Is size the next big thing in epidemiology?

A recent assessment of drugs that target the renin-angiotensin-aldosterone system and angioedema risk drew from a source population of more than 100 million people and 350 million person-years of observation time. The assessment identified 3.9 million eligible new users of angiotensin-converting enzyme inhibitors (aCeis), angiotensin receptor blockers (aRBs), the direct renin inhibitor aliskiren, or the common referent group beta-blockers (a class of drugs not thought to affect the risk of angioedema). more than 4500 outcome events were observed. The assessment replicated a well-known association between aCeis and angioedema, but the risk estimates were much more precise than those from prior studies. The assessment also generated new evidence for aRBs and aliskiren. not so long ago, an assessment of such scale existed only in our imaginations. Secondary uses of routinely collected electronic health information now enable us to conduct research using data from hundreds of thousands or even millions of patients. But certain studies or surveillance activities, especially those with rare exposure or outcome, demand data larger than any single extant source. Combining data from multiple sources would help solve the sample size problem, but sharing data has always been a challenge because of privacy, security, regulatory, legal, and proprietary concerns. how did the angioedema assessment accomplish this and what implications does it have for epidemiology?

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