As industry continues to move toward greater automation, an increasing number of work environments have removed the human from the exposure or the exposure from the human. But is our work force becoming healthier as a result of this freedom from occupational exposures? Not at all. A 2011 paper published in the American Journal of Public Health has shown that, despite advances in controlling workplace exposures, chronic diseases continue to affect all working Americans. Why? Because our bodies do not discriminate between exposures encountered at work, in the environment, or during our leisure activities. But imagine the positive impact we could make on health outcomes if we could monitor, evaluate, and control or even eliminate exposures within and outside the workplace through the creation of a total exposure profile. Using advancements in science, technology, and informatics, we have the capability to monitor workplace, environmental, and lifestyle exposures 24 hours a day, seven days a week, and associate those exposures to an individual’s DNA. ORIGIN OF TOTAL EXPOSURE HEALTH 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 need to monitor and document exposures was codified into public law by the National Defense Authorization Act for Fiscal Year 1998. The Act required the Department of Defense to develop a health surveillance system to detect, document, prevent, or minimize health problems arising from occupational and environmental exposures during deployments and operations. 

American Journal of Public Health: “The Workshop Working Group on Worksite Chronic Disease Prevention” (December 2011).  CDC: Morbidity and Mortality Weekly Report, “Vital Signs: Noise-Induced Hearing Loss Among Adults—United States 2011–2012” (February 2017). H.R.1119 - National Defense Authorization Act for Fiscal Year 1998, Subtitle F, Section 765 (November 1997). International Society of Exposure Science 27th Annual Meeting: “Bringing it All Together—Noise, a Common Exposure” (presentation, October 2017). Military Medicine: “Economic Burden of Hearing Loss for the U.S. Military: A Proposed Framework for Estimation” (April 2016). NIOSH: “Total Worker Health.” PLOS ONE: “Genetic Polymorphisms Associated with Hearing Threshold Shift in Subjects during First Encounter with Occupational Impulse Noise” (June 2015). United States Congress: National Defense Authorization Act, Section 313 (January 2013).
The DoD has made advances in health surveillance, but the 24/7 nature of deployments necessitated the development of a more comprehensive view of what exposure meant beyond the typical forty-hour workweek and workplace. Collecting, documenting, and acting upon occupational and environmental health exposures was therefore expanded to include lifestyle choices. This new way of characterizing worker well-being originated at NIOSH through Total Worker Health, which NIOSH defines 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 greater personalization by utilizing recent advances in the exposure sciences, genomics, sensor and data technologies, and health informatics. This knowledge would contribute to 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 of the DoD’s efforts is now referred to as Total Exposure Health, or TEH. Operationalizing TEH required partnering across multiple program areas within the military, federal agencies, and academia. TEH uses traditional and emerging exposure assessment technologies, including sensors and “omics-based” molecular biology, and leverages 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 risk factors, the immediate objective was to demonstrate the feasibility of TEH and 24/7 collection of exposure data to better understand the effects of total exposure. In the long term, TEH adds genomics and other datasets to translate findings about total exposure into clinically actionable recommendations to improve the health and well-being of both workers 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 The demonstration project focused on noise exposure for several reasons. Noise does not discriminate by age, race, gender, or socioeconomic status. It is costly—for example, the U.S. Department of Veterans Affairs spends $1.5 to $2 billion annually in benefits and medical costs related to hearing health, according to a paper in Military Medicine. And noise exposure is widespread—the Centers for Disease Control and Prevention estimates that noise affects approximately 10 percent of the U.S. population, with 40 million American adults showing signs of noise-induced hearing loss (NIHL).  The Noise Exposure Demonstration Project, or NEDP, used sensor technology to collect total noise exposure 24 hours a day, seven days a week. The wearable sensor used for the NEDP collected sound events from both ambient noise in the workplace and environment as well as in-ear sounds from earbuds used with devices playing digital media (such as music). A smartphone app recorded decibel readings.  Nineteen subjects participated in this study at Moody Air Force Base, Ga., over ten days in June 2018. We captured 12,680 total noise events of 70 dBA or above; 2,968 (23 percent) were 95 dBA or above. The average daily (24-hour) dose was about 75 dBA. About 10 percent of the subjects had total daily noise exposures under 70 dB and 10 percent over 80 dB, with the majority in the middle (see Figure 1).
We found high noise exposure (greater than 85 dB) at the workplace (46 percent of recorded events) and off-duty (52 percent). Multiple study participants experienced significant cumulative high noise exposure from both work and non-work sources for durations that ranged from three to 27 hours. We also identified geospatial “hotspot” locations of exposure across the population—that is, certain places on the base had significant noise exposure at certain times.  Overall, the NEDP met its objectives. We developed a low-cost noise dosimeter that monitors sound levels from external sources and smartphone devices around the clock; used advanced analytics to collate multiple sensor devices, with geospatial layering; and managed participant compliance with the protocols of the study.  Recognizing that development of NIHL varies among populations who have been exposed to the same levels of noise, we wondered if there were genetic markers associated with a predisposition. We identified ten published studies, with small to modest sample sizes, that have indeed established a link between multiple genetic variants associated with NIHL. The effect is surprisingly large, with odds ratios of 5.2 to 22.36 indicating an elevated risk, as explained in a 2015 paper published by PLOS One. Furthermore, within the broader U.S. Air Force population, of which the NEDP participants are a subset, 17 percent 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, they are susceptible to hearing loss due to noise. This finding led to new questions. Could we combine a person’s cumulative, daily exposure to noise with their genetic predisposition for NIHL to optimally protect against it? That is, could we predict, by aggregating Big Data and genomics, a person’s susceptibility to external health risk factors—and alleviate that susceptibility using  individualized health protocols?  We determined that an individual’s medical records allowed us to identify pre-existing conditions that would also increase the risk of NIHL, thus bringing together exposure data, genomics data, and medical record data to form a more holistic description of the person’s risk from a particular exposure. This estimate of individual health risk factors involved merging and analyzing data from sensors, medical records, and unstructured information as well as genomics data to identify relationships between them at the individual level.

The Individual Exposure Health Risk Profile, or IEHRP, integrates exposure data from both traditional and emerging exposure assessment technologies (including sensors and “omics-based” molecular biology) with clinical and genomics data. Combining the exposure measurement, the genetic proclivity associated with the exposure, and 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. The IEHRI is defined as
Figure 1. Daily (24-hour) equivalent continuous noise level (Leq).
Tap on the figure to open a larger version in your browser.
where v1,1 is the exposure measure, v1,2 is genetic proclivity to the exposure, and v1,3 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 IEHRIs for an individual, represented by this equation for (i) exposures:
With the IEHRP equipped to address multiple exposures, the limitations of the IEHRI became evident. The IEHRI accounted for only three variables to describe an individual exposure. This limitation was observed early in the NEDP, which revealed other variables that would affect the IEHRI(Noise), such as ototoxins and family history. A modified IEHRP that accounts for multiple confounding factors (see Figure 2) is represented by the following equation:
With the development of the IEHRP for multiple exposures and multiple variables, two key questions still needed to be addressed: which variable (vi,j) was the most important—that is, should some have more weight than others; and how do we account for the variability of each variable? To answer the first question (are genes more important than exposure measure?), the equation was enhanced with a weighting factor (WFi,j), a numerical value that would account for the importance of each variable (vi,j). To account for variability (confidence), the equation included a correction factor (CFi,j). Combining the CF with the WF resulted in the most recent iteration of the IEHRP:
This IEHRP is for an individual, so if we imagine N individuals, we would expect to see N distinct IEHRPs. Therefore, observing the collection of IEHRPs
would be of interest. For example, suppose we visualize the IEHRPs for two individuals (see Figure 3) and find that individual A has a high risk for noise whereas individual B has a high risk for radon. This visualization allows the healthcare provider or IH/OH professional to target and prioritize interventions based on the individual’s highest risk exposures. Modifying the visualization emphasizes that noise is prevalent in both A and B. A policymaker, for example, could use this visualization to stimulate policy development or direct resources toward high priority exposures. When fully developed, the IEHRP will provide an enhanced capability to describe individual health risks based on several variables that affect exposure health risk, including genetic factors; occupational, lifestyle, and environmental exposure factors; medical disposition; and protective factors.
Figure 2. The Individual Exposure Health Risk Index can have many variables.
Figure 3. 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 policymakers can use the visualization on the right to direct resources toward high priority risks across a population.
Tap on the figure to open a larger version in your browser.

CHALLENGES FOR TEH Several ethical and privacy-related issues must be navigated as TEH develops. The most complex of these issues involve identifiability of workers, the increasingly sensitive nature of genetic information, and the limits of confidentiality (particularly for military populations).  Key for the success of TEH will be the monitoring, collection, identification, and understanding of different types of exposures; the development of biomarkers, technologies, and sensors to measure exposures; and the integration of multiple data streams into a unified framework that can provide useful information to improve healthcare outcomes.  The biggest challenge is development of a common electronic health record, or EHR, which will require solutions to issues such as integration and interoperability, privacy, security, and the use of both structured and unstructured data, especially with the proliferation of sensors and devices. THE NEXT REVOLUTION At the dawn of the industrial revolution, Alice Hamilton pioneered the way we think about exposures in the workplace. Today, with rapid advances in science, technology, medicine, and informatics, we are on the cusp of another revolution. IH/OH professionals and exposure scientists are well positioned to usher in this revolution in much the same way Hamilton did in her time. We will better understand the effects of exposures not only in the workplace and environment, but also in our day-to-day activities. Through initiatives like TEH, we 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 that will improve worker health, performance, and productivity.  By embracing bold new ideas, we can transform the current state of disparate exposure monitoring, 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 or similar exposure groups. The IH/OH profession will leverage genomics, sensor and data technologies, data analytics, and health informatics in a future-focused and progressive approach that represents a disruptive but necessary paradigm shift to the exposure sciences. That shift is Total Exposure Health.   RICHARD HARTMAN, PhD, is an independent consultant for the Air Force Institute of Technology.  MARK OXLEY, PhD, is a full professor of Mathematics in the Department of Mathematics and Statistics, in the Graduate School of Engineering and Management at the Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio. Send feedback to The Synergist.

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The Promise of Total Exposure Health
A New Approach to the Exposure Sciences
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Disadvantages of being unacclimatized:
  • Readily show signs of heat stress when exposed to hot environments.
  • Difficulty replacing all of the water lost in sweat.
  • Failure to replace the water lost will slow or prevent acclimatization.
Benefits of acclimatization:
  • Increased sweating efficiency (earlier onset of sweating, greater sweat production, and reduced electrolyte loss in sweat).
  • Stabilization of the circulation.
  • Work is performed with lower core temperature and heart rate.
  • Increased skin blood flow at a given core temperature.
Acclimatization plan:
  • Gradually increase exposure time in hot environmental conditions over a period of 7 to 14 days.
  • For new workers, the schedule should be no more than 20% of the usual duration of work in the hot environment on day 1 and a no more than 20% increase on each additional day.
  • For workers who have had previous experience with the job, the acclimatization regimen should be no more than 50% of the usual duration of work in the hot environment on day 1, 60% on day 2, 80% on day 3, and 100% on day 4.
  • The time required for non–physically fit individuals to develop acclimatization is about 50% greater than for the physically fit.
Level of acclimatization:
  • Relative to the initial level of physical fitness and the total heat stress experienced by the individual.
Maintaining acclimatization:
  • Can be maintained for a few days of non-heat exposure.
  • Absence from work in the heat for a week or more results in a significant loss in the beneficial adaptations leading to an increase likelihood of acute dehydration, illness, or fatigue.
  • Can be regained in 2 to 3 days upon return to a hot job.
  • Appears to be better maintained by those who are physically fit.
  • Seasonal shifts in temperatures may result in difficulties.
  • Working in hot, humid environments provides adaptive benefits that also apply in hot, desert environments, and vice versa.
  • Air conditioning will not affect acclimatization.
Acclimatization in Workers