Constant exposure to environmental pollutants can sicken our bodies, and social stressors such as poverty and psychological burden can further aggravate the health effects. For example, traffic-related air pollution has been linked to the onset of childhood asthma. However, if children exposed to air pollutants also experience violence, their risks of developing asthma can be doubled or tripled. Researchers and Scientists consider ‘cumulative risks’ (details here) as the elevated risks from the combined effects of multiple environmental and social stressors or agents, in this case air pollution and exposure to violence.
Understanding cumulative risk is important because we know very little about how synergistic interactions of multiple chemical exposures and social factors can increase risk of severe diseases, even though we are exposed to numerous chemicals via air, water, soil and consumer products on a daily basis. Until recently, a systematic-review of human and animal evidence confirmed the cumulative adverse effects of prenatal-exposure to chemicals and psychosocial stress on fetal growth. Individuals and different health organizations can take more effective actions to address such health issues once we can gain better knowledge relevant to cumulative risks.
The number of cumulative risk and impact studies increased tremendously over the past decade, but the relevant modeling methods have been underdeveloped to evaluate the joint exposures. Proper selection and use of statistical modeling techniques will generate accurate scientific results, which will contribute to sound environmental and public health policies.
To summarize modeling methods utilized to quantify the cumulative effects of multiple stressors in previous studies, we performed the first ever review on statistical models used to evaluate cumulative risks. I conducted a systematic search to identify original peer-reviewed research articles published between Jan 1, 2012- June 21, 2017 that evaluated both environmental and social stressors, and analyzed their health effects. We focused on human subject studies that provided quantitative method information. Eventually, we identified 31 eligible articles– the majority used simple regression models and focused on air pollutants and socio-economic status (SES) with various health outcomes, as shown in the table below.
| Chemical Stressors Measured | Social Stressors Measured | Health Effects Studied |
| Air pollutants, drinking water pollutants, climate indicators, metals, silica, BPA | SES, race, neighborhood features, psychological factors, physical disorder, material hardship, education, employment, housing, urbanization, neighborhood features, access to health services, health prevention program, violent crime | Mortality, morbidity, nutritional status, IQ, cancer risk, child behavior, heart defects, blood lead level, blood pressure, respiratory disease, autoimmune disease, diabetes hospitalization, pregnancy outcomes |
We found that simple regression models, including multivariable and logistic regression models, are commonly used in cumulative risk studies, especially evaluating the combined effects of both chemical and non-chemical stressors. However, we wondered whether regression methods can properly cover all the combinations of different possible research settings such as research questions, study designs and data. Unfortunately, no. We found that regression methods, similar to other modeling techniques, has its own advantages and limitations (details here), and concluded that no single modeling technique can be applied universally to all cumulative risk studies.
With increasing knowledge in exposure science and the advent of more quantitative tools in the era of ‘big data’, we recommend that other data mining and machine learning techniques such as deep learning be considered in cumulative risk research, particularly when it comes to understanding the combined risks of multiple stressors. To provide public health protection for vulnerable populations such as pregnant women and children, risk assessment can take advantage of the established modeling methods while more approaches are being developed. Other authors on the study include Drs. Aolin Wang, Rachel Morello-Frosch (UC Berkeley), Juleen Lam, Marina Sirota, Amy Padula and Tracey Woodruff.
About the Author
Hongtai Huang, PhD is a former Postdoctoral Scholar for PRHE, and was jointly affiliated with the UCSF Institute for Computational Health Sciences. Prior to joining UCSF, he was a postdoctoral data scientist at the US EPA. Hongtai received his PhD in Environmental Health Engineering and Master’s degree in Environmental Economics and Management from the Johns Hopkins University.
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