Analyzing and Optimizing the Distribution of Blood Lead Level Testing for Children in New York City: A Data-Driven Approach: Analyzing and Optimizing the Distribution...: K. Afane and J. Chen.
This study investigates blood lead level (BLL) rates and testing among children under 6 years of age across the 42 neighborhoods in New York City from 2005 to 2021. Despite a citywide general decline in BLL rates, disparities at the neighborhood level persist and are not addressed in the official re...
| Publicado en: | Journal of Urban Health Vol. 102; no. 1; pp. 92 - 101 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Feb2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=183282470&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 183282470 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10993460 GMF jtl: Journal of Urban Health issn: 10993460 maglogo: N pubinfo: dt: Feb2025 vid: 102 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 183282470 10.1007/s11524-024-00920-5 ppf: 92 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P size: 940KB tig: atl: Analyzing and Optimizing the Distribution of Blood Lead Level Testing for Children in New York City: A Data-Driven Approach: Analyzing and Optimizing the Distribution...: K. Afane and J. Chen. aug: au: Afane, Khalifa Chen, Juntao affil: https://ror.org/03qnxaf80 Department of Computer and Information Sciences, Fordham University, New York, USA su: Consciousness raising Day care centers Clustering algorithms Statistical significance Allocation (Accounting) sug: subj: Consciousness raising Day care centers Child Day Care Services Clustering algorithms Statistical significance Allocation (Accounting) keyword: Blood lead levels in children Clustering Grid search Blood lead levels in children Clustering Grid search ab: This study investigates blood lead level (BLL) rates and testing among children under 6 years of age across the 42 neighborhoods in New York City from 2005 to 2021. Despite a citywide general decline in BLL rates, disparities at the neighborhood level persist and are not addressed in the official reports, highlighting the need for this comprehensive analysis. In this paper, we analyze the current BLL testing distribution and cluster the neighborhoods using a k-medoids clustering algorithm. We propose an optimized approach that improves resource allocation efficiency by accounting for case incidences and neighborhood risk profiles using a grid search algorithm. Our findings demonstrate statistically significant improvements in case detection and enhanced fairness by focusing on under-served and high-risk groups. Additionally, we propose actionable recommendations to raise awareness among parents, including outreach at local daycare centers and kindergartens, among other venues. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|