Association of short-term exposure to PM2.5 and PM10 with hospitaliza-tion risk and excess expenses burden of unstable angina pectoris patients in Yichang city
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摘要:
目的 了解细颗粒物（PM2.5）和可吸入颗粒物（PM10）短期暴露对不稳定型心绞痛（UAP）住院风险及超额住院经济负担的影响，为UAP的预防控制提供参考依据。 方法 收集湖北省宜昌市6家综合医院2019年1月1日 — 2021年12月31日11334例UAP住院患者的病案首页信息以及宜昌市城区5个国控环境空气质量自动监测站每日的污染物监测数据和城区气象数据，采用时间分层病例交叉设计通过条件logistic回归模型分析PM2.5和PM10暴露浓度每变化10 μg/m3时UAP患者住院风险的变化情况和超额经济负担。 结果 湖北省宜昌市11334例UAP住院患者的病例天数为11334 d，匹配的对照天数为38540 d。在单污染物模型中，在Lag0、Lag1、Lag01和Lag02时间段，宜昌市PM2.5和PM10暴露浓度每增加10 μg/m3，UAP患者住院风险分别增加0.92%和0.89%、0.73%和0.62%、1.00%和0.92%、0.84%和0.85%。在多污染物模型中，排除了二氧化硫（SO2）、二氧化氮（NO2）、一氧化碳（CO）、臭氧（O3）、温度（T）、湿度（RH）以及病例性别、年龄等因素的影响后，宜昌市PM2.5在Lag0～Lag4和Lag01～Lag05时间段对UAP患者的住院风险有显著影响，其中在Lag02时间段的效应最大（ER = 1.86%，95%CI = 0.46%～3.27%）；宜昌市PM10在Lag0、Lag1、Lag01和Lag02时间段对UAP患者的住院风险有显著影响，其中在Lag01时间段的效应最大（ER = 1.19%，95%CI = 0.23%～2.16%）。分层分析结果显示，PM2.5和PM10对UAP患者住院风险的显著效应在性别亚组和入院季节亚组中依然存在，在男性亚组中，PM2.5和PM10分别在Lag01（ER = 2.69%，95%CI = 0.40%～5.03%）和Lag0（ER = 1.59%，95%CI = 0.47%～2.72%）时间段的效应最大；在暖季亚组中，PM2.5和PM10均在Lag02时间段的效应最大（PM2.5：ER = 6.69%，95%CI = 1.47%～12.17%；PM10：ER = 4.80%，95%CI = 2.46%～7.19%）。按中国现行空气质量二级控制标准，研究期间宜昌市因PM2.5和PM10超标导致的UAP患者超额住院数分别为109例和28例，产生UAP超额住院经济负担155.4万元和37.3万元。 结论 宜昌市PM2.5和PM10短期暴露浓度升高可导致UAP住院风险及超额住院经济负担的显著增加。 -
关键词:
- 不稳定型心绞痛（UAP） /
- 住院风险 /
- 超额住院经济负担 /
- 细颗粒物（PM2.5) /
- 可吸入颗粒物（PM10） /
- 短期暴露 /
- 影响
Abstract:Objective To explore the association of short-term exposure to particulate matter ≤ 2.5 and ≤ 10 μm in aerodynamic diameter (PM2.5 and PM10) with hospitalization risk and excess expenses burden of unstable angina pectoris (UAP) patients for providing a reference to UAP prevention. Methods Medical records of 11 334 UAP patients hospitalized from 2019 through 2021 were collected from 6 hospitals in Yichang city, Hubei province. Local data over the same period on daily ambient air pollutants and meteorological factors were also collected from five national automatic monitoring stations and municipal weather bureau. Time-stratified case-crossover design and conditional logistic regression model were adopted to analyze the association of each 10 μg/m3 change in ambient air PM2.5 and PM10 concentration with UAP hospitalization risk and excess economic burden. Results In total, 11 334 UAP case days were matched to 38 540 control days. In single pollutant model analysis, a 10 μg/m3 increase in PM2.5 and PM10 was associated with the increased UAP hospitalization risk of 0.92% and 0.89% on lag day 0 (Lag0), 0.73% and 0.62% on Lag1, 1.00% and 0.92% over lag days 0 – 1 (Lag0 – 1), and 0.84% and 0.85% over Lag0 – 2, respectively. After adjusting for sulfur dioxide, nitrogen dioxide, carbon monoxide, ozone, temperature and relative humidity in multi-pollutant model analysis, PM2.5 was significantly associated with increased UAP hospitalization risk on days from Lag0 to Lag4 and over days from Lag0 – 1 to Lag0 – 5 and the highest increased risk was observed over Lag0 – 2 (excess risk [ER] = 1.86%, 95% confidence interval [95%CI]: 0.46% – 3.27%); PM10 showed a significant impact on UAP hospitalization risk on Lag0, Lag1, and over Lag0 – 1 and Lag0 – 2, and with the highest increased risk over Lag0 – 1 (ER = 1.19%, 95%CI: 0.23% – 2.16%). Further stratified analysis indicated that the significant association still existed for gender and seasonal subgroup patients. For males, PM2.5 and PM10 was associated with the highest increased risk over Lag0 – 1 (ER = 2.69%, 95%CI: 0.40% – 5.03%) and on Lag0 (ER = 1.59%, 95%CI: 0.47% – 2.72%). In warm season, PM2.5 and PM10 was associated with the highest increased risk over Lag0 – 2 (for PM2.5: ER = 2.69%, 95%CI: 0.40% – 5.03%; for PM10: ER = 1.59%, 95%CI: 0.47% – 2.72%). During 2019 – 2021 in Yichang city, the increments of ambient air PM2.5 and PM10 were associated with the increased UAP hospitalizations of 109 and 28 and excessive UAP-related economic burden of 1.554 and 0.373 million Chinese Yuan based on China's current secondary air quality control standards, respectively. Conclusion Short-term exposure to increased ambient air PM2.5 and PM10 can significantly increase hospitalization risk and excess expenditure of unstable angina pectoris patients in Yichang city. -
表 1 病例日与对照日污染物和气象条件分布情况比较（$\bar x \pm s$）
Table 1. Annual mean daily concentration of major ambient air pollutants and temperature and relative humidity by observed meteorological conditions by case days and control days in Yichang city, 2019 – 2021
项目 病例日 对照日 t 值 P 值 PM2.5（μg/m3） 45.1 ± 39.6 44.3 ± 39.0 – 1.79 0.073 PM10（μg/m3） 69.3 ± 44.8 68.4 ± 44.1 – 1.96 0.049 NO2（μg/m3） 30.7 ± 10.4 30.3 ± 10.4 – 3.55 < 0.001 SO2（μg/m3） 6.8 ± 2.6 6.8 ± 2.6 – 0.61 0.543 CO（mg/m3） 0.78 ± 0.28 0.78 ± 0.29 – 0.08 0.936 O3（μg/m3） 78.9 ± 41.8 80.4 ± 41.8 3.34 0.001 T（℃） 16.9 ± 8.0 16.9 ± 8.1 0.59 0.553 RH（%） 75.8 ± 14.4 75.6 ± 14.3 – 1.04 0.296 
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表 2 空气污染物与每日UAP住院人数的相关性分析（r）
Table 2. Correlation coefficients between annual mean daily concentration of major ambient air pollutants and daily number of unstable angina pectoris hospitalization from the analysis on the data of 11 334 case days and 38 540 control days in Yichang city, 2019 – 2021
项目 PM10 NO2 SO2 CO O3 每日住院UAP人数 PM2.5 0.92 a 0.67 a 0.57 a 0.75 a – 0.31 a – 0.02 PM10 0.71 a 0.62 a 0.64 a – 0.17 a 0.01 NO2 0.54 a 0.48 a – 0.36 a 0.16 a SO2 0.36 a – 0.08 a – 0.02 CO – 0.36 a – 0.09 a O3 – 0.06 a 注：a P < 0.05。
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表 3 PM2.5和PM10浓度每增加10 μg/m3的UAP住院超额风险单污染物和多污染物模型分析
Table 3. Lag day-specific estimated excess risk of unstable angina pectoris hospitalization associated with each 10 μg/m3 increase in PM2.5 and PM10 concentration derived from single-pollutant and multi-pollutants modeling analysis on the data of 11 334 case days and 38 540 control days in Yichang city, 2019 – 2021
时间段 单污染物模型 多污染物模型 a PM2.5 PM10 PM2.5 PM10 ER 95%CI ER 95%CI ER 95%CI ER 95%CI Lag0 0.92% 0.25%～1.59% b 0.89% 0.36%～1.42% b 1.28% 0.10%～2.47% b 1.13% 0.24%～2.03% b Lag1 0.73% 0.07%～1.39% b 0.62% 0.10%～1.14% b 1.79% 0.63%～2.97% b 0.99% 0.13%～1.86% b Lag2 0.10% – 0.56%～0.76% 0.24% – 0.27%～0.7%5 1.76% 0.62%～2.92% b 0.54% – 0.32%～1.41% Lag3 – 0.58% – 1.23%～0.07% – 0.19% – 0.69%～0.32% 1.64% 0.53%～2.77% b – 0.07% – 0.94%～0.80% Lag4 – 0.51% – 1.16%～0.15% – 0.28% – 0.79%～0.23% 1.25% 0.10%～2.41% b 0.74% – 0.14%～1.63% Lag5 – 0.49% – 1.13%～0.15% – 0.32% – 0.82%～0.19% 0.49% – 0.65%～1.65% 0.48% – 0.40%～1.37% Lag6 – 0.49% – 1.10%～0.13% – 0.35% – 0.85%～0.16% 0.99% – 0.07%～2.06% 0.27% – 0.60%～1.14% Lag01 1.00% 0.26%～1.74% b 0.92% 0.34%～1.51% b 1.77% 0.48%～3.08% b 1.19% 0.23%～2.16% b Lag02 0.84% 0.04%～1.64% b 0.85% 0.22%～1.48% b 1.86% 0.46%～3.27% b 1.11% 0.06%～2.18% b Lag03 0.31% – 0.55%～1.17% 0.46% – 0.21%～1.14% 1.66% 0.15%～3.18% b 0.86% – 0.28%～2.01% Lag04 – 0.07% – 0.99%～0.85% 0.17% – 0.55%～0.90% 1.68% 0.08%～3.30% b 1.03% – 0.19%～2.27% Lag05 – 0.42% – 1.38%～0.56% – 0.11% – 0.87%～0.66% 1.79% 0.10%～3.51% b 1.17% – 0.14%～2.49% Lag06 – 0.76% – 1.79%～0.27% – 0.38% – 1.19%～0.43% 1.77% – 0.09%～3.59% 1.32% – 0.08%～2.74% 注：a 多污染物模型中调整了SO2、NO2、CO、O3、RH、T以及病例性别、年龄等因素的影响；b P < 0.05。
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表 4 PM2.5和PM10浓度每增加10 μg/m3不同性别的UAP住院超额风险多污染物模型分析
Table 4. Lag day-specific estimated excess risk of unstable angina pectoris hospitalization by gender associated with each 10 μg/m3 increase in PM2.5 and PM10 concentration derived from multi-pollutants modeling analysis on the data of 11 334 case days and 38 540 control days in Yichang city, 2019 – 2021
时间段 男性 a 女性 a PM2.5 PM10 PM2.5 PM10 ER 95%CI ER 95%CI ER 95%CI ER 95%CI Lag0 2.06% 0.56%～3.59% b 1.59% 0.47%～2.72% b 2.21% 0.40%～4.05% b 1.72% 0.30%～3.16% b Lag1 1.67% 0.23%～3.14% b 1.37% 0.23%～2.53% b 1.71% – 0.15%～3.60% 0.97% – 0.38%～2.33% Lag2 1.35% – 0.13%～2.85% 1.03% – 0.10%～2.16% 1.36% – 0.46%～3.22% 0.69% – 0.66%～2.05% Lag3 1.04% – 0.48%～2.57% 0.37% – 0.76%～1.51% 1.59% – 0.16%～3.37% 0.15% – 1.21%～1.52% Lag4 0.35% – 1.12%～1.85% 1.17% 0.02%～2.33% b 1.09% – 0.73%～2.94% 0.98% – 0.40%～2.39% Lag5 1.44% – 0.03%～2.94% 1.00% – 0.15%～2.15% 0.69% – 1.12%～2.53% 0.61% – 0.77%～2.00% Lag6 1.01% – 0.37%～2.40% 0.78% – 0.34%～1.92% 1.00% – 0.66%～2.68% 0.42% – 0.94%～1.80% Lag01 2.69% 0.40%～5.03% b 1.56% 0.31%～2.82% b 2.90% 0.07%～5.80% b 1.55% 0.03%～3.09% b Lag02 2.36% 0.19%～4.58% b 1.47% 0.11%～2.85% b 2.68% – 0.01%～5.43% 1.47% – 0.18%～3.15% Lag03 1.92% 0.12%～3.76% b 1.17% – 0.31%～2.66% 2.29% – 0.24%～4.89% 1.27% – 0.51%～3.08% Lag04 1.78% 0.11%～3.47% b 1.28% – 0.30%～2.89% 1.80% – 0.24%～3.87% 1.51% – 0.40%～3.47% Lag05 1.65% – 0.27%～3.62% 1.41% – 0.28%～3.13% 1.76% – 0.45%～4.02% 1.67% – 0.38%～3.76% Lag06 1.93% – 0.12%～4.02% 1.55% – 0.26%～3.40% 1.66% – 0.73%～4.10% 1.84% – 0.35%～4.09% 注：a 多污染物模型中调整了SO2、NO2、CO、O3、RH、T以及病例性别、年龄等因素的影响；b P < 0.05。
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表 5 PM2.5和PM10浓度每增加10 μg/m3不同入院季节的UAP住院超额风险多污染物模型分析
Table 5. Lag day-specific estimated excess risk of unstable angina pectoris hospitalization in warm and cold seasons associated with each 10 μg/m3 increase in PM2.5 and PM10 concentration derived from multi-pollutants modeling analysis on the data of 11 334 case days and 38 540 control days in Yichang city, 2019 – 2021
时间段 暖季 a 冷季 a PM2.5 PM10 PM2.5 PM10 ER 95%CI ER 95%CI ER 95%CI ER 95%CI Lag0 3.81% 0.72%～6.99% b 3.02% 1.14%～4.94% b 2.27% 0.72%～6.99% b 1.07% 0.02%～2.14% b Lag1 4.78% 1.65%～8.01% b 3.43% 1.56%～5.33% b 1.88% 1.65%～8.01% b 1.07% 0.02%～2.13% b Lag2 3.19% 0.25%～6.22% b 2.53% 0.74%～4.35% b 1.32% 0.25%～6.22% b 1.03% – 0.03%～2.09% Lag3 1.23% – 1.66%～4.20% 0.98% – 0.77%～2.77% 1.16% – 1.66%～4.20% 0.51% – 0.50%～1.52% Lag4 2.20% – 0.67%～5.15% 0.89% – 0.89%～2.70% 1.09% – 0.67%～5.15% 0.29% – 0.74%～1.33% Lag5 1.73% – 1.12%～4.67% – 0.72% – 2.47%～1.06% 0.76% – 1.12%～4.67% 0.19% – 0.84%～1.22% Lag6 1.31% – 1.52%～4.23% 0.35% – 1.44%～2.18% 1.02% – 1.52%～4.23% – 0.18% – 1.21%～0.86% Lag01 5.78% 2.18%～9.51% b 4.16% 2.04%～6.32% b 2.41% 2.18%～9.51% b 1.28% 0.14%～2.42% b Lag02 6.69% 1.47%～12.17% b 4.80% 2.46%～7.19% b 2.24% 1.47%～12.17% b 1.26% 0.03%～2.51% b Lag03 6.33% 2.36%～10.45% b 4.61% 2.09%～7.19% b 1.88% 2.36%～10.45% b 0.95% – 0.56%～2.49% Lag04 5.86% 1.60%～10.3% b 4.59% 1.89%～7.36% b 1.39% 1.60%～10.30% b 0.94% – 0.69%～2.59% Lag05 6.27% 1.69%～11.05% b 4.04% 1.18%～6.98% b 1.41% 1.69%～11.05% b 0.85% – 0.48%～2.20% Lag06 6.48% 1.60%～11.59% b 4.18% 1.13%～7.34% b 1.49% 1.60%～11.59% b 0.81% – 0.61%～2.25% 注：a 多污染物模型中调整了SO2、NO2、CO、O3、RH、T以及病例性别、年龄等因素的影响；b P < 0.05。
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