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Emergency Medical Services (EMS) demand load has become a considerable burden for many government authorities, and EMS demand is often an early indicator for stress in communities, a warning sign of emerging problems. In this paper, we introduce Deep Planning and Policy Making Net (DeepPPMNet), a Long Short-Term Memory network based, global forecasting and inference framework to forecast the EMS demand, analyse causal relationships, and perform `what-if' analyses for policy-making across multiple local government areas. Unless traditional univariate forecasting techniques, the proposed method follows the global forecasting methodology, where a model is trained across all the available EMS demand time series to exploit the potential cross-series information available. DeepPPMNet also uses seasonal decomposition techniques, incorporated in two different training paradigms into the framework, to suit various characteristics of the EMS related time series data. We then explore causal relationships using the notion of Granger Causality, where the global forecasting framework enables us to perform `what-if' analyses that could be used for the national policy-making process. We empirically evaluate our method, using a set of EMS datasets related to alcohol, drug use and self-harm in Australia. The proposed framework is able to outperform many state-of-the-art techniques and achieve competitive results in terms of forecasting accuracy. We finally illustrate its use for policy-making in an example regarding alcohol outlet licenses.