Data Visualization

Blog of the Data Visualization & Communication Course at OSB-AUB

This is my favorite part about analytics: Taking boring flat data and bringing it to life through visualization” John Tukey

Where You Live Shapes the Care You Get: A Data Story from Lebanon

Where You Live Shapes the Care You Get: A Data Story from Lebanon

If you grew up in Lebanon, you’ve probably heard someone say: “If this illness happened in Beirut, things would’ve been easier.”

I’ve heard it from relatives and friends who had to drive for hours for a simple check-up.
Healthcare in Lebanon has never felt equal, but I always wondered: Is this just a feeling, or is the data telling the same story?

To explore this, I combined  two national datasets:

One mapping where chronic diseases and special needs appear across Lebanese towns, and another showing where healthcare facilities are actually located.

  • These visuals show that rural regions, especially Akkar, Baalbeck-Hermel, and the North, have the highest share of towns reporting chronic diseases, confirming that Lebanon’s heaviest health burdens fall on its most underserved areas.
  • We can see that most healthcare facilities are concentrated in urban Mount Lebanon, creating an imbalance where the regions with the greatest health needs have the least medical infrastructure.

To understand this imbalance more clearly, I looked at disease prevalence side-by-side with the availability of the healthcare resources that matter most for each condition.
The question was simple: when a disease appears in a town, is the right type of care actually nearby?

Therefore, I paired each condition with the resource most relevant to its management, based on clinical practice and literature:
• Hypertension → hospitals
• Diabetes → clinics
• Cardiovascular disease → pharmacies or medical centers
• Special needs → dedicated care centers

Once I paired each condition with the care it requires, a clear imbalance appeared:
• The regions most affected by disease had the least access to the services they needed.
• The regions with lighter disease presence had the strongest concentration of facilities.

A clear example is hypertension vs hospitals:
•Akkar, Baalbeck-Hermel, the North, and parts of the South showed high hypertension presence, yet had some of the lowest hospital capacity.
•Meanwhile, Mount Lebanon, with lower prevalence, had more hospitals than all of them combined.

This is more than an imbalance; it’s an access gap that shapes real health outcomes.

So, what does Lebanon need?

  1. Targeted decentralization, not more hospitals everywhere.
    Rural regions don’t need giant new medical complexes.
    They need strategically placed clinics, chronic-disease screening units, hypertension/diabetes corners, and even mobile health programs.
  2. Allocate resources based on data
    Mount Lebanon already has the largest medical footprint.
    But Akkar, Baalbeck-Hermel, the North, and the South need urgent investment.
  3. Build capacity where it matters.
    Even a single medical center, diagnostic pharmacy, or special-needs support unit can shift accessibility for hundreds of towns.
  4. Make data-driven planning routine.
    Lebanon produces far more data than most people realize, we just don’t use it.
    Dashboards and visual can guide ministries, municipalities, NGOs, donors, and health planners to invest where impact will be highest.

Lebanon doesn’t suffer from a lack of medical knowledge, it suffers from a lack of medical access. And the good news is that access can change.
If resources finally start following the data, rural Lebanon won’t stay medically invisible. The map is clear, now the planning needs to follow.

Understanding the Dynamics: Birth Rates and Youth Fertility Over Time

Understanding the Dynamics: Birth Rates and Youth Fertility Over Time

In our global journey towards the Sustainable Development Goals (SDGs), closely examining reproductive trends offers a window into the successes and ongoing challenges of public health initiatives. This post presents an updated analysis of birth rates over the decades and current adolescent fertility rates in key nations.

Our line graph traces the paths of birth rates in countries like Afghanistan, Cameroon, Ethiopia, Madagascar, Mauritania, and Yemen from the 1960s onward. While there is a shared downward trajectory, each country’s journey reflects unique socio-economic and healthcare factors influencing these rates.

Complementing the long-term view, the bar graph presents a snapshot of the current state of adolescent fertility rates. This data is critical as it highlights the fertility patterns among young women, which is a key indicator of access to education and reproductive health services. Madagascar and Cameroon exhibit the highest rates, signaling areas where interventions may be most needed.

Together, these visuals offer a comprehensive perspective on reproductive health. The historical data of birth rates inform us of overarching progress, while the adolescent fertility rates give us a focused understanding of where additional efforts are necessary, particularly in empowering young women.

These reproductive trends are intimately linked to SDGs 3, 4, and 5, which emphasize health, education, and gender equality. High adolescent fertility rates can hinder progress in these areas by affecting education completion rates for young women and impacting their health and economic prospects.

To address these complex issues, data-driven strategies are essential. Promoting comprehensive sexual education, enhancing healthcare access, and empowering young women with choices can lead to healthier societies and further progress in reducing birth rates in line with our sustainable development aspirations.

As we analyze these visuals, we’re reminded of the power of data to shape our understanding and our actions. Let’s use these insights to foster a world where every young person is equipped with the knowledge and resources to make informed decisions about their reproductive health.