Preterm birth, when a baby is born before 37 weeks, is a pressing public health problem because babies born early have greater risks of mortality and health complications and later disease in child and adulthood. In 2017 alone, California had more than 400,000 preterm births an increased rate of 8.6%compared to the previous year.
Though we know that genetic factors, exposures to chemicals including air pollutants, and social factors such as and race and poverty can all raise preterm birth risk, there’s a lot we still don’t know about the causes of preterm birth—especially when it comes to environmental and social stressors. We do know that pregnant women in California are exposed to multiple environmental pollutants from air, food, water and consumer products. We also know that pregnant women can experience social stress due to factors such poverty, food insecurity and discrimination.
So in our new study, we set out to investigate the relationship between preterm birth and cumulative burdens of multiple environmental exposure and social stressors. We used a novel integrative big data approach to link two large datasets—1.8 million California birth records with environmental exposure information from CalEnviroScreen, a database that contains thousands of data points on environmental pollution and social factors for every census tract in California. By leveraging large datasets, we were able to reveal new, and surprising, patterns.
We found that arsenic pollution in drinking water is significantly associated with an increase in preterm birth of 1%, by comparing preterm birth in populations with different levels of arsenic contamination in drinking water. This may seem very small, but because there are so many pregnant women exposed to arsenic in their drinking water, it can translate into sizeable population impacts.. In our study of almost 2 million births, most of the pregnant women are living in area with arsenic contamination in the drinking water. For example, if the preterm birth rate for population without arsenic exposure is 7.0%, our study suggests that the same population with arsenic exposure in drinking water will have 1% increase in preterm birth rate (7.07%), which is around 1400 additional preterm births across a population of 2 million because of arsenic in drinking water. This shows how a small increase in risk spread across many people can translate to big impacts.
An additional finding is that there are many people living in area where arsenic concentration in drinking water is higher than regulatory standard. For example, the U.S. EPA’s maximum contaminant levels (MCL) is 10 parts per billion (ppb), which suggests that many people living in those areas are at higher risks of arsenic exposure (shown in figure below).
U.S. EPA adopted this standard for arsenic in drinking water in 2001 with consideration for the various health effects associated with arsenic exposure, including “cancerous effects (skin, bladder, lung, kidney, nasal passages, liver and prostate) and non-cancerous effects: cardiovascular, pulmonary, immunological, neurological and endocrine (e.g., diabetes) effects”. While pregnant women exposed to arsenic in drinking water may have increased risk of preterm birth, the rest of the population may face different risks related to these other diseases.
In addition, we found that both environmental chemical exposures and social stressors such as PM2.5, nitrate in drinking water and neighborhood unemployment rate collectively are associated with increased risk of preterm birth. This study adds to the evidence that cumulative impacts of multiple environmental and social factors are associated with adverse health outcomes. Note that there are some limitations and assumptions made in this study. For example, given the design of the environmental database used, we assume constant pollution during the entire pregnancy, and therefore didn’t account for temporal variability.
The power of our study shows how data collected in California can be used to identify potential risk factors that deserve more attention in research, monitoring and efforts that prevent exposures to harmful pollutant levels in order to better improve prenatal health.
Other co-authors on this work include: Tracey J. Woodruff (UCSF), Rebecca J. Baer (UCSD), Komal Bangia (OEHHA), Laura M. August (OEHHA), Laura L. Jellife-Palowski (UCSF), Amy M. Padula (UCSF, senior author), Marina Sirota (UCSF, senior author).
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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