"Mendelian Randomization" Approach in Economic Assessment of Health Conditions.

Vipin Gupta,Mohinder P Sachdeva,Gagandeep Kaur Walia

doi:10.3389/fpubh.2019.00002

Abstract

The increased prevalence of non-communicable chronic diseases (NCDs) is reflected in the rising economic burden of health conditions. Observational studies conducted in health economics research are detecting associations of NCDs or related risk factors with economic measures like health insurance, economic inequalities, accessibility of jobs, education, annual income, health expenditure, etc. The inferences of such relationships do not prove causation and are limited to associations which are many times influenced by confounding factors and reverse causation. Mendelian randomization (MR) approach is a useful method for exploring causal relations between modifiable risk factors and measures of health economics. The application of MR in economic assessment of health conditions has been started and is producing fruitful results.

Highlights

The world is facing epidemiological transition that has been attributed the rise of non-communicable chronic diseases (NCDs) and shifted the goal-post from fatal communicable diseases [1]
Among the South Asian countries, India and Pakistan, had received the bulk of development assistance for health (DAH) which is a financial resource transferred from development agencies to low- and middle-income countries (LMICs) primarily for maintaining or improving health [4, 5]
The objective of this paper is to review the use of Mendelian randomization (MR) approach in studying health economic aspects of NCDs

Summary

Introduction

The world is facing epidemiological transition that has been attributed the rise of non-communicable chronic diseases (NCDs) and shifted the goal-post from fatal communicable diseases [1]. Testing of such reported associations fail in RCTs. The analytical approach of Mendelian randomization (MR) can be very helpful in preventing failures of RCTs by providing strong causal evidence between exposure and outcomes in comparison to typical observational studies. We need alternative approaches for testing causal relationships in research related to health economics that can overcome the potential biases of observational designs and provide reliable evidence for causal associations.

Objectives

Results

Conclusion