Exploring the risk and predictive study of outdoor air pollutants on the incidence and mortality of HIV/AIDS
Year: 2024
Type: article
Abstract: The rising incidence of environmental pollution has heightened concerns regarding the impact of pollutant variations on public health. Time series analysis models and BP neural network models were utilized to investigate both univariate and multivariate predictions of HIV/AIDS cases. To evaluate the combined effects of pollutants on HIV/AIDS cases, we employed weighted quantile sum (WQS) regression, a quantile-based g-computation approach (Qgcomp) and Bayesian kernel machine regression (BKMR). Additionally, sensitivity analyses were conducted to further validate our findings. The incidence and mortality rates of HIV/AIDS in Beijing have demonstrated an upward trend, primarily affecting individuals aged 20–35 years, who account for approximately 63.95 % of cases. In the univariate prediction, the parameters that yielded strong predictive performance for the incidence model were as follows: Holt-Winters: α=0.13, β=0.09, γ=0.34. For the mortality model, the parameters indicating good predictive performance were derived from the SARIMA model: (0,1,3) (0,1,2) [12]. The BP neural network model also exhibited robust predictive performance across various configurations of hidden layers (error ∈ [0.096, 1.324]). The WQS model indicated that only NO 2 had a significant effect, with an overall risk effect of the five mixed air pollutants on HIV/AIDS incidence represented as βWQS (95 %CI) = 0.10 (0.02, 0.18). Meanwhile, the Qgcomp model revealed that NO 2 and AQI have hazardous effects on disease incidence, with weights of 0.514 and 0.486, respectively. Additionally, SO 2 was found to have a harmful effect on disease mortality. In the Qgcomp index and BKMR model, the weights of PM 10 and PM 2.5 were predominant in the positive weights. Various time series and neural network models effectively predict the incidence and mortality rates of HIV/AIDS. Additionally, multiple mixed exposure analyses provide further evidence of significant associations between exposure to air pollution mixtures and HIV/AIDS incidence and mortality rates, with PM 2.5 and PM 10 being the primary drivers. • Different time series models can predict disease incidence and mortality. • Three models were interpreted jointly to yield a conclusion. • The mixed exposure to pollutants on HIV/AIDS was assessed. • PM was identified as the main contributor to the positive association between pollutants and HIV/AIDS.
Authors: Weiming Hou, Zhenyao Song
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W4404310596