SHEDDING LIGHT ON BLIND SPOTS:
EXPLORING WOMEN'S HEALTH THROUGH DATA
Sofia Rezgui Villaume, a digital project manager with a data-driven focus, recently completed an intensive data analytics program at @LeWagon (a training center ranked among the world’s top tech bootcamps). For her final project, she chose to tackle a complex topic: women’s cardiovascular health. This work, a blend of investigative research and scientific analysis, highlights the contributions of data to prevention efforts and lays the groundwork for discussions on public health.
A TROUBLING OBSERVATION
It all started with a simple statistic: while men are more frequently affected by cardiovascular diseases, women are more likely to die from them. A woman with a cardiovascular disease has a 64% higher risk of death compared to a man with the same condition. Overall, about 13% more women die each year in France. This data, derived from European statistical databases, caught the attention of Sofia and her team. “It really struck us,” she admits.
Why such a disparity?
To address this question, the team embarked on an exploratory approach. The datasets, anonymized and quantitative, provided insights into general trends. However, the work quickly revealed the complexity of the subject: cross-referencing epidemiological data, identifying correlations, and interpreting the results required rigorous methodology.
A METHODICAL APPROACH
The starting point?
Identifying the leading cause of cardiovascular mortality in women: stroke. Sofia explains: “We began by analyzing data in France, focusing on prevalence (the percentage of affected individuals) by region.” This first step included a comparison with medical deserts, but the correlations found did not identify a specific gender-based impact.
A first paradox.
The team then broadened the analysis to the European level, examining several classic risk factors: alcohol, tobacco, obesity, sedentary lifestyle, and poor diet. Surprisingly, women generally exhibited fewer risky behaviors than men. They smoked and drank less alcohol. However, they were more sedentary—a factor insufficient to explain the mortality gap.
THE HORMONAL FACTOR: A KEY TO UNDERSTANDING
The results pointed to a universal reality: across all European countries, women are more likely to die from strokes. A breakthrough in the analysis came with the inclusion of an additional data point: age group.
“Until the age of 55, the mortality curves for men and women are similar. But after 55, the female curve becomes exponential,” Sofia highlights.
This shift aligns with a universal biological event: menopause. During this period, the drop in female hormones—estrogen and progesterone—has significant effects:
THE HORMONAL FACTOR: A KEY TO UNDERSTANDING
The results pointed to a universal reality: across all European countries, women are more likely to die from strokes. A breakthrough in the analysis came with the inclusion of an additional data point: age group.
“Until the age of 55, the mortality curves for men and women are similar. But after 55, the female curve becomes exponential,” Sofia highlights.
This shift aligns with a universal biological event: menopause. During this period, the drop in female hormones—estrogen and progesterone—has significant effects:- A 20% increase in arterial stiffness within five years.
- A 15% rise in LDL cholesterol (“bad cholesterol”).
- 50% prevalence of hypertension after 55 (compared to 30% at 45).
- Increased inflammation, measured by a marker, C-reactive protein (+40%).
These changes create a fertile ground for cardiovascular diseases. “It’s both a biological explanation and a call to rethink prevention,” Sofia explains.
CHALLENGES IN PUBLIC HEALTH DATA
Despite these findings, many blind spots remain. The project faced limitations in available datasets:
- Data on stress or sleep, which are crucial for health, is rare and unreliable.
- Traditional metrics like BMI are considered outdated, as they fail to account for muscle or fat distribution.
For Sofia, the future lies in better leveraging data from connected devices (watches, smart scales). “These tools capture valuable information, but it often remains inaccessible, confined to the private sector,” she laments.
CHALLENGES IN PUBLIC HEALTH DATA
Despite these findings, many blind spots remain. The project faced limitations in available datasets:
- Data on stress or sleep, which are crucial for health, is rare and unreliable.
- Traditional metrics like BMI are considered outdated, as they fail to account for muscle or fat distribution.
For Sofia, the future lies in better leveraging data from connected devices (watches, smart scales). “These tools capture valuable information, but it often remains inaccessible, confined to the private sector,” she laments.
A PROJECT FOCUSED ON PREVENTION
To extend their reflection, Sofia and her team envisioned an interactive dashboard to monitor women’s health in real-time. This platform combines behavioral and biological data to produce a dynamic health index, regularly updated through a self-reported questionnaire. However, with only nine weeks of training, creating a fully reliable tool was impossible. “In health, we can’t settle for a 10% margin of error,” Sofia acknowledges. Nonetheless, it demonstrates the importance of using data as a lever to anticipate risks and personalize prevention.
TOWARDS A BETTER UNDERSTANDING OF WOMEN'S HEALTH “Working on this topic has been incredibly rewarding,” Sofia concludes. “This project showed me how much women’s health remains an area to explore and elevate.” By introducing digital tools in prevention, the project highlights a sometimes-overlooked reality: inequalities in research and care based on gender. In this context, data becomes a powerful tool to transform observations into tangible actions.
TOWARDS A BETTER UNDERSTANDING OF WOMEN'S HEALTH “Working on this topic has been incredibly rewarding,” Sofia concludes. “This project showed me how much women’s health remains an area to explore and elevate.” By introducing digital tools in prevention, the project highlights a sometimes-overlooked reality: inequalities in research and care based on gender. In this context, data becomes a powerful tool to transform observations into tangible actions.
