ArticleAnnals of epidemiology2022
The where of when: Geographic variation in the timing of recent increases in US county-level heart disease death rates.
Article in Annals of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Decline and Resurgence of Cardiovascular-Kidney-Metabolic Syndrome-Related Deaths Involving Malnutrition in the United States, 1999 to 2023.Journal of the American Heart Association · 2026Article
- Trends and widening inequities in cardiovascular-kidney-metabolic involvement in cardiovascular mortality: A national spatiotemporal analysis, 2014-2023.Clinical medicine (London, England) · 2026Article
- Global, regional, and national burden and projections of severe heart failure attributable to hypertensive, ischemic, and rheumatic heart diseases: an analysis from the global burden of disease study 2021.Frontiers in cardiovascular medicine · 2026Article
- National trends and county-level geographic disparities in mortality from operationally defined cardiovascular-kidney-metabolic stages 4 and 4b in the United States.Frontiers in cardiovascular medicine · 2026Article
- Article
- Applying a Bayesian spatiotemporal model to examine excess county-level cardiovascular disease death rates during the COVID-19 pandemic.American journal of epidemiology · 2025Article
- Improving Blood Pressure Control and Tobacco Use Cessation Intervention In Primary Care: Protocol for the Alabama Cardiovascular Cooperative Heart Health Improvement Project.JMIR research protocols · 2024Article
- State and Local Health Departments: Research, Surveillance, and Evidence-Based Public Health Practices.Preventing chronic disease · 2023Article
- Cause-specific mortality by county, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities.Lancet (London, England) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purposeWithin the context of local increases in US heart disease death rates, we estimated when increasing heart disease death rates began by county among adults aged 35-64 years and characterized geographic variation.
methodsWe applied Bayesian spatiotemporal models to vital statistics data to estimate the timing (i.e., the year) of increasing county-level heart disease death rates during 1999-2019 among adults aged 35-64 years. To examine geographic variation, we stratified results by US Census region and urban-rural classification.
resultsThe onset of increasing heart disease death rates among adults aged 35-64 years spanned the two-decade study period from 1999 to 2019. Overall, 43.5% (95% CI: 41.3, 45.6) of counties began increasing before 2011, with early increases more prevalent outside of the most urban counties and outside of the Northeast. Roughly one-in-five (18.4% [95% CI: 15.6, 20.7]) counties continued to decline throughout the study period.
conclusionsThis variation suggests that factors associated with these geographic classifications may be critical in establishing the timing of changing trends in heart disease death rates. These results reinforce the importance of spatiotemporal surveillance in the early identification of adverse trends and in informing opportunities for tailored policies and programs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.