Census Bureau Research Articles

Longitudinal evaluation of COVID-19 vaccine hesitancy in the United States: analysis of the US Census Household Pulse survey

Addressing the challenge of vaccine hesitancy is crucial in managing public health crises like the COVID-19 pandemic. This study leverages the U.S. Census Bureau’s Household Pulse Survey to examine COVID-19 vaccine hesitancy, with a specific focus on the longitudinal tracking of vaccination beliefs and the influence of varying risk perceptions. It investigates how public attitudes towards vaccines evolve, influenced by changing perceptions of COVID-19 risk and vaccine safety. By applying Secondary Risk Theory (SRT), the research elucidates the relationship between different risk perceptions and vaccine hesitancy. We find that threat perception of COVID-19 and lack of trust in the vaccine demonstrate strong predictive value of increased vaccine hesitancy in comparison to other reasons for hesitancy such as access to vaccines, vaccine efficacy beliefs, or perceived secondary risks about vaccine side effects. Respondents with very low primary threat beliefs that COVID-19 is not a threat were 5.65 times more likely (β = 1.732) to intend not to be vaccinated in January 2021, decreasing to 2.70 times more likely to avoid vaccination (β = 0.995) in the December 2021.

Congenital Anomalies among Offspring of Women Living in Low Socioeconomic Status Neighborhoods or Hispanic/Latino Enclaves, Texas, 1999-2018

Objectives and ApproachNeighborhood influences pregnancy outcomes through effects on maternal health. We evaluated prevalence of >140 congenital anomalies monitored by the population-based Texas Birth Defects Registry among residents of Hispanic/Latino enclaves (census tracts with high proportions of Hispanic/Latino residents, immigrants, and Spanish-speaking households) and tracts with low socioeconomic status (low nSES). We included all cases regardless of pregnancy outcome (1999-2018) and a reference population of all livebirths, identified by linkage to Texas vital records. We calculated Yost socioeconomic index and Hispanic/Latino enclave index scores using linked U.S. Census Bureau data. We used Poisson regression to estimate the prevalence ratio (PR) and 95% confidence interval of anomalies among residents of: low nSES/enclave (34.5%); low nSES/non-enclave (25.6%); high nSES/enclave (4.6%); and high nSES/non-enclave (referent; 35.3%) tracts. We adjusted for maternal age, education, race/ethnicity, parity, and pre-pregnancy body mass index. ResultsLow nSES was associated with microcephaly, pulmonary artery anomalies, atrial septal defect, and Down syndrome (PRs 1.1-1.6), and inversely associated with hypospadias and other male genital anomalies (PRs 0.8-0.9). Offspring of women in low nSES enclaves were at increased risk of several congenital heart defects relative to those in other neighborhoods. Conversely, prevalence of hypospadias was lowest among offspring of women in low nSES enclaves. ConclusionsLinkage between a population-based registry, vital records, and Census Bureau data revealed novel associations between neighborhood characteristics and congenital anomalies. ImplicationsFurther research is warranted to understand mechanisms linking neighborhood factors to congenital anomalies.

Census Bureau Household Panel

The Census Bureau Household Panel is a nationally-representative, address-based, probability-based internet panel (including non-internet households) designed to improve representativeness, significantly reduce burden on sampled households, and promote high-frequency data meeting the immediate needs of the government and public. The Panel facilitates near real-time analysis of national events that may impact social, economic, or demographic characteristics of the population. It supports research to improve surveys, including testing content changes, alternative methods for enhancing data with administrative and other data sources, and adaptive survey design procedures.

Anxiety and depression among individuals with long COVID: Associations with social vulnerabilities

BackgroundWe examined the association between symptoms of anxiety and depression among individuals with long COVID and five social vulnerabilities (expenses, employment, food insufficiency, housing, and insurance). MethodsData from the Census Bureau's Household Pulse Survey (HPS) detailing COVID incidence, duration, and symptoms between June 1st and November 14th, 2022 contained versions of the Generalized Anxiety Disorder (GAD-2) and the Patient Health Questionnaire (PHQ-2) questionnaires. Associations between anxiety, depression, and the five social vulnerabilities among respondents from different racial and ethnic groups experiencing long COVID were evaluated using generalized binomial logistic regression. Structural equation models tested whether social vulnerabilities mediated the pathway between race/ethnicity and anxiety/depression. ResultsBlacks, Asians/others, and Hispanics with long COVID were significantly more likely to report anxiety and depression and various social vulnerabilities than Whites. Anxiety among Blacks was significantly associated with difficulty with expenses [Odds Ratio (OR) = 1.743, 95 % Confidence Interval (CI) = 1.739, 1.747], employment (OR = 1.519, 95 % CI = 1.516, 1.523), and housing (OR = 1.192, 95 % CI = 1.19, 1.194). Anxiety among Hispanics was significantly associated with food insufficiency (OR = 1.048, 95 % CI = 1.044, 1.052). Depression among Blacks was significantly associated with trouble with expenses (OR = 1.201, 95 % CI = 1.198, 1.205) and employment (OR = 1.129, 95 % CI = 1.127, 1.132). Mediation analysis showed that the number of social vulnerabilities partially mediated the association between race and anxiety. LimitationsThis retrospective study utilized secondary, observational, self-reported data from the HPS. Therefore, results may not be generalizable outside of the context in which they were collected. ConclusionsThe development of tailored programs for population health should address the differential associations of anxiety and depression with social difficulties among racial and ethnic groups.

The Geographic Distribution of Vascular Surgeons and Trainees by State in 2023

Approximately 1.4 vascular surgeons/100,000 persons are estimated to fulfill current patient needs in the United States (US), but an ongoing shortage exists. The aims of this study are to provide an updated nationwide state-by-state workforce analysis and compare the distribution of practicing vascular surgeons and training opportunities. Vascular surgeons in the US were identified using the National Provider Identifier registry in 2023. Only board-certified and actively licensed vascular surgeons were included. To estimate the number of vascular surgery graduates per year in each state, integrated residency and fellowship-matched positions (trainees) were ascertained from the National Resident Matching Program website. Surgeons and trainees were totaled by state, and densities were calculated using the 2020 US Census Bureau state populations. These two cohorts were also examined together using simple linear regression and geographic mapping. This study included 3399 board-certified vascular surgeons and 228 newly matched trainees. The average densities of vascular surgeons and trainees in the US are 1/100,000 persons and 0.06/100,000 persons, respectively. The five states with the lowest densities of vascular surgeons are AR, ND, NV, OK, and WY, averaging 0.4/100,000 persons. Eight states (AK, ID, KS, ND, NM, NV, RI, WY) had zero training programs offering positions in 2023 and ranked in the lowest quartile for the number of practicing vascular surgeons (Figure 1). Simple linear regression demonstrated a statistically significant correlation between state rates of vascular surgeons and trainees (p < 0.001). States with zero training positions also have the fewest vascular surgeons per capita. Statewide attention to expanding vascular surgery training opportunities targeted in these areas could positively impact the current maldistribution and shortage of vascular surgeons.

Geographic Trends in the Oral and Maxillofacial Surgery Residency Match

Matching into Oral and Maxillofacial Surgery (OMS) residency in the United States (US) is competitive. Various surgical and medical specialties found that a relationship exists between the geographic location where an applicant attends medical school and the location where they attend residency. However, information regarding this geographic relationship does not exist in OMS. The purpose of this study was to investigate if a relationship exists between the location of an applicant's dental school and the location of the OMS program where they match for residency. Study team conducted a cross-sectional review of all civilian OMS applicants who participated in the Postdoctoral OMS Matching Program from 2012 to 2022. US States and the District of Columbia were subdivided into a specific Region and Division as defined by the US Census Bureau. The primary predictor variable was the geographic location of the applicant's dental school. The outcome variable was the geographic location where the applicant matched for residency. There were no covariates analyzed in the study. Frequency tables were created for the expected and observed values for each possible combination of dental school and residency location. MATLAB R2023a software was used for statistical analysis. χ2 test, odds ratio and confidence interval were calculated to investigate association between variables. P values were <.05. 1768 applicants were reviewed. The inclusion criteria consisted of civilian OMS applicants from 2012 to 2022 who matched into residency. Of the 968 applicants who met the inclusion criteria, 48.45% (469/968, P -value < .001) matched into a residency in the same US Region as their dental school; 36.26% (351/968, P value < .001) matched into the same US Division. Applicants were found to be significantly more likely to match into a program in the same US Region and Division as their dental school. It is likely that individuals will match into OMS residency in the same US Region and Division as their dental school. This information helps applicants and residency programs during the match process.

Counting everyone: evidence for inclusive measures of disability in federal surveys.

The US Census Bureau has used the American Community Survey six-question set (ACS-6) to identify disabled people since 2008. In late 2023, the Census Bureau proposed changes to these questions that would have reduced disability prevalence estimates by 42%. Because these estimates inform funding and programs that support the health and independence of people with disabilities, many disability researchers and advocates feared this change in data collection would lead to reductions in funding and services. While the Census has paused-but not ruled out-the proposed changes, it is critical that alternate, more inclusive disability questions be identified and tested. We used data from the 2023/2024 National Survey on Health and Disability to explore alternative questions to identify disabled people in national surveys. A single broad question about conditions identified 11.2% more people with disabilities, and missed significantly fewer people with psychiatric disabilities compared to the current ACS-6 questions. A combination of a broad question and the existing ACS-6 questions may be necessary to more accurately and inclusively identify people with disabilities.

Geographic Patterns of Ocular Oncologist Supply and Patient Demand for Uveal Melanoma Treatment in the United States: A Supply and Demand Analysis.

To study geographic patterns of supply and demand for uveal melanoma and other ocular oncology healthcare by ocular oncology physicians in the United States. Google search interest data was obtained through trends.google.com. The combined-state density of ocular oncology physicians was calculated by dividing the number of practicing ocular oncologists in each state and its surrounding states by the state population. Relative search volume (RSV) values were divided by ocular oncology physician density to calculate the Google relative demand index (gRDI) for each state.Medicare (mRDI) and IRIS® Registry (iRDI) relative demand indices were calculated using prevalence data obtained through the Vision and Eye Health Surveillance System (VEHSS). Data from the US Census Bureau and Centers for Disease Control (CDC) databases were also utilized to analyze associations with poverty rates, percent living in urban or rural areas, vision screening rates, and ocular neoplasm rates. Alabama showed the highest RSV (100), while the lowest was reported in New Mexico (20). Vermont had the highest density of combined-state ocular oncology ophthalmologists (1.85 per 100,000 residents). New Mexico had the lowest RDI (0.013 gRDI, 0.015 mRDI, 0.018 iRDI) with 32 combined-state ocular oncologists and a population of 2,114,371. Ocular neoplasm prevalence rates ranged between 1.32% and 5.40% and significantly correlated with RSV. Single-state gRDI correlated with rural status and negatively correlated with urban areas (≥50,000 individuals). Single-state ophthalmologist density correlated positively with percent living in urban areas and vision screening rates, and negatively with rural status. This study uncovered significant heterogeneity in the geographical distribution of ocular oncology physicians and RDI throughout the United States, highlighting potential undersupply scenarios. This may guide efforts to increase ocular oncology physician and surgeon availability in areas of need.

Health care policy and arthroscopic rotator cuff repair prices in the United States

BackgroundArthroscopic rotator cuff repair (ARCR) is one of the most common orthopedic procedures in the general population. Despite its prevalence, the price of ARCR varies significantly across regions, hospital models, and settings. The purpose of this study was to examine the effect of Geographic Region, Certificate of Need (CON) laws, and Medicaid expansion on ARCR pricing. MethodsThis cross-sectional observational study utilized hospital payer-specific ARCR prices from the Turquoise Health Database using CPT code 29827. These prices are negotiated rates or charges that hospitals establish with various payers, including insurance companies, Medicare, Medicaid, and self-pay patients, for medical services and treatments provided. Outliers below the 10th percentile and above the 90th percentile were excluded. State policies, including CON status and Medicaid expansion, were obtained from public sources, while additional socioeconomic and demographic data were sourced from the US Census. The state's region classification was determined based on one of four Geographic Regions defined by the US Census Bureau. A detailed analysis was also conducted for North Carolina, examining county-level data on urbanization and the Area Deprivation Index (ADI). ResultsThere were 57,270 ARCR prices from 2,503 hospitals across the United States, with a median interquartile range (IQR) listed price of $6,428.17 (IQR: $2,886.88). States with CON regulations had significantly lower ARCR prices compared to those without ($6,500 vs. $8,000, p<0.0001). Multivariable analysis indicated that hospitals in the Northeast and West Regions listed significantly higher prices for ARCR compared to those in the Midwest Region (p<0.0001). In contrast, hospitals in the South Region listed lower prices for ARCR compared to those in the Midwest Region (p<0.0001). Medicaid expansion was associated with increased ARCR prices (p<0.0001), while CON laws were linked to reduced prices (p< .0001). In North Carolina, ADI and urbanization status did not significantly affect ARCR prices. ConclusionThe prices listed for ARCR varied significantly depending on the Geographic Region where hospitals were located. Additionally, CON laws were associated with reduced ARCR prices, while Medicaid expansion correlated with increased prices. These findings highlight the complex interplay between healthcare policy, regulatory frameworks, and socioeconomic factors in determining surgical prices.

Neighborhood income inequality associated with functional independence after ischemic stroke: a cohort study

BackgroundIndividual measures of socioeconomic status have been associated with post-stroke disability in patients with ischemic stroke. However, it is not known whether the distribution of income in a community may have an impact on stroke recovery. We hypothesized that increased neighborhood income inequality (as measured by the Gini index) may be associated with a slower recovery after stroke. MethodsThis was a retrospective cohort study of adult patients hospitalized at a comprehensive stroke center with acute ischemic stroke between 1/1/2018-12/31/2019. Individual patient data was abstracted from the EHR, and zip code Gini index was obtained from the US Census Bureau. Binary logistic regression was used to assess the relationship between Gini index and functional independence (modified Rankin scale ≤2) at discharge and first outpatient follow-up. These models controlled for patient demographics, stroke risk factors, stroke severity, and stroke treatment. A second binary regression was also performed using a subset of patients to assess possible predictors of being discharged as recommended (i.e. having a discharge destination that was consistent with the recommendation of the inpatient medical team). ResultsThree hundred and thirty-seven patients were included in this analysis. The median time to first outpatient follow-up was 35 days. Zip code Gini index was not associated with functional independence at discharge but was associated with independence at follow-up (modified Rankin scale ≤2) such that patients from higher inequality neighborhoods had decreased odds of being independent. More specifically, each 1% increase in neighborhood Gini index was associated with 8% decreased odds of independence at follow-up (OR=0.923, 95% CI: 0.863-0.987). Being discharged as recommended was associated with increased odds of independence at follow-up (OR=3.359, 95% CI: 1.055-10.695). Greater income inequality (OR=0.909, 95% CI: 0.841-0.983) and Asian race (OR=0.093, 95% CI: 0.011-0.766) were associated with decreased odds of being discharged as recommended. ConclusionAmong a cohort of patients with ischemic stroke, increased neighborhood income inequality was associated with decreased odds of achieving functional independence up (modified Rankin scale ≤2) by the time of first outpatient follow-up (at a median of 35 days following discharge). This disparity may be driven by discharge destination and race.

Inference about a Common Mean Vector from Several Independent Multinormal Populations with Unequal and Unknown Dispersion Matrices

This paper addresses the problem of making inferences about a common mean vector from several independent multivariate normal populations with unknown and unequal dispersion matrices. We propose an unbiased estimator of the common mean vector, along with its asymptotic estimated variance, which can be used to test hypotheses and construct confidence ellipsoids, both of which are valid for large samples. Additionally, we discuss an approximate method based on generalized p-values. The paper also presents exact test procedures and methods for constructing exact confidence sets for the common mean vector, with a comparison of the local power of these exact tests. The performance of the proposed methods is demonstrated through a simulation study and an application to data from the Current Population Survey (CPS) Annual Social and Economic (ASEC) Supplement 2021 conducted by the U.S. Census Bureau for the Bureau of Labor Statistics.

Disability inclusion in national surveys.

National surveys are important for understanding the disparities that disabled people experience across social determinants of health; however, limited research has examined the methods used to include disabled people in these surveys. This study reviewed nationally representative surveys administered by the Centers for Disease Control and Prevention (CDC) and the US Census Bureau that collected data in the past 5 years and sampled adults ≥18 years. Data from both publicly available online survey documents and a questionnaire emailed to survey administrators were used to determine whether surveys (1) oversampled disabled people, (2) had a data-accessibility protocol to support data collection, and (3) provided multiple data-collection modalities (eg, phone, paper). Of the 201 surveys identified, 30 met the inclusion criteria for the study. Of these 30 surveys, 1 oversampled disabled people, none had a data-collection accessibility protocol, and 21 provided multiple data-collection modalities. This study highlights barriers and opportunities to including disabled people in national surveys, which is essential for ensuring survey data are generalizable to the US population.

A closer examination of the "rural mortality penalty": Variability by race, region, and measurement.

Racial health disparities are well documented and pervasive across the United States. Evidence suggests there is a "rural mortality penalty" whereby rural residents experience poorer health outcomes than their urban counterparts. However, whether this penalty is uniform across demographic groups and U.S. regions is unknown. To assess how rural-urban differences in mortality differ by race (Black vs. White), U.S. region, poverty status, and how rural-urban status is measured. Age-standardized mortality rates (ASMRs)/100,000 by U.S. county (2015-2019) were obtained by race (Black/White) from the CDC Wonder National Vital Statistics System (2015-2019) and were merged with county-level social determinants from the US Census Bureau and County Health Rankings. Multivariable generalized linear models assessed the associations between rurality (index of relative rurality [IRR] decile, rural-urban continuum codes, and population density) and race-specific ASMR, overall, and by Census region and poverty level. Overall, average ASMR was significantly higher in rural areas than urban areas for both Black (rural ASMR = 949.1 per 100,000vs. urban ASMR = 857.7 per 100,000) and White (rural ASMR = 903.0 per 100,000vs. urban ASMR = 791.6 per 100,000) populations. The Black-White difference was substantially higher (p < 0.001) in urban than in rural counties (65.1 per 100,000vs. 46.1 per 100,000). Black-White differences and patterns in ASMR varied notably by poverty status and U.S. region. Policies and interventions designed to reduce racial health disparities should consider and address key contextual factors associated with geographic location, including rural-urban status and socioeconomic status.

Perspective: Better Privacy Theorists for Better Data Stewards

The U.S. Census Bureau's use of differential privacy (DP) fundamentally changed how academic DP researchers perform outreach with official statistics stakeholders. In this perspectives piece, I propose ways for us in this community to improve those processes by being more receptive to the practical concerns raised by building DP systems. First, I discuss how academic DP work fundamentally differs from the policy decisions needed to implement DP systems and why this distinction has political consequences. Through examples and discussions from workshops, I show how the DP community largely asked applied stakeholders to communicate on DP's theoretical terms, when such an ask foreclosed important considerations relevant for the Census Bureau's policy problems. Second, I discuss how existing polarization between theoretical and empirical privacy researchers unintentionally seeped into the ways we communicated about DP, pointing to why both perspectives are necessary in different ways for policy conversations. Finally, I conclude by discussing how these issues are not unique to data privacy work, but instead reflect structural problems in translating theoretical science into practice. These ideas are presented in service of a single goal: to ensure DP theory supports substantive, privacy-aware data processing and dissemination in practice for essential data curators.

Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Hierarchical Models

Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a significant computational burden. This motivates us to consider finding expressions of the posterior distribution that are computationally straightforward to obtain independent replicates from directly. We focus on a broad class of Bayesian hierarchical models for spatially dependent data, which are often modeled via a latent Gaussian process (LGP). First, we derive a new class of distributions referred to as the generalized conjugate multivariate (GCM) distribution. The GCM distribution’s theoretical development follows that of the conjugate multivariate (CM) distribution with two main differences: the GCM allows for latent Gaussian process assumptions, and the GCM explicitly accounts for hyperparameters through marginalization. The development of GCM is needed to obtain independent replicates directly from the exact posterior distribution, which has an efficient regression form. Hence, we refer to our method as Exact Posterior Regression (EPR). Simulation studies with weakly stationary spatial processes and spatial basis function expansions are provided. We provide an analysis of poverty incidence from the U.S. Census Bureau, and an analysis of high-dimensional remote sensing data. Supplementary materials for this article are available online.

Assessment of the Indian Public Healthcare Delivery System and Its Comparison with Public Healthcare Delivery of Global Nations Based on Their Gross Domestic Products Potential as Criteria: A Comparative Study

Aim: The aim of this study is to analyze the Indian public healthcare delivery system and compare it with other countries based on their gross domestic product (GDP) classification. Materials and Methods: The study was conducted to assess the Indian healthcare delivery system and the GDP among global nations based on their GDP potential as criteria. The data from 24 high-, middle-, and low-income countries were gathered from the World Bank from year 2018 to 2021. The parameters included GDP, GDP per capita, total number of population, and healthcare delivery system. The data were collected from various electronic sources of the World Health Organization, the International Monetary Fund, and the US Census Bureau’s international data, National Health Profile 2021. The collected data were compiled and tabulated. The descriptive analysis was done using Microsoft Excel version 2016 and SPSS version 20. Results: India ranks in the high-income group based on the GDP, whereas when ranked based on GDP per capita, it drops down to the lower-middle-income group. This lag may be because of a larger population and lack of priority in public health delivery. In terms of overall expenditure, India spends US$63 million in total health expenditure, of which only US$20 million is spent by the government sector. On the other hand, the United States holds the highest rank with US$10,921 million of total health expenditure, of which US$5,552 million spent by the government sector and US$5,368 million by the private sector. Conclusion: The Indian healthcare delivery system is suffering from a shortage of public health workforce and infrastructure. In the last decade, India has seen remarkable economic development, particularly in terms of the GDP growth rate. Unfortunately, this progress does not accompany with the Indian healthcare delivery system. Still there is a shortage in public health workforce and infrastructure. India should prioritize the expenditure in healthcare delivery.

Food Insecurity across Age Groups in the United States during the COVID-19 Pandemic.

Food insecurity increased during the COVID-19 pandemic, but the impact varied across different age groups during the prolonged public health emergency. This study sought to describe national food insecurity prevalence by adult age group at multiple stages of the pandemic and explore differences by demographic characteristics. Data were from the nationally representative US Census Bureau's Household Pulse Survey from April 2020 to May 2023 (N = 4,153,462). Locally weighted scatterplot smoothing (LOESS) regression analysis identified change points in food insecurity trends, segmenting the timeline into three periods: (1) April 2020-March 2021, (2) April 2021-May 2022, and (3) June 2022-May 2023. Logistic regression models examined associations between age, time period, and self-reported household food insecurity; covariates included demographics, socioeconomic status, household structure, and food support program usage. Overall, 9.3% of respondents experienced food insecurity, ranging from 3.5% among those aged ≥75 to 12.2% for ages 35-44 years. Significant interaction between age group and time period indicated inconsistency in the age-food insecurity association during the pandemic (p < 0.001). From Period 1 to 3, the proportion of food-insecure adults aged ≥65 rose from 9.2% to 13.9%. Across all age groups, higher odds of food insecurity were found among Black, Hispanic/Latino, or Other/Multiracial respondents; those with less than a Bachelor's degree; those with incomes below USD 35,000; those unemployed for reasons other than retirement; and non-homeowners (p < 0.001). The results show that trends and characteristics associated with food insecurity varied across age groups and time periods. Continuous monitoring of food insecurity during emergencies is critical to identify vulnerable populations and timely interventions.

Understaffed and beleaguered: a national survey of chiefs of police about the post-George Floyd era

PurposeThe 2020 murder of George Floyd resulted in challenges to policing in the United States of America, but little is known about how police chiefs perceive them. At the same time, chiefs of police wield great influence over public perceptions of crime and disorder, the state of their profession, the laws and policies that govern the conduct of police officers and municipal public safety budgets. It is therefore critical to understand how police perceive the changes to their profession post-Floyd.Design/methodology/approachThis study surveyed a randomly selected national sample of 276 municipal chiefs of police. Items probed resignations, recruitment, efforts to defund departments, community support, officer morale, suspects’ likelihood of obeying lawful orders and career risks that could inhibit proactive police work. It examined associations between perceptions and Census Bureau region, length of tenure as chief, size of police department, population served and the urban or rural designation of the jurisdiction.FindingsChiefs overwhelmingly reported recruiting qualified candidates had become much harder, and the present risks of proactive police work encourage inaction. Chiefs of agencies in the Northeast perceived more challenges than those in the South. Respondents with more years of experience were less likely to perceive the current situation as dire. Approximately 13.5% reported an attempt to defund their department, 56.8% of which yielded some success. Our study suggests an increase in the number and scope of challenges perceived by chiefs of police. Results vary by region and police chief years of experience.Originality/valueThis study provides researchers and practitioners with the perspectives of chiefs about the post-Floyd era that influence their decisions, policies and initiatives.

Relationship Between Athletic Trainer Access, Socioeconomic Status, and Race and Ethnicity in United States Secondary Schools.

Extensive research has exposed healthcare disparities regarding socioeconomic status (SES) and race/ethnicity demographics. Previous research has shown significant differences in access to athletic training services (AT access) in the secondary school setting based on SES, but with limited samples. This study investigated differences in AT access based on race/ethnicity and SES on a national scale. Cross-sectional study. Database study using secondary analysis. Data were collected from the National Center for Education Statistics (NCES), Athletic Training Location and Services (ATLAS) database, and US Census Bureau. 10,983 public schools. Descriptive data was summarized by measures of central tendency. A one-way ANOVA determined differences between school characteristics: median household income (MHI), percent of students eligible for free and reduced lunch (%FRL), percent white students, and percent non-white students based on AT access: Full-time (FT-AT), part-time (PT-AT), and no athletic trainer (no-AT). A Bonferroni pairwise comparison was used for variables with significant main effects. Across all schools included in the study, 43.8% had no-AT (n=4,812), 23.5% had PT-AT access (n=2,581), and 32.7% had FT-AT access (n=3,590). There were significant effects between AT access and MHI (p<.001), %FLR (p<.001), percent white (p<.001), and percent non-white (p<.001). FT-AT schools had a higher SES when compared to PT-AT and no-AT schools. Significant differences existed between AT access groups and race/ethnicity of schools. Schools with FT-AT had a significantly lower percent of non-white students (31.3%) compared to schools with no-AT (46.0%) (p<.001). No significant differences between FT-AT and PT-AT based on race/ethnicity demographics presented (p≥.13). Schools with higher SES had greater AT access; whereas, schools with a higher percentage of non-white students were more likely to have no AT access, demonstrating the disparities in healthcare extends to athletic healthcare as well. To increase AT access, future initiatives should address the inequities where larger minority populations and counties of lower SES exist.

Healthcare Use and Expenditures in Rural Survivors of Hospitalization for Sepsis.

Sepsis survivors have greater healthcare use than those surviving hospitalizations for other reasons, yet factors associated with greater healthcare use in this population remain ill-defined. Rural Americans are older, have more chronic illnesses, and face unique barriers to healthcare access, which could affect postsepsis healthcare use. Therefore, we compared healthcare use and expenditures among rural and urban sepsis survivors. We hypothesized that rural survivors would have greater healthcare use and expenditures. To test this hypothesis, we used data from 106,189 adult survivors of a sepsis hospitalization included in the IBM MarketScan Commercial Claims and Encounters database and Medicare Supplemental database between 2013 and 2018. None. We identified hospitalizations for severe sepsis and septic shock using the International Classification of Diseases , 9th Edition (ICD-9) or 1CD-10 codes. We used Metropolitan Statistical Area classifications to categorize rurality. We measured emergency department (ED) visits, inpatient hospitalizations, skilled nursing facility admissions, primary care visits, physical therapy visits, occupational therapy visits, and home healthcare visits for the year following sepsis hospitalizations. We calculated the total expenditures for each of these categories. We compared outcomes between rural and urban patients using multivariable regression and adjusted for covariates. After adjusting for age, sex, comorbidities, admission type, insurance type, U.S. Census Bureau region, employment status, and sepsis severity, those living in rural areas had 17% greater odds of having an ED visit (odds ratio [OR] 1.17; 95% CI, 1.13-1.22; p < 0.001), 9% lower odds of having a primary care visit (OR 0.91; 95% CI, 0.87-0.94; p < 0.001), and 12% lower odds of receiving home healthcare (OR 0.88; 95% CI, 0.84-0.93; p < 0.001). Despite higher levels of ED use and equivalent levels of hospital readmissions, expenditures in these areas were 14% (OR 0.86; 95% CI, 0.80-0.91; p < 0.001) and 9% (OR 0.91; 95% CI, 0.87-0.96; p < 0.001) lower among rural survivors, respectively, suggesting these services may be used for lower-acuity conditions. In this large cohort study, we report important differences in healthcare use and expenditures between rural and urban sepsis survivors. Future research and policy work is needed to understand how best to optimize sepsis survivorship across the urban-rural continuum.