Prevalence of covid-19 and long covid in collegiate student athletes from spring 2020 to fall 2021: a retrospective survey.

BACKGROUND: Symptomatic COVID-19 and Long COVID, also referred to as post-acute sequelae of SARS-CoV-2 (PASC) or post-COVID conditions, have been widely reported in young, healthy people, but their prevalence has not yet been determined in student athletes. We sought to estimate the prevalence of reported COVID-19, symptomatic COVID-19, and Long COVID in college athletes in the United States attending 18 schools from spring 2020 to fall 2021. METHODS: We developed an online survey to measure the prevalence of student athletes who tested positive for COVID-19, developed Long COVID, and did not return to their sport during the relevant time period. We surveyed a convenience sample of 18 collegiate school administrators, representing about 7,000 student athletes. Of those schools surveyed, 16 responded regarding the spring 2020 semester, and 18 responded regarding the full academic year of fall 2020 to spring 2021 (both semesters). RESULTS: According to the survey responses, there were 9.8% of student athletes who tested positive for COVID-19 in spring 2020 and 25.4% who tested positive in the academic year of fall 2020 to spring 2021. About 4% of student athletes who tested positive from spring 2020 to spring 2021 developed Long COVID, defined as new, recurring, or ongoing physical or mental health consequences occurring 4 or more weeks after SARS-CoV-2 infection. CONCLUSIONS: This study highlights that Long COVID occurs among young, healthy athletes and is a real consequence of COVID-19. Understanding the prevalence of Long COVID in this population requires longer follow-up and further study.

The Effect of a Shared Decision-Making Process on Acceptance of Colorectal Cancer Screening.

VISUAL ABSTRACT.

COVID-19 Infection Control Measures in Long-Term Care Facility, Pennsylvania, USA.

Residents of long-term care facilities are at risk for coronavirus disease. We report a surveillance exercise at such a facility in Pennsylvania, USA. After introduction of a testing strategy and other measures, this facility had a 17-fold lower coronavirus disease case rate than neighboring facilities.

Geographic variation in the relationship between body mass index and the built environment.

Studies examining associations between weight status and neighborhood built environment (BE) have shown inconsistent results and have generally focused on urban settings. However, many Americans do not live in metropolitan areas and BE impacts may be different outside of metropolitan areas. We sought to examine whether the relationship between body mass index (BMI) and neighborhood BE exists and varies by geographic region across small towns in the United States. We conducted telephone surveys with 2156 adults and geographic information systems data in nine towns located within three geographic regions (Northeast, Texas, Washington) in 2011 and 2012. Multiple regression models examined the relationship between individual BMI and BE measures. Most physical activity variables were significantly associated with lower BMI in all geographic regions. We saw variation across geographic region in the relationship between characteristics of the BE variables and BMI. Some perceived and objectively-measured characteristics of the BE were significantly associated with adult BMI, but significant relationships varied by geographic region. For example, in the Northeast, perceived attractiveness of the neighborhood as a reason for why they chose to live there was associated with lower BMI; in Texas, the perceived presence of a fast food restaurant was negatively associated with BMI; in Washington, perceived presence of trees along the streets was associated with lower BMI. Our findings suggest that regional variation plays a role in the relationship between adult BMI and BE characteristics in small towns. Regardless of geographic location, interventions should encourage utilitarian walking and other forms of physical activity.

The built environment and utilitarian walking in small U.S. towns.

OBJECTIVES: The role of the built environment on walking in rural United States (U.S.) locations is not well characterized. We examined self-reported and measured built environment correlates of walking for utilitarian purposes among adult residents of small rural towns. METHODS: In 2011-12, we collected telephone survey and geographic data from 2152 adults in 9 small towns from three U.S. regions. We performed mixed-effects logistic regression modeling to examine relationships between built environment measures and utilitarian walking ("any" versus "none"; "high" [≥150min per week] versus "low" [<150min per week]) to retail, employment and public transit destinations. RESULTS: Walking levels were lower than those reported for populations living in larger metropolitan areas. Environmental factors significantly (p<0.05) associated with higher odds of utilitarian walking in both models included self-reported presence of crosswalks and pedestrian signals and availability of park/natural recreational areas in the neighborhood, and also objectively measured manufacturing land use. CONCLUSIONS: Environmental factors associated with utilitarian walking in cities and suburbs were important in small rural towns. Moreover, manufacturing land use was associated with utilitarian walking. Modifying the built environment of small towns could lead to increased walking in a sizeable segment of the U.S. population.

Retail tobacco exposure: using geographic analysis to identify areas with excessively high retail density.

INTRODUCTION: There is great disparity in tobacco outlet density (TOD), with density highest in low-income areas and areas with greater proportions of minority residents, and this disparity may affect cancer incidence. We sought to better understand the nature of this disparity by assessing how these socio-demographic factors relate to TOD at the national level. METHODS: Using mixture regression analysis and all of the nearly 65,000 census tracts in the contiguous United States, we aimed to determine the number of latent disparity classes by modeling the relations of proportions of Blacks, Hispanics, and families living in poverty with TOD, controlling for urban/rural status. RESULTS: We identified six disparity classes. There was considerable heterogeneity in relation to TOD for Hispanics in rural settings. For Blacks, there was no relation to TOD in an urban moderate disparity class, and for rural census tracts, the relation was highest in a moderate disparity class. CONCLUSIONS: We demonstrated the utility of classifying census tracts on heterogeneity of tobacco risk exposure. This approach provides a better understanding of the complexity of socio-demographic influences of tobacco retailing and creates opportunities for policy makers to more efficiently target areas in greatest need.

Predictors of tobacco outlet density nationwide: a geographic analysis.

OBJECTIVE: To elucidate how demographics of US Census tracts are related to tobacco outlet density (TOD). METHOD: The authors conducted a nationwide assessment of the association between socio-demographic US Census indicators and the density of tobacco outlets across all 64,909 census tracts in the continental USA. Retail tobacco outlet addresses were determined through North American Industry Classification System codes, and density per 1000 population was estimated for each census tract. Independent variables included urban/rural; proportion of the population that was black, Hispanic and women with low levels of education; proportion of families living in poverty and median household size. RESULTS: In a multivariate analysis, there was a higher TOD per 1000 population in urban than in rural locations. Furthermore, higher TOD was associated with larger proportions of blacks, Hispanics, women with low levels of education and with smaller household size. Urban-rural differences in the relation between demographics and TOD were found in all socio-demographic categories, with the exception of poverty, but were particularly striking for Hispanics, for whom the relation with TOD was 10 times larger in urban compared with rural census tracts. CONCLUSIONS: The findings suggest that tobacco outlets are more concentrated in areas where people with higher risk for negative health outcomes reside. Future studies should examine the relation between TOD and smoking, smoking cessation, as well as disease rates.

Objective measurement of sociability and activity: mobile sensing in the community.

PURPOSE: Automated systems able to infer detailed measures of a person's social interactions and physical activities in their natural environments could lead to better understanding of factors influencing well-being. We assessed the feasibility of a wireless mobile device in measuring sociability and physical activity in older adults, and compared results with those of traditional questionnaires. METHODS: This pilot observational study was conducted among a convenience sample of 8 men and women aged 65 years or older in a continuing care retirement community. Participants wore a waist-mounted device containing sensors that continuously capture data pertaining to behavior and environment (accelerometer, microphone, barometer, and sensors for temperature, humidity, and light). The sensors measured time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with 1 or more other people. The participants also completed 4 questionnaires: the 36-Item Short Form Health Survey (SF-36), the Yale Physical Activity Survey (YPAS), the Center for Epidemiologic Studies-Depression (CES-D) scale, and the Friendship Scale. RESULTS: Men spent 21.3% of their time walking and 64.4% stationary. Women spent 20.7% of their time walking and 62.0% stationary. Sensed physical activity was correlated with aggregate YPAS scores (r(2)=0.79, P=.02). Sensed time speaking was positively correlated with the mental component score of the SF-36 (r(2)=0.86, P = .03), and social interaction as assessed with the Friendship Scale (r(2)=0.97, P = .002), and showed a trend toward association with CES-D score (r(2)=-0.75, P = .08). In adjusted models, sensed time speaking was associated with SF-36 mental component score (P = .08), social interaction measured with the Friendship Scale (P = .045), and CES-D score (P=.04). CONCLUSIONS: Mobile sensing of sociability and activity is well correlated with traditional measures and less prone to biases associated with questionnaires that rely on recall. Using mobile devices to collect data from and monitor older adult patients has the potential to improve detection of changes in their health.

Identifying optimal COVID-19 testing strategies for schools and businesses: Balancing testing frequency, individual test technology, and cost.

BACKGROUND: COVID-19 test sensitivity and specificity have been widely examined and discussed, yet optimal use of these tests will depend on the goals of testing, the population or setting, and the anticipated underlying disease prevalence. We model various combinations of key variables to identify and compare a range of effective and practical surveillance strategies for schools and businesses. METHODS: We coupled a simulated data set incorporating actual community prevalence and test performance characteristics to a susceptible, infectious, removed (SIR) compartmental model, modeling the impact of base and tunable variables including test sensitivity, testing frequency, results lag, sample pooling, disease prevalence, externally-acquired infections, symptom checking, and test cost on outcomes including case reduction and false positives. FINDINGS: Increasing testing frequency was associated with a non-linear positive effect on cases averted over 100 days. While precise reductions in cumulative number of infections depended on community disease prevalence, testing every 3 days versus every 14 days (even with a lower sensitivity test) reduces the disease burden substantially. Pooling provided cost savings and made a high-frequency approach practical; one high-performing strategy, testing every 3 days, yielded per person per day costs as low as $1.32. INTERPRETATION: A range of practically viable testing strategies emerged for schools and businesses. Key characteristics of these strategies include high frequency testing with a moderate or high sensitivity test and minimal results delay. Sample pooling allowed for operational efficiency and cost savings with minimal loss of model performance.

Neighborhood Environments and Utilitarian Walking Among Older vs. Younger Rural Adults.

Introduction: Walking has the potential to promote health across the life span, but age-specific features of the neighborhood environment (NE), especially in rural communities, linked with walking have not been adequately characterized. This study examines the relationships between NE and utilitarian walking among older vs. younger adults living in US rural towns. Methods: Data for this cross-sectional study came from telephone interviews in 2011-2012 with 2,140 randomly sampled younger (18-64 years, n = 1,398) and older (65+ years, n = 742) adults, collecting personal and NE perception variables. NE around each participant's home was also measured objectively using geographic information system techniques. Separate mixed-effects logistic regression models were estimated for the two age groups, predicting the odds of utilitarian walking at least once a week. Results: Perceived presence of crosswalks and pedestrian signals was significantly related to utilitarian walking in both age groups. Among older adults, unattended dogs, lighting at night, and religious institutions were positively while steep slope was negatively associated with their walking. For younger adults, traffic speed (negative, -), public transportation (positive, +), malls (-), cultural/recreational destinations (+), schools (+), and resource production land uses such as farms and mines (-) were significant correlates of utilitarian walking. Conclusion: Different characteristics of NE are associated with utilitarian walking among younger vs. older adults in US rural towns. Optimal modifications of NE to promote walking may need to reflect these age differences.

Pooling in a Pod: A Strategy for COVID-19 Testing to Facilitate a Safe Return to School.

The COVID-19 pandemic prompted widespread closures of primary and secondary schools. Routine testing of asymptomatic students and staff members, as part of a comprehensive mitigation program, can help schools open safely. "Pooling in a pod" is a public health surveillance strategy whereby testing cohorts (pods) are based on social relationships and physical proximity. Pooled testing provides a single laboratory test result for the entire pod, rather than a separate result for each person in the pod. During the 2020-2021 school year, an independent preschool-grade 12 school in Washington, DC, used pooling in a pod for weekly on-site point-of-care testing of all staff members and students. Staff members and older students self-collected anterior nares samples, and trained staff members collected samples from younger students. Overall, 12 885 samples were tested in 1737 pools for 863 students and 264 staff members from November 30, 2020, through April 30, 2021. The average pool size was 7.4 people. The average time from sample collection to pool test result was 40 minutes. The direct testing cost per person per week was $24.24, including swabs. During the study period, 4 surveillance test pools received positive test results for COVID-19. A post-launch survey found most parents (90.3%), students (93.4%), and staff members (98.8%) were willing to participate in pooled testing with confirmatory tests for pool members who received a positive test result. The proportion of students in remote learning decreased by 62.2% for students in grades 6-12 (P < .001) and by 92.4% for students in preschool to grade 5 after program initiation (P < .001). Pooling in a pod is a feasible, cost-effective surveillance strategy that may facilitate safe, sustainable, in-person schooling during a pandemic.

Implementation of a pooled surveillance testing program for asymptomatic SARS-CoV-2 infections in K-12 schools and universities.

BACKGROUND: The negative impact of continued school closures during the height of the COVID-19 pandemic warrants the establishment of cost-effective strategies for surveillance and screening to safely reopen and monitor for potential in-school transmission. Here, we present a novel approach to increase the availability of repetitive and routine COVID-19 testing that may ultimately reduce the overall viral burden in the community. METHODS: We implemented a testing program using the SalivaClear࣪ pooled surveillance method that included students, faculty and staff from K-12 schools (student age range 5-18 years) and universities (student age range >18 years) across the country (Mirimus Clinical Labs, Brooklyn, NY). The data analysis was performed using descriptive statistics, kappa agreement, and outlier detection analysis. FINDINGS: From August 27, 2020 until January 13, 2021, 253,406 saliva specimens were self-collected from students, faculty and staff from 93 K-12 schools and 18 universities. Pool sizes of up to 24 samples were tested over a 20-week period. Pooled testing did not significantly alter the sensitivity of the molecular assay in terms of both qualitative (100% detection rate on both pooled and individual samples) and quantitative (comparable cycle threshold (Ct) values between pooled and individual samples) measures. The detection of SARS-CoV-2 in saliva was comparable to the nasopharyngeal swab. Pooling samples substantially reduced the costs associated with PCR testing and allowed schools to rapidly assess transmission and adjust prevention protocols as necessary. In one instance, in-school transmission of the virus was determined within the main office and led to review and revision of heating, ventilating and air-conditioning systems. INTERPRETATION: By establishing low-cost, weekly testing of students and faculty, pooled saliva analysis for the presence of SARS-CoV-2 enabled schools to determine whether transmission had occurred, make data-driven decisions, and adjust safety protocols. We provide strong evidence that pooled testing may be a fundamental component to the reopening of schools by minimizing the risk of in-school transmission among students and faculty. FUNDING: Skoll Foundation generously provided funding to Mobilizing Foundation and Mirimus for these studies.

Quality of care for cardiometabolic disease: associations with mental disorder and rurality.

BACKGROUND: This study examines quality of cardiometabolic care among veterans receiving care in the Veterans Affairs (VA) health system. We assess whether quality of care disparities by mental disorder status are magnified for individuals living in rural areas. RESEARCH DESIGN: We identified all patients in a 2005 national Veterans Administration cardiometabolic quality of care chart review. The intersection of this cohort and VA registries, that include patients with and without mental disorder, permitted identification of chart review patients with and without mental disorder. Using residential ZIP code, patients were assigned to rural-urban commuting area codes. We used logistic regression adjusting for age, demographics, comorbidities, and income. MEASURES: We assessed association between rural residence and 9 cardiometabolic care quality indicators including care processes and intermediate outcomes. RESULTS: Compared with those without mental disorder, patients with mental disorder were less likely to receive diabetes sensory foot exams (OR: 0.82; 95% CI: 0.72-0.94), retinal exams (OR: 0.82; 95% CI: 0.73-0.93), and renal tests (OR: 0.79; CI: 0.74-0.90). Rural residence was associated with no differences in quality measures. Primary care visit volume was associated with significantly greater likelihood of obtaining diabetic retinal examination and renal testing, but did not explain disparities among patients with mental disorder. CONCLUSIONS: Mental disorder is associated with lesser attainment of quality cardiometabolic care. In this integrated VA care system, rurality and visit volume did not explain this disparity. Other explanations for disparities must be explored to improve the health and health care of this population.

Alcohol retail density and demographic predictors of health disparities: a geographic analysis.

OBJECTIVES: We examined whether the geographic density of alcohol retailers was greater in geographic areas with higher levels of demographic characteristics that predict health disparities. METHODS: We obtained the locations of all alcohol retailers in the continental United States and created a map depicting alcohol retail outlet density at the US Census tract level. US Census data provided tract-level measures of poverty, education, crowding, and race/ethnicity. We used multiple linear regression to assess relationships between these variables and retail alcohol density. RESULTS: In urban areas, retail alcohol density had significant nonlinear relationships with Black race, Latino ethnicity, poverty, and education, with slopes increasing substantially throughout the highest quartile for each predictor. In high-proportion Latino communities, retail alcohol density was twice as high as the median density. Retail alcohol density had little or no relationship with the demographic factors of interest in suburban, large town, or rural census tracts. CONCLUSIONS: Greater density of alcohol retailers was associated with higher levels of poverty and with higher proportions of Blacks and Latinos in urban census tracts. These disparities could contribute to higher morbidity in these geographic areas.

Density estimation and adaptive bandwidths: a primer for public health practitioners.

BACKGROUND: Geographic information systems have advanced the ability to both visualize and analyze point data. While point-based maps can be aggregated to differing areal units and examined at varying resolutions, two problems arise 1) the modifiable areal unit problem and 2) any corresponding data must be available both at the scale of analysis and in the same geographic units. Kernel density estimation (KDE) produces a smooth, continuous surface where each location in the study area is assigned a density value irrespective of arbitrary administrative boundaries. We review KDE, and introduce the technique of utilizing an adaptive bandwidth to address the underlying heterogeneous population distributions common in public health research. RESULTS: The density of occurrences should not be interpreted without knowledge of the underlying population distribution. When the effect of the background population is successfully accounted for, differences in point patterns in similar population areas are more discernible; it is generally these variations that are of most interest. A static bandwidth KDE does not distinguish the spatial extents of interesting areas, nor does it expose patterns above and beyond those due to geographic variations in the density of the underlying population. An adaptive bandwidth method uses background population data to calculate a kernel of varying size for each individual case. This limits the influence of a single case to a small spatial extent where the population density is high as the bandwidth is small. If the primary concern is distance, a static bandwidth is preferable because it may be better to define the "neighborhood" or exposure risk based on distance. If the primary concern is differences in exposure across the population, a bandwidth adapting to the population is preferred. CONCLUSIONS: Kernel density estimation is a useful way to consider exposure at any point within a spatial frame, irrespective of administrative boundaries. Utilization of an adaptive bandwidth may be particularly useful in comparing two similarly populated areas when studying health disparities or other issues comparing populations in public health.

Breast cancer stage at diagnosis and geographic access to mammography screening (New Hampshire, 1998-2004).

INTRODUCTION: Early detection of breast cancer by screening mammography aims to increase treatment options and decrease mortality. Recent studies have shown inconsistent results in their investigations of the possible association between travel distance to mammography and stage of breast cancer at diagnosis. OBJECTIVE: The purpose of the study was to investigate whether geographic access to mammography screening is associated with the stage at breast cancer diagnosis. METHODS: Using the state's population-based cancer registry, all female residents of New Hampshire aged > or =40 years who were diagnosed with breast cancer during 1998-2004 were identified. The factors associated with early stage (stages 0 to 2) or later stage (stages 3 and 4) diagnosis of breast cancer were compared, with emphasis on the distance a woman lived from the closest mammography screening facility, and residence in rural and urban locations. RESULTS: A total of 5966 New Hampshire women were diagnosed with breast cancer during 1998-2004. Their mean driving distance to the nearest mammography facility was 8.85 km (range 0-44.26; 5.5 miles, range 0-27.5), with a mean estimated travel time of 8.9 min (range 0.0-42.2). The distribution of travel distance (and travel time) was substantially skewed to the right: 56% of patients lived within 8 km (5 miles) of a mammography facility, and 65% had a travel time of less than 10 min. There was no significant association between later stage of breast cancer and travel time to the nearest mammography facility. Using 3 categories of rural/urban residence based on Rural Urban Commuting Area classification, no significant association between rural residence and stage of diagnosis was found. New Hampshire women were more likely to be diagnosed with breast cancer at later stages if they lacked private health insurance (p<0.001), were not married (p<0.001), were older (p<0.001), and there was a borderline association with diagnosis during non-winter months (p=0.074). CONCLUSIONS: Most women living in New Hampshire have good geographical access to mammography, and no indication was found that travel time or travel distance to mammography significantly affected stage at breast cancer diagnosis. Health insurance, age and marital status were the major factors associated with later stage breast cancer. The study contributes to an ongoing debate over geographic access to screening mammography in different states, which have given contradictory results. These inconsistencies in the rural health literature highlight a need to understand the complexity of defining rural and urban residence; to characterize more precisely the issues that contribute to good preventive care in different rural communities; and to appreciate the efforts already made in some rural states to provide good geographic access to preventive care. In New Hampshire, specific subgroups such as the uninsured and the elderly remain at greatest risk of being diagnosed with later stage breast cancer and may benefit from targeted interventions to improve early detection.

A comparison of health care worker-collected foam and polyester nasal swabs in convalescent COVID-19 patients.

Both polyester and foam nasal swabs were collected from convalescent COVID-19 patients at a single visit and stored in viral transport media (VTM), saline or dry. Sensitivity of each swab material and media combination were estimated, three by three tables were constructed to measure polyester and foam concordance, and cycle threshold (Ct) values were compared. 126 visits had polyester and foam swabs stored in viral transport media (VTM), 51 had swabs stored in saline, and 63 had a foam swab in VTM and a polyester swab stored in a dry tube. Polyester and foam swabs had an estimated sensitivity of 87.3% and 94.5% respectively in VTM, 87.5% and 93.8% respectively in saline, and 75.0% and 90.6% respectively for dry polyester and foam VTM. Polyester and foam Ct values were correlated, but polyester showed decreased performance for cases with a viral load near the detection threshold and higher Ct values on average.

Computing travel time when the exact address is unknown: a comparison of point and polygon ZIP code approximation methods.

BACKGROUND: Travel time is an important metric of geographic access to health care. We compared strategies of estimating travel times when only subject ZIP code data were available. RESULTS: Using simulated data from New Hampshire and Arizona, we estimated travel times to nearest cancer centers by using: 1) geometric centroid of ZIP code polygons as origins, 2) population centroids as origin, 3) service area rings around each cancer center, assigning subjects to rings by assuming they are evenly distributed within their ZIP code, 4) service area rings around each center, assuming the subjects follow the population distribution within the ZIP code. We used travel times based on street addresses as true values to validate estimates. Population-based methods have smaller errors than geometry-based methods. Within categories (geometry or population), centroid and service area methods have similar errors. Errors are smaller in urban areas than in rural areas. CONCLUSION: Population-based methods are superior to the geometry-based methods, with the population centroid method appearing to be the best choice for estimating travel time. Estimates in rural areas are less reliable.

Using built environment characteristics to predict walking for exercise.

BACKGROUND: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. RESULTS: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. CONCLUSION: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.