This document presents an Extended Cost-Effectiveness Analysis (ECEA) of the Empower Health intervention compared to usual care in Kenya, following the methodology developed by Verguet et al. (2016). ECEA extends traditional cost-effectiveness analysis by incorporating: Financial risk protection (catastrophic health expenditures and poverty averted) Distributional consequences across wealth quintiles Equity weights to value health gains among the poor Insurance value (risk premium reduction) Health insurance coverage and its impact on out-of-pocket costs

This document presents an Extended Cost-Effectiveness Analysis (ECEA) of the Empower Health intervention compared to usual care in Kenya, following the methodology developed by Verguet et al. (2016). ECEA extends traditional cost-effectiveness analysis by incorporating: Financial risk protection (catastrophic health expenditures and poverty averted) Distributional consequences across wealth quintiles Equity weights to value health gains among the poor Insurance value (risk premium reduction) Health insurance coverage and its impact on out-of-pocket costs

This file presents an Extended Cost-Effectiveness Analysis (ECEA) of the Empower Health intervention compared to usual care in Kenya, following the methodology developed by Verguet et al. (2016). ECEA extends traditional cost-effectiveness analysis by incorporating: 1. Financial risk protection (catastrophic health expenditures and poverty averted) 2. Distributional consequences across wealth quintiles 3. Equity weights to value health gains among the poor Insurance value (risk premium reduction)

This analysis evaluates the distributional impact of Empower Health intervention compared to usual care across wealth quintiles, regions, and sex in Kenya. The analysis includes: Health benefits: DALYs averted (disability-adjusted life years) - discounted at 3% Life years gained: Years of life saved (mortality benefit only) - NOT discounted (undiscounted) Cost burden: Incremental costs by equity stratifiers - discounted at 3% Concentration curves for health benefits, life years, and costs Cost-effectiveness ICERs by equity metric Kenya GDP per capita = $2,200 (WTP threshold) Important Note on Concentration Curve Interpretation: Curve below diagonal = Positive C-index (+) For Health Benefits/Life Years: PRO-RICH (richer gain more benefit) - INEQUITABLE For Costs: PROGRESSIVE (richer pay more) - EQUITABLE Curve above diagonal = Negative C-index (-) For Health Benefits/Life Years: PRO-POOR (poorer gain more benefit) - EQUITABLE For Costs: REGRESSIVE (poorer pay more) - INEQUITABLE

This analysis evaluates the distributional impact of Empower Health intervention compared to usual care across wealth quintiles, regions, and sex in Kenya. The analysis includes: Health benefits (DALYs averted) and cost burden by equity stratifiers Concentration curves for health benefits and costs Cost-effectiveness ICERs by equity metric

This report presents a the meta-analysis of ideal cardiovascular health (CVH) metrics across African populations using real study data. The analysis synthesizes data from multiple studies to estimate pooled prevalence rates for each component of the American Heart Association’s Life’s Simple 7 (LS7) framework. The report includes data cleaning, descriptive statistics, meta-analytic models, and visualizations

This file processes PSA results from the simulation. It takes in the results dataframes from the 1000 PSA runs.

This is a report of the analyses of patients' SBP change by level of engagement with the program. This can be used to construct usual care arm of the intervention. Please review and send me your feedback. Thanks. James

This script presents the regressions from the steps dataset-Predicting systolic blood pressure

This script presents the results of the Microsimulation model. To be used for model checking as development proceeds

This script is for processing the Empower Health dataset for baseline mean systolic blood pressure comparisons with STEPS dataset

This script shows the steps followed in processing the Kenya STEPS dataset and creating subsets for comparison with the Empower Health Dataset

The following R script compares the statistics of two datasets: the Medtronic dataset and the STEPS dataset. It performs a Two-Way ANOVA to examine the effects of age group and dataset on blood pressure, and their interaction. The script also visualizes the results using boxplots and bar plots, computes mean blood pressure for each age group and dataset, and saves the results to CSV files.

Interactive map