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Problem Asthma is a chronic disease of the airways that requires ongoing medical management. Socioeconomic and demographic factors as well as health care use might influence health patterns in urban and rural areas. Persons living in rural areas tend to have less access to health care and health resources and worse health outcomes. Characterizing asthma indicators (i.e., prevalence of current asthma, asthma attacks, emergency department and urgent care center [ED/UCC] visits, and asthma-associated deaths) and determining how asthma exacerbations and health care use vary across the United States by geographic area, including differences between urban and rural areas, and by sociodemographic factors can help identify subpopulations at risk for asthma-related complications. Reporting Period 2006–2018. Description of System The National Health Interview Survey (NHIS) is an annual cross-sectional household health survey among the civilian noninstitutionalized population in the United States. NHIS data were used to produce estimates for current asthma and among them, asthma attacks and ED/UCC visits. National Vital Statistics System (NVSS) data were used to estimate asthma deaths. Estimates of current asthma, asthma attacks, ED/UCC visits, and asthma mortality rates are described by demographic characteristics, poverty level (except for deaths), and geographic area for 2016–2018. Trends in asthma indicators by metropolitan statistical area (MSA) category for 2006–2018 were determined. Current asthma and asthma attack prevalence are provided by MSA category and state for 2016–2018. Detailed urban-rural classifications (six levels) were determined by merging 2013 National Center for Health Statistics (NCHS) urban-rural classification data with 2016–2018 NHIS data by county and state variables. All subregional estimates were accessed through the NCHS Research Data Center. Results Current asthma was higher among boys aged

BACKGROUND: Epidemiological data on childhood idiopathic nephrotic syndrome (INS) are limited. We estimated childhood INS incidence in a racially and ethnically diverse U.S. population and performed a meta-analysis of published reports to examine differences by race, ethnicity, and time. METHODS: One hundred seventy-five children aged 1–17 years living in the Atlanta Metropolitan Statistical Area (MSA) between 2013 and 2018 were identified by retrospective chart review. Annual INS incidence was estimated by dividing cases by population data from the Georgia Department of Public Health. We calculated pooled incidence estimates using random-effects regression models in a meta-analysis of the current and prior studies. Subgroup incidence estimates by race, ethnicity, and time were compared and tested for heterogeneity. RESULTS: One hundred seventy-five children aged 1–17 were diagnosed with INS between 2013 and 2018 in the Atlanta MSA. Average annual incidence was 2.13/100,000 (95% CI, 1.83–2.47). Twenty-four studies were included in meta-analysis. Our study was the only one to report incidence for Hispanic children, 2.13/100,000/y (95% CI, 1.40–3.10). In meta-analysis, incidence was highest in Asian children (7.14/100,000/y; 95% CI, 4.73–9.54), followed by Black (3.53/100,000/y; 95% CI, 2.93–4.12), and Caucasian (1.83/100,000/y; 95% CI, 1.52–2.14). Annual incidence in the U.S. was stable comparing studies performed before and after 1984, 2.05 vs. 2.26/100,000 (p 0.08). CONCLUSIONS: Risk of INS may be higher among Asian and Black children compared to White children. Incidence appears stable over time in the U.S. Future studies should use standardized methodology and assess the contribution of demographic and genetic factors to INS incidence and long-term outcomes.

OBJECTIVES Self-insured employers cover more people than Medicare, Medicaid, or direct purchasers of private insurance.This study examined the ability of self-insured employers to negotiate hospital prices and the relationship between hospital prices and employer market power in the United States. STUDY DESIGN Repeated cross-section analysis of commercial claims. METHODS We used the US Census Bureau County Business Patterns data to estimate employer market power at the metropolitan statistical area (MSA)-year level and used the Truven Health MarketScan commercial claims to estimate mean hospital prices and price ratios at the MSA-year level (2010-2016). We calculated descriptive statistics for employer market power, mean hospitalization prices, and a case mix-adjusted price ratio measure during the study period and analyzed the 10 most concentrated labor markets. We estimated MSA-year-level ordinary least squares regressions of hospitalization price and the price ratio measure on employer market power. RESULTS Large self-insured employers had concentrated market power in very few MSAs in 2016. The mean value of our employer market power measure was 62 for 2016, compared with the mean value of 5410 for hospital market power in the United States. Regression analyses find a slight relationship: A 1-point increase in employer market power was associated with a $6.61 decrease in the hospitalization price (mean = $20,813), but this result becomes statistically insignificant once the models control for hospital wages. CONCLUSIONS Employer market power is low in most MSAs. Self-insured employers may consider building purchase alliances with state and local government employee groups to enhance their market power and lower negotiated prices for hospital services.

BACKGROUND: In 2018, influenza and pneumonia was the eighth leading cause of death in the United States. Since 1950, non-Hispanic blacks (NHBs) have experienced higher rates of mortality than non-Hispanic whites (NHWs). Previous studies have revealed geographic variation in mortality rates by race. The identification of areas with the greatest disparity in influenza and pneumonia mortality may assist policymakers in the allocation of resources, including for the coronavirus disease 2019 pandemic. RESEARCH QUESTION: Does geographic variation in racial disparity in influenza and pneumonia mortality exist? STUDY DESIGN AND METHODS: The Centers for Disease Control and Prevention database for Multiple Cause of Death between 1999 and 2018 for NHB and NHW decedents ≥ 25 years of age with a Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems code for influenza (J09-J11) and pneumonia (J12-J18) was used. Age-adjusted mortality rates (AAMRs) with 95% CIs were computed by race for Health & Human Services (HHS) regions and urbanization in NHBs and NHWs. RESULTS: In 1999 through 2018, there were 540,476 deaths among NHBs and NHWs 25 to 84 years of age. AAMRs were higher in NHBs than NHWs in each age group and in seven of 10 HHS regions. The greatest disparity was in HHS regions 2 (New York and New Jersey) and 9 (Arizona, California, Hawaii, and Nevada). In HHS region 2, NHBs (24.6; 95% CI, 24.1-25.1) were more likely to die than NHWs (15.7; 95% CI, 15.6-15.9). Similarly, in region 9, NHBs (23.2; 95% CI, 22.7-23.8) had higher mortality than NHWs (16.1; 95% CI, 15.9-16.2). Within these regions, disparities were greatest in the core of major metropolitan areas. A very high AAMR in NHBs was noted in large, central metropolitan areas of region 2: 28.2 (95% CI, 27.6-28.9). INTERPRETATION: In 1999 through 2018, the NHB-NHW disparity in AAMRs from influenza and pneumonia was greatest in central metropolitan areas of HHS regions 2 and 9.

Introduction Vaccination helps to prevent influenza infection and reduce associated costs but the influenza vaccination rate in Texas for adults between the ages of 18 to 64 years old is the lowest in the US. Pharmacies and alternative locations have been shown to help increase vaccination rates but many adults still go unvaccinated. Objective This research aims to determine the factors associated with obtaining influenza vaccination at the pharmacy compared to non-pharmacy locations in Texas. Method This study used pooled Texas Behavior Risk Factor Surveillance System datasets (2014 to 2018) for this assessment. The main outcome variable was categorized into pharmacy and non-pharmacy vaccination locations and analyzed using a logistic regression analysis. Further statistical analysis was done using a multinomial logistic regression after re-categorizing the outcome variable into pharmacy, doctor office, and other locations. Result Blacks were 63% (AOR 0.37, C.I. 0.26, 0.50) and Hispanics were 38% (AOR 0.62, C.I. 0.48, 0.80) less likely to receive influenza vaccinations at the pharmacy respectively when compared to Whites. Furthermore, those who did not live in a Metropolitan Statistical Area (MSA) were 33% (AOR 0.67, C.I 0.53, 0.84) less likely to receive influenza vaccinations at the pharmacy compared to those who lived in an MSA. While there was no observed difference in the likelihood of receiving influenza vaccination, the unemployed population were 40% (AOR 1.40, C.I 1.15, 1.71) more likely to be vaccinated in the pharmacy compared to the employed population. Conclusion There is potential for increased utilization of pharmacies as a source of influenza vaccination in Texas. Racial differences exist both for receiving influenza vaccinations and being vaccinated in the pharmacy. Influenza vaccination advocacy and education efforts may be necessary to improve pharmacy-based vaccination in Texas, especially for minorities and rural-dwelling Texans.

This study examines the intersection between residential mobility and housing consumption for seniors who migrate inward from the suburbs of the Baltimore metropolitan statistical area (MSA), with ...

Using MSA-level data over 1994–2014, we study the effect of bank failures on local business dynamics, in the form of net business formation and net job creation. We find that at least one bank failure in the metropolitan statistical area (MSA) with the mean population prevents approximately 475 net businesses from forming in that area, compared with MSAs that experience no bank failures, ceteris paribus. The equivalent effect on net job creation is 16,433 net job losses. Our results are even stronger for small businesses, which are usually more dependent on bank-firm relationships. These effects point to significant welfare losses stemming from bank failures, highlighting an important role for government intervention. We show that the Troubled Asset Relief Program (TARP) is effective in reducing the negative effects of bank failures on local business dynamics. This positive effect of TARP is quite uniform across small and large firms.

The role of transportation network companies (TNCs) in the urban transport system is under intense debate. In this study, we systematically assess three aspects of the net impacts of TNCs on urban mobility in the United States—road congestion, transit ridership and private vehicle ownership—and examine how these impacts have evolved over time. Based on a set of fixed-effect panel models estimated using metropolitan statistical area level data, we find that the entrance of TNCs led to increased road congestion in terms of both intensity (by 0.9%) and duration (by 4.5%), an 8.9% decline in transit ridership and an insignificant change in vehicle ownership. Despite the ideal of providing a sustainable mobility solution by promoting large-scale car sharing, our analysis suggests that TNCs have intensified urban transport challenges since their debut in the United States. The use and growth of ride-sharing apps in recent years has changed urban transportation patterns. This article examines their impacts on congestion, mass transit ridership and private car ownership in North American cities.

Objective: Studies assessing sociodemo­graphic disparities in the tobacco retail envi­ronment have relied heavily on non-spatial analytical techniques, resulting in potentially misleading conclusions. We utilized a spatial analytical framework to evaluate neighbor­hood sociodemographic disparities in the tobacco retail environment in Washington, DC (DC) and the DC metropolitan statistical area (DC MSA). Methods: Retail tobacco availability for DC (n=177) and DC MSA (n=1,428) census tract was assessed using adaptive-bandwidth kernel density estimation. Density surfaces were constructed from DC (n=743) and DC MSA (n=4,539) geocoded tobacco retailers. Sociodemographics were obtained from the 2011-2015 American Community Survey. Spearman’s correlations between sociodemographics and retail density were computed to account for spatial autocorre­lation. Bivariate and multivariate spatial lag models were fit to predict retail density. Results: DC and DC MSA neighborhoods with a higher percentage of Hispanics were positively correlated with retail density (rho = .3392, P = .0001 and rho = .1191, P = .0000, respectively). DC neighbor­hoods with a higher percentage of African Americans were negatively correlated with retail density (rho = -.3774, P = .0000). This pattern was not significant in DC MSA neighborhoods. Bivariate and multivariate spatial lag models found a significant inverse relationship between the percentage of African Americans and retail density (Beta = -.0133, P = .0181 and Beta = -.0165, P = .0307, respectively). Conclusions: Associations between neighborhood sociodemographics and retail density were significant, although findings regarding African Americans are inconsistent with previous findings. Future studies should analyze other geographic areas, and account for spatial autocorrela­tion within their analytic framework. Ethn Dis. 2020;30(3):479-488; doi:10.18865/ed.30.3.479

Purpose This paper aims to use statistical analyses to understand the trends on housing liquidity and pricing by accounting for macroeconomic factors that vary at national level and at metropolitan statistical area level for all metropolitan statistical areas in Texas. In addition, the authors test for seasonality in all the metropolitan statistical areas in Texas. Design/methodology/approach Using publicly available data from Zillow a listing website, the authors conduct an analysis of all housing markets at metropolitan statistical area level in Texas to understand the factors that drive the liquidity and pricing. The authors use two measures for liquidity, namely, time to sell the house and sales to list ratio. The authors also try to understand the decision to lower the price of the listed houses. In addition, the authors conduct a test for seasonality within the year in these housing markets. Findings The analyses conclude that there is a significant impact of listing prices, unemployment rates, 30-year mortgage rates, consumer sentiment and oil price changes on the liquidity of the housing markets and decisions of sellers to adjust the prices down. In addition, the authors provide evidence of the existence of seasonality in most metropolitan statistical areas in Texas both for pricing and volume of transactions. Originality/value This is the first study to look at housing liquidity and pricing trends for about 25 markets in Texas. In addition, the authors provide evidence of the importance of oil prices for the housing markets in Texas metropolitan statistical areas.

Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs. Research limitations/implications This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates. Practical implications This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences. Originality/value This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.

Objective:When 2017 Hurricane Harvey struck the coastline of Texas on August 25, 2017, it resulted in 88 fatalities and more than US $125 billion in damage to infrastructure. The floods associated with the storm created a toxic mix of chemicals, sewage and other biohazards, and over 6 million cubic meters of garbage in Houston alone. The level of biohazard exposure and injuries from trauma among persons residing in affected areas was widespread and likely contributed to increases in emergency department (ED) visits in Houston and cities receiving hurricane evacuees. We investigated medical surge resulting from these evacuations in Dallas–Fort Worth (DFW) metroplex EDs.Methods:We used data sourced from the North Texas Syndromic Surveillance Region 2/3 in ESSENCE to investigate ED visit surge following the storm in DFW hospitals because this area received evacuees from the 60 counties with disaster declarations due to the storm. We used the interrupted time series (ITS) analysis to estimate the magnitude and duration of the ED surge. ITS was applied to all ED visits in DFW and visits made by patients residing in any of the 60 counties with disaster declarations due to the storm. The DFW metropolitan statistical area included 55 hospitals. Time series analyses examined data from March 1, 2017–January 6, 2018 with focus on the storm impact period, August 14–September 15, 2017. Data from before, during, and after the storm were visualized spatially and temporally to characterize magnitude, duration, and spatial variation of medical surge attributable to Hurricane Harvey.Results:During the study period overall, ED visits in the DFW area rose immediately by about 11% (95% CI: 9%, 13%), amounting to ~16 500 excess total visits before returning to the baseline on September 21, 2017. Visits by patients identified as residing in disaster declaration counties to DFW hospitals rose immediately by 127% (95% CI: 125%, 129%), amounting to 654 excess visits by September 29, 2017, when visits returned to the baseline. A spatial analysis revealed that evacuated patients were strongly clustered (Moran’s I = 0.35, P < 0.0001) among 5 of the counties with disaster declarations in the 11-day window during the storm surge.Conclusions:The observed increase in ED visits in DFW due to Hurricane Harvey and ensuing evacuation was significant. Anticipating medical surge following large-scale hurricanes is critical for community preparedness planning. Coordinated planning across stakeholders is necessary to safeguard the population and for a skillful response to medical surge needs. Plans that address hurricane response, in particular, should have contingencies for support beyond the expected disaster areas.

Evacuation is frequently used by emergency managers and other officials as part of an overall approach to reducing the morbidity and mortality associated with hurricane landfall. In this study, the evacuation shelter capacity of the Houston-Galveston Metropolitan Statistical Area (MSA) was spatially assessed and shelter deficits in the region were estimated. These data provide essential information needed to eliminate shelter deficits and ensure a successful evacuation from a future storm. Spatial statistical methods-Global Moran's I, Anselin Local Moran's I (Local Indicators of Spatial Association [LISA]), and Hot Spot Analysis (Getis-Ord Gi*) were used to assess for regional spatial autocorrelation and clustering of evacuation shelters in the Houston-Galveston MSA. Shelter deficits were estimated in four ways-the aggregate deficit for the Houston-Galveston MSA, by evacuation Zip-Zone, by county, and by distance or radii of evacuation Zip-Zone. Evacuation shelters were disproportionately distributed in the region, with lower capacity shelters clustered closer to evacuation Zip-Zones (50 miles from the Coastal Zip-Zone), and higher capacity shelters clustered farther away from the zones (120 miles from the Coastal Zip-Zone). The aggregate shelter deficit for the Houston-Galveston MSA was 353,713 persons. To reduce morbidity and mortality associated with future hurricanes in the Houston-Galveston MSA, authorities should consider the development and implementation of policies that would improve the evacuation shelter capacity of the region. Eliminating shelter deficits, which has been done successfully in the state of Florida, is an essential element of protecting the public from hurricane impacts.