Those who are pregnant as well as their babies are often among the most overlooked groups in clinical research, a challenge magnified by Covid-19. Despite the increased risk the pandemic poses to them and their newborns, vaccines were not systematically evaluated for use during pregnancy before being widely distributed. This left pregnant patients and their doctors to make real-time, consequential health decisions with little data to inform their choice of whether or not to get vaccinated.
Unfortunately, this isn’t a new trend: pregnant women are “severely underrepresented” in clinical research globally. Due to legal, ethical, and logistical challenges of assessing the safety and effectiveness of medical products on them and the infants they carry, pregnant populations are often excluded from drug development altogether. This lack of research has designated pregnant and lactating people “therapeutic orphans,” as there are few treatments that have been validated and approved for use in the population. For people who belong to racial and ethnic minority groups or the LGBTQIA+ community and are pregnant, the disparities run even deeper.
The issue of representation in medical and drug development research is both clinically significant and a major contributor to disparities in health equity. Without reliable evidence on how health interventions affect pregnant patients and their infants, how can we ensure they’re receiving the same caliber of safe, effective, and high-quality treatment as their non-pregnant peers? True health equity requires better-informed pregnancy care – and it should start at birth.
Data limitations make it difficult to conduct studies on how therapies affect pregnant people and their children.
Ninety percent of women take some medication during pregnancy and postpartum, but of all the drugs approved by the U.S. Food and Drug Administration (FDA) between 2000 and 2010, nearly 75 percent reported no data on use in pregnant people.
In order to make informed decisions, regulators, life sciences companies, providers, and patients need to be able to answer fundamental questions about how medical interventions will impact those who are pregnant: Is this treatment safe and effective for them and their infants? What is the impact of leaving certain conditions untreated during pregnancy? How is this treatment used to treat pregnant patients in the real world, outside of highly controlled clinical studies?
To help address some of these uncertainties, FDA often requires manufacturers to report on the safety and effectiveness of their drugs on pregnant mothers and newborns after therapies hit the market. Datasets known as pregnancy exposure registries exist to detail real-world health information on individuals’ exposure to drugs, vaccines, and other products during pregnancy, but they have limitations: registries are costly and can take years to build with sufficient data to capture outcomes on the parent and child’s health over time.
In addition to the logistical challenges of completing a pregnancy registry, FDA has cited “the lack of standardization of data collection, inconsistencies in outcome definitions/inclusion/exclusion criteria, and variations in use of a comparison population” as shortcomings of these datasets. The European Medicines Agency (EMA) has acknowledged similar limitations, as well as low levels of enrollment in pregnancy registries, high levels of patients lost to follow up, and low statistical power.
Relevant and reliable data, combined with advanced analytics, can inform better decisions in pregnancy and postpartum clinical care.
It’s critical that the life sciences industry and healthcare system infuse drug development with evidence of how medical products affect those who are pregnant and their children.
Real-world data – when relevant, reliable, and fit for the purpose of answering a given research question – can be used to generate evidence on how health interventions perform in pregnant people. Where clinical trials may exclude these populations due to ethical or safety reasons, properly-generated real-world evidence can supplement, or sometimes replace, trials to inform regulatory and drug development decisions.
Equipped with the proper data and analytics tools, pharmaceutical manufacturers can be proactive in thinking about potential risks and adverse events in pregnant populations earlier in development. Real-world datasets that link those who are pregnant to their infants enable comprehensive tracking of health outcomes over time, which can help overcome challenges to working with today’s pregnancy registries. These powerful data and analytics capabilities could empower manufacturers to more effectively care for these underserved populations.
The issue of representation in clinical research remains a threat to health equity, as do the devastating rates of maternal and infant mortality in the United States, which disproportionately affect people of color and especially Black Americans. With the right tools in hand and innovations occurring across industry every day, the life sciences industry now has the opportunity and ability to ensure health equity begins at birth. Will we collectively rise to the occasion? Only time will tell.
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