Five (5) Types of Disability Data and Their Relevance to Inclusive Development

Prevalence data tells us how many people in a population have a disability and the kind of disability they have. The Kenya National Bureau of Statistics (KNBS) collects this data through national censuses and surveys. The 2019 Kenya Population and Housing Census estimated that about 2.2% of Kenyans have a disability, although the real figure is significantly higher because the questions that were asked and how they were asked left many persons with disabilities out.

Why does this matter? Prevalence data determines how much funding is allocated to disability services, how many inclusive schools or hospitals are there, and whether your county even acknowledges you in its development plan. If you are not counted, you are not planned for.

Functional data goes deeper, it looks at what a person can do in daily life, can they see well enough to read? Do they have difficulty walking long distances? Can they communicate without assistance? This kind of information, when gathered well, helps planners to shift from labels and understand the barriers experienced.

Past census tools used to capture functional data were not designed with the realities of Kenyan disability experiences in mind. The result was a significant undercount millions of Kenyans with disabilities were missed entirely because the questions did not reflect their experiences.

This is why the push for locally contextualised, community-informed data collection tools matters so much. When disability organisations, communities, and experts are involved in designing how questions are asked, the data that comes out is far more accurate and far more useful. Functional data gathered the right way helps match support to actual need, rather than assumptions about what disability looks like.

Disability does not exist in isolation. Socioeconomic data tells us how disability intersects with education, employment, gender among other things. Persons with disabilities are more likely to be unemployed, less likely to complete secondary education, and more likely to live below the poverty line not because of their disability alone, but because of systemic exclusion.

When this data is disaggregated meaning broken down by disability type, gender, age, and county it reveals where the most urgent gaps are. For example, data showing that women with disabilities face higher rates of gender-based violence is critical for designing the right protective services in the right places.

Service access data tracks whether persons with disabilities are actually getting services or products. For example, health services, education, assistive devices, legal aid, or social protection programs like the Inua Jamii cash transfer. It is one thing to have a program exist but it is another thing for it to actually work for you.

This data helps advocates and governments identify existing barriers, whether that is a hospital that lacks adjustable beds, a school with segregated classrooms, or a social protection program that requires paperwork impossible to access in rural areas.

Lived experience data gathered through interviews, focus groups, testimonials, and community consultations captures what numbers alone cannot. It tells the story behind the statistic. It gives a face and a voice to the barriers and triumphs of persons with disabilities in Kenya.

This type of data is especially powerful in advocacy. When a policymaker hears that a visually impaired student in Kisumu had to drop out of university because learning materials were never provided in Braille, it drives urgency in a way that percentages rarely can.