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Navigating Networks: The Intersection Between Data Ethics, Clinical Research, and Public Policy

In the age of modern technology, we are constantly talking about data. What data is being collected from us at any given time? What is my data being used for? Who has access to my data? Despite all these big data conversations, very few of them discuss a rather important type of data: healthcare data.

Healthcare data includes any information related to health conditions, treatments, outcomes, or behaviors. It spans from electronic health records and lab results to large scale public health databases. These databases are used to monitor disease patterns, vaccination coverage, and population health behaviors. Public health databases have become an essential tool for doctors, researchers, and policy makers alike, enabling them to improve patient care, respond to disease outbreaks, and shape public health strategies.

Examples of Healthcare Data (source)

Federal agencies like the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) manage many of these datasets and tools. One of these tools includes AtlasPlus, a resource that helps public health professionals explore decades of data on infectious diseases and social determinants of health. Through AtlasPlus, users can create customized maps, tables, and charts that visualize trends in conditions like HIV, hepatitis, tuberculosis, and sexually transmitted infections. This allows public health professionals to compare disease rates across different regions and populations. They are also able to view those rates alongside social factors such as poverty, education level, or access to care. Platforms such as this make it easier to identify which communities may be most vulnerable and to tailor solutions accordingly. They also support real-time decision making in hospitals and inform policies that address national health challenges.

One new and growing area of healthcare data use is network analysis, which allows researchers to study the relationships between individuals. This can give us insights into how infections spread, how patients are connected to their caregiving teams, or how health behaviors are adopted. While this information can be valuable, it may also raise privacy concerns. After all, studying the connections between people may lead individuals to being identified, even in datasets that are anonymous. As a result, researchers must navigate how to share data safely while still furthering scientific analysis. One way to do this is through restricted access and data agreements. Sensitive network datasets would only be shared with approved users under strict agreements that define how the data could be used, stored, and published. However, recent policy changes have complicated these efforts, raising new questions about access and transparency in health data.

Example of how social network analysis models look

In early 2025, following new executive orders, some federal agencies temporarily limited access to certain health data and paused publication of related research. Language restrictions were also introduced for scientific publications, impacting topics related to gender, diversity, and health equity. Several databases were removed or modified, prompting legal action and public concern about the implications this may have for medical research and healthcare solutions. For example, a CDC-maintained dataset tracking adolescent mental health trends was removed. This has disrupted ongoing studies on the impact of social media and school policies on youth mental health.

While access to some of these resources have since been restored, these events have sparked important conversations about how to ensure data transparency, privacy, and scientific independence in a rapidly evolving policy landscape.

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