Associations between a Novel Measure of Census Tract-Level Credit Insecurity and Frequent Mental Distress in US Urban Areas, 2020.

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

BACKGROUND: Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits. METHODS: We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery. RESULTS: Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76). CONCLUSIONS: Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

The Organization of Mitochondrial Quality Control and Life Cycle in the Nervous System In Vivo in the Absence of PINK1.

Maintenance of healthy mitochondria is crucial in cells, such as neurons, with high metabolic demands, and dysfunctional mitochondria are thought to be selectively degraded. Studies of chemically uncoupled cells have implicated PINK1 mitochondrial kinase, and Parkin E3 ubiquitin ligase in targeting depolarized mitochondria for degradation. However, the role of the PINK1/Parkin pathway in mitochondrial turnover is unclear in the nervous system under normal physiological conditions, and we understand little about the changes that occur in the mitochondrial life cycle when turnover is disrupted. Here, we evaluated the nature, location, and regulation of quality control in vivo using quantitative measurements of mitochondria in Drosophila nervous system, with deletion and overexpression of genes in the PINK1/Parkin pathway. We tested the hypotheses that impairment of mitochondrial quality control via suppression of PINK1 function should produce failures of turnover, accumulation of senescent mitochondria in the axon, defects in mitochondrial traffic, and a significant shift in the mitochondrial fission-fusion steady state. Although mitochondrial membrane potential was diminished by PINK1 deletion, we did not observe the predicted increases in mitochondrial density or length in axons. Loss of PINK1 also produced specific, directionally balanced defects in mitochondrial transport, without altering the balance between stationary and moving mitochondria. Somatic mitochondrial morphology was also compromised. These results strongly circumscribe the possible mechanisms of PINK1 action in the mitochondrial life cycle and also raise the possibility that mitochondrial turnover events that occur in cultured embryonic axons might be restricted to the cell body in vivo, in the intact nervous system.

Association between racial residential segregation and walkability in 745 U.S. cities.

Despite higher chronic disease prevalence, minoritized populations live in highly walkable neighborhoods in US cities more frequently than non-minoritized populations. We investigated whether city-level racial residential segregation (RRS) was associated with city-level walkability, stratified by population density, possibly explaining this counterintuitive association. RRS for Black-White and Latino-White segregation in large US cities was calculated using the Index of Dissimilarity (ID), and walkability was measured using WalkScore. Median walkability increased across increasing quartiles of population density, as expected. Higher ID was associated with higher walkability; associations varied in strength across strata of population density. RRS undergirds the observed association between walkability and minoritized populations, especially in higher population density cities.

Validation of a geospatial aggregation method for congressional districts and other US administrative geographies.

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.

Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities.

OBJECTIVES: To present the COVID Local Risk Index (CLRI), a measure of city- and neighborhood-level risk for SARS COV-2 infection and poor outcomes, and validate it using sub-city SARS COV-2 outcome data from 47 large U.S. cities. METHODS: Cross-sectional validation analysis of CLRI against SARS COV-2 incidence, percent positivity, hospitalization, and mortality. CLRI scores were validated against ZCTA-level SARS COV-2 outcome data gathered in 2020-2021 from public databases or through data use agreements using a negative binomial model. RESULTS: CLRI was associated with each SARS COV-2 outcome in pooled analysis. In city-level models, CLRI was positively associated with positivity in 11/14 cities for which data were available, hospitalization in 6/6 cities, mortality in 13/14 cities, and incidence in 33/47 cities. CONCLUSIONS: CLRI is a valid tool for assessing sub-city risk of SARS COV-2 infection and illness severity. Stronger associations with positivity, hospitalization and mortality may reflect differential testing access, greater weight on components associated with poor outcomes than transmission, omitted variable bias, or other reasons. City stakeholders can use the CLRI, publicly available on the City Health Dashboard (www.cityhealthdashboard.com), to guide SARS COV-2 resource allocation.