Remember the dilemma of Alan Turing from the movie The Imitation Game where to optimize for a war winning strategy he happened to decide on the fate of American soldiers?
Sometimes people have to play God as we have to accept the harsh truth of life that “everybody cannot be saved”, unless human species advances to the level of octopus-like-aliens from the movie Arrival!
Consider a similar scenario where the war ground is replaced by a hypothetical island hit by a pandemic and soldiers dying are replaced by patients affected by the pandemic and Guliver is the fortunate (or the unfortunate) health economist who has to decide on allocation of limited healthcare budget.
Also, let’s assume that historically the government has been spending all of the budget on curing insomnia which happened to be the only disease among people there. The mortality rate accrued to insomnia under no medication was estimated to be 2% which under medication reduces to 0% . For the pandemic the mortality rate is 5%. The government is ready to fund research for the treatment though there is no certainty of success even if they pool all the funds into research for pandemic cure.
So, there is an economics to health as governments have a limited budget to spend on healthcare services and it’s crucial to optimize this budget over various healthcare facilities and technologies.
Luckily, the island has good data practices which can help Guliver (the health economist) to design a mathematical problem with an objective function and budget constraints.
There are core assumptions in this simplified version which can be dropped going further. A few of them as follows:
Different individuals can have different chances of survival
All lives are equally important for the government.
Quality of life after treatment is assumed to be same for everyone
Individuals with the same level of health are assumed to be equally happy, hence maximising aggregate social welfare measured in terms of aggregate individual utility will give the same results.
So the problem narrows down to estimating survival probability for individuals which can depend on various factors such as –
Patient-specific factors- age, gender, medical history, sanitation, heredity, social exposure
Awareness-specific factors – social distancing measures, expenditure on awareness
Political and social factors – poverty, unemployment rates
Disease and treatment specific factors – Nature of the disease (or severity levels), available treatments, adverse effects
Other factors – qualification/ capability of researchers in the country, time given for research, ability to produce new research
Once the functional relationship between the individual survival probabilities and various affecting factors is determined (which can be done using traditional or advanced mathematical and statistical approaches), Guliver can weigh up the benefits and costs of healthcare services to decide whether they are good value for money (or within the threshold of budget) in the pandemic time. These analyses will help in decision-making, which allows the government to provide the best healthcare to people in the island.
This is a major component of a Health Economics and Outcomes Research (HEOR) research or job. The market for HEOR was 0.7 billion in the year 2019, and is predicted to continue to grow in the coming decade. The market size will rise even during or after the current COVID-19 pandemic time, as it is currently most essential than ever to conduct value-based analysis and evidence based research.
There will be a growing need for HEOR in emerging economies as the health technology assessment (HTA) systems sprout or develop in these countries. Regarding the future of HEOR- The emerging use of machine learning, real world evidence data, digital healthcare, and precision medicine is initiating a new wave of research into HEOR.
Let’s prioritize evidence-based research for safe, affordable and efficient healthcare delivery!
Author – Kunal Hriday