Core-based Statistical Areas Research Articles

Ambient particulate matter and frequency of outpatient visits for chronic rhinosinusitis in the United States.

Emerging evidence has underscored the harmful effects of air pollution on the upper airway. We investigated the relationship between ambient particulate matter (PM) level and the frequency of outpatient visits for chronic rhinosinusitis (CRS). We conducted an ecological cohort study of US adults enrolled in The Merative MarketScan outpatient database from 2007 to 2020. For each geographical subunit (core-based statistical area [CBSA]), we calculated the annual rate of CRS-related outpatient visits per 1000 well-patient checkup visits (CRS-OV). Using data from the Environmental Protection Agency's Air Quality System, we mapped the rolling statistical average of daily PM2.5 and PM10 over the preceding year onto each CBSA×year combination. We employed multivariable negative binomial regression modeling to estimate the association between PM levels and subsequent CRS-OV. Across 3933 observations (CBSA×year combinations), encompassing ∼4 billion visits, the median CRS-OV was 164 (interquartile range 110-267). The mean PM2.5 level was 8.9µg/m3 (SD 2.6) and the mean PM10 level was 20.2µg/m3 (SD 7.2). Adjusting for patient demographics and respiratory comorbidities, a compounded rise in subsequent CRS-OV was observed with increasing PM levels. Each µg/m3 rise in PM2.5 independently predicted a 10% increase in CRS-OV (adjusted incidence rate ratio [aIRR]) 1.10, 95% confidence interval [CI] 1.08-1.13) and each µg/m3 rise in PM10 independently predicted a 3% increase in CRS-OV (aIRR 1.03, 95% CI 1.02-1.04). Elevated ambient PM2.5 and PM10 levels are associated with a subsequent compounded increase in the frequency of CRS-OV, with PM2.5 predicting a more pronounced rise compared to PM10.

Detainer Requests Issued by ICE and Fair/Poor Self-Rated Health among Latines in the U.S., 2017–2020

AbstractU.S. Immigration and Customs Enforcement (ICE) issues detainer requests to local law enforcement agencies to hold detainees suspected of being undocumented immigrants until they can be transferred into ICE custody. We examined the association between area-level detainer requests and self-rated health among Latine adults. We linked health data from Latine adults included in the 2017–2020 Behavioral Risk Factor Surveillance System (n = 69,386) to detainer requests per 1,000 non-citizens in core-based statistical areas, (n = 152 across 49 states). We fit logistic regression models of self-rated fair/poor health on detainer requests, adjusted for individual- and area-level confounders. In adjusted analyses, we found that Latine adults living in areas with the highest quartile of requests had 24% higher odds of fair/poor health (OR 1.24, 95% CI = 1.05,1.47) relative to those in the lowest quartile. Local law enforcement agencies should limit cooperation with federal immigrant agencies to protect the health of Latine communities.

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods.

Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019. We gathered a diverse set of non-personally identifiable information from public data sources to better understand and predict COPD rates at the core-based statistical area (CBSA) level in the United States. Our objective was to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD. We integrated non-personally identifiable information from multiple Centers for Disease Control and Prevention sources and used them to analyze COPD with different types of methods. We included cigarette smoking, a well-known contributing factor, and race/ethnicity because health disparities among different races and ethnicities in the United States are also well known. The models also included the air quality index, education, employment, and economic variables. We fitted models with both multiple linear regression and machine learning methods. The most accurate multiple linear regression model has variance explained of 81.1%, mean absolute error of 0.591, and symmetric mean absolute percentage error of 9.666. The most accurate machine learning model has variance explained of 85.7%, mean absolute error of 0.456, and symmetric mean absolute percentage error of 6.956. Overall, cigarette smoking and household income are the strongest predictor variables. Moderately strong predictors include education level and unemployment level, as well as American Indian or Alaska Native, Black, and Hispanic population percentages, all measured at the CBSA level. This research highlights the importance of using diverse data sources as well as multiple methods to understand and predict COPD. The most accurate model was a gradient boosted tree, which captured nonlinearities in a model whose accuracy is superior to the best multiple linear regression. Our interpretable models suggest ways that individual predictor variables can be used in tailored interventions aimed at decreasing COPD rates in specific demographic and ethnographic communities. Gaps in understanding the health impacts of poor air quality, particularly in relation to climate change, suggest a need for further research to design interventions and improve public health.

A method for defining dispersed community territories

The transition from dispersed to aggregated forms of settlement reflects a critical shift in the relative value of social and primary (food) modes of production. However, investigating trade-offs between these different forms of settlement requires estimates of the extent of community territories, including their nearby arable land. Here we demonstrate a simple algorithm to do that. Our algorithm is analogous to that used to define core-based statistical areas for the US census, though instead of central business districts, we rely on community centers (or areas of known and persistent interaction between unrelated individuals). We provide examples of our algorithm by applying it to archaeological sites in the central Mesa Verde, northern Rio Grande, and Cibola regions in the US Southwest. A sensitivity analysis is also conducted to demonstrate how each tuning parameter contributes to the algorithm.

Tieboutian clubs revisited

This paper examines the evolution of Tieboutian clubs over time and across metropolitan regions. We address this topic by examining a large set of socioeconomic characteristics of metropolitan regions to assess empirically whether and how those member attributes may figure into a Tiebout sorting process. The method entails factor analysis to reduce an initial set of forty-nine variables to a much smaller number of factors that represent groups of intercorrelated variables. An analysis of variance is then undertaken on the corresponding factor scores to assess whether census tracts within municipalities are more alike than those between municipalities. We do this for the Los Angeles metropolitan region using census data from 1970, 1990 and 2020, and we repeat the process for the twenty largest metropolitan regions (core-based statistical areas) in the USA for 2020. Several key results emerge from this analysis. While in most cases seven or eight factors emerge repeatedly, four are most persistent and ubiquitous; these pertain to economic class, race, age cohort and immigration status. These are, in effect, the most enduring fundamental aspects of the socioeconomic landscape in metropolitan regions in the USA. The final question addressed by our work links back to the original motivation, which is to assess the role of municipalities in the formation of these clubs. We do so for the twenty largest metropolitan regions in the USA. The results from our analyses of variance are compelling. With very few exceptions we find that census tracts with similar club characteristics have a strong tendency to be co-located within the same municipalities. This strong result holds regardless of which metropolitan region we examined, or which decade. This affirms that municipalities are indeed Tieboutian clubs.

Comparison of Source Apportionment and Background PM2.5 Contribution in Six U.S. Metropolitan Areas

Total ambient PM2.5 concentrations within six major metropolitan core-based statistical areas (CBSAs) were evaluated using a previously published approach that combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. The results demonstrate differences and similarities across the cities, including seasonal patterns, the magnitude of total PM2.5 concentrations, and local vs. background apportionment. Los Angeles generally had the greatest total and local PM2.5 concentrations, and monitoring locations in Seattle generally had the lowest total and local PM2.5 concentrations. In terms of seasonal change, mean total PM2.5 concentrations varied the most in Dallas and the least in Tampa, while mean total PM2.5 concentrations varied the most spatially in Los Angeles and the least spatially in Dallas. Additionally, mean local PM2.5 concentrations varied the most seasonally in Los Angeles and the least seasonally in Tampa, while mean local PM2.5 concentrations varied the most spatially in Seattle and the least spatially in Charlotte and Dallas. Local attribution in Charlotte, Dallas, Pittsburgh, and Tampa were most similar. Local attribution varied more widely in Los Angeles and Seattle. The analyses found distance to major U.S. roadways as an important indicator for total and local ambient PM2.5 concentrations. Accurate distinctions of local vs. background contributions and point vs. non-point source contributions allow for regulatory, mitigatory, and community efforts to be optimized at the appropriate spatial levels (i.e., local, regional, state, or national) and target toward the most impactful source sectors when seeking to take steps that reduce total community PM2.5 exposures (e.g., reducing vehicular emissions).

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Appendicitis Hospitalization Care Costs Among Patients With Delayed Diagnosis of Appendicitis

Delayed appendicitis diagnosis is associated with worse outcomes. Appendicitis hospital care costs associated with delayed diagnosis are unknown. To determine whether delayed appendicitis diagnosis was associated with increased appendicitis hospital care costs. This cohort study used data from patients receiving an appendectomy aged 18 to 64 years in 5 states (Florida, Maryland, Massachusetts, New York, Wisconsin) that were captured in the Healthcare Cost and Utilization Project State Inpatient and Emergency Department databases for the years 2016 and 2017 with no additional follow-up. Data were analyzed January through April 2023. Delayed diagnosis was defined as a previous emergency department or inpatient hospital encounter with an abdominal diagnosis other than appendicitis, and no intervention 7 days prior to appendectomy encounter. The main outcome was appendicitis hospital care costs. This was calculated from aggregated charges of encounters 7 days prior to appendectomy, the appendectomy encounter, and 30 days postoperatively. Cost-to-charge ratios were applied to charges to obtain costs, which were then adjusted for wage index, inflation to 2022 US dollar, and with extreme outliers winsorized. A multivariable Poisson regression estimated appendicitis hospital care costs associated with a delayed diagnosis while controlling for age, sex, race and ethnicity, insurance status, care discontinuity, income quartile, hospital size, teaching status, medical school affiliation, percentage of Black and Hispanic patient discharges, core-based statistical area, and state. There were 76 183 patients (38 939 female [51.1%]; 2192 Asian or Pacific Islander [2.9%], 14 132 Hispanic [18.5%], 8195 non-Hispanic Black [10.8%], 46 949 non-Hispanic White [61.6%]) underwent appendectomy, and 2045 (2.7%) had a delayed diagnosis. Delayed diagnosis patients had median (IQR) unadjusted cost of $11 099 ($6752-$17 740) compared with $9177 ($5575-$14 481) for nondelayed (P < .001). Patients with delayed diagnosis had 1.23 times (95% CI, 1.16-1.28 times) adjusted increased appendicitis hospital care costs. The mean marginal cost of delayed diagnosis was $2712 (95% CI, $2083-$3342). Even controlling for delayed diagnosis, non-Hispanic Black patients had 1.22 times (95% CI, 1.17-1.28 times) the adjusted increased appendicitis hospital care costs compared with non-Hispanic White patients. In this cohort study, delayed diagnosis of appendicitis was associated with increased hospital care costs.

Abstract 4846: Geographic and physician-level variation in the use of hypofractionated radiotherapy for breast cancer in the U.S.: A cross-classified multilevel analysis

Abstract Hypofractionated whole-breast irradiation (HF-WBI), a radiotherapy with a larger dose per fraction and a shorter schedule for early-stage breast cancer patients post breast-conserving surgery, shows equivalent safety and efficacy compared to conventionally fractionated whole-breast irradiation (CF-WBI) regarding local recurrence and disease-free survival. While several countries have set HF-WBI as the preferred standard of care for these patients, its uptake in the U.S. has lagged behind expectations. We used the Health Care Cost Institute (HCCI) database 2008-2017, covering a third of the U.S. employer-sponsored insurance population, to identify early-stage breast cancer patients. Patients were clustered at different geographic levels (Census region, state, core-based statistical area (CBSA), hospital referral region of Dartmouth Atlas, and zip code) and the radiation oncologist level. These two levels are cross-nested. We used a Bayesian cross-classified multilevel logistic model to simultaneously model the geographic units variability and physician-level variability in HF-WBI use across the U.S. Variation metrics included intracluster correlation coefficient (ICC) and median odds ratios (MOR). ICC with a value of 0 is equivalent to MOR of 1, indicating identical outcomes across all levels. After accounting for these variations in the cross-classified multilevel model framework, we evaluated the association between HF-WBI use and clinical and demographic factors.Our study included 79,747 women (74.0%) who underwent CF-WBI and 27,999 (26.0%) who received HF-WBI. HF-WBI adoption increased over time (2008-2017). Variability across radiation oncologists (ICC = 0.272, MOR = 2.88) was notably larger than geographic areas variability. Variability across CBSA (ICC = 0.097, MOR = 1.76) was the strongest among all the geographic levels. The variation in HF-WBI use was mainly attributed to physician-level variability (ICC=0.222, MOR=2.59) based on the cross-classified multilevel model. No substantial differences were found between young (aged 64 or younger with commercial health plans) and older patients (aged 65 or older with Medicare Advantage plans) in terms of both geographic and physician-level variations. After accounting for variability in radiation oncologists and CBSAs, older age, non-reception of chemotherapy, and several community-level factors, including longer distance from home to facility and higher community education level, were associated with a higher likelihood of HF-WBI use. These results may lend insights into potential facilitators/barriers to HF-WBI uptake. Future studies can further investigate the impact of the 2018 American Society for Radiation Oncology (ASTRO) guideline and the Covid-19 pandemic on HF-WBI use, which would illuminate potential improvements of healthcare practices. Citation Format: Yijia Sun, Loren Saulsberry, Chuanhong Liao, Donald Hedeker, Dezheng Huo. Geographic and physician-level variation in the use of hypofractionated radiotherapy for breast cancer in the U.S.: A cross-classified multilevel analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4846.

Geographic and Physician-Level Variation in the Use of Hypofractionated Radiation Therapy for Breast Cancer in the U.S.: A Cross-Classified Multilevel Analysis

We aimed to assess geographic and physician-level variation for hypofractionated whole-breast irradiation (HF-WBI) use for early-stage breast cancer patients in the United States. We further evaluated the association between HF-WBI use and demographic factors after accounting for these variations. We performed a retrospective study of early-stage breast cancer patients using private employer-sponsored insurance claims from 2008 to 2017. Patients were clustered according to geographic level and by radiation oncologist. Bayesian cross-classified multilevel logistic models were used to examine the geographic heterogeneity and variation of radiation oncologists simultaneously. Intracluster correlation coefficient (ICC) and median odds ratios (MOR) were calculated to quantify the variation at different levels. We also used the cross-classified model to identify patient demographic factors associated with receiving HF-WBI. The study included 79,747 women (74.0%) who received conventionally fractionated whole-breast irradiation (CF-WBI) and 27,999 women (26.0%) who underwent HF-WBI. HF-WBI adoption increased significantly across time (2008-2017). The variation in HF-WBI utilization was attributed mostly to physician-level variability (MOR = 2.59). The variability of HF-WBI utilization across core-based statistical areas (CBSAs) (MOR = 1.55) was found to be the strongest among all geographic classifications. After accounting for variability in both CBSAs and radiation oncologists, age, receiving chemotherapy, and several community-level factors, including distance from home to facility, community education level, and racial composition, were found to be associated with HF-WBI utilization. This study demonstrated geographic and physician-level heterogeneity in the use of HF-WBI among early-stage breast cancer patients. HF-WBI utilization was also found to be associated with patient and community-level characteristics. Given observed physician-level variability, intervention through continuing medical education could help doctors to better understand the advantages of HF-WBI and promote the adoption of HF-WBI in the U.S. Influence of physician-level characteristics on HF-WBI utilization merits further study.

Subsidiary networks, connectivity, and urban-regional economic development

ABSTRACT This paper argues that urban-regional income development depends on a larger fabric of economic relations at the national and international levels. Focusing on Core-Based Statistical Areas (CBSAs) in the US, the paper identifies firms’ subsidiary networks across space and their changes over time. These networks form a basic architecture through which important growth impulses in production and innovation are transmitted that impact urban income levels. Using a balanced panel of U.S. CBSAs with LexisNexis Corporate Affiliations data from 1993 until 2017, we develop a model that examines the relationship between national and international connectivity and urban income levels, differentiated by origin/destination of ties, industrial sectors, and various interaction effects. Our results strongly support that linkages at both the national and international scale (particularly linkages with European locations) are significantly related to urban-regional income development.

Geospatial Analysis of the Proportion of Persons Defined as Underrepresented in Medicine for Each Medical School and Their Surrounding Core-Based Statistical Area.

The current approach to increasing diversity in medical education fails to consider local community demographics when determining medical school matriculation. We propose that medical schools better reflect their surrounding community, both because racially/ethnically concordant physicians have been shown to provide better care and to repair the historical and current racist impacts of these institutions that have criminalized, displaced, and excluded local Black and Brown communities. In this study, we used geospatial analysis to determine that medical school enrollments generally fail to reflect their surrounding community, represented as their core-based statistical area, within which the individual medical schools reside.

Proximity and Knowledge Spillovers: Evidence from the Introduction of New Airline Routes

This paper examines the causal relationship between proximity and knowledge diffusion by estimating the elasticity of core-based statistical area (CBSA) pair-level citations to variations in travel time induced by the introduction of new flight routes. The findings reveal that decreasing travel time between U.S. cities by 20% increases knowledge flow by 0.5%, which corresponds to an increase of over 15,000 citations at the aggregate level. Rather than boosting within-firm knowledge transfer, travel time reduction leads to a rise in knowledge spillovers primarily across firm boundaries, particularly among those that form joint ventures, have block holdings in each other, or form supply chain relationships. These effects are stronger among city pairs located farther away from each other, with higher absorptive capacity, in complex technology classes, and for newly developed technologies. Additional mechanism tests suggest that the most likely channel through which travel time reduction impacts knowledge spillover is by influencing the transfer of tacit knowledge via facilitating cross-CBSA inventor flow and information acquisition. This paper was accepted by Tomasz Piskorski, finance. Funding: S. Zhou receives support from the National Natural Science Foundation of China [Grant 71804155] and China Fundamental Research Funds for the Central Universities [Grant 20720181047]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01717 .

Racial, Ethnic, Socioeconomic, and Geographic Inequities in Access to Mechanical Circulatory Support

BackgroundHospital admissions for cardiogenic shock have increased in the United States. Temporary mechanical circulatory support (tMCS) can be used to acutely stabilize patients. We sought to evaluate the presence of racial, ethnic, and socioeconomic inequities in access to MCS in the United States among patients with cardiogenic shock. MethodsMedicare data were used to identify patients with cardiogenic shock admitted to hospitals with advanced tMCS (microaxial left ventricular assist device [mLVAD] or extracorporeal membranous oxygenation [ECMO]) capabilities within the 25 largest core-based statistical areas, all major metropolitan areas. We modeled the association between patient race, ethnicity, and socioeconomic status and use of mLVAD or ECMO. ResultsAfter adjusting for age and clinical comorbidities, dual eligibility for Medicaid was associated with a 19.9% (95% CI, 11.5%-27.4%) decrease in odds of receiving mLVAD in a patient with cardiogenic shock (P < .001). After adjusting for age, clinical comorbidities, and dual eligibility for Medicaid, Black race was associated with 36.7% (95% CI, 28.4%-44.2%) lower odds of receiving mLVAD in a patient with cardiogenic shock. Dual eligibility for Medicaid was associated with a 62.0% (95% CI, 60.8%-63.1%) decrease in odds of receiving ECMO in a patient with cardiogenic shock (P < .001). Black race was associated with 36.0% (95% CI, 16.6%-50.9%) lower odds of receiving ECMO in a patient with cardiogenic shock, after adjusting for Medicaid eligibility. ConclusionsWe identified large and significant racial, ethnic, and socioeconomic inequities in access to mLVAD and ECMO among patients presenting with cardiogenic shock to metropolitan hospitals with active advanced tMCS programs. These findings highlight systematic inequities in access to potentially lifesaving therapies.

Unfilled in emergency medicine: An analysis of the 2022 and 2023 Match by program accreditation, ownership, and geography.

The two most recent National Resident Matching Program (NRMP) Match cycles saw a high number of initially unfilled emergency medicine (EM) residency positions. We sought to identify the risk of EM residency program characteristics including accreditation duration, primary clinical site ownership status, and geography pertaining to not initially filling all positions. We performed a repeated cross-sectional observational study of EM residency programs participating in the 2022 and 2023 NRMP Match cycles and used publicly available data from the NRMP, the Accreditation Council for Graduate Medical Education, the Centers for Medicare & Medicaid Services, and the U.S. Department of Housing and Urban Development. Our primary outcome was the proportion of EM residency programs that did not initially fill positions, with analyses stratified by accreditation duration (>5 or ≤5 years), primary clinical site ownership status, and geographic core-based statistical areas (CBSAs). A total of 219 of 2921 (7.5%) positions in the 2022 Match and 554 of 3010 (18.4%) positions in the 2023 Match were initially unfilled. Over the 2-year period, EM residency programs accredited within the past 5 years had more than double the risk (relative risk [RR] 2.08, 95% confidence interval [CI] 1.69-2.57, chi-square p < 0.001) of not filling all positions compared to those accredited more than 5 years previously. EM residency programs with a primary clinical site under for-profit ownership had a 50% greater risk of not filling all positions when compared to those under nonprofit or governmental ownership (RR 1.50, 95% CI 1.14-1.98, chi-square p = 0.009). In 2023, several CBSAs had a high number of both offered and unfilled positions. EM residency programs accredited within the past 5 years or those with a primary clinical site under for-profit ownership had a greater risk of not filling all positions within the past two Match cycles.

“Born for a Storm”: Hard-Right Social Media and Civil Unrest

Does activity on hard-right social media lead to hard-right civil unrest? If so, why? We created a spatial panel dataset comprising hard-right social media use and incidents of unrest across the United States from January 2020 through January 2021. Using spatial regression analyses with core-based statistical area (CBSA) and month fixed effects, we find that greater CBSA-level hard-right social media activity in a given month is associated with an increase in subsequent unrest. The results of robustness checks, placebo tests, alternative analytical approaches, and sensitivity analyses support this finding. To examine why hard-right social media activity predicts unrest, we draw on an original dataset of users’ shared content and status in the online community. Analyses of these data suggest that hard-right social media shift users’ perceptions of norms, increasing the likelihood they will participate in contentious events they once considered taboo. Our study sheds new light on social media’s offline effects, as well as the consequences of increasingly common hard-right platforms.

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US

While research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services. To evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US. This population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis. A regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child's racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (>50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA. Among 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (β = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (>50% of the population: β = 0.05; <50% of the population: β = 0.07; P = .002) or Hispanic autistic children (>50% of the population: β = 0.04; <50% of the population: β = 0.07; P < .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (β = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (β = 0; 95% CI, 0-0.02; P < .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (β = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children. In this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.

The impact of the COVID-19 pandemic on racial disparities in patients undergoing total shoulder arthroplasty in the United States

The purpose of this study was to assess racial disparities in total shoulder arthroplasty (TSA) in the United States and to determine whether these disparities were affected by the COVID-19 pandemic. Centers for Medicare and Medicaid Services (CMS) 100% sample was used to examine primary TSA volume from April to December from 2019 to 2020. Utilization was assessed for White, Black, Hispanic, and Asian populations to determine if COVID-19 affected these groups differently. A regression model adjusted for age, sex, CMS-hierarchical condition categories (HCC) score, dual enrollment (proxy for socioeconomic status), time-fixed effects, and core-based statistical area fixed effects was used to study difference across groups. In 2019, the TSA volume per 1000 beneficiaries was 1.51 for White and 0.57 for non-White, with a 2.6-fold difference. In 2020, the rate of TSA in White patients (1.30/1000) was 2.9 times higher than non-White (0.45/1000) during the COVID-19 pandemic (P<.01). There was an overall 14% decrease in TSA volume per 1000 Medicare beneficiaries in 2020; non-White patients had a larger percentage decrease in TSA volume than White (21% vs. 14%, estimated difference; 8.7%, P=.02). Black patients experienced the most pronounced disparity with estimated difference of 10.1%, P=.05, compared with White patients. Similar disparities were observed when categorizing procedures into anatomic and reverse TSA, but not proximal humerus fracture. During the COVID-19 pandemic, overall TSA utilization decreased by 14% with White patients experiencing a decrease of 14%, and non-White patients experiencing a decrease of 21%. This trend was observed for elective TSA, while disparities were less apparent for proximal humerus fracture.

Delineating and comparing local labor market geographies of Millennials, Generation Xers, and Baby Boomers

There has been an increasing share of older workers in America’s workforce as Baby Boomers continue to age into this group. This raises questions about what their local labor markets look like in space and how they differ from Millennials and Generation Xers who are also active in today’s workforce. However, even though significant generational (or age) differences in commuting distance have been well documented, questions about generational disparities in local labor market geographies have not been investigated. Although several definitions of market areas exist, they were delineated using all commuting trips made by all workers in a region and thus may not be representative of subgroups of workers. The aim of this research is to delineate generation-specific local labor market areas and compare how they differ from one another. Using flow visualizations and three employment self-containment indices applied to a Longitudinal Employer-Household Dynamics dataset of Florida, this research finds that the core-based statistical areas, a popular definition of local labor market areas, understate the true market geographies across generational cohorts. It then delineates generation-specific market areas using a popular community detection method and compares their geographies using the self-containment indices. Results demonstrate largely consistent market geographies across the three generational cohorts in most regions in Florida. Most of these consistent market areas encompass two or more metros, indicating the emergence of functional megaregions centering on major metropolitan hubs. Substantial generational differences in market geographies do exist and concentrate in regions appealing to seniors along the Gulf of Mexico.

Geographic Employment Outcomes for Graduating Residents in Radiation Oncology: 2021

PurposeIn light of the persistent disparity in the prevalence of radiation oncologists between rural and urban areas in the United States, we sought to characterize the geographic employment outcomes of the radiation oncology (RO) residency class of 2021. Methods and MaterialsWe identified the employment positions accepted by 2021 RO residency graduates using publicly available information. Then, we classified each position as academic or nonacademic and determined the rural–urban continuum code and core-based statistical area (CBSA) of each position, residency program, and medical school associated with the 2021 graduates. Last, we compared the geographic employment outcomes of the 2021 class with those of the 2019 class. ResultsMost 2021 graduates accepted employment positions outside of the counties, CBSAs, and states in which they attended medical school or residency. A total of 116 graduates accepted nonacademic positions, and 65 graduates accepted academic positions. Nine graduates accepted fellowships and were excluded from this analysis. As with the class of 2019, most 2021 graduates (n = 104; 57.5%) accepted positions in the largest metropolitan areas, and few (n = 11; 6.1%) accepted positions in nonmetropolitan areas. Female residents were more likely to accept academic (48.9% vs 31.3%; P = .03) and academic main site (36.2% vs 21.6%; P = .05) positions than male residents. The distribution of jobs in both the 5 and 10 largest CBSAs was not significantly different from what would have been expected if jobs were equally distributed across the country on the basis of population (P = .81 and P = .87, respectively). However, the distribution was significantly different from what would have been expected if the distribution of such positions reflected the number of residents who graduated from residency programs in those CBSAs (P < .01 and P < .01, respectively). ConclusionsAs with the class of 2019, the majority of graduates in the class of 2021 accepted employment positions in large metropolitan areas. Relatively few graduates accepted positions in nonmetropolitan areas. How key RO stakeholders respond to the challenge of maintaining a stable supply of rural radiation oncologists while also ensuring that jobs available to graduating RO residents reflect their preferences will have a significant effect on the field going forward.