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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.22080 |
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| _version_ | 1866917042064457728 |
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| author | Von Hoene, Emma Gupta, Aanya Kavak, Hamdi Roess, Amira Anderson, Taylor |
| author_facet | Von Hoene, Emma Gupta, Aanya Kavak, Hamdi Roess, Amira Anderson, Taylor |
| contents | This study introduces the Spatial Health and Population Estimator (SHAPE), a spatial microsimulation framework that applies hierarchical iterative proportional fitting (IPF) to estimate two health risk behaviors and eleven health outcomes across multiple spatial scales. SHAPE was evaluated using county-level direct estimates from the Behavioral Risk Factor Surveillance System (BRFSS) and both county and census tract level data from CDC PLACES for New York (2021) and Florida (2019). Results show that SHAPE's SAEs are moderately consistent with BRFSS (average Pearson's correlation coefficient r of about 0.5), similar to CDC PLACES (average r of about 0.6), and are strongly aligned with CDC PLACES model-based estimates at both county (average r of about 0.8) and census tract (average r of about 0.7) levels. SHAPE is an open, reproducible, and transparent framework programmed in R that meets a need for accessible SAE methods in public health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System Von Hoene, Emma Gupta, Aanya Kavak, Hamdi Roess, Amira Anderson, Taylor Applications Multiagent Systems This study introduces the Spatial Health and Population Estimator (SHAPE), a spatial microsimulation framework that applies hierarchical iterative proportional fitting (IPF) to estimate two health risk behaviors and eleven health outcomes across multiple spatial scales. SHAPE was evaluated using county-level direct estimates from the Behavioral Risk Factor Surveillance System (BRFSS) and both county and census tract level data from CDC PLACES for New York (2021) and Florida (2019). Results show that SHAPE's SAEs are moderately consistent with BRFSS (average Pearson's correlation coefficient r of about 0.5), similar to CDC PLACES (average r of about 0.6), and are strongly aligned with CDC PLACES model-based estimates at both county (average r of about 0.8) and census tract (average r of about 0.7) levels. SHAPE is an open, reproducible, and transparent framework programmed in R that meets a need for accessible SAE methods in public health. |
| title | Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System |
| topic | Applications Multiagent Systems |
| url | https://arxiv.org/abs/2510.22080 |