Physicians per 10,000 population

Key: who_physdens | Secondary data | Category: Health

Physician density (per 10 000 population), including generalists, specialist medical practitioners and medical doctors not further defined, in the given national and/or subnational area. Depending on the nature of the original data source, the data may include practising (active) physicians only or all registered physicians. The ISCO-08 codes included here are 221, 2211, 2212. The WHO framework for classifying health workers draws on the latest revisions of international classifications, including the International Standard Classification of Occupations (revision 2008), the International Standard Classification of Education (revision 2011) and the International Standard Industrial Classification of All Economic Activities (revision 4).

Data Layer information

Source file is available (Show file)

import pandas as pd

from shapes.models import Shape
from datalayers.datasources.base_layer import BaseLayer


class WhoPhysdens(BaseLayer):
    def __init__(self) -> None:
        super().__init__()

    def download(self):
        pass

    def process(self, shapes=None):
        if shapes is None:
            shapes = Shape.objects.all()

        df = pd.read_csv(self.get_data_path() / "GHA_who_physdens.csv")

        for shape in shapes:
            pcode = shape.get_property("pcode")
            dfx = df[df["ADMIN0"] == pcode]

            for _, row in dfx.iterrows():
                self.add_value(shape, row["year"], row[self.key])

Data is available locally

Data is loaded into the database

Log | Files

Operation	--
Database unit	individuals / 10.000 population
Value type	`value`
Temporal resolution	`year`

Data availability per spatial unit within the integrated time period
Shape type	First	Last	Availability
Country			89.47 %
Total	2004	2022	0.32 %

Physicians per 10,000 population

Data Layer information

Analytical overview

Source information

Physicians per 10,000 population

Data Layer information

src/datalayer/who_physdens.py

Analytical overview

Source information