COVID-19 has forced people around the world to make drastic changes to their lives and routines. In the midst of these changes, digital health companies find themselves in a unique position to help people stay active, eat nutritious foods, sleep well, and manage stress during these challenging times. They are also partnering with public health and research partners in order to leverage behavior and biometric data to help detect and prevent the spread of COVID-19.

Fitbit’s Medical Director John Moore and Scripps Research Epidemiologist Jennifer Radin will provide insights as to how wearables can help keep people healthy in times of change, and help detect the spread of influenza-like illnesses, including COVID-19.

Join us to learn:

• How wearables can help keep people healthy in times of change
• How digital health companies can use their products and services to better support users during the COVID-19 pandemic
• How Scripps research harnesses data from Fitbit and other digital health platforms in order to improve detection of influenza-like illnesses

   

Speakers: 

Jonathan Moore
Medical Director, Fitbit

John Moore is a physician, engineer and the Medical Director at Fitbit. He is the former CEO of Twine Health, a Cambridge based company recently acquired by Fitbit. John studied biomedical engineering and then medicine at Boston University. He left the clinical career path, determined to develop solutions to improve healthcare delivery, and earned a PhD from MIT. His research included the intersection of health psychology, learning science, and human-computer interaction, which formed the health behavior change foundation of Twine Health that is now being leveraged at Fitbit. John was recently recognized by Employee Benefits News, as one of the 2019 Digital Innovators: Transforming HR. John finds his fit with various ocean-related activities, including surfing.

Jennifer Radin
Epidemiologist, Scripps Research

Jennifer Radin is an epidemiologist at Scripps Research, where she conducts research to improve disease prediction and prevention by incorporating digital devices, sensors and platforms. Before joining Scripps, she worked with the Operational Infectious Disease Department at the Naval Health Research Center and the Influenza Division at the Centers for Disease Control and Prevention. Jennifer received her doctoral degree in Epidemiology from the University of California, San Diego and San Diego State University. She also holds a master's of public health, specializing in Epidemiology of Microbial Diseases, from Yale University and a bachelor's degree in Biology from the College of William and Mary.

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Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study

Body CT scans are frequently done for a wide range of clinical indications, but potentially valuable biometric information typically goes unused. We aimed to compare the prognostic ability of automated CT-based body composition biomarkers derived from previously developed deep-learning and feature-based algorithms with that of clinical parameters (Framingham risk score [FRS] and body-mass index [BMI]) for predicting major cardiovascular events and overall survival in an adult screening cohort.

The full article from The Lancet can be downloaded below.  

Population Health: Proactive Solutions for Healthy Outcomes

Social environments contribute directly to a wide range of health outcomes. The social determinants of health refer to conditions in the environments in which people live, work, play, worship, and age. Traditionally, the public health sector factored the social determinants of health into practice, while the hospital sector focused on individual factors, such as illness and the provision of episodic curative services. However, health care in the US is evolving. These separate views are no longer sufficient, and population health is now considered the solution. Population health addresses the full range of the determinants of health and involves measuring and optimizing the health of groups by embracing the traditional social determinants of health as well as health care delivery. The purpose of this article is to provide an overview of population health, highlight examples of how it is taking shape in Hawai‘i, and discuss how the University of Hawaiʻi at Mānoa School of Nursing and Dental Hygiene (UHM SONDH) is preparing its graduate nursing students for new roles in population health in Hawaiʻi.

The full Spotlight on Nursing column can be viewed at this link.  

Designing a multifaceted telehealth intervention for a rural population using a model for developing complex interventions in nursing

Telehealth interventions offer an evidenced-based approach to providing cost-effective care, education, and timely communication at a distance. Yet, despite its widespread use, telehealth has not reached full potential, especially in rural areas, due to the complex process of designing and implementing telehealth programs. The objective of this paper is to explore the use of a theory-based approach, the Model for Developing Complex Interventions in Nursing, to design a pilot telehealth intervention program for a rural population with multiple chronic conditions.

In order to develop a robust, evidenced based intervention that suits the needs of the community, stakeholders, and healthcare agencies involved, a design team comprised of state representatives, telehealth experts, and patient advocates was convened. Each design team meeting was guided by major model constructs (i.e., problem identification, defining the target population and objectives, measurement theory selection, building and planning the intervention protocol). Overarching the process was a review of the literature to ensure that the developed intervention was congruent with evidence-based practice and underlying the entire process was scope of practice considerations.

Ten design team meetings were held over a six-month period. An adaptive pilot intervention targeting home and community-based Medicaid Waiver Program participants in a rural environment with a primary objective of preventing re-institutionalizations was developed and accepted for implementation. To promote intervention effectiveness, asynchronous (i.e., remote patient monitoring) and synchronous (i.e., nursing assessment of pain and mental health and care coordination) telehealth approaches were selected to address the multiple comorbidities of the target population. An economic evaluation plan was developed and included in the pilot program to assess intervention cost efficiency.

The Model for Developing Complex Interventions in Nursing provided a simple, structured process for designing a multifaceted telehealth intervention to minimize re-institutionalization of participants with multiple chronic conditions. This structured process may promote efficient development of other complex telehealth interventions in time and resource constrained settings. This paper provides detailed examples of how the model was operationalized.

The full article can be downloaded below.  

Money Can’t Buy You Health: Disconnection In U.S. Between Healthcare Spending And Population Health

Let me begin this sobering post by saying there are aspects of the U.S. healthcare system that are admirable, especially regarding the use of cutting-edge innovative medicines and medical procedures. In cancer care, in particular, the U.S. has been at the forefront of a number of advances which have delivered miraculous benefits to patients. Yet, on the whole, America’s healthcare system appears “woefully dysfunctional.”

The U.S.  spends about twice as much on healthcare as other Organization for Economic Cooperation and Development (OECD) countries, but ranks near the bottom in terms of life expectancy, and that gap has widened sharply in recent years.

The full Forbes article can be viewed at this link.  

The Polypill Revisited: Why We Still Need Population-Based Approaches in the Precision Medicine Era

Nearly 2 decades ago, Wald and Law proposed “a strategy to reduce cardiovascular disease by more than 80%” by administering a polypill to everyone 55 years of age and older. Their bold proposal had its roots in the debate surrounding risk-based versus population-based approaches to prevention, as described by Rose. In risk-based approaches, preventive measures are targeted specifically at higher risk individuals, with medication therapy tailored to each patient’s risk factor profile. The identification of higher risk patients typically relies on clinical and laboratory-based prediction algorithms, the traditional approach endorsed in most practice guidelines. In contrast, population-based approaches aim to shift the entire risk distribution, even modestly, with measures implemented at the population level. The latter necessitates interventions that are low in cost and have a low incidence of side effects. These are among the proposed advantages of the polypill, a fixed-dose combination of cardiovascular medications, usually including a statin and several antihypertensive drugs.

One of the objections to the Wald and Law proposal was that large numbers of low-risk individuals would end up receiving unneeded and/or unindicated drug therapy. Thus, despite randomized trials supporting the tolerability of various polypill formulations and regulatory approval in multiple countries outside the United States, momentum in the field shifted toward viewing the polypill primarily as a strategy for high-risk individuals with established cardiovascular disease. The problem is that a one-size-fits-all approach to pharmacotherapy may not be optimal for patients with established disease, for whom aggressive cholesterol and blood pressure targets often require titration of multiple medications. Furthermore, secondary prevention patients often have comorbidities such as diabetes that influence the choice of therapy.

Thus, several decades since Wald and Law’s original proposal, there remains little clarity regarding the role of the polypill in cardiovascular care. This has coincided with the rising interest in precision medicine, a contemporary embodiment of the risk-based approach in the Rose framework. A natural question, then, is whether there is any place for a population-based strategy using the polypill in the present era with so much focus on precision medicine.

The full perspective article can be downloaded below.  

Closing the Gap: Identifying Rates and Reasons for Nonadherence in a Specialty Population

Adherence to specialty and nonspecialty medications is often calculated using pharmacy claims data. However, specialty medication regimens are complex and may require periods of intentional gaps in therapy. Common adherence calculations are insufficient in identifying reasons for gaps in therapy. Because adherence reporting is a growing measure of quality care for specialty pharmacy accreditation and payer and manufacturer contracts, a better understanding of the rates and reasons for nonadherence within a specialty population is needed.

The objective was to identify rates and reasons for misidentified and true nonadherence in patients who are prescribed specialty medications.

A single center, retrospective cohort study was conducted using pharmacy claims data between March 2017 and February 2018. Medication adherence was calculated using proportion of days covered (PDC). Electronic medical records of a random 10% sample of nonadherent patients (PDC<80%) were manually reviewed to identify reasons for nonadherence. Patients were then classified as either (a) misidentified as nonadherent (i.e., a provider-directed discontinuation or disruption of treatment that varies from the prescribed administration schedule or transfer of the prescription to an external pharmacy) or (b) truly nonadherent (discontinuation or disruption of treatment that varies from the prescribed administration instruction that is not directed or recommended by the provider or health care team).

Of the 7,488 included prescription records from 18 specialty areas, 1,059 met criteria for nonadherence. 105 prescription records (representing 105 unique patients) were manually reviewed; most of these patients (58%) were truly nonadherent, driven by inability to contact patients for refills (59%). However, 40% were misidentified as nonadherent, most due to provider-directed medication holding (69%). Two percent of patients were nonadherent for unknown reasons.

Many patients classified as nonadherent based on pharmacy claims experienced gaps in therapy due to medically appropriate reasons. Methods to better measure and identify true nonadherence are needed to efficiently and adequately affect specialty medication adherence behavior.

The full article can be downloaded below.  

Dissecting racial bias in an algorithm used to manage the health of populations

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

The full article can be downloaded below.  

Population Health Vs. Personalized Medicine: Lost In Translation?

Evidence-based medicine, it seems commonsensical; who could argue about using the best evidence available to make treatment decisions? The difficulty, of course, is that the evidence comes from studies of large populations, often expressed in terms of average responses; and as a clinician, you want to tailor the care to the one member of the population in front of you, your patient. How do you reconcile population-based evidence with the desire for personalized care?

The full article from the American Council on Science and Health can be viewed at this link.