Back pain data collection using scalable vector graphics and geographical information systems

Abstract

According to a Department of Health survey, in Britain back pain affects 40% of the adult population, 5% of which have to take time off to recover (Boucher, 1999). This causes a large strain on the health system, with some 40% of back pain sufferers consulting a GP for help and 10% seeking alternative medicine therapy (Boucher, 1999).Dueto the largenumber of people affected, back pain alone cost industry£9090 million in 1997/8 (Frank and De Souza, 2000), with between 90 and 100 million days of sickness and invalidity benefit paid out per year for back pain complaints (Frank and De Souza, 2000;Main, 1983; Papageorgiou et al., 1995). Back pain is not confined to the UK alone, but is a worldwide problem: in the United States, for instance, 19% of all workers' compensation claims are made with regard to back pain. Although this is a lot less than the percentage of people affected by back pain in the UK, it should be noted that in the United States not all workers are covered by insurance and not all workers will make a claim for back pain (Jefferson and McGrath, 1996). Any improvement in the way that patients with back pain can be analyzed should therefore be viewed as one potentially capable of significantly saving both benefit expenditure and lost person-hours. The problem with back pain is that "there exist no standardised clinical tests or investigations by which all peoplewith lowback pain can be evaluated" (Papageorgiou et al., 1995). Nor will there ever be, as different people have different pain thresholds and will be affected differently. It is also difficult for medical personnel to know what has caused the back pain, as there are potentially many different causes behind it (Frank and De Souza, 2000). Due to the debilitating effect that back pain has on society, our research aimed to find a method thatwould allowcorrelations and patterns to be found between patients' data, and therefore allow the medical world to draw conclusions as to the cause and effect of back pain. Within our research we devised and implemented four ways of visualization and accessing back pain datasets, all of which would enable the user to carry out deeper analysis on a dataset than is usually possible using standard database queries. This is the case as the human is able to compare and contrast information diagrammatically far faster and to a higher degree than just relying on statistical or numerical values.