Analyzing and Optimizing the Distribution of Blood Lead Level Testing for Children in New York City: A Data-Driven Approach

2025-11-22

AI Summary

This study addresses uneven blood lead level (BLL) testing coverage among children under six across 42 neighborhoods in New York City and insufficient screening of high-risk populations. We propose a data-driven resource optimization framework that integrates k-medoids clustering with grid search optimization, coupled with spatial epidemiological analysis and multivariate regression modeling to identify geographic disparities in testing access and quantify associations between service gaps and community-level risk factors—including poverty rate and prevalence of pre-1950 housing. Results reveal persistent neighborhood-level testing deficits and demonstrate a significant improvement in detection yield for at-risk children and equitable distribution of screening services. Building on these findings, we recommend targeted interventions—such as parent education and point-of-care BLL screening in preschools and daycare centers—to enhance early identification and intervention. The framework provides a methodologically rigorous and operationally feasible approach for optimizing public health resource allocation in urban settings.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This study investigates blood lead level (BLL) rates and testing among children under six 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.

Problem

• Analyzing blood lead level testing disparities across NYC neighborhoods
• Optimizing resource allocation for child lead testing efficiency
• Improving case detection fairness in underserved high-risk groups

Innovation

• Used k-medoids clustering algorithm for neighborhood analysis
• Applied grid search algorithm to optimize resource allocation
• Focused on under-served high-risk groups for case detection