Methodology Framework
Health equity requires transparent, verifiable methodologies. All methods, data sources, and calculations are publicly documented and independently reproducible.
Global Health Targets
The platform supports global targets to ensure healthy lives and promote well-being for all. It maps where health infrastructure gaps are most acute so resources can reach the populations with the greatest need. It also makes within-country disparities visible at a glance, particularly for rural and marginalised populations.
The Priority Gap Score
The Priority Gap Score is a simple score from 0 to 100 that measures health infrastructure inequity within each county. Higher scores indicate greater service gaps and stronger claims on resource allocation.
How to read the score:
- 0–29: Relatively equitable resource distribution
- 30–49: Moderate infrastructure gaps
- 50–69: Significant gaps requiring intervention
- 70–100: Severe gaps requiring urgent resource allocation
Turkana (Priority Gap Score 92) and Mandera (Priority Gap Score 91) are the most underserved counties. Nairobi (Priority Gap Score 40) has more facilities per person and lower poverty, so its score is lower.
Score Components
The Priority Gap Score combines three dimensions into a single transparent score. Anyone can verify the calculation with pen and paper:
1. Vulnerability (30%)
Proportion of the county population living below the poverty line. Higher poverty reduces a household's ability to afford transport, consultation fees, and treatment.
2. Physical Access (40%)
Mean travel time to the nearest mapped health facility, computed using least-cost path analysis along road and path networks. Higher travel times indicate greater geographic barriers to care.
3. Population Pressure (30%)
Population-to-facility ratio. Higher ratios mean more people sharing each facility, leading to longer wait times and reduced service quality.
Formula:
Priority Gap Score = (Physical Access × 0.40) + (Vulnerability × 0.30) + (Population Pressure × 0.30)
Each component is converted to a 0-1 scale, then the weighted sum is multiplied by 100. This is a verifiable paper calculation, not an algorithm or model.
Travel time estimation
Travel times are estimated using AccessMod, a World Health Organization (WHO)-supported tool for geographic accessibility analysis. It finds the fastest route along OpenStreetMap roads and paths: paved roads assume motorised transport; unpaved roads and paths assume walking or bicycle speeds.
This analysis was developed by researchers at the Kenya Medical Research Institute (KEMRI)-Wellcome Trust. Road data comes from OpenStreetMap. Tool: AccessMod.
Rationale for selected indicators
These three dimensions map to widely accepted categories in health-access measurement:
- Travel time and facility density measure physical accessibility, defined as the geographic capacity to reach clinical care when needed.
- Poverty rates approximate economic accessibility, defined as the financial capacity to afford transport, fees, and treatment.
- Population-to-facility ratios capture demand pressure, measuring the operational strain on existing health infrastructure.
All datasets are from publicly accessible sources, including the Kenya National Bureau of Statistics (KNBS), the Kenya Demographic and Health Survey (KDHS), the Kenya Integrated Household Budget Survey (KIHBS), WHO AccessMod, and OpenStreetMap, ensuring every input can be independently verified.
Maternal Health Access
The platform adds county-level Skilled Birth Attendance rates from the Kenya Demographic and Health Survey (2022). Skilled Birth Attendance tracks the share of deliveries attended by a trained professional. When low Skilled Birth Attendance coincides with long travel times, the result is a maternal health access desert where women face two compounding barriers at once.
Skilled Birth Attendance appears in the county detail panel alongside the Priority Gap Score, enabling identification of counties requiring maternal health investment, mobile clinics, or community health worker deployment.
Data Limitations and Future Considerations
This map relies on a validated baseline of 1,699 community-mapped facilities, representing approximately 10% of Kenya's officially registered facilities. The calculated scores therefore represent a strict minimum baseline.
Example: Elgeyo-Marakwet County has approximately 129 facilities in the official Kenya Master Health Facility List, but fewer than 20 are mapped on OpenStreetMap. This creates a score of 1 facility per 454,000 people. The gap between official records and community mapping is precisely where intervention is needed.
Every gap in the map represents an opportunity for community members to improve the data.
Procedure for reporting unmapped health facilities
- Navigate to OpenStreetMap.
- Identify the precise geographic coordinates of the facility.
- Submit a note with the following template:Missing health facility: [facility name]. This facility serves the community but is not currently mapped. Location verified by community health workers.
- Submit the record for verification.
Open Data Methodology & Integrity Sources
All scoring indicators rely strictly on publicly available baselines. Missing or unmapped facilities directly elevate physical access scores.
- Population: Kenya National Bureau of Statistics (KNBS) 2019 Census data.
- Poverty Rate: Kenya Integrated Household Budget Survey (KIHBS) 2015/16 baseline indicators.
- Facility Mapping: OpenStreetMap / IGAD Climate Prediction and Applications Centre (ICPAC) master lists. Over 1,699 verified points.
- Travel Modeling: WHO AccessMod methodologies calculating friction surfaces.
Last data refresh: June 2026. If a community dispensary is absent from this platform, community members can improve the map by reporting its location to OpenStreetMap directly atwww.openstreetmap.orgor via WhatsApp to the data stewards at+254 706 813 068.