Exploring the risk and predictive study of outdoor air pollutants on the incidence and mortality of HIV/AIDS

Background

The rising incidence of environmental pollution has heightened concerns regarding the impact of pollutant variations on public health.

Methods

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.

Results

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 NO₂ 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 NO₂ and AQI have hazardous effects on disease incidence, with weights of 0.514 and 0.486, respectively. Additionally, SO₂ was found to have a harmful effect on disease mortality. In the Qgcomp index and BKMR model, the weights of PM₁₀ and PM_2.5 were predominant in the positive weights.

Conclusions

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 PM₁₀ being the primary drivers.