Dynamic Simulation Model for Socio-economic Impact of E-health Tools in Cervical Cancer Management in Kenya

Abstract

Apart from cost effectiveness, e-health tools optimize cervical cancer management. An understanding of the impact of e-health interventions will inform policy. The aim of this study was to assess the clinical and social economic impact of e-health in Cancer management in addition to traditional vaccination and screening interventions. System Dynamics(SD) modelling was applied. Ethical clearance was sought from relevant research bodies and study institutions. The study comprised of four phases. In Phase onequalitative evaluation to establish experiences, opportunities and challenges- Cervical Cancer managers in Kenya was done. 33 Cervical cancer managers drawn from 4 provincial hospitals and 2 main National public referral hospitals were interviewed .Their responses were audio recorded, transcribed verbatim ,content analyzed in emerging themes.Four themes related to; Patients, health care providers, Health facility and Information Technology were identified. Mobile phones were highly accessibility. Negative attitudes towards screening procedure and Cervical Cancer patients need urgent attention.In phase 2, a cross sectional survey to establish; the extent of use of e-health tools by Cervical Cancers clients, the characteristics of patients and identify barriers faced in internet use was done. Stratified random sampling of 199 Cervical Cancer clients from two main National referral hospitals. A structured questionnaire was administered. Low level (7.5%) use of the internet was reported. The main barriers to internet use were; lack of IT knowledge, no access to computer and high cost at cyber. In phase 3SD Simulation model to evaluate possible effects of vaccination, screening and e-health tools wad developed using iThink™ version 10.0.6. Virtual experiments to assess socio-economic impact of various interventions were done. Secondary vaccination was found to have the highest impact followed by , primary vaccination and screening . e-health would be a complementary measure.In Phase 4, Differential equations for population in each stage of diagnosed and undiagnosed cervical cancer were generated. Base Year (2010).Co efficients of model were estimated using published data. Equations were run through Matlab TM Output graphs showing the rate of change of population in each stage of disease were generated. The SD model may act as an informed policy guide in management of Cervical Cancer in Kenya