Closing the gender data gap in healthcare

When the first covid-19 vaccines became publicly available in India in 2021, there was a palpable relief. We defeated a deadly adversary who had paralysed our modern, tech-enabled world for at least a year. Many women experienced terrible side effects after a single dose of the vaccine. Some doctors and medical researchers received reports from reputed women scientists around the world whose studies showed vaccine side effects on women. The scientists were unsuccessful in convincing vaccine researchers and manufacturers to consider data on women affected.

More than ever before, data has taken center stage for doctors, life sciences and healthcare. Data is leading the way to support decision-making with regard to diagnosis and treatment, prediction of disease and medical innovation, and managing patient outcomes. Women make up nearly 50% of the world population and have significantly more health expenditure than men. As of 2021, more than 80% of pre-clinical studies meant to assess drug safety and efficacy are conducted on male mice only. Females account for only 29%-34% of participants in Phase 1 trials by the pharmaceutical industry.

This gap in data generated across healthcare is significant where it concerns women’s health. The lack of gender equity is across the data value chain. This chain comprises 4 stages. The effects of gaps in data across the chain are summarised below.

  1. Definition of women’s health – with a lack of consistency and consensus around women’s health, data definitions are correspondingly affected.
  2. Documenting diagnoses of women – The prevalence of conditions affecting women is underdiagnosed nearly 5 times as much as men. It has been noted that several implicit biases are associated with diagnostic uncertainty. For example, a female physician is more likely to diagnose a condition with a female patient than a male physician. This then skews the data gathered during diagnosis.
  3. Data Aggregation – There is unevenness in both quantity and quality of data collected on women’s health. The availability of gender-specific health data, when aggregated at national levels, varies widely by country.
  4. Data Analysis – Since there are gaps in the aggregation and metrics derived, predictions related to disease burden cannot be effectively interpreted. This affects overall populations, healthcare providers, policymakers, and researchers. Since female-specific conditions are poorly captured, health metrics fail to represent the suffering associated with these conditions.

The effects of these data gaps have a widespread impact on areas such as effective measurement, safety and guidelines of drug administration since metabolism varies with gender. Women are likely to experience more adverse drug reactions than men. This also adversely impacts any clinical research and development focused on women and medical technology advancements that actively address women-specific health conditions.

There have been conscious efforts since the ‘90s to improve representation of women-centric data and there’s much more to be done. Opportunities to narrow the gaps are:

  1. Creating a greater awareness around the importance of gender while defining and treating diseases. This means re-evaluating older data sets, updating, and adding the right definitions to women-specific diseases and laying emphasis on educating the healthcare ecosystem about the biological relevance of gender to managing health. It’s time that information gathering around women’s health widens extensively to cover the gamut of their health experiences.
  2. Providing greater for better gender representation in clinical research starting with the sponsors giving women a seat at the table while strategizing, designing, planning, and executing clinical trials. This also means reducing barriers to enrol women in trials often put up as cost reduction measures or non-consideration of data due to more adverse events.
  3. Re-examining existing metrics at a global level. This activity must include policymakers, governments, researchers, public health experts and clinicians to examine how studies, metrics and recommendations can be changed to incorporate gender-specific data.
  4. Obtain exclusive investment for women’s health-related data. This could potentially open up innovation avenues for new drugs, new methods of treatment, and most important, bring about awareness and understanding of the impact of gender-specific differences on health outcomes.

Becoming patient-centric is key to gathering the right data and knowing when it has been missed. This needs to include gender-specific definitions, diagnosis, and analysis to improve understanding, treatment and overall health outcomes for entire populations. This in turn can influence how policies are developed and implemented that result in meaningful health outcomes for all populations.

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