geohealth analysis

by Geoffrey M. Jacquez^1,2 and Robert Rommel²

1.  Department of Geography, State University of New York at Buffalo, Buffalo, NY

2. BioMedware, Ann Arbor MI

Introduction: Perhaps one of the greatest challenges and limitations in environmental health and epidemiology is that of measurement of individual health outcomes, their causes, and correlates.  Data on risk factors and exposures are often measured with imperfect instruments such as surveys that are inherently inaccurate and subject to recall and other bias.  At present, biomarkers may provide reasonable estimates of exposures, but can be difficult to obtain and are available for only a small number of compounds.  Ideally, epidemiologists and exposure assessment scientists would have timely information regarding measurements of individual-level physiology and ambient environment (e.g. at the human boundary layer), the specific times and locations where these measurements were collected, and in what settings.

We are at the beginning of a revolution in measurement that has the potential to transform environmental health and epidemiology (Swan 2013).  We argue this transformation will require new ways of thinking about data sharing, consent, privacy and confidentiality, and the formation of an organization to foster the use of personal, crowd-sourced data for the common good.  We begin with a working definition of the genome+, exposome and behavome, and examples of how technology is revolutionizing their measurement.  Next, we consider technology trends in the quantifiable self, and where these will lead in the near future.  We predict these trends will culminate in a new era for epidemiology and environmental health provided mechanisms are established to foster sharing of individual information from these new data streams.  We close with a call for action to form an organization charged with governance, data security, and mechanisms for data sharing, with the ultimate mission of advancing epidemiology, the environmental health sciences, and human health.

Genome+, exposome and behavome:  Consider a conceptual model of three important determinants of health (Figure 1).  Both illness and well-being are treated as core outcomes, whose expression is influenced by the genome+, exposome and behavome.  An individual’s “Genome +” is comprised of their genome (genetic composition), regulome (which controls gene expression), proteome (their compliment of amino acids and proteins) and metabalome (the basis of metabolism and homeostasis).   Together, these constitute a good portion of an individual’s biological makeup.  The exposome is defined as the totality of exposures over a person’s life course (Wild 2005). We define the behavome as the totality of an individual’s health-related behaviors over their life course.  Wild’s definition of the exposome included behavioral determinants of exposure; we treat the behavome separately to clarify the role of human behavior in mediating the exposome (through health behaviors such as smoking, exercise, diet and so on), as well as interactions between the exposome and the genome+ (for example, many behaviors are now recognized to have a genetic component, such as a predilection to alcohol and substance abuse).  These determinants of human health act through place, defined as the geographic, environmental, social and societal milieus experienced over a person’s life course.

Technology trends in measurement of the genome+, exposome and behavome: Measurement of the genome+, exposome and behavome is an enormous challenge.  Nonetheless, the last few years have seen major advances in measurement.  We now provide a few examples of such advances, before considering implications of these technology trends for environmental health and epidemiology.

Measurement of the genome+:  Continued improvements in sequencing technology are dramatically reducing the cost of sequencing individual genomes. In 2000 the Human Genome Project was completed, having sequenced the first whole human genome, at a cost in excess of USD$2 billion (Davies 2010). In 2012 the 1000 genomes project released their phase 1 sequencing data (Pybus et al. 2014).  This project is first to sequence the genomes of over 1,000 individuals, sampled to document human genetic variation across 25 populations from around the globe (The_1000_Genomes_Project_Consortium 2012). When it began, costs for fully sequencing an entire genome were high, and as a result only a portion of each genome was sequenced.  But the cost of whole genome sequencing continues to drop, and the USD$1,000 whole sequence genome is now available (Hayden 2014). In medical practice and research whole genome sequencing is posing ethical challenges regarding the amount of information to disclose to the individual, especially given incomplete knowledge of the genetic basis of disease (Yu et al. 2013).  Nonetheless, we expect whole genome sequencing for individuals to soon be a commodity available for USD$100 or less.  That sequence data will support viable business models is being proven out by companies such as 23andme (https:src=”//www.23andme.com/; see src=”//www.isogg.org/wiki/List_of_personal_genomics_companies for a list of personal genomics companies), which offer partial sequencing using saliva samples to explore ancestral origins and disease risks.  These dramatic reductions in cost of measurement are also occurring in the exome, epigenome, and other constituents of the genome+  (Zentner and Henikoff 2012, Weinhold 2012, Meissner 2012, Mefford 2012).  It is clear that measurements of the genome+ will soon be widely and inexpensively available, and will be incorporated into individual electronic health records, notwithstanding the informatics and ethical challenges posed by their integration (Kho et al. 2013, Flintoft 2014, Tarczy-Hornoch et al. 2013, Hazin et al. 2013).

Near real-time physiological measurement is expanding rapidly in clinical medicine as well as in the burgeoning “wearables” marketplace.  In 2012 Qualcomm established their Tricorder XPrize (src=”//www.qualcommtricorderxprize.org/), with the goal of creating a wireless handheld device that monitors and diagnoses a patient’s health conditions using personal health metrics.  The top three entries are to be announced in May, 2014. Wearable and implantable wireless sensors for healthcare monitoring include cancer detection, glucose monitoring, seizure warning, cardiac rate and rhythm, and heart attack detection, among others (Darwish and Hassanien 2011).  Google is now testing a glucose monitor incorporated into a contact lens (Landen 2014), and “smart” t-shirts for monitoring pulse, respiration and stress levels will soon be on the market (src=”//www.omsignal.com/).  Activity sensors such as those from Jawbone and fitbit are currently available, priced at around USD$100, and provide a record of daily activity (e.g. steps taken, distance traveled, time asleep).  Data from wearable sensors is already being used to monitor physical activity levels in pediatric patients (Yan et al. 2014).  At the 2014 Consumer Electronics Show wearables were prominent and recognized as an emerging market segment (Rowinski 2014).  While still small, the digital health market segment received $1.9B in venture funding in 2013, and posted 39% growth.  It has more than doubled since 2011 (RockHealth 2014).  Even so, the marketplace is nascent and fragmented, it is unclear what will succeed and what will not, and we have only an imperfect understanding of what people will adopt (Figure 2).  It seems reasonable, however, to assume that wearables as a technology will rapidly evolve and that their adoption and use will expand quickly.  This potentially poses a great opportunity for measurement in the environmental and health sciences.

Measurement of the exposome:   The challenge for quantification of an individual’s exposome is measurement of the ambient environment at the human boundary layer – the epidermis, mouth, mucosa, and nasal passages – where contaminants and pathogens enter the body (Balshaw and Kwok 2012).  This requires wearable sensors integrated into clothing (e.g. smart shirts, pants and shoes), jewelry and bracelets, or wearable on the lapel ( see (Windmiller and Wang 2013) for a review).   Recognizing their importance for quantification of the exposome, the National Institutes of Health has funded several initiatives to develop such “environmental tricorders” (see for example src=”//grants.nih.gov/grants/guide/rfa-files/RFA-ES-09-005.html). Environmental tricorders are already on the market, although the environmental factors they monitor are somewhat limited, including Volatile Organic Compounds, dust, light, sound, ionizing radiation, carbon dioxide and others.  Examples include products from Valarm and Sensorcon, among others.   The Knight foundation recently funded prototyping of the “Global Sensor Web”, whose objective is to create an online platform for aggregating geo-tagged data sets from public and personal data sources (src=”//www.knightfoundation.org/grants/201347663/), although these are not necessarily data from wearable sensors.  As noted below, there currently is a distance between sensors of sufficient quality, accuracy and precision to be of immediate use in environmental epidemiology, and the low-cost sensors currently being adopted in the consumer marketplace.

Measurement of the behavome:  We think of the behavome as separate from Wild’s exposome, as health behaviors are key mediators of exposures.  Behavioral recognition methods for assessing what an individual is doing has for decades been an important topic of health research.  With the advent of sensors in residences, health care facilities, and wearable on patients, the issue of multisensor data fusion for activity recognition has become an important topic.   These technologies are already being deployed and assessed in nursing home and assisted living facilities. Recent research has demonstrated these methods can identify risky behaviors with good accuracy and low deployment costs (Palumbo et al. 2013).  The “internet of things” including smart homes, smart cars and smart workplaces, is in the early phase of what many predict to be explosive growth (Ashton 2009).  In 2008 the number of devices on the Internet exceeded the number of people, and in 2020 will exceed 50 billion devices (Swan 2012). Information on when, where and how we use appliances, electronic devices, machinery and environmental  controls in home and workplace settings, and while commuting, have yet to be used to quantify the behavome.  The value of near real-time data on ambient temperatures and how often and when we use the refrigerator may have enormous value for quantifying, for example, personal energy budgets.  A variety of different approaches for assessing health behaviors have been suggested using technologies such as inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. Most promising is the sensor fusion approach that combines data from several sensors simultaneously (Lowe and ÓLaighin 2014).  To our knowledge technologies such as Google Glass have yet to be used for capturing video images to chronicle dietary intake and other health-related activities.  Other potential applications include quantification of personal energy budgets, individual walkability (e.g. (Mayne et al. 2013)), and documentation of other personalized environmental metrics.  Once health-related behaviors are known, the possibility of using gamification (Whitson 2013) and other approaches to encourage salubrious behaviors become possible  (Schoech et al. 2013).

Where will these measurement trends lead?  At present there are two domains for measuring the quantified self, the high-end approach focused on measurement accuracy and precision, and the quantified self as a commodity that is focused on capturing the consumer market (refer to Figure 3).  We see the possibility of a future convergence and emergence of low-cost sensors of sufficient quality to support a common good – high quality environmental health research, made possible by volunteered information provided by enlightened citizen scientists.  But achievement of this goal likely will require the establishment of appropriate mechanisms of data sharing, oversight and governance, and the creation of a user community of sufficient market mass to influence the development of COTS sensors of sufficient quality to support research in environmental health and epidemiology.

What benefits might be realized?  There is a growing recognition that new ways of measuring ourselves require new ways of understanding what “normal” means (McFedries 2013).  We know amazingly little about the ambient environments individuals experience through the course of their daily lives.  Similarly, we know very little about the local environments experienced by the members of local communities across the US and around the world.  How much temporal and spatial variability is there in air quality at the human boundary layer for individuals in diverse communities?  What are the exposure profiles of children as they move about their daily lives in our neighborhoods and schools?  A national baseline environmental assessment, incorporating data on the quantified self collected by citizen scientists, may begin to address such questions.  Important issues will need to be addressed regarding data quality, data sharing, sampling design and governance, but these do not appear to be insurmountable  (Goldberg et al. 2013).

A call for action.  It seems reasonable to the authors to assume the technology trends in figure 3 will indeed result in more accurate measurement of individual-level physiology, exposures and genetics at decreased costs.  In fact, the commoditization of the quantifiable self is rapidly taking place, as demonstrated by products such as Google Glass, fitbit, the advent of environmental tricorders from sensorcon, Valarm and others.  The data collected by these technologies is a highly valued business asset, and as such is not likely to be shared with research scientists for advancing epidemiology and public health. This balkanization of data for measuring the quantifiable self may be ameliorated by appealing directly to the individual to act both in their own self interest and also for the common good.  We must provide an alternative for the highly vested, motivated citizen scientist, so they may choose to share their personal measurements for the good of all.  This will require the formation of an organization charged with governance, data sharing, data security, and oversight of research and data use, with the overall mission of advancing human health.  Rather than a balkanization of measurements on the quantifiable self into isolated information silos under the control of corporations, we envision the enlightened sharing of such personal measurements that will lead to safer neighborhoods, workplaces and communities.  We believe our professional organizations, the AAAS, Sigma Xi, ISEE, and the AAG, are best positioned to take a leadership role in addressing this need.  We encourage readers to consider bringing this to the attention of their professional organizations.

Research sensors, such as those funded by grants from NIH and Qualcomm’s Xprize challenge, strive for accuracy and high quality, but are expensive. The emergence of the wearables marketplace is resulting in sensors as commodities, COTS (commercial off the shelf) sensors that are relatively inexpensive but not necessarily of sufficient quality (e.g. accuracy and precision of measurement) to support environmental health research. We see a trend towards higher quality, low cost sensors (Future bubble) that may be suited for baseline environmental and population-level exposure assessment.
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