Review on the validity of self-report to assess work-related diseases

Self-report is an efficient and accepted means of assessing population characteristics, risk factors, and diseases. Little is known on the validity of self-reported work-related illness as an indicator of the presence of a work-related disease. This study reviews the evidence on (1) the validity of workers' self-reported illness and (2) on the validity of workers' self-assessed work relatedness of an illness. A systematic literature search was conducted in four databases (Medline, Embase, PsycINFO and OSH-Update). Two reviewers independently performed the article selection and data extraction. The methodological quality of the studies was evaluated, levels of agreement and predictive values were rated against predefined criteria, and sources of heterogeneity were explored. In 32 studies, workers' self-reports of health conditions were compared with the "reference standard" of expert opinion. We found that agreement was mainly low to moderate. Self-assessed work relatedness of a health condition was examined in only four studies, showing low-to-moderate agreement with expert assessment. The health condition, type of questionnaire, and the case definitions for both self-report and reference standards influence the results of validation studies. Workers' self-reported illness may provide valuable information on the presence of disease, although the generalizability of the findings is limited primarily to musculoskeletal and skin disorders. For case finding in a population at risk, e.g., an active workers' health surveillance program, a sensitive symptom questionnaire with a follow-up by a medical examination may be the best choice. Evidence on the validity of self-assessed work relatedness of a health condition is scarce. Adding well-developed questions to a specific medical diagnosis exploring the relationship between symptoms and work may be a good strateg

Using Intermicrophone Correlation to Detect Speech in Spatially Separated Noise

This paper describes a system for determining intervals of "high" and "low" signal-to-noise ratios when the desired signal and interfering noise arise from distinct spatial regions. The correlation coefficient between two microphone signals serves as the decision variable in a hypothesis test. The system has three parameters: center frequency and bandwidth of the bandpass filter that prefilters the microphone signals, and threshold for the decision variable. Conditional probability density functions of the intermicrophone correlation coefficient are derived for a simple signal scenario. This theoretical analysis provides insight into optimal selection of system parameters. Results of simulations using white Gaussian noise sources are in close agreement with the theoretical results. Results of more realistic simulations using speech sources follow the same general trends and illustrate the performance achievable in practical situations. The system is suitable for use with two microphones in mild-to-moderate reverberation as a component of noise-reduction algorithms that require detecting intervals when a desired signal is weak or absent.National Institute on Deafness and Other Communication Disorders (U.S.)National Institute on Deafness and Other Communication Disorders (U.S.) (Grant 1-R01-DC00117