This essay is part of a series exploring what is required for scientific progress to be made in our understanding of women’s health and our ability to treat the conditions that impact the lives of women everywhere. Learn more about why this is necessary and explore the other challenges and topics.
Women’s Health Researchers Lack the Data Needed to Make Progress
A lack of data, samples, and sharing hinders researchers' progress.
When data is collected and shared, understanding can happen faster. This was shown in 2021 by researchers at the Oregon Health and Science University in Portland, Ore, and the private company Natural Cycles. Within a few months of the Covid vaccine being released, women started to report that the vaccine had affected their menstrual cycles and caused “unusually heavy flows and even breakthrough bleeding among some people who hadn’t menstruated in years.” Many turned to the clinical trial data for answers and while women were included in the trial, data on menstrual cycles was not collected.
In August of 2021, the NICHD granted a total of $1.67 million to 5 institutes to study potential links between the vaccine and menstrual changes¹. The first of these grants was able to publish results by January 2022, just 5 months later, due to this partnership. Natural Cycles, a fertility and birth control app, allows women to track their menstrual cycles and provided menstruation and vaccination data on 3,595 women who had opted in to participate in research. This sped up the research because the data did not have to be collected. It already existed and researchers were then able to evaluate and publish the results that much faster.
There is no data.
Most research starts with data. Scientists either need access to the data required to conduct their research or they need to gather the data. Research often builds on previous work done and data is often repurposed to ask another question. Additionally, data often comes from biosamples provided by patients experiencing the condition being studied. These samples are then stored to be used over and over.
However, for most researchers interested in studying women’s health, accessing the data needed is not so simple. Due to a century of excluding female models from research and women from clinical trials, there is a significant lack of data on women’s health and when women are included, the relevant data is often not collected. The NIH did not require scientists to consider sex in their research until 2016 and the result is that for many interesting questions, there just isn’t data to support the research.
Additionally, scientists lack standards and knowledge around how to measure, track and curate data on women’s health conditions. This requires research to start at the beginning and gather new samples and new data. This work is often considered high-risk research and challenging to get funding for.
There is no sharing.
There is also a data-sharing problem in science and it is not limited to women’s health. Data sharing and collaboration are just not embraced. This results in a significant amount of work being duplicated and slows progress.
In 2003, the NIH required that researchers whose work is funded by the NIH share their data publicly. However, there is a mentality to share the minimum amount possible. This challenge is exacerbated by the lack of infrastructure, funding, and in some cases standards to easily share datasets.
In addition to challenges within the research community, a proliferation of technology companies are gathering data on patient populations (such as Apple, 23andMe, and Clue) and are not motivated to share their data. The data they collect is the basis for their intellectual property and competitive edge. Some companies are interested in selling their data to researchers, which is generally not something academic researchers can afford, but less interested in making it openly available. Others are interested in doing the research internally and using it for marketing or product development.
This results in scientists spending a lot of time duplicating work and often starting over to gather the data they need. This is particularly true for data around women’s health conditions since there has been less focus and research on these conditions.
There are no samples.
One of the key data collection methods that scientists use is that of biospecimens (tissue, blood, urine, cerebral spinal fluid, feces, etc) from patients. These are generally collected by physicians working in conjunction with researchers and then stored for the institute or researchers to use in a biobank. There are four main types of biobanks²,
- Academic biobanks are collected by clinicians and located within hospitals or research organizations. These currently hold the majority of biosamples.
- Industry biobanks are collected during research or clinical trials and located within the private institute that conducted the trial.
- Commercial biobanks collect biosamples to sell to industry researchers.
- Public biobanks collect citizen data and make it available to researchers. These are much more common in Europe.
The biosamples collected are rarely shared outside of the institution that collects them, or made accessible to other researchers, which presents one more problem. Each researcher has to go about finding the collaboration needed to gather the samples needed for their research. In some cases, they can buy samples, but they then need the funding to be able to do so.
For women’s health researchers the challenges go one step further. The largest public tissue biobanks in the world are located in Europe and searching the Biobank Graz and the BBMRI-ERIC Directory show that while there are extensive collections of breast cancer samples and a few ovarian samples, there are no collections of other necessary women’s health-specific biospecimens such as menstrual blood or endometrial tissue. As a result, women’s health researchers have to work harder to gather these biospecimens for their work and researchers are less likely to receive NIH funding to support the gathering of these biosamples.
What can we do?
Data is crucial to researchers’ progress and challenges around data sharing, collaboration, and funding to collect novel new data set all present challenges to women’s health researchers. Without data, researchers cannot gain a deeper understanding of women’s health. For progress to be made, a unified, pubic women’s health dataset is necessary. Additionally, scientists need to embrace open science and learn from the lessons of the technology community on how to collaborate. Reducing friction for researchers interested in women’s health is required for us to see greater progress towards understanding female biology.
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