Validation of an instrument for assessing the medicolegal death investigation system: A public health approach

Background. This study validated the content of an instrument designed to assess the performance of the medicolegal death investigation system. The instrument was modified from Version 2.0 of the Local Public Health System Performance Assessment Instrument (CDC) and is based on the 10 Essential Public Health Services. ^ Aims. The aims were to employ a cognitive testing process to interview a randomized sample of medicolegal death investigation office leaders, qualitatively describe the results, and revise the instrument accordingly. ^ Methods. A cognitive testing process was used to validate the survey instrument's content in terms of the how well participants could respond to and interpret the questions. Twelve randomly selected medicolegal death investigation chiefs (or equivalent) that represented the seven types of medicolegal death investigation systems and six different state mandates were interviewed by telephone. The respondents also were representative of the educational diversity within medicolegal death investigation leadership. Based on respondent comments, themes were identified that permitted improvement of the instrument toward collecting valid and reliable information when ultimately used in a field survey format. ^ Results. Responses were coded and classified, which permitted the identification of themes related to Comprehension/Interpretation, Retrieval, Estimate/Judgment, and Response. The majority of respondent comments related to Comprehension/Interpretation of the questions. Respondents identified 67 questions and 6 section explanations that merited rephrasing, adding, or deleting examples or words. In addition, five questions were added based on respondent comments. ^ Conclusion. The content of the instrument was validated by cognitive testing method design. The respondents agreed that the instrument would be a useful and relevant tool for assessing system performance. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^