Global health burden signals (obesity trends, PM2.5 exposure, anomalies)
Annual ML-enriched population-health signals for ~190 territories, derived from WHO Global Health Observatory data: adult obesity prevalence by sex, the 5-year obesity trend, annual mean PM2.5 concentration and the PM2.5 exceedance multiple versus the WHO 15 µg/m³ guideline, with 30-period change volatility, 3-year momentum, 3-sigma anomaly flags versus a trailing 10-year baseline, naive-drift 1-year forecasts, and a per-year cross-country volatility rank. Raw data: World Health Organization (non-commercial). All rows carry ISO alpha-3 country_code and join cleanly with other health datasets.
Qualité
Attribution
World Health Organization (Global Health Observatory; derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Reference year (January 1) of the WHO GHO annual estimate. |
| country | string | |
| country_code | string | |
| series_id | string | Signal code: OBESITY_ALL / OBESITY_FEMALE / OBESITY_MALE (adult obesity prevalence by sex), OBESITY_TREND_5Y (5-year pp change), PM25_MEAN (annual mean PM2.5), PM25_WHO_EXCEED (PM2.5 / WHO 15 µg/m³ guideline). |
| series_label | string | Human-readable label for the signal series, derived from the WHO GHO indicator names. |
| value | float | Signal value in native units: percent for the obesity series; percentage points for OBESITY_TREND_5Y; µg/m³ for PM25_MEAN; a multiple of the WHO guideline for PM25_WHO_EXCEED. |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank |
|---|---|---|---|---|---|---|---|---|---|---|
| 1980-01-01 | Afghanistan | AFG | OBESITY_ALL | Adult obesity prevalence, both sexes, % | 0.97484551 | — | — | 0 | — | — |
| 1980-01-01 | Angola | AGO | OBESITY_ALL | Adult obesity prevalence, both sexes, % | 1.7267242 | — | — | 0 | — | — |
| 1980-01-01 | Albania | ALB | OBESITY_ALL | Adult obesity prevalence, both sexes, % | 10.364953 | — | — | 0 | — | — |
| 1980-01-01 | Andorra | AND | OBESITY_ALL | Adult obesity prevalence, both sexes, % | 10.086311 | — | — | 0 | — | — |
| 1980-01-01 | United Arab Emirates | ARE | OBESITY_ALL | Adult obesity prevalence, both sexes, % | 12.776726 | — | — | 0 | — | — |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
Utiliser avec un LLM
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/health_signals/global_health_burden_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/health_signals/global_health_burden_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API: https://datazimuts.com/v1/datasets/health_signals/global_health_burden_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.