Data is critical to the effectiveness of disease surveillance initiatives

Dr Vikram Venkateswaran, Member-Healthcare Working Group, IET Future Tech Panel in an interaction with Viveka Roychowdhury talks about his company’s disease surveillance project and highlights that the project is ambitious approach at looking at disease surveillance from the view of unstructured data

What difference has the COVID pandemic made to the practice of disease surveillance in India? What has been the quantum of investment in such activities?

COVID-19 has been a big driver for the growth of digital health in India. Initiatives like telemedicine have received a big boost and this has enabled the growth of digital platforms for consultation and growth. Disease surveillance has also seen a big boost, with the tracking of COVID infections, hospitalisation, RTPCR tests and serological studies during this period. Also, the vaccination program along with the digital certificates have also shown how preventive surveillance can be tracked nationally. The government had earlier earmarked almost 35,000 crores for the programs including surveillance and vaccination. It has spent almost 20,000 crores so far on these activities. Similarly, the investments in the telemedicine platforms have increased significantly and today the telemedicine market stands at almost USD 1.3 billion.

How effective have surveillance programmes for diseases like TB, malaria, polio, etc been in India? What have been the gaps and learnings that can be adopted to prevent future outbreaks?

India started the IDSP, Integrated Disease Surveillance Program with funding from the World Bank in 2004. This was a 10-year program to track 33 disease conditions in India. The key drivers for this program were the Cholera outbreak in Delhi in 1988 and the Plague outbreak in Surat in 1994. Now this program has really scaled over the years and today is run nation wide under the National Center for Disease Control in Delhi. While the program is remarkable in many ways and has helped identify and control many conditions in various parts of the country, including tackling the Nipah Virus outbreak in Kerala some years ago there are certain gaps that are yet to be filled. We have been very successful in managing Polio, and  before that we eradicated Small Pox, moderately successful in managing conditions like TB, but a lot more needs to be done to manage conditions like Malaria which are cyclical and have been affecting us for years. Even during COVID the IDSP managed to track and stop the spread of Acute Diarrheal Infection in Sangrur in Punjab in September 2021, which shows the program is effective but definitely needs an upgrade. Here are some key points to consider:

• The program is not real-time and there is a lag between collecting the information and the actions taken
• These are mainly due to the manual entry that is done at Primary Health Centres, Labs, District Hospitals and other sources. Forms like P Forms, L Forms Admissions Forms are manually field scanned and then sent to Delhi for analysis
• Leveraging smartphones the program did create a mobile application for tracking the disease condition, but the implementation has not gone according to plan and leaves a lot to be desired

What are the benefits of a predictive and scalable planning model for healthcare infrastructure? What would be the scale of funding required and what are the models to create in infrastructure?

A country like India cannot afford to deliver care uniformly across the entire length and breadth of the country. Our best strategy is to be proactive and determine where disease conditions are developing, so that we can move the entire infrastructure human capital, technology and supply chains to the affected area. Hence in this context the disease surveillance programs are very important. That way both the government and private organisations can proactively tackle emerging conditions.

This would require the following

• Real-time integration of hospital data- admissions and prescriptions with other sources like lab data
• Leveraging the telemedicine platforms to get a real time view of the consultations for a particular local area
• Coordination with local block officers to effective tackling of the situation on the ground, similar to the COVID-19 committees made by various state and local administrations
• Implementation of the national health stack for authorities to integrate all these data sources
• Merging of unstructured data from digital media with structured data to get a 360-degree view of the disease conditions

NITI Ayog has been working on a vision 2035 document incorporating many of these into a strategy that would be an upgrade to the IDSP and move us in the direction of an integrated disease surveillance program. A program of this size would require a budget upwards of USD 500 million. The US for example has allocated a budget of $200 million to tackle disease surveillance. India today spends close to 1.2 per cent of the GDP on health.

What are the steps in IET’s Disease Surveillance Project? What are the objectives, stages and tentative timelines?

IET’s disease surveillance project is a very radical and ambitious approach at looking at disease surveillance from the view of unstructured data. The project looks at three disease conditions- Malaria, Dengue and Chikungunya, and will track all publicly available data on digital media to track their spread and patterns in the growth of the conditions. The timing of the project is perfect as are in summer now, leading to monsoons. These are the times where these three disease conditions spike. Based on our initial analysis, we would identify regions, and cities where the diseases are spiking, and correlate them with the on-ground data to build a model that can be scaled and used across the country for all key disease conditions. This is first-of-its-kind project and we are collaborating with our partners like PATH and Siemens Healthineers, along with volunteers from IET who collectively bring together almost 60 years of experience in healthcare.

Right now, we are at the initial analysis stages but in the initial 2 months, we have looked at more than 20,000 data sources of information for these three conditions. In the next stage, we will start triangulating the data with the local on-ground situation. Finally, in the last stage, we will bring up a correlation model that can be scaled and used nationally.

Public data can be effectively utilised to prevent disease outbreaks but India’s data has been doubted by several international agencies. Like the current controversy about WHO’s estimate of deaths due to COVID in India being much more than the estimates of the Indian government. What steps can we take to make our data more robust, given the gaps in our public health data?

Data is the key to the success of disease surveillance programs. While I would not like to comment on the WHO controversy, the key is to have a single source of truth, like a golden key in collating data. Today ,that is possible due to the creation of the Aadhar Card which by itself is the biggest success of the digital transformation program in India.

 

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