66 Years of Philippine Health Data in One Dashboard

In 1953, the average Filipino could expect to live about 50 years. By 2019, that number had climbed to 71. Twenty-one extra years of life in just over six decades. That's a big number, but it hides a lot of complexity.

What drove those gains? Was progress steady or did it come in bursts? And where does the Philippines stand compared to its Southeast Asian neighbors? These were the questions that got me started on this project.

What I Put Together

I built a dashboard that tracks Philippine health indicators from 1953 to 2019 using WHO datasets. It covers life expectancy, infant mortality, maternal mortality, and the shifting burden of disease — from infectious diseases like tuberculosis to chronic conditions like heart disease and diabetes.

The dashboard lets you trace these trends over time and compare the Philippines against regional benchmarks.

The Motivation

Health data gets cited in news articles all the time, but usually as isolated numbers. "Infant mortality dropped by X percent." "The Philippines ranks Yth in Southeast Asia." I wanted to see the full arc — not just snapshots, but the trajectory. Where did progress stall? Where did it accelerate?

There's also a personal angle. Like most Filipinos, I've seen how uneven healthcare access is depending on where you live. The data confirms what many of us already sense, but puts hard numbers behind it.

Wrangling 66 Years of Messy Data

The biggest technical challenge wasn't analysis — it was cleaning. WHO datasets spanning this many decades don't come in a neat format. Column names changed. Reporting standards shifted. Some years are missing entirely for certain indicators.

I used linear interpolation for small gaps (1-2 years) and flagged larger gaps in the visualization so users know when data is estimated versus reported. For disease classification, I mapped older ICD codes to current categories so the trend lines would be consistent across decades.

One specific headache: maternal mortality data before the 1990s was sparse and used different measurement methodologies. I ended up creating two separate trend lines — one using the older methodology and one using the modern definition — rather than trying to force them into a single series.

Key Patterns in the Data

The headline finding is that life expectancy gain of 21 years. But the pace wasn't uniform. The fastest gains came between the 1960s and 1980s, driven largely by reductions in infant and child mortality. Progress slowed after 2000.

The disease burden tells a fascinating story of transition. In the 1950s, infectious diseases — tuberculosis, pneumonia, diarrheal diseases — were the leading killers. By 2019, the top causes of death had shifted to heart disease, stroke, and diabetes. The Philippines is in the middle of an epidemiological transition that most developed countries went through decades earlier.

• Infant mortality fell from roughly 100 per 1,000 live births to under 20
• Tuberculosis death rates dropped dramatically but remain higher than regional peers
• Heart disease became the number one cause of death by the 2000s
• The Philippines trails Thailand and Vietnam in life expectancy despite similar GDP levels

That last point is striking. Economic development alone doesn't explain health outcomes. Public health investment and policy matter enormously.

What I'd Do Differently

If I had more time, I'd add sub-national data. The Philippines is a country of stark regional differences — health outcomes in Metro Manila versus rural Mindanao are worlds apart. The WHO data is national-level only, so capturing that gap would need DOH regional data, which is harder to get in clean form.

I'd also love to overlay health spending data to see if budget increases correlate with outcome improvements. My gut says the relationship isn't as straightforward as politicians suggest, but I'd rather let the data show it.