Primary care service areas: a new tool for the evaluation of primary care services

Development and Evaluation of Pediatric Acute Care Hospital Referral Regions in Eight States

OBJECTIVE

To develop a set of pediatric acute care hospital referral regions for use in studying pediatric acute care delivery and test their utility relative to other regional systems.

STUDY DESIGN

We used state-level administrative databases capturing all pediatric acute care in 8 states to construct novel referral regions. We first constructed pediatric hospital service areas (PHSAs) based on 5 837 464 pediatric emergency department encounters. We then aggregated these PHSAs to pediatric hospital referral regions (PHRRs) based on 344 440 pediatric hospitalizations. Finally, we used 3 measures of spatial accuracy (localization index, market share index, and net patient flow) to compare this novel region system with the Dartmouth Atlas, designed originally to study adult specialty care, and the Pittsburgh Atlas, designed originally to study adult acute care.

RESULTS

The development procedure resulted in 717 novel PHSAs, which were then aggregated to 55 PHRRs across the included states. Relative to hospital referral regions in the Dartmouth and Pittsburgh Atlases, PHRRs were fewer in number and larger in area and population. PHRRs more accurately captured patterns of pediatric hospitalizations, (eg, mean localization index: 69.1 out of 100, compared with a mean of 58.1 for the Dartmouth Atlas and 62.4 for the Pittsburgh Atlas).

CONCLUSIONS

The use of regional definitions designed specifically to study pediatric acute care better captures contemporary pediatric acute care delivery than the use of existing regional definitions. Future work should extend these definitions to all US states to enable national analyses of pediatric acute care delivery.

Access to Federally Qualified Health Centers and HIV Outcomes in the U.S. South

INTRODUCTION

Federally Qualified Health Centers may increase access to HIV prevention, care, and treatment for at-risk populations.

METHODS

A pooled cross section of ZIP Code Tabulation Areas from cites in the U.S. South with high HIV diagnoses were used to examine Federally Qualified Health Center density and indicators of HIV epidemic control. The explanatory variable was Federally Qualified Health Center density-number of Federally Qualified Health Centers in a ZIP Code Tabulation Areas' Primary Care Service Area per low-income population-high versus medium/low (2019). Outcomes were 5-year (2015-2019 or 2014-2018) (1) number of new HIV diagnoses, (2) percentage late diagnosis, (3) percentage linked to care, and (4) percentage virally suppressed, which was assessed over 1 year (2018 or 2019). Multiple linear regression was used to examine the relationship, including ZIP Code Tabulation Area-level sociodemographic and city-level HIV funding variables, with state-fixed effects, and data analysis was completed in 2022-2023. Sensitivity analyses included (1) examining ZIP Code Tabulation Areas with fewer non-Federally Qualified Health Center primary care providers, (2) controlling for county-level primary care provider density, (3) excluding the highest HIV prevalence ZIP Code Tabulation Areas, and (4) excluding Florida ZIP Code Tabulation Areas.

RESULTS

High-density ZIP Code Tabulation Areas had a lower percentage of late diagnosis and virally suppressed, a higher percentage linked to care, and no differences in new HIV diagnoses (p<0.05). In adjusted analysis, high density was associated with a greater number of new diagnoses (number or percentage=5.65; 95% CI=2.81, 8.49), lower percentage of late diagnosis (-3.71%; 95% CI= -5.99, -1.42), higher percentage linked to care (2.13%; 95% CI=0.20, 4.06), and higher percentage virally suppressed (1.87%; 95% CI=0.53, 2.74) than medium/low density.

CONCLUSIONS

Results suggest that access to Federally Qualified Health Centers may benefit community-level HIV epidemic indicators.

Optimizing the medical equipment investment in primary care centres in rural China: evidence from a panel threshold model

BACKGROUND

The previous "one-size-fits-all" practice in resource allocation can no longer adapt to the spatial variation in population and health needs. This study aimed to investigate the spatially heterogeneous effect of medical equipment investment in the township health centres in rural China to optimize the investment strategies.

METHODS

Based on the national-scale stratified multistage cluster sampling, 319 township health centres from six provinces were included in the study. The retrospective data from 2013 to 2017 were collected for each sampled township health centres and the corresponding township community. The panel threshold regression model was applied to estimate the nonlinear effect of medical equipment increment on the service utilization due to the township communities' urbanization degree. The influence of township community remoteness on the effects of equipment increment was investigated through subgroup analysis.

RESULTS

Among the township health centres in the neighbouring towns of the county seat (travel time to the county seat < 1 h), the significant effect of medical equipment increment was only found in the township health centres of the towns with high urbanization degrees (the proportion of the residents living in the built-up area > 69.89%), of which the effect size was 774.81 (95% CI 495.63, 1053.98, p < 0.05). Among the township health centres in the remote towns (travel time ≥ 1 h), the effect of medical equipment increment in the township health centres of the low urbanized towns (urban ≤ 5.99%, β = 1052.54, p < 0.01) was around four times the size of that of the counterparts (urban > 5.99%, β = 237.00, p < 0.01).

CONCLUSION

This study demonstrated the spatially heterogeneous effect of medical equipment investment in the primary care centres in rural China. The priority of the equipment investment was suggested to be given to the township health centres in the remote towns with a low urbanization degree and those in the highly-urbanized neighbouring towns of the county seats.

Development and Evaluation of Rehabilitation Service Areas for the United States

BACKGROUND

Geographic areas have been developed for many healthcare sectors including acute and primary care. These areas aid in understanding health care supply, use, and outcomes. However, little attention has been given to developing similar geographic tools for understanding rehabilitation in post-acute care. The purpose of this study was to develop and characterize post-acute care Rehabilitation Service Areas (RSAs) in the United States (US) that reflect rehabilitation use by Medicare beneficiaries.

METHODS

A patient origin study was conducted to cluster beneficiary ZIP (Zone Improvement Plan) code tabulation areas (ZCTAs) with providers who service those areas using Ward's clustering method. We used US national Medicare claims data for 2013 to 2015 for beneficiaries discharged from an acute care hospital to an inpatient rehabilitation facility (IRF), skilled nursing facility (SNF), long-term care hospital (LTCH), or home health agency (HHA). Medicare is a US health insurance program primarily for older adults. The study population included patient records across all diagnostic groups. We used IRF, SNF, LTCH and HHA services to create the RSAs. We used 2013 and 2014 data (n = 2,730,366) to develop the RSAs and 2015 data (n = 1,118,936) to evaluate stability. We described the RSAs by provider type availability, population, and traveling patterns among beneficiaries.

RESULTS

The method resulted in 1,711 discrete RSAs. 38.7% of these RSAs had IRFs, 16.1% had LTCHs, and 99.7% had SNFs. The number of RSAs varied across states; some had fewer than 10 while others had greater than 70. Overall, 21.9% of beneficiaries traveled from the RSA where they resided to another RSA for care.

CONCLUSIONS

Rehabilitation Service Areas are a new tool for the measurement and understanding of post-acute care utilization, resources, quality, and outcomes. These areas provide policy makers, researchers, and administrators with small-area boundaries to assess access, supply, demand, and understanding of financing to improve practice and policy for post-acute care in the US.

Racial and Ethnic Disparities in Hospital-Based Care Among Dual Eligibles Who Use Health Centers

Introduction

Health center use may reduce hospital-based care among Medicare-Medicaid dual eligibles, but racial and ethnic disparities in this population have not been widely studied. We examined the extent of racial and ethnic disparities in hospital-based care among duals using health centers and the degree to which disparities occur within or between health centers.

Methods

We used 2012-2018 Medicare claims and health center data to model emergency department (ED) visits, observation stays, hospitalizations, and 30-day unplanned returns as a function of race and ethnicity among dual eligibles using health centers.

Results

In rural and urban counties, age-eligible Black individuals had more ED visits (7.9 [4.0, 11.7] and 13.7 [10.0, 17.4] per 100 person-years) and were more likely to experience an unplanned return (1.4 [0.4, 2.4] and 1 [0.4, 1.6] percentage points [pp]) than White individuals, but were less likely to be hospitalized (-3.3 [-3.9, -2.8] and -1.2 [-1.6, -0.9] pp). In urban counties, age-eligible Black individuals were 1.2 [0.9, 1.5] pp more likely than White individuals to have observation stays. Other racial and ethnic groups used the same or less hospital-based care than White individuals. Including state and health center fixed effects eliminated Black versus White disparities in all outcomes, except hospitalization. Results were similar among disability-eligible duals.

Conclusion

Racial and ethnic disparities in hospital-based care among dual eligibles are less common within than between health centers. If health centers are to play a more central role in eliminating racial and ethnic health disparities, these differences across health centers must be understood and addressed.

Health center use and hospital-based care among individuals dually enrolled in Medicare and Medicaid, 2012-2018

OBJECTIVE

To examine the relationship between federally qualified health center (FQHC) use and hospital-based care among individuals dually enrolled in Medicare and Medicaid.

DATA SOURCES

Data were obtained from 2012 to 2018 Medicare claims.

STUDY DESIGN

We modeled hospital-based care as a function of FQHC use, person-level factors, a Medicare prospective payment system (PPS) indicator, and ZIP code fixed effects. Outcomes included emergency department (ED) visits (overall and nonemergent), observation stays, hospitalizations (overall and for ambulatory care sensitive conditions), and 30-day unplanned returns. We stratified all models on the basis of eligibility and rurality.

DATA EXTRACTION METHODS

Our sample included individuals dually enrolled in Medicare and Medicaid for at least two full consecutive years, residing in a primary care service area with an FQHC. We excluded individuals without primary care visits, who died, or had end-stage renal disease.

PRINCIPAL FINDINGS

After the Medicare PPS was introduced, FQHC use in rural counties was associated with fewer ED and nonemergent ED visits per 100 person-years among both age-eligible (-14.8 [-17.5, -12.1]; -6.6 [-7.5, -5.6]) and disability-eligible duals (-11.3 [-14.4, -8.3]; -6 [-7.4, -4.6]) as well as a lower probability of observation stays (-0.8 pp age-eligible; -0.4 pp disability-eligible) and unplanned returns (-2.1 pp age-eligible; -1.9 pp disability-eligible). In urban counties, FQHC use was associated with more ED and nonemergent ED visits per 100 person-years (10.6 [8.4, 12.8]; 4.0 [2.6, 5.4]) among disability-eligible duals (a decrease of more than 60% compared with the pre-PPS period) and increases in the probability of hospitalization (1.1 pp age-eligible; 0.8 pp disability-eligible) and ACS hospitalization (0.5 pp age-eligible; 0.3 pp disability-eligible) (a decrease of roughly 50% compared with the pre-PPS period).

CONCLUSIONS

FQHC use is associated with reductions in hospital-based care among dual enrollees after introduction of the Medicare PPS. Further research is needed to understand how FQHCs can tailor care to best serve this complex population.

Delineation of Cancer Service Areas Anchored by Major Cancer Centers in the United States

Defining a reliable geographic unit pertaining to cancer care is essential in its assessment, planning, and management. This study aims to delineate and characterize the cancer service areas (CSA) accounting for the presence of major cancer centers in the United States. We used the Medicare enrollment and claims from January 1, 2014 to September 30, 2015 to build a spatial network from patients with cancer to cancer care facilities that provided inpatient and outpatient care of cancer-directed surgery, chemotherapy, and radiation. After excluding those without clinical care or outside of the United States, we identified 94 NCI-designated and other academic cancer centers from the members of the Association of American Cancer Institutes. By explicitly incorporating existing specialized cancer referral centers, we refined the spatially constrained Leiden method that accounted for spatial adjacency and other constraints to delineate coherent CSAs within which the service volumes were maximal but minimal between them. The derived 110 CSAs had a high mean localization index (LI; 0.83) with a narrow variability (SD = 0.10). The variation of LI across the CSAs was positively associated with population, median household income, and area size, and negatively with travel time. Averagely, patients traveled less and were more likely to receive cancer care within the CSAs anchored by cancer centers than their counterparts without cancer centers. We concluded that CSAs are effective in capturing the local cancer care markets in the United States. They can be used as reliable units for studying cancer care and informing more evidence-based policy.

Significance

Using the most refined network community detection method, we can delineate CSAs in a more robust, systematic, and empirical manner that incorporates existing specialized cancer referral centers. The CSAs can be used as a reliable unit for studying cancer care and informing more evidence-based policy in the United States. The cross-walk tabulation of ZIP code areas, CSAs, and related programs for CSAs delineation are disseminated for public access.

Individual and Neighborhood Influences on the Relationship Between Waist Circumference and Coronary Heart Disease in the REasons for Geographic and Racial Differences in Stroke Study

INTRODUCTION

The objective of this study was to describe how the relationship between waist circumference and incident coronary heart disease (CHD) is influenced by individual and neighborhood factors in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study.

METHODS

REGARDS is a cohort study of 30,239 US adults. The primary exposure was sex-specific quartiles of waist circumference. Individual covariates included sociodemographic characteristics, health status, health behavior, and usual source of care. Neighborhood (ie, zip code-level) covariates included access to primary care, poverty, rurality, and racial segregation. The main outcome was incident CHD from baseline (2003) through 2017. We used descriptive statistics, Kaplan-Meier curves, and Cox proportional hazard models to analyze the overall sample and race-sex subgroups.

RESULTS

During the study period, 23,042 study participants had 1,499 CHD events. We found a higher risk of incident CHD in the upper quartile of waist circumference compared with the first quartile in all 4 race-sex subgroups except African American men, among whom we found no relationship between waist circumference and incident CHD. Covariates did not attenuate these relationships.

CONCLUSION

In all groups except African American men, waist circumference in the highest quartile was associated with increased risk of incident CHD. Individual and neighborhood factors did not influence the relationship between waist circumference and development of CHD but differentially influenced incident CHD among race-sex subgroups.

Geographic access to federally qualified health centers before and after the affordable care act

Background

The Affordable Care Act (ACA) increased funding for Federally Qualified Health Centers (FQHCs). We defined FQHC service areas based on patient use and examined the characteristics of areas that gained FQHC access post-ACA.

Methods

We defined FQHC service areas using total patient counts by ZIP code from the Uniform Data System (UDS) and compared this approach with existing methods. We then compared the characteristics of ZIP codes included in Medically Underserved Areas/Populations (MUA/Ps) that gained access vs. MUA/P ZIP codes that did not gain access to FQHCs between 2011–15.

Results

FQHC service areas based on UDS data vs. Primary Care Service Areas or counties included a higher percentage of each FQHC’s patients (86% vs. 49% and 71%) and ZIP codes with greater use of FQHCs among low-income residents (29% vs. 22% and 22%), on average. MUA/Ps that gained FQHC access 2011–2015 included more poor, uninsured, publicly insured, and foreign-born residents than underserved areas that did not gain access, but were less likely to be rural (p < .05).

Conclusions

Measures of actual patient use provide a promising method of assessing FQHC service areas and access. Post-ACA funding, the FQHC program expanded access into areas that were more likely to have higher rates of poverty and uninsurance, which could help address disparities in access to care. Rural areas were less likely to gain access to FQHCs, underscoring the persistent challenges of providing care in these areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07685-0.

GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods

Since the Dartmouth hospital service areas (HSAs) were proposed three decades ago, there has been a large body of work using the unit in examining the geographic variation in health care in the U.S. for evaluating health care system performance and informing health policy. However, many studies question the replicability and reliability of the Dartmouth HSAs in meeting the challenges of ever-changing and a diverse set of health care services. This research develops a reproducible, automated, and efficient GIS tool to implement Dartmouth method for defining HSAs. Moreover, the research adapts two popular network community detection methods to account for spatial constraints for defining HSAs that are scale flexible and optimize an important property such as maximum service flows within HSAs. A case study based on the state inpatient database in Florida from the Healthcare Cost and Utilization Project is used to evaluate the efficiency and effectiveness of the methods. The study represents a major step toward developing HSA delineation methods that are computationally efficient, adaptable for various scales (from a local region to as large as a national market), and automated without a steep learning curve for public health professionals.

Creating accountable hospital service areas in China: a case analysis of health expenditure in the metropolis of Chengdu

OBJECTIVES

To delineate hospital service areas (HSAs) using the Dartmouth approach in China and identify the hypothesised demand-side, supply-side and region-specific factors of health expenditure within HSAs.

DESIGN

Population-based descriptive study.

SETTING

We selected the metropolis of Chengdu, one of the three most populous cities in China as a case for the analysis, where approximately 16.33 million residents living.

PARTICIPANTS

Individual-level in-patient discharge records (n=904 298) during the fourth quarter of 2018 (from 1 September to 31 December) were extracted from Sichuan Health Commission. Cases of non-residents of Chengdu were excluded from the datasets.

METHODS

We conducted three sets of analyses: (1) apply Dartmouth approach to delineate HSAs; (2) use Geographic Information System (GIS)-based method to demonstrate health expenditure variations across delineated HSAs and (3) employ a three-level multilevel linear model to examine the association between health expenditure and demand-side, supply-side and region-specific factors.

RESULTS

A total of 113 HSAs with a median population of 60 472 (ranging from 7022 to 827 750) was delineated. Total in-patient expenditure per admission varied more than threefold across HSAs after adjusting for age and gender. Apart from a list of demand-side factors, an increased number of physicians, healthcare facilities at higher levels and for-profit healthcare facilities were significantly associated with increased total in-patient expenditures. At the HSA level, the proportion of private healthcare facilities located in a single HSA was associated with increased total in-patient expenditure generated by that HSA, while the increased number of healthcare facilities in a HSA was negatively associated with the total in-patient expenditures.

CONCLUSION

HSAs were delineated to help establish an accountable healthcare delivery system, which serves as local hospital markets to provide in-patient healthcare via connecting demanders with suppliers inside particular HSAs. Policy-makers should adopt HSAs to identify variations of total in-patient expenditures among different areas and the potential associated factors. Findings from the HSA-based analysis could inform the formulation of relevant health policies and the optimisation of healthcare resource allocations.

Association of opioid fills with centers for disease control and prevention opioid guidelines and payer coverage policies: physician, insurance and geographic factors

Background The Centers for Disease Control and Prevention (CDC) issued guidelines and certain healthcare payers have made pharmacy coverage changes (PCC) focusing on regulating prescription opioids. Aim We evaluated differences in the rate of first-time opioid fills at doses ≥ 50 morphine milligram equivalents (MME)/day and first-time opioid fills with benzodiazepine fill overlap following the CDC guidelines and following a PCC between provider types, geographic locations, and insurance types. Method We used OptumLabs® Data Warehouse claims data between 2014 and 2018. Subjects were opioid naïve non-cancer care patients, 18 years and older who had an identified chronic pain condition ICD diagnosis within 2 weeks prior to their first-time opioid fill. We used multiple treatment period segmented regression analysis with interaction terms to test the differences between primary care providers (PCPs) and specialist providers (SPs), urban and rural primary care service areas (PCSAs), and Medicare Advantage (MA) and commercially insured patients (CIPs) in their first-time opioid fill patterns. Results Prescribing first-time opioid fills at doses ≥ 50MME/day declined following the CDC guidelines and PCC, the decline was greater among SPs than PCPs and in rural PCSAs than urban PCSAs. Also, following the CDC guidelines, the decline was greater among MA patients however following the PCC the decline was greater among CIPs. There were no differences in rate of first-time opioid fill with benzodiazepine overlap between groups. Conclusion Responses to the CDC opioid guidelines and a PCC differed between PCPs and SPs, urban and rural PCSAs, and when prescribing to MA and CIPs. Understanding these differences is important to help inform future guidelines.

Access to primary care and cognitive impairment: results from a national community study of aging Americans

BACKGROUND

Despite a growing burden of Alzheimer's Disease and related dementias (ADRD) in the US, the relationship between health care and cognitive impairment prevention is unclear. Primary care manages risk causing conditions and risk reducing behaviors for dementia, so we examine the association between individual and area-level access to primary care and cognitive impairment in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study.

METHODS

REGARDS participants with a cognitive assessment and vascular measurements at their baseline visit were included in this cross-sectional analysis. Cognitive impairment was defined as a Six-Item Screener (SIS) score < 5. Primary care supply, primary care utilization and emergency department (ED) utilization were measured at the primary care service area (PCSA) level based on participant's address. Individual access to care was self-reported. Models were adjusted for confounding by demographics, socioeconomic status and behavioral risk factors.

RESULTS

Among 25,563 adults, living in a PCSA with low primary care supply was associated with 25% higher odds of cognitive impairment (OR 1.25 CI 1.07-1.45). Not having a regular source of medical care was associated with 14% higher odds of cognitive impairment (OR 1.14 CI 1.02-1.28), and living in a PCSA with high emergency department utilization was associated with 12% higher odds of cognitive impairment (OR 1.12 CI 1.02-1.23).

CONCLUSIONS

Our results are an important first step in understanding how health care may prevent cognitive impairment. They highlight the importance of primary care and suggest future work clarifying its role in preventing cognitive decline is imperative.

Association of 3 CDC opioid prescription guidelines for chronic pain and 2 payer pharmacy coverage changes on opioid initiation practices

BACKGROUND: Due to the US opioid epidemic, in March of 2016, the Centers for Disease Control and Prevention (CDC) published new guidelines for primary care providers on opioid prescribing for chronic pain. Payer coverage changes were also implemented to help modify opioid prescribing behavior. Whether these initiatives were associated with changes in opioid initiation patterns is unknown. OBJECTIVE: To assess the association between 3 of the 2016 CDC guidelines and 2 subsequent payer pharmacy coverage changes with changes in opioid initiation behavior across different provider specialties. METHODS: We conducted a real-world evidence study using claims data from OptumLabs Data Warehouse between January of 2014 and December of 2018. Subjects were continuously enrolled opioid naive patients, aged at least 18 years, who had at least 1 chronic pain diagnosis within 2 weeks before their first (first-time) opioid prescription. The study used multiple treatment period segmented regression analysis to evaluate the association, across different provider specialties, between the CDC guideline release and the payer pharmacy coverage changes with immediate change in level and overall change in the rate of first-time extended-release opioid prescriptions, firsttime opioid prescriptions at doses of at least 50 MME (morphine milligram equivalent) per day, and first-time opioid prescriptions with overlapping benzodiazepine prescription. RESULTS: The CDC guidelines were not associated with any change in the rate of first-time prescriptions of extended-release opioids. However, a January 2017 payer pharmacy coverage change was associated with a reduction over time in first-time extended-release opioid prescription rates by 22.15 in every 100,000 prescriptions (CI = -40.04 to -2.92, P = 0.013). The CDC guidelines were associated with an immediate decline in level of first-time opioid prescription at doses of at least 50 MME per day by 74.00 in every 10,000 prescriptions (CI = -124.86 to -23.13, P = 0.004) and an increased rate of decline over time by 13.64 in every 10,000 prescriptions (CI = -17.07 to -10.21, P < 0.001). These associations varied across provider types and specialties. The March 2018, payer coverage change was associated with an immediate reduction in level of first-time opioid prescriptions at doses of at least 50 MME per day across all specialties and an increased reduction over time among surgeons. The CDC guidelines were associated, respectively, with a reduction in the rate of overlapping first-time opioid prescriptions with benzodiazepines among family medicine, internal medicine, surgeons, emergency medicine providers, and providers with unknown specialty by 6.11, 5.10, 2.89, 11.43, and 9.11 in every 10,000 prescriptions monthly (CI = -9.48 to -2.73, -9.86 to -0.35, -5.40 to -0.38, -17.26 to -5.61 and -11.96 to -6.25, respectively, P < 0.001, P = 0.035, P = 0.024, P < 0.001 and P < 0.001). CONCLUSIONS: Some specialist providers also adopted the CDC guidelines, and the response to the guidelines differed across various provider specialties. Some CDC guidelines were associated with a reduction in high-risk first-time opioid prescriptions. Payer pharmacy coverage changes reinforced the guidelines both in situations where the CDC guidelines did and did not show any association. DISCLOSURE: This research was funded by Agency for Healthcare Research and Quality (R01 HS025164; PI: Karaca-Mandic). Karaca-Mandic reports grants from the American Cancer Society and Sempre Health, along with fees from Tactile Medical and Precision Health Economics, unrelated to this study. The other authors have nothing to disclose.

Spatial Behavior of Cancer Care Utilization in Distance Decay in the Northeast Region of the U.S

PURPOSE

Spatial behavior of patients in utilizing health care reflects their travel burden or mobility, accessibility for medical service, and subsequently outcomes from treatment. This paper derives the best-fitting distance decay function to capture the spatial behaviors of cancer patients in the Northeast region of the U.S., and examines and explains the spatial variability of such behaviors across sub-regions.

PRINCIPAL RESULTS

(1) 46.8%, 85.5%, and 99.6% of cancer care received was within a driving time of 30, 60 and 180 minutes, respectively. (2) The exponential distance decay function is the best in capturing the travel behavior of cancer patients in the region and across most sub-regions. (3) The friction coefficient in the distance decay function is negatively correlated with the mean travel time. (4) The best-fitting function forms are associated with network structures. (5) The variation of the friction coefficient across sub-regions is related to factors such as urbanicity, economic development level, and market competition intensity.

MAJOR CONCLUSIONS

The distance decay function offers an analytic metric to capture a full spectrum of travel behavior, and thus a more comprehensive measure than average travel time. Examining the geographic variation of travel behavior needs a reliable analysis unit such as organically defined "cancer service areas", which capture relevant health care market structure and thus are more meaningful than commonly-used geopolitical or census area units.

Understanding Variation in Postacute Care: Developing Rehabilitation Service Areas Through Geographic Mapping

OBJECTIVE

The aims of the study were to demonstrate a method for developing rehabilitation service areas and to compare service areas based on postacute care rehabilitation admissions to service areas based on acute care hospital admissions.

DESIGN

We conducted a secondary analysis of 2013-2014 Medicare records for older patients in Texas (N = 469,172). Our analysis included admission records for inpatient rehabilitation facilities, skilled nursing facilities, long-term care hospitals, and home health agencies. We used Ward's algorithm to cluster patient ZIP Code Tabulation Areas based on which facilities patients were admitted to for rehabilitation. For comparison, we set the number of rehabilitation clusters to 22 to allow for comparison to the 22 hospital referral regions in Texas. Two methods were used to evaluate rehabilitation service areas: intraclass correlation coefficient and variance in the number of rehabilitation beds across areas.

RESULTS

Rehabilitation service areas had a higher intraclass correlation coefficient (0.081 vs. 0.076) and variance in beds (27.8 vs. 21.4). Our findings suggest that service areas based on rehabilitation admissions capture has more variation than those based on acute hospital admissions.

CONCLUSIONS

This study suggests that the use of rehabilitation service areas would lead to more accurate assessments of rehabilitation geographic variations and their use in understanding rehabilitation outcomes.

How to Identify Team-based Primary Care in the United States Using Medicare Data

BACKGROUND

Studying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers.

METHODS

We assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams.

RESULTS

The team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure: 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively.

CONCLUSIONS

Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.

Assessing Association Between Team Structure and Health Outcome and Cost by Social Network Analysis

BACKGROUND/OBJECTIVE

To assess the impact of team structure composition and degree of collaboration among various providers on process and outcomes of primary care.

DESIGN

Cross-sectional study.

SETTING

Data from 20% randomly selected primary care service areas in the 2015 Medicare claims were used to identify primary care practices.

PARTICIPANTS

449,460 patients with diabetes, heart failure, or chronic obstructive pulmonary disease cared for by the identified primary care practices.

MEASUREMENTS

Social network analysis measures, including edge density, degree centralization, and betweenness centralization for each practice.

RESULTS

When compared with practices with MDs and nurse practitioners (NPs) or/and physicians assistants (PAs), the practices with MDs had only lower degree of centralization and higher MD-to-MD connectedness. Within the primary care practices comprising MDs, NPs, or/and PAs, the nonphysician providers were more connected (measured as edge density) to all providers in the practice but with higher degree of centralization compared with the MDs in the practice. After adjusting for patient characteristics and type of practice, higher edge density was associated with lower odds of hospitalization (odds ratio (OR) = 0.89, 95% confidence interval (CI) = 0.79-0.99), emergency department (ER) admission (OR = 0.80, 95% CI = 0.70-0.92), and total spending (cost ratio (CR) = 0.86, standard error of the mean (SE) = 0.038). Conversely, higher degree centralization was associated with higher rates of hospitalization (OR = 1.15, 95% CI = 1.03-1.28), ER admission (OR = 1.23, 95% CI = 1.08-1.40), and total spending (CR = 1.14, SE = 0.037). However, higher degree centralization was associated with lower rates of potentially inappropriate medications (OR = 0.90, 95% CI = 0.81-0.99). Team leadership by an NP versus an MD was similar in the rate of ER admissions, hospitalizations, or total spending.

CONCLUSION

Our findings showed that highly connected primary care practices with high collaborative care and less top-down MD-centered authority have lower odds of hospitalization, fewer ER admissions, and less total spending; findings likely reflecting better communication and more coordinated care of older patients.

Primary care competition and quality of care: Empirical evidence from Medicare

In this paper, we explore the effects of primary care physician (PCP) practice competition on five distinct quality metrics directly tied to screening, follow-up care, and prescribing behavior under Medicare Part B and D. Controlling for physician, practice, and area characteristics as well as zip code fixed effects, we find strong evidence that PCP practices in more concentrated areas provide lower quality of care. More specifically, PCPs in more concentrated areas are less likely to perform screening and follow-up care for high blood pressure, unhealthy bodyweight, and tobacco use. They are also less likely to document current medications. Furthermore, PCPs in more concentrated areas have a higher amount of opioid prescriptions as a fraction of total prescriptions.

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns

BACKGROUND

Prescription opioid overprescribing is a focal point for legislators, but little is known about opioid prescribing patterns of primary care nurse practitioners (NPs) and physician assistants (PAs).

OBJECTIVE

To identify prescription opioid overprescribers by comparing prescribing patterns of primary care physicians (MDs), nurse practitioners (NPs), and physician assistants (PAs).

DESIGN

Retrospective, cross-sectional analysis of Medicare Part D enrollee prescription data.

PARTICIPANTS

Twenty percent national sample of 2015 Medicare Part D enrollees.

MAIN MEASURES

We identified potential opioid overprescribing as providers who met at least one of the following: (1) prescribed any opioid to > 50% of patients, (2) prescribed ≥ 100 morphine milligram equivalents (MME)/day to > 10% of patients, or (3) prescribed an opioid > 90 days to > 20% of patients.

KEY RESULTS

Among 222,689 primary care providers, 3.8% of MDs, 8.0% of NPs, and 9.8% of PAs met at least one definition of overprescribing. 1.3% of MDs, 6.3% of NPs, and 8.8% of PAs prescribed an opioid to at least 50% of patients. NPs/PAs practicing in states with independent prescription authority were > 20 times more likely to overprescribe opioids than NPs/PAs in prescription-restricted states.

CONCLUSIONS

Most NPs/PAs prescribed opioids in a pattern similar to MDs, but NPs/PAs had more outliers who prescribed high-frequency, high-dose opioids than did MDs. Efforts to reduce opioid overprescribing should include targeted provider education, risk stratification, and state legislation.

Multilevel Risk Factors for Hospital Readmission Among Patients With Opioid Use Disorder in Selected US States: Role of Socioeconomic Characteristics of Patients and Their Community

Research Objective

Using a multilevel framework, the study examines the association of socioeconomic characteristics of the individual and the community with all-cause 30-day readmission risks for patients hospitalized with a principal diagnosis of opioid use disorder (OUD).

Study Design

The study uses hospital discharge data of adult (18+) patients in 5 US states for 2014 from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality, linked to community and hospital characteristics using data from Health Resources and Services Administration and American Hospital Association, respectively. A multilevel logistic regression model is applied on data pooled over 5 states adjusting for patient, hospital, and community characteristics.

Principal Findings

Higher primary care access, as measured by density of primary care providers, is associated with reduced readmission risks among patients with OUD. Medicare is associated with the highest readmission risk (odds ratio [OR] = 2.0, P < .01) compared to private coverage, while Medicaid coverage is also associated with elevated risk (OR = 1.71, P < .01). Being self-pay or covered by other payers carried a similar risk to private coverage. Urban patients had higher readmission rates than rural patients.

Conclusions

Patients' risk of readmission following hospitalization for OUD varies according to availability of primary care providers, expected payer, and geographic location. Understanding which patients are most at risk may allow policy makers to design interventions to prevent readmissions and improve patient outcomes. Future studies may wish to focus on understanding when a decreased readmission rate represents better patient outcomes and when it represents difficulty accessing health care.

Health Care Disparities in Race-Ethnic Minority Communities and Populations: Does the Availability of Health Care Providers Play a Role?

OBJECTIVES

To examine disparities in use and access to different health care providers by community and individual race-ethnicity and to test provider supply as a potential mediator.

DATA SOURCES

National secondary data from 2014 Medical Expenditure Panel Survey, 5-year estimates (2010-2014) from American Community Survey, and 2014 InfoUSA.

STUDY DESIGN

Multiple logistic regression models examined the association of community and individual race-ethnicity with reported health care visits and access. Mediation analyses tested the role of provider supply.

DATA EXTRACTION METHODS

Individual-level survey data were linked to race-ethnic composition and health business counts of the respondent's primary care service area (PCSA).

PRINCIPAL FINDINGS

Minority PCSAs are significantly and independently associated with lower odds of having a visit to a physician assistant/nurse practitioner, dentist, or other health professionals and having a usual care provider (all p < 0.05). Few significant associations were observed for integrated PCSAs or for health provider supply. A modest mediation effect for provider supply was observed for travel time to usual care provider and visit to other health professionals.

CONCLUSIONS

Use of a range of health services is lower in minority communities and individuals. However, provider supply was not an important explanatory factor of these disparities.

Automated delineation of cancer service areas in northeast region of the United States: A network optimization approach

OBJECTIVE

Derivation of service areas is an important methodology for evaluating healthcare variation, which can be refined to more robust, condition-specific, and empirically-based automated regions, using cancer service areas as an exemplar.

DATA SOURCES/STUDY SETTING

Medicare claims (2014-2015) for the nine-state Northeast region were used to develop a ZIP-code-level origin-destination matrix for cancer services (surgery, chemotherapy, and radiation). This population-based study followed a utilization-based approach to delineate cancer service areas (CSAs) to develop and test an improved methodology for small area analyses.

DATA COLLECTION/EXTRACTION METHODS

Using the cancer service origin-destination matrix, we estimated travel time between all ZIP-code pairs, and applied a community detection method to delineate CSAs, which were tested for localization, modularity, and compactness, and compared to existing service areas.

PRINCIPAL FINDINGS

Delineating 17 CSAs in the Northeast yielded optimal parameters, with a mean localization index (LI) of 0.88 (min: 0.60, max: 0.98), compared to the 43 Hospital Referral Regions (HRR) in the region (mean LI: 0.68; min: 0.18, max: 0.97). Modularity and compactness were similarly improved for CSAs vs. HRRs.

CONCLUSIONS

Deriving cancer-specific service areas with an automated algorithm that uses empirical and network methods showed improved performance on geographic measures compared to more general, hospital-based service areas.

Trends in Sociodemographic Disparities in Colorectal Cancer Staging and Survival: A SEER-Medicare Analysis

INTRODUCTION

Race, ethnicity, and socioeconomic status are known to influence staging and survival in colorectal cancer (CRC). It is unclear how these relationships are affected by geographic factors and changes in insurance coverage for CRC screening. We examined the temporal trends in the association between sociodemographic and geographic factors and staging and survival among Medicare beneficiaries.

METHODS

We identified patients 65 years or older with CRC using the 1991-2010 Surveillance, Epidemiology, and End Results-Medicare database and extracted area-level sociogeographic data. We constructed multinomial logistic regression models and the Cox proportional hazards models to assess factors associated with CRC stage and survival in 4 periods with evolving reimbursement and screening practices: (i) 1991-1997, (ii) 1998-June 2001, (iii) July 2001-2005, and (iv) 2006-2010.

RESULTS

We observed 327,504 cases and 102,421 CRC deaths. Blacks were 24%-39% more likely to present with distant disease than whites. High-income areas had 7%-12% reduction in distant disease. Compared with whites, blacks had 16%-21% increased mortality, Asians had 32% lower mortality from 1991 to 1997 but only 13% lower mortality from 2006 to 2010, and Hispanics had 20% reduced mortality only from 1991 to 1997. High-education areas had 9%-12% lower mortality, and high-income areas had 5%-6% lower mortality after Medicare began coverage for screening colonoscopy. No consistent temporal trends were observed for the associations between geographic factors and CRC survival.

DISCUSSION

Disparities in CRC staging and survival persisted over time for blacks and residents from areas of low socioeconomic status. Over time, staging and survival benefits have decreased for Asians and disappeared for Hispanics.

The Policy and Practice Legacy of the New Jersey Medicaid ACO Demonstration Project

The New Jersey Medicaid Accountable Care Organization (ACO) Demonstration was created with a unique combination of features regarding ACO geography, involvement of managed care organizations (MCOs), and shared savings parameters. Ultimately, the Demonstration did not lead to a sustainable accountable care financing model and shared savings were deemphasized. Instead, the ACOs evolved into community health coalitions focused on coordinating and enhancing a wide range of activities in partnership with state government, private health systems, community leaders, and MCOs. Currently, the state is developing policy parameters to reposition the ACOs as regional partners to implement state-directed population health initiatives.

Factors Associated with Psychiatrist Opt-out from US Medicare: an Observational Study

BACKGROUND

Concerns exist about availability and access to psychiatric services in the USA. For Medicare beneficiaries, one impediment to psychiatric services is the extent to which psychiatrists have opted out of the Medicare program.

OBJECTIVE

This study describes geographic variation in rates that psychiatrists opt out of Medicare, and assesses physician-level and geographic-level predictors of opt-out.

DESIGN

Retrospective cross-sectional analysis of data describing psychiatrists' opt-out status as of March 2017 linked to data on psychiatrist location, psychiatrist characteristics (obtained from a comprehensive US physician database), and market area-level characteristics.

PARTICIPANTS

27,838 psychiatrists in the USA MAIN MEASURES: Whether a psychiatrist had opted out of Medicare as of March 2017.

KEY RESULTS

Overall, 7.0% of psychiatrists (1940/27,838) opted out of Medicare as of March 2017. Opt-out rates varied substantially across states and within states. Physician-level factors independently associated with opt-out included: older age (psychiatrists > 65 years were 2.6 percentage points more likely to opt vs. psychiatrists < 35 years old, p = 0.03), greater years of experience, female gender (female psychiatrists were 2.6 percentage points more likely to opt out than male psychiatrists, p < 0.001), graduation from a top-20 ranked medical school (1.7 percentage points more likely to opt out of Medicare, p < 0.001), and domestic medical graduate (domestic graduates were 7.3 percentage points more likely to opt out of Medicare vs. foreign graduates, p < 0.001). Adjusting for other individual- and geographic-level factors, psychiatrists who practiced in areas with more psychiatrists per Medicare beneficiary were less likely to opt out (p < 0.001).

CONCLUSIONS

The overall likelihood that psychiatrists opt out of Medicare is significant and varies considerably across regions and by characteristics of psychiatrists.

Early Effects of an Accountable Care Organization Model for Underserved Areas

BACKGROUND

The Centers for Medicare and Medicaid Services (CMS) developed the Accountable Care Organization (ACO) Investment Model (AIM) to encourage the growth of Medicare Shared Savings Program (MSSP) ACOs in rural and underserved areas. AIM provides financial support to eligible MSSP ACOs by means of prepayment of shared savings. Estimation of the performance of AIM ACOs on measures of spending and utilization in their first performance year would be useful for understanding the viability of ACOs located in these areas.

METHODS

We analyzed Medicare claims and enrollment data for a group of fee-for-service beneficiaries who had been attributed to 41 AIM ACOs and for a comparable group of beneficiaries who resided in the ACO markets but were served primarily by non-ACO providers. We used a difference-in-differences study design to compare changes in outcomes from the baseline period (2013 through 2015) to the performance period (2016) among beneficiaries attributed to AIM ACOs with concurrent changes among beneficiaries in the comparison group. The primary outcome of interest was total Medicare Part A and B spending.

RESULTS

Provider participation in AIM was associated with a differential reduction in total Medicare spending of $28.21 per beneficiary per month relative to the comparison group, which amounted to an aggregate decrease of $131.0 million. Over the same period, CMS made $76.2 million in prepayments and paid an additional $6.2 million in shared savings to ACOs in which shared savings exceeded the prepayments. After we accounted for this $82.4 million in CMS spending, the aggregate net reduction was $48.6 million, which corresponded to a net reduction of $10.46 per beneficiary per month. Decreases in the number of hospitalizations and use of institutional post-acute care contributed to the observed reduction in overall spending.

CONCLUSIONS

With up-front investments, participation in ACO shared savings contracts by providers serving rural and underserved areas was associated with lower Medicare spending than that among non-ACO providers. (Funded by the Centers for Medicare and Medicaid Services.).

Change in geographic access to community health centers after Health Center Program expansion

OBJECTIVE

To examine geographic access to community health centers (CHC accessibility) before and after Health Center Program expansion in three Southern states.

DATA SOURCES

Community health center data were from the Health Resources and Services Administration (1967-2016). Population estimates and sociodemographic characteristics were from the American Community Survey (2006-2015).

STUDY DESIGN

We used the two-step floating catchment area method to calculate CHC accessibility for census tracts in 2008 and 2016. We mapped census tract-level variation and used spatial regression to assess to what extent indicators of potential CHC need were associated with change in accessibility from 2008 to 2016.

PRINCIPAL FINDINGS

Community health center accessibility increased by 192 percent overall, and the proportion of tracts with no accessibility decreased by 65 percent. Indicators of potential need were not associated with greater gains in CHC accessibility from 2008 to 2016, but census tracts with less accessibility at baseline saw larger accessibility increases.

CONCLUSIONS

Community health center accessibility substantially increased from 2008 to 2016, but increases did not differentially impact groups with greater potential need. This approach for measuring CHC accessibility offers significant improvement in granularity over traditional CHC accessibility measures.

Team-Based Clinic Redesign of Opioid Medication Management in Primary Care: Effect on Opioid Prescribing

PURPOSE

Six key elements of opioid medication management redesign in primary care have been previously identified. Here, we examine the effect of implementing these Six Building Blocks on opioid-prescribing practices.

METHODS

Six rural-serving organizations with 20 clinic locations received support for 15 months during the period October 2015 to May 2017 to implement the Six Building Blocks. Patients undergoing long-term opioid therapy (LtOT) at these study sites were compared with patients undergoing LtOT enrolled in a regional health plan who did not receive care at the study sites but who resided in the same primary care service areas (control group). Outcomes were monthly trend in the proportion of patients undergoing LtOT prescribed a ≥100 morphine equivalent dose (MED) of opioids daily and the total number of patients receiving an opioid prescription. An interrupted time series using difference-indifference analysis was used for tests of significance.

RESULTS

The proportion of patients prescribed a ≥100 MED of opioids daily decreased 2.2% (11.8% to 9.6%) among patients at the intervention clinics and 1.3% (14.0% to 12.7%) among patients in the control group. The rate of decrease was significantly greater among study patients than among patients in the control group (P = .018). The rate of decrease in the number of patients on LtOT at intervention clinics increased during the intervention period compared with the preintervention period (P <.001).

CONCLUSIONS

Efforts to redesign opioid medication management in primary care resulted in a significant decrease in opioid prescribing. Future research is needed to determine if these results are generalizable to other settings and to assess implications for patient-reported outcomes.

Geographic Expansion of Federally Qualified Health Centers 2007-2014

PURPOSE

Federally Qualified Health Centers (FQHCs), which were expanded under the Affordable Care Act, are federally funded health centers that aim to improve access to primary care in underserved areas. With continued federal support, the number of FQHCs in the United States has increased >80% within a decade. However, the expansion patterns and their impact on the population served are unknown.

METHODS

A pre (2007)-post (2014) study of FQHC locations. FQHC locations were identified from the Provider of Services Files then linked to primary care service areas (PCSAs), which represent the service markets that FQHCs served. Road-based travel time was estimated from each 2007 FQHC to the nearest new FQHC as an indicator of geographic expansion in access. PCSA-level characteristics were used to compare 2007 and 2014 FQHC service markets.

FINDINGS

Between 2007 and 2014, there was greater expansion in the number of FQHCs (3,489 vs 6,376; 82.7%) than in the number of service markets (1,835 vs 2,695; 46.9%). Nearly half of 2007 FQHCs (47%) had at least one new FQHC within 30 minutes travel time. Most newly certified FQHCs (81%) were located in urban areas. Compared to 2007 service markets, the new 2014 markets (N = 174) were much less likely to be in areas with >20% of the population below poverty (31.4% vs 14.9%, P < .001).

CONCLUSIONS

The latest expansion of FQHCs was less likely to be in rural or high poverty areas, suggesting the impact of expansion may have limitations in improving access to care among the most financially disadvantaged populations.

Association of Geography and Access to Health Care Providers With Long-Term Prescription Opioid Use in Medicare Patients With Severe Osteoarthritis: A Cohort Study

OBJECTIVE

To evaluate the variation in long-term opioid use in osteoarthritis (OA) patients according to geography and health care access.

METHODS

We designed an observational cohort study among OA patients undergoing total joint replacement (TJR) in the Medicare program (2010 through 2014). The independent variables of interest were the state of residence and health care access, which was quantified at the primary care service area (PCSA) level as categories of number of practicing primary care providers (PCPs) and categories of rheumatologists per 1,000 Medicare beneficiaries. The percentage of OA patients taking long-term opioids (≥90 days in the 360-day period immediately preceding TJR) within each PCSA was the outcome variable in a multilevel, generalized linear regression model, adjusting for case-mix at the PCSA level and for policies, including rigor of prescription drug monitoring programs and legalized medical marijuana, at the state level.

RESULTS

A total of 358,121 patients with advanced OA, with a mean age of 74 years, were included from 4,080 PCSAs. The unadjusted mean percentage of long-term opioid users varied widely across states, ranging from 8.9% (Minnesota) to 26.4% (Alabama), and this variation persisted in the adjusted models. Access to PCPs was only modestly associated with rates of long-term opioid use between PCSAs with highest (>8.6) versus lowest (<3.6) concentration of PCPs (adjusted mean difference 1.4% [95% confidence interval 0.8%, 2.0%]), while access to rheumatologists was not associated with long-term opioid use.

CONCLUSION

We note a substantial statewide variation in rates of long-term treatment with opioids in OA, which is not fully explained by the differences in access to health care providers, varying case-mix, or state-level policies.

Neighborhood Environment and Disparities in Health Care Access Among Urban Medicare Beneficiaries With Diabetes: A Retrospective Cohort Study

Older adults' health is sensitive to variations in neighborhood environment, yet few studies have examined how neighborhood factors influence their health care access. This study examined whether neighborhood environmental factors help to explain racial and socioeconomic disparities in health care access and outcomes among urban older adults with diabetes. Data from 123 233 diabetic Medicare beneficiaries aged 65 years and older in New York City were geocoded to measures of neighborhood walkability, public transit access, and primary care supply. In 2008, 6.4% had no office-based "evaluation and management" (E&M) visits. Multilevel logistic regression indicated that this group had greater odds of preventable hospitalization in 2009 (odds ratio = 1.31; 95% confidence interval: 1.22-1.40). Nonwhites and low-income individuals had greater odds of a lapse in E&M visits and of preventable hospitalization. Neighborhood factors did not help to explain these disparities. Further research is needed on the mechanisms underlying these disparities and older adults' ability to navigate health care. Even in an insured population living in a provider-dense city, targeted interventions may be needed to overcome barriers to chronic illness care for older adults in the community.

Racial/Ethnic Disparities in Readmissions in US Hospitals: The Role of Insurance Coverage

We examine differences in rates of 30-day readmissions across patients by race/ethnicity and the extent to which these differences were moderated by insurance coverage. We use hospital discharge data of patients in the 18 years and above age group for 5 US states, California, Florida, Missouri, New York, and Tennessee for 2009, the latest year prior to the start of Centers for Medicare & Medicaid Services’ Hospital Compare program of public reporting of hospital performance on 30-day readmissions. We use logistic regression models by state to estimate the association between insurance status, race, and the likelihood of a readmission within 30 days of an index hospital admission for any cause. Overall in 5 states, non-Hispanic blacks had a slightly higher risk of 30-day readmissions relative to non-Hispanic whites, although this pattern varied by state and insurance coverage. We found higher readmission risk for non-Hispanic blacks, compared with non-Hispanic whites, among those covered by Medicare and private insurance, but lower risk among uninsured and similar risk among Medicaid. Hispanics had lower risk of readmissions relative to non-Hispanic whites, and this pattern was common across subgroups with private, Medicaid, and no insurance coverage. Uninsurance was associated with lower risk of readmissions among minorities but higher risk of readmissions among non-Hispanic whites relative to private insurance. The study found that risk of readmissions by racial ethnic groups varies by insurance status, with lower readmission rates among minorities who were uninsured compared with those with private insurance or Medicare, suggesting that lower readmission rates may not always be construed as a good outcome, because it could result from a lack of insurance coverage and poor access to care, particularly among the minorities.

Individual and Environmental Determinants of Provider Continuity Among Urban Older Adults With Heart Failure: A Retrospective Cohort Study

Objective: Continuity in patient–provider relationships is important to providing high-quality care for older adults with chronic conditions. We investigated individual and environmental determinants of provider continuity for office-based physician visits among urban older adults with heart failure. Method: We linked Medicare claims with data on neighborhood characteristics for a retrospective cohort of community-dwelling Medicare beneficiaries with heart failure in New York City (N = 50,475). Results: Mean continuity using the Bice–Boxerman index was 0.33 (SD = 0.22) (possible range of 0 [no continuity] to 1 [perfect continuity]). Multivariable regression indicated that provider continuity was higher among older, female, and dually eligible beneficiaries. Those with more chronic conditions had higher continuity, controlling for number of medical specialties seen. Continuity was lower for beneficiaries in neighborhoods with high median income and high primary care density. Conclusion: Individual and environmental predictors of provider continuity among urban older adults with heart failure could help to identify those at risk of care fragmentation.

Health Care Market Concentration Trends In The United States: Evidence And Policy Responses

Policy makers and analysts have been voicing concerns about the increasing concentration of health care providers and health insurers in markets nationwide, including the potential adverse effect on the cost and quality of health care. The Council of Economic Advisers recently expressed its concern about the lack of estimates of market concentration in many sectors of the US economy. To address this gap in health care, this study analyzed market concentration trends in the United States from 2010 to 2016 for hospitals, physician organizations, and health insurers. Hospital and physician organization markets became increasingly concentrated over this time period. Concentration among primary care physicians increased the most, partially because hospitals and health care systems acquired primary care physician organizations. In 2016, 90 percent of Metropolitan Statistical Areas (MSAs) were highly concentrated for hospitals, 65 percent for specialist physicians, 39 percent for primary care physicians, and 57 percent for insurers. Ninety-one percent of the 346 MSAs analyzed may have warranted concern and scrutiny because of their concentration levels in 2016 and changes in their concentrations since 2010. Public policies that enhance competition are needed, such as stricter enforcement of antitrust laws, reducing barriers to entry, and restricting anticompetitive behaviors.

Multiple Chronic Conditions and Disparities in 30-Day Hospital Readmissions Among Nonelderly Adults

This study examines the patterns of 30-day hospital readmissions by race/ethnicity and multiple chronic conditions (MCC) burden among nonelderly adult patients. We used hospital discharge data of patients in the 18- to 64-year age group in 5 US states, California, Florida, Missouri, New York, and Tennessee, for 2009 from the Healthcare Cost and Utilization Project State Inpatient Database (HCUP-SID) of the Agency for Healthcare Research and Quality, linked to contextual and provider data from the Health Resources and Services Administration. A multilevel logistic regression model was used for data pooled over 5 states, adjusting for patient, hospital, and community characteristics. Controlling for other covariates, the study found that a higher MCC burden was associated with a higher all-cause 30-day readmission risk. We found considerable heterogeneity in levels of readmission risk among racial/ethnic subgroups stratified by chronic conditions. Among patients with a lowest MCC burden, African Americans had the highest risk of readmission, but with a higher MCC burden, the risk of readmission increased most for Hispanics.

Use of medical care biases associations between Parkinson disease and other medical conditions

OBJECTIVE

To examine how use of medical care biases the well-established associations between Parkinson disease (PD) and smoking, smoking-related cancers, and selected positively associated comorbidities.

METHODS

We conducted a population-based, case-control study of 89,790 incident PD cases and 118,095 randomly selected controls, all Medicare beneficiaries aged 66 to 90 years. We ascertained PD and other medical conditions using ICD-9-CM codes from comprehensive claims data for the 5 years before PD diagnosis/reference. We used logistic regression to estimate age-, sex-, and race-adjusted odds ratios (ORs) between PD and each other medical condition of interest. We then examined the effect of also adjusting for selected geographic- or individual-level indicators of use of care.

RESULTS

Models without adjustment for use of care and those that adjusted for geographic-level indicators produced similar ORs. However, adjustment for individual-level indicators consistently decreased ORs: Relative to ORs without adjustment for use of care, all ORs were between 8% and 58% lower, depending on the medical condition and the individual-level indicator of use of care added to the model. ORs decreased regardless of whether the established association is known to be positive or inverse. Most notably, smoking and smoking-related cancers were positively associated with PD without adjustment for use of care, but appropriately became inversely associated with PD with adjustment for use of care.

CONCLUSION

Use of care should be considered when evaluating associations between PD and other medical conditions to ensure that positive associations are not attributable to bias and that inverse associations are not masked.

Racial and Ethnic Differences in Medicaid Acceptance by Primary Care Physicians: A Geospatial Analysis

Physician acceptance is an important dimension of access to care, especially for Medicaid patients. We constructed two new measures to quantify primary care physician (PCP) acceptance of Medicaid patients using geocoded Virginia physician addresses and population data and geospatial methods. For each Census block group, we measured the shares of "accessible PCPs" accepting any Medicaid patients or new Medicaid patients. Accessible PCPs were defined as those located within 30-minute travel from patient locations and patient locations were proxied by Census block group geographic centroids. We found that the shares of accessible PCPs accepting Medicaid varied within Virginia, and were significantly lower in urban communities where larger fractions of the population were Hispanic, even controlling for unobserved market-level traits associated with Medicaid acceptance. Policy makers and Medicaid program officials should continue to improve nonfinancial access to primary care, especially by addressing access barriers in communities with high shares of minority residents.

The Population Burden of Cancer: Research Driven by the Catchment Area of a Cancer Center

Cancer centers, particularly those supported by the National Cancer Institute, are charged with reducing the cancer burden in their catchment area. However, methods to define both the catchment area and the cancer burden are diverse and range in complexity often based on data availability, staff resources, or confusion about what is required. This article presents a review of the current literature identifying 4 studies that have defined various aspects of the cancer burden in a defined geographical area and highlights examples of how some cancer centers and other health institutions have defined their catchment area and characterized the cancer burden within it. We then present a detailed case study of an approach applied by the University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center to define its catchment area and its population cancer burden. We cite examples of how the Cancer Center research portfolio addresses the defined cancer burden. Our case study outlines a systematic approach to using publicly available data, such as cancer registry data, that are accessible by all cancer centers. By identifying gaps and formulating future research directions based on the needs of the population within the catchment area, epidemiologic studies and other types of cancer research can be directed to the population served. This review can help guide cancer centers in developing an approach to defining their own catchment area as mandated and applying research findings to this defined population.

Oregon's Emphasis On Equity Shows Signs Of Early Success For Black And American Indian Medicaid Enrollees

In 2012 Oregon transformed its Medicaid program, providing coverage through sixteen coordinated care organizations (CCOs). The state identified the elimination of health disparities as a priority for the CCOs, implementing a multipronged approach that included strategic planning, community health workers, and Regional Health Equity Coalitions. We used claims-based measures of utilization, access, and quality to assess baseline disparities and test for changes over time. Prior to the CCO intervention there were significant white-black and white-American Indian/Alaska Native disparities in utilization measures and white-black disparities in quality measures. The CCOs' transformation and implementation of health equity policies was associated with reductions in disparities in primary care visits and white-black differences in access to care, but no change in emergency department use, with higher visit rates persisting among black and American Indian/Alaska Native enrollees, compared to whites. States that encourage payers and systems to prioritize health equity could reduce racial and ethnic disparities for some measures in their Medicaid populations.

Defining Primary Care Shortage Areas: Do GIS-based Measures Yield Different Results?

PURPOSE

To examine whether geographic information systems (GIS)-based physician-to-population ratios (PPRs) yield determinations of geographic primary care shortage areas that differ from those based on bounded-area PPRs like those used in the Health Professional Shortage Area (HPSA) designation process.

METHODS

We used geocoded data on primary care physician (PCP) locations and census block population counts from 1 US state to construct 2 shortage area indicators. The first is a bounded-area shortage indicator defined without GIS methods; the second is a GIS-based measure that measures the populations' spatial proximity to PCP locations. We examined agreement and disagreement between bounded shortage areas and GIS-based shortage areas.

FINDINGS

Bounded shortage area indicators and GIS-based shortage area indicators agree for the census blocks where the vast majority of our study populations reside. Specifically, 95% and 98% of the populations in our full and urban samples, respectively, reside in census blocks where the 2 indicators agree. Although agreement is generally high in rural areas (ie, 87% of the rural population reside in census blocks where the 2 indicators agree), agreement is significantly lower compared to urban areas. One source of disagreement suggests that bounded-area measures may "overlook" some shortages in rural areas; however, other aspects of the HPSA designation process likely mitigate this concern. Another source of disagreement arises from the border-crossing problem, and it is more prevalent.

CONCLUSIONS

The GIS-based PPRs we employed would yield shortage area determinations that are similar to those based on bounded-area PPRs defined for Primary Care Service Areas. Disagreement rates were lower than previous studies have found.

A Conceptual Framework for Examining Healthcare Access and Navigation: A Behavioral-Ecological Perspective

This paper introduces a conceptual framework for investigating individual ability to navigate healthcare in the contexts of the built environment, social environment, and healthcare infrastructure in which a person is embedded. Given the complexity of healthcare delivery in the United States, consumers are expected to have an increasingly sophisticated set of skills in order to effectively navigate and benefit from the healthcare resources available to them. Addressing barriers to navigation in vulnerable populations may be essential to reducing health disparities. This paper builds on previous conceptual developments in the areas of healthcare use, navigation, and ecological perspectives on health in order to present a behavioral-ecological framework for examining healthcare navigation and access. The model posits that healthcare navigation is an ecologically informed process not only because of the spatial distribution of health services, but because of the spatial distribution of individual and environmental factors that influence decision-making and behavior with respect to service use. The paper discusses areas for further research on healthcare navigation, challenges for research, and implications for reducing health disparities.

The New Zealand Indices of Multiple Deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New Zealand

For the past 20 years, the New Zealand Deprivation Index (NZDep) has been the universal measure of area-based social circumstances for New Zealand (NZ) and often the key social determinant used in population health and social research. This paper presents the first theoretical and methodological shift in the measurement of area deprivation in New Zealand since the 1990s and describes the development of the New Zealand Index of Multiple Deprivation (IMD).

We briefly describe the development of Data Zones, an intermediary geographical scale, before outlining the development of the New Zealand Index of Multiple Deprivation (IMD), which uses routine datasets and methods comparable to current international deprivation indices. We identified 28 indicators of deprivation from national health, social development, taxation, education, police databases, geospatial data providers and the 2013 Census, all of which represented seven Domains of deprivation: Employment; Income; Crime; Housing; Health; Education; and Geographical Access. The IMD is the combination of these seven Domains. The Domains may be used individually or in combination, to explore the geography of deprivation and its association with a given health or social outcome.

Geographic variations in the distribution of the IMD and its Domains were found among the District Health Boards in NZ, suggesting that factors underpinning overall deprivation are inconsistent across the country. With the exception of the Access Domain, the IMD and its Domains were statistically and moderately-to-strongly associated with both smoking rates and household poverty.

The IMD provides a more nuanced view of area deprivation circumstances in Aotearoa NZ. Our vision is for the IMD and the Data Zones to be widely used to inform research, policy and resource allocation projects, providing a better measurement of area deprivation in NZ, improved outcomes for Māori, and a more consistent approach to reporting and monitoring the social climate of NZ.

The Role of Neighborhood Characteristics in Late Stage Melanoma Diagnosis among Hispanic Men in California, Texas, and Florida, 1996-2012

Background

Hispanics diagnosed with cutaneous melanoma are more likely to present at advanced stages but the reasons for this are unknown. We identify census tracts at high risk for late stage melanoma diagnosis (LSMD) and examine the contextual predictors of LSMD in California, Texas, and Florida.

Methods

We conducted a cross-sectional study using geocoded state cancer registry data. Using hierarchical multilevel logistic regression models we estimated ORs and 95% confidence intervals for the impact of socioeconomic, Hispanic ethnic concentration, index of dissimilarity, and health resource availability measures on LSMD.

Results

We identified 12,493 cases. In California, late stage cases were significantly more likely to reside within census tracts composed mostly of Hispanics and immigrants. In Texas, LSMD was associated with residence in areas of socioeconomic deprivation and a higher proportion of immigrants. In Florida, living in areas of low education attainment, high levels of poverty, and a high percentage of Hispanic residents was significantly associated with LSMD. Residential segregation did not independently affect LSMD.

Conclusion

The influence of contextual predictors on LSMD varied in magnitude and strength by state, highlighting both the cosegregation of social adversity and poverty and the complexity of their interactions.

Community Characteristics and Qualified Health Plan Selection during the First Open Enrollment Period

OBJECTIVE

To examine state and community factors that contributed to geographic variation in qualified health plan selection during the first open enrollment period.

DATA SOURCES/STUDY SETTING

Administrative data on qualified health plan selections at the ZIP code area merged with survey estimates from the American Community Survey.

STUDY DESIGN

Descriptive and regression analyses.

DATA COLLECTION/EXTRACTION METHODS

Data were generated by healthcare.gov and from a household survey.

PRINCIPAL FINDINGS

Thirty-one percent of the variation in qualified health plan selection ratios resulted from between-state differences, and the rest was driven by local area differences. Education, language, age, gender, and the ethnic composition of communities contributed to disparate levels of plan selection. Medicaid expansion states had a qualified health plan selection ratio that was 4.4 points lower than non-Medicaid expansion states, controlling for covariates.

CONCLUSIONS

Our results suggest community-level differences in the intensity or receptiveness to outreach and enrollment activities during the first open enrollment period.

Regional Variation of Cost of Care in the Last 12 Months of Life in Switzerland: Small-area Analysis Using Insurance Claims Data

Supplemental Digital Content is available in the text.

Background:

Health care spending increases sharply at the end of life. Little is known about variation of cost of end of life care between regions and the drivers of such variation. We studied small-area patterns of cost of care in the last year of life in Switzerland.

Methods:

We used mandatory health insurance claims data of individuals who died between 2008 and 2010 to derive cost of care. We used multilevel regression models to estimate differences in costs across 564 regions of place of residence, nested within 71 hospital service areas. We examined to what extent variation was explained by characteristics of individuals and regions, including measures of health care supply.

Results:

The study population consisted of 113,277 individuals. The mean cost of care during last year of life was 32.5k (thousand) Swiss Francs per person (SD=33.2k). Cost differed substantially between regions after adjustment for patient age, sex, and cause of death. Variance was reduced by 52%–95% when we added individual and regional characteristics, with a strong effect of language region. Measures of supply of care did not show associations with costs. Remaining between and within hospital service area variations were most pronounced for older females and least for younger individuals.

Conclusions:

In Switzerland, small-area analysis revealed variation of cost of care during the last year of life according to linguistic regions and unexplained regional differences for older women. Cultural factors contribute to the delivery and utilization of health care during the last months of life and should be considered by policy makers.

Characteristics and Service Use of Medicare Beneficiaries Using Federally Qualified Health Centers

BACKGROUND

Federally Qualified Health Centers (FQHCs) provide primary care for millions of Americans, but little is known about Medicare beneficiaries who use FQHCs.

OBJECTIVE

To compare patient characteristics and health care service use among Medicare beneficiaries stratified by FQHC use.

RESEARCH DESIGN

Cross-sectional analysis of 2011 Medicare fee-for-service beneficiaries aged 65 years and older.

SUBJECTS

Subjects included beneficiaries with at least 1 evaluation and management (E&M) visit in 2011, categorized as FQHC users (≥1 E&M visit to FQHCs) or nonusers living in the same primary care service areas as FQHC users. Users were subclassified as predominant if the majority of their E&M visits were to FQHCs.

MEASURES

Demographic characteristics, physician visits, and inpatient care use.

RESULTS

Most FQHC users (56.6%) were predominant users. Predominant and nonpredominant users, compared with nonusers, markedly differed by prevalence of multiple chronic conditions (18.2%, 31.7% vs. 22.7%) and annual mortality (2.8%, 3.8% vs. 4.0%; all P<0.05). In adjusted analyses (reference: nonusers), predominant users had fewer physician visits (RR=0.81; 95% CI, 0.81-0.81) and fewer hospitalizations (RR=0.84; 95% CI, 0.84-0.85), whereas nonpredominant users had higher use of both types of service (RR=1.18, 95% CI, 1.18-1.18; RR=1.09, 95% CI, 1.08-1.10, respectively).

CONCLUSIONS

Even controlling for primary care delivery markets, nonpredominant FQHC users had a higher burden of chronic illness and service use than predominant FQHC users. It will be important to monitor Medicare beneficiaries using FQHCs to understand whether primary care only payment incentives for FQHCs could induce fragmented care.