International journal of epidemiology
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Multicenter Study
Effect of misclassification of causes of death in verbal autopsy: can it be adjusted?
Verbal autopsy (VA) is an indirect method of ascertaining cause of death from information about symptoms and signs obtained from bereaved relatives. This method has been used in several settings to assess cause-specific mortality. However, cause-specific mortality estimates obtained by VA are susceptible to bias due to misclassification of causes of death. One way of overcoming this limitation of VA is to adjust the crude VA estimate of cause-specific mortality fractions (CSMF) using the sensitivity and specificity of the VA tool. This paper explores the application of sensitivity and specificity of VA data obtained from a hospital-based validation study for adjusting the effect of misclassification error in VA data obtained from a demographic surveillance system. ⋯ Estimates of sensitivity and specificity obtained from hospital-based validation studies must be used cautiously as a de facto 'gold standard' for adjusting the misclassification error in CSMF derived from VA. It is not possible to use sensitivity and specificity estimates derived from a location-specific validation study to adjust for misclassification in VA data from populations with substantially different patterns of cause-specific mortality.
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Exposure to infections, particularly in early life, may modify the risk of developing childhood diabetes. Population mixing, based on the number and diversity of incoming migrants to an area can be used as a proxy measure for exposure to infections. We tested the hypothesis that incidence of childhood Type 1 diabetes is higher in areas of low population mixing. ⋯ The incidence of childhood diabetes is highest in areas where limited childhood population mixing occurs and the diversity of origins of incoming children is low; those over 4 years are at greatest risk. This is consistent with an infectious hypothesis where absence of stimulation to the developing immune system increases vulnerability to late infectious exposure, which may precipitate diabetes.
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Review
Use of comorbidity scores for control of confounding in studies using administrative databases.
Comorbidity scores are increasingly used to reduce potential confounding in epidemiological research. Our objective was to compare metrical and practical properties of published comorbidity scores for use in epidemiological research with administrative databases. ⋯ Comorbidity scores, particularly the CDS or D'Hoore's CI based on three-digit ICD-9 codes, may be useful in exploratory data analysis. However, residual confounding by comorbidity is inevitable, given how these scores are derived. How much residual confounding usually remains is something that future studies of comorbidity scores should examine. In any given study, better control for confounding can be achieved by deriving study-specific weights, to aggregate comorbidities into groups with similar relative risks of the outcomes of interest.