ArticlePreventing chronic disease2019
Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER.
Article in Preventing chronic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Temporal and geographic patterns of breast cancer-related google search activity and its association with CDC-reported incidence in the United States (2005-2023).Annals of medicine and surgery (2012) · 2026Article
- Trends and widening inequities in cardiovascular-kidney-metabolic involvement in cardiovascular mortality: A national spatiotemporal analysis, 2014-2023.Clinical medicine (London, England) · 2026Article
- Understanding Mortality Data: A Step-by-Step Guide to CDC WONDER, Joinpoint Analysis, and Forecasting Models.Journal of epidemiology and global health · 2026Review
- Trends in Unintentional Drowning Mortality Among U.S. Adults Aged ≥25 Years, 1999-2024: A U.S. Surveillance Analysis.Healthcare (Basel, Switzerland) · 2026Article
- Mortality trends involving fatal-arrhythmias and anemia in the United States: A retrospective analysis of 25 years.Medicine · 2026Observational
- A multiscale geospatial analysis integrating explainable machine learning to characterize spatial disparities in melanoma mortality associated with environmental justice and healthcare access in the contiguous United States.Frontiers in public health · 2026Article
- Trends and disparities in deaths involving atherosclerotic cardiovascular disease and stroke-related conditions among U.S. adults, 1999-2025.Frontiers in cardiovascular medicine · 2026Article
- Analyzing the Centers for Disease Control and Prevention Mortality Data Using Weekly Exceedance in Mortality Count and Weekly Change in Mortality Indicator: A Time Series Study.Health science reports · 2025Article
- Article
- Unbiased temperature-related mortality estimates using weekly and monthly health data: a new method for environmental epidemiology and climate impact studies.The Lancet. Planetary health · 2024Article
- An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.American journal of epidemiology · 2024Article
- Reliable event rates for disease mapping.Journal of official statistics · 2024Article
- Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis.Environmental health perspectives · 2023Article
- Twenty-Year Disparity Trends in United States Stroke Death Rate by Age, Race/Ethnicity, Geography, and Socioeconomic Status.Neurology · 2023Article
- Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts.PloS one · 2023Article
- Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.Scientific reports · 2021Article
- A Novel Imputation Approach for Sharing Protected Public Health Data.American journal of public health · 2021Article
- Geographic Visualization of Mortality in the United States as Related to Healthcare Access by County.Cureus · 2021Article
- Commentary: The Incidence of Road Traffic Crashes Among Young People Aged 15-20 Years: Differences in Behavior, Lifestyle and Sociodemographic Indices in the Galilee and the Golan.Frontiers in public health · 2021Article
- Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice.Preventing chronic disease · 2019Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCDC WONDER is a system developed to promote information-driven decision making and provide access to detailed public health information to the general public. Although CDC WONDER contains a wealth of data, any counts fewer than 10 are suppressed for confidentiality reasons, resulting in left-censored data. The objective of this analysis was to describe methods for the analysis of highly censored data.
methodsA substitution approach was compared with 1) a simple, nonspatial Bayesian model that smooths rates toward their statewide averages and 2) a more complex Bayesian model that accounts for spatial and between-age sources of dependence. Age group-specific county-level data on heart disease mortality were used for the comparisons.
resultsAlthough the substitution and nonspatial approach provided age-standardized rate estimates that were more highly correlated with the true rate estimates, the estimates from the spatial Bayesian model provided a superior compromise between goodness-of-fit and model complexity, as measured by the deviance information criterion. In addition, the spatial Bayesian model provided rate estimates with greater precision than the nonspatial approach; in contrast, the substitution approach did not provide estimates of uncertainty.
conclusionBecause of the ability to account for multiple sources of dependence and the flexibility to include covariate information, the use of spatial Bayesian models should be considered when analyzing highly censored data from CDC WONDER.
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