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thank you very much for for that invitation and and this is does seem like a timely point to be thinking about how we do strengthen the possibilities for generating some meaningful in evidence about what public health interventions do what sorts of health they foster what they don't and in what circumstances and i guess increasingly our evaluation teams do consist of collaborators from across different disciplines and we're often generating very different kinds of data sets and what i want to do today is just kind of focus on um i guess first of all one particular scenario where we're evaluating the primary qualitative data and often some quantitative data um [Music] uh using some sort of statistical analysis and then briefly flag qualitative comparative analysis approaches as one way of integrating data and then maybe just reflect a little bit on um some of the things that i think help in a project to generate a bit more integration of those data sets um just from my own experience so the context i guess is that um like most people i've been involved in a number of um public health evaluations that do generate a mix of data both for health service evaluations and more recently for public interventions that are impacting in some way on the public health and largely what's happened is that those two sets of um research happen in parallel i'm leading on a qualitative component somebody else is leading on a more epidemiological component and we kind of do it side by side and we write it up in separate journals the final big report for nihr or whatever has the different chapters with those different um different sorts of data in them and we end up not really integrating them but i do wonder sometimes whether we're just missing uh and sometimes that's fine that they're answering different kinds of questions we learn from each other as we go along and perhaps they they don't need to fit together too well but i do wonder whether we're sometimes missing a bit of an opportunity to do rather more um particularly in the light of increasing interest in comp what we might call complex causation and of course uh almost all of the the things that we're looking at in public health if we're looking upstream are complex we're trying to understand not just what happens narrowly when one thing affects another but we're trying to understand what happens to a system when something changes in it and what the multiple effects across different kinds of health outcomes might be and this is where i think some sort of better better ways of integrating the different sorts could we just go back a slide or two um the different sorts of um uh um data that we're integrating might generate now an underlying challenge i guess for doing that is that the sort of logics of causal inference might differ across the...