Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.
Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and compl...
| Publicado en: | Journal of Urban Health Vol. 92; no. 5; pp. 864 - 910 |
|---|---|
| Autores principales: | , , , |
| Formato: | journal article |
| Publicado: |
Springer Nature
Oct2015
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=110400771&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 110400771 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10993460 GMF jtl: Journal of Urban Health issn: 10993460 maglogo: N pubinfo: dt: Oct2015 vid: 92 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 110400771 10.1007/s11524-015-9972-1 ppf: 864 ppct: 46 formats: fmt: @attributes: type: P size: 1.6MB tig: atl: Small-Area Estimation of Spatial Access to Care and Its Implications for Policy. aug: au: Gentili, Monica Isett, Kim Serban, Nicoleta Swann, Julie affil: School of Public Policy, Georgia Institute of Technology, Atlanta 30332 USA H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta 30332 USA su: California Georgia Public health Health policy Health equity Health services accessibility Medical decision making Primary care Mathematical optimization Statistics Statistical models sug: subj: Public health Health policy Health equity Health services accessibility California Georgia Administration of Public Health Programs Health and Welfare Funds Medical decision making Primary care Mathematical optimization Statistics Statistical models keyword: Optimization Small-area estimates Spatial access Optimization Small-area estimates Spatial access ab: Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|