Relationship Between Primary Care Physician Capacity and Usual Source of Care.

BACKGROUND: The NASEM Primary Care Report and Primary Care scorecard highlighted the importance of primary care physician (PCP) capacity and having a usual source of care (USC). However, research has found that PCP capacity and USC do not always correlate. This exploratory study compares geographic patterns and the characteristics of counties with similar rates of PCP capacity but varying rates of USC. METHODS: Our county-level, cross-sectional approach includes estimates from the Robert Graham Center and data from the Robert Wood Johnson County Health Rankings (CHR). We utilized conditional mapping methods to first identify US counties with the highest rates of social deprivation (SDI). Next, counties were stratified based on primary care physician (PCP) capacity and usual source of care (USC) terciles, allowing us to identify 4 types of counties: (1) High-Low (high PCP capacity, low USC); (2) High-High (high PCP capacity, high USC); (3) Low-High (low PCP capacity, high USC); and (4) Low-Low (low PCP capacity, low USC). We use t test to explore differences in the characteristics of counties with similar rates of primary care capacity. RESULTS: The results show clear geographic patterns: High-High counties are located primarily in the northern and northeastern US; High-Low counties are located primarily in the southwestern and southern US. Low-High counties are concentrated in the Appalachian and Great Lakes regions; Low-Low counties are concentrated in the southeastern US and Texas. Descriptive results reveal that rates of racial and ethnic minorities, the uninsured, and social deprivation are highest in counties with low rates of USC for both high PCP and low PCP areas. CONCLUSIONS: Recognizing PCP shortages and improving rates of USC are key strategies for increasing access to high-quality, primary care. Targeting strategies by geographic region will allow for tailored models to improve access to and continuity of primary care. For example, we found that many of the counties with the lowest rates of USC are found in non-Medicaid expansion states (Texas, Georgia, and Florida) with high rates of uninsured populations, suggesting that expanding Medicaid and improving access to health insurance are key strategies for increasing USC in these states.

Calling Your Aunt Bertha for Social Assets: Family Medicine and Social Determinants of Health.

PURPOSE: The sociopolitical determinants of health drive health outcomes and inequities in the United States. Primary care practices are, increasingly, expected by payers and policy makers to assess patients' social needs. Resource referral platforms provide physicians with information and referral systems for community resources. One commonly used platform is Aunt Bertha/Find Help (AB/FH). The American Academy of Family Physicians (AAFP) Neighborhood Navigator (NN) tool allows physicians and laypeople to search for resources using AB/FH. We sought to describe what users were searching for and to identify patterns to inform resource allocation. METHODS: This was a descriptive study of the AAFP's NN tool. Searches of NN were analyzed to describe what users were searching for. RESULTS: From 2018 to April 2022 there were 168 135 searches. The most common searches were for food and housing insecurity (22%, 21%) and health care referral (20.6%) with 22% more searches in the winter than the spring. There was a 119% increase in searches between 2018 and 2022, and a 47% increase in searches during the COVID-19 Pandemic. In the "Health" category the top 20 subcategories accounted for over 77% of searches. CONCLUSIONS: Family physicians and their patients use NN to search AB/FH for community resources to address adverse social determinants of health (SDOH). As expected, searches increased during the COVID-19 pandemic. This type of analysis may help individual clinicians, practices, and health systems prepare for the most common social needs of their patients. Social resource platforms might serve as a robust measure for primary care practice screening and referral for SDOH.

Using State All-Payer Claims Data to Identify the Active Primary Care Workforce: A Novel Study in Virginia.

PURPOSE: Primary care is the foundation of the health care workforce and the only part that extends life and improves health equity. Previous research on the geographic and specialty distribution of physicians has relied on the American Medical Association's Masterfile, but these data have limitations that overestimate the workforce. METHODS: We present a pragmatic, systematic, and more accurate method for identifying primary care physicians using the National Plan and Provider Enumeration System (NPPES) and the Virginia All-Payer Claims Database (VA-APCD). Between 2015 and 2019, we identified all Virginia physicians and their specialty through the NPPES. Active physicians were defined by at least 1 claim in the VA-APCD. Specialty was determined hierarchically by the NPPES. Wellness visits were used to identify non-family medicine physicians who were providing primary care. RESULTS: In 2019, there were 20,976 active physicians in Virginia, of whom 5,899 (28.1%) were classified as providing primary care. Of this primary care physician workforce, 52.4% were family medicine physicians; the remaining were internal medicine physicians (18.5%), pediatricians (16.8%), obstetricians and gynecologists (11.8%), and other specialists (0.5%). Over 5 years, the counts and relative percentages of the workforce made up by primary care physicians remained relatively stable. CONCLUSIONS: Our novel method of identifying active physicians with a primary care scope provides a realistic size of the primary care workforce in Virginia, smaller than some previous estimates. Although the method should be expanded to include advanced practice clinicians and to further delineate the scope of practice, this simple approach can be used by policy makers, payers, and planners to ensure adequate primary care capacity.

Identifying health centers in areas with low COVID-19 vaccination rates & high rates of vaccine hesitancy.

Context: Large numbers of US adults are vaccinated, but COVID-19 vaccine hesitancy remains high. Health centers funded by the Health Resources and Services Administration (HRSA) have played a major role in COVID-19 vaccinations and have the potential to vaccinate even larger numbers of people. Objective: To identify U.S. counties with low COVID-19 vaccination rates and high rates of vaccine hesitancy, explore the characteristics of these counties and health center presence in these areas, and identify priority health centers for targeted vaccine outreach. Study Design: Cross-sectional geospatial analysis of county-level COVID-19 vaccination rates and COVID-19 vaccination hesitancy. Bivariate Local Moran's I using GeoDa software to identify clusters of counties with low COVID-19 vaccination rates and high rates of COVID-19 vaccine hesitancy. Geographic Information Systems (GIS) mapping to overlay health centers with county-level data. Setting or Dataset: U.S. counties; vaccine hesitancy data from U.S. Department of Health & Human Services Office of the Assistant Secretary for Planning and Evaluation (ASPE); vaccination rates from the Centers for Disease Control and Prevention (CDC); and data on Health Center Program awardees from the HRSA. Population studied: U.S. Counties (n=2,825) for which data on COVID-19 vaccination and COVID-19 vaccine hesitancy are available; and HRSA-funded health centers, excluding Puerto Rico and Pacific Islands (n=1,559). Outcome Measures: COVID-19 vaccine hesitancy and COVID-19 vaccination rates. Results: We identified 219 counties that are part of clusters of high rates of vaccine hesitancy and low COVID-19 vaccination rates. In general, these counties have higher rates of poverty, larger percentages of black and Hispanic populations, and are located in the Southeast (Alabama, Georgia) and West Virginia. Sixty health center awardees are located within these counties, serving almost 700,000 patients. Conclusions: While almost one-half of US adults have been vaccinated, younger adults have much lower rates of vaccination and large numbers are still unvaccinated. Further, vaccine rates vary by race and ethnicity, with less than one-fifth of Hispanic and black adults having been vaccinated. Targeting areas with high rates of vaccine hesitancy and low vaccination rates supports strategic planning, optimizes finite resources, and better assists health centers in creating culturally competent outreach addressing vaccine hesitancy.

Improving Access to Treatment for Opioid Use Disorder in High-Need Areas: The Role of HRSA Health Centers.

Introduction: Despite the opioid epidemic adversely affecting areas across the U.S. for more than two decades and increasing evidence that medication-assisted treatment (MAT) is effective for patients with opioid use disorder (OUD), access to treatment is still limited. The limited access to treatment holds true in the Appalachia region despite being disproportionately affected by the crisis, particularly in rural, central Appalachia. Purpose: This research identifies opportunities for health centers located in high-need areas based on drug poisoning mortality to better meet MAT care gaps. We also provide an in-depth look at health center MAT capacity relative to need in the Appalachia region. Methods: The analysis included county-level drug poisoning mortality data (2013-2015) from the National Center for Health Statistics (NCHS) and Health Center Program Awardee and Look-Alike data (2017) on the number of providers with a DATA waiver to provide medication-assisted treatment (MAT) and the number of patients receiving MAT for the U.S. Several geospatial methods were used including an Empirical Bayes approach to estimate drug poisoning mortality, excess risk maps to identify outliers, and the Local Moran's I tool to identify clusters of high drug poisoning mortality counties. Results: High-need counties were disproportionately located in the Appalachia region. More than 6 in 10 health centers in high-need counties have the potential to expand MAT delivery to patients. Implications: The results indicate an opportunity to increase health center capacity for providing treatment for opioid use disorder in high-need areas, particularly in central and northern Appalachia.

Identifying Priority and "Bright-Spot" Counties for Diabetes Preventive Care in Appalachia: An Exploratory Analysis.

Introduction: Type 2 diabetes mellitus (T2DM) prevalence and mortality in Appalachian counties is substantially higher when compared to non-Appalachian counties, although there is significant variation within Appalachia. Purpose: The objectives of this research were to identify low-performing (priority) and high-performing (bright spot) counties with respect to improving T2DM preventive care. Methods: Using data from the Centers for Medicare and Medicaid (CMS), the Dartmouth Atlas of Health Care, and the Appalachia Regional Commission, conditional maps were created using county-level estimates for T2DM prevalence, mortality, and annual hemoglobin A1c (HbA1c) testing rates. Priority counties were identified using the following criteria: top 33rd percentile for T2DM mortality; top 33rd percentile for T2DM prevalence; bottom 50th percentile for A1c testing rates. Bright spot counties were identified as counties in the bottom 33rd percentile for T2DM mortality, the top 33rd percentile for T2DM prevalence; and the top 50th percentile for HbA1c testing rates. Results: Forty-one priority counties were identified (those with high T2DM mortality, high T2DM prevalence, and low HbA1c testing rates), which were located primarily in Central and North Central Appalachia; and 17 bright spot counties were identified (high T2DM prevalence, low T2DM mortality, and high HbA1c testing rates), which were scattered throughout Appalachia. Eight of the 17 bright spot counties were adjacent to priority counties. Implications: By employing conditional mapping to T2DM, multiple variables can be summarized into a single, easily interpretable map. This could be valuable for T2DM-prevention programs seeking to prioritize diagnostic and intervention resources for the management of T2DM in Appalachia.

Congruence of Patient Self-Rating of Health with Family Physician Ratings.

BACKGROUND: A single self-rated health (SRH) question is associated with health outcomes, but agreement between SRH and physician-rated patient health (PRPH) has been poorly studied. We studied patient and physician reasoning for health ratings and the role played by patient lifestyle and objective health measures in the congruence between SRH and PRPH. METHODS: Surveys of established family medicine patients and their physicians, and medical record review at 4 offices. Patients and physicians rated patient health on a 5-point scale and gave reasons for the rating and suggestions for improving health. Patients' and physicians' reasons for ratings and improvement suggestions were coded into taxonomies developed from the data. Bivariate relationships between the variables and the difference between SRH and PRPH were examined and all single predictors of the difference were entered into a multivariable regression model. RESULTS: Surveys were completed by 506 patients and 33 physicians. SRH and PRPH ratings matched exactly for 38% of the patient-physician dyads. Variables associated with SRH being lower than PRPH were higher patient body mass index (P = .01), seeing the physician previously (P = .04), older age, (P < .001), and a higher comorbidity score (P = .001). Only 25.7% of the dyad reasons for health status rating and 24.1% of needed improvements matched, and these matches were unrelated to SRH/PRPH agreement. Physicians focused on disease in their reasoning for most patients, whereas patients with excellent or very good SRH focused on feeling well. CONCLUSIONS: Patients' and physicians' beliefs about patient health frequently lack agreement, confirming the need for shared decision making with patients.

How Other Countries Use Deprivation Indices-And Why The United States Desperately Needs One.

Integrating public health and medicine to address social determinants of health is essential to achieving the Triple Aim of lower costs, improved care, and population health. There is intense interest in the United States in using social determinants of health to direct clinical and community health interventions, and to adjust quality measures and payments. The United Kingdom and New Zealand use data representing aspects of material and social deprivation from their censuses or from administrative data sets to construct indices designed to measure socioeconomic variation across communities, assess community needs, inform research, adjust clinical funding, allocate community resources, and determine policy impact. Indices provide these countries with comparable data and serve as a universal language and tool set to define organizing principles for population health. In this article we examine how these countries develop, validate, and operationalize their indices; explore their use in policy; and propose the development of a similar deprivation index for the United States.

"Community vital signs": incorporating geocoded social determinants into electronic records to promote patient and population health.

Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, "community vital signs" (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.

Using geographic information systems (GIS) to identify communities in need of health insurance outreach: An OCHIN practice-based research network (PBRN) report.

BACKGROUND: Our practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients. METHODS: Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n = 228,284). RESULTS: Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates. DISCUSSION: EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions.

The impact of a clinic move on vulnerable patients with chronic disease: a Geographic Information Systems (GIS) analysis.

BACKGROUND: Changing locations disrupts the populations served by primary health care clinics, and such changes may differentially affect access to care for vulnerable populations. METHODS: Online geographic information systems mapping tools were used to define how the relocation of a family medicine center impacted access to care for black and Hispanic patients with chronic disease. RESULTS: Maps created from practice management data revealed a distinct shift in black and Hispanic patients with chronic disease being served in the new location. CONCLUSIONS: Geographic information systems tools are valuable aids in defining changing service areas of primary health care clinics.

Is access sufficient? An examination of the effects of the MedShare program to expand access to prescription drugs for indigent populations.

We conduct an evaluation of MedShare, a program designed to enhance access to prescription drugs for indigent patients in the Greater Cincinnati area. The program expands access to drugs by providing subsidies to reduce the costs paid by patients for their prescriptions. The assumption is that by expanding access to prescription drugs, participant health outcomes as measured by quality of life improve. Although the program appears outwardly successful, we found little difference between program participants and comparison groups. We feel that these findings point to a major flaw with existing health policy: access alone is not sufficient to improve health outcomes. Too often programs are created and, provided they show outwards signs of success (e.g., enrollment and utilization), are assumed to be improving the health of the community. Our findings indicate that one must look beyond just expanding access to ensure that programs are indeed achieving their overall objectives.

Development and implementation of a quality assurance infrastructure in a multisite home visitation program in Ohio and Kentucky.

As home visitation programs go to scale, numerous challenges are faced in implementation and quality assurance. This article describes the origins and implementation of Every Child Succeeds, a multisite home visitation program in southwestern Ohio and Northern Kentucky. In order to optimize quality assurance and generate new learning for the field, a Web-based system (eECS) was designed to systematically collect and use data. Continuous quality assurance procedures derived from business and industry have been established. Findings from data collection have documented outcomes, and have identified clinical needs that potentially undermine the impact of home visitation. An augmented module approach has been used to address these needs, and a program to treat maternal depression is described as an example of this approach. Challenges encountered are also discussed.

Does financial self-efficacy explain gender differences in retirement saving strategies?

Research indicates that women remain less financially prepared for retirement than are men. Little research has examined the gender difference in use of retirement plans. The present research assessed the gender difference in use of employer-sponsored and private retirement plans, and sought to account for this difference. Social status and human capital factors, occupation and industry of employment, and sense of financial self-efficacy were expected to account for gender differences. Findings indicated gender has no relationship with use of private retirement plans. However, females were found to be less likely to use an employer-sponsored plan, and this was largely accounted for by gender differences in occupation.

Every user succeeds.

By applying proven models in user-interface design and training, we deployed a Web-based data collection and reporting system that has been enthusiastically embraced by users with limited computer equipment and skills. Social workers and nurses now submit forms paperlessly as they visit thousands of new mothers across seven counties. The value of the underlying database has been enhanced by immediate validity checks, up-to-the-minute summary reports, and instantaneous access to all previous entries.

Facilitating heart transplant candidate evaluation throughout Ohio.

The Ohio Solid Organ Transplantation Consortium (OSOTC) has a mission to ensure equitable access to transplant services to those most likely to benefit, regardless of personal resources. Patients are selected according to criteria established by the OSOTC, which are delineated in the Ohio Department of Health rule, and consistent with national criteria. The University of Cincinnati's Institute for Health Policy and Health Services Research (IHPHSR) has implemented a secure internet-based system to replace the OSOTC's existing fax-based procedures. This system meets both the patient evaluation and basic research needs of the OSOTC.

The Israel Penn International Transplant Tumor Registry.

The Israel Penn International Transplant Tumor Registry is literally the world's premier repository of information on patients who have developed malignancies after organ transplants. The administrators of the Registry not only collect information but also provide consulting services based on the accumulated knowledge that the Registry contains. By creating a secure Web-based front end, we have made it possible for the Registry to keep pace with its burgeoning international caseload.