School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China.
School of Public Health, Imperial College London, London, UK.
Environ Health. 2024 Apr 13;23(1):36. doi: 10.1186/s12940-024-01083-1.
Multifaceted SARS-CoV-2 interventions have modified exposure to air pollution and dynamics of respiratory diseases. Identifying the most vulnerable individuals requires effort to build a complete picture of the dynamic health effects of air pollution exposure, accounting for disparities across population subgroups.
We use generalized additive model to assess the likely changes in the hospitalisation and mortality rate as a result of exposure to PM2.5 and O over the course of COVID-19 pandemic. We further disaggregate the population into detailed age categories and illustrate a shifting age profile of high-risk population groups. Additionally, we apply multivariable logistic regression to integrate demographic, socioeconomic and climatic characteristics with the pollution-related excess risk.
Overall, a total of 1,051,893 hospital admissions and 34,954 mortality for respiratory disease are recorded. The findings demonstrate a transition in the association between air pollutants and hospitalisation rates over time. For every 10 µg/m increase of PM2.5, the rate of hospital admission increased by 0.2% (95% CI: 0.1-0.7%) and 1.4% (1.0-1.7%) in the pre-pandemic and dynamic zero-COVID stage, respectively. Conversely, O-related hospitalization rate would be increased by 0.7% (0.5-0.9%) in the pre-pandemic stage but lowered to 1.7% (1.5-1.9%) in the dynamic zero-COVID stage. Further assessment indicates a shift of high-risk people from children and young adolescents to the old, primarily the elevated hospitalization rates among the old people in Lianyungang (RR: 1.53, 95%CI: 1.46, 1.60) and Nantong (RR: 1.65, 95%CI: 1.57, 1.72) relative to those for children and young adolescents. Over the course of our study period, people with underlying diseases would have 26.5% (22.8-30.3%) and 12.7% (10.8-14.6%) higher odds of having longer hospitalisation and over 6 times higher odds of deaths after hospitalisation.
Our estimates provide the first comprehensive evidence on the dynamic pollution-health associations throughout the pandemic. The results suggest that age and underlying diseases collectively determines the disparities of pollution-related health effect across population subgroups, underscoring the urgency to identifying the most vulnerable individuals to air pollution.
多方面的 SARS-CoV-2 干预措施改变了人们接触空气污染和呼吸道疾病动态的方式。确定最脆弱的人群需要努力全面了解空气污染暴露的动态健康影响,同时考虑到人口亚组之间的差异。
我们使用广义加性模型来评估 COVID-19 大流行期间,由于暴露于 PM2.5 和 O3 而导致的住院率和死亡率可能发生的变化。我们进一步将人群细分为详细的年龄类别,并说明了高风险人群年龄分布的变化。此外,我们应用多变量逻辑回归将人口统计学、社会经济和气候特征与与污染相关的超额风险结合起来。
总体而言,共记录了 1051893 例因呼吸道疾病住院和 34954 例死亡。研究结果表明,随着时间的推移,空气污染物与住院率之间的关联发生了转变。PM2.5 每增加 10µg/m3,住院率分别增加 0.2%(95%置信区间:0.1-0.7%)和 1.4%(1.0-1.7%),在前疫情阶段和动态零 COVID 阶段分别增加。相反,与 O3 相关的住院率在前疫情阶段将增加 0.7%(0.5-0.9%),但在动态零 COVID 阶段将降低至 1.7%(1.5-1.9%)。进一步评估表明,高危人群从儿童和青少年转移到老年人,主要是连云港(RR:1.53,95%CI:1.46,1.60)和南通(RR:1.65,95%CI:1.57,1.72)的老年人的住院率升高与儿童和青少年相比。在我们的研究期间,患有基础疾病的人会有 26.5%(22.8-30.3%)和 12.7%(10.8-14.6%)更高的住院时间延长和住院后死亡的风险。
我们的估计提供了关于整个大流行期间动态污染与健康关联的第一个全面证据。结果表明,年龄和基础疾病共同决定了人口亚组之间与污染相关的健康影响差异,这凸显了确定最易受空气污染影响的人群的紧迫性。