Measures of Social Deprivation That Predict Health Care Access and Need within a Rational Area of Primary Care Service Delivery
ABSTRACT 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.
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ABSTRACT: The aim of this study was to describe prevalence, awareness, and treatment of high blood pressure (HBP) and associated factors among the elderly in Florianópolis, Santa Catarina State, Brazil. This cross-sectional population-based study used a complex sampling design. HBP was defined as elevated blood pressure (by direct measurement), use of antihypertensive medication, or prior diagnosis. The association of outcomes with independent variables was assessed by Poisson regression. One-thousand seven hundred and five participants were interviewed. Of these, 84.6% presented HBP, 77.5% were aware of their condition, and 79.1% were on antihypertensive medication. Prevalence of HBP was associated with age, functional capacity, and body mass index (BMI). Awareness of the condition was associated with age, gender, BMI, self-rated health, and recent medical consultation. Treatment was associated with gender, functional capacity, self-rated health, and recent medical consultation. Although public health policies should include everyone, unequal distribution of HBP in the population should be addressed through targeted preventive, diagnostic, and therapeutic measures.Cadernos de saúde pública / Ministério da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública 03/2013; 29(3):507-21. · 0.89 Impact Factor
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ABSTRACT: Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere.Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets.BMC Health Services Research 09/2013; 13(1):343. DOI:10.1186/1472-6963-13-343 · 1.66 Impact Factor
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ABSTRACT: Purpose: Facing rising numbers of insured with implementation of the Affordable Care Act, policy makers are interested in building teams of providers that can accommodate a growing demand for primary care services. Nurse Practitioners (NPs), Physician Assistants (PAs), and Certified Nurse Midwives (CNMs) already augment the physician workforce, particularly in rural areas. Our objective was to determine what physician and areal-level characteristics were associated with working with NPs, PAs or CNMs. Methods: The sample consisted of a convenience sample of physicians through the American Board of Family Medicine (ABFM) website in the fall of 2011. We linked these data to demographic and practice information collected by the ABFM and with provider information supplied from the National Provider Identifier file aggregated at the Primary Care Service Area level. Hierarchical logistic regression models were used to determine variables associated with working with NPs, PAs, or CNMs. Findings: Of the 3,855 family physicians in our sample, 60% reported routinely working with NPs, PAs, or CNMs. In regression analysis, characteristics positively associated with working with NPs, PAs, or CNMs were providing gynecological care (Odds Ratio = 1.23 [95% confidence interval, 1.06-1.42]), multispecialty group practice (OR = 1.72 [1.36-2.18]), any rural setting, and higher availability of PAs (OR = 1.40 [1.10-1.79]). Restrictive NP scope of practice laws failed to reach significance (OR = 0.86 [0.71-1.05]). Conclusions: This study suggests that the number of family physicians routinely working with NPs, PAs, and CNMs continues to increase, which may allow for improved access to health care, particularly in rural areas.The Journal of the American Board of Family Medicine 12/2013; 26(3):244-5. DOI:10.3122/jabfm.2013.03.120312 · 1.85 Impact Factor