Dataset Information

Identifying and Chronicling Childhood Lead Poisoning Prevention Program Achievements With "Success Stories".

ABSTRACT: Success stories showcase a public health program's progress toward achieving population health objectives. The Centers for Disease Control and Prevention's (CDC's) Childhood Lead Poisoning Prevention Program (CLPPP) develops "success stories" in partnership with state and local cooperative agreement recipients as one way to highlight lead poisoning prevention achievements. Success stories can be used to inform policy makers, stakeholders, and the general public. Over time, the process for collecting and developing CLPPP "successes" has evolved. Early efforts to collect success stories from funded recipients resulted in broad or unfocused reports that diminished the program's perceived impact. CDC's CLPPP revised the approach to success story development in order to better articulate the context, intervention or activity, and results related to programs' successes. The new approach results in stronger products ensuring that both CDC and program recipients can use the success stories to demonstrate achievement of key program objectives. We describe how success stories can be used to identify, chronicle, and mobilize public health program achievements using the example of lead poisoning prevention. Success stories allow programs to increase mission awareness, build stakeholder support, generate community interest, and collectively demonstrate progress toward meeting national program objectives.

SUBMITTER: Lockamy-Kassim E

PROVIDER: S-EPMC6433369 | biostudies-literature | 2019 Jan/Feb

REPOSITORIES: biostudies-literature

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Publications

Identifying and Chronicling Childhood Lead Poisoning Prevention Program Achievements With "Success Stories".

Lockamy-Kassim Elise E Friedberg Jared J Newby Christina C Lecours Carolina C Credle Kimball K Leonard Monica M

Journal of public health management and practice : JPHMP 20190101

Success stories showcase a public health program's progress toward achieving population health objectives. The Centers for Disease Control and Prevention's (CDC's) Childhood Lead Poisoning Prevention Program (CLPPP) develops "success stories" in partnership with state and local cooperative agreement recipients as one way to highlight lead poisoning prevention achievements. Success stories can be used to inform policy makers, stakeholders, and the general public. Over time, the process for collec ...[more]

PMID: 30507779

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Project description:ImportanceChildhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention.ObjectiveTo validate a machine learning (random forest) prediction model of elevated blood lead levels (EBLLs) by comparison with a parsimonious logistic regression.Design, setting, and participantsThis 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.ExposuresBlood lead level test results; lead investigation findings; housing characteristics, permits, and violations; and demographic variables.Main outcomes and measuresIncident 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.ResultsAmong 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 relevanceThe 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.

Dataset Information

Identifying and Chronicling Childhood Lead Poisoning Prevention Program Achievements With "Success Stories".

Publications

Identifying and Chronicling Childhood Lead Poisoning Prevention Program Achievements With "Success Stories".

Similar Datasets

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