Korin MR, Araya F, Idris MY, Brown H, Claudio L. Community-based Organizations as effective partners in the battle against misinformation. Front Public Health 15 March 2022. https://doi.org/10.3389/fpubh.2022.853736
SUMMARY: Misinformation has real-world implications on population health in today's fast-changing pandemic environment. Traditionally, professional middlemen such as reporters and formal media outlets were placed between information creators and the public. We no longer have these intermediates, allowing disinformation to spread. Public health information is frequently not customized to the needs of those most at risk, exacerbating health inequities and increasing suspicion and skepticism.
While public health authorities sometimes employ community-based organizations (CBOs) for outreach activities, they lose opportunities to actively engage them in all phases of the Health Communication Cycle. Fighting the infodemic requires many steps, including understanding what information is required, producing suitable and relevant messages, and sharing these messages in ways that are tailored to the recipients. Community-based organizations are already trustworthy bodies thoroughly engaged in the communities they serve and can help counteract disinformation. As a result of their unique and intangible characteristics and inroads in the communities they serve, CBOs are ideally positioned to play an active part in the health communication cycle, encouraging health equality. Thus, to successfully communicate and fight against health misinformation, particularly in groups with deep-seated mistrust or inadequate health literacy, we must incorporate and engage CBOs in all parts of health communication.
In this new opinion paper, co-written by our research team and our community partners, we provide background to support engaging CBOs in addressing health literacy and ehealth equity.
Idris MY, Korin M, Araya F, et al. Including the Public in Public eHealth: The Need for Community Participation in the Development of State-Sponsored COVID-19-Related Mobile Apps. JMIR Mhealth and Uhealth. 2022 Mar;10(3):e30872. DOI: 10.2196/30872. PMID: 35113793.
The COVID-19 epidemic has swamped healthcare systems globally, especially in communities of color with high prevalence of pre-existing conditions. Many state governments and healthcare organizations responded by expanding telemedicine capabilities and developing illness tracking smartphone apps. Based on our observations, many state-sponsored eHealth technologies did not involve community participation, thereby contributing to the growing digital health inequities. We propose that as the use of eHealth tools grows, more emphasis be paid to their equitable distribution, accessibility, and use. We offer our experience engaging in a Community Advisory Board working on the dissemination of the COVID Alert NY mobile app to show the relevance of public participation in app development. We also offer suggestions for involving community members in the app development process. We argue that involving communities in the app creation process enhances buy-in, trust, and utilization of digital technology in communities where it is most needed.
We published new research: Inequalities in Exposure to Ambient Air Neurotoxicants and Markers of Neurodevelopment in Children by Maternal Nativity Status
Araya F, Stingone JA, Claudio L. Inequalities in exposure to ambient air neurotoxicants and disparities in markers of neurodevelopment in children by maternal nativity status. Int J. Environ Res Public Health 2021, 18 (14) 7512 https://doi.org/10.3390/ijerph18147512
SUMMARY: Exposure to hazardous air pollutants (HAPs) varies greatly amongst populations. Inequities in HAP exposure among groups can lead to disparities in neurodevelopmental outcomes in children. Our study's goal was to see if HAP exposure was influenced by maternal nativity, a demographic characteristic generally disregarded in health disparities research. We also looked at whether unequal HAP exposure levels may affect young children's neurodevelopment.
To do this, we used data from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B), a nationally representative sample of children born in 2001 (n = 4750). Early cognitive development was assessed using Bayley's Short Form–Research Edition (BSF–R), a standardized test often used to assess brain development.
We found that participants at age nine months were exposed to ten possibly neurotoxic HAPs in their homes. Using linear regression models, we found that maternal nativity and HAP exposure had a synergistic effect on neurodevelopment. For instance, 32 percent of children born to foreign-born mothers were exposed to high levels of HAPs, compared to 21 percent of children born to U.S.-born moms.
One example of a particular HAP was the air pollutant isophorone, a measure of industrial pollution. After adjusting for socioeconomic characteristics, both isophorone exposure and maternal nativity status were linked with worse BSF-R mental scores in children. There was no statistically significant interaction between nativity status and isophorone exposure, but the change in mental scores was higher in children of foreign-born mothers than in children of U.S.-born mothers (0.12, vs. 0.03, p = 0.2).
In conclusion, children of foreign-born mothers were more likely than US-born mothers to be exposed to HAPs in the highest levels, showing disparities in pollutant exposure by nativity status within metropolitan populations. Exposures linked to nativity status may harm children's neurodevelopment.
We published new research: Creating a new machine learning algorithm to study the effects of air pollution on children with asthma
In a new study we published this week in The Journal of Clinical Investigation, our multidisciplinary team of computer scientists, physicians, and epidemiologists at the Icahn School of Medicine at Mount Sinai developed a novel machine-learning algorithm. We used this new machine learning method to identify previously unknown mixtures of toxic air pollutants that appear to be linked to poor asthma outcomes in children.
The issue we addressed with this investigation was that most studies assess the toxicity of pollutants one at a time. But in the real world, people are exposed to mixtures of pollutants that cause different health effects. Gaurav Pandey, PhD, Assistant Professor of Genetics and Genomic Sciences and a senior author of the study, said: "Traditionally, for technical reasons, it has been difficult to study the health effects of more than one toxic at a time. We overcame this by tapping into the power of machine learning algorithms."
To do this, we examined early exposure to dozens of pollutants to which 151 children with mild to severe asthma were potentially exposed early in their lives, as measured by the Environmental Protection Agency's National Air Toxics Assessment resource. We developed and applied a novel algorithm, named "Data-driven ExposurE Profile (DEEP) Extraction, to determine the possible ways that each pollutant, alone or in combination with others, could explain asthma outcomes in the children. The algorithm development was led by Yan-Chak Li, MPhil, a bioinformatician, and Hsiao-Hsien Leon Hsu, ScD, Assistant Professor of Environmental Medicine and Public Health.
We found that some disease cases could be linked to an individual chemical. One example of an individual chemical showing an effect was the ammonia-scented waterproofing agent trimethylamine, which raised the chances that a child with asthma would spend a night in the hospital.
Other pollutants could act alone or in mixtures. One example was acrylic acid, a chemical used in plastics, coatings, medical products, and detergents. Exposure to acrylic acid raised the chances that a child would need daily medication. Exposure to acrylic acid combined with other chemicals further increased this possibility, and in addition, it boosted the chances of emergency room visits and overnight hospitalizations among the asthmatic children who participated in the study. (See illustration)
In all, 34 individual chemicals were found to be linked to poor asthma outcomes. Importantly, some asthma cases appeared to be linked to mixtures of pollutants that had never been associated with asthma.
"As a physician who treats children with asthma, I was struck by how many potential air toxics are not on our radar," said Supinda Bunyavanich, MD, MPH, MPhil, Professor of Pediatrics, and Genetics and Genomic Sciences, and a senior author of the study. "These results changed my view of the heightened risk some children face."
"Our study is an example of how machine learning has the potential to alter medical research," said Dr. Pandey. "It is allowing us to understand how a wide variety of environmental factors—or the exposome—influences our health. In the future, we plan to use DEEP and other computer science techniques to tackle environmental factors associated with other complex disorders."
This work was supported by the Department of Genetics and Genomic Sciences at Mount Sinai, Scientific Computing at Icahn Mount Sinai, and the National Institutes of Health (AI118833, HG011407, HL147328, OD023337, and ES023515).
Citation: Li, Y.C., Hsu, H.H.L., Chun Y, Chiu PH, Arditi Z, Claudio L, Pandey G, Bunyavanich S. Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes. Journal of Clinical Investigation, October 5, 2021, DOI: 10.1172/JCI152088
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Dr. Luz Claudio is an environmental health scientist, mother and consultant, originally from Puerto Rico. She is a tenured professor of environmental medicine and public health. Luz recently published her first book: How to Write and Publish a Scientific Paper: The Step-by-Step Guide. Dr. Claudio has internship programs and resources for young scientists. Opinions expressed in this blog are solely her own and may not reflect her employer's views.