Narcosurveillance methods to reduce drug-related harms
Drug use can be associated with significant harms, including the blood-borne virus hepatitis C. Addressing the harms associated with drug use, including hepatitis C, is a global priority, especially as new drugs can now cure it. The Australian government has an ambitious goal to eliminate hepatitis C by 2030, investing billions of dollars by adding new drugs to the Pharmaceutical Benefits Scheme. Over 100,000 people have been treated, but thousands have not, and new infections and reinfections continue. We are struggling to find those affected by the virus. Such groups are said to be ‘hidden’, or ‘hard to reach’. There are many reasons for this. Most importantly, drug use is criminalised. When use is uncovered, people experience significant stigma and discrimination, and face serious legal and social consequences (e.g. loss of housing, employment, incarceration). For these reasons, people often conceal their drug use and disengage from mainstream systems, or are unable to safely access them. These various forces can enable and exacerbate drug-related harms. We urgently need new methods to locate and support these populations, to reduce drug-related harms and eliminate hepatitis C by 2030. Bold claims are being made that big data methods can solve this challenge. Big data methods include healthcare surveillance, data linking and drug-use prediction algorithms. Researchers and governments are experimenting with such methods, including machine learning tools to predict potential drug users. In the unique context of criminalisation, experimentation with such methods is both promising, and especially risky. As the Robodebt scandal showed, experimentation on vulnerable groups can lead to flawed results and tragic outcomes, eroding trust in governments and institutions. This project explores the benefits and risks of big data methods to arrest rising drug-related harms.
Project team:
Professor Kate Seear
Professor kylie valentine (University of New South Wales)
Emily Lenton (La Trobe University)
Dr Alejandra Zuluaga Duque (Deakin University; University of New South Wales)