Xenobiotic’s And Chronic Illness, What You Need To Know!

Xenobiotics And Chronic Illness

Xenobiotic

From Wikipedia, the free encyclopedia

A xenobiotic is a chemical substance found within an organism that is not naturally produced or expected to be present within the organism. It can also cover substances that are present in much higher concentrations than are usual.

Natural compounds can also become xenobiotics if they are taken up by another organism, such as the uptake of natural human hormones by fish found downstream of sewage treatment plant outfalls, or the chemical defenses produced by some organisms as protection against predators.[1]

The term xenobiotics, however, is very often used in the context of pollutants such as dioxins and polychlorinated biphenyls and their effect on the biota, because xenobiotics are understood as substances foreign to an entire biological system, i.e. artificial substances, which did not exist in nature before their synthesis by humans. The term xenobiotic is derived from the Greek words ξένος (xenos) = foreigner, stranger and βίος (bios, vios) = life, plus the Greek suffix for adjectives -τικός, -ή, -ό (tic).

Xenobiotics may be grouped as carcinogens, drugs, environmental pollutants, food additives, hydrocarbons, and pesticides.

Xenobiotic metabolism

The body removes xenobiotics by xenobiotic metabolism. This consists of the deactivation and the excretion of xenobiotics, and happens mostly in the liver. Excretion routes are urine, faeces, breath, and sweat. Hepatic enzymes are responsible for the metabolism of xenobiotics by first activating them (oxidation, reduction, hydrolysis and/or hydration of the xenobiotic), and then conjugating the active secondary metabolite with glucuronic acid, sulphuric acid, or glutathione, followed by excretion in bile or urine. An example of a group of enzymes involved in xenobiotic metabolism is hepatic microsomal cytochrome P450. These enzymes that metabolize xenobiotics are very important for the pharmaceutical industry, because they are responsible for the breakdown of medications.

Although the body is able to remove xenobiotics by reducing it to a less toxic form through xenobiotic metabolism then excreting it, it is also possible for it to be converted into a more toxic form in some cases. This process is referred to as bioactivation and can result in structural and functional changes to the microbiota.[2] Exposure to xenobiotics can disrupt the microbiome community structure, either by increasing or decreasing the size of certain bacterial populations depending on the substance. Functional changes that result vary depending on the substance and can include increased expression in genes involved in stress response and antibiotic resistance, changes in the levels of metabolites produced, etc.[3]

Organisms can also evolve to tolerate xenobiotics. An example is the co-evolution of the production of tetrodotoxin in the rough-skinned newt and the evolution of tetrodotoxin resistance in its predator, the Common Garter Snake. In this predator–prey pair, an evolutionary arms race has produced high levels of toxin in the newt and correspondingly high levels of resistance in the snake.[4] This evolutionary response is based on the snake evolving modified forms of the ion channels that the toxin acts upon, so becoming resistant to its effects.[5] Another example of a xenobiotic tolerance mechanism is the use of ATP-binding cassette (ABC) transporters, which is largely exhibited in insects.[6] Such transporters contribute to resistance by enabling the transport of toxins across the cell membrane, thus preventing accumulation of these substances within cells.

Xenobiotics in the environment

Main article: Environmental xenobiotic

Xenobiotic substances are an issue for sewage treatment systems, since they are many in number, and each will present its own problems as to how to remove them (and whether it is worth trying to)

Some xenobiotics are resistant to degradation. Xenobiotics such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and trichloroethylene (TCE) accumulate in the environment due to their recalcitrant properties and have become an environmental concern due to their toxicity and accumulation. This occurs particularly in the subsurface environment and water sources, as well as in biological systems, having the potential to impact human health.[7] Some of the main sources of pollution and the introduction of xenobiotics into the environment come from large industries such as pharmaceuticals, fossil fuels, pulp and paper bleaching and agriculture.[8] For example, they may be synthetic organochlorides such as plastics and pesticides, or naturally occurring organic chemicals such as polyaromatic hydrocarbons (PAHs) and some fractions of crude oil and coal.

Microorganisms may be a viable solution to the issue of environmental pollution by the production of xenobiotics; a process known as bioremediation.[9] Microorganisms are able to adapt to xenobiotics introduced into the environment through horizontal gene transfer, in order to make use of such compounds as energy sources.[8] This process can be further altered to manipulate the metabolic pathways of microorganisms in order to degrade harmful xenobiotics under specific environmental conditions at a more desirable rate.[8] Mechanisms of bioremediation include both genetically engineering microorganisms and isolating the naturally occurring xenobiotic degrading microbes.[9] Research has been conducted to identify the genes responsible for the ability of microorganisms to metabolize certain xenobiotics and it has been suggested that this research can be used in order to engineer microorganisms specifically for this purpose.[9] Not only can current pathways be engineered to be expressed in other organisms, but the creation of novel pathways is a possible approach.[8]

Xenobiotics may be limited in the environment and difficult to access in areas such as the subsurface environment.[8] Degradative organisms can be engineered to increase mobility in order to access these compounds, including enhanced chemotaxis.[8] One limitation of the bioremediation process is that optimal conditions are required for proper metabolic functioning of certain microorganisms, which may be difficult to meet in an environmental setting.[7] In some cases a single microorganism may not be capable of performing all metabolic processes required for degradation of a xenobiotic compound and so “syntrophic bacterial consortia” may be employed.[8] In this case, a group of bacteria work in conjunction, resulting in dead end products from one organism being further degraded by another organism.[7] In other cases, the products of one microorganisms may inhibit the activity another, and thus a balance must be maintained.[8]

Many xenobiotics produce a variety of biological effects, which is used when they are characterized using bioassay. Before they can be registered for sale in most countries, xenobiotic pesticides must undergo extensive evaluation for risk factors, such as toxicity to humans, ecotoxicity, or persistence in the environment. For example, during the registration process, the herbicide, cloransulam-methyl was found to degrade relatively quickly in soil.[10]

References[edit]

  1. Jump up ^ Mansuy D (2013). “Metabolism of xenobiotics: beneficial and adverse effects”. Biol Aujourdhui. 207 (1): 33–37. doi:1051/jbio/2013003. PMID23694723.
  2. Jump up ^ Park, B.K.; Laverty, H.; Srivastava, A.; Antoine, D.J.; Naisbitt, D.; Williams, D.P. “Drug bioactivation and protein adduct formation in the pathogenesis of drug-induced toxicity”. Chemico-Biological Interactions. 192 (1–2): 30–36. doi:1016/j.cbi.2010.09.011.
  3. Jump up ^ Lu, Kun; Mahbub, Ridwan; Fox, James G. (2015-08-31). “Xenobiotics: Interaction with the Intestinal Microflora”. ILAR Journal. 56 (2): 218–227. doi:1093/ilar/ilv018. ISSN1084-2020. PMC 4654756.
  4. Jump up ^ Brodie ED, Ridenhour BJ, Brodie ED (2002). “The evolutionary response of predators to dangerous prey: hotspots and coldspots in the geographic mosaic of coevolution between garter snakes and newts”. Evolution. 56 (10): 2067–82. doi:1554/0014-3820(2002)056[2067:teropt]2.0.co;2. PMID12449493.
  5. Jump up ^ Geffeney S, Brodie ED, Ruben PC, Brodie ED (2002). “Mechanisms of adaptation in a predator–prey arms race: TTX-resistant sodium channels”. Science. 297 (5585): 1336–9. doi:1126/science.1074310. PMID12193784.
  6. Jump up ^ Broehan, Gunnar; Kroeger, Tobias; Lorenzen, Marcé; Merzendorfer, Hans (2013-01-16). “Functional analysis of the ATP-binding cassette (ABC) transporter gene family of Tribolium castaneum”. BMC Genomics. 14: 6. doi:1186/1471-2164-14-6. ISSN1471-2164.
  7. ^ Jump up to: a b c Biodegradation and bioremediation. Singh, Ajay, 1963-, Ward, Owen P., 1947-. Berlin: Springer. 2004. ISBN3540211012. OCLC 54529445.
  8. ^ Jump up to: a b c d e f g h Díaz, Eduardo (September 2004). “Bacterial degradation of aromatic pollutants: a paradigm of metabolic versatility”. International Microbiology: The Official Journal of the Spanish Society for Microbiology. 7 (3): 173–180. ISSN1139-6709. PMID 15492931.
  9. ^ Jump up to: a b c Singleton, Ian (January 1994). “Microbial metabolism of xenobiotics: Fundamental and applied research”. Journal of Chemical Technology AND Biotechnology. 59 (1): 9–23. doi:1002/jctb.280590104.
  10. Jump up ^ Wolt JD, Smith JK, Sims JK, Duebelbeis DO (1996). “Products and kinetics of cloransulam-methyl aerobic soil metabolism”. J. Agric. Food Chem. 44: 324–332. doi:1021/jf9503570.CS1 maint: Multiple names: authors list (link)

 

 

 

We are what we eat: Regulatory gaps in the United States that put our health at risk

Maricel V. Maffini,1,* Thomas G. Neltner,2 and Sarah Vogel2

Maricel V. Maffini

1 Independent consultant, Germantown, Maryland, United States of America

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Thomas G. Neltner

2 Environmental Defense Fund, Washington DC, United States of America

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Sarah Vogel

2 Environmental Defense Fund, Washington DC, United States of America

Find articles by Sarah Vogel

Linda S. Birnbaum, Editor

1 Independent consultant, Germantown, Maryland, United States of America

2 Environmental Defense Fund, Washington DC, United States of America

National Institute of Environmental Health Sciences, United States of America,

MVM is a paid consultant for Environmental Defense Fund. MVM and TGN authored the food additive petition to FDA asking the agency to revoke the approvals of perchlorate’s uses. The Environmental Defense Fund is a party in challenging FDA’s 2017 ruling on continuing uses of perchlorate in dry food packaging and handling equipment.

* E-mail: moc.liamg@amvmrd

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Copyright © 2017 Maffini et al

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This article has been cited by other articles in PMC.

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Abstract

The American diet has changed dramatically since 1958, when Congress gave the United States Food and Drug Administration (FDA) the authority to ensure the safety of chemicals in food. Since then, thousands of chemicals have entered the food system. Yet their long-term, chronic effects have been woefully understudied, their health risks inadequately assessed. The FDA has been sluggish in considering scientific knowledge about the impact of exposures—particularly at low levels and during susceptible developmental stages. The agency’s failure to adequately account for the risks of perchlorate—a well-characterized endocrine-disrupting chemical—to vulnerable populations is representative of systemic problems plaguing the regulation of chemicals in food. Today, we are faced with a regulatory system that, weakened by decades of limited resources, has fallen short of fully enforcing its mandates. The FDA’s inability to effectively manage the safety of hundreds of chemicals is putting our children’s health at risk.

This Perspective is part of the Challenges in Environmental Health: Closing the Gap between Evidence and Regulations Collection.

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Introduction

Over the course of the 1950s, Congress debated legislation that sought to address rising concerns about the safety of hundreds of new, industrially produced chemicals transforming the way Americans grew, packaged, processed, and transported the food they ate. In 1958, the Food Additive Amendment (FAA) to the 1938 Federal Food, Drug and Cosmetics Act (FFDCA) [1] was signed into law, providing new authority to the Food and Drug Administration (FDA) to ensure the safety of chemicals added to the food supply. [2] Among other considerations, Congress intended that the FDA address the chronic and cumulative exposures to chemicals in the food supply when considering safety. [3] The American diet, just like that of most developed countries, has changed dramatically in the nearly 60 years since the law passed. Today, there are more than 10,000 chemicals [4]—commonly referred to as food additives—allowed in food, which presents a critical challenge to the FDA’s ability to effectively assess and manage the safety of all of these chemicals. This challenge has become particularly evident in the face of mounting scientific evidence that some of these chemicals—including endocrine-disrupting agents—can interact with biological systems at exceedingly low, chronic levels of exposure and result in adverse health impacts, especially when exposures occur during pregnancy or early childhood [5].

The ongoing presence of various chemicals in the food supply that have been associated with significant health risks [6,7] indicates that the FDA’s scientific decision-making processes are inadequate to take into account the best available evidence and methods for assessing the chronic and cumulative effects of chemicals. In 1982, a select committee of experts provided the FDA with recommendations to improve chemical safety. Among their main issues were to pay special attention to the effects of chemicals on behavioral changes and the endocrine system; not to assume that chemicals below a certain concentration are not hazardous; and to ensure susceptible populations were protected [8]. Unfortunately, the FDA has not incorporated the great majority of these recommendations made over three decades ago [9]. As a result, the health of children and vulnerable populations are particularly at risk.

There are a number of interrelated factors that contribute to creating a gap between the intent of the law regulating chemicals in food and the agency’s practices; these include limited agency funding that reduces available resources for updating scientific practices and guidance and the fact that an absence of internal resources generates a dependency on the regulated industry, including its implementation and interpretation of the law. Importantly, the interpretation of the law to allow companies to make safety determinations for “generally recognized as safe” (GRAS) substances without notifying the FDA has tied the agency’s hands, making it difficult to obtain information about a chemical’s safety and generating a strong disincentive to update agency science.

Adequately addressing this gap between intent and practice demands more than just a dedication to science; it also will require a renewed commitment to the agency itself through resources that can bring new talent and ideas to the agency and the authority to get the information it needs, such as reporting on new uses [10]. At a time when federal agencies in charge of protecting public health face potential budget cuts and political leaders undermine the very practice of scientific inquiry, many health safeguards we take too easily for granted are at stake [11]. Among them are the safety of the chemicals in the food we eat every day.

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A case study: Perchlorate

The chemical perchlorate illustrates an expanding disconnect between the original intent of the 1958 food law as it relates to managing chronic health impacts of chemicals and the agency’s practice for allowing chemicals into the food supply. Because the chemical’s mode of action is well understood and undisputed—inhibition of the transport of iodine from the blood to the thyroid gland—it also makes for a compelling example. Perchlorate is a naturally occurring and man-made chemical that quickly dissolves in water and organic solvents and persists in the environment [12]. It has been found in the urine of all Americans tested [13]. While it leaves the body quickly, perchlorate persists in the environment for many years. The risks of chronic exposure to perchlorate are well described, as is the evidence of its widespread presence in the environment [14,15,12]. The FDA has approved perchlorate’s use as a food contact substance twice. The first time was in 1963 for its use in sealing gaskets for food containers; and then, in 2005, it was approved for use as a conductivity enhancer or antistatic agent in dry food packaging [16].

Perchlorate is perhaps most commonly known for its uses in rocket fuel, explosives, and fireworks and as a contaminant of nitrate-based Chilean fertilizer [12]. Perchlorate enters the body through food and water, with food being the main contributor [17]; perchlorate contaminates food through two primary uses: as an antistatic agent in any plastic material that contacts dry food, including final and bulk packaging, and as a contaminant of hypochlorite bleach, which oxidizes to form chlorate and then perchlorate with time and inadequate management [18]. Bleach—a pesticide—is widely used to sanitize food surfaces in food-manufacturing and -processing facilities; it is also a food additive approved by the FDA to wash and peel fruits and vegetables [19].

Perchlorate primarily affects the normal functioning of the thyroid gland by inhibiting the transport of iodine from the blood into the organ. Iodine is an essential element needed to produce thyroid hormone—which plays an important role in controlling metabolism and is critical in regulating fetal and infant brain development. Because perchlorate is such a strong inhibitor of iodine transport [20], pregnant women, infants, and children with inadequate iodine consumption are the most vulnerable, and exposure to the chemical greatly increases the risk of impaired neurodevelopment [14].

At least 20% of pregnant American women consume so little iodine that any exposure to perchlorate presents a significant risk of adverse neurological development in the fetus [21]. Effects caused by lowering thyroid hormone levels during brain development are likely to be subtle and may be manifested as intellectual deficiencies and other chronic developmental delays. Emerging scientific evidence demonstrates that perchlorate exposure decreases thyroid hormone levels during pregnancy [14] and that children born to mothers with borderline thyroid deficiency and who were exposed to perchlorate during the first trimester had decreased intelligence quotient (IQ) [15].

It is very likely that the FDA’s first approval of perchlorate use in contact with food more than 50 years ago was based on limited or no safety data. However, available scientific evidence on perchlorate targeting the thyroid gland and contemporary controversies about safe levels of perchlorate exposure from drinking water [22] make its 2005 approval [23] puzzling. Over the past decade, the evidence for the adverse neurological effects of low-level exposure to perchlorate has become stronger, yet the agency continues to stand behind its approval of perchlorate’s use in dry food packaging and food handling equipment [16].

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Outdated scientific methods built on flawed assumptions led to bad decisions

It’s worth then exploring how the FDA considers perchlorate to be safe for use in food. A fundamental, long-standing assumption informing the agency’s determination of safe uses is that, unless the chemical is a carcinogen, there is a threshold effect. That is, below a certain level of exposure, there is no adverse effect. The FDA assumed that perchlorate was safe at low doses. In 1982, a committee of experts told the agency the assumption of a threshold effect was “scientifically untenable” and ignored the possibility of irreversible organ function alteration [9]. And yet, in 1995, the FDA responded by issuing a Threshold of Regulation rule [24] that codified exemptions of chemicals from regulation as food additives if the amount stayed below a predetermined level in the diet of 0.5 parts per billion without a rigorous evaluation of its safety.

The second reason for allowing endocrine disruptors such as perchlorate can be found in the FDA’s guidance for chemicals used in packaging or food-handling equipment. The guidance is based on tiers of exposure below which limited (e.g., gene toxicity) or no safety data are expected to be generated [25]. The FDA has also fallen short in its efforts to update its scientific practices. A case in point is the 1982 publication of the Redbook, the agency’s guidance for the food industry on chemical testing methods [26]. Although it was reviewed in the 1990s and 2000 [27], none of the major scientific advances in neurodevelopment, endocrinology, reproductive biology, and immunology were reflected in the guidance. For example, unlike the Organization for Economic Cooperation and Development [28], the FDA does not have guidelines for testing developmental neurotoxicity [27]. In 2014, the FDA announced it was taking “steps to strengthen its program to assess the safety of chemicals in food,” which includes another revision of the Redbook [29].

Importantly, the agency lacks guidance on the best practice for evaluating chronic diseases caused by a single chemical or multiple chemicals. The result is that there are no testing requirements to demonstrate the effects of very low or cumulative exposures that occur in the diet. The agency’s decision on perchlorate under the Threshold of Regulation rule did not consider the cumulative effect of perchlorate, nitrates, and thiocyanate. These latter two chemicals are also present in the diet and, like perchlorate, affect the thyroid gland by inhibiting the transport of iodine, although they are weaker inhibitors compared to perchlorate [20]. This is a clear-cut example of what Congress intended when it mandated the FDA to assess the cumulative effects of co-exposures and investigate their potential chronic health effects.

In sum, the FDA has fallen short in its responsibility to identify and thoroughly evaluate what chemicals may cause the type of long-lasting diseases that have become so prevalent, including behavioral and neurodevelopmental disorders associated with perchlorate exposure. In considering the safety of perchlorate, the agency assumed a threshold effect and did not require data on the neurological effects of low-dose, chronic exposures nor consider the impact of cumulative effects of other chemicals also affecting the thyroid gland. And today, the agency continues to maintain that there is no health risk for children consuming perchlorate. This interpretation has become alarming given the agency’s own recent studies [30,17] that show an increase in perchlorate consumption by infants and toddlers since the chemical’s approval for use in dry food plastic packaging and food-handling equipment.

The case of perchlorate demonstrates the disconnect between the health risk posed by real-world exposure, especially for vulnerable populations with iodine deficiencies, and a regulatory system that appears to have stagnated when it comes to scientific principles and methods used in decision-making. There is an urgency to effectively close this gap between regulatory mandate and scientific practice. But it is not a problem that is easily solved. It will require a combination of statutory changes, a renewed commitment by the agency itself, and the difficult task of changing institutional culture and practices.

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Good science requires solid information and independent judgement

While the 1958 food law emphasized the need for testing chemicals and ensuring that they were safe before being allowed in food, the statute gives the FDA little authority to systematically collect chemical hazard and exposure information from businesses or to develop a postmarket monitoring program to keep track of uses of chemicals (that is, how much is used and in which foods). As a result, the agency has little information for evaluating safety or monitoring it over time—consider that a chemical approved for use in food in the 1960s may be used today in more foods and in packaging or processing without any additional review by the agency. The change in the levels used in food along with the advancements in science in the subsequent 50 years suggests that some review of the chemical’s safety is warranted. But without an ability to track uses of chemicals, the agency has no visibility into the volume or frequency of chemical use, nor does it regularly obtain new information on toxicity that could be used to prioritize re-evaluations of chemicals used in food.

Another statutory provision hindering the agency’s ability to know whether all the chemicals in the food supply are safe is the “generally recognized as safe” or GRAS loophole. [4] With this exemption, Congress intended that additives such as vinegar and oils should not undergo safety testing because of a long history of safe uses. In other words, food manufacturers could determine a substance’s use was GRAS without informing the FDA of its safety. Unfortunately, the FDA has interpreted this provision to mean that chemical and food manufacturers can declare any new substance or new use of a substance to be GRAS with no obligation to tell the agency about the identity of the substance, where it was used, how much of it was used, and if it was safe [31].

In a universe of more than 10,000 chemicals, at least 1,000 have completely avoided FDA scrutiny through the GRAS exemption [4]. In an effort to entice companies to inform the FDA about the safety of their GRAS chemicals, the agency created a voluntary program where manufacturers could send the safety assessment of a chemical use they determined was GRAS and ask for FDA review. The agency then offers a nonbinding opinion; in other words, the regulator becomes a peer reviewer rather than the decision-maker. This process effectively creates a conflict-laden safety assessment process by allowing an employee or consultant to a company that profits from a given chemical to make the decision about the safety of that chemical. This voluntary system is further problematic in that it allows a company not to submit the safety decision to the FDA for its nonbinding opinion as it leaves the agency entirely unaware of the presence of the chemical in the food supply and its safety [32]. Furthermore, under this voluntary system, a company may also withdraw a notice even after the FDA flags a concern, and any company could move ahead and put that questionable chemical on the market without any requirement to resubmit to the FDA for review. This process ties the hands of the agency entirely because it no longer has any authority to limit a chemical’s use if, in its review, the substance is found to raise safety concerns. The substance can still be marketed as GRAS, and no one—not competitors nor consumers—will know that there might be safety concerns.

By effectively delegating regulatory authority to the industry itself, the agency’s power and authority is significantly weakened. It might not then be surprising that the agency is slow to challenge its long-held scientific practices. Raising new scientific questions or demanding more information from the industry would presume a position of power and authority, as it risks putting the agency in conflict with the regulated industry.

Further, the agency’s lack of access to information on the toxicity and exposure of chemicals extends beyond GRAS substances. Consider the following example: for chemicals purposely added to food such as flavors, preservatives, and sweeteners, the FDA recommends the industry perform a month-long feeding study in laboratory animals. Yet less than 22% of almost 4,000 chemicals have sufficient data to estimate how much is safe to eat, and less than 7% were tested for developmental or reproductive effects [33]. The paucity of information is astonishing.

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Looking forward

It is too tempting to put all the blame at the foot of the FDA. Certainly, when it comes to managing the safety of chemicals in food, the FDA has been sluggish to modernize its science and is falling far short in effectively accounting for the safe use of thousands of chemicals in use today, including well-known hazardous substances like perchlorate. But there is an important factor to consider in evaluating the effectiveness of our regulatory process: resources, both human and financial. Current efforts to roll back and repeal health and safety standards and dramatically cut agency budgets is a central component of President Trump’s political agenda. Cutting the agency’s funding even further will not solve the problem of regulatory ineffectiveness but rather will amplify it, potentially further eroding the independence of the agency from the industry it regulates.

The FDA Office of Food Additives Safety—one of 12 offices in the Center for Food Safety and Applied Nutrition (CFSAN) [34]—responsible for regulating more than 10,000 chemicals and a multi-billion-dollar industry—has a little over 100 full-time technical staff. The fiscal year (FY) 2017 budget for CFSAN and related field activities (e.g., implementation of the Food Safety Modernization Act of 2011) in the Office of Regulatory Affairs is about US$1 billion [35]. This budget pales in comparison to the US$371 billion in packaged foods sales in 2015 [36].

Additional resources are a critical component of a multipronged approach needed to improve the FDA’s processes to ensure that chemicals in the food we eat are safe. If the FDA is to integrate new scientific understandings into its consideration of chemical safety, it needs access to adequate and up-to-date information; it needs to be able to incentivize the regulated entities to provide such information without statutory changes; and it needs to be able to conduct systematic safety reviews of priority chemicals that were approved decades ago and never since re-examined.

Today, more than ever, this country needs to reinvest in the scientific institutions that we all have long relied on to protect our health. The FDA’s efforts to manage the tens of thousands of chemicals in use in our food supply have fallen far short of the task at hand given the rapid changes in food technology and scientific research on chemicals. As the case of perchlorate shows, this failure is putting the health of our children at risk.

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Abbreviations

CFSAN Center for Food Safety and Applied Nutrition
FAA Food Additive Amendment
FDA Food and Drug Administration
FFDCA Federal Food, Drug and Cosmetics Act
FY fiscal year
GRAS generally recognized as safe
IQ intelligence quotient

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References

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  21. US Environmental Protection Agency (2016) Biologically Based Dose Response Models for the Effect of Perchlorate on Thyroid Hormones in the Infant, Breast Feeding Mother, Pregnant Mother, and Fetus: Model Development, Revision, and Preliminary Dose-Response Analyses. Accessed June 1, 2017. https://www.regulations.gov/document?D=EPA-HQ-OW-2016-0438-0002
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An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health

Wullianallur Raghupathi1 and Viju Raghupathi2,*

Wullianallur Raghupathi

1Gabelli School of Business, Fordham University, New York, NY 10023, USA; ude.mahdrof@ihtapuhgaR

Find articles by Wullianallur Raghupathi

Viju Raghupathi

2Koppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA

Find articles by Viju Raghupathi

1Gabelli School of Business, Fordham University, New York, NY 10023, USA; ude.mahdrof@ihtapuhgaR

2Koppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA

*Correspondence: ude.ynuc.nylkoorb@ihtapuhgarV; Tel.: +1-(718)-951-5000

Author information Article notes Copyright and License information Disclaimer

Received 2018 Jan 12; Accepted 2018 Feb 27.

Copyright © 2018 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Abstract

In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Five main categories of variables are studied, namely chronic disease conditions, behavioral health, mental health, demographics, and overarching conditions. These are analyzed in the context of regions and states within the U.S. to discover possible correlations between variables in several categories. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is fundamental to developing insights that will help in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, health providers, and society as a whole, address these adverse effects in a timely and efficient manner.

Keywords: behavioral health, chronic disease, comorbidity, overarching condition, population health, preventive health

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  1. Introduction

A chronic condition “is a physical or mental health condition that lasts more than one year and causes functional restrictions or requires ongoing monitoring or treatment” [1,2]. Chronic diseases are among the most prevalent and costly health conditions in the United States. Nearly half (approximately 45%, or 133 million) of all Americans suffer from at least one chronic disease [3,4,5], and the number is growing. Chronic diseases—including, cancer, diabetes, hypertension, stroke, heart disease, respiratory diseases, arthritis, obesity, and oral diseases—can lead to hospitalization, long-term disability, reduced quality of life, and death [6,7]. In fact, persistent conditions are the nation’s leading cause of death and disability [6].

Globally, chronic diseases have affected the health and quality of life of many citizens [8,9]. In addition, chronic diseases have been a major driver of health care costs while also impacting workforce patterns, including, of course, absenteeism. According to the Centers for Disease Control, in the U.S. alone, chronic diseases account for nearly 75 percent of aggregate healthcare spending, or an estimated $5300 per person annually. In terms of public insurance, treatment of chronic diseases comprises an even larger proportion of spending: 96 cents per dollar for Medicare and 83 cents per dollar for Medicaid [4,10,11,12]. Thus, the understanding, management, and prevention of chronic diseases are important objectives if, as a society, we are to provide better quality healthcare to citizens and improve their overall quality of life.

More than two thirds of all deaths are caused by one or more of these five chronic diseases: heart disease, cancer, stroke, chronic obstructive pulmonary disease, and diabetes. Additional statistics are quite stark [5,13]: chronic diseases are responsible for seven out of 10 deaths in the U.S., killing more than 1.7 million Americans each year; and more than 75% of the $2 trillion spent on public and private healthcare in 2005 went toward chronic diseases [5]. What makes treating chronic conditions (and efforts to manage population health) particularly challenging is that chronic conditions often do not exist in isolation. In fact, today one in four U.S. adults have two or more chronic conditions [5], while more than half of older adults have three or more chronic conditions. And the likelihood of these types of comorbidities occurring goes up as we age [5]. Given America’s current demographics, wherein 10,000 Americans will turn 65 each day from now through the end of 2029 [5], it is reasonable to expect that the overall number of patients with comorbidities will increase greatly.

Trends show an overall increase in chronic diseases. Currently, the top ten health problems in America (not all of them chronic) are heart disease, cancer, stroke, respiratory disease, injuries, diabetes, Alzheimer’s disease, influenza and pneumonia, kidney disease, and septicemia [14,15,16,17,18]. The nation’s aging population, coupled with existing risk factors (tobacco use, poor nutrition, lack of physical activity) and medical advances that extend longevity (if not also improve overall health), have led to the conclusion that these problems are only going to magnify if not effectively addressed now [19].

A recent Milken Institute analysis determined that treatment of the seven most common chronic diseases coupled with productivity losses will cost the U.S. economy more than $1 trillion dollars annually. Furthermore, compared with other developed nations, the U.S. has ranked poorly on cost and outcomes. This is predominantly because of our inability to effectively manage chronic disease. And yet the same Milken analysis estimates that modest reductions in unhealthy behaviors could prevent or delay 40 million cases of chronic illness per year [11]. If we learn how to effectively manage chronic conditions, thus avoiding hospitalizations and serious complications, the healthcare system can improve quality of life for patients and greatly reduce the ballooning cost burden we all share [10].

The success of population health and chronic disease management efforts hinges on a few key elements: identifying those at risk, having access to the right data about this population, creating actionable insights about patients, and coaching them toward healthier choices. Methods such as data-driven visual analytics help experts analyze large amounts of data and gain insights for making informed decisions regarding chronic diseases [10,20]. According to the U.S.-based Institute of Medicine and the National Research, the vision for 21st century healthcare includes increased attention to cognitive support in decision making [21]. This encompasses computer-based tools and techniques that aid comprehension and cognition. Visualization techniques offer cognitive support by offering mental models of the information through a visual interface [22]. They combine statistical methods and models with advanced interactive visualization methods to help mask the underlying complexity of large health data sets and make evidence-based decisions [23]. Chronic diseases are characterized by high prevalence among populations, rising complication rates, and increased incidence of people with multiple chronic conditions, to name a few. In this scenario, visualization can represent association between preventive measures and disease control, summary health dimensions across diverse patient populations and, timeline of disease prevalence across regions/populations, to offer actionable insights for effective population management and national development [24]. Additionally, visual techniques offer the ability to analyze data at multiple levels and dimensions starting from population to subpopulation to the individual [25]. This paper addresses the challenge of understanding large amounts of data related to chronic diseases by applying visual analytics techniques and producing descriptive analytics. Our overall goal is to gain insight into the data and make policy recommendations.

Given that large segments of the U.S. population suffer from one or more chronic disease conditions, a data-driven approach to the analysis of the data has the potential to reveal patterns of association, correlation, and causality. We therefore studied the variables extracted from a highly reliable source, the Centers for Disease Control. Data for variables pertaining to several categories, namely chronic condition (“condition” is used interchangeably with “disease”), behavioral health, mental health, preventive health, demographics, overarching conditions, and location for several years (typically 2012 to 2014). We analyzed relationships within each category and across categories to obtain multi-dimensional views and insight into the data. The analytics provide insights and implications that suggest ways for the healthcare system to better manage population health.

This paper is organized as follows: Section 1 offers an introduction to the research, Section 2 discusses the methodology, Section 3 presents and discusses the visual charts and results, Section 4 contains the scope and limitations of the research, Section 5 describes the policy implications and future research, and Section 6 presents our conclusions.

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  1. Materials and Methods

This study analyzes the characteristics of chronic diseases in the U.S. and explores the relationships between demographics, behavior habits, and other health conditions and chronic diseases, thereby revealing information for public health practice at the state-specific level. In this data-driven study we use visual analytics [26], conducting primarily descriptive analytics [20] to obtain a panoramic insight into the chronic diseases data set pulled from the Centers for Disease Control and Prevention web site. The discipline of visual analytics aims to provide researchers and policymakers with better and more effective ways to understand and analyze large data sets, while also enabling them to act upon their findings in real time. Visual analytics integrates the analytic capabilities of the computer and the abilities of human analysts, thus inviting novel discoveries and empowering individuals to take control of the analytical process. It sheds light on unexpected and hidden insights, which may lead to beneficial and profitable innovation [27,28]. Driving visual analytics is the aim of turning information overload into opportunity; just as information visualization has changed our view on databases, the goal of visual analytics is to make our way of processing data and information transparent and accessible for analytic discourse. The visualization of these processes provides the means for examining the actual processes and not just the results. Visual analytics applies such technology as business intelligence (BI) tools to combine human analytical skill with computing power. Clearly, this research is highly interdisciplinary, involving such areas as visualization, data mining, data management, data fusion, statistics, and cognitive science, among others. One key understanding of visual analytics is that the integration of these diverse areas is a scientific discipline in its own right [29,30].

Historically, automatic analysis techniques, such as statistics and data mining, were developed independently of visualization and interaction techniques. One of the most important steps in the direction of visual analytics research was the need to move from confirmatory data analysis (using charts and other visual representations to present results) to exploratory data analysis (interacting with the data), first introduced to the statistics research community by John W. Tukey in his book, Exploratory Data Analysis [31].

With improvements in graphical user interfaces and interaction devices, the research community devoted its efforts to information visualization [27]. Eventually, this community recognized the potential of integrating the user’s perspective into the knowledge discovery and data mining process through effective and efficient visualization techniques, interaction capabilities, and knowledge transfer. This led to visual data exploration and visual data mining [29] and widened considerably the scope of applications of visualization, statistics, and data mining—the three pillars of analytics. In visual analytics is defined as “the science of analytical reasoning facilitated by interactive human-machine interfaces” [29]. A more current definition says “visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding reasoning and decision-making on the basis of very large and complex data sets” (both reported in [27]). In their book Illuminating the Path, Thomas and Cook define visual analytics as the science of analytical reasoning facilitated by interactive visual interfaces.

One application of visualization is descriptive analytics, the most commonly used and most well understood type of analytics. It was the earliest to be introduced and the easiest by far to implement and understand in that it describes data “as is” without complex calculations. Descriptive analytics is more data-driven than other models. Most health data analyses start with descriptive analytics, using data to understand past and current health patterns and trends and to make informed decisions [20]. The models in descriptive analytics categorize, characterize, aggregate, and classify data, converting it into information for understanding and analyzing business decisions, outcomes, and quality. Such data summaries can be in the form of meaningful charts and reports, and responses to queries using SQL. Descriptive analytics uses a significant amount of visualization. One could, for example, obtain standard and customized reports and drill down into the data, running queries to better understand, say, the sales of a product [20]. Descriptive analytics helps answer such questions as: How many patients with diabetes also have obesity? Which of the chronic diseases are more prevalent in different regions of the country? What behavioral habits are correlated to the chronic diseases? Which groups of patients suffer from more than one chronic condition? Is there an association between health insurance (and lack thereof) and chronic diseases? What are cost trade-offs between chronic disease prevention and management? What are typical patient profiles for various chronic diseases?

This study concentrates on chronic condition indicators and related demographics, behavior habits, preventive health, and oral health factors. As mentioned, the data source for this study is the Center for Disease Control and Prevention (CDC) [32]. The CDC’s Division of Population Health offers a crosscutting set of 124 indicators that were developed by consensus. Those indicators are integrated from multiple resources, with the help of the Chronic Disease Indicator web site, which serves as a gateway to additional information and data sources. In this research we downloaded secondary data for the United States from the CDC dataset, for the years 2012 to 2014. The data is for states, territories, and large metropolitan areas in the U.S., including the 50 states and District of Columbia, Guam, Puerto Rico, and the U.S. Virgin Islands. Data cleaning, integration, and transformation were conducted on the raw data set. The main categories of variables included—chronic condition, mental health, behavior habits, preventative health, and demographics. In addition, overarching conditions and location were also studied. Table 1 summarizes the categories and variables.

Table 1

Chronic diseases and related indicators.

Category Sub-Category Variables (Measure) Definition
Chronic condition Diabetes Diabetes (%) Prevalence of diagnosed diabetes among adults aged ≥18 years—2012–2014
Hospital diabetes (number) Hospitalization with diabetes as diagnosis; 2010 and 2013
Mortality diabetes (per 100,000) Mortality rate due to diabetes listed as cause of death, 2010–2014
Arthritis Arthritis (%) Prevalence of arthritis among adults aged ≥18 years; 2013–2014
Fair or poor health—arthritis (%) Prevalence of fair or poor health among adults aged ≥18 years with arthritis—2013–2014
Obesity—arthritis (%) Prevalence of Arthritis among adults aged ≥18 years who are obese—2013–2014
Asthma Asthma (%) Current asthma prevalence among adults aged ≥18 years, through 2012–2014
Mortality—asthma (case per 100,000) Asthma mortality rate through 2010–2014
Hospital—asthma (case per 100,000) Hospitalizations for Asthma
Chronic Kidney Disease Kidney (%) Prevalence of chronic kidney disease among adults aged ≥18 years—2012–2014
Mortality—kidney (case per 100,000) Mortality with end stage renal disease, through 2010 to 2014
Chronic Obstructive Pulmonary Disease Pulmonary (%) Prevalence of chronic obstructive pulmonary disease among adults aged ≥18 years, through 2012 to 2014
Hospital—pulmonary (case per 100,000) Hospitalization for chronic obstructive pulmonary disease as any diagnosis of 2010 and 2013
Mortality—pulmonary (case per 100,000) Mortality with chronic obstructive pulmonary disease as underlying cause among adults aged ≥45 years, through 2010 and 2014.
Mental health Mental health Mental—women (%) The crude prevalence rate of at least 14 recent mentally unhealthy days among women aged 18–44 years, through 2012 to 2014
Postpartum (%) The crude prevalence rate of Postpartum depressive symptoms in 2011
Mental (number) The aged-adjusted mean of recently mentally unhealthy days among adults aged ≥18 years, through 2012 to 2014
Behavioral Habits Alcohol Binge drink (%) Binge drinking prevalence among adults aged ≥18 years, through 2012 to 2014
Heavy drink (%) Heavy drinking among adults aged ≥18 years, through 2012 to 2014
Nutrition, Physical Activity, and Weight Status Physical activity (%) No leisure-time physical activity among adults aged ≥18 years, through 2012 to 2014
Tobacco—smokeless (%) Current smokeless tobacco use among adults aged ≥18 years, through 2012 to 2014
Tobacco (%) Current smoking among adults aged ≥18 years, through 2012 to 2014
Obesity (%) Obesity among adults aged ≥18 years, through 2012 to 2014
Preventive health Pneumococcal vaccination Pneumonia—smoke (%) Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years who smoke, through 2012 to 2014
Pneumonia—heart (%) Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with a history of coronary heart disease, through 2012 to 2014
Pneumonia—asthma (%) Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with asthma, through 2012 to 2014
Pneumonia—diabetes (%) Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with diagnosed diabetes, through 2012 to 2014
Immunization Influenza—asthma (%) Influenza vaccination among noninstitutionalized adults aged 18–64 years with asthma, through 2012 to 2014;
Influenza—diabetes (%) Influenza vaccination among noninstitutionalized adults aged 18–64 years with diagnosed diabetes, through 2012 to 2014
Influenza—heart (%) Influenza vaccination among noninstitutionalized adults aged 18–64 years with a history of coronary heart disease or stroke
Influenza (%) Influenza vaccination among noninstitutionalized adults aged ≥18 years, through 2012 to 2014
Smoke Quit (number) Quit attempts in the past year among current smokers, through 2012 to 2014
Demographics Gender Gender (character) Male and female
Ethnicity Race (character) Race
Location State location Location (character) 50 states and District of Columbia, Guam, Puerto Rico, Virgin Islands
Overarching Conditions Overarching Conditions Insurance (%) Current lack of health insurance among adults aged 18–64 years, through 2012 to 2014
Poor—self rate (%) Fair or poor self-rated health status among adults aged ≥18 years
Sleep (%) Prevalence of sufficient sleep among adults aged ≥18 years

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  1. Results

We use visualization and descriptive analytics to explore chronic conditions, preventive healthcare, mental health, and overarching conditions, with the objective of deciphering relationships and patterns that emerge from the visualization. We would like to point out that since our sample includes adults aged 18 and over our results are applicable for adults in that age group.

Figure 1 models the average prevalence of diagnosed diabetes among adults aged ≥18 years in the period 2012 to 2014. Puerto Rico leads the pack, followed by Mississippi.

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Figure 1

Diabetes by state.

As Figure 2 below shows, Puerto Rico has the highest number of citizens among adults aged ≥18 years, in fair or poor health with arthritis for the period 2013 to 2014. Puerto Rico is followed by Tennessee and Mississippi.

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Figure 2

Arthritis by state.

The current asthma prevalence among adults aged ≥18 years for the period 2012 to 2014 is indicated in Figure 3. West Virginia has a higher prevalence of the condition compared to other states.

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Figure 3

Asthma by state.

With regard to end-stage renal disease, Figure 4 shows that the condition is dispersed widely among various areas.

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Figure 4

End-stage renal disease by region.

The average value for hospitalization for chronic obstructive pulmonary disease for all diagnoses between 2010 and 2013 is shown in Figure 5. Kentucky and West Virginia have higher hospitalizations compared to other states. Most of the areas are below 45 cases per 100,000.

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Figure 5

Chronic obstructive pulmonary disease by state.

In exploring chronic conditions by location in the U.S., we see that some conditions, such as diabetes, arthritis, and obstructive pulmonary diseases, are more prevalent in eastern states, while others, such as asthma, occur more often in northeastern states. For diabetes, listed as a cause of death for the years 2010 to 2014, the states of Oklahoma and West Virginia had the relatively high average threshold of over 100 (age adjusted rate per 100,000). In the case of asthma, West Virginia has the highest prevalence of the condition (among adults), while Maryland, Massachusetts, and New York had the highest number of hospitalizations. With regard to chronic obstructive pulmonary disease, Kentucky and West Virginia had the most hospitalizations compared to other states. The majority of states are indeed below 45 cases per 100,000. With respect to arthritis among adults, a majority of states average below 25%, with the exception of West Virginia, which averaged 34.15%. In summary, West Virginia ranks high in prevalence for most chronic conditions, such as diabetes, asthma, chronic pulmonary disease, and arthritis when compared to all other states for the period 2000 to 2014.

We looked at the distribution of chronic conditions by gender and race to identify relevant trends and patterns (Figure 6 and Figure 7). Chronic conditions differ by gender. Women tend to have significantly higher cases per 100,000 of hospitalizations for asthma. Whereas men tend to have a higher mortality rate from chronic obstructive pulmonary disease, diabetes, chronic kidney, and other conditions, as shown in Figure 6.

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Figure 6

Chronic condition by gender.

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Figure 7

Chronic conditions by race.

We also examined all chronic conditions by race (Figure 7) and found that non-Hispanic Blacks have higher mortality rate for pulmonary disease and asthma and a higher hospitalization from diabetes. They are followed by Pacific Islander and American Indians. All categories of arthritis are fairly evenly distributed among Black, non-Hispanic, Multiracial, Whites, and other.

Females have a higher hospitalization rate for asthma (per 100,000), while in terms of mortality rate for chronic obstructive pulmonary disease, diabetes, and chronic kidney disease, males have the higher hospitalization rate. Again, American Indian or Alaskan Natives have higher mortality rate for chronic obstructive pulmonary disease, diabetes, and kidney disease. They’re followed by Blacks and non-Hispanics.

3.1. Mental Health by Gender and Race

Mental health is an important aspect of national healthcare impacting chronic diseases. We analyzed mental health by gender (Figure 8) and by race (Figure 9). When we examine how many days an individual feels “mentally unhealthy” for the years 2012 to 2014, women are more likely to have more unhealthy days than men, as shown in Figure 8.

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Figure 8

Mental health by gender.

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Figure 9

Mental health by race.

Simultaneously, multi-racial, non-Hispanic women in the age group 18 to 44 have a higher crude prevalence rate of at least 14 recent “mentally unhealthy” days. This group is followed by black non-Hispanics.

We then studied behavioral habits in the data set to gain insight into noticeable patterns, if in fact any exist.

3.2. Behavioral Habits by Gender and Race

Figure 10 charts behavioral habits by gender.

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Figure 10

Behavioral habits by gender.

As seen in the chart above, men display higher numbers in the alcohol categories of “binge drinking” and “heavy drinking”, as well as in “current smokeless” tobacco use among adults. In terms of engaging in “current smoking”, “obesity”, and “no leisure-time” physical activity, both men and women experience similar complications, that highlights the need for positive behavior modification.

Figure 11 illustrates the analysis of behavioral habits by race.

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Figure 11

Behavioral habits by race.

Figure 11 reveals that for the behavioral habits of “obesity” and “no leisure-time” physical activity among adults aged 18 and over, the black non-Hispanic and Hispanic races have the highest frequency, while white non-Hispanics have the lowest. By and large, in most behavioral habits, the other non-Hispanics have the lowest frequency.

3.3. Preventive Health and Chronic Conditions

We analyzed the data to detect associations between demographics and preventive health. As Figure 12 indicates, both men and women appear to engage in preventive health, though women have the edge. With regard to race, Blacks and Hispanics engage less in preventive health overall, as shown in Figure 13.

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Figure 12

Preventive health by gender.

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Figure 13

Preventive health by race.

While all chronic conditions are debilitating on the economy, for the sake of scope, we selectively analyze the influence of a few conditions such as diabetes and asthma. By 2034, the population with diabetes is expected to increase by 100% and the cost expected to increase by 53% [33]. Figure 14 depicts the association between diabetes and pneumococcal vaccination for diabetes.

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Figure 14

Diagnosed diabetes ratio by pneumococcal vaccination ratio. (#: number).

As indicated in Figure 14, there is a significant negative relationship between the average pneumococcal vaccination among diabetes patients and the average diagnosed diabetes ratio among the population (p < 0.0001). As the average pneumococcal vaccination among diabetes patients increases, the average diagnosed diabetes ratio decreases (fewer cases of diabetes). Given the importance of asthma as another prevalent chronic condition, we decided to analyze the relationship between the mortality ratio and influenza vaccinations for asthma to determine the efficiency of preventive measures (Figure 15).

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Figure 15

Mortality ratio for asthma and influenza vaccination for asthma.

Figure 15 shows a significant negative association (p < 0.0001): as the rate of influenza vaccination for asthma increases, the mortality ratio of asthma declines. Analysis of the above preventive health variables shows that resources and efforts dedicated to preventive healthcare offer promise. The importance of managing chronic diseases is also highlighted when we examine the association between behavioral habits and overarching conditions.

3.4. Behavioral Health and Overarching Conditions

Overarching conditions represent situations or factors that directly or indirectly influence the area of study. In our research we look at the influence of these conditions on chronic diseases, behavioral health, and preventive health. The overarching conditions include lack of health insurance (%), self-rated health status (good, fair, poor), and prevalence of sufficient sleep (%) for which data was available.

We explored the association of self-assessed health statuses among adults with the behavioral habits of binge drinking and heavy drinking (Figure 16).

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Figure 16

Binge/heavy drink and poor self-rated health status.

There is a significant negative correlation (p < 0.0001) between binge drinking and self-assessment of health. That is to say that the lower the health self-assessment, the higher is the percentage of binge drinking. A decrease of less than 1% (0.69%) in self-assessed health is associated with a 1% increase in binge drinking. Likewise, there is a significant negative association (p < 0.0001) between self-assessment of health and percentage of heavy drinking. A decrease of 1.6% in self-assessed health is associated with a 1% increase in heavy drinking. We can surmise that reduced self-assessment of health has a stronger influence on heavy drinking than binge drinking among adults.

Next, we looked at the association between current smoking prevalence and presence of sufficient sleep among adults (Figure 17).

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Figure 17

Current smoking prevalence by presence of sufficient sleep among adults.

Figure 17 above shows a significant negative association (p < 0.0001) between prevalence of current smoking and prevalence of sufficient sleep. When current smoking prevalence decreases by less than 1% (0.38%), the prevalence of sufficient sleep increases by 1%.

The relationship between poor self-rated health status and obesity is positive (Figure 18). The higher the prevalence of fair or poor self-rated health, the higher is the prevalence of obesity. When poor self-rated health increases by 1%, the prevalence of obesity increases by 0.468779%.

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Figure 18

Obesity by poor self-rated health status.

Similarly, poor self-rated health has a positive association with current smoking, as indicated in Figure 19. As the prevalence of poor self-rated health increases by 1%, the prevalence of current smoking increases by 0.30425%.

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Figure 19

Smoking by self-rated health status.

3.5. Chronic Conditions and Overarching Conditions

In the analysis of various chronic conditions, there are significant clusters of conditions among men and women, such as the prevalence of asthma, with the women tending to have a higher prevalence of asthma than men. Regarding such chronic conditions as diabetes, there is a significant positive relationship (p < 0.001) between lack of health insurance and prevalence of diagnosed diabetes (Figure 20).

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Figure 20

Current lack of health insurance by diagnosed diabetes.

We notice in Figure 20 that the distribution of lack of health insurance is sparse compared to that of diagnosed diabetes among adults aged 18 and older. Likewise, for chronic kidney disease (Figure 21) there is a significant positive relationship (p < 0.0001) with lack of health insurance.

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Figure 21

Lack of insurance by chronic kidney disease.

The relationship between lack of insurance and hospitalization for chronic pulmonary disease is positive and significant (p < 0.0001), as shown in Figure 22. An increase in the lack of insurance is associated with an increase in hospitalization for chronic pulmonary disease.

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Figure 22

Lack of insurance by pulmonary disease.

3.6. Association between Chronic Conditions

We analyzed for any associations between different chronic conditions. It is important to incorporate gender as a factor in the association and prevalence of chronic diseases, so as to develop customized plans for diagnoses and treatments. A linear trend model was developed for the relationship between asthma and diabetes (Figure 23).

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Figure 23

Asthma by diabetes.

The model in Figure 23 shows a significant negative relationship (p < 0.01) between asthma and diabetes. We can see gender clusters for the prevalence of asthma. Women tend to have higher prevalence of asthma compared to men. Overall, prevalence of asthma is negatively related to the prevalence of diabetes. On average, a high prevalence of asthma is associated with a low prevalence of diabetes. In terms of gender differences our results are consistent with other studies that have shown that women are more prone to develop asthma. Contributing factors include puberty, menstruation, pregnancy, menopause, and oral contraceptives [34,35]. There is potential for more research in this area.

The association between diabetes and kidney disease is shown in Figure 24.

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Figure 24

Diabetes by kidney disease.

Figure 24 shows a moderate, positive association (p < 0.01) between prevalence of kidney disease and diabetes. As the prevalence of diagnosed diabetes increases by 1%, the prevalence of chronic kidney disease increases by 0.09%. There are no obvious differences in gender here.

The association between diabetes and chronic pulmonary disease is shown in Figure 25, and that between arthritis and asthma is shown in Figure 26.

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Figure 25

Diabetes by obstructive pulmonary disease.

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Figure 26

Arthritis by asthma.

In Figure 25, we find a significant positive association between diabetes and chronic pulmonary disease (p < 0.001).

When it comes to prevalence of arthritis and asthma, there clearly are clusters for men and women, as shown in Figure 26. There is a positive association such that an increase of 1% in prevalence of arthritis is associated with a 0.4% increase in prevalence of asthma.

Figure 27 shows the association between arthritis and chronic pulmonary disease.

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Figure 27

Arthritis by chronic obstructive pulmonary disease.

Although there are no defined clusters for men and women with regard to the prevalence of arthritis and chronic obstructive pulmonary disease, there is a significant positive association (p < 0.0001), as Figure 27 illustrates. An increase of 1% in prevalence of arthritis is associated with an increase of 0.3% in chronic obstructive pulmonary disease.

3.7. Summary of Results

The visual analytics figures above offer insight into a representative cross section of the data. They provide a bird’s eye view of the dimensions and correlations of chronic diseases “conditions”, behavioral health, and preventive health condition in the U.S. In addition, associations between mental health and chronic conditions, preventive health and chronic conditions, and among chronic conditions themselves highlight the dynamics of interplay between these categories. This understanding is useful to policymakers in framing appropriate health policies. Preventive healthcare and mental health are both important elements in the management, mitigation, and prevention of chronic conditions. By exploring these in the context of chronic conditions, we offer insight on allocation and prioritization of resources in mitigation and prospective eradication of chronic diseases at a national level. Overarching conditions, including a lack of health insurance, influence the access to necessary health services, including preventive care. This lack of availability is associated with poor health and the prevalence of chronic diseases. Similarly, self-assessed health status is a good indicator of overall health status, correlating with subsequent health service use, functional status, and mortality [36]. Poor mental health interferes with social functioning as well as health condition and should therefore be monitored in chronic disease mitigation. Experiencing activity limitation due to poor physical or mental health undermines efforts to achieve a healthy lifestyle and therefore should be addressed at individual, state, and national levels.

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  1. Scope and Limitations

Our research has a few limitations. First, our study is cross-sectional and covers only the years 2012 to 2014, the years for which data is available. Second, we included only a limited set of variables (indicators) from the large data repository on the CDC website. A more comprehensive study could draw from other sources and a larger set of variables. Third, as population and public health have emerged as key disciplines in the contemporary health ecosystem, more scalable, macro-level, and drill-down studies would inform greater understanding of chronic diseases. Fourth, one would assume that the quality of publicly available data is high and error-free. Lastly, the study is limited to examining associations and correlations and does not investigate causality. Furthermore, we only apply visual analytics and descriptive analytics, which have limitations in and of themselves.

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  1. Implications

This study has analyzed chronic conditions in conjunction with several demographic variables, including gender and race. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. For some chronic diseases—such as diabetes, arthritis, and obstructive pulmonary —the prevalence in the east is higher than in other regions, while, there is higher prevalence for other conditions, such as asthma, in the northeast. The south and midwest also show their own prevalence of chronic diseases. Likewise, there are variations for hospitalization and mortality rates. In addition, there are gender differences related to chronic conditions. For example, women tend to have higher cases per 100,000 for asthma-related hospitalizations. Men, on the other hand, appear to have higher mortality rates for chronic obstructive pulmonary disease, diabetes, chronic kidney, and others. Also, when we examined chronic conditions by race, we noticed that American Indian or Alaska Natives had higher mortality rates for chronic obstructive pulmonary disease, diabetes, chronic kidney, and so on, followed by Black and non-Hispanic groups.

In addition, the study analyzed demographics of mental health, behavior habits, and preventive health. The associations between behavioral health and chronic conditions and between preventive health and chronic conditions were also analyzed. There is a positive relationship between average female coronary heart disease mortality ratio and average female tobacco use ratio. There is a negative relationship between the average pneumococcal vaccination among diabetes patients and the average diagnosed diabetes ratio among the population. Referring to the relationship between behavioral health and overarching conditions, the study found a negative correlation between age-adjusted prevalence percentage of fair or poor self-rated health status among adults aged ≥18 years and binge drinking adults. The current smoking prevalence and sufficiency of sleep among adults is negatively related. The current lack of health insurance is negatively related to both prevalence of current smoking and that of current smokeless tobacco use. The relationship between obesity and poor self-rated health status is positively related. Similarly, current smoking prevalence has a strong, positive correlation with fair or poor self-rated health status. There are different negative or positive correlations between overarching conditions and chronic conditions. For instance, there is a significant positive relationship between the prevalence of a lack of health insurance and that of diagnosed diabetes. But the relationship between prevalence of a lack of health insurance and prevalence of asthma is negatively related.

Finally, we conducted analyses of the differences among chronic conditions. There are obvious clusters between men and women for asthma, although women tend to have a higher prevalence of asthma than men. Overall, prevalence of asthma is negatively related to the prevalence of diabetes. There is a moderate, positive correlation between prevalence of kidney and diabetes, which is akin to the positive correlation between the prevalence of chronic obstructive pulmonary disease and diabetes, arthritis and asthma, arthritis and chronic obstructive pulmonary disease, and asthma and chronic obstructive pulmonary.

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  1. Conclusions

The study makes multiple essential contributions to chronic disease analysis at the patient/physician and the state levels. At the patient level, analysis of chronic conditions and related behavioral factors allows patients to be proactive in managing their conditions as well as modifying behavioral health. In this day and age, patients are eager to assimilate health information from various sources [37,38]. Being informed allows patients to self-monitor and seek appropriate and timely medical care [39,40], contributing to an ultimate care model that is increasingly personalized.

Similar to patients, physicians too have varying information needs in healthcare that need to be satisfied [41]. To physicians, information on chronic conditions and more importantly, associations between multiple conditions and between categories of healthcare, enable developing personalized treatment plans based on patient-specific profiles that integrate various symptoms with environmental and other health data [42]. Additionally, the array of information increases their ability to guide patients in towards lifestyle medicine (making lifestyle changes in healthy diet, exercise etc.) in the management of chronic diseases [43]. The road from sickness to wellness requires integrated efforts from physicians and patients—physicians can coach and guide the patients but the ultimate cross-over to wellness lies in the patients’ hands.

Whereas most studies on chronic diseases focus on specific chronic diseases and are somewhat limited, this study offers comprehensive analysis over multiple categories of chronic diseases at the state-level. By utilizing visual analytics and descriptive analytics, our study offers methods for gaining insight into the relationships between behavior habits, preventative health and demographics, and chronic conditions. Moreover, this study contributes in terms of the methodology of analytics used in the research. It demonstrates the efficacy of data-driven analytics, which can help make informed decisions on chronic diseases.

Going forward, more theoretical and empirical research is needed. Additional studies can address the relationship between chronic disease conditions and other indicators, such as economic, financial, and social. While chronic disease management has become the focus in modern medicine as our population ages and medical costs continue to rise, research should focus on preventive and mitigating policies. The benefits of prevention and its potential to reduce costs and improve outcomes have received the attention of insurance companies, health care plans, and the U.S. Congress. Healthcare systems are now incentivized to reduce readmissions and physicians are encouraged to meet evidence-based quality measures to provide the best outcomes for patients with chronic disease states.

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Author Contributions

Both the authors contributed equally to the data analysis, design, and development of the manuscript.

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Conflicts of Interest

The authors declare no conflict of interest.

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The National Center For Chronic Disease Control.

Now they have this as a way of apeasing the american public!

They have never helped anyone heal completley or regain their health again like all of these agencies for chronic disease, and never will.