Statistical Areas Research Articles

Bidirectional associations between depressive symptoms and cigarette, e-cigarette, cannabis, and alcohol use: Cross-lagged panel analyses among young adults before and during COVID-19

BackgroundThe literature regarding bidirectional relationships of depressive symptoms to cigarette and alcohol use is mixed, and limited regarding e-cigarette and cannabis use. Moreover, COVID-19 has significantly impacted mental health and substance use, especially among young adults. Thus, this is a critical period for focused research on these relationships among young adults. MethodsWe analyzed longitudinal data (assessments in Fall 2018, 2019, and 2020) from 3,006 young adults (Mage = 24.56 [SD = 4.72], 54.8% female, 31.6% sexual minority, 71.6% White, 5.3% Black, 12.2% Asian, 11.4% Hispanic) from 6 US metropolitan statistical areas. Cross-lagged panel models were conducted to examine bidirectional associations between depressive symptoms and past 30-day use of cigarettes, e-cigarettes, cannabis, and alcohol (respectively), controlling for sociodemographics. ResultsDuring the study period, depressive symptoms decreased before the pandemic but increased during, cigarette and e-cigarette use decreased in both periods, alcohol use showed no change before but increases during the pandemic, and cannabis use increased in both periods. Additionally, each outcome demonstrated greater stability before versus during COVID-19. Finally, greater antecedent depressive symptoms correlated with more days of subsequent cigarette (β = 0.03, SE = 0.01, p =.011) and e-cigarette use (β = 0.03, SE = 0.01, p =.021), but fewer days of alcohol use (β = -0.02, SE = 0.01, p =.035). W2 cannabis use and alcohol use, respectively, were related to W3 depressive symptoms (cannabis: β = 0.09, SE = 0.02, p <.001; alcohol: β = 0.06, SE = 0.02, p =.002). No other cross-lagged associations were significant. ConclusionsIntervention efforts targeting depression and substance use should explicitly address the potential for onset and escalation of substance use and depressive symptoms, respectively, especially during societal stressors.

Pneumococcal vaccination coverage among adults newly diagnosed with underlying medical conditions and regional variation in the U.S.

BackgroundThe Centers for Disease Control and Prevention’s Advisory Committee on Immunization Practices recommends pneumococcal vaccination for adults with chronic or immunocompromising conditions to prevent pneumococcal disease, yet vaccination rates are low and have limited information on regional variation. This study examines factors associated with pneumococcal vaccination in adults with underlying conditions and describes regional variation in vaccination across the U.S. MethodsUsing IBM MarketScan Commercial Database and Medicare Supplemental Database, this retrospective cohort study included adults ages 19–64 newly diagnosed with chronic (i.e. diabetes, chronic heart, lung, or liver disease, alcohol or tobacco dependence) or immunocompromising (i.e. cancer, chronic renal disease, organ transplant, HIV/AIDS, and asplenia) conditions in 2013. Adults were followed up until the time of pneumococcal vaccination, death, or December 31, 2019, whichever came first. Cox proportional hazards model was used to examine factors associated with vaccination. Vaccination rate was calculated by metropolitan statistical area (MSA) and visually represented on a U.S. map. Results255,330 adults were included. Vaccination rate increased from 6.0% to 21.1% among adults with one year and five years of follow-up, respectively. It took 2.4 years on average for adults to receive vaccination after initial diagnosis. Adults ages 50–64, 35–49 (relative to 19–34) or receiving influenza vaccination were more likely to receive pneumococcal vaccinations. Adults with HIV/AIDS were more likely and those with other conditions were less likely to be vaccinated than those with diabetes. Adults being diagnosed by other providers were less likely to be vaccinated than those diagnosed by primary care providers. Vaccination rate varied largely across MSAs, ranging from 0.0% (Ames, IA; Cheyenne, WY) to 34.0% (Ann Arbor, MI). ConclusionPneumococcal vaccination remains low and most adults with underlying conditions are unvaccinated. Insights into factors associated with vaccination, including regional variability, can help to increase pneumococcal vaccination.

Growth of Private Pay Senior Housing Communities in Metropolitan Statistical Areas in the United States: 2015-2019.

Older adults needing assistance with activities of daily living can receive support in various settings. Senior housing communities, such as independent living, assisted living, and continuing care retirement communities, are an increasingly popular option for adults not requiring nursing home-level care. However, limited research exists due to a dearth of data on these types of communities. We use a proprietary data set to describe the market of private pay senior housing and community-level characteristics in 140 metropolitan statistical areas, from 2015 to 2019. Although the number of senior housing communities increased substantially, the supply of senior housing options supporting the continuum of care has not necessarily kept up with population growth. Describing the supply of senior housing communities across the spectrum of levels of care provides a more complete description of the formal noninstitutional long-term care supply among the 140 most populated metropolitan statistical areas.

House price dynamics in Iberian Metropolitan Statistical Areas: slope heterogeneity, cross-sectional dependence and elasticities

PurposeThis paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.Design/methodology/approachThe authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.FindingsThe results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.Practical implicationsRecent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.Originality/valueTo the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

Spatial Mismatch and the Share of Black, Hispanic, and White Students Enrolled in Charter Schools

How are patterns of segregation related to families’ engagement in public-school choice policies across U.S. metropolitan areas? This article examines how segregation in urban public schools and the spatial mismatch between school-age children and relatively high-performing schools relate to the shares of Black, Hispanic, and White students enrolled in charter schools, one particular school choice mechanism. Drawing on Core-Based Statistical Area–level data, I find that charter-school enrollment among Black students is positively associated with spatial mismatch. As the degree of geographic imbalance between Black and, to a lesser extent, Hispanic school-age children and high-performing schools increases, so too does the share of Black and Hispanic students who enroll in charter schools. There is no such relationship for White students, whose enrollment in charter schools is higher when school segregation is relatively low—that is, when they would be more likely to attend neighborhood public schools with Black children.

Comprehensive Process Mapping and Qualitative Interviews to Inform Implementation of Rapid Linkage to HIV Care Programs in a Mid-Sized Urban Setting in the Southern United States.

Rapid antiretroviral therapy (ART) initiation, in which people living with HIV start ART within days of diagnosis, is a key component of the US Ending the HIV Epidemic initiative. The Memphis Metropolitan Statistical Area ranked fourth in the United States for the highest HIV incidence per 100,000 population in 2018. Rapid ART programs are limited in the Memphis Metropolitan Statistical Area, and our objective was to identify local implementation barriers. We conducted participatory process mapping and in-depth interviews to detail steps between HIV testing at the municipal health department's Sexually Transmitted Infections Clinic and ART prescription from a nearby high-volume Ryan White-funded HIV Clinic. Process mapping identified 4 modifiable, rate-limiting rapid ART barriers: (1) requiring laboratory-based confirmatory HIV results, (2) eligibility documentation requirements for Ryan White-funded services, (3) insufficient HIV Clinic medical provider availability, and (4) variability in ART initiation timing among HIV Clinic providers. Staff at both sites highlighted suboptimal communication and sense of shared management between facilities, limited resources to address important social determinants of health, and lack of Medicaid expansion in Tennessee as key barriers. In-depth interview themes negatively affecting rapid ART initiation included clinic burden; provider knowledge, attitudes, and beliefs; and client psychosocial needs. Our preimplementation work identified modifiable and systemic barriers to systems flow and patient-level outcomes. This work will inform the design and implementation of a locally relevant rapid ART program in Memphis, a community disproportionately affected by the HIV epidemic.

Why has public transit ridership declined in the United States?

Between 2012 and 2018, bus ridership in the United States declined 15% and rail ridership declined 3%. These losses are widespread and in contrast to trends in other countries. Using data from 215 Metropolitan Statistical Areas (MSAs) prior to the COVID-19 pandemic, we identify the factors responsible for this decline and quantify the contribution of each. We show that expanded transit service and land-use changes increased ridership 4.7% on bus and 10.7% on rail. However, losses due to other factors exceed these gains. Ride-hailing is the biggest contributor to transit ridership decline over this period, reducing bus ridership by 10%. Ride-hailing’s effect on rail varies by metropolitan area size: it has little effect on rail ridership in the largest metropolitan areas but decreases rail ridership 10% in mid-sized metropolitan areas. Lower gas prices and higher fares contribute to lower transit ridership, as do higher incomes, more teleworking and higher car ownership. By providing a clear understanding of the causes of transit ridership decline, our research provides the foundation on which communities can craft an effective response to the problem.

Does polycentric development produce less transportation carbon emissions? Evidence from urban form identified by night-time lights across US metropolitan areas

Identifying the comprehensive metropolitan urban form is important to propose effective policies to mitigate transportation carbon emissions. A publicly accessible night-time light dataset was used to identify urban centers and develop two polycentric indices to compute the composition and configuration of urban form, respectively. We used the most populous 103 U.S. metropolitan statistical areas (MSAs), with their corresponding transportation carbon emissions, polycentric indices, population sizes, gross domestic product (GDP) per capita, and road network densities. We first explored the typology of urban form and classified MSAs into six types based on two polycentric indices. We then introduced correlation analysis and statistical models to test the relationships between polycentric urban form and transportation carbon emissions. We found: (1) more urban centers lead to more emissions (compositional dimension), (2) more spatially distributed urban centers result in less emissions (configurational dimension), and (3) population and GDP per capita are positively related to carbon emissions. These findings suggest the importance of measuring two polycentric dimensions separately but using them together. Urban planners should consider mixed strategies that combine the traditional intra-center-based smart growth principles and the metropolitan-level inter-centers spatial plan to effectively counteract climate change.

Sharper Sub-Weibull Concentrations

Constant-specified and exponential concentration inequalities play an essential role in the finite-sample theory of machine learning and high-dimensional statistics area. We obtain sharper and constants-specified concentration inequalities for the sum of independent sub-Weibull random variables, which leads to a mixture of two tails: sub-Gaussian for small deviations and sub-Weibull for large deviations from the mean. These bounds are new and improve existing bounds with sharper constants. In addition, a new sub-Weibull parameter is also proposed, which enables recovering the tight concentration inequality for a random variable (vector). For statistical applications, we give an ℓ2-error of estimated coefficients in negative binomial regressions when the heavy-tailed covariates are sub-Weibull distributed with sparse structures, which is a new result for negative binomial regressions. In applying random matrices, we derive non-asymptotic versions of Bai-Yin’s theorem for sub-Weibull entries with exponential tail bounds. Finally, by demonstrating a sub-Weibull confidence region for a log-truncated Z-estimator without the second-moment condition, we discuss and define the sub-Weibull type robust estimator for independent observations {Xi}i=1n without exponential-moment conditions.

The Effect of CMS's Comprehensive Care for Joint Replacement Bundled Payment Model on Trajectories of Post-acute Rehabilitation Care After Total Hip Arthroplasty

ObjectiveTo evaluate the effect of the Comprehensive Care for Joint Replacement (CJR) policy on the 90-day trajectory of post-acute care after a total hip arthroplasty (THA). DesignMultivariable difference-in-difference models applied to Medicare beneficiaries undergoing a THA prior to (2014-2015) and post-CJR implementation (2017) in areas subjected to or exempt from the policy. SettingHospitals in standard metropolitan statistical areas. Participants357,844 elderly Medicare patients nationwide undergoing THA (N=357,844). InterventionsNone. Main Outcome MeasuresEscalation in care to institutionalization (ie, admission to an inpatient rehabilitation or skilled nursing facility during 90-days postdischarge for those initially discharged to the community and return to the community at the end of the episode of care among those initially discharged to an institutional setting). ResultsOf the 357,844 elderly Medicare patients nationwide undergoing THA during the study period, 47.6% were discharged directly to the community and 52.4% received post-acute care in an institution. Patients discharged to an institution post-policy in a CJR area were about 10% less likely to return to the community (odds ratio=0.91; 95% confidence interval, 0.84-0.98; P=.02) at the end of the 90-day episode of care than those treated in policy-exempt areas. Despite the large magnitude, estimates of escalation in care among patients treated in bundling areas post-CJR implementation were not statistically significant. ConclusionsOur findings support further exploration of unanticipated effects of mandatory bundled payment policies on outcomes, as well as further examination of outcomes among policy-relevant subgroups of patients undergoing hip replacement in the United States.

How to Select the Best Fit Model among Bayesian Latent Growth Models for Complex Data

Bayesian approach is becoming increasingly important as it provides many advantages in dealing with complex data. However, there is no well-defined model selection criterion or index in a Bayesian context. To address the challenges, new indices are needed. The goal of this study is to propose new model selection indices and to investigate their performances in the framework of latent growth mixture models with missing data and outliers in a Bayesian context. We consider latent growth models because they are very flexible in modeling complex data and becoming increasingly popular in statistical, psychological, behavioral, and educational areas. Specifically, this study conducted five simulation studies to cover different cases, including latent growth curve models with missing data, latent growth curve models with missing data and outliers, growth mixture models with missing data and outliers, extended growth mixture models with missing data and outliers, and latent growth models with different classes. Simulation results show that almost all proposed indices can effectively identify the true model. This study also illustrated the application of these model selection indices in real data analysis.

Disparities in Human Papillomavirus vaccination coverage among adolescents in Australia: A geospatial analysis

AimThis ecological study aimed to examine the geographic patterns in Human Papillomavirus (HPV) vaccination rates among boys and girls aged 15 years across locations in Australia, in addition to assessing contextual area-level factors that may explain the variations in HPV vaccination coverage. MethodsAggregate HPV vaccination data for Australian girls and boys aged 15 years from 2015 to 16 was obtained from the Australian Institute of Health and Welfare for each Statistical Area level 4 (SA4). A Gradient Boosting Machine learning model was applied to assess the predictors’ importance for the study outcomes. Geographically weighted regression (GWR) models were run to assess whether substantially different relationships between predictors and outcomes occur at different locations in space. ResultsCompleted HPV vaccination across the 88 SA4 regions ranged from 57.6% to 90.6% among girls, and from 53.6% to 85.5% among boys. The 2016 SEIFA Index of Economic Resources was the variable with the highest contribution to the predictions of both girls’ and boys’ HPV vaccination rates. Selected predictors explained 45% and 72% of the geographic variance in vaccination rates among boys and girls, respectively. Normalised coefficients for both GWR models showed a high variation in the associations between predictors and HPV vaccination rates across regions. ConclusionSocioeconomic and education factors were important predictors for HPV vaccination rates among Australian boys and girls aged 15 years, although no variable presented a uniform effect on HPV vaccination across SA4 regions. Important spatial heterogeneity in the effect of predictors was identified across the study area.

Changes in social behavior and impacts of the COVID-19 pandemic on regional housing markets: Independence and risk

This paper explores changes in social behavior since the start of the COVID-19 pandemic, which are characterized by reduction in relocation, mobility, and community engagement, and how the correlations between regional housing markets are affected by these changes. Because changes in mobility and engagement are the most apparent in large cities, the present study calculates the independence indicator of regional housing markets in the 50 largest metropolitan statistical areas (MSAs) in the United States and determines their relationship with Mobility and Engagement Index values. The empirical results show that as mobility and community engagement decline in a certain area, housing market fluctuations become more independent, indicating correlations between regional housing markets in the US might decrease after the COVID-19 outbreak. This paper also finds that there are more MSAs having significantly decreased in volatility since the outbreak of the pandemic. This paper provides evidence indicating that housing markets may be impacted differently by the COVID-19 pandemic than other asset markets, particularly stock markets. Changes in mobility and engagement can be used as an indicator to assess whether the correlation between regional housing markets would decline, which means that, compared with financial instruments, more factors from real aspects need to be considered when determining the changes in real estate affected by the epidemic.

Housing affordability and commute distance

ABSTRACT The growing affordable housing crisis in high-cost metropolitan areas may force households to seek lower cost housing in the outer reaches of metropolitan areas contributing to the recent increase in commute distance. To explore this assertion, we test the relationship between the availability of affordable housing relative to jobs and commute distance in two diverse metropolitan statistical areas in Southern California: Los Angeles-Orange (higher cost, coastal, older, more urban) and Riverside-San Bernardino (lower cost, inland, newer, more suburban). A worse “fit” between the number of low-wage jobs and affordable housing rentals is associated with longer commute distances in LA-Orange, but is not statistically significant in Riverside-San Bernardino. This study’s findings highlight the differences in housing dynamics and commute distances between higher cost coastal regions and lower cost inland regions—and underscore the importance of protecting and expanding the supply of affordable housing in job-rich neighborhoods located in more expensive, coastal cities.

#70 Predicting place-based factors for Staphylococcus aureus infections within pediatric population using machine learning

Abstract Background Community-onset Staphylococcus aureus (CO-S. aureus) infections, methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA), have a high burden on United States (US) Emergency Departments and urgent care, particularly within the pediatric population. Although individual risk factors have been well-studied, the influence of specific geographic locality and place-based risks for CO-S. aureus infections have not been well-characterized. Maximum entropy (MaxEnt) is a machine learning technique for ecological niche modeling, which predicts the distribution of disease vectors and their possible disease transmissions using environmental and other relevant risk factors. The aim of this study is to predict socioecological factors that contribute to the spread of CO-S. aureus in a major urban area. Method Electronic medical records from children with staphylococcal infections, who were treated at two pediatric hospitals from 2002 to 2016, were retrospectively reviewed. Children were included in the analyses if they had a confirmed S. aureus infection within 48 hours of hospital admission (i.e., CO-S. aureus), less than 19 years old, and a geo-referenced address within Atlanta’s metropolitan statistical area (MSA). The timespan was divided into two periods, 2002-2005 (early) and 2006-2010 (later), mimicking the trend of CO-S. aureus. Fourteen place-based factors, obtained at the US Census Bureau block group level, were included in the MaxEnt model: population under 18 years old, Caucasian, African American, ethnicity, poverty, low education (high school diploma), high education (Bachelor’s degree and above), crowding, nursery school enrollment, kindergarten enrollment, distance to K-12 school, distance to a children’s hospital, distance to a daycare center, and population density. These were based on factors previously determined a priori. A total of four models (CO-MRSA early, CO-MSSA early, CO-MRSA later, and CO-MSSA later) were run using the MaxEnt software (v.3.4.1). For each model, 75% and 25% of data was randomly assigned to training and testing groups, respectively. Models were assessed by jack-knife tests. Results 16,124 records met the eligibility criteria and were included in the MaxEnt models. Preliminary analyses of data from 2002-2010 suggest training Area Under the Curve (AUC) ranging from 0.802 to 0.828 and the test AUC ranging from 0.796 to 0.809, demonstrating these models are performing very well. Population density had the highest contribution in predicting CO-MRSA and CO-MSSA locations, which was confirmed by jack-knife tests. Conclusion By applying MaxEnt to pediatric CO-S. aureus infections in the Atlanta MSA, it was found that higher risks of CO-S. aureus infections may exist in more densely populated areas. MaxEnt can be utilized to identify potential future areas of CO-MRSA and CO-MSSA infections based on estimated or predicted changes to the place-based factors used to build these models, most notably population density. Predicted risk areas should have more frequent monitoring to prevent S. aureus infection outbreaks, which will also allow for more time to pool public health resources for these areas to quickly and effectively control outbreaks.

Social distancing and related factors during the COVID-19 pandemic in relation to COVID-19 symptoms and diagnosis and mental health.

BackgroundMany states, local authorities, organizations, and individuals have taken action to reduce the spread of COVID-19, particularly focused on restricting social interactions. Such actions have raised controversy regarding their implications for the spread of COVID-19 versus mental health.MethodsWe examined correlates of: (1) COVID symptoms and test results (i.e., no symptoms/tested negative, symptoms but not tested, tested positive), and (2) mental health symptoms (depressive/anxiety symptoms, COVID-related stress). Data were drawn from Fall 2020 surveys of young adults (n = 2576; Mage = 24.67; 55.8% female; 31.0% sexual minority; 5.4% Black; 12.7% Asian; 11.1% Hispanic) in six metropolitan statistical areas (MSAs) with distinct COVID-related state orders. Correlates of interest included MSA, social distancing behaviors, employment status/nature, household composition, and political orientation.ResultsOverall, 3.0% tested positive for COVID-19; 7.0% had symptoms but no test; 29.1% reported at least moderate depressive/anxiety symptoms on the PHQ-4 Questionnaire. Correlates of testing positive (vs. having no symptoms) included residing in Oklahoma City vs. Boston, San Diego, or Seattle and less social distancing adherence; there were few differences between those without symptoms/negative test and those with symptoms but not tested. Correlates of greater depressive/anxiety symptoms included greater social distancing adherence, being unemployed/laid off (vs. working outside of the home), living with others (other than partners/children), and being Democrat but not Republican (vs. no lean); findings related to COVID-specific stress were similar.ConclusionDespite curbing the pandemic, social distancing and individual (e.g., political) and environmental factors that restrict social interaction have negative implications for mental health.

Minimax estimation of smooth densities in Wasserstein distance

We study nonparametric density estimation problems where error is measured in the Wasserstein distance, a metric on probability distributions popular in many areas of statistics and machine learning. We give the first minimax-optimal rates for this problem for general Wasserstein distances for two classes of densities: smooth probability densities on [0,1]d bounded away from 0, and sub-Gaussian densities lying in the Hölder class Cs, s∈(0,1). Unlike classical nonparametric density estimation, these rates depend on whether the densities in question are bounded below, even in the compactly supported case. Motivated by variational problems involving the Wasserstein distance, we also show how to construct discretely supported measures, suitable for computational purposes, which achieve the minimax rates. Our main technical tool is an inequality giving a nearly tight dual characterization of the Wasserstein distances in terms of Besov norms.

608-P: Intersectional Identities, Social Risk Factors, and Quality of Life in Men and Women with Diabetes

Background: More than 34 million adults ≥18 years have diabetes in the U.S. Adverse social conditions associated with poor health, or social risk factors, contribute to poorer access to care, quality of care, and clinical outcomes for adults with diabetes. Intersectionality, defined as having multiple marginalized/disadvantaged social identities that reflect larger-scale inequalities and systems of oppression, may contribute to diabetes disparities. The study objective was to assess differences in the relationship between intersectionality and social risk factors between men and women with diabetes. Methods: Data of 2,476 adults (weighted sample = 1,144,573) from the Behavioral Risk Factor Surveillance System (BRFSS) were analyzed. The outcomes were physical and mental health quality of life, and the predictor was intersectionality summed as Non-Hispanic White, age ≥65 years, unemployment, less than high school education, and income &amp;lt;$25K. Covariates included comorbidities, insurance, region, and metropolitan statistical area. Logistic regression models were found for each of the outcomes to assess their independent association with intersectionality. Results: In the fully adjusted model, women with 3+ intersecting social identities were 4x more likely to have poorer physical health (OR=4.13 (95% CI 0.54, 7.73)) compared to women reporting no intersectionality. Men with food insecurity were 5x more likely to have poorer mental health (OR=4.93, 95% CI 1.02, 8.84) than men without food insecurity, whereas women with food insecurity were 3x more likely to have poorer mental health (OR=2.56, 95% CI 0.61, 4.51) compared to women without food insecurity. There were no significant associations between intersectionality and housing instability in this sample. Conclusions: These findings suggest the need for understanding the influence of intersectionality among women with diabetes and social risk factors, especially food insecurity, among men and women with diabetes. Disclosure J.S.Williams: None. A.Thorgerson: None. M.N.Ozieh: None. L.E.Egede: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (K24DK093699, R01DK118038, R01DK120861 and R21DK123720)

Does a municipality's wellness affect its business growth?

This study focuses on the health of a given population to determine if health indicators can be used as a predictor of business growth in a geographic region. A study that was focused on the wellness in a firm, concluded: "that firms can increase operational productivity through socially responsible health policies that improve both workers' wellness and economic value" (Gubner et.al., 2018). That study was typical of most papers on wellness. It analyzed the impact of a corporate wellness program on worker productivity. While interesting, it was severely limited in scope as it was based on only one firm. Most studies that examined the factors that contribute to growth in major areas are confined largely to cities and cite factors that do not include wellness. The factors most often mentioned are a city's: (i) natural resources surplus; (ii) industrialization and commercialization; (iii) development of transport and communication; (iv) economic pull; and (v) educational and recreational facilities. Other factors mentioned include a well-developed infrastructure, good quality, available human capital, and a decent ecosystem. Wellness is normally absent as a factor or is rarely mentioned. This study is different. It is focused on the role that wellness could play in the economic growth of a major area in the country. That major area is a Municipal Statistical Area (MSA) in the United States of America (USA). An MSA is not limited to a city, but may extend from one major city to several adjoining industrial areas in close proximity. Thus, this study takes a much more robust, macro-oriented approach. The research used multiple-regression modeling. It determined that there may well exist a positive relationship between the wellness of an MSA and the percent increase in business establishments in that MSA. The yearly percent increase in business establishments in an MSA was used as the response variable i.e., the business growth in an MSA. Two control variables were necessarily employed in the study to improve accuracy. The first was whether the MSA was in a Right-to-Work (RTW) state. The second was if the MSA existed in the Rust Belt (RB) area of the USA. A second research portion of this study used different statistical methods (ANOVA and MANOVA) to examine the impact of wellness on business growth and two additional explanatory variables (i.e., unemployment and annual salaries in an MSA). This analysis validated that increased MSA wellness is a driver variable that increases business growth and leads to lower unemployment and higher annual salaries in an MSA.

Cervical cancer geographical burden analyzer: An interactive, open-access tool for understanding geographical disease burden in patients with recurrent or metastatic cervical cancer.

5523 Background: Examining geographic distributions of high and low proportions of recurrent or metastatic cervical cancer (r/mCC) can identify regions with high need of intervention and advance our understanding of r/mCC community-specific risk factors. Previous studies of epidemiologic or geographic clusters in late-stage cervical cancer were conducted prior to the availability of modern treatments for r/mCC. Due to the rapidly evolving treatment landscape for r/mCC use of commercial claims is an important way to follow dynamic practice changes. Our study objective was to understand recent geographical disparities among r/mCC patients in the US. Methods: We developed a web-based tool, Cervical Cancer Geographical Disease Burden Analyzer, that allows users to quantify r/mCC disease burden across metropolitan statistical areas (MSA) over multiple years. The inputs to the web-based tool were extracted through a retrospective analysis of the 2015-2020 MarketScan commercial claims database using a previously validated methodology. We then calculated the r/mCC rate as the ratio of r/mCC cases over cervical cancer (CC) cases for each of the &gt;400 MSA considered. The calculated MSA-specific r/mCC rate was visualized in an interactive US map,. The online interactive tool allowed the users to filter the results by year, age group, and minimum number of diagnoses at the MSA level. Results: Our findings showed that there is large variation with respect to r/mCC disease burden across MSA in the US, with a range of 0-45%. The top five MSA with the highest r/mCC rates from 2018-2020 are presented in the table. In particular, r/mCC rates in Sacremento-Roseville-Arden-Arcade (SRAA), CA and Boston-Cambridge-Newton (BCN), MA were on an increasing trajectory (33% in 2018 to 50% in 2020 in SRAA, CA; and from 41% in 2018 to 50% in 2020 in BCN, MA). On the other hand, while disease burden remained high, we observed decreasing r/mCC rates in Grand Rapids, MI (55% in 2018 and 31% in 2020, resulting in an average r/mCC rate of 42%). Finally, in Cape Coral-Fort Myers, FL and Baltimore-Columbia-Towson, MD the r/mCC rates have fluctuated across time but were consistently &gt;30%. Conclusions: Our web-based online interactive tool can help with identifying areas with high need of intervention, and inform how access, prevention and new or emerging therapies may potentially change the distribution of r/mCC disease burden. To better understand underlying drivers of geographic disparities observed, further efforts should be targeted towards identifying local-level factors. [Table: see text]