Redlining-associated methylation in breast tumors: the impact of contemporary structural racism on the tumor epigenome.

Purpose: Place-based measures of structural racism have been associated with breast cancer mortality, which may be driven, in part, by epigenetic perturbations. We examined the association between contemporary redlining, a measure of structural racism at the neighborhood level, and DNA methylation in breast tumor tissue. Methods: We identified 80 Black and White women diagnosed and treated for a first-primary breast cancer at Emory University Hospitals (2008-2017). Contemporary redlining was derived for census tracts using the Home Mortgage Disclosure Act database. Linear regression models were used to calculate the association between contemporary redlining and methylation in breast tumor tissue. We also examined epigenetic age acceleration for two different metrics, regressing ß values for each cytosine-phosphate-guanine dinucleotide (CpG) site on redlining while adjusting for covariates. We employed multivariable Cox-proportional hazards models and 95% confidence intervals (CI) to estimate the association between aberrant methylation and mortality. Results: Contemporary redlining was associated with 5 CpG sites after adjustment for multiple comparisons (FDR<0.10). All genes were implicated in breast carcinogenesis, including genes related to inflammation, immune function and stress response (ANGPT1, PRG4 and PRG4). Further exploration of the top 25 CpG sites, identified interaction of 2 sites (MRPS28 and cg11092048) by ER status and 1 site (GDP1) was associated with all-cause mortality. Contemporary redlining was associated with epigenetic age acceleration by the Hannum metric (ß=5.35; CI 95%=0.30,10.4) and showed positive but non-significant correlation with the other clock. Conclusion: We identified novel associations between neighborhood contemporary redlining and the breast tumor DNA methylome, suggesting that racist policies leading to inequitable social and environmental exposures, may impact the breast tumor epigenome. Additional research on the potential implications for prognosis is needed.

Opportunities and Challenges to Advance Health Equity Using Digital Health Tools in Underserved Communities in Southeast US: A Mixed Methods Study.

INTRODUCTION: Over the last 30 years, the adoption of health information technology and digital health tools (DHTs) into the US health system has been instrumental to improving access to care, especially for people living in rural, underserved, and underrepresented communities. Despite widespread adoption of DHTs by primary care clinicians, documented challenges have contributed to inequitable use and benefit. The COVID-19 pandemic necessitated rapid adoption of DHTs, accelerated by state and federal policy changes, in order to meet patient needs and ensure access to care. METHODS: The Digital Health Tools Study employed a mixed methods approach to assess adoption and use of DHTs by primary care clinicians in southeastern states and identify individual- and practice-level barriers and facilitators to DHT implementation. A survey was conducted using a multi-modal recruitment strategy: newsletters, meeting/conference presentations, social media, and emails/calls. Focus groups were conducted to assess priorities, barriers, and facilitators and were recorded/transcribed verbatim. Descriptive statistics were calculated for survey results, produced for the whole sample, and stratified by state. Thematic analysis was conducted of focus group transcripts. RESULTS: There were 1215 survey respondents. About 55 participants who had missing demographic information were excluded from the analysis. About 99% of clinicians used DHTs in the last 5 years, modalities included: telehealth (66%), electronic health records (EHRs; 66%), patient portals (49%), health information exchange (HIE; 41%), prescription drug monitoring programs (39%), remote/home monitoring (27%), and wearable devices (22%). Time (53%) and cost (51%) were identified as barriers. About 61% and 75% of clinicians reported being "satisfied" to "very satisfied" with telemedicine and EHRs, respectively. Seven focus groups with 25 clinicians were conducted and indicated COVID-19 and the use of supplemental tools/apps to connect patients to resources as major motivators for adopting DHTs. Challenges included incomplete and difficult-to-utilize HIE interfaces for providers and internet/broadband access and poor connectivity for patients. CONCLUSIONS: This study describes the impact adopting DHTs by primary care clinicians has on expanded access to healthcare and reducing health disparities in regions with longstanding health and social inequities. The findings identify opportunities to leverage DHTs to advance health equity and highlight opportunities for policy improvement.

Population-Level Disparities in Exposure to COVID-19 Mitigation Policies, April 2020-April 2021.

CONTEXT: Studies have found that COVID-19 stay-at-home orders (SHOs) and face mask policies (FMPs) were associated with reduced COVID-19 transmission and deaths. But it is unknown whether exposure to these policies varied by sociodemographic characteristics across the US population. OBJECTIVE: The goal of this study was to quantify and characterize the sociodemographic characteristics and geographic distribution of populations exposed to evidence-based COVID-19 mitigation policies. DESIGN: We obtained statewide SHOs and FMPs for all US counties from April 10, 2020, to April 10, 2021, calculated median policy lengths, and categorized counties into 4 groups based on length of policy exposure: low SHO-low FMP, high SHO-low FMP, low SHO-high FMP, and high SHO-high FMP. We described exposure groups by COVID-19 cumulative case/death and vaccination rates and county sociodemographic characteristics. SETTING: In total, 3142 counties from all 50 states and Washington, District of Columbia, were included in the analysis. MAIN OUTCOME MEASURES: County-level sociodemographic factors and county cumulative rates for COVID-19 cases, deaths, and vaccinations. RESULTS: The largest percentage of the US population lived in counties with high exposure to SHOs and FMPs. However, populations living in high SHO-high FMP counties had the lowest percent non-Hispanic Black (NHB) and highest percent non-Hispanic White (NHW) populations. Populations living in high SHO-low FMP counties had the highest percent NHB and Hispanic populations and the lowest percent NHW population. CONCLUSION: This study identified county-level racial, ethnic, and sociodemographic disparities in exposure to evidence-based statewide COVID-19 mitigation policies. POLICY IMPLICATIONS: Exposure to evidence-based policies is an important consideration for studies evaluating the root causes of health inequities.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Creation of a Longitudinal Legal Data Set to Support Legal Epidemiology Studies of Mental Health Insurance Legislation.

OBJECTIVE: This article describes policy surveillance methodology used to track changes in the comprehensiveness of state mental health insurance laws over 23 years, resulting in a data set that supports legal epidemiology studies measuring effects of these laws on mental health outcomes. METHODS: Structured policy surveillance methods, including a coding protocol, blind coding of laws in 10% of states, and consensus meetings, were used to track changes in state laws from 1997 through 2019-2020. The legal database Westlaw was used to identify relevant statutes. The legal coding instrument included six questions across four themes: parity, mandated coverage, definitions of mental health conditions, and enforcement-compliance. Points (range 0-7) were assigned to reflect the laws' comprehensiveness and aid interpretation of changes over time. RESULTS: The search resulted in 147 coding time periods across 51 jurisdictions (50 states, District of Columbia). Intercoder consensus rates increased from 89% to 100% in the final round of blinded duplicate coding. Since 1997, average comprehensiveness scores increased from 1.31 to 3.82. In 1997, 41% of jurisdictions had a parity law, 28% mandated coverage, 31% defined mental health conditions, and 8% required state agency enforcement. In 2019-2020, 94% of jurisdictions had a parity law, 63% mandated coverage, 75% defined mental health conditions, and 29% required state enforcement efforts. CONCLUSIONS: Comprehensiveness of state mental health insurance laws increased from 1997 through 2019-2020. The State Mental Health Insurance Laws Dataset will enable evaluation research on effects of comprehensive legislation and cumulative impact.

Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.

Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties.

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.

Percentage of Black Population and Primary Care Shortage Areas Associated with Higher COVID-19 Case and Death Rates in Georgia Counties.

OBJECTIVES: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional shortage areas (PCHPSAs) would be associated with higher COVID-19 death rates, and the lack of primary care access would exacerbate racial disparities in death rates. We undertook this study to test these hypotheses and discern the independent effects of racial composition, socioecologic characteristics, and healthcare system factors on COVID-19 cases and deaths in Georgia counties. METHODS: We used county-level COVID-19 cases and deaths on April 23, 2020 from the Johns Hopkins Coronavirus Resource Center and estimates of 2019 county-level populations from the US Census Bureau to calculate the cumulative event rates for the state of Georgia. We used multiple regression models to examine crude and adjusted associations of socioecologic and health system variables with county-level COVID-19 case and mortality rates. RESULTS: After adjustment, a 1% increase in the proportion of Black people in the county resulted in a 2.3% increase in the county COVID-19 confirmed case rate and a 3.0% increase in the death rate (relative risk 1.03, 95% confidence interval 1.01-1.05, P < 0.001). Primary care shortage areas had a 74% higher death rate (relative risk 1.74, 95% confidence interval 1.00-3.00, P = 0.049). CONCLUSIONS: These results highlight the impact of racial disparities on the spatial patterns of COVID-19 disease burden in Georgia, which can guide interventions to mitigate racial disparities. The results also support the need for robust primary care infrastructure throughout the state.

County-Level Concentration of Selected Chronic Conditions Among Medicare Fee-for-Service Beneficiaries and Its Association with Medicare Spending in the United States, 2017.

Multiple chronic conditions (MCC) reduce quality of life and are associated with high per capita health care spending. One potential way to reduce Medicare spending for MCC is to identify counties whose populations have high levels of spending compared to level of disease burden. Using a nationally representative sample of Medicare Fee-for-Service beneficiaries, this paper presents a method to measure the collective burden of several chronic conditions in a population, which the authors have termed the concentration of chronic conditions (CCC). The authors observed a significantly positive linear relationship between the CCC measure and county-level per capita Medicare spending. This area-level measure can be operationalized to identify counties that might benefit from targeted efforts designed to optimally manage and prevent chronic illness.

Neighborhood-Level Redlining and Lending Bias Are Associated with Breast Cancer Mortality in a Large and Diverse Metropolitan Area.

BACKGROUND: Structural inequities have important implications for the health of marginalized groups. Neighborhood-level redlining and lending bias represent state-sponsored systems of segregation, potential drivers of adverse health outcomes. We sought to estimate the effect of redlining and lending bias on breast cancer mortality and explore differences by race. METHODS: Using Georgia Cancer Registry data, we included 4,943 non-Hispanic White (NHW) and 3,580 non-Hispanic Black (NHB) women with a first primary invasive breast cancer diagnosis in metro-Atlanta (2010-2014). Redlining and lending bias were derived for census tracts using the Home Mortgage Disclosure Act database. We calculated hazard ratios and 95% confidence intervals (CI) for the associations of redlining, lending bias on breast cancer mortality and estimated race-stratified associations. RESULTS: Overall, 20% of NHW and 80% of NHB women lived in redlined census tracts, and 60% of NHW and 26% of NHB women lived in census tracts with pronounced lending bias. Living in redlined census tracts was associated with a nearly 1.60-fold increase in breast cancer mortality (hazard ratio = 1.58; 95% CI, 1.37-1.82) while residing in areas with substantial lending bias reduced the hazard of breast cancer mortality (hazard ratio = 0.86; 95% CI, 0.75-0.99). Among NHB women living in redlined census tracts, we observed a slight increase in breast cancer mortality (hazard ratio = 1.13; 95% CI, 0.90-1.42); among NHW women the association was more pronounced (hazard ratio = 1.39; 95% CI, 1.09-1.78). CONCLUSIONS: These findings underscore the role of ecologic measures of structural racism on cancer outcomes. IMPACT: Place-based measures are important contributors to health outcomes, an important unexplored area that offers potential interventions to address disparities.

Estimating health service utilization potential using the supply-concentric demand-accumulation spatial availability index: a pulmonary rehabilitation case study.

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P < 0.001) than was the 2SFCA (Morans I = 0.321, P < 0.001). These results suggest that the measures of spatial availability may be a better way to estimate the health care utilization potential than measures of spatial accessibility.

Primary Care Comprehensiveness and Care Coordination in Robust Specialist Networks Results in Lower Emergency Department Utilization: A Network Analysis of Medicaid Physician Networks.

Background: Care coordination is an essential and difficult to measure function of primary care. Objective: Our objective was to assess the impact of network characteristics in primary/specialty physician networks on emergency department (ED) visits for patients with chronic ambulatory care sensitive conditions (ACSCs). Subjects and Measures: This cross-sectional social network analysis of primary care and specialty physicians caring for adult Medicaid beneficiaries with ACSCs was conducted using 2009 Texas Medicaid Analytic eXtract (MAX) files. Network characteristic measures were the main exposure variables. A negative binomial regression model analyzed the impact of network characteristics on the ED visits per patient in the panel. Results: There were 42 493 ACSC patients assigned to 5687 primary care physicians (PCPs) connected to 11 660 specialist physicians. PCPs whose continuity patients did not visit a specialist had 86% fewer ED visits per patient in their panel, compared with PCPs whose patients saw specialists. Among PCPs connected to specialists in the network, those with a higher number of specialist collaborators and those with a high degree of centrality had lower patient panel ED rates. Conclusions: PCPs providing comprehensive care (ie, without specialist consultation) for their patients with chronic ACSCs had lower ED utilization rates than those coordinating care with specialists. PCPs with robust specialty networks and a high degree of centrality in the network also had lower ED utilization. The right fit between comprehensiveness of primary care, care coordination, and adequate capacity of specialty availability in physician networks is needed to drive outcomes.

Differences in Travel Time to Cancer Surgery for Colon versus Rectal Cancer in a Rural State: A New Method for Analyzing Time-to-Place Data Using Survival Analysis.

PURPOSE: Rectal cancer is rarer than colon cancer and is a technically more difficult tumor for surgeons to remove, thus rectal cancer patients may travel longer for specialized treatment compared to colon cancer patients. The purpose of this study was to evaluate whether travel time for surgery was different for colon versus rectal cancer patients. METHODS: A secondary data analysis of colorectal cancer (CRC) incidence data from the Iowa Cancer Registry data was conducted. Travel times along a street network from all residential ZIP Codes to all cancer surgery facilities were calculated using a geographic information system. A new method for analyzing "time-to-place" data using the same type of survival analysis method commonly used to analyze "time-to-event" data is introduced. Cox proportional hazard model was used to analyze travel time differences for colon versus rectal cancer patients. RESULTS: A total of 5,844 CRC patients met inclusion criteria. Median travel time to the nearest surgical facility was 9 minutes, median travel time to the actual cancer surgery facilities was 22 minutes, and the median number of facilities bypassed was 3. Although travel times to the nearest surgery facilities were not significantly different for colon versus rectal cancer patients, rectal cancer patients on average traveled 15 minutes longer to their actual surgery facility and bypassed 2 more facilities to obtain surgery. DISCUSSION: In general, the survival analysis method used to analyze the time-to-place data as described here could be applied to a wide variety of health services and used to compare travel patterns among different groups.

Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States.

INTRODUCTION: All-cause mortality in the United States declined from 1935 through 2014, with a recent uptick in 2015. This national trend is composed of disparate local trends. We identified distinct groups of all-cause mortality rate trajectories by grouping US counties with similar temporal trajectories. METHODS: We used all-cause mortality rates in all US counties for 1999 through 2016 and estimated discrete mixture models by using county level mortality rates. Proc Traj in SAS was used to detect how county trajectories clustered into groups on the basis of similar intercepts, slopes, and higher order terms. Models with increasing numbers of groups were assessed on the basis of model fit. We created county-level maps of mortality trajectory groups by using ArcGIS. RESULTS: Eight unique trajectory groups were detected among 3,091 counties. The average mortality rate in the most favorable trajectory group declined 29.4%, from 592.3 deaths per 100,000 in 1999 to 418.2 in 2016. The least favorable mortality trajectory group declined 3.4% over the period, from 1,280.3 deaths per 100,000 to 1,236.9. We saw significant differences in the demographic and socioeconomic profiles and geographic patterns across the trajectory categories, with favorable mortality trajectories in the Northeast, Midwest, and on the West Coast and unfavorable trajectories concentrated in the Southeast. CONCLUSIONS: County-level disparities in all-cause mortality rates widened over the past 18 years. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by our research could inform interventions to achieve equitable distribution of county mortality rates.

Using spatially adaptive floating catchments to measure the geographic availability of a health care service: Pulmonary rehabilitation in the southeastern United States.

A spatially adaptive floating catchment is a circular area that expands outward from a provider location until the estimated demand for services in the nearest population locations exceeds the observed number of health care services performed at the provider location. This new way of creating floating catchments was developed to address the change of spatial support problem (COSP) by upscaling the availability of the service observed at a provider location to the county-level so that its geographic association with utilization could be measured using the same spatial support. Medicare Fee-for-Service claims data were used to identify beneficiaries aged ≥ 65 years who received outpatient pulmonary rehabilitation (PR) in the Southeastern United States in 2014 (n = 8798), the number of PR treatments these beneficiaries received (n = 132,508), and the PR providers they chose (n = 426). The positive correlation between PR availability and utilization was relatively low, but statistically significant (r = 0.619, p < 0.001) indicating that most people use the nearest available PR services, but some travel long distances. SAFCs can be created using data from health care systems that collect claim-level utilization data that identifies the locations of providers chosen by beneficiaries of a specific health care procedure.

Racial Disparities in Breast Cancer Outcomes in the Metropolitan Atlanta Area: New Insights and Approaches for Health Equity.

BACKGROUND: Racial disparities in breast cancer (BC) outcomes persist where non-Hispanic black (NHB) women are more likely to die from BC than non-Hispanic white (NHW) women, and the extent of this disparity varies geographically. We evaluated tumor, treatment, and patient characteristics that contribute to racial differences in BC mortality in Atlanta, Georgia, where the disparity was previously characterized as especially large. METHODS: We identified 4943 NHW and 3580 NHB women in the Georgia Cancer Registry with stage I-IV BC diagnoses in Atlanta (2010-2014). We used Cox proportional hazard regression to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing NHB vs NHW BC mortality by tumor, treatment, and patient characteristics on the additive and multiplicative scales. We additionally estimated the mediating effects of these characteristics on the association between race and BC mortality. RESULTS: At diagnosis, NHB women were younger-with higher stage, node-positive, and triple-negative tumors relative to NHW women. In age-adjusted models, NHB women with luminal A disease had a 2.43 times higher rate of BC mortality compared to their NHW counterparts (95% CI = 1.99 to 2.97). High socioeconomic status (SES) NHB women had more than twice the mortality rates than their white counterparts (HR = 2.67, 95% CI = 1.65 to 4.33). Racial disparities among women without insurance, in the lowest SES index, or diagnosed with triple-negative BC were less pronounced. CONCLUSIONS: In Atlanta, the largest racial disparities are observed in luminal tumors and most pronounced among women of high SES. More research is needed to understand drivers of disparities within these treatable features.

Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015-2060) in adults aged ≥65 years.

INTRODUCTION: Alzheimer's disease and related dementias (ADRD) cause a high burden of morbidity and mortality in the United States. Age, race, and ethnicity are important risk factors for ADRD. METHODS: We estimated the future US burden of ADRD by age, sex, and race and ethnicity by applying subgroup-specific prevalence among Medicare Fee-for-Service beneficiaries aged ≥65 years in 2014 to subgroup-specific population estimates for 2014 and population projection data from the United States Census Bureau for 2015 to 2060. RESULTS: The burden of ADRD in 2014 was an estimated 5.0 million adults aged ≥65 years or 1.6% of the population, and there are significant disparities in ADRD prevalence among population subgroups defined by race and ethnicity. ADRD burden will double to 3.3% by 2060 when 13.9 million Americans are projected to have the disease. DISCUSSION: These estimates can be used to guide planning and interventions related to caring for the ADRD population and supporting caregivers.

Neighborhood Environmental Health and Premature Death From Cardiovascular Disease.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in the United States and disproportionately affects racial/ethnic minority groups. Healthy neighborhood conditions are associated with increased uptake of health behaviors that reduce CVD risk, but minority neighborhoods often have poor food access and poor walkability. This study tested the community-driven hypothesis that poor access to food at the neighborhood level and poor neighborhood walkability are associated with racial disparities in premature deaths from CVD. METHODS: We examined the relationship between neighborhood-level food access and walkability on premature CVD mortality rates at the census tract level for the city of Atlanta using multivariable logistic regression models. We produced maps to illustrate premature CVD mortality, food access, and walkability by census tract for the city. RESULTS: We found significant racial differences in premature CVD mortality rates and geographic disparities in food access and walkability among census tracts in Atlanta. Improved food access and walkability were associated with reduced overall premature CVD mortality in unadjusted models, but this association did not persist in models adjusted for census tract population composition and poverty. Census tracts with high concentrations of minority populations had higher levels of poor food access, poor walkability, and premature CVD mortality. CONCLUSION: This study highlights disparities in premature CVD mortality and neighborhood food access and walkability at the census tract level in the city of Atlanta. Improving food access may have differential effects for subpopulations living in the same area. These results can be used to calibrate neighborhood-level interventions, and they highlight the need to examine race-specific health outcomes.

Integrated care coordination by an interprofessional team reduces emergency department visits and hospitalisations at an academic health centre.

People with chronic behavioural and physical health conditions have higher healthcare costs and mortality rates than patients with chronic physical conditions alone. As a result, there has been promotion of integrated care for this group. It is important to train primary care residents to practice in integrated models of care with interprofessional teams and to evaluate the effectiveness of integrated care models to promote high-quality care for this at-risk group. We implemented an integrated, interprofessional care management programme for adults with chronic mental and physical health needs as part of a curriculum for family medicine and family medicine psychiatry residents. We then evaluated the clinical effectiveness of this programme by describing participants' healthcare utilisation patterns pre- and post-enrolment. Patients enrolled in the programme were approximately 60-70% less likely to utilise the emergency room and 50% less likely to be admitted to the hospital after enrolment in the programme compared to before enrolment. The odds of individual attendance at outpatient primary care and mental health visits improved after enrolment. In the context of the implementation of integrated behavioural and physical healthcare in primary care, this interprofessional care management programme reduced emergency department utilisation and hospitalisations while improving utilisation of primary care and psychiatry outpatient care. Further studies should focus on replication of this model to further discern the model's cost-savings and health promotion effects.

How Socioeconomic Status Affects Patient Perceptions of Health Care: A Qualitative Study.

INTRODUCTION: Clinician perceptions of patients with low socioeconomic status (SES) have been shown to affect clinical decision making and health care delivery in this group. However, it is unknown how and if low SES patients perceive clinician bias might affect their health care. METHODS: In-depth interviews with 80 enrollees in a state Medicaid program were analyzed to identify recurrent themes in their perceptions of care. RESULTS: Most subjects perceived that their SES affected their health care. Common themes included treatment provided, access to care, and patient-provider interaction. DISCUSSION: This study highlights complex perceptions patients have around how SES affects their health care. These results offer opportunities to reduce health care disparities through better understanding of their impact on the individual patient-provider relationship. This work may inform interventions that promote health equity via a multifaceted approach, which targets both providers and the health care system as a whole.