ArticleHealth services research2015
Modeling Area-Level Health Rankings.
Article in Health services research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- The Covid-19 hospitalization risk associated with air pollution in New York state counties after the 2023 Quebec wildfires.Journal of public health research · 2025Article
- Health differences between rural and non-rural Texas counties based on 2023 County Health Rankings.BMC health services research · 2025Article
- The association of wildfire air pollution with COVID-19 incidence in New South Wales, Australia.The Science of the total environment · 2022Article
- Environmental and sociodemographic risk factors associated with environmentally transmitted zoonoses hospitalisations in Queensland, Australia.One health (Amsterdam, Netherlands) · 2021Article
- Developing a surveillance system of sub-county data: Finding suitable population thresholds for geographic aggregations.Spatial and spatio-temporal epidemiology · 2020Article
- Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil.The American journal of tropical medicine and hygiene · 2019Article
- The Importance of Health and Social Services Spending to Health Outcomes in Texas, 2010-2016.Southern medical journal · 2019Article
- Revisiting area risk classification of visceral leishmaniasis in Brazil.BMC infectious diseases · 2019Article
- County Health Rankings and the Cult of the Imperfect.Health services research · 2015Article
- Just How Useful Are Health Rankings?Health services research · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
objectiveRank county health using a Bayesian factor analysis model. DATA SOURCES: Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009). STUDY
designOur model builds on the existing county health rankings (CHRs) by using data-derived weights to compute ranks from mortality and morbidity variables, and by quantifying uncertainty based on population, spatial correlation, and missing data. We apply our model to Wisconsin, which has comprehensive data, and Texas, which has substantial missing information. DATA COLLECTION
methodsThe data were downloaded from www.countyhealthrankings.org. PRINCIPAL
findingsOur estimated rankings are more similar to the CHRs for Wisconsin than Texas, as the data-derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHRs and our ranks are 0.89 for Wisconsin and 0.65 for Texas. Uncertainty is especially severe for Texas given the state's substantial missing data.
conclusionsThe reliability of comprehensive CHRs varies from state to state. We advise focusing on the counties that remain among the least healthy after incorporating alternate weighting methods and accounting for uncertainty. Our results also highlight the need for broader geographic coverage in health data.
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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.