Introduction

In the continuum from health to disease, there are several key transition periods. The first is from healthy to presymptomatic, where an individual still feels well and is asymptomatic but has developed a disease. An example of this health state is the beginning of cardiovascular disease when an individual is developing plaque but they are unaware of it. The second is from presymptomatic to disease diagnosis and the third is from diagnosis to disease status, which can be either well controlled or uncontrolled. Health risk assessments (HRAs) are an essential component of the healthy period. Their purpose is to estimate an individual’s risk for developing common chronic diseases (see, e.g., Table 1.1 ) allowing clinicians to tailor preventive care, screening, and testing to each individual’s level of risk—with the goal of keeping healthy people healthy. Personalized care plans developed with the aid of HRAs balance effectiveness and harms with risk, in a way that maximizes benefit and minimizes harm not only for each individual, but when taken as a whole, for the population as well. Unfortunately, HRAs are not widely used in primary care, where they would be most effective, due to a number of constraints. This chapter discusses how HRAs were developed, their key aspects, and what needs to occur in order to integrate them into primary care settings.

In the Beginning

In 1948, Joseph Mountain, the Assistant Surgeon General, initiated the Framingham Heart Study, an innovative longitudinal study arising from the field of epidemiology. The goal, as devised by the director, Thomas Dawber, was to closely follow a group of individuals living in Framingham, Massachusetts, collecting as much data as possible over the course of many years in order to develop a risk prediction model for heart disease . This was the first time the phrase “factor of risk,” more commonly termed risk factor today, was introduced . Despite initial skepticism among both the research and medical communities, the trial was successful beyond expectations and the field of HRA was born. In 2009, when Clay Christensen coined the term “Precision Medicine,” he defined it as precisely predicting a medical outcome by combining a variety of data into rules . By this definition, HRAs are simply the application of precision medicine to those who are healthy.

Today, most HRAs include the following components: data collection (either through a web-based or paper questionnaire), risk calculation, and report of risk results. This last component, the report, may or may not provide guidance about how to manage your risk. Some are exceptionally detailed and even indicate how much your risk can be lowered by initiating one or more recommended preventive actions, while others merely indicate that you are at increased risk for the specified condition. For the first component, data collection, the data collected varies depending upon which conditions are included in the risk assessment, but at a minimum they all include: demographics, lifestyle, personal health history, family health history , and biometrics (such as blood pressure, weight, cholesterol, etc.). Other types of data, such as genetic/genomic and individual preferences, are just now starting to be incorporated into some risk assessment models and have the potential to not only refine the accuracy of risk calculations but to also improve shared decision-making with medical providers .

Why Family Health History is Central to HRAs

Family health history is an unassuming and often overlooked, but essential data element in HRAs. For many conditions, family health history is the strongest predictor of disease risk and for some, such as hereditary cardiovascular syndromes, it is the only predictor (and thus the only component of the HRA) (see Table 1.1 ). An example of the impact of family health history on disease risk is type II diabetes, where a first degree relative (parent or child) with the disease increases an individual’s risk from an average of 3.2% to 14.3% . In some cases, excluding a family health history can lead to missing those at highest risk for developing a condition. For example, many risk assessments for chronic obstructive pulmonary disease ask about environmental exposures (such as smoking and asbestos) but do not ask about family history; however, those with alpha-1-antitrypsin deficiency, a hereditary condition that runs in families, are at the highest risk of developing chronic obstructive pulmonary disease even without an environmental exposure . Renal cell carcinoma, a tumor of the kidney, is another example. Almost all risk assessments include smoking, alcohol, and exercise, and some include family members with renal cell carcinoma, but most do not ask about a family history of other cancers even though renal cell carcinoma is part of the constellation of cancers that can occur in two hereditary cancer syndromes, Lynch and Von Hippel–Lindau . While those with hereditary cancer syndromes or alpha-1-antitrypsin deficiency are only a small proportion of those developing these two conditions, they are the ones at the highest risk of developing disease.

As mentioned earlier, the Framingham study and the resultant Framingham risk scores inaugurated the risk assessment field and pushed it steadily forward for the first two decades. However, currently, the three widely used cardiovascular risk scores: Framingham (which has multiple versions and multiple outcomes including atrial fibrillation, atherosclerotic heart disease, and congestive heart failure) ( https://www.framinghamheartstudy.org/risk-functions/index.php ), Reynold’s risk score (for heart and stroke risk) ( http://www.reynoldsriskscore.org/ ), and the pooled equation for atherosclerotic cardiovascular disease risk recommended in the 2013 ACC/AHA Guidelines ( http://tools.acc.org/ascvd-risk-estimator/ ), do not include family health history of cardiovascular disease though an earlier version of Framingham did. To adjust for this missing information, the Canadian Cholesterol guidelines multiply the Framingham risk score by 2 for an individual who has a first degree relative with a cardiovascular event before age 60 . Similarly, European guidelines recommend multiplying the risk score results by 1.7 in women and 2.0 in men .

In addition to being highly predictive, family health history also serves as the basis for a number of evidence-based guidelines that not only indicate the level of disease risk associated with a given combination of affected relatives but also actions to take to manage risk. For example, the National Comprehensive Cancer Network’s guidelines for breast and ovarian cancer recommend BRCA testing if an individuals’ first degree relative (parents or child) developed breast cancer at age 45 or younger . Another example is abdominal aortic aneurysm screening. If an individual has a relative with the condition, then screening is recommended when they are aged 50 or older .

Despite the fact that the cardiovascular field was early to begin to explore the benefits of risk assessment, there is a complete absence of literature and guidelines addressing the role of family history, even for the many hereditary cardiovascular syndromes, except for Familial Hypercholesterolemia (FH). For FH, there are now four different screening tools (Simon Broom, Dutch Lipid Clinic Network, Med Ped, and the newly published FAMCAT)—all include family history. One important aspect of these hereditary conditions, which include many cardiomyopathies and arrhythmias, as well as FH, is that (for most) there are steps that can be taken to manage risk. For example, there is now a drug class (PCSK-9 inhibitors) approved specifically for use in individuals with FH, echocardiogram screening for cardiomyopathies, and EKG screening for arrhythmias. Another highly important point to consider is the risk to relatives when a family member is found to have a hereditary condition. In these cases, it’s important to inform and screen those individuals at-risk. Processes to facilitate this screening, called cascade screening, are being explored and have been most closely studied in Europe around FH.

Thus, family health history is the only data element in HRAs that is both highly predictive and actionable in combination with other data elements and by itself. Unfortunately, family health history is often hard to obtain. Individuals often do not know much about their relatives’ health and what they do know is often piecemeal or may be inaccurate . This leads to the problem that one of the most informative data elements in HRAs is also one of the more difficult to collect.

An Implementation Crisis

Despite the acclaim surrounding the publication of the Framingham Heart Study results, there was little movement in the field of HRA until 1980 when the Center for Disease Control (CDC) released a publicly available HRA tool . Incidentally, 1980 was also a time when employers and insurers were being faced with rapidly increasing health-care costs. In their search for a way to manage these costs, they turned to HRAs . To explore the impact of this resource, Prudential funded updates to the CDC’s tool, which ultimately showed that use of a HRA tool in the workplace could lower company health-care expenditures, as well as reduce absenteeism and increase productivity . These results and Prudential’s takeover of the program in 1986 led to rapid uptake among US employers and insurance companies; however, uptake continued to be anemic in the health-care setting .

Explanations for why implementation in the health-care system failed to take root include the disconnect between public health and health care, increasing demands on primary care providers, and a perverse incentive system that rewarded interventions over maintaining health . The combination of these factors encouraged the development of a health-care system, incapable of responding to the needs of the healthy segment of the population, quickly leading to a negative feedback loop dominated by sick patients getting sicker, less time to manage risk among healthy patients, and ultimately healthy patients getting sick . In this environment, it is easy to see how adoption of HRA in clinical practice was slow.

Fortunately, recent studies have highlighted these findings and their unsustainable impact on the US health-care system. In particular, the 5 Mirror, Mirror studies performed by the Commonwealth Fund to assess health-care quality and cost in 11 international health-care systems between 2004 and 2014, not only ranked the US last in quality and highest in expenditures but showed little improvement over the 10-year period . In addition, the Affordable Care Act enacted in 2010 has emphasized the need for improvements in quality of care, maintaining health, and lowering costs. HRAs are neatly aligned with these objectives and are now viewed as a useful tool for redesigning health-care systems. That being said, there are still a number of practical barriers to overcome before implementation in primary care can become widespread: ease of use for providers, ease of use for patients, quality of the data-entered into the HRA (particularly family health history data), and it’s potential to improve quality of care in primary care populations. Each of these is describe in detail below.

Will Providers Use It?

Providers, primary care providers in particular, are frequently overloaded by the number of tasks to achieve within the constraints of the health-care visit, and with face times shrinking to just over 9 minutes for most appointments, many lower priority and/or complex tasks often lose out to higher acuity concerns . Because HRA data collection, risk calculation, and evidence synthesis are complex and time consuming (particularly for family health history data), it often poses a significant challenge for integration into normal workflow . In particular, algorithms are often complex requiring a computer to calculate; however, the calculators are typically scattered across the internet and not integrated into electronic medical record (EMR) systems. In addition, the sheer magnitude of the literature available makes synthesizing an actionable risk-management plan difficult and efforts to initiate provider education around these topics have fallen flat for many of the same reasons that implementing HRAs have .

One solution to these complex and interrelated barriers is to leverage the burgeoning field of health IT. Patient-facing web- or computer-based HRA tools can eliminate the data collection component from the physician’s office, moving it to the patient’s home, and provide risk calculations and actionable risk-management plans to the provider at the point of care. Similarly, mobile health technologies (mHealth) are beginning to demonstrate that they can be used to facilitate risk-related data collection (environmental, behavioral, psychological, and biological) and communication between patients and their health-care system. To date, a number of family health history-based HRA tools have been built with just such capabilities . See Table 1.2 for examples of currently available tools and their characteristics. Of note, only one of these tools currently incorporates clinical decision support for cardiovascular diseases. Uptake of these tools has been anecdotally promising. Currently, there is only one published study that reports physician experience with and uptake of a HRA tool. In this study, performed by the authors, primary care providers indicated that the HRA tool was easy to incorporate into workflow (100%), improved their quality of care (85%), made their practice easier (75%), and enhanced their understanding of the importance of family health history (62%) (see Fig. 1.1 for sample provider report). These results suggest that with the right combination of features, electronic HRA tools can gain acceptance by busy primary care clinicians.

Table 1.2

Examples of Patient-Facing Electronic Family Health History-Based Health Risk Assessment Tools

FHH-Based CDS Software Programs for Adult	No. of Conditions Collected	Decision Support Diseases	Completed by	Validation of Data Accuracy	Who Receives Output	Available at Point of Care	Action-Oriented Recomm.
MeTree a	100	27 (Colon, breast, lung, and ovarian cancer; thrombosis, aortic aneurysm, heart disease, stroke, diabetes, hereditary cancer syndromes, hereditary cardiovascular syndromes, hereditary liver diseases)	Patient online or physician’s office	Yes	Patient and physician	Yes	Yes
Schroy et al. b	1	1 (Colon cancer)	Physician	?	Physician	Yes	No
GRACE c	1	1 (Breast cancer)	Patient in physician’s office	No	Patient, clinical nurse specialist, physician	Yes	No
Family Healthware d	6	6 (Coronary heart disease, diabetes, stroke, colon, breast, and ovarian cancer)	Patient online	Yes	Patient	No	No
Family HealthLink e	38 (35 Cancers)	Coronary heart disease, cancer	Patient online	?	Patient	No	No
Cancer Risk Intake System (CRIS) f	3	3 (Breast, ovarian, and colon cancer)	Patient in physician’s office	Yes	Patient and physician	Yes	No
Hughes Risk Apps g	~5 (Cancers)	Breast cancer and hereditary cancer syndromes	Patient—clinician can revise online or physician’s office	Yes	Patient and physician	Yes	No
Health Heritage h	87	15 (Cancers, diabetes, neuromuscular diseases, and cardiovascular disease)	Patient online	No	Patient	No	No
Invitae i	6	Hereditary cancer and hereditary cardiac disease genetic testing	Physician online	No	Patient and Physician	?	No
MyFamily j	?	Cancer, cardiology, GI (proprietary risk algorithms)	Patient	No	Physician	Yes	Yes
Myriad k	29 Cancers	Hereditary cancer genetic testing	Patient	No	Patient	No	No
Power Lineage l	Cancer	6 Hereditary cancers (breast, colon, endometrial, ovarian, pancreatic, melanoma)	Patient and Physician	No	Physician	Yes	Yes
My Family Health Portrait m	~21	2 (Diabetes and colon cancer)	Patient	Yes	Patient	No	Yes

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