Towards realistic methods for evaluating public health interventions using time-series data

Researchers and practitioners in the field of public health are routinely faced with the task of evaluating the effect of an intervention on an outcome of interest. Often, these evaluations rely on observational time-series data from a small number of units of intervention, such as hospitals or geographical regions, of which some receive the intervention (treated units) and some do not (control units). Increasing availability of such data has led to a pressing need for statistical methodology that can be used to draw causal conclusions in this context, whilst accounting for the problem of counfounding present in observational studies. Despite recent developments, current methods cannot accommodate important facets of an intervention that are typically of interest in a public health-related context. Firstly, the strong dependence of intervention effects on the characteristics of a unit, which leads to great effect heterogeneity. Secondly, the possibility that unobserved confounders change over time and that complex interactions exist between the observed confounders. Thirdly, the presence of correlations between neighbouring units, which, while could be accounted for in models to improve statistical power,  also raise the question of how to deal with interference, i.e. the fact that the intervention will also affect the units surrounding those that have been treated. Fourthly, violation of the requirement that control units exist throughout the study, and that interventions cannot be withdrawn once delivered. I propose to develop statistical methodology that tackles these challenges, thus offering generic and much needed tools to assess public health interventions. These Bayesian methods will provide uncertainty quantification to aid policy making, and include easy-to-use software to facilitate future use. I will apply my methods to studies from the UK, arising from three key substantive areas: the evaluation of ongoing efforts to reduce Hepatitis C virus prevalence among people who inject drugs, which are part of UK's plan to eliminate the virus by 2030;  the impact that various non-pharmaceutical interventions had in containing the spread of COVID-19 during the recent pandemic; and the effects of the Soft Drinks Industry Levy on drinks containing added sugar on improving health-related outcomes such as sales of products containing added sugar.