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Related Experiment Video

Updated: Jan 9, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
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Broad Applications of Distributed Lag Non-Linear Model in Public Health: A Comprehensive Review.

Ambreen Shafqat1, Eunsik Park1

  • 1Department of Mathematics and Statistics Chonnam National University Gwangju South Korea.

Geohealth
|December 11, 2025
PubMed
Summary

The distributed lag non-linear model (DLNM) effectively analyzes environmental exposures and health outcomes, revealing complex temporal relationships. This review highlights its utility in public health research, despite challenges in standardization.

Keywords:
distributed lag non‐linear model (DLNM)environmental exposureexposure‐response relationshipspublic health outcomestime series analysis

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Area of Science:

  • Environmental health
  • Biostatistics
  • Epidemiology

Background:

  • The distributed lag non-linear model (DLNM) is increasingly utilized in public health research.
  • Understanding the complex temporal dynamics between environmental exposures and health outcomes is crucial.

Purpose of the Study:

  • To review the application of DLNM in analyzing environmental exposures and health outcomes.
  • To identify trends, challenges, and future directions for DLNM in environmental health research.

Main Methods:

  • A systematic literature search was conducted across Embase, PubMed, Web of Science, and Scopus (January 2020 - November 2024).
  • Studies using DLNM to assess environmental factors (temperature, air pollutants) and health outcomes were screened and analyzed.
  • Data from 274 selected studies across 36 countries were synthesized.

Main Results:

  • Morbidity was the most frequently reported adverse health outcome (n=102), followed by hospitalization (n=39) and hospital admission (n=40).
  • The review identified diverse data sources for climate (174) and air pollutants (131), noting a lack of standardized heat thresholds.
  • DLNM demonstrated utility in capturing lagged effects of environmental exposures on health.

Conclusions:

  • DLNM is a valuable tool for investigating environmental health issues, particularly for understanding delayed health impacts.
  • Standardization of data and computational efficiency remain challenges, but ongoing developments are improving DLNM's applicability.
  • Future research should integrate advanced statistical methods like machine learning and expand DLNM applications to broader environmental health scenarios.