Uncovering the true burden of disease: The significance of surveillance data towards prevention and wellness

Endemic to Australasia, Ross River Virus (RRV) is an abrovirus that causes arthritic symptoms among infected patients. As RRV is under-diagnosed, it is necessary to determine the reliability of general practitioners (GPs) in accurately diagnosing disease. Since most current research about RRV is based on surveillance data, findings from this study will have an impact on the quality of the surveillance data for RRV research and also provide insight into medical training. We provided GPs with three musculoskeletal case studies; one of which described a classical presentation of RRV. Using a cross-sectional design, we mailed surveys to 413 practicing GPs registered in Australian Capital Territory and examined provider characteristics (e.g., age, sex, location of medical training, etc.) of those would have appropriately referred patients for serological testing. Preliminary results suggest that GPs who identify RRV as a differential diagnosis are more likely to appropriate RRV reporting protocol (p<0.0265, N=116). Findings from subgroup analysis show that domestically trained GPs have a greater likelihood of performing laboratory test than foreign-trained physicians (p<0.0320, n=88). Additionally, female GPs were more likely to perform RRV serology than males (p<0.1751, n=99) and full-time GPs were more likely to test for RRV than part-time and specialists (p<0.1297, n=114). An accurate measure of the effects of RRV provides useful information about the trend of the disease, which will inform public health authorities as what actions are necessary to prevent further outbreak. This information also will assist in the development of correction factors that will adjust surveillance data to portray a more accurate burden of disease. Furthermore, the findings provide evidence that the use of surveillance data is either a non-biased representation of the burden of disease or if there is a need for a correction factor applied to surveillance data to adjust for the inconsistency of diagnosis.