Supreme Court Questions Cancer Drug Costs as New Dengue Forecasting Tool Emerges

UPDATE - follow-up to Health Wrap Episode 53 discuss cancer drug pricing, medicine misuse and dengue: The newest podcast episode from The Hindu examines major concerns raised by the Supreme Court regarding heavy markups on critical oncology treatments, alongside new tools for tracking vector-borne illness.

Drug Pricing Concerns

Hosted by Senior Assistant Editor Zubeda Hamid and Senior Reporter Athira Elssa Johnson, the latest discussion highlights a case where an oncology treatment costing about twenty-seven hundred rupees to supply was sold to patients for twenty-seven thousand rupees. The bench of judges termed this massive gap an open daylight robbery. The judiciary also focused on hospitals forcing patients to purchase supplies exclusively from in-house pharmacies, proposing a fixed sixteen percent profit margin, and questioned how strictly the Drugs Prices Control Order is being enforced.

Health Risks

Beyond costs, the episode warns about the dangers of taking painkillers and antibiotics without proper medical guidance. Misusing painkillers can lead to serious kidney damage, while taking antibiotics needlessly creates drug resistance that makes future infections much harder to treat.

Dengue Tracking

In Keralam, experts at the Institute of Advanced Virology are building a machine learning tool to predict dengue outbreaks. This warning system uses past disease records along with weather details like rain, temperature, and air moisture to create weekly forecasts for every district. Trained on data gathered from March 2020 through February 2026, the tool hopes to help officials spot outbreaks early, manage mosquitoes better, and warn the public in time once testing finishes.

Context

Drug pricing in India is closely regulated to keep essential treatments affordable for the general public, though large gaps can sometimes emerge through various distribution channels. Meanwhile, vector-borne illnesses like dengue tend to surge during specific seasonal shifts, making early prediction tools helpful for local health departments trying to stop outbreaks before they grow.