Interdisciplinary data science to advance environmental health research and improve birth outcomes

Ambient Environment and the Epidemiology of Preterm Birth

Preterm birth (PTB) is associated with substantial mortality and morbidity. We describe environmental factors that may influence PTB risks. We focus on exposures associated with an individual's ambient environment, such as air pollutants, water contaminants, extreme heat, and proximities to point sources (oil/gas development or waste sites) and greenspace. These exposures may further vary by other PTB risk factors such as social constructs and stress. Future examinations of risks associated with ambient environment exposures would benefit from consideration toward multiple exposures - the exposome - and factors that modify risk including variations associated with the structural genome, epigenome, social stressors, and diet.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

OBJECTIVES

The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

METHODS

We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

RESULTS

Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

CONCLUSION

In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses

Multiple systematic reviews and meta-analyses linked prenatal exposure to ambient air pollutants to adverse birth outcomes with mixed findings, including results indicating positive, negative, and null associations across the pregnancy periods. The objective of this study was to systematically summarise systematic reviews and meta-analyses on air pollutants and birth outcomes to assess the overall epidemiological evidence. Systematic reviews with/without meta-analyses on the association between air pollutants (NO₂, CO, O₃, SO₂, PM_2.5, and PM₁₀) and birth outcomes (preterm birth; stillbirth; spontaneous abortion; birth weight; low birth weight, LBW; small-for-gestational-age) up to March 30, 2022 were included. We searched PubMed, CINAHL, Scopus, Medline, Embase, and the Web of Science Core Collection, systematic reviews repositories, grey literature databases, internet search engines, and references of included studies. The consistency in the directions of the effect estimates was classified as more consistent positive or negative, less consistent positive or negative, unclear, and consistently null. Next, the confidence in the direction was rated as either convincing, probable, limited-suggestive, or limited non-conclusive evidence. Final synthesis included 36 systematic reviews (21 with and 15 without meta-analyses) that contained 295 distinct primary studies. PM_2.5 showed more consistent positive associations than other pollutants. The positive exposure-outcome associations based on the entire pregnancy period were more consistent than trimester-specific exposure averages. For whole pregnancy exposure, a more consistent positive association was found for PM_2.5 and birth weight reductions, particulate matter and spontaneous abortion, and SO₂ and LBW. Other exposure-outcome associations mostly showed less consistent positive associations and few unclear directions of associations. Almost all associations showed probable evidence. The available evidence indicates plausible causal effects of criteria air pollutants on birth outcomes. To strengthen the evidence, more high-quality studies are required, particularly from understudied settings, such as low-and-middle-income countries. However, the current evidence may warrant the adoption of the precautionary principle.

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.