County Population Research Articles

Current patterns of trauma center proliferation have not led to proportionate improvements in access to care or mortality after injury: An ecologic study.

Timely access to high-level (I/II) trauma centers (HLTCs) is essential to minimize mortality after injury. Over the last 15 years, there has been a proliferation of HLTC nationally. The current study evaluates the impact of additional HLTC on population access and injury mortality. A geocoded list of HLTC, with year designated, was obtained from the American Trauma Society, and 60-minute travel time polygons were created using OpenStreetMap data. Census block group population centroids, county population centroids, and American Communities Survey data from 2005 and 2020 were integrated. Age-adjusted nonoverdose injury mortality was obtained from CDC Wide-ranging Online Data for Epidemiologic Research and the Robert Wood Johnson Foundation. Geographically weighted regression models were used to identify independent predictors of HLTC access and injury mortality. Over the 15-year (2005-2020) study period, the number of HLTC increased by 31.0% (445 to 583), while population access to HLTC increased by 6.9% (77.5-84.4%). Despite this increase, access was unchanged in 83.1% of counties, with a median change in access of 0.0% (interquartile range, 0.0-1.1%). Population-level age-adjusted injury mortality rates increased by 5.39 per 100,000 population during this time (60.72 to 66.11 per 100,000). Geographically weighted regression controlling for population demography and health indicators found higher median income and higher population density to be positively associated with majority (≥50%) HLTC population coverage and negatively associated with county-level nonoverdose mortality. Over the past 15 years, the number of HLTC increased 31%, while population access to HLTC increased only 6.9%. High-level (I/II) trauma center designation is likely driven by factors other than population need. To optimize efficiency and decrease potential oversupply, the designation process should include population level metrics. Geographic information system methodology can be an effective tool to assess optimal placement. Prognostic and Epidemiological; Level IV.

Increasing demand for ophthalmic pathology: time trends in a laboratory with nationwide coverage

PurposeTo report the time trends in basic patient characteristics and the number of specimens received at a national referral center for ophthalmic pathology.MethodsData on patient sex, age at surgical resection and geographical location of the referring unit were obtained for all specimens received at the St. Erik Ophthalmic Pathology laboratory, Stockholm, Sweden, between January 1st, 1959, and December 31st, 2021.ResultsA total of 33 057 specimens had been received, of which 14 560 (44%) came from men and 18 477 (56%) from women (for 20 patients, the sex was not specified). The average annual percent change (AAPC) in the number specimens received was + 10.5%, whereas the Swedish population increased with 0.5% per year. Patients became older throughout the period, with an average yearly increase of patient age at surgery of 0.3 years (AAPC 0.2%). Overall, women were three years older than men at surgery (59.4 versus 56.4 years, P < 0.0001) The number of specimens increased with patient age from the first to the 8th decade, after which it decreased to zero in the 11th decade. The largest portion of patients had undergone their surgery in one of the hospitals or clinics in the capital region, with four of the five largest sources corresponding to the most populous counties in the country.ConclusionsDuring six decades, the growth in number of specimens sent to our national referral center for ophthalmic pathology has greatly outpaced the growth of the population, indicating an increasing demand for subspecialized services. Throughout the period, patients have become older, and a higher number of specimens have been submitted from female patients.

The effect of changing COVID-19 restrictions on the transmission rate in a veterinary clinic

With the declaration of the COVID-19 pandemic by the World Health Organization on March 11, 2020, the University of Tennessee College of Veterinary Medicine (UTCVM), like other institutions, restructured their services to reduce the potential spread of the COVID-19 virus while simultaneously providing critical and essential veterinary services. A mathematical model was developed to predict the change in the level of possible COVID-19 infections due to the increased number of potential contacts within the UTCVM hospital. A system of ordinary differential equations with different compartments for UTCVM individuals and the Knox county population was formulated to show the dynamics of transmission and the number of confirmed cases. Key transmission rates in the model were estimated using COVID-19 case data from the surrounding county and UTCVM personnel. Simulations from this model show the increasing number of COVID-19 cases among UTCVM personnel as the number of daily clients and the number of veterinary staff in the clinic are increased. We also investigate how changes within the Knox county community impact the UTCVM hospital. These scenarios show the importance of understanding the effects of re-opening scenarios in veterinary teaching hospitals.

Effect of cities and distance on COVID-19 spreading in the United States

The COVID-19 pandemic has evolved over time through multiple spatial and temporal dynamics. The varying extent of interactions among different geographical areas can result in a complex pattern of spreading so that influences between these areas can be hard to discern. Here, we use cross-correlation analysis to detect synchronous evolution and potential interinfluences in the time evolution of new COVID-19 cases at the county level in the United States. Our analysis identified two main time periods with distinguishable features in the behavior of correlations. In the first phase, there were few strong correlations that only emerged between urban areas. In the second phase of the epidemic, strong correlations became widespread and there was a clear directionality of influence from urban-to-rural areas. In general, the effect of distance between two counties was much weaker than that of the counties' population. Such analysis can provide possible clues on the evolution of the disease and may identify parts of the country where intervention may be more efficient in limiting the disease spread.

Association of Variation in US County-Level Rates of Liver Surgical Resection for Colorectal Liver Metastasis With Poverty Rates in 2010

Among patients with colorectal liver metastasis (CRLM) who are eligible for curative-intent liver surgical resection, only half undergo liver metastasectomy. It is currently unclear how rates of liver metastasectomy vary geographically in the US. Geographic differences in county-level socioeconomic characteristics may, in part, explain variability in the receipt of liver metastasectomy for CRLM. To describe county-level variation in the receipt of liver metastasectomy for CRLM in the US and its association with poverty rates. This ecological, cross-sectional, and county-level analysis was conducted using data from the Surveillance, Epidemiology, and End Results Research Plus database. The study included the county-level proportion of patients who had colorectal adenocarcinoma diagnosed between January 1, 2010, and December 31, 2018, underwent primary surgical resection, and had liver metastasis without extrahepatic metastasis. The county-level proportion of patients with stage I colorectal cancer (CRC) was used as a comparator. Data analysis was performed on March 2, 2022. County-level poverty in 2010 obtained from the US Census (proportion of county population below the federal poverty level). The primary outcome was county-level odds of liver metastasectomy for CRLM. The comparator outcome was county-level odds of surgical resection for stage I CRC. Multivariable binomial logistic regression accounting for clustering of outcomes within a county via an overdispersion parameter was used to estimate the county-level odds of receiving a liver metastasectomy for CRLM associated with a 10% increase in poverty rate. In the 194 US counties included in this study, there were 11 348 patients. At the county level, the majority of the population was male (mean [SD], 56.9% [10.2%]), White (71.9% [20.0%]), and aged between 50 and 64 (38.1% [11.0%]) or 65 and 79 (33.6% [11.4%]) years. The adjusted odds of undergoing a liver metastasectomy was lower in counties with higher poverty in 2010 (per 10% increase; odds ratio, 0.82 [95% CI, 0.69-0.96]; P = .02). County-level poverty was not associated with receipt of surgery for stage I CRC. Despite the difference in rates of surgery (mean county-level rates were 0.24 for liver metastasectomy for CRLM and 0.75 for surgery for stage I CRC), the variance at the county-level for these 2 surgical procedures was similar (F370, 193 = 0.81; P = .08). The findings of this study suggest that higher poverty was associated with lower receipt of liver metastasectomy among US patients with CRLM. Surgery for a more common and less complex cancer comparator (ie, stage I CRC) was not observed to be associated with county-level poverty rates. However, county-level variation in surgical rates was similar for CRLM and stage I CRC. These findings further suggest that access to surgical care for complex gastrointestinal cancers such as CRLM may be partially influenced by where patients live.

Population in floodplains or close to sea level increased in US but declined in some counties—especially among Black residents

Previous studies estimating US population vulnerable to climate change have assumed that population is uniformly distributed within the geographical unit of analysis, typically a census block. That assumption overstates vulnerable population in places where people have avoided the most hazardous areas. After using independent samples of housing location and building footprints to validate a revised approach, this letter estimates the US population close to sea level and in floodplains based on the assumption that population in a hazard zone is proportional to the fraction of buildings in the hazard zone, for the period 1990–2020. The building-based assumption reduces population estimates about 30%. Over the 30-year period, the US population below 1 m increased 14%–18% from 1.85 (±0.19) million in 1990 to 2.2 (±0.25) million; population below 3 m increased 31%. Sea level rise accounts for 75% of the increase in population below 1 m, but only 14% of the increase below 3 m. Nevertheless, in 21 counties, net emigration from land below 1 m was greater than 1% of county population. Although this emigration was particularly great in the 2000s after Hurricane Katrina, it totaled 170 000 for the entire 30-year period. Similarly, the US population of inland 100-year floodplains increased 7%, but several hundred counties saw a steady emigration throughout the 30-year period which totaled about 240 000. Black residents accounted for 19% of the population below 1 m but only 12% of the nation’s population in 2020, meaning that Black residents were 63% more likely to live below 1 m than the general population nationwide. This disproportionality is more attributable to high Black populations in the most vulnerable localities (especially New Orleans) than to historic segregation. Black people also are almost five times as likely to have emigrated from land below 1 m than the general population since 1990.

Non-pharmaceutical Interventions and COVID-19: Do County- and State-Level Policies Predict the Spread of COVID-19?

This study examines the impact of county- and state-level policies on the spread and severity of COVID-19 in communities in the USA during the first wave of the COVID-19 pandemic. We use county-level COVID-19 death and case data to examine the impact of county- and state-level mandates and non-pharmaceutical interventions (NPIs) on the spread and severity of COVID-19. Following previous work by Amuendo-Dorantes et al. (2020), we utilize a strategy that incorporates the duration of NPI implementation within a county. Specifically, we examine aggregated measures of mask mandates, daycare closures, stay-at-home orders, and restaurant and bar closures. In addition to the implementation and duration of NPI policy, we examine the role of pre-existing factors that contribute to social determinants of health in a locality. We incorporate information on the incidence of prior health conditions, socio-economic factors, and demographics including racial and ethnic composition, share of immigrant population of counties, and state governance in our estimations. To alleviate the possible endogeneity of COVID-19 outcomes and NPIs, we use instrumental variable estimation and our results show that collectively NPIs decreased the intensity of the pandemic by decreasing the total deaths and cases. Furthermore, we find the magnitude of the impact of NPIs increases the longer they are implemented. We also estimate a specification that allows for heterogeneity of NPI impact based on the racial and ethnic composition of counties. Our results suggest that NPIs have a non-uniform impact in counties with different racial and ethnic compositions.

Confirmation and detection of novel acetolactate synthase- and protoporphyrinogen oxidase–inhibiting herbicide-resistant redroot pigweed (Amaranthus retroflexus) populations in North Carolina

AbstractComplaints of control failures with acetolactate synthase (ALS)- and protoporphyrinogen oxidase (PPO)-inhibiting herbicides on redroot pigweed (Amaranthus retroflexus L.) were reported in conventional soybean [Glycine max (L.) Merr.] fields in North Carolina. Greenhouse dose–response assays confirmed that the Camden County and Pasquotank County populations were less sensitive to ALS- and PPO-inhibiting herbicides compared with susceptible A. retroflexus populations, suggesting the evolution of resistance to these herbicides. Sanger sequencing of target genes determined the Camden County population carried a Trp-574-Leu mutation in the ALS gene and an Arg-98-Gly mutation in the PPX2 gene, while the Pasquotank County population carried a His-197-Pro mutation in the ALS gene (first documentation of the mutation in the Amaranthus genus), but no mutation was detected in the PPX2 gene. Single-nucleotide polymorphism (SNP) genotyping assays were developed to enable efficient screening of future control failures in order to limit the spread of these herbicide-resistant populations. In addition, preliminary testing of these assays revealed the three mutations were ubiquitous in the respective populations. These two populations represent the first confirmed cases of PPO-inhibiting herbicide-resistant A. retroflexus in the United States, as well as the first confirmed cases of this particular herbicide-resistance profile in A. retroflexus inhabiting North America. While no mutation was found in the PPX2 gene of the Pasquotank County population, we suggest that this population has evolved resistance to PPO-inhibiting herbicides, but the mechanism of resistance is to be determined.

Storm Surge Risk Assessment in Coastal Communities in the Rio Grande Valley: An Application of GIS-Based Spatial Multicriteria Decision Analysis with Analytical Hierarchy Process

Kyne, D., 2023. Storm surge risk assessment in coastal communities in the Rio Grande Valley: An application of GIS-based spatial multicriteria decision analysis with analytical hierarchy process. Journal of Coastal Research, 39(3), 471–483. Charlotte (North Carolina), ISSN 0749-0208. Cameron County, which is located in the Rio Grande Valley, maintains records of storm surges associated with noticeable property damage, fatalities, and injuries. This study investigates storm surge inundation risk in Cameron County using storm surge hazard datasets from the National Oceanic and Atmospheric Administration, along with American Community Survey 2019 block group datasets. Using a GIS-based spatial multicriteria decision analysis with an analytical hierarchy process method, the study estimates that storm surge water levels could be above 6.1 m (20 ft) in category 4 and 5 hurricane events, whereas about 37% of the county's population (159,659 people) could be exposed to floodwater in the event of a category 5 hurricane. The inundation risk map shows that in the event of a category 4 or 5 hurricane, about 28 and 42%, respectively, of county's area could be exposed to a very high level of inundation risk. Because of the destructive power of such storms and their potential consequences, the findings suggest that storm surge risk should not be underestimated. All stakeholders should proactively prepare for potential storm surge events.

Habitat value of constructed breeding pools for the endangered Sonoma population of California tiger salamander

AbstractHabitat creation is a common element of efforts to recover populations threatened by altered habitat. Unfortunately, human‐created habitats do not necessarily support re‐establishment of target species. We investigated the value of constructed vernal pools as breeding habitat for the endangered Sonoma County population of California tiger salamanders (Ambystoma californiense) by conducting 19 years of larval surveys in 118 pools that were natural (n = 40), constructed (n = 70), or natural with substantial human alterations (human‐altered, n = 8). California tiger salamander larval densities in human‐altered and natural pools declined over the study period, while larval densities in constructed pools showed non‐negative trends. Larval declines in natural pools may be attributed to long‐term degradation of the surrounding terrestrial habitat. Mean larval densities over the course of the study were equivalent between constructed and natural pools, when corrected for pool morphometry. In contrast, human‐altered pools had lower larval densities. To assure consistent California tiger salamander breeding, pools needed to be ≥30 cm deep and 240 m2 in area, with deeper (50–70 cm) and larger (1,600–1,800 m2) pools providing increased larval productivity. Over the study, there was no evidence of decrease in the rate of colonization of new pools. We conclude that constructed pools can be important tools in the recovery of imperiled pool‐breeding amphibians if proper design elements for the target species are considered.

Estimating County Level Health Indicators Using Spatial Microsimulation.

Given the importance of understanding health outcomes at fine spatial scales, iterative proportional fitting (IPF), a form of small area estimation, was applied to a fixed number of health-related variables (obesity, overweight, diabetes) taken from regionalized 2019 survey responses (n = 5474) from the Idaho Behavioral Risk Factor Surveillance System (BRFSS). Using associated county-level American Community Survey (ACS) census data, a set of constraints, which included age categorization, race, sex, and education level, were used to create county-level weighting matrices for each variable, for each of the seven (7) Idaho public health districts. Using an optimized modeling construction technique, we identified significant constraints and grouping splits for each variable/region, resulting in estimates that were internally and externally validated. Externally validated model results for the most populated counties showed correlations ranging from .79 to .85, with p values all below .05. Estimates indicated higher levels of obesity and overweight individuals for midsouth and southwestern Idaho counties, with a cluster of higher diabetes estimates in the center of the state (Gooding, Lincoln, Minidoka, and Jerome counties). Alternative external sources for health outcomes aligned extremely well with our estimates, with wider confidence intervals in more rural counties with sparse populations.

An Analysis of Indiana Youth Group Demographics

This study examines the demographics of the youth attending Indiana Youth Group (IYG) programs and how the demographics of the IYG attendees compare to youth in Marion County, Indiana at large. Indiana Youth Group is a center based in Indianapolis that serves LGBTQ+ youth ages 12-24 that creates safer spaces to build community and offers harm-reduction programs that empower youth to build confidence, explore their identities, and foster friendships. IYG provides a wide array of programs free of charge, including basic needs, support groups, arts and crafts, sexual health education, STI/HIV testing, case management, mental health counseling, and affinity groups to lessen the impact of stressors most faced by LGBTQ+ youth and increase self-efficacy. Based on our analysis of demographic data from the 2020 IYG Annual Report and the 2020 Indiana Youth Institute (IYI) County Snapshots, we found that IYG attendees are less diverse than the greater Marion County youth population in terms of race in 2020. IYG serves a higher proportion of white youth and a lower proportion of Black and Hispanic/Latinx compared to the Marion County population. Our study proposes the following strategies to broaden outreach to racially minoritized populations: identify and build relationships with trusted community-based organizations (CBOs) within Black, Indigenous, and People of Color (BIPOC) communities, invest in health education and health literacy, and increase language access by conveying program information through trusted community messengers. Organizational changes could include cultural competency and social justice training for staff that specifically focuses on the intersection between systemic racism and LGBTQ+ oppression.

Impact of the V410L kdr mutation and co-occurring genotypes at kdr sites 1016 and 1534 in the VGSC on the probability of survival of the mosquito Aedes aegypti (L.) to Permanone in Harris County, TX, USA.

Harris County, TX, is the third most populous county in the USA and upon detection of arboviruses Harris County Public Health applies insecticides (e.g., pyrethroid-based Permanone 31-66) against adults of Culex quinquefasciatus to prevent disease transmission. Populations of Aedes aegypti, while not yet a target of public health control, are likely affected by pyrethroid exposure. As this species is a vector of emerging arboviruses, its resistance status to Permanone and the kdr mutations in the voltage-gated sodium channel (VGSC) associated with pyrethroid resistance were investigated. We examined females of known genotype at the V1016I and F1534C sites (N = 716) for their genotype at the 410 amino acid position in the VGSC, and for the influence of their kdr genotype on survival to Permanone at three different distances from the insecticide source in field tests. Most females (81.8%) had at least one resistant L allele at the 410 position, being the first report of the V410L mutation in Ae. aegypti for Texas. When only genotypes at the 410 position were analyzed, the LL genotype exhibited higher survivorship than VL or VV. Out of 27 possible tri-locus kdr genotypes only 23 were found. Analyses of the probability of survival of tri-locus genotypes and for the V410L genotype using a multivariate logistic regression model including area, distance, and genotype found significant interactions between distance and genotype. When only the most common tri-locus genotypes were analyzed (LL/II/CC, 48.2%; VL/II/CC, 19.1%; and VV/II/CC, 10.1%) genotype had no effect on survival, but significant interactions of distance and genotype were found. This indicated that the V410L kdr allele increased survival probability at certain distances. Genotypes did not differ in survivorship at 7.62-m, but LL/II/CC had higher survivorship than VL/II/CC at 15.24- and 22.86-m. The model also identified differences in survivorship among the operational areas investigated.

Population-level predictors of sexually transmitted infection rate changes in Missouri: an ecological study

BackgroundSexually transmitted infection rates continue to increase across the US, further developing health disparities and economic burdens of disease, especially as migration occurs. In this study, we aim to assess the relationship between STI rates and population-level variables from 2008 to 2017 at the county level in Missouri.MethodsTwo data sources were used: STI rates of chlamydia, gonorrhea, syphilis, HIV reported to Missouri DHSS and ACS 1-year county population estimates. Linear regression models and ANOVA tests were conducted in SPSS for each STI from year-to-year and 2008–2017. Covariates included in the analyzes were county-level income, employment rate, race, ethnicity, age, and percent poverty. Further, Akaike Information Criterion tests were performed to indicate the best predictor models and averaged standardized beta values.ResultsSignificant relationships among STI rates and population growth were identified. Chlamydia, syphilis, and HIV were positively associated with population growth from 2008 to 2017 (β = 0.15; β = 0.01; β = 0.05, respectively). Gonorrhea was negatively associated with population growth (β = − 0.02) but positively associated with unemployment rates (β = 0.01) highlighting the need to address population growth, as well as other variables in a population.ConclusionsThere seems to be a positive relationship among population change and rates of STIs. As populations change, rates of STIs change. Moving forward, quantitative work should be conducted in various states and the nation to understand this relationship in different contexts. Future studies should be qualitative word focused on county health departments and community health improvement plans. Lastly, public policy should be implemented to buffer the impact of migration on health outcomes.

Extreme temperatures, mortality, and adaptation: Evidence from the county level in China.

Extreme temperatures are known to cause adverse health outcomes. Yet knowledge on the magnitude of this effect in developing countries is limited due to data availability and reliability issues. Collecting data for 2872 counties in China, we estimate the effects of daily temperatures on the monthly mortality rate. The results indicate that an additional day for which the maximum temperature is 38°C or above on average increases the monthly mortality rate by about 1.7% relative to if that day's maximum temperature had been in the range 16-21°C. This is after deducting deaths harvested from the subsequent month. Higher gross domestic product per capita at the county level is associated with lower mortality effects of hot and cold days. Improved dwelling conditions are found to be associated with a lower mortality effect of hot days and improved local healthcare infrastructure to be associated with a lower mortality effect of cold days. In the absence of strong adaptation efforts, the estimates suggest net upward pressure on annual mortality rates over coming decades in many populous counties, especially under more extreme climate change scenarios.

Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776936 cases and 1362545 controls in the state of Indiana, USA.

To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. EHR data of 776936 COVID-19 cases and 1362545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

A spatial evaluation method for earthquake disaster using optimized BP neural network model

Rapid spatial evaluation of seismic disaster after earthquake occurrence is required in disaster emergency rescue management, because of its importance in decreasing casualties and property losses. Among many categories of seismic disaster, evaluation of earthquake-affected population is of great significance to clarify the severity of earthquake disaster. For simple classic regression model, it is difficult to describe the strong nonlinear relationship between multiple influencing factors and earthquake disasters. In present study, an optimized BP neural network model considering spatial characteristic of influencing factors is proposed to evaluate the population distribution affected by earthquake. The correlation between earthquake-affected population and influencing factors is analysed using data of 2013 Ms7.0 Lushan earthquake. Ten influencing factors including elevation, slope angle, population density, per capita GDP, distance to fault, distance to river, NDVI, PGA, PGV, and distance to the epicentre, were classified into environmental and seismic factors. Correlation analysis revealed that per capita GDP and PGA factor had a stronger correlation with the earthquake-affected population. The earthquake-affected population was evaluated using a BP neural network by optimizing training samples considering spatial characteristics of per capita GDP and PGA factors. Different numbers of sample points, instead of a random distribution of sample points, were generated in areas with different value intervals of the influencing factors. The optimized samples improved the convergence speed and generalization capability of neuron network compared to random samples. The trained network was applied to the 2017 Ms7.0 Jiuzhaigou earthquake to verify its prediction accuracy. The MAE of the estimated earthquake-affected populations of different counties under Jiuzhaigou earthquake were 1.276 people/km2 using network model from optimized samples, smaller than the results of network model from random samples and linear regression model. The results indicate that BP neural network, which considers correlation characteristics of factors, has capability to evaluate spatial earthquake disaster.

Surveying and localized screening of soil contamination with the growth of urban community agriculture in Northern Utah

AbstractUrban soil contamination has become an increasing concern within residential developments and with the expansion of urban agriculture, which has grown across the United States. In Salt Lake County, Utah, there are 39 community gardens and urban farms, with more in development. Only limited data exist to determine whether these soils may be considered contaminated. The purpose of this study was to conduct an urban soil survey in Salt Lake County for initial screening of soils in community gardens and urban farms for common contaminants, including the trace elements lead and arsenic (total concentrations), along with organic contaminant benzo (A) pyrene (BAP). Site selection included an emphasis on sites serving at least nine families and those with greater potential for soil contamination based on identified risk factors. Measured soil test concentrations were compared to US EPA regional screening levels (RSLs) and other US background levels. Lead concentrations ranged from 14.8 to 516 mg kg−1, with a mean concentration of 91.8 mg kg−1. Measured arsenic ranged from 2.83 to 39.3 mg kg−1 with a mean of 11.8 mg kg−1. All measured arsenic concentrations exceeded the US EPA RSL of 0.68 mg kg−1. BAP was the primary hydrocarbon of concern, and concentrations above detectable limits ranged from 0.01 to 0.44 mg kg−1, with a mean concentration of 0.09 mg kg−1. By determining the baseline degree of soil contamination in key urban agricultural sites in Utah's most populated county, local screening levels were developed into soil management plans for producers.

МИГРАЦИОННЫЕ ПРОЦЕССЫ НА АЛТАЕ В 1917–1926 ГОДАХ: ИНФОРМАЦИОННЫЙ ПОТЕНЦИАЛ СТАТИСТИЧЕСКИХ ИСТОЧНИКОВ

The study presents an attempt to assess the information potential of official publications of central and regional statistical institutional bodies in the early 20th century for studying migration processes in Altai. The aim of the research is to generate the information system on the historical statistics of Altai of the second half of the 19th century – the first third of the 20th century. The modern historiography of migration processes in the early 20th century is full of theoretical and practical concepts of resettlement in the early Soviet period. Nevertheless, the issues of population dynamics in Altai and the role of migration processes in the early Soviet period remain relevant. The official publications of the central and provincial statistical institutional bodies, materials of the population censuses of 1916–1917, 1920, 1923, and 1926, publications of the Altai Provincial Statistical Bureau are among the sources for studying migration processes in Altai in 1917–1926. The authors state that official statistical materials, despite certain difficulties in comparing and analyzing data, allow them to trace the main trends, as well as the role and significance of migration processes for Altai. The dynamic ranks in terms of the population of counties and cities for 1917–1923, made by the Altai Provincial Statistical Bureau, as well as data from population censuses of 1917, 1920, 1923, and 1926, are the most informative sources. Special characteristics of migrations in Altai compared to similar Siberian processes are revealed in the paper. The authors conclude that, by 1926, migrations of the early Soviet period played a key role in the formation of the Altai population.

Assessing 2019 Hurricane Harvey Recovery in Aransas County, Texas Through Community Assessment for Public Health Emergency Response (CASPER).

The objective of this study was to assess recovery of Aransas County, Texas households 2 years after Category 4 Hurricane Harvey made landfall. A 2-stage cluster sampling method used to conduct a Community Assessment for Public Health Emergency Response (CASPER) on May 3 - 4 and May 18 - 19, 2019. A household-based survey was administered through face-to-face interviews, selected through systematic random sampling using weighted analysis of the county population. Field teams collected 175 surveys (83.3% completion rate). Approximately 57% households experienced repairable damage, 23% had destroyed homes, and 19% had minimal damage. 38% stated having 'no need,' 18% needed financial assistance, 16% needed household repairs, and over 8% had behavioral health needs. 17% experiencing a behavioral health concern were seeking services. Of the 35% of households who did not seek services, 14% felt there was no need, and 4% were not aware of the resources available. Households reported high levels of preparedness, but gaps remain in evacuation intention and behavioral health care access. CASPERs are effective in assessing long-term recovery of communities impacted by major disasters.