Visualizing Neighborhood COVID-19 Levels, Trends, and Inequities in Wastewater: An Equity-Centered Approach and Comparison to CDC Methods.

CONTEXT: Monitoring neighborhood-level SARS-CoV-2 wastewater concentrations can help guide public health interventions and provide early warning ahead of lagging COVID-19 clinical indicators. To date, however, U.S. Centers for Disease Control and Prevention's (CDC) National Wastewater Surveillance System (NWSS) has provided methodology solely for communicating national and state-level "wastewater viral activity levels." PROGRAM: In October 2022, the Boston Public Health Commission (BPHC) began routinely sampling wastewater at 11 neighborhood sites to better understand COVID-19 epidemiology and inequities across neighborhoods, which vary widely in sociodemographic and socioeconomic characteristics. We developed equity-centered methods to routinely report interpretable and actionable descriptions of COVID-19 wastewater levels, trends, and neighborhood-level inequities. APPROACH AND IMPLEMENTATION: To produce these data visualizations, spanning October 2022 to December 2023, we followed four general steps: (1) smoothing raw values; (2) classifying current COVID-19 wastewater levels; (3) classifying current trends; and (4) reporting and visualizing results. EVALUATION: COVID-19 wastewater levels corresponded well with lagged COVID-19 hospitalizations and deaths over time, with "Very High" wastewater levels coinciding with winter surges. When citywide COVID-19 levels were at the highest and lowest points, levels and trends tended to be consistent across sites. In contrast, when citywide levels were moderate, neighborhood levels and trends were more variable, revealing inequities across neighborhoods, emphasizing the importance of neighborhood-level results. Applying CDC/NWSS state-level methodology to neighborhood sites resulted in vastly different neighborhood-specific wastewater cut points for "High" or "Low," obscured inequities between neighborhoods, and systematically underestimated COVID-19 levels during surge periods in neighborhoods with the highest COVID-19 morbidity and mortality. DISCUSSION: Our methods offer an approach that other local jurisdictions can use for routinely monitoring, comparing, and communicating neighborhood-level wastewater levels, trends, and inequities. Applying CDC/NWSS methodology at the neighborhood-level can obscure and perpetuate COVID-19 inequities. We recommend jurisdictions adopt equity-focused approaches in neighborhood-level wastewater surveillance for valid community comparisons.

Characterizing self-reported physical activity before and during a subsequent pregnancy among parents in a familial autism cohort.

LAY ABSTRACT: Parents of autistic children may have limited time and resources to participate in physical activity, a key aspect of health. Previous studies have been small and included mostly mothers, rather than fathers. No studies have examined physical activity in these parents during another pregnancy, when physical activity is especially important for maternal and fetal health. We aimed to fill this gap by examining physical activity levels among mothers and fathers caring for an autistic child before and during a subsequent pregnancy. We used data from a study which followed pregnant individuals who already had a child with autism. We asked mothers and fathers to report their levels of moderate and vigorous physical activity. We found that mothers and fathers of autistic children reported lower physical activity levels than the national average and were unlikely to meet Physical Activity Guidelines for Americans. Pregnant mothers were the least likely to participate in physical activity, particularly if their autistic child scored highly on a measure of autistic traits. Given that parental physical activity has benefits for parents and children, family-based interventions may be needed to help support parents' physical activity levels.

Spatial and spatio-temporal statistical implications for measuring structural racism: A review of three widely used residential segregation measures.

Social determinants of health are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning and quality of life outcomes and risks - these social determinants of health often aid in explaining the racial and ethnic health inequities present in the United States (US). The root cause of these social determinants of health has been tied to structural racism, and residential segregation is one such domain of structural racism that allows for the operationalization of the geography of structural racism. This review focuses on three residential segregation measures that are often utilized to capture segregation as a function of race/ethnicity, income, and simultaneously race/ethnicity and income. Empirical findings related to the spatial and spatio-temporal heterogeneity of these residential segregation measures are presented. We also discuss some of the implications of utilizing these three residential segregation measures.

Associations between Historical Redlining and the Risk of Pregnancy Complications and Adverse Birth Outcomes in Massachusetts, 1995-2015.

Objective: To assess the impact of historical redlining on the risk of pregnancy complications and adverse birth outcomes in Massachusetts (MA) from 1995 to 2015. Methods: In total, 288,787 pregnant people from the MA Birth Registry had information on parental characteristics, pregnancy factors, and redlining data at parental residences at the time of delivery. Historic redlining data were based on MA Home Owners' Loan Corporation (HOLC) security maps, with grades assigned (A "best," B "still desirable," C "definitely declining," and D "hazardous"). We used covariate-adjusted binomial regression models to examine associations between HOLC grade and each chronic condition and pregnancy/birth outcome. Results: Living in HOLC grades B through D compared with A was associated with an increased risk of entering pregnancy with chronic conditions and adverse pregnancy/birth outcomes. The strongest associations were seen with pregestational diabetes (adjusted risk ratio [RR] Grade D: 1.7, 95% confidence interval [CI]: 1.3, 2.4) and chronic hypertension (adjusted RR Grade D: 1.5, 95% CI: 1.1, 1.9). Conclusions: Historical redlining policies from the 1930s were associated with adverse pregnancy outcomes and chronic conditions; associations were strongest for chronic conditions in pregnancy.

Restricted spatial models for the analysis of geographic and racial disparities in the incidence of low birthweight in Pennsylvania.

The incidence of low birthweight is a common measure of public health due to the increased risk of complications associated with infants having low and very low birthweights. Moreover, many factors that increase the risk of an infant having a low birthweight can be linked to the mother's socioeconomic status, leading to large racial/ethnic disparities in its incidence. Our objective is thus to analyze the incidence of low and very low birthweight in Pennsylvania counties by race/ethnicity. Due to the small number of births in many Pennsylvania counties when stratified by race/ethnicity, our methods must walk a fine line: While we wish to leverage spatial structure to improve the precision of our estimates, we also wish to avoid oversmoothing the data, which can yield spurious conclusions. As such, we develop a framework by which we can measure (and control) the informativeness of our spatial model. To analyze the data, we first model the Pennsylvania birth data using the conditional autoregressive model to demonstrate the extent to which it can lead to oversmoothing. We then reanalyze the data using our proposed framework and highlight its ability to detect (or not detect) evidence of racial/ethnic disparities in the incidence of low birthweight.

A two-stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties.

Racialized economic segregation, a key metric that simultaneously accounts for spatial, social and income polarization in communities, has been linked to adverse health outcomes, including morbidity and mortality. Due to the spatial nature of this metric, the association between health outcomes and racialized economic segregation could also change with space. Most studies assessing the relationship between racialized economic segregation and health outcomes have always treated racialized economic segregation as a fixed effect and ignored the spatial nature of it. This paper proposes a two-stage Bayesian statistical framework that provides a broad, flexible approach to studying the spatially varying association between premature mortality and racialized economic segregation while accounting for neighborhood-level latent health factors across US counties. The two-stage framework reduces the dimensionality of spatially correlated data and highlights the importance of accounting for spatial autocorrelation in racialized economic segregation measures, in health equity focused settings.

Prenatal air pollution and children's autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods.

Background: Maternal nutrient intake may moderate associations between environmental exposures and children's neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. Methods: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 µm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children's autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Results: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. Conclusion: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

A Segmented Regression Analysis of Household Income and Recurrent Falls Among Adults in a National Cohort Study.

Falls can have life-altering consequences for older adults, including extended recovery periods and compromised independence. Higher household income may mitigate the risk of falls by providing financial resources for mobility tools, remediation of environmental hazards, and needed supports, or it may buffer the impact of an initial fall on subsequent risk through improved assistance and care. Household income has not had a consistently observed association with falls in older adults; however, a segmented association may exist such that associations are attenuated above a certain income threshold. In this study, we utilized segmented negative binomial regression analysis to examine the association between household income and recurrent falls among 2,302 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited between 2003 and 2007. Income-fall association segments separated by changes in slope were considered. Model results indicated a 2-segment association between household income and recurrent falls in the past year. In the range below the breakpoint, household income was negatively associated with the rate of recurrent falls across all age groups examined; in a higher income range (from $20,000-$49,999 to ≥$150,000), the association was attenuated (weaker negative trend) or reversed (positive trend). These findings point to potential benefits of ensuring that incomes for lower-income adults exceed the threshold needed to confer a reduced risk of recurrent falls.

Addressing spatial misalignment in population health research: a case study of US congressional district political metrics and county health data.

Areal spatial misalignment, which occurs when data on multiple variables are collected using mismatched boundary definitions, is a ubiquitous obstacle to data analysis in public health and social science research. As one example, the emerging sub-field studying the links between political context and health in the United States faces significant spatial misalignment-related challenges, as the congressional districts (CDs) over which political metrics are measured and administrative units, e.g., counties, for which health data are typically released, have a complex misalignment structure. Standard population-weighted data realignment procedures can induce measurement error and invalidate inference, which has prompted the development of fully model-based approaches for analyzing spatially misaligned data. One such approach, atom-based regression models (ABRM), holds particular promise but has scarcely been used in practice due to the lack of appropriate software or examples of implementation. ABRM use "atoms", the areas created by intersecting all sets of units on which variables of interest are measured, as the units of analysis and build models for the atom-level data, treating the atom-level variables (generally unmeasured) as latent variables. In this paper, we demonstrate the feasibility and strengths of the ABRM in a case study of the association between political representatives' voting behavior (CD-level) and COVID-19 mortality rates (county-level) in a post-vaccine period. The adjusted ABRM results suggest that more conservative voting record is associated with an increase in COVID-19 mortality rates, with estimated associations smaller in magnitude but consistent in direction with those of standard realignment methods. The results also indicate that ABRM may enable more robust confounding adjustment and more realistic uncertainty estimates, properly representing the uncertainties arising from all analytic procedures. We also implement the ABRM in modern optimized Bayesian computing programs and make our code publicly available, which may enable these methods to be more widely adopted.

Racialized economic segregation and potentially preventable hospitalizations among Medicaid/CHIP-enrolled children.

OBJECTIVE: To examine geographic variation in preventable hospitalizations among Medicaid/CHIP-enrolled children and to test the association between preventable hospitalizations and a novel measure of racialized economic segregation, which captures residential segregation within ZIP codes based on race and income simultaneously. DATA SOURCES: We supplement claims and enrollment data from the Transformed Medicaid Statistical Information System (T-MSIS) representing over 12 million Medicaid/CHIP enrollees in 24 states with data from the Public Health Disparities Geocoding Project measuring racialized economic segregation. STUDY DESIGN: We measure preventable hospitalizations by ZIP code among children. We use logistic regression to estimate the association between ZIP code-level measures of racialized economic segregation and preventable hospitalizations, controlling for sex, age, rurality, eligibility group, managed care plan type, and state. DATA EXTRACTION METHODS: We include children ages 0-17 continuously enrolled in Medicaid/CHIP throughout 2018. We use validated algorithms to identify preventable hospitalizations, which account for characteristics of the pediatric population and exclude children with certain underlying conditions. PRINCIPAL FINDINGS: Preventable hospitalizations vary substantially across ZIP codes, and a quarter of ZIP codes have rates exceeding 150 hospitalizations per 100,000 Medicaid-enrolled children per year. Preventable hospitalization rates vary significantly by level of racialized economic segregation: children living in the ZIP codes that have the highest concentration of low-income, non-Hispanic Black residents have adjusted rates of 181 per 100,000 children, compared to 110 per 100,000 for children in ZIP codes that have the highest concentration of high-income, non-Hispanic white residents (p < 0.01). This pattern is driven by asthma-related preventable hospitalizations. CONCLUSIONS: Medicaid-enrolled children's risk of preventable hospitalizations depends on where they live, and children in economically and racially segregated neighborhoods-specifically those with higher concentrations of low-income, non-Hispanic Black residents-are at particularly high risk. It will be important to identify and implement Medicaid/CHIP and other policies that increase access to high-quality preventive care and that address structural drivers of children's health inequities.

The Association between Changes in Built Environment and Changes in Walking among Older Women in Portland, Oregon.

Some cross-sectional evidence suggests that the objectively measured built environment can encourage walking among older adults. We examined the associations between objectively measured built environment with change in self-reported walking among older women by using data from the Study of Osteoporotic Fractures (SOF). We evaluated the longitudinal associations between built environment characteristics and walking among 1253 older women (median age = 71 years) in Portland, Oregon using generalized estimating equation models. Built environment characteristics included baseline values and longitudinal changes in distance to the closest bus stop, light rail station, commercial area, and park. A difference of 1 km in the baseline distance to the closest bus stop was associated with a 12% decrease in the total number of blocks walked per week during follow-up (eß = 0.88, 95% CI: 0.78, 0.99). Our study provided limited support for an association between neighborhood transportation and changes in walking among older women. Future studies should consider examining both objective measures and perceptions of the built environment.

Examining the Geospatial Distribution of Health and Support Services for Transgender, Gender Nonbinary, and Other Gender Diverse People in New York City.

A geospatial analysis of services that support transgender and gender diverse ("trans") people in New York City (NYC) was conducted to investigate associations with neighborhood-level sociodemographic characteristics. In June 2019, there were 5.3 services for every 100,000 of the general NYC population; controlling for other covariates, they were more commonly located in neighborhoods with larger populations of non-Hispanic Black (rate ratio [RR]=1.02, 95% confidence interval [CI]: 1.00-1.04), Hispanic/Latino (RR=1.03, 95% CI: 1.00-1.06), and gay/lesbian people (RR=1.53, 95% CI: 1.03-2.34). These findings suggest that the distribution of trans-focused services in NYC is proximal to communities that are most in need, but research should examine proximity to trans people specifically and distribution in nonurban areas.

Examining Associations between Adverse Childhood Experiences and Posttraumatic Stress Disorder Symptoms among Young Survivors of Urban Violence.

Our study examines the association between Adverse Childhood Experience (ACE) exposure and posttraumatic stress disorder (PTSD) symptoms among survivors of violence. In this cross-sectional study, an ACE questionnaire and PTSD Checklist for DSM-5 (PCL-5) were completed by 147 participants ≤ 3 months after presenting to a Philadelphia, PA emergency department between 2014 and 2019 with a violent injury. This study treated ACEs, both separate and cumulative, as exposures and PTSD symptom severity as the outcome. Most participants (63.3%) met criteria for provisional PTSD, 90% reported experiencing ≥ 1 ACE, and 39% reported experiencing ≥ 6 ACEs. Specific ACEs were associated with increasing PCL-5 scores and increased risk for provisional PTSD. Additionally, as participants' cumulative ACE scores increased, their PCL-5 scores worsened (b = 0.16; p < 0.05), and incremental ACE score increases predicted increased odds for a positive provisional PTSD screen. Results provide further evidence that ACEs exacerbate the development of PTSD in young survivors of violence. Future research should explore targeted interventions to treat PTSD among survivors of interpersonal violence.

Prenatal Diet as a Modifier of Environmental Risk Factors for Autism and Related Neurodevelopmental Outcomes.

PURPOSE OF REVIEW: Environmental chemicals and toxins have been associated with increased risk of impaired neurodevelopment and specific conditions like autism spectrum disorder (ASD). Prenatal diet is an individually modifiable factor that may alter associations with such environmental factors. The purpose of this review is to summarize studies examining prenatal dietary factors as potential modifiers of the relationship between environmental exposures and ASD or related neurodevelopmental outcomes. RECENT FINDINGS: Twelve studies were identified; five examined ASD diagnosis or ASD-related traits as the outcome (age at assessment range: 2-5 years) while the remainder addressed associations with neurodevelopmental scores (age at assessment range: 6 months to 6 years). Most studies focused on folic acid, prenatal vitamins, or omega-3 fatty acids as potentially beneficial effect modifiers. Environmental risk factors examined included air pollutants, endocrine disrupting chemicals, pesticides, and heavy metals. Most studies took place in North America. In 10/12 studies, the prenatal dietary factor under study was identified as a significant modifier, generally attenuating the association between the environmental exposure and ASD or neurodevelopment. Prenatal diet may be a promising target to mitigate adverse effects of environmental exposures on neurodevelopmental outcomes. Further research focused on joint effects is needed that encompasses a broader variety of dietary factors, guided by our understanding of mechanisms linking environmental exposures with neurodevelopment. Future studies should also aim to include diverse populations, utilize advanced methods to optimize detection of novel joint effects, incorporate consideration of timing, and consider both synergistic and antagonistic potential of diet.

Spatial predictors of heavy metal concentrations in epiphytic moss samples in Seattle, WA.

The use of bio-indicators is an emerging, cost-effective alternative approach to identifying air pollution and assessing the need for additional air monitoring. This community science project explores the use of moss samples as bio-indicators of the distribution of metal air particulates in two residential neighborhoods of the industrial Duwamish Valley located in Seattle, WA (USA). We applied geographically weighted regression to data from 61 youth-collected samples to assess the location-specific area-level spatial predictors of the concentrations of 25 elements with focus on five heavy metals of concern due to health and environmental considerations. Spatial predictors included traffic volume, industrial land uses, major roadways, the airport, dirt roads, the Duwamish River, impervious surfaces, tree canopy cover, and sociodemographics. Traffic volume surrounding sample locations was the most consistent positive predictor of increasing heavy metal concentration. Greater distance from the heavy-industry corridor surrounding the Duwamish River predicted lower concentrations of all metals, with statistically significant associations for chromium and lead in some areas. As the distance from dirt roads increased, the concentration of arsenic and chromium decreased significantly. Percent tree canopy within 200 m of sample locations was a significant protective factor for cadmium concentrations. In addition, percent people of color was significantly positively associated with increasing lead, chromium and nickel concentrations. Our findings underscore the potential influence of heavy industry and mobile sources on heavy metal concentrations, the buffering potential of trees in local environments, and persistent opportunity to improve environmental justice.

Spatially varying racial inequities in cardiovascular health and the contribution of individual- and neighborhood-level characteristics across the United States: The REasons for geographic and racial differences in stroke (REGARDS) study.

Black-White inequities in cardiovascular health (CVH) pose a significant public health challenge, with these disparities also varying geographically across the US. There remains limited evidence of the impact of social determinants of health on these inequities. Using a national population-based cohort from the REasons for Geographic and Racial Differences in Stroke study, we assessed the spatial heterogeneity in Black-White differences in CVH and determined the extent to which individual- and neighborhood-level characteristics explain these inequities. We utilized a Bayesian hierarchical statistical framework to fit spatially varying coefficient models. Results showed overall and spatially varying inequities, where Black participants had significantly poorer CVH. The maps of the state level random effects also highlighted how inequities vary. The evidence produced in this study further highlights the importance of multilevel approaches - at the individual- and neighborhood-levels - that need to be in place to address these geographic and racial differences in CVH.

Associations Between Historical Redlining and Present-Day Heat Vulnerability Housing and Land Cover Characteristics in Philadelphia, PA.

Historical, institutional racism within the housing market may have impacted present-day disparities in heat vulnerability. We quantified associations between historically redlined areas with present-day property and housing characteristics that may enhance heat vulnerability in Philadelphia, PA. We used color-coded Home Owners Loan Corporation (HOLC) maps and tax assessment data to randomly select 100 present-day (2018-2019) residential properties in each HOLC grade area (A = Best; B, C, and D = Most hazardous; N = 400 total). We conducted virtual inventories of the properties using aerial and streetview imagery for land cover and housing characteristics (dark roof color, flat roof shape, low or no mature tree canopy, no recently planted street trees) that may enhance heat vulnerability. We used modified Poisson regression models to estimate associations of HOLC grades with the property characteristics, unadjusted and adjusted for historical and contemporary measures of the neighborhood sociodemographic environment. Compared to grade A areas, higher proportions of properties in grade B, C, and D areas had dark roofs, low/no mature tree canopy, and no street trees. Adjusting for historical sociodemographics attenuated associations, with only associations with low or no tree canopy remaining elevated. Adjusting for present-day concentrated racial and socioeconomic deprivation did not substantially impact overall findings. In Philadelphia, PA, HOLC maps serve as spatial representations of present-day housing and land cover heat vulnerability characteristics. Further analyses incorporating longitudinal data on urban redevelopment, reinvestment, and neighborhood change are needed to more fully represent complex relationships among historical racism, residential segregation, and heat vulnerability.

Historical redlining and cardiovascular health: The Multi-Ethnic Study of Atherosclerosis.

We investigated historical redlining, a government-sanctioned discriminatory policy, in relation to cardiovascular health (CVH) and whether associations were modified by present-day neighborhood physical and social environments. Data included 4,779 participants (mean age 62 y; SD = 10) from the baseline sample of the Multi-Ethnic Study of Atherosclerosis (MESA; 2000 to 2002). Ideal CVH was a summary measure of ideal levels of seven CVH risk factors based on established criteria (blood pressure, fasting glucose, cholesterol, body mass index, diet, physical activity, and smoking). We assigned MESA participants' neighborhoods to one of four grades (A: best, B: still desirable, C: declining, and D: hazardous) using the 1930s federal Home Owners' Loan Corporation (HOLC) maps, which guided decisions regarding mortgage financing. Two-level hierarchical linear and logistic models, with a random intercept to account for participants nested within neighborhoods (i.e., census tracts) were used to assess associations within racial/ethnic subgroups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Chinese). We found that Black adults who lived in historically redlined areas had a 0.82 (95% CI -1.54, -0.10) lower CVH score compared to those residing in grade A (best) neighborhoods, in a given neighborhood and adjusting for confounders. We also found that as the current neighborhood social environment improved the association between HOLC score and ideal CVH weakened (P < 0.10). There were no associations between HOLC grade and CVH measures or effect modification by current neighborhood conditions for any other racial/ethnic group. Results suggest that historical redlining has an enduring impact on cardiovascular risk among Black adults in the United States.