Developing a National-Scale Exposure Index for Combined Environmental Hazards and Social Stressors and Applications to the Environmental Influences on Child Health Outcomes (ECHO) Cohort.

Tools for assessing multiple exposures across several domains (e.g., physical, chemical, and social) are of growing importance in social and environmental epidemiology because of their value in uncovering disparities and their impact on health outcomes. Here we describe work done within the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study to build a combined exposure index. Our index considered both environmental hazards and social stressors simultaneously with national coverage for a 10-year period. Our goal was to build this index and demonstrate its utility for assessing differences in exposure for pregnancies enrolled in the ECHO-wide Cohort Study. Our unitless combined exposure index, which collapses census-tract level data into a single relative measure of exposure ranging from 0-1 (where higher values indicate higher exposure to hazards), includes indicators for major air pollutants and air toxics, features of the built environment, traffic exposures, and social determinants of health (e.g., lower educational attainment) drawn from existing data sources. We observed temporal and geographic variations in index values, with exposures being highest among participants living in the West and Northeast regions. Pregnant people who identified as Black or Hispanic (of any race) were at higher risk of living in a "high" exposure census tract (defined as an index value above 0.5) relative to those who identified as White or non-Hispanic. Index values were also higher for pregnant people with lower educational attainment. Several recommendations follow from our work, including that environmental and social stressor datasets with higher spatial and temporal resolutions are needed to ensure index-based tools fully capture the total environmental context.

Geospatial analysis of firearm injuries in an urban setting: Individual rather than community characteristics affect firearm injury risk.

BACKGROUND: The relationship between individual/socioeconomic characteristics and firearm injury risk in an urban center was evaluated. METHODS: A hospital registry was used to identify individuals in Baltimore City who experienced interpersonal firearm injury in 2019 (FA). Injuries that did not satisfy this criterion were used as a comparison group (NF). Socioeconomic characteristics were linked to home address at the block group level. Regression analysis was used to determine predictors of firearm injury. Clusters of high and low firearm relative to non-firearm injuries were identified. RESULTS: A total of 1293 individuals were included (FA = 277, NF = 1016). The FA group lived in communities with lower income (p = 0.005), higher poverty (p = 0.007), and more Black residents (p < 0.001). Individual level factors were stronger predictors of firearm injury than community factors on multivariate regression with Black race associated with 5x higher odds of firearm injury (p < 0.001). Firearm injury clustered in areas of low socioeconomic status. CONCLUSIONS: Individual versus community factors have a greater influence on firearm injury risk. Prevention efforts should target young, Black men in urban centers.

Returning to our roots: The use of geospatial data for nurse-led community research.

In the early 20th century, public health nurse, Lillian Wald, addressed the social determinants of health (SDOH) through her work in New York City and her advocacy to improve policy in workplace conditions, education, recreation, and housing. In the early 21st century, addressing the SDOH is a renewed priority and provides nurse researchers with an opportunity to return to our roots. The purpose of this methods paper is to examine how the incorporation of geospatial data and spatial methodologies in community research can enhance the analyses of the complex relationships between social determinants and health. Geospatial technologies, software for mapping and working with geospatial data, statistical methods, and unique considerations are discussed. An exemplar for using geospatial data is presented regarding associations between neighborhood greenspace, neighborhood violence, and children's asthma control. This innovative use of geospatial data illustrates a new frontier in investigating nontraditional connections between the environment and SDOH outcomes.

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011.

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions.

Malaria risk maps may be used to guide policy decisions on whether vector control interventions should be targeted and, if so, where. Active surveillance for malaria was conducted through household surveys in Nchelenge District, Zambia from April 2012 through December 2014. Households were enumerated based on satellite imagery and randomly selected for study enrollment. At each visit, participants were administered a questionnaire and a malaria rapid diagnostic test (RDT). Logistic regression models were used to construct spatial prediction risk maps and maps of risk uncertainty. A total of 461 households were visited, comprising 1,725 participants, of whom 48% were RDT positive. Several environmental features were associated with increased household malaria risk in a multivariable logistic regression model adjusting for seasonal variation. The model was validated using both internal and external evaluation measures to generate and assess root mean square error, as well as sensitivity and specificity for predicted risk. The final, validated model was used to predict and map malaria risk including a measure of risk uncertainty. Malaria risk in a high, perennial transmission setting is widespread but heterogeneous at a local scale, with seasonal variation. Targeting malaria control interventions may not be appropriate in this epidemiological setting.

Neighborhood incivilities, perceived neighborhood safety, and walking to school among urban-dwelling children.

BACKGROUND: Walking to school is an important source of physical activity among children. There is a paucity of research exploring environmental determinants of walking to school among children in urban areas. METHODS: A cross-sectional secondary analysis of baseline data (2007) from 365 children in the "Multiple Opportunities to Reach Excellence" (MORE) Study (8 to 13 years; Mean 9.60 years, SD 1.04). Children and caregivers were asked about walking to school and perceived safety. Objective measures of the environment were obtained using a validated environmental neighborhood assessment. RESULTS: Over half (55.83%) of children reported walking to school most of the time. High levels of neighborhood incivilities were associated with lower levels of perceived safety (OR: 0.39, 95% CI: 0.21 to 0.72). Living on a block above the median in incivilities was associated with a 353% increase in odds of walking to school (OR: 3.53; 95% CI: 1.68 to 7.39). CONCLUSIONS: Children residing in neighborhoods high in incivilities are more likely to walk to school, in spite of lower levels of perceived safety. As a high proportion of children residing in disadvantaged neighborhoods walk to school, efforts should be directed at minimizing exposure to neighborhood hazards by ensuring safe routes to and from school.