Farmworker Exposure to Pesticides: Methodologic Issues for the Collection of Comparable Data

The exposure of migrant and seasonal farmworkers and their families to agricultural and residential pesticides is a continuing public health concern. Pesticide exposure research has been spurred on by the development of sensitive and reliable laboratory techniques that allow the detection of minute amounts of pesticides or pesticide metabolites. The power of research on farmworker pesticide exposure has been limited because of variability in the collection of exposure data, the predictors of exposure considered, the laboratory procedures used in analyzing the exposure, and the measurement of exposure. The Farmworker Pesticide Exposure Comparable Data Conference assembled 25 scientists from diverse disciplinary and organizational backgrounds to develop methodologic consensus in four areas of farmworker pesticide exposure research: environmental exposure assessment, biomarkers, personal and occupational predictors of exposure, and health outcomes of exposure. In this introduction to this mini-monograph, first, we present the rationale for the conference and its organization. Second, we discuss some of the important challenges in conducting farmworker pesticide research, including the definition and size of the farmworker population, problems in communication and access, and the organization of agricultural work. Third, we summarize major findings from each of the conference’s four foci—environmental exposure assessment, biomonitoring, predictors of exposure, and health outcomes of exposure—as well as important laboratory and statistical analysis issues that cross-cut the four foci

Development and validation of a short version of the Partnership Self-Assessment Tool (PSAT) among professionals in Dutch disease-management partnerships

Background: The extent to which partnership synergy is created within quality improvement programmes in the Netherlands is unknown. In this article, we describe the psychometric testing of the Partnership Self-Assessment Tool (PSAT) among professionals in twenty-two disease-management partnerships participating in quality improvement projects focused on chronic care in the Netherlands. Our objectives are to validate the PSAT in the Netherlands and to reduce the number of items of the original PSAT while maintaining validity and reliability. Methods. The Dutch version of the PSAT was tested in twenty-two disease-management partnerships with 218 professionals. We tested the instrument by means of structural equation modelling, and examined its validity and reliability. Results: After eliminating 14 items, the confirmatory factor analyses revealed good indices of fit with the resulting 15-item PSAT-Short version (PSAT-S). Internal consistency as represented by Cronbach's alpha ranged from acceptable (0.75) for the 'efficiency' subscale to excellent for the 'leadership' subscale (0.87). Convergent validity was provided with high correlations of the partnership dimensions and partnership synergy (ranged from 0.512 to 0.609) and high correlations with chronic illness care (ranged from 0.447 to 0.329). Conclusion: The psychometric properties and convergent validity of the PSAT-S were satisfactory rendering it a valid and reliable instrument for assessing partnership syne

DMMAM: Deep multi-source multi-task attention model for intensive care unit diagnosis

Disease diagnosis can provide crucial information for clinical decisions that influence the outcome in acute serious illness, and this is particularly in the intensive care unit (ICU). However, the central role of diagnosis in clinical practice is challenged by evidence that does not always benefit patients and that factors other than disease are important in determining patient outcome. To streamline the diagnostic process in daily routine and avoid misdiagnoses, in this paper, we proposed a deep multi-source multi-task attention model (DMMAM) for ICU disease diagnosis. DMMAM exploits multi-sources information from various types of complications, clinical measurements, and the medical treatments to support the diagnosis. We evaluate the proposed model with 50 diseases of 9 classifications on an extensive collection of real-world ICU Electronic Health Records (EHR) dataset with 151729 ICU admissions from 46520 patients. Experiments results demonstrate the effectiveness and the robustness of our model