In the United States, rates of preterm birth (deliveries before 37 weeks gestation) have risen for the past four years, to 10.02% in 2018¹; and indeed the frequency of preterm birth has increased in the US for four decades.² Incidence of low birth weight has also increased 3% since 2014.¹ There are well-documented disparities in these rates, with Black women having almost double the risk of having preterm births than White women.^1,3 Preterm birth and low birthweight remain a substantial public health problem as they have a significant impact on an infant’s survival, development and long-term health.^4–6 Decades of research have provided evidence that environmental exposures contribute to both preterm birth and low birthweight.^7–14 These exposures include air pollution, pesticides, extreme temperatures and aspects of the built environment.

Despite the breadth of the exposures investigated for associations with preterm birth and low birthweight, there has been limited translation to public health policies and interventions that reduce the frequencies of these outcomes, particularly in vulnerable communities. Evidence for prevention would be strengthened by simultaneously investigating the manifold exposures experienced by women during pregnancy and then employing analytic techniques that have the ablity to disentangle effects and identify targeted points for intervention. Characterizing the complex exposure-response relationships that contribute to preterm birth and low birthweight requires both epidemiologic and toxicologic studies that include the examination of multiple exposures simultaneously, account for temporal variability in exposure throughout pregnancy and explore the potential for interaction between environmental exposures, socioeconomic contexts and genetics. Current study designs and analytic methods often fail to capture this complexity.¹⁵ In addition, there has been limited research that combines epidemiology of ambient environmental pollutants with biomarker-based research of biological responses to fully characterize the pathways that define how population-level exposures can contribute to these adverse birth outcomes.^16,17 This knowledge is critical for informing successful public health policy and population-level interventions aimed at prevention. These approaches require collaborations between computational scientists skilled at the analysis of large, complex data with biomarker-based laboratory researchers with the knowledge and techniques to determine the mechanistic paths that connect external exposures to adverse birth outcomes. An interdisciplinary data science framework enables these collaborations and allows for the holistic analysis of complex environmental data to extract primary risk drivers and to guide biological, mechanistic and biomarker-based research, enabling reduction and prevention of preterm birth, low birthweight and other adverse birth outcomes.

Much has been written about the promise of data science in advancing the understanding of public health.¹⁸ There have been numerous commentaries arguing for the integration of data science methods into environmental health research.^19,20 Many of these focus on examining the complexity of the exposome, a paradigm describing the totality of endogenous and exogenous exposures that occur throughout a lifetime.^21–26 We are beginning to see examples of research that have successfully adapted data science methods to address the challenges of characterizing the exposome and documenting its effects on adverse birth outcomes.^27,28 While this is a clear sign of progress, applying more complex analytics to ever larger datasets can lead to more questions than answers. This is because data-driven analyses, including those that have marked this initial phase of environmental health data science, are limited by the assumptions required of purely statistical approaches and often fail to include the existing knowledge generated within other fields such as known biophysical pathways underlying disease pathology or toxicological knowledge of environmental pollutants.²⁷ The causal inference needed to inform public health policy and interventions requires an interdisciplinary data science, one that enables the integration of knowledge across research fields including epidemiologic and toxicologic knowledge with environmental health data on large populations.

The objective of this commentary is to propose an interdisciplinary research framework that integrates data science across the multiple disciplines within environmental health. The proposed framework utilizes multiple lines of scientific inquiry, such as epidemiology, data-driven analytics and biomarker-based investigations, in order to connect the external exposures, modifiable through policy and interventions, to the internal measures of biologic response that may serve as more proximal causes of preterm birth and low-birthweight. In addition, we provide recommendations aimed at the environmental health community to support the interdisciplinary collaborations needed to advance this work.

There are two primary reasons to support an interdisciplinary data science framework for environmental health: the first is to address the limitations of evidence produced by existing approaches and the second is to advance the field of data-driven analytic approaches to navigate the space of hypotheses more efficiently.

As discussed above, epidemiologic studies of environmental contributors to adverse birth outcomes have traditionally focused on a single exposure. This fails to account for the complexities of exposure, pregnancy and fetal development.²⁹ These complexities include epidemiologic analysis of simultaneous exposure to multiple chemical and non-chemical stressors, temporal variability in both exposure and vulnerability throughout pregnancy and the potential for interaction between environmental exposures, socioeconomic contexts and genetics. Recent studies have attempted to tackle many of these complexities. Previous work in California took a comprehensive approach to assess both spatial and temporal variation in pesticides to explore associations with pregnancy outcomes.³⁰ A number of studies have applied complex analytic techniques, including distributed lag models and others, to identify critical windows of exposure during pregnancy.^30–33 Smaller cohort studies have begun to utilize biological specimens, such as maternal urine collected during pregnancy and cord blood collected at delivery, to assess more proximal exposures to the fetus.^34–36 While making considerable advances in our knowledge, these studies have tackled the individual limitations of previous studies. In many instances, they remain subject to other challenges, that could be addressed by a more interdisciplinary data-science approach. For example, a study to identify critical exposure windows of a single exposure using statistical approaches remains prone to the confounding influences of unmeasured co-exposures and often only considers biological knowledge about fetal development when interpreting results, not in the analysis itself. Thus, each individual study may be limited in its ability to generate causal inference at the level needed for policy and intervention planning.

Additionally, data science methods have traditionally focused on building robust predictive statistical models. State-of-the-art feature selection algorithms have the ability to select, among large sets of covariates, a set of variables that are maximally predictive for the target variable, and discard the rest as non-significant contributors. This omission is particularly important in environmental health data sets, where exposures can often co-occur or have a spurious association with the outcome due to their correlation with other causal exposures, and therefore carry similar information for the outcome.³⁷ For example, in Figure 1 we illustrate the challenge of outcome-equivalent co-exposure sets and how to detect them on a county-level data set on preterm birth from California. In this example, the chemicals chloroform and ethylene oxide were found to be interchangeable in a linear model that included 8 other air toxics as covariates. Figure 1 shows the corresponding residuals for preterm birth rates, using chloroform and ethylene oxide. The residuals are almost identical, suggesting that the variables are interchangeable in the model. In regression analysis methods used for feature selection, such as LASSO (least absolute shrinkage and selection operator), chloroform would be discarded as a non-significant contributor, without any consideration of toxicologic knowledge about which exposure may be more relevant to preterm birth. This example illustrates a common issue in environmental health research: the presence of variables that carry similar outcome information can result in some of the variables being overlooked as potential risk factors for the outcome of interest.

Solutions to these limitations require not just expertise in complex analytics, but the ability to integrate data across a variety of fields within the broader domain of environmental health research including epidemiology, toxicology and the omics technologies (e.g. genomics, proteomics, metabolomics, etc). Approaches that incorporate previous knowledge on toxicity of exposures while adapting study designs and analytic methods to address multiple limitations simultaneously can advance our knowledge of environmental contributors to adverse birth outcomes and accelerate efforts to translate research into prevention.

We propose an interdisciplinary data science framework that integrates epidemiologic data with toxicological knowledge of exposures when applying complex analytic methods and then using generated results to inform targeted biomarker-based studies of biological mechanisms.

The proposed framework includes multiple techniques from data science that span the disciplines of epidemiology, bioinformatics and laboratory science: i) the re-use of existing public health data, environmental monitoring and biospecimens to efficiently access information on large, representative populations; ii) the use of bioinformatics toxicological knowledge within quantitative and statistical approaches; and iii) the translation of results to inform biomarker-based investigations of biological mechanisms. Our proposed framework is not meant to serve as a “how-to” for conducting complex analytics related to environmental contributors to adverse birth outcomes. Rather, we aim to encourage a more holistic interpretation of data science, marked by the integration of interdisciplinary approaches and tailored to the research question of interest. Figure 2 illustrates an example of how this framework could be applied to investigations of adverse birth outcomes, such as preterm birth, using publicly-available public health data. We will refer to this example throughout the rest of this commentary. However, we encourage readers to consider the study populations, exposure data sources, analytic techniques and biomarker-based investigations that best fit the scientific complexities of their research question and context.

We present three recommendations to support and advance the use of the proposed interdisciplinary framework. First, we encourage researchers across the multiple disciplines within environmental health to establish interdisciplinary research teams. The challenges facing the field of environmental health are complex and require a broad set of skills to maximize the knowledge generated by the diverse types of environmental health data. The expansion of research teams is not limited to the inclusion of data scientists with expertise in analytic approaches, but also chemists knowledgeable in the use of bioinformatics resources, clinicians and biologists with training in physiological pathways and public health professionals to advise on optimal translation strategies. The formation and function of these interdisciplinary research teams will need to be actively supported by funding agencies, professional organizations and academic centers, through grants and programs that support collaboration and consortia development. An excellent example is the Data Science Innovation Labs currently supported by NIH that led to the creation of the authors’ interdisciplinary group.⁷⁵ Initially supported by the NIH Big Data to Knowledge Initiative (BD2K), the Data Science Innovation Labs are an annual intensive workshop, created specifically to foster the development of interdisciplinary teams focused on specific biomedical challenges that could benefit from increased use of data science techniques. Guided by professional facilitators and experienced mentors, investigators from diverse disciplines form teams to solve a data science challenge related to a broad biomedical theme.

Second, to encourage efficient and timely interdisciplinary research designs, we recommend greater use of existing biorepositories for research into adverse birth outcomes. In our proposed framework, we discussed the use of archived residual NDBS as a potential source for biomarker-based research. New technologies have enhanced our ability to re-use these public health resources for multiple omics research. Many states have demonstrated the ability to share these resources with researchers, while still protecting individuals’ privacy and ensuring data security. A greater commitment to sharing data across birth cohorts could also provide access to larger resources of biospecimens linked with extensive epidemiologic data. The HELIX study in Europe provides a blueprint for constructing novel study designs aimed at integrating interdisciplinary data science within existing birth cohort studies.

As a third recommendation, we encourage the cross-training of both environmental health and data science investigators to improve communication and integration of skills within interdisciplinary research teams. NIEHS has acknowledged this need through an approved concept to advance workforce development for environmental health data science. Their proposed concept included initiatives to support environmental health training of data scientists and educational resources for skill development to enhance data-intensive environmental health research.

Incorporating interdisciplinary data science techniques and greater use of big public health data into environmental health research can address limitations of prior research. We call for the creation and support of interdisciplinary research teams to implement our proposed framework, connecting population-level environmental exposures to biomarker-based targeted interventions of biological mechanisms. By leveraging expertise across the domains of environmental health research and utilizing diverse techniques of data science, we will enable integrative research to generate translatable knowledge on environmental contributors to the etiologies and prevention of adverse birth outcomes.