Licenced Hospitals, Clinics and Dispensaries in Kenya, Year 2025

An animated plot of the Kenya population increase.

data-science
Author

Kabui, Charles

Published

20 March 2025

Keywords

kenya, health-facilities, year-2025, hospitals, clinics, licensed, dispensaries, counties

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Introduction

Using the data from Licenced HealthFacilities for the year 2025 1, we analyze hospital distribution in kenya per county.

Kenya’s healthcare system employs a six-tiered structure. Level 1 Community Health Units (CHUs) focused on preventive and promotive healthcare care through community health workers. Level 2 dispensaries and clinics focus on basic outpatient services for common ailments. Level 3 health centers provide curative and preventative care, including maternal and child health. Level 4 sub-county and district hospitals offer primary referral services. Level 5 county referral and teaching hospitals handle secondary referrals and specialized care. Finally, Level 6 national referral hospitals, such as Kenyatta National Hospital provide the highest level of tertiary care 2 3.

Analysis

Importing Libraries

import matplotlib.pyplot as plt
import pygadm
import pandas as pd
import requests
from io import StringIO
import geopandas as gpd
from adjustText import adjust_text
from IPython.display import display, Markdown
import matplotlib.colors as colors

Data

# Get the data
health_facilities_data = requests.get(
    "https://kmpdc.go.ke/Registers/H-Facilities.php").text
# Parse the data
health_facilities_df = pd.read_html(StringIO(health_facilities_data))[0]
# Drop columns that are not useful
health_facilities_df = health_facilities_df.drop([
    'View', # Empty column
    'status', # All values are 'Active'
    'Reg_No', # Not useful because it's unique and obscured
], axis=1)
# Make levels categorical
health_facilities_df['Level'] = pd.Categorical(
    health_facilities_df['Level'].str.lower().str.strip().str.upper())
# drop duplicates
health_facilities_df = health_facilities_df.drop_duplicates()
health_facilities_df
Facility_Name Address Facility_Type Level Bed_Capacity County
0 A.C.K ST. PAULS MIHUTI DISPENSARY P.O BOX 227-10202 KANGEMA DISPENSARY LEVEL 2 0 MURANG'A
1 NEEMA MEDICARE MEDICAL CENTRE P.O BOX 59461-00200 NAIROBI MEDICAL CENTRE LEVEL 3B 0 NAIROBI
2 AMUNG\'ENTI CATHOLIC DISPESARY P.O BOX 75-60600 MAUA DISPENSARY LEVEL 2 0 MERU
3 LIFOG CENTRE MEDICAL CLINIC P.O BOX 84-80100 MOMBASA MEDICAL CLINIC LEVEL 2 0 MOMBASA
4 RAPHA JOY MEDICAL CLINIC P.O BOX 244-00221 LAIKIPIA MEDICAL CLINIC LEVEL 2 0 KIAMBU
... ... ... ... ... ... ...
14094 NJORO HUDUMA MEDICAL CLINIC NAKURU P.O BOX 86-20107 NJORO NURSING HOME LEVEL 3A 43 NAKURU
14095 DYNAMED MEDICAL CLINIC P.O BOX 454-01001 KALIMONI MEDICAL CLINIC LEVEL 2 0 KIAMBU
14096 SLENMARK MEDICAL CLINIC P.O BOX 267-10304 KUTUS MEDICAL CENTRE LEVEL 3B 0 KIRINYAGA
14097 SLENMAC CARE CENTRE LIMITED P.O BOX 267- KERUGOYA MEDICAL CENTRE LEVEL 3B 40 KIRINYAGA
14098 LUIZ HOASIS MEDICAL CENTRE EBATE P.O BOX 366-40200 KISII MEDICAL CENTRE LEVEL 3B 16 NYAMIRA

14098 rows × 6 columns

def health_facilities_county(title: str, levels = None):
    display(Markdown(
        "\n"
        f"### {title}"
        "\n"))
    df = health_facilities_df\
        if levels == None\
        else health_facilities_df[health_facilities_df['Level'].isin(levels)]
    sizes = df.groupby('County').size()
    sizes.sort_values(ascending=False)\
        .plot(kind='bar', figsize=(20, 10))
    plt.title(title)
    plt.ylabel('Number of Health Facilities')
    plt.xlabel('County')
    plt.grid(True, alpha=0.3, axis='y',)
    plt.show()

    print(sizes.describe())

health_facilities_county(
    'Health Facilities by County, year 2025 [All Levels]')

Health Facilities by County, year 2025 [All Levels]

count 47.000000 mean 299.957447 std 247.828654 min 61.000000 25% 180.500000 50% 261.000000 75% 342.500000 max 1718.000000 dtype: float64

health_facilities_county(
    'Health Facilities by County, year 2025 [LEVEL 2]',
    ['LEVEL 2'])

Health Facilities by County, year 2025 [LEVEL 2]

count     47.000000
mean     183.297872
std      122.494350
min       35.000000
25%      111.500000
50%      170.000000
75%      217.000000
max      786.000000
dtype: float64
health_facilities_county(
    'Health Facilities by County, year 2025 [LEVEL 3]',
    ['LEVEL 3A', 'LEVEL 3B'])

Health Facilities by County, year 2025 [LEVEL 3]

count     47.000000
mean      92.510638
std      116.786850
min       13.000000
25%       36.500000
50%       60.000000
75%      104.000000
max      752.000000
dtype: float64
health_facilities_county(
    'Health Facilities by County, year 2025 [LEVEL 4]',
    ['LEVEL 4', 'LEVEL 4B'])

Health Facilities by County, year 2025 [LEVEL 4]

count     47.000000
mean      21.510638
std       22.531980
min        3.000000
25%        8.500000
50%       15.000000
75%       30.000000
max      145.000000
dtype: float64
health_facilities_county(
    'Health Facilities by County, year 2025 [LEVEL 5 and LEVEL 6]',
    ['LEVEL 5', 'LEVEL 6A', 'LEVEL 6B'])

Health Facilities by County, year 2025 [LEVEL 5 and LEVEL 6]

count    33.000000
mean      3.757576
std       6.015762
min       1.000000
25%       1.000000
50%       2.000000
75%       4.000000
max      35.000000
dtype: float64

Only three counties have more than 500 helath facilities (Nairobi, Kiambu and Nakuru). Nairobi county leads with 1,718 facilities while Isiolo county tails with 61 facilities.

Hospital Density

If we gave each health facility a score depending on the level, and sum the scores for each county, we can get a simplistic estimate of the healthcare density in each county. The score is calculated as follows:

facility_level_values = { 
    j: i 
    for i, j 
    in enumerate(
        health_facilities_df['Level'].sort_values().unique().tolist(), start = 1)
}
facility_level_values
{'LEVEL 2': 1,
 'LEVEL 3A': 2,
 'LEVEL 3B': 3,
 'LEVEL 4': 4,
 'LEVEL 4B': 5,
 'LEVEL 5': 6,
 'LEVEL 6A': 7,
 'LEVEL 6B': 8}
density_data = [
    [
        county, 
        int(pd.to_numeric(facilities["Level"].apply(lambda x: facility_level_values[x])).sum()),
        int(facilities["Level"].count()),
    ] 
    for county, facilities 
    in health_facilities_df.groupby('County')
]
density_df = pd.DataFrame(density_data, columns=['County', 'Density', 'Count'])\
    .sort_values('Density', ascending=False)\
    .reset_index(drop=True)
density_df
County Density Count
0 NAIROBI 3671 1718
1 KIAMBU 1257 688
2 MANDERA 1076 478
3 NAKURU 949 606
4 HOMA BAY 813 392
5 WAJIR 706 361
6 MERU 698 456
7 KISUMU 691 368
8 MACHAKOS 672 440
9 KAKAMEGA 654 384
10 KISII 649 318
11 MOMBASA 632 323
12 KAJIADO 631 375
13 BUNGOMA 599 309
14 MIGORI 528 309
15 UASIN GISHU 524 296
16 KITUI 511 347
17 KILIFI 491 338
18 SIAYA 452 274
19 GARISSA 447 209
20 MAKUENI 436 314
21 NYERI 433 278
22 MURANG'A 395 285
23 KIRINYAGA 386 204
24 KERICHO 379 283
25 EMBU 371 238
26 BARINGO 366 261
27 NYAMIRA 362 198
28 NAROK 356 244
29 TURKANA 331 237
30 KWALE 328 244
31 NANDI 315 224
32 BUSIA 305 184
33 TRANS NZOIA 292 178
34 LAIKIPIA 283 183
35 BOMET 258 196
36 THARAKA NITHI 236 167
37 VIHIGA 231 137
38 MARSABIT 227 152
39 WEST POKOT 211 168
40 ELGEYO/ MARAKWET 201 151
41 NYANDARUA 194 129
42 TAITA TAVETA 193 121
43 SAMBURU 162 113
44 TANA RIVER 141 89
45 LAMU 113 70
46 ISIOLO 111 61

Get the geometry of the counties in Kenya, GADM (Global Administrative Areas).

gdf: gpd.geodataframe.GeoDataFrame = pygadm.Items(
    name="Kenya", content_level=1).rename(columns={"NAME_1": "County"})
gdf = gdf.drop(columns=list(set(gdf.columns) - set(["geometry", "County"])), 
              axis=1)
gdf.head(5)
geometry County
0 MULTIPOLYGON (((35.7616 -0.1904, 35.7243 -0.19... Baringo
1 MULTIPOLYGON (((35.2613 -1.0159, 35.2583 -1.02... Bomet
2 MULTIPOLYGON (((34.8778 0.8339, 34.9138 0.8524... Bungoma
3 MULTIPOLYGON (((34.0292 -0.0142, 34.0158 -0.02... Busia
4 MULTIPOLYGON (((35.5272 0.2167, 35.5261 0.2183... Elgeyo-Marakwet

Merge the densities and the geometries

def format_county_name(county_name: pd.Series) -> pd.Series:
    return county_name.str.strip().str.lower().str.replace(r'[^a-zA-Z0-9]+', '-', regex=True).str.title().unique()
density_df['County'] = format_county_name(density_df['County'])
gdf['County'] = format_county_name(gdf['County'])
merged_gdf = gdf.merge(density_df, on='County', how='left')
merged_gdf.head(5)
geometry County Density Count
0 MULTIPOLYGON (((35.7616 -0.1904, 35.7243 -0.19... Baringo 366 261
1 MULTIPOLYGON (((35.2613 -1.0159, 35.2583 -1.02... Bomet 258 196
2 MULTIPOLYGON (((34.8778 0.8339, 34.9138 0.8524... Bungoma 599 309
3 MULTIPOLYGON (((34.0292 -0.0142, 34.0158 -0.02... Busia 305 184
4 MULTIPOLYGON (((35.5272 0.2167, 35.5261 0.2183... Elgeyo-Marakwet 201 151

Plotting the simplistic densities gives us the following map.

# Create figure and axis
fig, ax = plt.subplots(1, 1, figsize=(12, 12))

# Create a custom colormap from green to red
cmap = colors.LinearSegmentedColormap.from_list('custom_cmap', ['#f2fff2', '#ff0000'])

# Plot with hospital count determining the color
merged_gdf.plot(
    ax=ax, 
    column='Density', 
    cmap=cmap, 
    legend=True,
    legend_kwds={'label': "Hospital Density", 'orientation': "vertical"},
    linewidth=0.1, edgecolor='black'
)

texts = [
    ax.text(
        row.geometry.centroid.x, 
        row.geometry.centroid.y, 
        f"{row['County']}-{row['Density']}",  # Just show the count
        fontsize=9,
        ha='center',
        va='center',
        color='black',
        # fontweight='bold',
        # bbox=dict(facecolor='white', alpha=0.7, boxstyle='round,pad=0.3')
    )
    for idx, row 
    in merged_gdf.iterrows()]
adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black', lw=0.5))

# Add title and remove axes
plt.title('Kenya Health Facilities Density by County', fontsize=15)
plt.axis('off')

# Show the map
plt.tight_layout()
plt.show()

Disclaimer: For information only. Accuracy or completeness not guaranteed. Illegal use prohibited. Not professional advice or solicitation. Read more: /terms-of-service

Footnotes / Citations / References

  1. Archive - Licenced HealthFacilities for the year 2025↩︎

  2. Explainer: Six levels of hospitals and services they offer↩︎

  3. Healthcare in Kenya↩︎

Reuse

GNU GENERAL PUBLIC LICENSE v3.0(View License)

Citation

BibTeX citation:
@misc{kabui2025,
  author = {{Kabui, Charles}},
  title = {Licenced {Hospitals,} {Clinics} and {Dispensaries} in
    {Kenya,} {Year} 2025},
  date = {2025-03-20},
  url = {https://toknow.ai/posts/licenced-hospitals-clinics-and-dispensaries-in-kenya-year-2025/index.html},
  langid = {en-GB}
}
For attribution, please cite this work as:
Kabui, Charles. 2025. “Licenced Hospitals, Clinics and Dispensaries in Kenya, Year 2025.” https://toknow.ai/posts/licenced-hospitals-clinics-and-dispensaries-in-kenya-year-2025/index.html.