The Individual Exposure Health Risk Profile (IEHRP)— Developing a Risk Profile Tool beyond Dose Response
Richard Hartman
Independent Consultant
Mark Oxley
Air Force Institute of Technology
Introduction
As industry continues to move toward greater degrees of automation, the concerns of workplace exposures will change as well. More and more work environments have now removed the human from the exposure or the exposure from the human. But is the workforce free from exposure-based risks as a result of the reduction in occupational exposures?
Not completely. Despite the advances to control workplace exposures, chronic diseases continue to affect workers (Sorensen et al. 2011). Why? Because the human body does not discriminate between exposures encountered at work, in the environment, or during leisure activities. But imagine the positive impact which could be made on health outcomes if there was a way to monitor, evaluate, and control or even eliminate exposures within and outside the workplace, creating a total exposure profile.
Total Exposure Health (TEH) is capitalizing on advancements in science, technology, and informatics to provide a framework which can be used to deliver this capability. This new approach to the exposure sciences also will associate exposures to the lowest common denominator—the individual’s genes (N=1) and monitoring for workplace, environmental, and lifestyle exposures whenever and wherever needed.
Total Exposure Health (TEH)
After the military’s experience of health consequences relating to Agent Orange exposure during the Vietnam War and the significant adverse health claims by service members deployed to the Persian Gulf War, the necessity to monitor and document exposures was codified into the National Defense Authorization Act for 1998 (Public Law 1997) whereupon the Department of Defense (DoD) was mandated to develop a deployment health surveillance system to detect, document, prevent, or minimize health problems arising as a result of occupational and environmental exposures during deployments and operations.
And while the DoD has made advances in health surveillance, the issues to collect, document, and act upon occupational and environmental health exposures while deployed remain a challenge (US Congress 2013). This is due to the nature of deployments, which are 24/7 workplaces, so the need to develop a more comprehensive view of what exposure meant beyond the typical 40-hour workweek and workplace was necessary. This meant expanding the practice of monitoring occupational and environmental exposures to include lifestyle choices.
This new way of characterizing worker well-being originated at the National Institute for Occupational Safety and Health (NIOSH) through Total Worker Health (TWH) (NIOSH 2018) as a holistic approach to worker well-being that acknowledges risk factors related to work that contribute to health problems previously considered unrelated to work.
The DoD recognized the validity of this approach but sought to realize a greater level of personalization by utilizing recent advances in the exposure sciences, genomics, sensor and data technologies, and health informatics which would aid in a more complete understanding of an individual’s health risks, root causes of disease and injury, and innovative but accessible methods for primary prevention. The result is what is now referred to as Total Exposure Health (TEH).
Operationalizing TEH required partnering across multiple program areas, using traditional and emerging exposure assessment technologies (including sensors and -omics-based molecular biology), as well as leveraging a Big Data Infrastructure and Advanced Analytics.
While the near-term goal of TEH is to improve exposure characterization and understand individual variability, susceptibility, and vulnerability to cumulative exposures and risks factors, the first achievement was to demonstrate the feasibility of TEH and the ability to collect exposure data 24/7 to better understand the effects of total exposure. In the long term, TEH adds genomics and other data sets to translate these findings into clinically actionable recommendations for health prevention to improve both the health and well-being of both the workforce and their beneficiaries. Though the ultimate implementation of TEH will monitor a wide range of physical risks such as radiation and chemical exposure from the workplace and environment as well as lifestyle risks from recreational activities and diet, the first focus was on noise exposure.
Noise Exposure Demonstration Project (NEDP)
Noise was the focus for this demonstration project because it is: (1) an exposure that does not discriminate by age, race, gender, and/or socioeconomic status; (2) costly (currently hearing health expenditures range from $1.5 billion to $2 billion annually for the US Department of Veterans Affairs in benefits and medical costs) (Alamgir et al. 2016); and (3) affects approximately 10% of the US population according to a 2017 report by the Centers for Disease Control and Prevention, where 40 million American adults show signs of noise-induced hearing loss (NIHL) (Carroll et al.
2012).
Noise Exposure Demonstration Project (NEDP) used sensor technology to collect total noise exposure 24hours a day, 7days a week. Unique to the noise monitoring devices developed for NEDP, the wearable sensor collects sound events from both ambient noise in the workplace and environment as well as in-ear sounds from ear buds used with smartphones and other devices playing digital media (such as music) with a smartphone app recording those decibel (dB) readings.
Nineteen subjects participated in this study at Moody Air Force Base, Georgia, over 10 days in June 2018. There were 12,680 noise events of 70 dB А-weighted (dBA) or above captured, with 2,968 events, or 23%, being 95 dBA or above. (A-weighted decibels are adjusted to deemphasize sounds outside of normal human hearing.) Figure 2.1 shows the average daily (24-hour) dB dose was about 75 dBA. About 10% of the subjects had total daily noise exposures under 70 dB, and 10% over 80 dB, with the majority in the middle.
High noise exposure was found at both the workplace (46%) and away from the workplace, off-duty, in the defense lexicon, (52%), and significant cumulative high-noise individual exposures were identified (3-27 hours over the 10-day study period). Geospatial “hotspot” locations of exposure were also identified across the population.
Overall, the NEDP met its objectives as a successful TEH demonstration project. The study therefore developed a low-cost noise dosimeter/sensor that monitors
FIGURE 2.1 Daily (24-hour) equivalent continuous noise level (Leq).
external and media smartphone device noise sound levels around the clock; used advanced analytics to collate multiple sensor devices, with geospatial layering; and managed participant compliance (Montgomery 2017). A summary of the NEDP is available from Yamamoto et al. (2019).
Recognizing that development of NIHL varies among populations who have been exposed to the same levels of noise, the researchers asked if there were genetic markers associated with a predisposition. A thorough review of published research within the field of genetics identified ten published studies, with small-to-modest sample sizes, which have indeed established a link between multiple genetic variants associated with NIHL. The effect is surprisingly large, with odds ratios of 5.2-22.36 indicating an elevated risk (Grondin et al. 2015).
Furthermore, within the broader United States Air Force (USAF) population, of which the NEDP participants are a subset, Figure 2.2 shows 17% of the 2,000 who have had their genes fully sequenced showed a particular gene variant, rs7598759, indicating they may be at a substantially increased risk for a hearing threshold shift—that is, of a hearing loss susceptibility due to noise.
This finding led to a new question: What if an IH or exposure scientist could take a person’s cumulative, daily exposure to noise and combine this data with their genetic predisposition for NIHL to optimally protect against it? More broadly speaking, what if the IH and the care providers could predict, by aggregating big data
FIGURE 2.2 Gene variant associated with NIHL where the CC genotype is dominant trait and TT is the recessive trait (Phillips 2017, 2018).
and genomics, a person’s susceptibility to external health risk factors—and take action to alleviate it using individualized health protocols?
The study concluded that by using an individual’s medical records, it would be possible to identify pre-existing conditions that would also increase the risk of NIHL, thus bringing exposure data, genomics data, and medical record data together to describe a more holistic montage of the person’s risk to a particular exposure.
This estimate/calculation of individual health risk factors involved the need for a mathematical/informatics process that could merge and analyze various data from sensors, medical records, and unstructured information as well as genomics data to identify relationships between them at the individual level called the Individual Exposure Health Risk Profile (IEHRP).
Individual Exposure Health Risk Profile (IEHRP)
The IEHRP integrates exposure data from both the traditional and cutting-edge emerging exposure assessment technologies (including sensors and “-omics”-based molecular biology) with clinical and genomic data. Combining the three: (1) the exposure measurement, (2) the genetic proclivity associated with the exposure, and (3) current clinical history associated with the exposure, provides a better description of the individual’s risk than would a focus on any one variable. This combination originally resulted in the Individual Exposure Health Risk Index (IEHRI) defined by
where vu is the exposure measure, vl 2 is the genetic proclivity to the exposure, and v, з is the clinical effect per the evidence in the individual’s medical record (medical history). For example, when evaluating noise, the IEHRI becomes
Accounting for multiple exposures leads to the development of the IEHRP, which combines multiple IEHRI’s for an individual, represented by equation (2.3) for (i) exposures.
With the IEHRP equipped to address multiple exposures, the limitations to the IEHRI became evident, which only accounted for three variables to describe an individual exposure. This was observed early in the NEDP, which revealed other variables that would affect the IEHRI(Noise), such as ototoxins or family history. This required the IEHRP to be modified so it could account for multiple confounding factors (Figure 2.3) and is represented by equation (2.4) with (j) confounding factors.
FIGURE 2.3 The IEHRI can have many variables (Phillips 2017, 2018).
With the development of the IEHRP for multiple exposures and variables, two key questions still needed to be addressed: (1) Which variable (v, j) was the most important? That is, should some weigh more than others? and (2) How does an IH account for the variability of each variable?
To answer the first question (e.g. are genes more important than exposure measure?), the equation was enhanced with a “Weighting Factor” (WFLJ), a numerical value that would account for importance of each variable (v,j). To account for the variability (confidence), the equation included a “Correction Factor” (CFj j), a numerical value. Combining the CF with the WF resulted in the most recent iteration of the IEHRP, equation (2.5)
This IEHRP is for an individual, so in a group of N individuals with similar or different exposures, the expectation would be to see N distinct IEHRPs. Therefore, observing the collection of IEHRPs
would be of interest.
For example, if two individuals are considered the IEHRPs set would be N = 2 (Figure 2.4), individual A has a high risk for noise whereas individual В has a high risk for radon. This visualization allows the provider or IH/OH professional to target and prioritize interventions based on the individual’s highest risk exposure(s).
Now modify the visualized set with the same data to accommodate a policy maker, it would be apparent that noise is prevalent in both A and B, allowing the policy maker to identity the high priority exposure for either policy development/ modification or proper resourcing versus focusing on a lower priority exposure.
When fully developed, the IEHRP will provide an enhanced capability to describe individual health risks based on genetic factors and occupational, lifestyle, and environmental exposure factors, medical disposition, protective factors, and other variables that affect exposure health risk.
Conclusion
At the rise of the industrial revolution, Alice Hamilton pioneered the industrial hygiene profession and by building on the past knowledge and looking to the future of industry expanded began the way industrial hygienists think about exposures in the workplace. With rapid advances of science, technology, medicine, and informatics, society is on the cusp of a Fourth Industrial Revolution and is beginning to recognize the opportunity that comes with the change it brings.
IH/OH professionals and exposure scientists are well positioned to usher in this revolution in much the same way Dr. Alice Hamilton did in her time. In practice, the IH/OH professions will better understand the effects of exposures not only in the workplace and environment but also in the day-to-day activities of the
FIGURE 2.4 IEHRP data visualization for two individuals. Healthcare providers can use the visualization on the left to target interventions based on individual risks (noise for Person A and radon for Person B), while policy makers can use the visualization on the right to direct resources toward high priority risks across a population.
population. Through initiatives like TEH, the exposure communities will uncover a new' understanding of the relationships between an individual’s genetic predispositions, epigenetic factors, and exposures from lifestyle, occupation, and the environment to support the development of diagnostic approaches, treatment methods, and intervention strategies to truly institute primary prevention to improve worker health, performance, and productivity. The work TEH framework created by Col Kirk Phillips was placed into an end-to-end system development graphic and presented at the closing keynotes of the American Industrial Hygiene Association Fall Conference 2017 and Conference and Exposition in 2018 (Phillips 2017, 2018) (Figure 2.5).
By embracing new' bold ideas as a profession, the current state of disparate exposure monitoring will be transformed, research studies, data collection, and controls into holistic and integrated systems that quantitate total exposure and inform health outcomes into precise actionable insights and initiatives for individuals and/ or similar exposure groups.
The IH/OH profession will leverage and advance genomics, sensor and data technologies, data analytics, and health informatics into a future-focused and progressive approach that represents a disruptive but necessary paradigm shift to the exposure sciences—TEH.
FIGURE 2.5 TEH end-to-end system development (Phillips 2017, 2018).
References
Alamgir, H., et al.: Economic Burden of Hearing Loss for the U.S. Military: A Proposed Framework for Estimation. Military Medicine, 181(4): 301-306, April 2016.
Carroll, Y.I., et al.: Vital Signs: Noise-Induced Hearing Loss Among Adults - United States 2011-2012. Morbidity and Mortality Weekly Report (MMWR), 66: 139-144. 2017. Grondin, Y., et al.: Genetic Polymorphisms Associated with Hearing Threshold Shift in Subjects during First Encounter with Occupational Impulse Noise. PLOS ONE, 10(6): e0130827, 2015.
National Institute for Occupational Safety and Health (NIOSH): “Total Worker Health.” Available at https://www.cdc.gov/niosh/twh/default.html Montgomery, K. and R. Hartman: “Bringing It All Together - Noise a Common Exposure.” Presented at the International Society of Exposure Scientist (ISES), Research Triangle, NC. 18 Oct 2017.
Phillips, K.: “Total Exposure Health: A Revolutionary Way to Think of Exposure and Primary Prevention.” Presented at the American Industrial Hygiene Fall Conference
2017, 31 Oct 2017.
Phillips, K: “Total Exposure Health: A Revolutionary Way to Think of Exposure and Primary Prevention.” Presented at the American Industrial Hygiene Conference and Exposition
2018, 23 May 2018.
Public Law 105-85 (HR 1119): National Defense Authorization Act for Fiscal Year 1998, Subtitle F, Section 765, November 1997.
Sorensen, G., et al.: The Workshop Working Group on Worksite Chronic Disease Prevention. American Journal of Public Health, 101(Suppl 1): S196-S207, December 2011.
United States Congress. National Defense Authorization Act (NDAA): Section 313, January 2013.
Yamamoto, D.P., J.W. Kurzdorfer, and K.L. Fullerton: U.S. Air Force Noise Exposure Demonstration Project, Final Report for December 2016 to July 2018 (AFRL-SA- WP-TR-2019-0010). Air Force Research Laboratory, 711th Human Performance Wing. Available from the Defense Technical Information Center (DTIC) at: https://apps.dtic. mil/dtic/tr/fulltext/u2/1071653.pdf.
О In Pursuit of Total Exposure Health
