global-health-data

name	global-health-data
description	Global health data access and analysis: WHO GHO API, IHME GBD, Our World in Data, DALYs, age-standardization, health inequality, and choropleth mapping.
tags	["public-health","epidemiology","who","global-health","health-inequality","geopandas"]
version	1.0.0
authors	[{"name":"awesome-rosetta-skills contributors","github":"@awesome-rosetta-skills"}]
license	MIT
platforms	["claude-code","codex","gemini-cli","cursor"]
dependencies	["requests>=2.31.0","pandas>=2.0.0","numpy>=1.24.0","scipy>=1.11.0","matplotlib>=3.7.0","geopandas>=0.14.0","shapely>=2.0.0","seaborn>=0.12.0","statsmodels>=0.14.0"]
last_updated	2026-03-17

Global Health Data Access and Analysis

This skill provides tools for accessing and analyzing global health data from major open sources: the WHO Global Health Observatory API, IHME Global Burden of Disease study data, and Our World in Data health datasets. It covers computing burden-of-disease metrics (DALYs, YLLs, YLDs), age-standardized rates, health inequality measures, and publication-quality choropleth maps.

Prerequisites

pip install requests pandas numpy scipy matplotlib geopandas shapely seaborn statsmodels

For GBD data downloads you may need an IHME account. Set API tokens via environment variables (no hardcoded credentials):

export WHO_API_KEY="<paste-your-who-api-key>"       # currently optional for public endpoints
export IHME_API_TOKEN="<paste-your-ihme-token>"     # for authenticated GBD API endpoints

Core Functions

1. WHO Global Health Observatory (GHO) API

import os
import time
import requests
import pandas as pd
import numpy as np
from typing import Optional


# WHO GHO OData API v3 base URL (public, no API key required for most indicators)
WHO_GHO_BASE = "https://ghoapi.azureedge.net/api"


def get_who_indicator(
    indicator_code: str,
    countries: Optional[list[str]] = None,
    years: Optional[list[int]] = None,
    dimension_filter: Optional[dict] = None,
    page_size: int = ,
    verbose:  = ,
) -> pd.DataFrame:
    
    filters = []
     countries:
        country_filter = .join([  c  countries])
        filters.append()
     years:
        year_filter = .join([  y  years])
        filters.append()
     dimension_filter:
         key, val  dimension_filter.items():
            filters.append()

    odata_filter = .join(filters)  filters  

    records = []
    skip = 
    session = requests.Session()
    headers = {}

    
    api_key = os.environ.get()
     api_key:
        headers[] = api_key

     :
        params = {: page_size, : skip}
         odata_filter:
            params[] = odata_filter

        url = 
        :
            resp = session.get(url, params=params, headers=headers, timeout=)
            resp.raise_for_status()
         requests.HTTPError  e:
            ()
            
         requests.RequestException  e:
            ()
            time.sleep()
            

        data = resp.json()
        batch = data.get(, [])
          batch:
            

         item  batch:
            records.append({
                : indicator_code,
                : item.get(, ),
                : item.get(, ),
                : item.get(),
                : item.get(),
                : item.get(),
                : item.get(),
                : item.get(, ),
                : item.get(, ),
                : item.get(, ),
            })

        skip += page_size
         verbose:
            ()

         (batch) < page_size:
            
        time.sleep()

    df = pd.DataFrame(records)
    df[] = pd.to_numeric(df[], errors=)
    df[] = pd.to_numeric(df[], errors=)
    df[] = pd.to_numeric(df[], errors=)
    df[] = pd.to_numeric(df[], errors=)

     verbose:
        ()

     df


 () -> pd.DataFrame:
    
    url = 
    params = {}
     search_term:
        params[] = 

    resp = requests.get(url, params=params, timeout=)
    resp.raise_for_status()
    items = resp.json().get(, [])
     pd.DataFrame([{
        : it[],
        : it[],
    }  it  items])