Background: Sepsis continues to cause substantial mortality rates and economic burden around the world. Mortality is estimated to be over 25-30%, up to 50% if the patient is in chock. The true burden of sepsis remains largely unknown. Epidemiological data differs notable regarding both incidence and cost, ICD-identification of sepsis have shown do differ substantially from clinical validation in prospective studies. Thus, to know about Sepsis, there is great need of reliable and robust models to further access prevalence, risk factors, protective factors, and outcome. With this, we would be able to further our care of this common and deadly disease. Objective: Our objective is with hospital data on income status together with parameters and treatment to access the prevalence and outcome of sepsis and compare this with epidemiological estimations. We also aim to analyse predictive outcomes and risk factors to sepsis. This includes different types of treatment, level of care, income status, lab status. To do this, we will combine data to create a robust model of sepsis and risk stratification and use this to analyse outcome, risk factors, and protective factors. Questions 1. Does the estimation on prevalence and outcomes of sepsis hold up to verified hospital data? 2. How does level of care effect the outcome of sepsis in different clinical situations? 3. How does type of treatment effect the outcome of sepsis in different clinical situations? 4. Can we find unknown risk factors to sepsis? 5. Can we find unknown protective factors to sepsis? Methods We aim to use the Uppsala ambulance registry with data collected between 2017-2019, combined with hospital data on medications, lab data, microbiology, ward, level of care and outcomes to create a robust model on patient income status, treatment, and outcome. Income status, lab results and microbiology will be used create a verifiable sepsis model. We will use given medicine, microbiology results, lab results and level of care to map progress within hospital treatment. Finally, we will use outcome data om ICD-10-codes and death as main outcomes to access treatment efficiency in different situations. This includes income status, given medicine, level of care, and time to treatment. This allows us to create risk-estimation models and an epidemiological model of sepsis and compare this with known known risk factors, and possibly finding new ones.