Surprisingly, prioritizing global health issues scarcely relies on demographic analysis so far. If health programs are aligned with mortality often only basic indicators are considered, such as prevalences and death counts of fatal diseases. Leveraging demographic knowledge we use sensitivity analysis and sorting algorithms to identify the top age-&-cause of death combinations that could best increase life expectancy and decrease lifespan inequality worldwide in upcoming years, based on data of the UN World Population Prospects and the Global Burden of Disease study. Compared to analyzing only death counts of leading causes, our study reveals that HIV/AIDS is indeed more important and that ages of timely intervention often are substantially younger. Consequently, health planning that is based on only basic indicators could perhaps motivate to implement counter-productive actions that are meant well but increase health inequality-an unintended but serious shortcoming that could be overcome with our proposed methodology.