Measuring Impact: Econometric Evaluation of Health Policy Training Course

Introduction

In an increasingly evidence-driven world, the effective design and implementation of health policies hinge on a rigorous understanding of their true impact. Policymakers and public health professionals often introduce interventions with the best intentions, but without robust evaluation, it's challenging to discern what truly works, for whom, and why. Econometrics offers a powerful toolkit to move beyond simple before-and-after comparisons, allowing for the isolation of causal effects and providing a solid foundation for evidence-based decision-making in public health.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of econometric methods for evaluating health policies. From mastering the principles of causal inference and the challenges of selection bias to applying cutting-edge quasi-experimental designs, you will gain the expertise to rigorously assess policy effectiveness, unintended consequences, and distributional impacts. This empowers you to conduct impactful evaluations, contribute to the optimization of health systems, and advocate for policies that genuinely improve population health and well-being.

Target Audience

  • Health policymakers and planners in government, ministries of health, and public health agencies.
  • Monitoring and Evaluation (M&E) specialists in health programs and organizations.
  • Public health researchers and epidemiologists.
  • Economists and data analysts working in the health sector or related fields.
  • Academics and graduate students (Master's and PhD) in health economics, public health, health policy, or applied econometrics.
  • Professionals from international development organizations and NGOs focused on health outcomes.
  • Regulatory bodies overseeing healthcare markets and interventions.
  • Consultants specializing in health program evaluation.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the fundamental principles of causal inference and its importance in health policy evaluation.
  • Grasp the common challenges in evaluating health policies, including selection bias and confounding factors.
  • Analyze various econometric methods for assessing the causal impact of health interventions.
  • Comprehend the design and application of quasi-experimental approaches (e.g., Difference-in-Differences, Regression Discontinuity).
  • Evaluate the effectiveness of health policies on various outcomes, such as health behaviors, health status, and healthcare utilization.
  • Develop practical skills in applying econometric techniques to real-world health policy data using statistical software (e.g., R, Python, Stata).
  • Navigate the complexities of data requirements, data quality, and model specification in health policy evaluation.
  • Formulate robust, evidence-based recommendations for health policy improvement and resource allocation.

Course Content

  1. Introduction to Health Policy Evaluation and Causal Inference
  • What is health policy evaluation? Purpose, scope, and types (formative, summative, process, outcome)
  • The fundamental problem of causal inference: counterfactuals and potential outcomes
  • Why econometrics is essential for causal evaluation
  • Challenges in health policy evaluation: selection bias, confounding, reverse causality
  • Logic models and theory of change for health interventions
  1. Data for Health Policy Evaluation
  • Sources of health data: administrative records (claims data, electronic health records), surveys (household, patient), clinical trial data
  • Types of data: cross-sectional, time series, panel data, repeated cross-sections
  • Data cleaning, preparation, and management for econometric analysis
  • Addressing missing data and measurement error in health datasets
  • Ethical considerations and data privacy in health research
  1. Basic Econometric Models for Evaluation
  • Review of Ordinary Least Squares (OLS) regression: assumptions and interpretation
  • Dummy variables and interaction terms for policy effects
  • Interpreting coefficients in the context of policy interventions
  • Limitations of simple before-and-after comparisons and cross-sectional regressions for causal inference
  • Measuring health outcomes and healthcare utilization as dependent variables
  1. Panel Data Models for Policy Evaluation
  • Advantages of panel data: controlling for unobserved heterogeneity
  • Fixed Effects (FE) models: identifying within-unit policy impacts
  • Random Effects (RE) models: assumptions and estimation
  • Pooled OLS vs. FE vs. RE: model selection
  • Applications in evaluating state-level health reforms or hospital interventions
  1. Difference-in-Differences (DiD) Estimation
  • The core assumption of DiD: parallel trends
  • Constructing treatment and control groups and defining the policy period
  • Estimating DiD models: two-way fixed effects and interactions
  • Visualizing DiD results and checking the parallel trends assumption
  • Extensions of DiD: staggered adoption, multiple time periods
  • Case studies of DiD in health policy (e.g., impact of insurance expansions)
  1. Instrumental Variables (IV) and Regression Discontinuity Design (RDD)
  • Instrumental Variables (IV): Addressing endogeneity and omitted variable bias
    • Identifying valid instruments: relevance and exogeneity
    • Two-Stage Least Squares (2SLS) estimation
    • Weak instruments and overidentification tests
    • Applications in health (e.g., health behavior, healthcare access)
  • Regression Discontinuity Design (RDD): Exploiting policy thresholds
    • Sharp vs. fuzzy RDD
    • Graphical analysis and estimation
    • Local average treatment effect (LATE)
    • Applications in health (e.g., impact of eligibility cutoffs for health programs)
  1. Matching Methods and Propensity Score Analysis
  • Selection bias: observable vs. unobservable characteristics
  • Propensity Score Matching (PSM): balancing covariates between treatment and control groups
  • Different matching algorithms: nearest neighbor, caliper, kernel
  • Checking balance and sensitivity of PSM results
  • Inverse Probability Weighting (IPW) and Doubly Robust Estimation
  • Applications in evaluating health interventions where randomization is not feasible
  1. Discrete Choice Models in Health Policy
  • Modeling binary outcomes: Logit and Probit models (e.g., likelihood of insurance uptake, healthy behavior adoption)
  • Interpreting odds ratios and marginal effects
  • Count data models: Poisson and Negative Binomial regression (e.g., number of doctor visits, hospitalizations)
  • Two-part and hurdle models for healthcare expenditures
  • Multinomial and ordered choice models for multiple health outcomes
  1. Policy Simulations and Forecasting
  • Using estimated econometric models for policy simulations
  • Predicting the effects of hypothetical policy changes
  • Forecasting future health outcomes under different policy scenarios
  • Incorporating uncertainty into policy simulations
  • Limitations of forecasting and scenario analysis
  1. Communication and Dissemination of Evaluation Findings
  • Presenting complex econometric results to non-technical audiences
  • Writing effective policy briefs and evaluation reports
  • Visualizing data and key findings for impact
  • Engaging stakeholders throughout the evaluation process
  • Ethical considerations in reporting and using evaluation results for policy.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 

Measuring Impact: Econometric Evaluation Of Health Policy Training Course in Kenya
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