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Studie provides visual representations of lead concentration in Santa Ana cross matching it with vulnerability risk.

“Also of note when interpreting our results is that this study did not take into consideration the ingestion of heavy metals through the dietary route. Had we considered this additional exposure pathway, our calculated chronic daily intake levels of heavy metals would have been greater, resulting in higher estimated risk (particularly for metals such as Pb, As, and Cd which have been widely documented in various foods)” (Marsi et al. 2021)

“Both cancer and non-cancer risk at the Census tract level exhibited positive correlations with indicators of social as well as physiological vulnerability” (Marsi et al. 2021)

Exposure to heavy metals has been associated with adverse health effects and disproportionately impacts communities of a lower socio-economic status.  

This study used a community-based participatory research approach to collect and analyze a large number of randomly sampled soil measurements to yield a high spatially resolved understanding of the distribution of heavy metals in the Santa Ana soil, in an effort to exposure misclassification. This study looks into average metal  concentrations at the Census tract level and by land use type, which helps map potential sources of heavy metals in the soil and better understand the association between socioeconomic status and soil contamination (Marsi et al. 2021). 

In 2018, soil samples of eight heavy metals including lead (Pb), arsenic (As), manganese (Mn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), and zinc (Zn) were collected across Santa Ana. These were analyzed at a high resolution using XRF analysis. Then, metal concentrations were mapped out and American Community Survey data was used to assess the metals throughout Census tracts in terms of social and economic variables. Risk assessment was conducted to evaluate carcinogenic risk. The results of the concentrations of soil metals were categorized according to land-use type and socioeconomic factors. “Census tracts where the median household income was under $50 000 had 90%, 92.9%, 56.6%, and 54.3% higher Pb, Zn, Cd, and As concentrations compared to high-income counterparts” (Marsi et al. 2021). All Census tracts in Santa were above hazard inder >1, which implies non-carcinogenic effects, and almost all Census tracts showed a cancer risk above 104, which implies greater than acceptable risk. Risk was found to be driven by childhood exposure.

It was concluded that the issue of elevated soil contamination relates back to environmental justice due to overlap between contaminated areas and neighborhoods of lower socioeconomic status. Marsi et al. (2021) found there needs to be more community-driven recommendations for policies and other actions to address disproportionate solid contamination and prevent adverse health outcomes.      

Published in May 2021, amid the coronavirus pandemic where in-person community workshops and meetings turned into weekly virtual meetings. 

-> Authors:

Shahir Masri: Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine; air pollution scientist.

Alana M. W. LeBrón: Department of Health, Society, and Behavior, University of California, Irvine; Assistant Professor, Chicano/Latino Studies; Interests: structural racism and health, health of Latina/o communities, community-based participatory research.

Michael D. Logue: Department of Chicano/Latino Studies, University of California, Irvine

Enrique Valencia: Orange County Environmental Justice, Santa Ana

Abel Ruiz: Jóvenes Cultivando Cambios, Santa Ana; CRECE Urban Farming Cooperative member

Abigail Reyes: Community Resilience, University of California, Irvine

Jun Wu: Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine

Creators of the Student Health Index recommend using the tool in combination with qualitative data collection and stakeholder/community engagement (e.g. working with school leaders, local community leaders, and healthcare providers).

A full guide to using the dashboard is available here.

Data sources utilized by the index are not always the most current due to data collection limitations (e.g. covid-19 has caused disruptions in the collection of CDE data).

The Index is limited in that it does not offer data for schools that were not large enough to warrant the construction of a School-based Health Center. Thus, schools that did not meet specific enrollment targets were excluded from the dashboard. This includes rural schools (designed as such by the USDA) with an enrollment under 500 students, urban schools (without a high school) with less than 500 students, and urban schools (with a high school) with less than 1000 students. California had more than 10,000 active public schools in 2020-21. The final dashboard for the Student Health Index includes 4,821 schools.

The lack of available data on health indicators at a school-level restricted the Student Health Index to using proxies for the health outcomes. Some health indicators are included, but they are not school-specific, instead linked to specific schools geographically through the census tract. However, community-level data does not always accurately reflect the characteristics of a school’s population. As a result, school-level indicators in the Index were weighted more heavily than community-level indicators.

Additionally, race was not included as a measure in the Student Health Index because of California’s Proposition 20, which prohibits the allocation of public resources based on race and ethnicity. However, the dataset does contain measures of non-white students at each school. 

The Index has also been limited as a quantitative measure of need, which may overlook the influence of other factors that might be better illuminated through qualitative evidence (e.g. stakeholder engagement, focus groups, interviews, etc.).

No information thus far

The Student Health Index can produce visualizations that represent data on conditions, school characteristics and risk factors that affect education outcomes and could be improved through access to school-based health care. These visualizations can be used to demonstrate need for expanding school-based health care access in California.

In addition to maps, the index can also be used to generate graphs and visual displays of data (e.g. ratio of highest need schools to all schools, by county).

The visualizations can be used to demonstrate the correlations between final need scores and race, the impact of specific indicators in health, and the concentration of need to certain regions of California (hot spot analysis).