, nor severity of influenza seasons showed evidence of spatial autocorrelation (0.02 ≤ Moran's I ≤ 0.23). After aggregating data to the state level, agreement between the seasonal severity at the CBSA level and the state level was fair (median Cohen's weighted κ = 0.32, interquartile range = 0.26-0.39). Based on seasonal severity, relative timing, and geographic place, we used hierarchical agglomerative clustering to join CBSAs into influenza zones for each season. Seasonal maps of influenza zones showed no obvious patterns that might assist in predicting influenza zones for future seasons. Because of the large number of prescriptions, these data may be especially useful for characterizing influenza activity and geographic distribution during low severity seasons, when other data sources measuring influenza activity are likely to be sparse.
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