WHO's Data: How Reliable | Gist of Rajya Sabha TV / RSTV (now Sansad TV) - UPSC PDF Download

Introduction

India has raised strong objections regarding the mathematical models used by the World Health Organization (WHO) to project excessively high death estimates linked to the coronavirus pandemic. Last week WHO had released a report that said 14.9 million people were killed either by Covid-19 directly or due to the pandemic’s impact on health systems and society, which included 4.7 million fatalities in India. India has said that the validity and robustness of the models used and methodology of data collection by WHO are questionable. As per the official data released by Union Health Ministry India reported 5 Lakh 24 Thousand 93 deaths till 8 AM on 9th May 2022. India had also informed WHO that in view of the availability of authentic data published through Civil Registration System (CRS) by the Registrar General of India (RGI), mathematical models should not be used for projecting excess mortality numbers for India.

Significance of data in handling pandemic

• The need for timely data sharing lies in its utility through analysis and its power resides in its interpretation. The current pandemic is a prime example.
• It has revealed the need for continuous and repeated tracking of case numbers, fatalities and recoveries. The epidemiological concept of flattening the curve and its predictions are results of data analysis and modelling.
• The first wave showed us how certain underlying illnesses, comorbidities, could impact the outcome of the disease.
• Understanding testing adequacy or lack thereof allows us to measure our preparedness, prognostic versus diagnostic ability, and shape our responses to identify, manage, and care for new cases.
• Epidemic outbreak data like case data, medical and treatment data can be used to understand disease pathogenesis and severity.
• Genome sequencing surveillance helps identify and track viral genome sequence variants in real time and the evolution of the virus.
• Data is most valuable when you compare it to a control. Hence, background data defining underlying characteristics of the resident population including demographics, movement data, health infrastructure, water supply system and hygiene infrastructure are critical.

Vitality of data to governments in decision-making

• The pre-requisite for evidence-based policy is that the data must be trustworthy, and it depends upon the quality of the data and the quality of the professional statisticians.
• Evidence-based policy-making (EBP) assists in making decisions about projects, programmes and policies, by placing the best available evidence from research conducted at the heart of policy development and implementation.
• It also makes explicit what is known through scientific evidence. In contrast to opinion-based policy-making, evidence-based policy-making needs an evidence base at all stages in the policy cycle to define issues, shape agendas, make choices of action, identify options, deliver them, and monitor their impact and outcomes.
• Basically, it is a set of methods which informs the policy process, rather than aiming to directly affect the eventual objectives of the policy.
• Thereby, EBP advocates a more systematic, rational and rigorous approach to produce better outcomes.

Way forward

• Part of the challenge in evidence-based decision-making pertains to the standardisation of data collection, curation, annotation and the seamless integration of data analytics pipelines for outbreak analytics.
• Thus, ensuring data availability and quality under operational constraints is critical.
• The use of data standards instils consistency, reduces errors and enables transparency.
• Embedded in the idea of data sharing lies the concept of data security and confidentiality.