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

TL;DR

This paper analyzes blood lead level testing distribution in NYC children, identifies disparities, and proposes an optimized, data-driven approach to improve detection and fairness in resource allocation.

Contribution

It introduces a novel data-driven method combining clustering and optimization to enhance testing efficiency and address neighborhood disparities in lead exposure detection.

Findings

Significant improvement in case detection rates.

Enhanced fairness in testing distribution.

Identification of high-risk neighborhoods for targeted intervention.

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.