Triggered cagedSTORM microscopy.

In standard SMLM methods, the photoswitching of single fluorescent molecules and the data acquisition processes are independent, which leads to the detection of single molecule blinking events on several consecutive frames. This mismatch results in several data points with reduced localization precision, and it also increases the possibilities of overlapping. Here we discuss how the synchronization of the fluorophores' ON state to the camera exposure time increases the average intensity of the captured point spread functions and hence improves the localization precision. Simulations and theoretical results show that such synchronization leads to fewer localizations with 15% higher sum signal on average, while reducing the probability of overlaps by 10%.

Identifying Individuals with Highest Social Risk in Adults with Type 2 Diabetes Using Item Response Theory.

OBJECTIVE: The aim of this analysis was to create a parsimonious tool to screen for high social risk using item response theory to discriminate across social risk factors in adults with type 2 diabetes. METHODS: Cross-sectional data of 615 adults with diabetes recruited from two primary care clinics were used. Participants completed assessments including validated scales on economic instability (financial hardship), neighborhood and built environment (crime, violence, neighborhood rating), education (highest education, health literacy), food environment (food insecurity), social and community context (social isolation), and psychological risk factors (perceived stress, depression, serious psychological distress, diabetes distress). Item response theory (IRT) models were used to understand the association between a participant's underlying level of a particular social risk factor and the probability of that response. A two-parameter logistic IRT model was used with each of the 12 social determinant factors being added as a separate parameter in the model. Higher values in item discrimination indicate better ability of a specific social risk factor in differentiating participants from each other. RESULTS: Rate of crime reported in a neighborhood (discrimination 3.13, SE 0.50; item difficulty - 0.68, SE 0.07) and neighborhood rating (discrimination 4.02, SE 0.87; item difficulty - 1.04, SE 0.08) had the highest discrimination. CONCLUSIONS: Based on these findings, crime and neighborhood rating discriminate best between individuals with type 2 diabetes who have high social risk and those with low social risk. These two questions can be used as a parsimonious social risk screening tool to identify high social risk.

Catastrophic health expenditures: a disproportionate risk in uninsured ethnic minorities with diabetes.

BACKGROUND: Chargemaster prices are the list prices that providers and health systems assign to each of their medical services in the US. These charges are often several factors of magnitude higher than those extended to individuals with either private or public insurance, however, these list prices are billed in full to uninsured patients, putting them at increased risk of catastrophic health expenditures (CHE). The objective of this study was to examine the risk of CHE across insurance status, diabetes diagnosis and to examine disparity gaps across race/ethnicity. METHODS: We perform a retrospective observational study on a nationally representative cohort of adult patients from the Medical Expenditure Panel Survey for the years 2002-2017. Using logistic regression models we estimate the risk of CHE across insurance status, diabetes diagnosis and explore disparity gaps across race/ethnicity. RESULTS: Our fully adjusted results show that the relative odds of having CHE if uninsured is 5.9 (p < 0.01) compared to if insured, and 1.1 (p < 0.01) for patients with a diabetes diagnosis (compared to those without one). We note significant interactions between insurance status and diabetes diagnosis, with uninsured patients with a diabetes diagnosis being 9.5 times (p < 0.01) more likely to experience CHE than insured patients without a diabetes diagnosis. In terms of racial/ethnic disparities, we find that among the uninsured, non-Hispanic blacks are 13% (p < 0.05), and Hispanics 14.2% (p < 0.05), more likely to experience CHE than non-Hispanic whites. Among uninsured patients with diabetes, we further find that Hispanic patients are 39.3% (p < 0.05) more likely to have CHE than non-Hispanic white patients. CONCLUSIONS: Our findings indicate that uninsured patients with diabetes are at significantly elevated risks for CHE. These risks are further found to be disproportionately higher among uninsured racial/ethnic minorities, suggesting that CHE may present a channel through which structural economic and health disparities are perpetuated.

Historic Redlining and Impact of Structural Racism on Diabetes Prevalence in a Nationally Representative Sample of U.S. Adults.

OBJECTIVE: We investigated direct and indirect relationships between historic redlining and prevalence of diabetes in a U.S. national sample. RESEARCH DESIGN AND METHODS: Using a previously validated conceptual model, we hypothesized pathways between structural racism and prevalence of diabetes via discrimination, incarceration, poverty, substance use, housing, education, unemployment, and food access. We combined census tract-level data, including diabetes prevalence from the Centers for Disease Control and Prevention PLACES 2019 database, redlining using historic Home Owners' Loan Corporation (HOLC) maps from the Mapping Inequality project, and census data from the Opportunity Insights database. HOLC grade (a score between 1 [best] and 4 [redlined]) for each census tract was based on overlap with historically HOLC-graded areas. The final analytic sample consisted of 11,375 U.S. census tracts. Structural equation modeling was used to investigate direct and indirect relationships adjusting for the 2010 population. RESULTS: Redlining was directly associated with higher crude prevalence of diabetes within a census tract (r = 0.01; P = 0.008) after adjusting for the 2010 population (χ2(54) = 69,900.95; P < 0.001; root mean square error of approximation = 0; comparative fit index = 1). Redlining was indirectly associated with diabetes prevalence via incarceration (r = 0.06; P < 0.001), poverty (r = -0.10; P < 0.001), discrimination (r = 0.14; P < 0.001); substance use (measured by binge drinking: r = -0.65, P < 0.001; and smoking: r = 0.35, P < 0.001), housing (r = 0.06; P < 0.001), education (r = -0.17; P < 0.001), unemployment (r = -0.17; P < 0.001), and food access (r = 0.14; P < 0.001) after adjusting for the 2010 population. CONCLUSIONS: Redlining has significant direct and indirect relationships with diabetes prevalence. Incarceration, poverty, discrimination, substance use, housing, education, unemployment, and food access may be possible targets for interventions aiming to mitigate the impact of structural racism on diabetes.

Community Social Capital and Population Health Outcomes.

Importance: While the association between economic connectedness and social mobility has now been documented, the potential linkage between community-level economic connectedness and population health outcomes remains unknown. Objective: To examine the association between community social capital measures (defined as economic connectedness, social cohesion, and civic engagement) and population health outcomes (defined across prevalence of diabetes, hypertension, high cholesterol, kidney disease, and obesity). Design, Setting, and Participants: This cross-sectional study included communities defined at the zip code tabulation area (ZCTA) level in all 50 US states. Data were collected from January 2021 to December 2022. Main Outcomes and Measures: Multivariable regression analyses were used to examine the association between population health outcomes and social capital. Adjusted analyses controlled for area demographic variables and county fixed effects. Heterogeneities within the associations based on the racial and ethnic makeup of communities were also examined. Results: In this cross-sectional study of 17 800 ZCTAs, across 50 US states, mean (SD) economic connectedness was 0.88 (0.32), indicating friendship sorting on income; the mean (SD) support ratio was 0.90 (0.10), indicating that 90% of ties were supported by a common friendship tie; and the mean (SD) volunteering rate was 0.08 (0.03), indicating that 8% of individuals within a given community were members of volunteering associations. Mean (SD) ZCTA diabetes prevalence was 10.8% (2.9); mean (SD) high blood pressure prevalence was 33.2% (6.2); mean (SD) high cholesterol prevalence was 32.7% (4.2), mean (SD) kidney disease prevalence was 3.0% (0.7), and mean (SD) obesity prevalence was 33.4% (5.6). Regression analyses found that a 1% increase in community economic connectedness was associated with significant decreases in prevalence of diabetes (-0.63%; 95% CI, -0.67% to -0.60%); hypertension (-0.31%; 95% CI, -0.33% to -0.29%); high cholesterol (-0.14%; 95% CI, -0.15% to -0.12%); kidney disease (-0.48%; 95% CI, -0.50% to -0.46%); and obesity (-0.28%; 95% CI, -0.29% to -0.27%). Second, a 1% increase in the community support ratio was associated with significant increases in prevalence of diabetes (0.21%; 95% CI, 0.16% to 0.26%); high blood pressure (0.16%; 95% CI, 0.13% to 0.19%); high cholesterol (0.16%; 95% CI, 0.13% to 0.19%); kidney disease (0.17%; 95% CI, 0.13% to 0.20%); and obesity (0.08%; 95% CI, 0.06% to 0.10%). Third, a 1% increase in the community volunteering rate was associated with significant increases in prevalence of high blood pressure (0.02%; 95% CI, 0.01% to 0.02%); high cholesterol (0.03%; 95% CI, 0.02% to 0.03%); and kidney disease (0.02%; 95% CI, 0.01% to 0.02%). Additional analyses found that the strength of these associations varied based on the majority racial and ethnic population composition of communities. Conclusions and Relevance: In this study, higher economic connectedness was significantly associated with better population health outcomes; however, higher community support ratios and volunteering rates were both significantly associated with worse population health. Associations also differed by majority racial and ethnic composition of communities.

Historic Residential Redlining and Present-Day Social Determinants of Health, Home Evictions, and Food Insecurity within US Neighborhoods.

OBJECTIVE: Examine the association between historic residential redlining and present-day racial/ethnic composition of neighborhoods, racial/ethnic differences in social determinant of health domains, and risk of home evictions and food insecurity. RESEARCH DESIGN AND METHODS: We examined data on 12,334 (for eviction sample), and 8996 (for food insecurity sample), census tracts in 213 counties across 37 states in the USA with data on exposure to historic redlining. First, we examined relationships between Home Owners' Loan Corporation (HOLC) redlining grades (A="Best", B="Still Desirable", C="Definitely Declining", D="Hazardous") and present-day racial/ethnic composition and racial/ethnic differences in social determinant of health domains of neighborhoods. Second, we examined whether historic redlining is associated with present-day home eviction rates (measured across eviction filings rates, and eviction judgment rates for 12,334 census tracts in 2018) and food insecurity (measured across low supermarket access, low supermarket access and income, low supermarket access and low car ownership for 8996 census tracts in 2019). Multivariable regression models were adjusted for census tract population, urban/rural designation, and county level fixed effects. RESULTS: Relative to areas with a historic HOLC grading of "A (Best)", areas with a "D (Hazardous)" grading had a 2.59 (95%CI=1.99-3.19; p-value<0.01) higher rate of eviction filings, and a 1.03 (95%CI=0.80-1.27; p-value<0.01) higher rate of eviction judgments. Compared to areas with a historic HOLC grading of "A (Best)", areas rated with a "D (Hazardous)" had a 16.20 (95%CI=15.02-17.79; p-value<0.01) higher rate of food insecurity based on supermarket access and income, and a 6.15 (95%CI =5.53-6.76; p-value<0.01) higher rate of food insecurity based on supermarket access and car ownership. CONCLUSIONS: Historic residential redlining is significantly associated with present-day home evictions and food insecurity, highlighting persistent associations between structural racism and present-day social determinants of health.

Hospital cost efficiency: an examination of US acute care inpatient hospitals.

The use of stochastic frontier models for inference on hospital efficiency is complicated by the inability to fully control for quality differences across hospitals. Additionally, the potential existence of cross-sectional dependence due to the presence of unobserved common factors leads to endogeneity problems that can bias both cost function and efficiency estimates. Using a panel consisting of 1518 hospitals for the years 1996-2013 (T = 18), I adopt techniques for dealing with long, cross-sectionally dependent panel data in order to estimate cost parameters and hospital specific efficiency. In particular, I employ the estimation technique proposed by Bai (Econometrica 77(4):1229-1279, 2009), which assumes that the unobservable heterogenous effects have a factor structure. I find evidence of considerable scale economies and that hospital cost inefficiencies have been increasing during the period of 1996-2013, and that the growth in expenditures is, in part, driven by spending that increases patient satisfaction, but that does not significantly contribute to improved patient health outcomes.

Structural Racism as an Upstream Social Determinant of Diabetes Outcomes: A Scoping Review.

OBJECTIVE: To evaluate the evidence on the role of structural racism as an upstream factor impacting diabetes outcomes, identify current gaps, and recommend areas for future work. RESEARCH DESIGN AND METHODS: A reproducible search of Medline and Ovid was used. Structural factors based on the World Health Organization social determinants of health framework (governance, macroeconomic policy, social policy, public policy, and cultural and societal values) had to be included as measured variables or contextual factors discussed as upstream influences. Outcomes included 1) hemoglobin A1c (HbA1c), 2) LDL, 3) BMI, 4) quality of life, 5) self-efficacy, 6) mortality, 7) years of life lost, and 8) self-care behaviors. RESULTS: Thirteen articles were included for final synthesis. Ten studies focused on governance, two on social policies, one on public policies, and one on cultural and societal values. Results highlight significant associations between structural racism and poorer clinical outcomes (HbA1c and blood pressure), worse self-care behaviors (diet and physical activity), lower standards of care, higher mortality, and more years of life lost for adults with diabetes. CONCLUSIONS: There is a paucity of work investigating the relationship between structural racism and diabetes outcomes. Five areas for future work include 1) more rigorous research on the relationship between structural racism, downstream social determinants, and health outcomes in diabetes, 2) policy assessments specific to diabetes outcomes, 3) research designed to examine pathways and mechanisms of influence, 4) intervention development to mitigate the impact of structural racism, and 5) tracking and monitoring of change over time.

Modern Day Consequences of Historic Redlining: Finding a Path Forward.

There is emerging evidence that structural racism is a major contributor to poor health outcomes for ethnic minorities. Structural racism captures upstream historic racist events (such as slavery, black code, and Jim Crow laws) and more recent state-sanctioned racist laws in the form of redlining. Redlining refers to the practice of systematically denying various services (e.g., credit access) to residents of specific neighborhoods, often based on race/ethnicity and primarily within urban communities. Historical redlining is linked to increased risk of diabetes, hypertension, and early mortality due to heart disease with evidence suggesting it impacts health through suppressing economic opportunity and human capital, or the knowledge, skills, and value one contributes to society. Addressing structural racism has been a rallying call for change in recent years-drawing attention to the racialized impact of historical policies in the USA. Unfortunately, the enormous scope of work has also left people feeling incapable of effecting the very change they seek. This paper highlights a path forward by briefly discussing the origins of historical redlining, highlighting the modern-day consequences both on health and at the societal level, and suggest promising initiatives to address the impact.

Benchmarking Thiolate-Driven Photoswitching of Cyanine Dyes.

Carbocyanines are among the best performing dyes in single-molecule localization microscopy (SMLM), but their performance critically relies on optimized photoswitching buffers. Here, we study the versatile role of thiols in cyanine photoswitching at varying intensities generated in a single acquisition by a microelectromechanical systems (MEMS) mirror placed in the excitation path. The key metrics we have analyzed as a function of the thiolate concentration are photon budget, on-state and off-state lifetimes and the corresponding impact on image resolution. We show that thiolate acts as a concentration bandpass filter for the maximum achievable resolution and determine a minimum of ∼1 mM is necessary to facilitate SMLM measurements. We also identify a concentration bandwidth of 1-16 mM in which the photoswitching performance can be balanced between high molecular brightness and high off-time to on-time ratios. Furthermore, we monitor the performance of the popular oxygen scavenger system based on glucose and glucose oxidase over time and show simple measures to avoid acidification during prolonged measurements. Finally, the impact of buffer settings is quantitatively tested on the distribution of the glucose transporter protein 4 within the plasma membrane of adipocytes. Our work provides a general strategy for achieving optimal resolution in SMLM with relevance for the development of novel buffers and dyes.

Association Between State-Level Medicaid Expansion and Eviction Rates.

Importance: Prior research has identified associations between housing insecurity and poor health outcomes. Objective: To evaluate the association between US state Medicaid expansions and reductions in eviction; to examine the persistence of these associations and how they vary across US states and counties. Design, Setting, and Participants: This cohort study of 25 398 county-year observations (across 40 states) used US eviction and census data for the years 2002 through 2018 (ie, 17 years). County-level associations were estimated using interactive fixed effects counterfactual estimators, and models were selected using cross validation. Across-county treatment association heterogeneities were assessed using multivariable regression methods. Analyses were performed in July of 2022. Exposure: State-level Medicaid expansion under the Patient Protection and Affordable Care Act. Main Outcomes and Measures: Eviction judgments; eviction judgments per 100 renter-occupied households. Results: Among a total of 774 treated counties (with Medicaid expansion) and 720 control counties (untreated, without Medicaid expansion), mean (SD) eviction judgments for treated counties were 534.78 (1945.84) eviction judgments in the pre-2014 period (mean [SD] eviction rate, 2.25 [2.18] per 100 households), which decreased to 463.67 (1499.39) eviction judgments in the post-2014 period (mean [SD] eviction judgment rate, 2.02 [1.81] per 100 households). Control group mean (SD) county eviction judgments were 477.22 (1592.18) eviction judgments (mean [SD] eviction judgment rate, 1.91 per 100 households) pre-2014, and 490.22 (1575.19) eviction judgments (mean [SD] eviction judgment rate, 1.89 per 100 households) post-2014. Model estimates indicate that Medicaid expansion was associated with reductions in county eviction judgments by -66.49 (95% CI, -132.50 to -0.48; P = .047) and reductions of the eviction judgment rate by -0.25 (95% CI, -0.35 to -0.14; P < .001). Associations remained broadly consistent between 2014 and 2018, although some diminishment of associations occurred in 2018. Approximately 29% of the across-county treatment association variation was explained by across-state differences, while 9% was explained by county-level demographic and uninsurance differences. Conclusions and Relevance: In this cohort study, Medicaid expansion was associated with reductions in eviction judgments and eviction judgment rates; however, these associations were found to vary considerably both across as well as within states (across counties). These findings suggest that the channel between Medicaid expansion and evictions is sensitive to state environments as well as county specific population demographics and uninsurance levels.

GLUT4 translocation and dispersal operate in multiple cell types and are negatively correlated with cell size in adipocytes.

The regulated translocation of the glucose transporter, GLUT4, to the surface of adipocytes and muscle is a key action of insulin. This is underpinned by the delivery and fusion of GLUT4-containing vesicles with the plasma membrane. Recent studies have revealed that a further action of insulin is to mediate the dispersal of GLUT4 molecules away from the site of GLUT4 vesicle fusion with the plasma membrane. Although shown in adipocytes, whether insulin-stimulated dispersal occurs in other cells and/or is exhibited by other proteins remains a matter of debate. Here we show that insulin stimulates GLUT4 dispersal in the plasma membrane of adipocytes, induced pluripotent stem cell-derived cardiomyocytes and HeLa cells, suggesting that this phenomenon is specific to GLUT4 expressed in all cell types. By contrast, insulin-stimulated dispersal of TfR was not observed in HeLa cells, suggesting that the mechanism may be unique to GLUT4. Consistent with dispersal being an important physiological mechanism, we observed that insulin-stimulated GLUT4 dispersal is reduced under conditions of insulin resistance. Adipocytes of different sizes have been shown to exhibit distinct metabolic properties: larger adipocytes exhibit reduced insulin-stimulated glucose transport compared to smaller cells. Here we show that both GLUT4 delivery to the plasma membrane and GLUT4 dispersal are reduced in larger adipocytes, supporting the hypothesis that larger adipocytes are refractory to insulin challenge compared to their smaller counterparts, even within a supposedly homogeneous population of cells.

An observational study of health care provider collaboration networks and heterogenous hospital cost efficiency and quality outcomes.

Provider network structure has been linked to hospital cost, utilization, and to a lesser degree quality, outcomes; however, it remains unknown whether these relationships are heterogeneous across different acute care hospital characteristics and US states. The objective of this study is to evaluate whether there are heterogeneous relationships between hospital provider network structure and hospital outcomes (cost efficiency and quality); and to assess the sources of measured heterogeneous effects. We use recent causal random forest techniques to estimate (hospital specific) heterogeneous treatment effects between hospitals' provider network structures and their performance (across cost efficiency and quality). Using Medicare cost report, hospital quality and provider patient sharing data, we study a population of 3061 acute care hospitals in 2016. Our results show that provider networks are significantly associated with costs efficiency (P < .001 for 7/8 network measures), patient rating of their care (P < .1 in 5/8 network measures), heart failure readmissions (P < .01 for 3/8 network measures), and mortality rates (P < .02 in 5/8 cases). We find that fragmented provider structures are associated with higher costs efficiency and patient satisfaction, but also with higher heart failure readmission and mortality rates. These effects are further found to vary systematically with hospital characteristics such as capacity, case mix, ownership, and teaching status. This study used an observational design. In summary, we find that hospital treatment responses to different network structures vary systematically with hospital characteristics..

Hospital Price Transparency in the United States: An Examination of Chargemaster, Cash, and Negotiated, Price Variation for 14 Common Procedures.

BACKGROUND: Effective January 1, 2021, US hospitals were required to upload information on their chargemaster prices (database of list prices), discounted cash prices (commonly charged to self-pay patients), and payer-specific negotiated prices. OBJECTIVE: Examine how prices vary and are associated with hospital characteristics, market competition, and hospital quality. DESIGN SETTING AND PARTICIPANTS: This observational study used data on 14 common medical services across 1599 hospitals in 2021. Descriptive and regression analyses were used to study price variation. Analyses adjust for hospital characteristics, market competition and state fixed effects. RESULTS: Ninetieth -to-10th-percentile price markups factors (ratios) range between 3.2 and 11.5 for chargemaster; 6.1 and 19.7 for cash; and 6.6 and 30.0 for negotiated prices. Adjusted regression results indicate that hospitals' cash prices are on average 60% ( P <0.01) higher, and list prices are on average 164% ( P <0.01) higher, than negotiated prices. Systematic pricing differences across hospitals were noted, with urban hospitals having 14% ( P <0.01) lower prices than rural hospitals, teaching hospitals having 3% ( P <0.01) higher prices than nonteaching hospitals, and nonprofit hospitals pricing 9% ( P <0.01), and for-profit hospitals 39% ( P <0.01), higher than government owned hospitals. In addition, hospitals that contract with more insurance plans have higher prices, hospitals in more competitive markets have lower prices, and higher quality hospitals have on average 5% ( P <0.01) lower prices than lower quality hospitals. CONCLUSIONS: Prices all vary considerably across US hospitals. High quality hospitals are associated with lower pricing across all three sets of prices examined. Hospital price transparency may help consumers better identify hospitals that provide both high quality, and low cost, care.

Do Chargemaster Prices Matter?: An Examination of Acute Care Hospital Profitability.

BACKGROUND: It remains widely debated whether chargemaster price markups are tied to hospital profitability. OBJECTIVE: To evaluate the effect of chargemaster markups on hospital profitability in the presence of unobserved hospital-specific (time-invariant) confounders, and cross-sectional dependence due to latent (common) policy shocks. DESIGN: We use interactive fixed effects methods to address concerns of unobserved hospital-specific (time-invariant) confounders, and cross-sectional dependence. SETTING: US acute care hospitals, 1996 through 2017 (ie, 22 y). PARTICIPANTS: Using primarily Medicare cost report data, we construct an unbalanced panel of 3499 acute care hospitals per year, or a total of 76,972 hospital-year observations. MEASUREMENTS: Chargemaster markups (above cost), profits per hospital inpatient discharge. RESULTS: Between 1996 and 2017, chargemaster markups increased (on average) by 155%, and the SD of the chargemaster markup distribution increased by 324%-indicating growing variability in the average markup strategies pursued by hospitals. Our preferred model specification implies that a unit increase of the hospital chargemaster markup is associated with a $261 ( P <0.01; 95% confidence interval: $232-$291) increase in profits per hospital inpatient discharge. These results are robust to a wide set of model specifications, the use of alternative profitability measurements, and the use of an alternative instrumental variable identification strategy. Additional subsample analysis that controls for a rich set of hospital quality measures and system affiliation information also yields similar results. CONCLUSION: We show that higher chargemaster markups are associated with higher hospital profitability. Additional research is needed to understand how chargemaster pricing impact health outcomes and health care disparities.

Nonmedical Interventions For Type 2 Diabetes: Evidence, Actionable Strategies, And Policy Opportunities.

This systematic review identified studies of nonmedical interventions designed to reduce risk for and improve clinical outcomes for type 2 diabetes. Specifically, this review sought to identify interventions that target structural racism and social determinants of health. To be included, studies were published in English; published between database initiation and January 2022; conducted in the United States; measured an intervention effect using a clinical trial, quasi-experimental, or pre-post design; included a population of adults at risk for or with type 2 diabetes; and targeted hemoglobin A1c levels, blood pressure, lipids, self-care, or quality of life as outcomes. The findings of our review indicate that interventions with targeted, multicomponent designs that combine both medical and nonmedical approaches can reduce risk for and improve clinical outcomes for type 2 diabetes. HbA1c levels improved significantly with the use of food supplementation with referral and diabetes support; the use of financial incentives with education and skills training; the use of housing relocation with counseling support; and the integration of nonmedical interventions into medical care using the electronic medical record. Our findings demonstrate that the literature on nonmedical interventions designed to address relevant social factors and target structural racism is limited. The article offers actionable strategies and identifies policy opportunities for targeting structural inequalities and decreasing social risk among adults with type 2 diabetes.

Historic Residential Redlining and Present-day Diabetes Mortality and Years of Life Lost: The Persistence of Structural Racism.

OBJECTIVE: The association between structural racism, as captured by historic residential redlining practices under the Home Owners' Loan Corporation (HOLC), and present-day diabetes mortality, and years of life lost (YLL), remains unknown. RESEARCH DESIGN AND METHODS: Age-standardized mortality and YLL data were combined with historic HOLC redlining data for the city of Seattle, WA (a sample of 109 census tract-level observations) for each of the years 1990 through 2014 (25 years). Spatial autoregressive regression analyses were used for assessment of the association between an area's historic HOLC redlining score and diabetes (and all-cause) mortality and YLL. RESULTS: Spatial autoregressive model estimates indicate that an area's HOLC redlining score explains 45%-56% of the variation in the census tract-level diabetes mortality rate and 51%-60% of the variation in the census tract diabetes YLL rate between the years of 1990 and 2014. For 2014, estimates indicate that areas with a unit-higher HOLC grade are associated with 53.7% (95% CI 43.3-64.9; P < 0.01) higher diabetes mortality rates and 66.5% (53.7-80.4; P < 0.01) higher diabetes YLL rate. Magnitudes of marginal effects were consistently larger for diabetes than for all-cause outcomes. CONCLUSIONS: Results indicate sizable, and statistically significant, associations between historic redlining practices and present-day diabetes mortality and YLL rates. In addition, the persistence of these associations across the 1990-2014 period highlight a need for targeted action to undo the impact of historical redlining on current health.

Retrospective observational study of the robustness of provider network structures to the systemic shock of COVID-19: a county level analysis of COVID-19 outcomes.

OBJECTIVE: To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic. DESIGN: Using multivariable regression analysis, we measure the effect that provider network structure, derived from Medicare patient sharing data, has on county level COVID-19 outcomes (across mortality and case rates). Our adjusted analysis includes county level socioeconomic and demographic controls, state fixed effects, and uses lagged network measures in order to address concerns of reverse causality. SETTING: US county level COVID-19 population outcomes by 3 September 2020. PARTICIPANTS: Healthcare provider patient sharing network statistics were measured at the county level (with n=2541-2573 counties, depending on the network measure used). PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate. RESULTS: We find that provider network structures where primary care physicians (PCPs) are relatively central, or that have greater betweenness or eigenvector centralisation, are associated with lower county level COVID-19 death rates. For the adjusted analysis, our results show that increasing either the relative centrality of PCPs (p value<0.05), or the network centralisation (p value<0.05 or p value<0.01), by 1 SD is associated with a COVID-19 death reduction of 1.0-1.8 per 100 000 individuals (or a death rate reduction of 2.7%-5.0%). We also find some suggestive evidence of an association between provider network structure and COVID-19 case rates. CONCLUSIONS: Provider network structures with greater relative centrality for PCPs when compared with other providers appear more robust to the systemic shock of COVID-19, as do network structures with greater betweenness and eigenvector centralisation. These findings suggest that how we organise our health systems may affect our ability to respond to systemic shocks such as the COVID-19 pandemic.

Trends in charges and association with defaults on medical payments in uninsured Americans: a disproportionate burden in ethnic minorities - a retrospective observational study.

OBJECTIVE: To evaluate whether medical event charges are associated with uninsured patients' probability of medical payment default and whether there exist racial/ethnic disparity gaps in medical payment defaults. DESIGN: We use logistic regression models to analyse medical payment defaults. Our adjusted estimates further control for a rich set of patient and medical visit characteristics, region and time fixed effects. SETTING: Uninsured US adult (non-elderly) population from 2002 to 2017. PARTICIPANTS: We use four nationally representative samples of uninsured patients from the Medical Expenditure Panel Survey across office-based (n=39 967), emergency (n=3269), outpatient (n=1739) and inpatient (n=340) events. PRIMARY AND SECONDARY OUTCOME MEASURES: Payment default, medical event charges and medical event payments. RESULTS: Relative to uninsured non-Hispanic white (NHW) patients, uninsured non-Hispanic black (NHB) patients are 142% (p<0.01) more likely to default on medical payments for office-based visits, 27% (p<0.05) more likely to default on emergency department visit payments and 82% (p<0.1) more likely to default on an outpatient visit bill. Hispanic patients are 46% (p<0.01) more likely to default on an office-based visit, but 25% less likely to default on emergency department visit payments than NHW patients. Within our fully adjusted model, we find that racial/ethnic disparities persist for office-based visits. Our results further suggest that the probabilities of payment defaults for office-based, emergency and outpatient visits are all significantly (p<0.01) and positively associated with the medical event charges billed. CONCLUSIONS: Medical event charges are found to be broadly associated with payment defaults, and we further note disproportionate payment default disparities among NHB patients.

Association of County Race and Ethnicity Characteristics With Number of Insurance Carriers and Insurance Network Breadth.

Importance: While the Affordable Care Act (ACA) set out to eliminate insurer discrimination based on preexisting conditions, the ACA health exchanges allow insurers to select what markets to enter and afford them great freedom on how they design their physician networks. Strategic market participation and physician network design based on population race, ethnicity, and health characteristics may give rise to a present-day form of redlining within health insurance markets-ie, a systematic underprovision of insurance plans and in-network practitioners within areas that are populated with higher proportions of non-Hispanic Black residents. Objective: To examine if markets with relatively higher non-Hispanic Black populations have systematically fewer insurers and lower network inclusion of physicians residing within these areas. Design, Setting, and Participants: This cohort study conducted a regression analysis of the US ACA health insurance exchange marketplace across 34 states with federal exchanges and physicians located within the 500 most populous US cities in 2014. County-level data were sourced from individual market and issuer enrollment databases and county health rankings; census tract data came from a national database of physician networks in 2014 marketplace plans, US Census Bureau data, and the Centers for Disease Control and Prevention's PLACES database. Adjustment was made for a rich set of county (or census tract) controls and state fixed effects to capture broad market and/or policy differences across states. Analyses were performed in June 2021. Main Outcomes and Measures: The raw count of insurers within a county and the mean percentage of insurance networks that physicians participate in within each census tract. Results: A total of 2270 counties were examined within our first analyses. In the counties analyzed, a mean (SD) of 23.0% (3.2%) of the population was aged 18 years or younger, and a mean (SD) of 11.0% (15.8%) of the population had non-Hispanic Black race and ethnicity. For the second analysis, 16 006 to 25 096 census tracts were examined (depending on physician specialty). With adjustment for population size, age, and race and ethnicity, a 1-SD increase in the county non-Hispanic Black population was associated with a 14.1% reduction in the number of insurers (mean [SE] marginal effect size, -2.18 [0.13]; P < .001). Accounting for additional county-level risk selection controls and state fixed effects, a 1-SD increase in the non-Hispanic Black population was associated with a 2.3% reduction in available insurers (marginal effect size, -0.36 [0.17]; P = .04). For practitioners network breadth inclusion, a 1-SD increase in the non-Hispanic Black population was associated with a 15.8% (marginal effect size, -0.32 [0.01]; P < .001) to 24.7% (marginal effect size, -0.14 [0.02]; P < .001) reduction in the physicians' network participation depending on their specialty. Adjusting for additional state fixed effects yielded estimates of 6% (marginal effect size, -0.08 [0.01]; P < .001) to 13.5% (marginal effect size, -0.12 [0.02]; P < .001) reductions in practitioner network participation. Conclusions and Relevance: These findings suggest that strategic decisions by insurers may contribute toward markets with higher racial or ethnic minority populations having systematically fewer participating insurers, as well as a higher prevalence of local physicians not included in coverage networks. These findings call for further examination of potential insurance redlining within the ACA marketplaces.