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Validation of a Machine Learning Model to Predict Childhood Lead Poisoning
Importance: Childhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention. Objective: To validate a machine learning (random forest) prediction model of elevated blood lead levels (EBLLs) by comparison with a parsimonious logistic regression.Design, Setting, and Participants: This prognostic study for temporal validation of multivariable prediction models used data from the Women, Infants, and Children (WIC) program of the Chicago Department of Public Health. Participants included a development cohort of children born from January 1, 2007, to December 31, 2012, and a validation WIC cohort born from January 1 to December 31, 2013. Blood lead levels were measured until December 31, 2018. Data were analyzed from January 1 to October 31, 2019. Exposures: Blood lead level test results; lead investigation findings; housing characteristics, permits, and violations; and demographic variables. Main Outcomes and Measures: Incident EBLL (≥6 μg/dL). Models were assessed using the area under the receiver operating characteristic curve (AUC) and confusion matrix metrics (positive predictive value, sensitivity, and specificity) at various thresholds.Results: Among 6812 children in the WIC validation cohort, 3451 (50.7%) were female, 3057 (44.9%) were Hispanic, 2804 (41.2%) were non-Hispanic Black, 458 (6.7%) were non-Hispanic White, and 442 (6.5%) were Asian (mean [SD] age, 5.5 [0.3] years). The median year of housing construction was 1919 (interquartile range, 1903-1948). Random forest AUC was 0.69 compared with 0.64 for logistic regression (difference, 0.05; 95% CI, 0.02-0.08). When predicting the 5% of children at highest risk to have EBLLs, random forest and logistic regression models had positive predictive values of 15.5% and 7.8%, respectively (difference, 7.7%; 95% CI, 3.7%-11.3%), sensitivity of 16.2% and 8.1%, respectively (difference, 8.1%; 95% CI, 3.9%-11.7%), and specificity of 95.5% and 95.1% (difference, 0.4%; 95% CI, 0.0%-0.7%). Conclusions and Relevance: The machine learning model outperformed regression in predicting childhood lead poisoning, especially in identifying children at highest risk. Such a model could be used to target the allocation of lead poisoning prevention resources to these children.</p
Coordinated Health Care Interventions for Childhood Asthma Gaps in Outcomes (CHICAGO) plan
Background: Evidence-based strategies to improve outcomes in minority children with uncontrolled asthma discharged from the emergency department (ED) are needed. Objectives: This multicenter pragmatic clinical trial was designed to compare an ED-only intervention (decision support tool), an ED-only intervention and home visits by community health workers for 6 months (ED-plus-home), and enhanced usual care (UC). Methods: Children aged 5 to 11 years with uncontrolled asthma were enrolled. The change over 6 months in the Patient-Reported Outcomes Measurement Information System Asthma Impact Scale score in children and Satisfaction with Participation in Social Roles score in caregivers were the primary outcomes. The secondary outcomes included guideline-recommended ED discharge care and self-management. Results: Recruitment was significantly lower than expected (373 vs 640 expected). Of the 373 children (64% Black and 31% Latino children), only 63% completed the 6-month follow-up visit. In multivariable analyses that accounted for missing data, the adjusted odds ratios and 98% CIs for differences in Asthma Impact Scores or caregivers’ Satisfaction with Participation in Social Roles scores were not significant. However, guideline-recommended ED discharge care was significantly improved in the intervention groups versus in the UC group, and self-management behaviors were significantly improved in the ED-plus-home group versus in the ED-only and UC groups. Conclusions: The ED-based interventions did not significantly improve the primary clinical outcomes, although the study was likely underpowered. Although guideline-recommended ED discharge care and self-management did improve, their effect on clinical outcomes needs further study