Application of Business Intelligence Techniques using SAS on Open Data: Analysing Health Inequality in English Regions

Health inequality is a widely reported problem. There is an existing body of work that links health inequality and geographical location. This means that one might be more disadvantage health-wise if one was born in one region compared to another. Existing health inequality related work in various developed and developing countries rely on population census or survey data. Effective conclusions drawn require large scale data with multiple parameters. There is a new phenomenon in countries (e.g. the UK), where governments are opening up citizen-centric data for transparency purposes and to facilitate data-informed policy making. There are many health organisations, including NHS and sister organisations (e.g. HSCIC), which participate in this drive to open up data. These health-related datasets can be exploited health inequality analytics. This work presents a novel approach of analysing health inequality in English regions solely based on open data. A methodological and systematic approach grounded in CRISP-DM methodology is adhered to for the analyses of the datasets. The analysis utilises a well-cited work on health inequality in children and the corresponding parameters such as Preterm birth, Low birth weight, Infant mortality, Excessive weight in children, Breastfeeding prevalence and Children in poverty. An authority in health datasets, called Public Health Outcomes(PHO) Framework, is chosen as a data source that contains data with these parameters. The analysis is carried out using various SAS data mining techniques such as clustering, and time series analysis. The results show the presence of health inequality in English regions. The work clearly identifies the English regions on the right and wrong side of the divide. The policy and future work recommendations based on these findings are articulated in this research. This work presented in this paper is novel as it applies SAS based BI techniques to analyse health inequality for children in the UK solely based on open data.