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The impact of radiofrequency exposure on Aedes aegypti (Diptera: Culicidae) development

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Nik Abdull Halim NMH, Mohd Jamili AF, Che Dom N, Abd Rahman NH, Jamal Kareem Z, Dapari R · 2024

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RF-EMF exposure, particularly at 900 MHz, may impair the development of Aedes aegypti mosquitoes from larvae to adults, potentially altering their life cycle and transmission dynamics.

Plain English Summary

Summary written for general audiences

This 2024 study examined the effects of radiofrequency electromagnetic field (RF-EMF) exposure on Aedes aegypti mosquito development by exposing eggs to three conditions: baseline, 900 MHz, and 18 GHz frequencies. The researchers found that the 900 MHz exposure group had the highest hatching rate (79%) but the lowest adult emergence rate (33%), with statistically significant differences observed between exposure groups (p = 0.03).

Why This Matters

The study addresses a relevant question about whether higher RF frequencies used in modern wireless communications (including 5G) could affect disease-vector insects through dielectric heating. The experimental design using multiple frequencies allows for comparison of effects across the RF spectrum used in telecommunications.

Exposure Information

Specific exposure levels were not quantified in this study.

Cite This Study
Nik Abdull Halim NMH, Mohd Jamili AF, Che Dom N, Abd Rahman NH, Jamal Kareem Z, Dapari R (2024). The impact of radiofrequency exposure on Aedes aegypti (Diptera: Culicidae) development.
Show BibTeX
@article{nik_abdull_halim_nmh_mohd_jamili_af_che_dom_n_abd_rahman_nh_jamal_kareem_z_dapari_r_ce3802,
  author = {Nik Abdull Halim NMH and Mohd Jamili AF and Che Dom N and Abd Rahman NH and Jamal Kareem Z and Dapari R},
  title = {The impact of radiofrequency exposure on Aedes aegypti (Diptera: Culicidae) development},
  year = {2024},
  doi = {10.1016/S2589-7500(24)00065-7},
  
}
DOI: 10.1016/S2589-7500(24)00065-7PubMed: 38412167

Quick Questions About This Study

The study analyzed data from 123,512 patients across 1,884 hospitals in 114 countries, making it one of the largest international surgical outcome studies ever conducted for developing risk prediction models.
Lung complication rates varied by dataset: 2.0% in the main development group, 3.9% in the COVID-era validation group, and 4.7% in the pre-pandemic validation group from UK and Australasia.
The model showed good discrimination with area under the curve values of 0.773 in internal validation, 0.746 in COVID-era external validation, and 0.716 in pre-pandemic validation, indicating acceptable predictive accuracy.
LASSO regression performed similarly to XGBoost (0.786 vs 0.785 area under curve) but was chosen for the final model because it was more explainable and required fewer variables.
The study mentions ten predictor variables in the final model but doesn't list them specifically in the abstract. These are simple factors available before surgery to estimate pulmonary complication risk.