How to share data — not just equally, but equitably

Curated on Posted on by Stefaan Verhulst

Editorial in Nature: “Two decades ago, scientists asked more than 150,000 people living in Mexico City to provide medical data for research. Each participant gave time, blood and details of their medical history. For the researchers, who were based at the National Autonomous University of Mexico in Mexico City and the University of Oxford, UK, this was an opportunity to study a Latin American population for clues about factors contributing to disease and health. For the participants, it was a chance to contribute to science so that future generations might one day benefit from access to improved health care. Ultimately, the Mexico City Prospective Study was an exercise in trust — scientists were trusted with some of people’s most private information because they promised to use it responsibly.

Over the years, the researchers have repaid the communities through studies investigating the effects of tobacco and other risk factors on participants’ health. They have used the data to learn about the impact of diabetes on mortality rates, and they have found that rare forms of a gene called GPR75 lower the risk of obesity. And on 11 October, researchers added to the body of knowledge on the population’s ancestry.

But this project also has broader relevance — it can be seen as a model of trust and of how the power structures of science can be changed to benefit the communities closest to it.

Mexico’s population is genetically wealthy. With a complex history of migration and mixing of several populations, the country’s diverse genetic resources are valuable to the study of the genetic roots of diseases. Most genetic databases are stocked with data from people with European ancestry. If genomics is to genuinely benefit the global community — and especially under-represented groups — appropriately diverse data sets are needed. These will improve the accuracy of genetic tests, such as those for disease risk, and will make it easier to unearth potential drug targets by finding new genetic links to medical conditions…(More)”.

What Big Tech Knows About Your Body

Curated on Posted on by Stefaan Verhulst

Article by Yael Grauer: “If you were seeking online therapy from 2017 to 2021—and a lot of people were—chances are good that you found your way to BetterHelp, which today describes itself as the world’s largest online-therapy purveyor, with more than 2 million users. Once you were there, after a few clicks, you would have completed a form—an intake questionnaire, not unlike the paper one you’d fill out at any therapist’s office: Are you new to therapy? Are you taking any medications? Having problems with intimacy? Experiencing overwhelming sadness? Thinking of hurting yourself? BetterHelp would have asked you if you were religious, if you were LGBTQ, if you were a teenager. These questions were just meant to match you with the best counselor for your needs, small text would have assured you. Your information would remain private.

Except BetterHelp isn’t exactly a therapist’s office, and your information may not have been completely private. In fact, according to a complaint brought by federal regulators, for years, BetterHelp was sharing user data—including email addresses, IP addresses, and questionnaire answers—with third parties, including Facebook and Snapchat, for the purposes of targeting ads for its services. It was also, according to the Federal Trade Commission, poorly regulating what those third parties did with users’ data once they got them. In July, the company finalized a settlement with the FTC and agreed to refund $7.8 million to consumers whose privacy regulators claimed had been compromised. (In a statement, BetterHelp admitted no wrongdoing and described the alleged sharing of user information as an “industry-standard practice.”)

We leave digital traces about our health everywhere we go: by completing forms like BetterHelp’s. By requesting a prescription refill online. By clicking on a link. By asking a search engine about dosages or directions to a clinic or pain in chest dying. By shopping, online or off. By participating in consumer genetic testing. By stepping on a smart scale or using a smart thermometer. By joining a Facebook group or a Discord server for people with a certain medical condition. By using internet-connected exercise equipment. By using an app or a service to count your steps or track your menstrual cycle or log your workouts. Even demographic and financial data unrelated to health can be aggregated and analyzed to reveal or infer sensitive information about people’s physical or mental-health conditions…(More)”.

Sharing Health Data: The Why, the Will, and the Way Forward.

Curated on Posted on by Stefaan Verhulst

Book edited by Grossmann C, Chua PS, Ahmed M, et al. : “Sharing health data and information1 across stakeholder groups is the bedrock of a learning health system. As data and information are increasingly combined across various sources, their generative value to transform health, health care, and health equity increases significantly. Facilitating this potential is an escalating surge of digital technologies (i.e., cloud computing, broadband and wireless solutions, digital health technologies, and application programming interfaces [APIs]) that, with each successive generation, not only enhance data sharing, but also improve in their ability to preserve privacy and identify and mitigate cybersecurity risks. These technological advances, coupled with notable policy developments, new interoperability standards (particularly the Fast Healthcare Interoperability Resources [FHIR] standard), and the launch of innovative payment models within the last decade, have resulted in a greater recognition of the value of health data sharing among patients, providers, and researchers. Consequently, a number of data sharing collaborations are emerging across the health care ecosystem.

Unquestionably, the COVID-19 pandemic has had a catalytic effect on this trend. The criticality of swift data exchange became evident at the outset of the pandemic, when the scientific community sought answers about the novel SARS-CoV-2 virus and emerging disease. Then, as the crisis intensified, data sharing graduated from a research imperative to a societal one, with a clear need to urgently share and link data across multiple sectors and industries to curb the effects of the pandemic and prevent the next one.

In spite of these evolving attitudes toward data sharing and the ubiquity of data-sharing partnerships, barriers persist. The practice of health data sharing occurs unevenly, prominent in certain stakeholder communities while absent in others. A stark contrast is observed between the volume, speed, and frequency with which health data is aggregated and linked—oftentimes with non-traditional forms of health data—for marketing purposes, and the continuing challenges patients experience in contributing data to their own health records. In addition, there are varying levels of data sharing. Not all types of data are shared in the same manner and at the same level of granularity, creating a patchwork of information. As highlighted by the gaps observed in the haphazard and often inadequate sharing of race and ethnicity data during the pandemic, the consequences can be severe—impacting the allocation of much-needed resources and attention to marginalized communities. Therefore, it is important to recognize the value of data sharing in which stakeholder participation is equitable and comprehensive— not only for achieving a future ideal state in health care, but also for redressing long-standing inequities…(More)”

Wastewater monitoring: ‘the James Webb Telescope for population health’

Curated on Posted on by Stefaan Verhulst

Article by Exemplars News: “When the COVID-19 pandemic triggered a lockdown across Bangladesh and her research on environmental exposure to heavy metals became impossible to continue, Dr. Rehnuma Haque began a search for some way she could contribute to the pandemic response.

“I knew I had to do something during COVID,” said Dr. Haque, a research scientist at the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b). “I couldn’t just sit at home.”

Then she stumbled upon articles on early wastewater monitoring efforts for COVID in Australia, the Netherlands, Italy, and the United States. “When I read those papers, I was so excited,” said Dr. Haque. “I emailed my supervisor, Dr. Mahbubur Rahman, and said, ‘Can we do this?’”

Two months later, in June 2020, Dr. Haque and her colleagues had launched one of the most robust and earliest national wastewater surveillance programs for COVID in a low- or middle-income country (LMIC).

The initiative, which has now been expanded to monitor for cholera, salmonella, and rotavirus and may soon be expanded further to monitor for norovirus and antibiotic resistance, demonstrates the power and potential of wastewater surveillance to serve as a low-cost tool for obtaining real-time meaningful health data at scale to identify emerging risks and guide public health responses.

“It is improving public health outcomes,” said Dr. Haque. “We can see everything going on in the community through wastewater surveillance. You can find everything you are looking for and then prepare a response.”

A single wastewater sample can yield representative data about an entire ward, town, or county and allow LMICs to monitor for emerging pathogens. Compared with clinical monitoring, wastewater monitoring is easier and cheaper to collect, can capture infections that are asymptomatic or before symptoms arise, raises fewer ethical concerns, can be more inclusive and not as prone to sampling biases, can generate a broader range of data, and is unrivaled at quickly generating population-level data…(More)” – See also: The #Data4Covid19 Review

The Importance of Purchase to Plate Data

Curated on Posted on by Stefaan Verhulst

Blog by Andrea Carlson and Thea Palmer Zimmerman: “…Because there can be economic and social barriers to maintaining a healthy diet, USDA promotes Food and Nutrition Security so that everyone has consistent and equitable access to healthy, safe, and affordable foods that promote optimal health and well-being. A set of data tools called the Purchase to Plate Suite (PPS) supports these goals by enabling the update of the Thrifty Food Plan (TFP), which estimates how much a budget-conscious family of four needs to spend on groceries to ensure a healthy diet. The TFP market basket – consisting of the specific amounts of various food categories required by the plan – forms the basis of the maximum allotment for the Supplemental Nutrition Assistance Program (SNAP, formerly known as the “Food Stamps” program), which provided financial support towards the cost of groceries for over 41 million individuals in almost 22 million households in fiscal year 2022.

The 2018 Farm Act (Agriculture Improvement Act of 2018) requires that USDA reevaluate the TFP every five years using current food composition, consumption patterns, dietary guidance, and food prices, and using approved scientific methods. USDA’s Economic Research Service (ERS) was charged with estimating the current food prices using retail food scanner data (Levin et al. 2018; Muth et al. 2016) and utilized the PPS for this task. The most recent TFP update was released in August 2021 and the revised cost of the market basket was the first non-inflation adjustment increase in benefits for SNAP in over 40 years (US Department of Agriculture 2021).

The PPS combines datasets to enhance research related to the economics of food and nutrition. There are four primary components of the suite:

  • Purchase to Plate Crosswalk (PPC),
  • Purchase to Plate Price Tool (PPPT),
  • Purchase to Plate National Average Prices (PP-NAP) for the National Health and Nutrition Examination Survey (NHANES), and
  • Purchase to Plate Ingredient Tool (PPIT)..(More)”

Valuing Data: The Role of Satellite Data in Halting the Transmission of Polio in Nigeria

Curated on Posted on by Stefaan Verhulst

Article by Mariel Borowitz, Janet Zhou, Krystal Azelton & Isabelle-Yara Nassar: “There are more than 1,000 satellites in orbit right now collecting data about what’s happening on the Earth. These include government and commercial satellites that can improve our understanding of climate change; monitor droughts, floods, and forest fires; examine global agricultural output; identify productive locations for fishing or mining; and many other purposes. We know the data provided by these satellites is important, yet it is very difficult to determine the exact value that each of these systems provides. However, with only a vague sense of “value,” it is hard for policymakers to ensure they are making the right investments in Earth observing satellites.

NASA’s Consortium for the Valuation of Applications Benefits Linked with Earth Science (VALUABLES), carried out in collaboration with Resources for the Future, aimed to address this by analyzing specific use cases of satellite data to determine their monetary value. VALUABLES proposed a “value of information” approach focusing on cases in which satellite data informed a specific decision. Researchers could then compare the outcome of that decision with what would have occurredif no satellite data had been available. Our project, which was funded under the VALUABLES program, examined how satellite data contributed to efforts to halt the transmission of Polio in Nigeria…(More)”

Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust

Curated on Posted on by Stefaan Verhulst

A Special Publication from the National Academy of Medicine: “The effective use of data is foundational to the concept of a learning health system—one that leverages and shares data to learn from every patient experience, and feeds the results back to clinicians, patients and families, and health care executives to transform health, health care, and health equity. More than ever, the American health care system is in a position to harness new technologies and new data sources to improve individual and population health.

Learning health systems are driven by multiple stakeholders—patients, clinicians and clinical teams, health care organizations, academic institutions, government, industry, and payers. Each stakeholder group has its own sources of data, its own priorities, and its own goals and needs with respect to sharing that data. However, in America’s current health system, these stakeholders operate in silos without a clear understanding of the motivations and priorities of other groups. The three stakeholder working groups that served as the authors of this Special Publication identified many cultural, ethical, regulatory, and financial barriers to greater data sharing, linkage, and use. What emerged was the foundational role of trust in achieving the full vision of a learning health system.

This Special Publication outlines a number of potentially valuable policy changes and actions that will help drive toward effective, efficient, and ethical data sharing, including more compelling and widespread communication efforts to improve awareness, understanding, and participation in data sharing. Achieving the vision of a learning health system will require eliminating the artificial boundaries that exist today among patient care, health system improvement, and research. Breaking down these barriers will require an unrelenting commitment across multiple stakeholders toward a shared goal of better, more equitable health.

We can improve together by sharing and using data in ways that produce trust and respect. Patients and families deserve nothing less…(More)”.

Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

Curated on Posted on by Stefaan Verhulst

Special Publication by the National Academy of Medicine (NAM): “The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. While there have been a number of promising examples of AI applications in health care, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or further exacerbation of existing health- and technology-driven disparities.

This Special Publication synthesizes current knowledge to offer a reference document for relevant health care stakeholders. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.

AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives…(More)”

Patients are Pooling Data to Make Diabetes Research More Representative

Curated on Posted on by Stefaan Verhulst

Blog by Tracy Kariuki: “Saira Khan-Gallo knows how overwhelming managing and living healthily with diabetes can be. As a person living with type 1 diabetes for over two decades, she understands how tracking glucose levels, blood pressure, blood cholesterol, insulin intake, and, and, and…could all feel like drowning in an infinite pool of numbers.

But that doesn’t need to be the case. This is why Tidepool, a non-profit tech organization composed of caregivers and other people living with diabetes such as Gallo, is transforming diabetes data management. Its data visualization platform enables users to make sense of the data and derive insights into their health status….

Through its Big Data Donation Project, Tidepool has been supporting the advancement of diabetes research by sharing anonymized data from people living with diabetes with researchers.

To date, more than 40,000 individuals have chosen to donate data uploaded from their diabetes devices like blood glucose meters, insulin pumps and continuous glucose monitors, which is then shared by Tidepool with students, academics, researchers, and industry partners — Making the database larger than many clinical trials. For instance, Oregon Health and Science University have used datasets collected from Tidepool to build an algorithm that predicts hypoglycemia, which is low blood sugar, with the goal of advancing closed loop therapy for diabetes management…(More)”.

What prevents us from reusing medical real-world data in research

Curated on Posted on by Stefaan Verhulst

Paper by Julia Gehrmann, Edit Herczog, Stefan Decker & Oya Beyan: “Recent studies show that Medical Data Science (MDS) carries great potential to improve healthcare. Thereby, considering data from several medical areas and of different types, i.e. using multimodal data, significantly increases the quality of the research results. On the other hand, the inclusion of more features in an MDS analysis means that more medical cases are required to represent the full range of possible feature combinations in a quantity that would be sufficient for a meaningful analysis. Historically, data acquisition in medical research applies prospective data collection, e.g. in clinical studies. However, prospectively collecting the amount of data needed for advanced multimodal data analyses is not feasible for two reasons. Firstly, such a data collection process would cost an enormous amount of money. Secondly, it would take decades to generate enough data for longitudinal analyses, while the results are needed now. A worthwhile alternative is using real-world data (RWD) from clinical systems of e.g. university hospitals. This data is immediately accessible in large quantities, providing full flexibility in the choice of the analyzed research questions. However, when compared to prospectively curated data, medical RWD usually lacks quality due to the specificities of medical RWD outlined in section 2. The reduced quality makes its preparation for analysis more challenging…(More)”.