Joseph Kanini

Joseph Kanini

Student Bio

Noel Kanini Joseph: MSc. Biometry, 2020 and BSc Economics and Statistics (Statistics Major), 2015. She was a recipient of VLIR-OUS Biostatistics Team Project funding to support her MSc. project. While a graduate student at the University of Nairobi, She hosted a discussion to aid the understanding of the appropriate use of Demographic Health Surveys data at the school of mathematics and attended a training on modelling survival for cancer outcomes. Her main research interest is understanding health outcomes particularly within the domains of health equity, access to care and determinants of health in Kenya and beyond. For her MSc thesis, she sought to understand the spatial distribution of hypertension and diabetes prevalence in Kenya at counties (health planning units in the country) and the associated risk factors under the guidance of Prof. Samuel Mwalili and Dr Nelson Owour. Noel works as a research assistant (Epidemiology and Statistics) at KEMRI-Wellcome Trust Research programme in Population Health Unit Department.

Project Summary

Project Title : Small area estimation with an application to bivariate spatial modelling of hypertension and diabetes prevalence in Kenya


Comorbidity of Hypertension and diabetes leads to significant risks of mortality and other non-communicable diseases (NCDs) such as heart attacks and strokes. Kenya, like many low and middle-income countries (LMICs), faces a rapid increase in non-communicable diseases (NCDs) burden. However, sub-national burden profiles to inform health policy at the county level; the current health planning units are implausible due to small sample sizes from the existing NCDs data sources in Kenya. The main objective of this study was to determine the distribution of hypertension and diabetes disease prevalence at county units in Kenya using small area estimation methods. Methods: Data from a nationally representative Kenya STEPwise survey for non-communicable diseases risk factors (STEPs-2015) was used. The survey collected health information (physical and biochemical measurements), risky behaviour and demographic indicators related to NCDs for 4,500 persons aged 18-69 years. Multivariate conditional autoregressive models that account for spatial autocorrelation and dependence between diseases (latent effects) were used to estimate the county-specific prevalence of hypertension and diabetes. Simple multivariate improper CAR, improper multivariate CAR, proper multivariate CAR and M-model latent effects were explored. A mixed-effects multinomial logistic regression model was t to identify the macro-risk factors of hypertension and diabetes in Kenya. Results: The M-model was selected as the best fit based on DIC. Substantial geographical variation in the prevalence of hypertension ranging from 9% in Wajir county and 54% in Nyeri county while diabetes ranged from 0.1% in Narok to 8.1% in Makueni were observed. Overall, 47% (22 counties) and 36% (17 counties) had hypertension and diabetes prevalence estimates above the national burden, 26.4% and 2.7% respectively. Notably, Mombasa, Kiambu, Embu and Nyeri had a substantial burden of both hypertension and diabetes. High cholesterol, central obesity, age, BMI, harmful alcohol intake and high sugar intake were significantly associated with hypertension and diabetes. Conclusion: The county-specific prevalence estimates provide the first evaluation of hypertension and diabetes burden that policymakers can use to inform interventions aimed at the prevention and treatment of NCDs in Kenya. Implementation of comprehensive screening programs and awareness building for NCDs control is crucial in reducing hypertension and diabetes burden in Kenya.