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Covid and the public health math

The world has recognised that the total number of affected persons is of great importance, not just the disease’s severity, in determining how to respond

Published: 18th March 2020 04:00 AM  |   Last Updated: 18th March 2020 02:11 AM   |  A+A-

amit bandre

The WHO has finally declared Covid-19 a pandemic, after days of hoping it could be contained through effective national actions. While this novel virus has much lower death rates than its cousins (SARS: 9.5%; MERS: 34%), it has already claimed many more lives than those, even at a much lower death rate of around 3%. This is because of its high infectivity rate, with many more persons receiving the virus from a person harbouring it. (3% among 1,00,000 is more than 10% among 1,000.) The world has recognised that the total number of affected persons is of great public health importance, and not just the severity of the disease, in determining how to respond. In scientific terms, this is called the ‘population attributable fraction’. Simply put, even a moderate risk in a large fraction of the population will yield more cases than severe risk in a small fraction of the population. Covid has emerged as the winner in the killer stakes when pitted against its more aggressive cousins. 

This perspective has been previously emphasised in assessing the risk of chronic diseases like cardiovascular diseases or cancer in populations. However, popular perception and even policymaker attention tend to focus on the smaller ‘high risk’ fraction of the population in case of those chronic diseases. This arises from a false perception of risk as an ‘all or none’ phenomenon rather than as a continuous graded relationship. Most physiological variables, like blood pressure, body weight and blood sugar have a gradient of health and disease risk across a continuous upward slope. With systolic blood pressure, for example, risk of heart disease, stroke and kidney failure starts progressively rising above 110 mm of mercury. And similarly, for diastolic pressures above 70 mm. Systolic blood pressure of 180 carries more risk than 170, which carries higher risk than 140, which still poses greater risk than 120. Distinctions of ‘hypertension’ and ‘normal blood pressure’ are arbitrary in that context and are merely terms of clinical convenience. The continuous relationship exists for non-physiological variables too, like number  of cigarettes smoked in a day (rising harm) or number of helpings of fruits and vegetables consumed in a day (rising benefit).  

Most of the cases of coronary heart disease in any population arise from the large segment of the population that lies in the mid-range of the risk factor rather than the narrow segment at the top with a fewer number of persons. This, despite the fact that the persons at the top range have higher individual risk than those at the middle. So the burden of risk in the population, for coronary heart disease differs from the quantum of risk at the individual level. For an analogy, a large departmental store that sells many items at a small profit margin makes higher overall profit than a small boutique that sells fewer items at higher profit margin. Further, the risk of chronic diseases such as heart disease in any individual is not determined only by the level of just a single risk factor like blood pressure but also the manner in which multiple risk factors, each with their own risk gradients, interact with other risk or protective factors to predicate the cumulative future risk of disease for that person.

These factors include age, gender, diabetic and smoking status, obesity, blood cholesterol and its subfractions. This ‘absolute’ risk varies from individual to individual and is determined from risk stratification charts. At any level of a single risk factor, the levels of co-existing risk factors also matter. A smoker’s risk of a heart attack rises multifold compared to a non-smoker, depending on the number of cigarettes and years smoked, at the same level of high blood pressure. A person with diabetes experiences cardiovascular risk at lower levels of blood pressure than a person who is non-diabetic.  
Estimation of ‘absolute’ or comprehensive risk helps to decide on thresholds for initiating drug therapy or deferring it in favour of non-drug measures only, such as diet and exercise. Those at high risk get drug therapy straight away. Those at low absolute risk get non-drug therapy.

Those at moderate risk have an initial trial of non-drug measures and drug therapy is initiated if that does not reduce risk by itself. Health system resources, often scarce, can be optimally directed to those at higher absolute risk. Clinical management is also directed towards treating all the modifiable contributors to the overall risk. The whole person, with all the personal characteristics contributing to risk, will have to be carefully looked at instead of mechanically treating high blood pressure as a disease of the right arm. Similarly, in case of Covid, the overall risk of serious complications depends on a person’s age, nutritional status, smoking history, co-existent clinical conditions like hypertension, diabetes, heart or respiratory disease.

If there is a large-scale spread of the epidemic, there will be a need to prioritise patients who require admission to hospitals and those who require intensive care. Such decisions are best guided by comprehensive risk assessment, rather than one parameter alone. Such composite risk scores can be developed based on accumulating experience. Artificial intelligence can be utilised to develop predictive algorithms to provide early alerts of potential risk of serious illness. 

Healthcare providers have to get comfortable with the idea of estimating comprehensive absolute risk as a composite measure for grading risk and prognostication in many clinical conditions ranging from Covid infection to coronary heart disease. The idea of grading and treating single risk factors is not only reductionist but also a defective therapeutic approach. Both in public health and in clinical medicine, a holistic yet quantitative risk prediction is possible and useful. 

Dr K Srinath Reddy
President, Public Health Foundation of India. Adjunct Professor of Epidemiology at the Harvard T H Chan School of Public Health. Views are personal Email: ksrinath.reddy@phfi.org

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