Survey Data on Chronic Disease Burden and Multimorbidity in Kenya

This dataset is derived from the Kenya Demographic and Health Survey and contains harmonized individual and household level information on sociodemographic characteristics living conditions health behaviors healthcare access and chronic disease status among middle aged and older adults in Kenya. The analytic sample includes women aged 40 to 49 years and men aged 45 to 54 years consistent with age eligibility criteria for noncommunicable disease modules in the survey.

The dataset includes core demographic variables such as sex age county of residence and urban or rural location alongside socioeconomic indicators including education literacy employment status employment type and household wealth quintile. Household composition and living environment are characterized through measures of household size living arrangement dwelling type floor material cooking fuel and access to improved water and sanitation facilities.

Health system access and financial protection are captured through variables on health insurance coverage out of pocket health expenditure and forgone medical care due to cost. Psychosocial and wellbeing indicators include self reported emotional distress financial stress health cost related stress sleep problems and experience of domestic violence among women. Behavioral and social factors such as alcohol use religion and ethnicity are also included.

Chronic disease status is measured using self reported diagnoses of hypertension diabetes heart disease and asthma. These conditions are aggregated into multiple indicators of disease burden including a count of chronic conditions presence of any chronic condition and a multiclass measure distinguishing no condition one condition and two or more conditions. Nutritional status is assessed using body mass index and standardized body mass index categories. Functional health is further captured through self reported disability and self rated health status.

All variables are accompanied by sampling weights that account for the complex survey design and enable nationally representative estimates. The dataset is suitable for descriptive epidemiology inequality assessment and predictive modeling of chronic disease burden and multimorbidity in population based research.