Close the Gap (ASEAN)
The hardest part was a data bug that looked correct. A self-referencing Power BI filter made First Rate and Last Rate identical for every country, so the "Rate of Change" chart showed 0.00% everywhere without erroring. Separately, a slicer left on "All" caused several measures to silently sum three mortality indicators together, so the headline 52x gap was actually a combined burden score, not pure maternal mortality as first labeled. Neither issue crashed or errored, both looked plausible. Winning would mean a lot, honestly. We put in real hours on this, way more than the deadline probably called for, and it would just feel good to have that recognized. Beyond the prize itself, it's the kind of thing you can point to later, on a CV, in an interview, when someone asks "give me an example of a project you're proud of." It's validation that we're capable of this level of work, and that matters when you're still building a track record.
Voting is not currently open.
Project Information
Project Name
Close the Gap (ASEAN)
One-Line Problem Statement
ASEAN's maternal and child mortality gap, as wide as 52 times between countries and widening across most of the region, is driven mainly by physician shortages rather than underfunding, within a fragmented, data poor healthcare system.
Project Description
Close the Gap ASEAN analyzes a decade of mortality data across all ten ASEAN countries and finds the gap between best and worst performing countries is as wide as 52 times, and widening in most of the region. Testing four resource factors, physician density shows the strongest link to outcomes (correlation 0.72), far stronger than health spending (0.31), pointing to a workforce shortage, not a funding gap. Our response is a physician reach platform: community health workers capture risk data offline, an AI layer triages cases and flags early outbreak signals, and a remote physician reviews only what truly needs one, extending scarce doctors across many more clinics. Built edge first for climate-related disruptions, with blockchain-backed record-keeping, patient data secure and portable.
Methodology
We first established where ASEAN's health equity gap is worst and whether it's improving, then tested why it exists by correlating outcomes against resource data, so the resulting solution targets the strongest root cause rather than a generic fix. Data WHO/World Bank indicators across all ten ASEAN countries, 2004 to 2015: infant, maternal, and under-5 mortality, plus health expenditure, physician, nurse, and pharmacist density. Since reporting coverage varies by country and year, all calculations use each country's own latest available year rather than a fixed cutoff. Tools Power BI and Power Query for data cleaning, modeling, and the interactive dashboard; Python for validating figures and calculating correlation coefficients between mortality and each resource factor; PowerPoint for the final presentation. Process We cleaned and merged seven raw files into related outcome and infrastructure tables, built trend and disparity visuals, then tested four resource factors against mortality to isolate the strongest driver. Along the way we caught and corrected a measure that had inadvertently combined three mortality indicators, and documented the fix rather than hide it. Implementation The strongest finding, physician density, shaped the solution: an offline-capable app for community health workers to capture risk data, an AI triage layer to flag high-risk cases, and remote physician review only where needed. It runs edge-first for resilience during climate disruptions.
Team Bio
Pod Lyra brings together diverse analytical talents united by a shared passion for data-driven storytelling. We collaborate closely to solve complex problems, uncover trends, and translate numbers into clear, actionable business insights.
Last updated just now
…