Biological Aging Measures and Hearing-Screening Equity

Authors

  • Peter Lawson Author
  • Mark Stevens Author
  • Scott Franklin Author

Abstract

A central question for occupational hearing-risk assessment is not simply whether an output is accurate, but whether it survives scrutiny of selection. The article develops a research agenda through critical comparison of published evidence. The starting source uses sex-stratified cross-sectional modeling, longitudinal generalized estimating equations, and Cox-model nomograms using baseline high-frequency audiometry. Selection shapes who appears in data and who remains observable. Conditioning on employment, participation, surgery, or follow-up can alter associations independently of the mechanism of interest. Map eligibility and observation pathways. Compare conclusions under plausible selection mechanisms and disclose which populations are excluded from the claim. The proposed comparison concerns survey weights, worker selection, age markers; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between noise-exposed workers and clinical follow-up and any stronger downstream decision.

References

Yu, Xiao, et al. “Prediction of Risk of Hearing Loss by Industry Noise from Cross-Sectional and Longitudinal Data.” Communications Medicine, vol. 6, 2026, https://doi.org/10.1038/s43856-026-01463-3.

Li, Yuanhao, et al. “BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models.” arXiv, version 4, 18 Sept. 2026, https://arxiv.org/abs/2605.09134v4. Preprint.

Dai, Junjian, et al. “Interaction between Noise Exposure and Phenotypic Age Acceleration in Hearing Loss: An NHANES Study.” ENT Updates, vol. 16, no. 3, 2026, pp. 1–12, https://doi.org/10.54963/entu.v16i3.100576.

Sang, Yinghao. “Adaptive Quantization Strategies for Robust ML Inference under Distribution Shift.” Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy, ACM, 2026, pp. 362–68, https://doi.org/10.1145/3819836.3819892.

Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.

Rusu, Andrei A., et al. “Policy Distillation.” arXiv, 2015, arXiv:1511.06295.

Schulman, John, et al. “Proximal Policy Optimization Algorithms.” arXiv, 2017, arXiv:1707.06347.

Tishby, Naftali, Fernando C. Pereira, and William Bialek. “The Information Bottleneck Method.” Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing, 1999, pp. 368–77.

Alemi, Alexander A., et al. “Deep Variational Information Bottleneck.” International Conference on Learning Representations, 2017.

National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.

Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data, vol. 3, 2016, article 160018.

Hernán, Miguel A., and James M. Robins. Causal Inference: What If. Chapman & Hall/CRC, 2020.

Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.

Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.

Downloads

Published

2026-10-14

Issue

Section

Reviews and Perspectives