© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Health impact assessment (HIA) focuses on minimizing inequities when studying the effects of a policy on the population’s health. Nevertheless, it is seldom simultaneously quantified, multivariate, and visually graphically comprehensible for non-statisticians. This paper aims to address that gap, assessing a policy promoting the quality of Electronic Health Records, linking hospital and primary health care data (Blood Pressure, Cholesterol, Triglycerides, Waist Circumference, Body Mass Index) to mortality outcomes and regional inequities. Acute Myocardial Infarction patients admitted in the hospital are then followed regularly in Portuguese NHS Primary Care. Regional disparities regarding recorded information are observed and different association patterns with mortality identified, ranked, and visualized through adjusted ORs for sex, age, and indicators of severity of hospital admission, complemented with multivariate correspondence analysis. A pathway to handling equity within quantitative HIA shows that complexity in data and methods may generate simplicity and clarity through visual graphical aids. Tackling Big Data with Data Science in HIA may even be at the center of future health reforms, assessing impacts of health promotion and chronic disease policies. This work was partially supported by the CUTEHeart Project – Comparative Use of Technologies for Coronary Heart Disease, funded by FCT, QREN, COMPETE [grant number HMSP-ICT/0013/2011] (http://cuteheart.med.up.pt). info:eu-repo/semantics/publishedVersion