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Home Research Research Library Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery 2013 Author(s) Butler, Danielle C, Petterson, Stephen M, Phillips, Robert L, and Bazemore, Andrew W Topic(s) Role of Primary Care Keyword(s) Measurement, and Shortage Areas Volume Health Services Research Source Health Services Research OBJECTIVE: To develop a measure of social deprivation that is associated with health care access and health outcomes at a novel geographic level, primary care service area. DATA SOURCES/STUDY SETTING: Secondary analysis of data from the Dartmouth Atlas, AMA Masterfile, National Provider Identifier data, Small Area Health Insurance Estimates, American Community Survey, Area Resource File, and Behavioural Risk Factor Surveillance System. Data were aggregated to primary care service areas (PCSAs). STUDY DESIGN: Social deprivation variables were selected from literature review and international examples. Factor analysis was used. Correlation and multivariate analyses were conducted between index, health outcomes, and measures of health care access. The derived index was compared with poverty as a predictor of health outcomes. DATA COLLECTION/EXTRACTION METHODS: Variables not available at the PCSA level were estimated at block level, then aggregated to PCSA level. PRINCIPAL FINDINGS: Our social deprivation index is positively associated with poor access and poor health outcomes. This pattern holds in multivariate analyses controlling for other measures of access. A multidimensional measure of deprivation is more strongly associated with health outcomes than a measure of poverty alone. CONCLUSIONS: This geographic index has utility for identifying areas in need of assistance and is timely for revision of 35-year-old provider shortage and geographic underservice designation criteria used to allocate federal resources. ABFM Research Read all 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2015 Only one third of family physicians can estimate their patient panel size Go to Only one third of family physicians can estimate their patient panel size 2011 Variation over time in preventable hospitalization rates across counties Go to Variation over time in preventable hospitalization rates across counties
Author(s) Butler, Danielle C, Petterson, Stephen M, Phillips, Robert L, and Bazemore, Andrew W Topic(s) Role of Primary Care Keyword(s) Measurement, and Shortage Areas Volume Health Services Research Source Health Services Research
ABFM Research Read all 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries 2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning 2015 Only one third of family physicians can estimate their patient panel size Go to Only one third of family physicians can estimate their patient panel size 2011 Variation over time in preventable hospitalization rates across counties Go to Variation over time in preventable hospitalization rates across counties
2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries
2021 Advancing primary care with Artificial Intelligence and Machine Learning Go to Advancing primary care with Artificial Intelligence and Machine Learning
2015 Only one third of family physicians can estimate their patient panel size Go to Only one third of family physicians can estimate their patient panel size
2011 Variation over time in preventable hospitalization rates across counties Go to Variation over time in preventable hospitalization rates across counties