title: ASCEND Decision Observatory
emoji: ↗
colorFrom: green
colorTo: gray
sdk: static
app_file: index.html
pinned: false
short_description: Global health-system and surgical-access observatory
tags:
- health-systems
- global-surgery
- decision-analysis
- data-visualization
- world-bank
- who
ASCEND Decision Observatory
ASCEND is an independent, exploratory observatory of health systems and surgical access. It links three things that are usually kept apart: what is observed (public indicators for every World Bank economy), what is assumed (a transparent, editable decision model), and what is published (retrievable literature and registry leads). Every number is either traceable to a public source or explicitly labelled as an illustrative assumption.
Live app: https://mmrech-ascend.static.hf.space · Author: Matheus Machado Rech (MD; MPH candidate, Harvard T.H. Chan School of Public Health).
Three views
World lens — 217 World Bank economies, 2000–2024, on an interactive 3D choropleth globe, a population-sized scatterplot and a sortable table. Indicators are grouped as:
| Group | Indicators | Source |
|---|---|---|
| System context | Health spending per person (current US$ and PPP), physicians per 1,000, life expectancy, out-of-pocket share, population | World Bank WDI |
| Surgical system (LCoGS) | Specialist surgical workforce (SAO) per 100,000; surgical procedures per 100,000; share at risk of catastrophic and of impoverishing expenditure for surgical care | World Bank WDI (compiled from the Lancet Commission on Global Surgery indicator programme) |
| Coverage & financial protection | UHC service coverage index (SDG 3.8.1); population with household health spending above 10% of consumption (SDG 3.8.2) | WHO Global Health Observatory |
| Geographic access (modelled) | Population within 1 h and 2 h of the nearest healthcare facility and population-weighted mean travel time, motorised and walking-only | Computed by access_pipeline.py from Malaria Atlas Project 2019 travel-time surfaces × WorldPop 2020 (1 km) over Natural Earth 1:10m polygons |
"Latest available" mode shows each economy's most recent observation with its year; fixed-year mode never carries values forward. Missing values stay visibly missing. The Lancet Commission's 2030 targets (≥20 SAO providers and ≥5,000 procedures per 100,000; 100% protection against catastrophic and impoverishing expenditure) are drawn as reference lines and progress bars, not as judgements.
Decision landscape — an illustrative 12-month flow model of a surgical pathway (arrivals → referral → staffed capacity → funded slots). Sliders change budget, usable capacity and referral improvement; a decision map shows which of four options maximises completed procedures and where the preferred option flips. It is a sensitivity map, not a forecast, and is calibrated to no country.
Evidence atlas — Europe PMC publications on surgical access, capacity and LCoGS indicators (most-cited first), live Europe PMC search, and ClinicalTrials.gov registrations. Discovery leads only; nothing here calibrates the model.
Data provenance
- World Bank Indicators API (CC BY 4.0): https://api.worldbank.org/v2/ — all economies,
date=2000:2024. - WHO Global Health Observatory OData API: https://ghoapi.azureedge.net/api/ —
UHC_INDEX_REPORTED,FINPROTECTION_CATA_TOT_10_POP(national total). - Natural Earth 1:110m (public domain) country polygons and boundary lines; see
assets/ATTRIBUTION.md. - Malaria Atlas Project travel time to healthcare 2019 (CC BY 4.0) via the MAP GeoServer WCS; WorldPop 2020 1 km population counts (CC BY 4.0); Natural Earth 1:10m admin-0 polygons.
- Europe PMC REST API; ClinicalTrials.gov API v2.
python3 refresh_data.py rebuilds data.json from the live APIs and writes a request manifest with status, bytes and timestamps for every call. If a live call fails after retries and a previously retrieved raw response exists in data_raw/, that response is reused and the source is marked cached with its file timestamp; nothing is ever synthesised. python3 refresh_data.py --validate re-checks the committed snapshot.
python3 access_pipeline.py --surface motorized (then --surface walking) streams the 1 km travel-time surface tile by tile from the MAP WCS, weights it by the WorldPop mosaic (download ppp_2020_1km_Aggregated.tif into geo/ first, 870 MB) and writes geo/access_summary.json, which refresh_data.py merges as the "Geographic access (modelled)" group. python3 make_overlay.py motorized walking paints the globe overlay PNGs. The whole run takes about 25 minutes on two cores and stores no large intermediate files.
UN System Data Commons and MCP
ASCEND treats the UN System Data Commons (United Nations Statistics Division) as a third source and as an interoperability layer. Seven indicators carry a Data Commons identifier (health spending per person, out-of-pocket share, life expectancy, population, physician density, UHC service coverage index, catastrophic health spending above 10%) and SAO density is cross-checked as a derived quantity from WHO head-counts; PPP spending, surgical volume and the surgical-expenditure risks have no counterpart there. The full comparison, with agreement rates and the method differences found, is in docs/undc_crosscheck.md. The refresh script stores each economy's latest Data Commons value beside the World Bank/WHO series and reports agreement at the latest common year; the World lens shows both, offers a per-country live check, and can recolour the globe from the Commons' public V2 observation endpoint (https://unsd-datacommons.gcp.un-icc.cloud/core/api/v2), which allows cross-origin requests. Live values are session-only and always marked.
The same identifiers work with the UN System Data Commons MCP server (https://unsd-datacommons.gcp.un-icc.cloud/mcp). tools/undc_probe.py is a dependency-free client that re-runs the server's discover → qualify → retrieve playbook for ASCEND's concepts (python3 tools/undc_probe.py audit), documenting why each series was adopted, cross-checked only, or found to have no counterpart (surgical volume, perioperative mortality, PPP spending, surgical-expenditure risks).
Build, test, run
npm install # esbuild only
npm run data # optional: refresh data.json from public APIs
npm run build # bundles app.mjs + style.css + data.json into the standalone index.html
npm test # model, globe geometry and data-snapshot tests (node:test)
python3 -m http.server # or open source.html via any static server for the unbundled modules
index.html is a generated artefact: do not edit it by hand. Three.js r160 is vendored locally; the app makes no network calls except the optional live Europe PMC search.
Limitations
- National averages describe systems; they do not measure any individual's access to surgery.
- LCoGS indicators are compiled from heterogeneous national reports with irregular years; the latest value for an economy can be a decade old and is always shown with its year.
- Two of the six LCoGS indicators (2-hour access to Bellwether capacity; perioperative mortality) have no comparable country series in these sources. The modelled access layer measures travel time to any healthcare facility and therefore overstates surgical access.
- The decision model is deterministic and illustrative; it carries no uncertainty distribution and no causal claim.
- Natural Earth geometry expresses no position on disputed territories; 47 small economies without 1:110m polygons appear as capital markers.
Provenance
ASCEND is an independent research prototype. Earlier health-system explainers were an inspiration only; no framework, code or multipliers were reproduced. Version history: v1.0.0 (18 Oct 2026) twelve-country prototype; v2.0.0 (19 Oct 2026) all-economy World lens with LCoGS and SDG 3.8 indicators, choropleth globe, build pipeline; v2.1.0 modelled geographic access and travel-time overlay; v2.2.0 UN System Data Commons cross-checks, live refresh and MCP probe; v2.3.0 cross-check extended to every indicator (eight counterparts, four documented gaps). Tags and the v1.0-launch branch preserve each release.