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Where modern public health developed techniques to calculate probability, potentiality, risk and uncertainty, contemporary finance introduces instruments that redeploy these. This article traces possibilities for interrogating the connection between health and financialisation as it is arising in one particular example - the health impact bond. It locates the development of this very recent financial innovation in an account of public health's role within governance strategies over the 20th century to the present. We examine how social impact bonds for chronic disease prevention programmes bring two previously distinct ways of thinking about and addressing risk into the same domain. Exploring the derivative-type properties of health impact bonds elucidates the financial processes of exchange, hedging, bundling and leveraging. As tools for speculation, the functions of health impact bonds can be delinked from any particular outcome for participants in health interventions. How public health techniques for knowing and acting on risks to population health will contest, rework or be subsumed within finance's speculative response to risk, is to be seen.

The findings show that some groups who inject IPEDs may be more vulnerable to blood-borne virus transmission and/or less likely to know their blood-borne virus status. From design to delivery, IPED harm minimisation strategies should pay attention to the needs of CALD groups.

In Australia, one in three people are born overseas, and one in five households speak languages other than English. This study explores substance use prevalence, related harms, and attitudes among these large groups in the population. Analysis was conducted using cross-sectional data (N = 22, 696) from the 2013 National Drug Strategy Household Survey. General linear model and binary logistic regression were used to assess substance use and harms, using stabilized inverse propensity score weighting to control for potential confounding variables. Between culturally and linguistically diverse populations and the population born in Australia, United Kingdom, or New Zealand who speak only English at home, there is no statistically significant variation in the likelihood of current smoking; using analgesics, tranquilizers, or sleeping pills; or administering drugs via injection. Culturally diverse populations are less likely to drink alcohol or use cannabis or methamphetamines. No difference between these two major groups in the population is observed in substance-related abuse from strangers; but culturally diverse respondents are less likely to report substance-related abuse from known persons. Lower substance use prevalence is not observed among people from culturally diverse backgrounds who have mental health issues. Australian-, UK-, or New Zealand-born respondents who speak only English at home are more likely to oppose drug and tobacco policies, including a range of harm reduction policies. We discuss the practical and ethical limitations of this major Australian data set for examining the burden of drug-related harms experienced by specific migrant populations. Avenues for potential future research are outlined.

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ (SDOH) in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely.