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Jim Black

Publications and source records attributed to Jim Black.

5 recordsLinked to original sources
PubMed2004

Malaria in travelers: a review of the GeoSentinel surveillance network.

BACKGROUND: Malaria is a common and important infection in travelers. METHODS: We have examined data reported to the GeoSentinel surveillance network to highlight characteristics of malaria in travelers. RESULTS: A total of 1140 malaria cases were reported (60% of cases were due to Plasmodium falciparum, 24% were due to Plasmodium vivax). Male subjects constituted 69% of the study population. The median duration of travel was 34 days; however, 37% of subjects had a travel duration of < or =4 weeks. The majority of travellers did not have a pretravel encounter with a health care provider. Most cases occurred in travelers (39%) or immigrants/refugees (38%). The most common reasons for travel were to visit friends/relatives (35%) or for tourism (26%). Three-quarters of infections were acquired in sub-Saharan Africa. Severe and/or complicated malaria occurred in 33 cases, with 3 deaths. Compared with others in the GeoSentinel database, patients with malaria had traveled to sub-Saharan Africa more often, were more commonly visiting friends/relatives, had traveled for longer periods, presented sooner after return, were more likely to have a fever at presentation, and were less likely to have had a pretravel encounter. In contrast to immigrants and visitors of friends or relatives, a higher proportion (73%) of the missionary/volunteer group who developed malaria had a pretravel encounter with a health care provider. Travel to sub-Saharan Africa and Oceania was associated with the greatest relative risk of acquiring malaria. CONCLUSIONS: We have used a global database to identify patient and travel characteristics associated with malaria acquisition and characterized differences in patient type, destinations visited, travel duration, and malaria species acquired.

Africa↗
PubMed2004

Artificial neural networks and job-specific modules to assess occupational exposure.

Job-specific modules (JSMs) were used to collect information for expert retrospective exposure assessment in a community-based non-Hodgkins Lymphoma study in New South Wales, Australia. Using exposure assessment by a hygienist, artificial neural networks were developed to predict overall and intermittent benzene exposure among the module of tanker drivers. Even with a small data set (189 drivers), neural networks could assess benzene exposure with an average of 90% accuracy. By appropriate choice of cutoff (decision threshold), the neural networks could reliably reduce the expert's workload by approximately 60% by identifying negative JSMs. The use of artificial neural networks shows promise in future applications to occupational assessment by JSMs and expert assessment.

Benzene↗