Routine dipstick urinalysis in daily practice of Belgian occupational physicians

BACKGROUND: Little work has been done to assess the quality of health care and the use of evidence-based methods by occupational physicians in Belgium. Therefore, the main objective is to describe one aspect of occupational health assessments, namely the common use of dipstick urinalysis, and to compare the current practice with international guidelines. METHODS: A self-administered questionnaire was mailed to 211 members of the Scientific Association of Occupational Medicine in the Dutch speaking part of Belgium. RESULTS: A total of 120 occupational physicians responded, giving a response rate of 57%. Dipstick urinalysis was a routine investigation for the vast majority of physicians (69%). All test strips screened for protein and in 90% also for blood. Occupational health services offered clinical tests to satisfy customer wants as international guidelines do not recommend screening for haematuria and proteinuria in asymptomatic adults. A lack of knowledge concerning positive testing and referral criteria was demonstrated in almost half of the study participants. CONCLUSIONS: Belgian occupational physicians still routinely perform dipstick testing although there is no evidence to support this screening in healthy workers. To practice evidence-based medicine, occupational physicians need more instruction and training. Development and implementation of more guidelines is not only of use for the individual practitioner, it may also enhance professionalization and efficiency of occupational health care

Robust bootstrap procedures for the chain-ladder method

Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures