ArticleBMC public health2023
Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics.International journal of environmental research and public health · 2024Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
backgroundLocal governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA.
methodsThe first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County.
resultsThe ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates.
conclusionsThese SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.
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Registered trials
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