Recent news headlines claimed that the number of Covid-19 cases in India might rise to 13 lakh by May if no interventions are put in place to slow its spread. Where do these estimates come from?
The novel coronavirus has brought into focus the work of a previously little-known community of researchers – epidemiologists. They are, in a sense, disease detectives who ask questions like: Who is sick? When and where did they get sick? What are the symptoms?
Epidemiologists look for the causes of diseases, both infectious and non-communicable; work out where the outbreak started; and identify people at risk by tracking details of those affected – like the patient’s age, for instance. Such estimates have allowed us to understand, for instance, that Covid-19 likely originated in a market in the Chinese city of Wuhan and those with existing health conditions like diabetes are at greater risk.
Epidemiologists also make recommendations on how to intervene or control the spread of a disease. An example of an intervention that stems from epidemiologists’ data is lockdown, which can slow down the spread of a virus.
Ever since the coronavirus pandemic broke out, we are being bombarded with numbers and data has become a part of mainstream discussions. Numbers are crucial for the simple reason that if we can’t measure, we can’t manage.
At a basic level, epidemiological data does three things. First, it describes the disease and affected populations. Where do these people live? What age group or gender was more affected? This allows them to undertake the second step – predict whether the outbreak will affect other populations with similar demographics. Third, epidemiologists identify causal relationships – for example, smoking can cause lung cancer.
Why data is key
What distinguishes epidemiologists from other researchers is that they look at how social, political, and environmental factors interact to affect disease risk. Policymakers and governments rely on such information, generated through a method known as modelling, while taking decisions.
For instance, projections by a team at Imperial College London stated that if no interventions were implemented in the United States, Covid-19 could kill 2.2 million. This estimate came from a mathematical model based on available data and certain assumptions. These models change constantly as new data becomes available.
Even when the immediacy of a public health crisis subsides, public health data is vital for any country. For example, ongoing research in India shows that health needs in some areas are shifting from infectious diseases like tuberculosis to chronic conditions like cardiovascular disease and diabetes.
Since the symptoms of cardiovascular disease appear long before they manifest into disease, having this data allows epidemiologists to ask questions like: What are the most common symptoms shown by at-risk people? Do they smoke? Which specific gender or socioeconomic group is most at risk? Where do they live? What is their diet?
These questions can be answered and addressed with data and helps policymakers prioritise the allocation of funding and policy development.
Building a robust system
A major takeaway from the coronavirus pandemic is that investment in India’s health system must happen now. If we have robust public health data, epidemiologists can understand the underlying causes and contribute to disease prevention.
In the coming years, India needs to invest in standardised and well-organised systems to collect public health data at both, the national and state levels, given the country’s diverse settings and populations. For example, Uttar Pradesh has different health needs from Kerala, owing to a number of factors such as air pollution in winter and education levels.
However, we need national data too. A centralised repository of key diseases and risk factors, collected over a long period of time, will allow India to understand and ameliorate its overall disease burden.
Another key bit of data majorly lacking in India is cause of death. In the 2016 Global Burden of Disease Study, which focussed on India, researchers found that out of the total deaths in India in 2016, two-thirds were premature and only one-third was due to disability. But unfortunately, we have little data on what is causing these premature deaths, and so we take no action to prevent them.
Importantly, data is also essential for reducing India’s widespread health inequities. One of the biggest public health successes of the last decade was identification of diarrhoea as a major contributor to infant mortality in India, and disproportionately affected the poorest. Once we knew this, we knew how to intervene – oral rehydration solutions saved millions. Improved data is vital to protect population health in India.
This is not impossible – we have successfully done this with polio in the past. A disease that was once firmly entrenched in India has been eradicated. This happened when the government took ownership of the initiative at the central, state, and district levels. It set up disease surveillance systems that surpassed international standards and collected good quality data. This means that when motivated enough, the government can fine-tune its strategy to reach high-risk groups and marginalised communities.
In short, epidemiological data is not only critical during a pandemic but essential to minimise the larger healthcare issues this country faces. Developing this centralised and state repository of health records will be a monumental task, but there are no shortcuts. Having a suitable public health data system in place can considerably reduce the number of people that become patients. It will also be the cornerstone in reducing the impact of the next inevitable pandemic.
Tishya Venkatraman is a PhD candidate at Imperial College London’s Department of Primary Care and Public Health.
