Policies and opportunities for physical activity engagement in Austrian schools: a census survey.

Background: Compelling evidence suggests that schools' infrastructure and policy represent potential predictors of health and physical activity (PA) in children and adolescents. However, the translation of these findings into practice has not been examined. This study investigated the activity friendliness of Austrian schools. Methods: Using a census sampling method, principals of Austrian schools (n = 342) were invited to participate in an online survey assessing 1) the availability of PA facilities (e.g. playgrounds, sports courts, swimming halls) and 2) applied PA policies (e.g. cooperation with sports clubs and involvement in PA projects). Results: A total of 130 principals answered the survey which corresponds to a minimal response rate of 38%. While most schools (87.4%, n = 111) had a gymnastic hall, only one third (37%, n = 47) had access to a swimming hall. On average, the schools had 4.2 ± 2 PA facilities with significant variation between school types (i.e., high schools: 5 ± 2 vs. primary schools: 3.5 ± 2, p < 0.05). The most common facilities were meadow areas (89%, n = 113), sports fields (71.7%, n = 91), and playgrounds (64.6%, n = 82). Almost half of the schools were part of a PA project (e.g. "active break", 45%, n = 56) and offered extracurricular PA programs and courses (54%, n = 67), but only one in five (22%, n = 27) regularly conducted fitness tests. PA policies varied between school types (p < 0.05) but almost all principals (94%, n = 117) would welcome a stronger promotion of activity friendliness at their school. Conclusion: Schools are mostly activity-friendly regarding infrastructure although the limited possibilities for swimming lessons seem worrisome. PA promotion through projects, extracurricular PA offerings and fitness tests may be expanded.

Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data.

Background: The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology. Objective: This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census. Methods: The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles. Results: A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9%) or New Zealand (n=130, 11.8%), with most (n=620, 56.3%) aged 35-54 years and identifying as female (n=720, 65.3%). The top four occupational specialties were health informatics (n=179, 20.2%), health information management (n=175, 19.8%), health information technology (n=128, 14.4%), and health librarianship (n=104, 11.7%). Nearly all (n=797, 90%) participants identified as a manager or professional. Less than half (430/1019, 42.2%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26%) held a credential in a digital health area. While two-thirds (502/763, 65.7%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological. Conclusions: Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology.

Description of census-tract-level social determinants of health in cancer surveillance data.

BACKGROUND: Disparities in cancer incidence, stage at diagnosis, and mortality persist by race, ethnicity, and many other social determinants, such as census-tract-level socioeconomic status (SES), poverty, and rurality. Census-tract-level measures of these determinants are useful for analyzing trends in cancer disparities. METHODS: The purpose of this paper was to demonstrate the availability of the Surveillance, Epidemiology, and End Results Program's specialized census-tract-level dataset and provide basic descriptive cancer incidence, stage at diagnosis, and survival for 8 cancer sites, which can be screened regularly or associated with infectious agents. We present these analyses according to several census-tract-level measures, including the newly available persistent poverty as well as SES quintile, rurality, and race and ethnicity. RESULTS: Census tracts with persistent poverty and low SES had higher cancer incidence rates (except for breast and prostate cancer), higher percentages of cases diagnosed with regional or distant-stage disease, and lower survival than non-persistent-poverty and higher-SES tracts. Outcomes varied by cancer site when analyzing based on rurality as well as race and ethnicity. Analyses stratified by multiple determinants showed unique patterns of outcomes, which bear further investigation. CONCLUSIONS: This article introduces the Surveillance, Epidemiology, and End Results specialized dataset, which contains census-tract-level social determinants measures, including persistent poverty, rurality, SES quintile, and race and ethnicity. We demonstrate the capacity of these variables for use in producing trends and analyses focusing on cancer health disparities. Analyses may inform interventions and policy changes that improve cancer outcomes among populations living in disadvantaged areas, such as persistent-poverty tracts.

Suspected Counterfeit M-30 Oxycodone Pill Exposures and Acute Withdrawals Reported from a Single Hospital - Toxicology Investigators Consortium Core Registry, U.S. Census Bureau Western Region, 2017-2022.

Availability of counterfeit prescription pills (counterfeit pills) containing illegally made fentanyl, including counterfeit M-30 oxycodone (counterfeit M-30) pills, has risen sharply in the United States and has been increasingly linked to overdose deaths. In 2023, approximately 115 million counterfeit pills were seized in U.S. High Intensity Drug Trafficking Areas. However, clinical data on counterfeit pill-related overdoses are limited. Medical toxicology consultations during 2017-2022 from one U.S. Census Bureau Western Region hospital participating in the Toxicology Investigators Consortium Core Registry were analyzed. A total of 352 cases suspected to involve counterfeit M-30 pills, including 143 (40.6%) cases of fentanyl exposure and 209 (59.4%) cases of acute withdrawal were identified; consultations increased from three in 2017, to 209 in 2022. Patients aged 15-34 years accounted for 95 (67.4%) exposure cases. Among all patients with exposures, 81.1% were hospitalized, 69.0% of whom were admitted to an intensive care unit. Additional substances were detected in 131 (91.6%) exposures. Providing outreach to younger persons misusing prescription pills, improving access to and distribution of harm reduction tools including fentanyl test strips and naloxone, and promoting linkage of persons treated for overdose in hospitals to harm reduction and substance use treatment services are strategies to reduce morbidity associated with use of counterfeit M-30.

New Evidence From Census 2020 on the Residential Segregation of Same-Sex Households: A Research Note.

The 2020 decennial census provides new insights into the demography of same-sex households and can shed light on ongoing debates in urban and gayborhood studies. Although the U.S. Census gives a vast undercount of the LGBTQ population, it is still the largest source of nationally representative data on same-sex households and is accessible over three time points (2000, 2010, 2020). In this research note, we use 2020 census data to examine the residential patterns of same-sex households down to the neighborhood level. By employing the index of dissimilarity, we present results for the 100 largest U.S. cities and 100 largest metropolitan areas that demonstrate moderate yet persistent segregation. In a continuation of prior trends, male same-sex households remain more segregated from different-sex households than do female same-sex households. We find moderate levels of within-group segregation by gender and marital status-representing new demographic trends. Finally, metropolitan areas have a higher dissimilarity index than cities, revealing greater levels of segregation when factoring in suburban areas. We discuss these trends in light of debates regarding the spatial organization of sexuality in residential contexts and outline future avenues for research utilizing recently released 2020 census data.

Quantification and visualization of US racial geography using the National Racial Geography Dataset 2020.

Racial geography studies the spatial distributions of multiracial populations. Technical challenges arise from the fact that US Census data, upon which all US-based studies rely, is only available in the form of spatial aggregates at a few levels of granularity. This negatively affects spatial analysis and, consequently, the quantification of racial segregation, especially on a smaller length scale. A recent methodology called the Racial Landscape (RL) stochastically disaggregates racial data at the level of census block aggregates into a grid of monoracial cells. RL-transformed racial data makes possible pattern-based, zoneless analysis, and visualization of racial geography. Here, we introduce the National Racial Geography Dataset 2020 (NRGD2020)-a collection of RL-based grids calculated from the 2020 census data and covering the entire conterminous US. It includes a virtual image layer for a bird's-eye-like view visualization of the spatial distribution of racial sub-populations, numerical grids for calculating racial diversity and segregation within user-defined regions, and precalculated maps of racial diversity and segregation on various length scales. NRGD2020 aims to facilitate and extend spatial analyses of racial geography and to make it more interpretable by tightly integrating quantitative analysis with visualization (mapping).

Use of computer simulation to identify effects on hospital census with reduction of transfers for non-procedural patients in community hospitals.

OBJECTIVE: In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred. PATIENTS AND METHODS: We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days. RESULTS: Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70. CONCLUSION: Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems.

Assessing the heterogeneity of ethnic categories for patient records in primary care.

BACKGROUND: UK general practice surgeries collect data regarding patient ethnicities, typically at registration. These data are subsequently used in both clinical care and research, for example, when embedded in risk modelling tools. The published standard list of ethnic categories exists, but little is known about what happens in frontline practice. AIM: To document the variation in ethnic categories available on online patient registration forms across GP surgeries in Oxfordshire. METHOD: Of all 67 GP surgeries in Oxfordshire, 56 had online registration forms that included an option list for ethnicity reporting. The authors compared these against the 2001, 2011, and 2021 UK census ethnic group categorisation. RESULTS: Significant heterogeneity was identified across practices. The number of options for ethnicity group ranged from 5 to 84, with a median of 14, compared to the census lists that comprise of 19 (2021), 18 (2011), and 16 (2001) groups. Of the 56 practices, six used the 2001 census list, five used the 2011 census list, and none used the 2021 census list. Overall, 45 practices used lists that differed from any census list, including categories not typically considered to be ethnic, for example 'Muslim' or 'Buddhist', meaning individuals could potentially identify with multiple options. CONCLUSION: High-quality research and healthcare data that includes patient ethnicity is essential to understand, document, and mitigate against health inequalities. However, this may be compromised by poorly conceived ethnic categorisations and a lack of standardisation. This pilot/exploratory study suggests that the ethnicity records in primary health care may be neither standardised nor meaningful.

A national census for the off-label treatment of complex aortic aneurysms.

OBJECTIVE: Despite regulatory challenges, device availability, and rapidly expanding techniques, off-label endovascular repair of complex aortic aneurysms (cAAs) has expanded in the past decade. Given the lack of United States Food and Drug Administration-approved endovascular technology to treat cAAs, we performed a national census to better understand volume and current practice patterns in the United States. METHODS: Targeted sampling identified vascular surgeons with experience in off-label endovascular repair of cAAs. An electronic survey was distributed with institutional review board approval from the University of Rochester to 261 individuals with a response rate of 38% (n = 98). RESULTS: A total of 93 respondents (95%) reported off-label endovascular repair of cAAs. Mean age was 45.7 ± 8.3 years, and 84% were male. Most respondents (59%) were within the first 10 years of practice, and 69% trained at institutions with a high-volume of off-label endovascular procedures for complex aortic aneurysms with or without a physician-sponsored investigational device exemption (PS-IDE). Twelve respondents from 11 institutions reported institutional PS-IDEs for physician-modified endografts (PMEGs), in-situ laser fenestration (ISLF), or parallel grafting technique (PGT), including sites with PS-IDEs for custom-manufactured devices. Eighty-nine unique institutions reported elective off-label endovascular repair with a mean of 20.2 ± 16.5 cases/year and ∼1757 total cases/year nationally. Eighty reported urgent/emergent off-label endovascular repair with a mean of 5.7 ± 5.4 cases/year and ∼499 total cases/year nationally. There was no correlation between high-volume endovascular institutions (>15 cases/year) and institutions with high volumes of open surgical repair for cAAs (>15 cases/year; odds ratio, 0.7; 95% confidence interval, 0.3-1.5; P = .34). Elective techniques included PMEG (70%), ISLF (30%), hybrid PMEG/ISLF (18%), and PGT (14%), with PMEG being the preferred technique for 63% of respondents. Techniques for emergent endovascular treatment of complex aortic disease included PMEG (52%), ISLF (40%), PGT (20%), and hybrid-PMEG/ISLF (14%), with PMEG being the preferred technique for 41% of respondents. Thirty-nine percent of respondents always or frequently offer referrals to institutions with PS-IDEs for custom-manufactured devices. The most common barrier for referral to PS-IDE centers included geographic distance (48%), longitudinal relationship with patient (45%), and costs associated with travel (33%). Only 61% of respondents participate in the Vascular Quality Initiative for complex endovascular aneurysm repair, and only 57% maintain a prospective institutional database. Eighty-six percent reported interest in a national collaborative database for off-label endovascular repair of cAA. CONCLUSIONS: Estimates of off-label endovascular repair of cAAs are likely underrepresented in the literature based on this national census. PMEG was the most common technique for elective and emergent procedures. Under-reported off-label endovascular repair of cAA outcomes data appears to be limited by non-standardized PS-IDE reporting to the United States Food and Drug Administration, and the lack of Vascular Quality Initiative participation and prospective institutional data collection. Most participants are interested in a national collaborative database for endovascular repair of cAAs.

Long-term health conditions among household families in Aotearoa New Zealand: cross-sectional analysis of integrated Census and administrative data.

AIM: Little is known about the extent to which families in Aotearoa New Zealand are affected by long-term health conditions (HCs). This study aimed to explore the rates of nine selected HCs among New Zealand family members within the same household. METHOD: Linked population and administrative health data were obtained for families living in the same household according to the 2013 New Zealand Census (N=1,043,172). Health data (2008-2013) were used to ascertain whether people in these families (N=3,137,517) received treatment or services for nine selected HCs: cancer, chronic obstructive pulmonary disease, heart disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI), or mental health/behaviour conditions (MHBCs). RESULTS: Over 60% of families included at least one person with a HC, and this rate was higher among multi-generation families (73.9%). The most common HCs were MHBCs (39.4% of families), diabetes (16.0%) and TBI (13.9%). At the highest level of socio-economic deprivation, 57.6% of children aged under 18 years lived with a family member who had a HC. CONCLUSION: Three in five New Zealand household families included someone with at least one of nine selected HCs, with differences in the proportion affected according to family composition, socio-economic status and an individual's ethnicity. This suggests that there are a substantial number of people at risk of the poor outcomes associated with the experience of HCs within their family.

Differential Privacy Protections in 2020 U.S. Decennial Census Data Do Not Impede Measurement of Racial and Ethnic Disparities.

Census data are vital to health care research but must also protect respondents' confidentiality. The 2020 decennial Census employs a new Differential Privacy framework; this study examines its effect on the accuracy of an important tool for measuring health disparities, the Bayesian Improved Surname and Geocoding (BISG) algorithm, which uses Census Block Group data to estimate race and ethnicity when self-reported data are unavailable. Using self-reported race and ethnicity data as our standard, we compared the accuracy of BISG estimates calculated using the original 2010 Census counts to the accuracy of estimates calculated using 2010 data but with 2020 Differential Privacy in place. The Differential Privacy methodology slightly decreases BISG accuracy for American Indian and Alaska Native people but has little effect for other groups, suggesting that the methodology will not impede health disparities research that employs BISG and similar methods.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

PURPOSE: By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS: We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS: US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

A Research Note on Community Resilience Estimates: New U.S. Census Bureau Data With an Application to Excess Deaths From COVID-19.

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic.

Reweighting UK Biobank corrects for pervasive selection bias due to volunteering.

BACKGROUND: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. METHODS: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. RESULTS: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. CONCLUSIONS: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available.

Piloting a Measure of Segregation at the Census Tract Level: Associations with Place and Racial/Ethnic Disparities in Life Expectancy.

This study considers residential segregation as a critical driver of racial/ethnic health disparities and introduces a proxy measure of segregation that estimates the degree of segregation at the census tract level with a metric capturing the overrepresentation of a racialized/ethnic group in a census tract in relation to that group's representation at the city level. Using Dallas, Texas as a pilot city, the measure is used to investigate mean life expectancy at birth for relatively overrepresented Hispanic, non-Hispanic white, non-Hispanic Black, and Asian census tracts and examine for significant differences between mean life expectancy in relatively overrepresented census tracts and that group's mean life expectancy at the state level. Multivariable linear regression analysis was utilized to assess how segregation measured at the census tract level associates with life expectancy across different racialized/ethnic groups, controlling for socioeconomic disparities. This study aimed to expose the need to consider the possibility of neighborhood mechanisms beyond socioeconomic characteristics as an important determinant of health and draw attention to the importance of critically engaging the experience of place in examinations of racial and ethnic health disparities. Multivariable linear regression modeling resulted in significant findings for non-Hispanic Black, non-Hispanic white, and Asian groups, indicating increased census tract-level life expectancy for Black and white residents in highly segregated census tracts and decreased life expectancy for residents of tracts in which the Asian community is overrepresented when compared to state means. Unadjusted models demonstrated socioeconomic inequities between first and fourth quartile census tracts and pointed to the importance of mixed methods in health disparities research and the importance of including the voice of community members to account for places of daily lived experience and people's relationships with them.

Application of citizen science with the nationwide bird census.

Citizen science (CS) is the most effective tool for overcoming the limitations of government and/or professional data collection. To compensate for quantitative limitations of the 'Winter Waterbird Census of Korea', we conducted a total of four bird monitoring via CS from 2021 to 2022. To use CS data alongside national data, we studied CS data quality and improvement utilizing (1) digit-based analysis using Benford's law and (2) comparative analysis with national data. In addition, we performed bird community analysis using CS-specific data, demonstrating the necessity of CS. Neither CS nor the national data adhered to Benford's law. Alpha diversity (number of species and Shannon index) was lower, and total beta diversity was higher for the CS data than national data. Regarding the observed bird community, the number of species per family was similar; however, the number of individuals per family/species differed. We also identified the necessity of CS by confirming the possibility of predicting bird communities using CS-specific data. CS was influenced by various factors, including the perceptions of the survey participants and their level of experience. Therefore, conducting CS after systematic training can facilitate the collection of higher-quality data.