The government of India’s goal to eradicate tuberculosis by 2025 is both admirable and ambitious. Tuberculosis was not only responsible for the death of 1.6 million people in 2017, but is also one of the top causes of death from a single infection agent, even topping AIDS (WHO, 2018).
For consecutive years, India has held the troubling title of most deaths from tuberculosis, with 2016 reflecting almost a third of total deaths in the world. The good news is that there has been a 12% drop in deaths in 2017 as compared to 2016, but for a disease that is both curable and preventable it is surprising that new TB cases only dropped by 3% in the same year.
Goats and Soda report that:
“A high mortality tells us several things. One is that there is a delay in diagnosis. And that people who get diagnosed aren’t given adequate treatment,” says Dr. Jennifer Furin, a lecturer at the Harvard Medical School in the Department of Global Health and Social Medicine.
Unfortunately, the numbers could be far worse than projected. The United States Agency for International Development (USAID) estimates that 850,000 people go undetected or untreated, or are diagnosed privately and given substandard treatment/drugs which not only fail to eliminate TB, but also contribute to the increasing problem of drug resistant TB. Reaching India’s target will require innovation and technology.
Mobile Data to the Rescue
India is one of the leading middle/low income countries to see a dramatic rise in the use of mobile phones only falling behind China in the use of smartphones. Taylor & Schroeder (2015) maintain that data emitted as a byproduct of mobile technology, such as location information, have the potential to fill in problematic data gaps available to aid organizations, governments and policymakers. Could big data be the new weapon that wins the TB war?
Airtel Case Study
Be Mobile, a join initiative of the WHO and the International Telecommunications Union, along with the telecommunications giant Bharti Airtel, Be He@althy, and the GSMA have put their heads together to figure out how mobile data can be used to generate relevant insights into fast-tracking the end of tuberculosis (GSMA, 2018). A proof of concept (PoC) was developed and initiated in the ‘high TB burden’ states of Uttar Pradesh and Gujarat with the aim of pinpointing which areas were at high risk of increasing TB incidence. By combining anonymous aggregated mobile data, TB expertise, analytical expertise and local knowledge, the PoC was able to determine that the movement of people is the critical factor in spreading of TB, not the proximity of high TB areas. In other words, areas that receive a regular flow of people from high TB incidence rates could be TB transmission hotspots or already host large numbers of unreported/undiagnosed cases. Through identifying patterns of movement, targeted health interventions can be deployed and efforts to eradicate TB can be strengthened (GSMA, 2018).
The Unsexy Bits
So, big data supported by multidisciplinary collaboration has contributed new insights. Now what? It remains to be seen whether this big data contribution will fall into what Taylor & Schroeder (2015) call the gap between data and effective development, with some mobile data projects yielding more gains to data science than development itself. One hot criticism of using mobile data for development is that subscribers of one mobile provider are not accurately representative of the population and may omit some extremely marginalized communities altogether (Taylor & Schroeder, 2015). Although in this case, Airtel was able to collect data on only 40 million out of 280 million, it can be argued that some data is better than none at all, and that some patterns can still be recognized even if the numbers are not 100% representative.
Although all big data efforts risk ethical pitfalls and challenges, some positive indicators are that this PoC involved local stakeholders and that data was reportedly anonymous. In addition, unlike other data efforts, no extreme claims are made except to “help ending tuberculosis” (GSMA, 2018).
It’s Still Complicated
According to Goats and Soda, part of the reason for TB delay of diagnosis is old equipment, slow processes and a large margin for human error. Using the new data in conjunction with other relevant knowledge will be a mammoth but crucial task and the devil, as usual, will lie in the details.
What market opportunities this war opens up remains to be seen, with Johnson and Johnson already offering its new tuberculosis (TB) drug, Bedaquiline, at $900 (Rs 70,000) once its conditional access programme with the government of India ends in 2019. It must be said that this drug has not been without controversy, with critique of ethical infractions during clinical trials and calls for more data on side effects. Additionally, targeted marketing of substandard drugs to identified high-risk TB locations could exasperate the problem of increasingly drug-resistant TB.
All the sexy-data and unsexy ethics aside, I for one am looking forward to seeing the big data on the results of the big data in this case. Tuberculosis, surrender already!