Examining select sociodemographic characteristics of sub-county geographies for public health surveillance.

BACKGROUND: Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them. METHODS: Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract. RESULTS: The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program's aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level. CONCLUSIONS: The Tracking Program's sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

The COVID-19 Pandemic and Its Effects on Mental Health-A before, during, and after Comparison Using the U.S. Census Bureau's Household Pulse Survey.

BACKGROUND: Although significantly increased mental health concerns were noted globally during the first year of the COVID-19 pandemic, much less is known about the mental health trends during the COVID-19 recovery period. We aimed to compare current anxiety or depression rates to those before and during the first year of the pandemic and to evaluate demographic differences. METHODS: We analyzed Household Pulse Survey data prospectively collected from a representative U.S. population sample. We compared the anxiety or depression rates from the first pandemic year (04/2020-05/2021) and recovery period (06/2023-03/2024) from the national cohort and demographic subgroups using two-sided paired t-tests and regression analyses and compared these to pre-pandemic (01/2019-12/2019) rates using one-sided t-tests. RESULTS: The national estimates for anxiety or depression improved during the recent COVID-19 recovery period as compared to the first year (29.5 ± 5.5 vs. 37.6 ± 3.1; p < 0.0001) but did not return to the pre-pandemic benchmark (29.5% vs. 10.8%; p < 0.001). Higher rates were noted in younger individuals aged 18-29 years (p < 0.0001), in individuals with less than a high school diploma (p < 0.0001), or with disabilities (p < 0.0001). Non-Hispanic Asians reported the lowest rates (p < 0.0001), and no significant gender differences were noted. CONCLUSION: The U.S. population's mental health concerns have improved since the first year of the pandemic but remain above pre-pandemic benchmarks. Certain demographic subgroups are at higher risk, indicating the need for targeted health care and economic policy interventions to address these disparities.

Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.

Background: Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity. ER is a fundamental challenge in data science, and a common barrier to ER research and development is that the data fields used for this fuzzy matching are personally identifiable information, such as name, address, and date of birth. The necessary restrictions on accessing and sharing these authentic data have slowed the work in developing, testing, and adopting new methods and software for ER. We recently released pseudopeople, a Python package that allows users to generate simulated datasets with configurable noise approaching the scale and complexity of the data on which large organizations and federal agencies, like the US Census Bureau regularly perform ER. With pseudopeople, researchers can develop new algorithms and software for ER of US population data without needing access to personal and confidential information. Methods: We created the simulated population data available for noising with pseudopeople using our Vivarium simulation platform. Our model simulates individuals and their families, households, and employment dynamics over time, which we observe through simulated censuses, surveys, and administrative data collection systems. Results: Our simulation process produced over 900 gigabytes of simulated censuses, surveys, and administrative data for pseudopeople, representing hundreds of millions of simulants. A sample simulated population of thousands of simulants is now openly available to all users of the pseudopeople package, and large-scale simulated populations of millions and hundreds of millions of simulants are also available by online request through GitHub. These simulated population data are structured for use by the pseudopeople package, which includes additional affordances to add various kinds of noise to the data to provide realistic, sharable challenges for ER researchers.

Blending census and paleolimnological data allows for tracking the establishment and growth of a major gannet colony over several centuries.

Seabird colonies with long-term monitoring records, i.e., > 50 years, are rare. The population data for northern gannets (Morus bassanus) in Cape St. Mary's (CSM) Ecological Reserve (Newfoundland and Labrador, Canada) is robust, extending back to 1883 when the colony was presumed established. We inferred the colony's historical population shifts by measuring ornithogenic proxies in a dated sediment record collected from a nearby pond. Our record extended to the early eighteenth century, but the proxy data only began to show significant signs of seabird presence between ca. 1832 and 1910, aligning with the period gannets were first observed at CSM. Through the twentieth century, we observed significant increases in δ15N, P, Zn, Cd, and chlorophyll a, coeval with a shift in the dominant diatom species, indicating rapid colony growth. The proxies were overall highest in ca. 2005, corresponding to the reported historical maximum of the gannet colony in 2009. Our results validate that paleo-reconstructions using ornithogenic proxies can accurately reflect population trends and provide a stronger understanding of the colony's establishment and growth. This study highlights the value of applying paleolimnological methods in seabird population studies to frame the history of a colony's dynamics and inform conservation efforts.

Open defecation among Kenyan households: an analysis of demographic and health survey 2022 and census report of 2019.

BACKGROUND: Open defecation (OD) is the disposal of human excreta in the fields, bushes, water bodies and other open spaces. It poses a public health risk as it can lead to the spread of diarrhoea, cholera, soil-transmitted helminths and trachoma. Kenya aims to achieve 100% open defecation free status by 2030 in line with Sustainable development goal number 6. This study sought to determine factors influencing OD at the household level as well as quantify the number of households practicing OD in each of the 47 Kenyan counties. METHODS: Data from the household questionnaire of the Kenya Demographic and Health Survey, 2022 was analysed. Bivariate logistic regression was done with open defecation status as the dependent variable. Independent variables were poverty status, place of residence, ownership of farm animals, gender and educational level of household head. The number of households practicing OD per county were determined using the Kenya Census report of 2019. RESULTS: Poverty was the strongest predictor of a household practicing OD (OR 43.8 95% CI 26.1-73.8) followed by educational status of the household head (OR 3.3 95% CI 2.3-4.6 ) and the household not owning livestock ( OR 0.7 95% CI 0.6-0.9). An estimated 7.4% of households practice OD. These are estimated to be 814,223 households. Out of these, 686,051 households (84.3%) are found in the 15 counties ranked as having a high population practicing OD. Five counties have managed to eliminate OD and another nine have OD rates of less than 0.5%. CONCLUSION: Kenya has made commendable progress in eliminating OD. Poverty is a significant predictor of OD at the household level. To eliminate OD, it is advised that more efforts be targeted towards poor households as well as the 15 counties having a high number of OD-practicing households.

The COVID-19 pandemic and associated declines in cancer incidence by race/ethnicity and census-tract level SES, rurality, and persistent poverty status.

BACKGROUND: The COVID-19 pandemic had a significant impact on cancer screening and treatment, particularly in 2020. However, no single study has comprehensively analyzed its effects on cancer incidence and disparities among groups such as race/ethnicity, socioeconomic status (SES), persistent poverty (PP), and rurality. METHODS: Utilizing the recent data from the United States National Cancer Institute's Surveillance, Epidemiology, and End Results Program, we calculated delay- and age-adjusted incidence rates for 13 cancer sites in 2020 and 2015-2019. Percent changes (PCs) of rates in 2020 compared to 2015-2019 were measured and compared across race/ethnic, census tract-level SES, PP, and rurality groups. RESULTS: Overall, incidence rates decreased from 2015-2019 to 2020, with varying PCs by cancer sites and population groups. Notably, NH Blacks showed significantly larger PCs than NH Whites in female lung, prostate, and colon cancers (e.g., prostate cancer: NH Blacks -7.3, 95% CI: [-9.0, -5.5]; NH Whites: -3.1, 95% CI: [-3.9, -2.2]). Significantly larger PCs were observed for the lowest versus highest SES groups (prostate cancer), PP versus non-PP groups (prostate and female breast cancer), and all urban versus rural areas (prostate, female breast, female and male lung, colon, cervix, melanoma, liver, bladder, and kidney cancer). CONCLUSIONS: The COVID-19 pandemic coincided with reduction in incidence rates in the U.S. in 2020 and was associated with worsening disparities among groups, including race/ethnicity, SES, rurality, and PP groups, across most cancer sites. Further investigation is needed to understand the specific effects of COVID-19 on different population groups of interest.

Structural Stigma and Disparities in Long-Term Health Conditions Among Australians in Same-Sex Relationships: 2021 Australian Census.

Objectives.  To explore the extent to which structural stigma (sociocultural and institutional constraining factors) is associated with sexual orientation disparities in long-term health conditions. Methods.  We measured structural stigma using the regional percentage of votes against same-sex marriage from Australia's 2017 Marriage Equality Survey and mapped this to the 2021 Census survey of 10 093 399 and 136 988 individuals in different-sex and same-sex relationships, respectively. Controlling for individual and area-level confounders, we used logistic regression analyses to examine the association between quartiles of structural stigma and sexual orientation disparities in long-term health conditions (e.g., any, mental health, asthma, cardiovascular). Results.  In the lowest stigma quartile, individuals in same-sex relationships had 56% higher odds of reporting any long-term health condition (odds ratio [OR] = 1.56; 95% confidence interval [CI] = 1.53, 1.59) and this increased to 63% in the highest stigma quartile (OR = 1.63; 95% CI = 1.58, 1.68). Effects were particularly pronounced for cardiovascular, respiratory, and mental health conditions as well as for men, younger populations, and those living in socioeconomically deprived regions. Conclusions.  Living in stigmatizing environments may have deleterious health effects for sexual minorities in Australia. Policy action and enhanced protections for sexual minorities are urgently required. (Am J Public Health. 2024;114(10):1110-1122. https://doi.org/10.2105/AJPH.2024.307759).

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.

Evaluating Geospatial Sampling Frames with a Novel Field Census for a Malaria Household Survey in Artibonite, Haiti.

The Ministry of Public Health and Population in Haiti is committed to malaria elimination. In 2017, we used novel methods to conduct a census, monitor progress, and return to sampled households (HH) before a cross-sectional survey in La Chapelle and Verrettes communes in Artibonite department ("the 2017 Artibonite HH census"). Geospatial PDFs with digitized structures and basemaps were loaded onto tablets. Enumerators captured GPS coordinates and details of each HH and points of interest. The census used 1 km2 enumeration areas (EAs) to draw a representative sample. Three remote sampling frames were compared with the 2017 Artibonite HH census. First, 2003 census EAs with 2012 population estimates from the Haitian Institute of Statistics and Informatics were standardized to the study EAs. The second sampling frame used the 2016 LandScanTM population estimates and study EAs. The third sampling frame used structures ≥3 m2 manually digitized using Maxar satellite images. In each study EA, 70% of structures were estimated to be inhabited with 4.5 persons/HH. The census identified 33,060 inhabited HHs with an estimated population of 121,593 and 6,126 points of interest. Using daily coverage maps and including digitized structures were novel methods that improved the census quality. Manual digitization was closest to the census sampling frame results with 30,514 digitized structures in the study area. The LandScanTM method performed better in urban areas; however, it produced the highest number of HHs to sample. If a census is not possible, when feasible, remotely digitizing structures and estimating occupancy may provide a close estimate.

Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level.

Physical environment plays a key role in determining human health risks. Exposure to toxins, weather extremes, degraded air and water quality, high levels of noise and limited accessibility to green areas can negatively affect health. Furthermore, adverse environmental exposures are often correlated with each other and with socioeconomic status, thereby compounding disadvantages in marginalized populations. Moreover, despite their importance in determining human health risks, the role of multiple environmental exposures is not well studied, and only a few resources contain aggregate environmental exposure data and only for selected areas of the contiguous US. To fill these gaps, we took a cumulative approach to measuring the environment by generating a composite Multi-Exposure Environmental Index (MEEI) as a US Census Tract-level summary of key environmental factors with known health effects. This measure quantifies multiple environmental exposures in the same area that can result in additive and synergistic effects on health outcomes. This information is crucial to better understand and possibly leverage environmental determinants of health for informed policy-making and intervention.

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).

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.

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.

Associations between flood risk and US Census tract-level health outcomes.

Human-induced climate change has led to more frequent and severe flooding around the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation data by census tract and used principal component analysis to derive a set of 5 interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on these data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We show that 3 flood risk principal components had small but significant associations with each of the health outcomes across the different stratifications of social vulnerability. Our analysis gives, to our knowledge, the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards. This article is part of a Special Collection on Environmental Epidemiology.