蚌埠市某区2015—2020年空气污染对恶性肿瘤死亡风险影响的队列研究

蔡军; 张文浩; 张珮瑶; 赵珂; 贾贤杰; 芈静

doi:10.16766/j.cnki.issn.1674-4152.003000

蚌埠市某区2015—2020年空气污染对恶性肿瘤死亡风险影响的队列研究

doi: 10.16766/j.cnki.issn.1674-4152.003000

蚌埠医学院公共卫生学院流行病学与卫生统计学教研室, 安徽蚌埠 233033

基金项目:

安徽省高校自然科学研究项目 KJ2021A0710

详细信息

通讯作者:
芈静，E-mail: xiaomi05011@sina.com

中图分类号: R122.7 R730.1
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出版历程
- 收稿日期: 2022-11-05

A cohort study on the effect of air pollution on the risk of malignant tumor mortality in a district of Bengbu City from 2015 to 2020

Department of Epidemiology and Health Statistics, School of Public Health, Bengbu Medical College, Bengbu, Anhui 233033, China

摘要

摘要: 目的探讨6种空气污染物对恶性肿瘤患者死亡风险的影响。方法本研究是采用蚌埠市禹会区2015—2020年恶性肿瘤的发病和死亡信息进行的一项回顾性队列研究。利用中国高分辨率的空气污染数据集(CHAP)收集PM2.5、PM10、NO₂、CO、SO₂和O₃浓度，并分析6种空气污染物之间的Spearman等级相关系数。以6种空气污染物的第1三分位数等分点(Q1组)为基线组，应用Cox比例风险回归模型估计6种空气污染物与恶性肿瘤患者死亡风险的风险比(HR)及其95%的置信区间(95% CI)。结果本研究最终纳入蚌埠市禹会区2015—2020年恶性肿瘤患者3 840例，其中死亡人数为1 443例。Spearman相关分析显示O₃与其他污染物之间呈负相关关系，而其他5种污染物互相之间呈正相关关系。Cox比例风险回归模型分析结果显示：除O₃外，其他空气污染物在高浓度时与恶性肿瘤的死亡风险呈正相关关系，其中PM2.5的HR(4.553，95% CI：3.853~5.381)最大。O₃与恶性肿瘤的死亡风险呈负相关关系。结论空气污染可以影响致癌过程，对确诊的恶性肿瘤患者死亡风险具有显著的影响。本研究为降低恶性肿瘤患者的死亡风险提供了重要的公共卫生意义。
- 空气污染 /
- 恶性肿瘤 /
- 死亡风险 /
- Cox比例风险回归模型
Abstract: Objective To explore the impact of six types of air pollution on the risk of death in patients with malignancies. Methods This was a retrospective cohort study using the information on incidence and mortality of malignant tumors in Yuhui District, Bengbu City, from 2015 to 2020. PM2.5, PM10, NO₂, CO, SO₂, and O₃ concentrations were collection of using a high-resolution air pollution dataset from China (CHAP) and analyzed by Spearman' s rank correlation coefficients between the six air pollutants. Using the first tri quantile (Q1 group) of the six air pollutants as the baseline group, risk ratios (HR) and their 95% confidence intervals (95% CI) for six types of air pollution and the risk of death in patients with malignancies were estimated by Cox proportional hazards models. Results This study finally included 3 840 cases of malignant tumors in Yuhui District, Bengbu City, from 2015 to 2020, of which 1 443 cases died. Spearman' s correlation showed a negative correlation between O₃ and the other pollutants, while the other five had a positive correlation. Cox proportional risk regression model analysis showed that air pollutants other than O₃ were positively associated with the risk of death from malignancy at high concentrations, with PM2.5 having the most significant HR (4.553, 95% CI: 3.853-5.381). O₃ was negatively associated with the risk of death from malignancy. Conclusion Air pollution can influence carcinogenic processes and significantly impact the risk of death in patients with diagnosed malignancies. Since environmental factors are not usually used as survival healing factors after diagnosis, this study provides important public health implications.
- Air pollution /
- Malignant tumors /
- Risk of death /
- Cox proportional risk regression model
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表 1 2015—2020年蚌埠市禹会区恶性肿瘤患者的基本特征

Table 1. Basic characteristics of patients with malignant tumors in Bengbu Yuxiang District, 2015-2020

变量	数值
死亡例数[例(%)]	1 443(37.58)
性别[例(%)]
男性	2 189(57.01)
女性	1 651(42.99)
年龄(x ±s，岁)	63.64±14.24
建成环境[M(P₂₅, P₇₅)]
人口密度(人/km²)	6 325(781, 10 503)
建筑密度	0.55(0.16, 0.85)
交叉口密度(个/km²)	84(20, 84)
土地利用混合度	1.20(0.22, 1.55)
公交站点密度(个/km²)	15.10(1.30, 20.00)
NDVI	0.27(0.26, 0.46)

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表 2 6种空气污染物三分位点浓度(x ±s)

Table 2. Trichotomous concentrations of six air pollutants(x ±s)

空气污染物	Q1	Q2	Q3	总计
PM2.5(μg/m³)	45.18±3.44	52.20±1.38	57.79±2.84	51.72±5.82
PM10(μg/m³)	87.68±4.08	95.42±1.04	100.25±3.93	94.45±6.15
NO₂(μg/m³)	30.95±2.85	39.62±2.98	46.98±2.68	39.18±7.14
CO(mg/m³)	0.81±0.07	1.06±0.08	1.46±0.23	1.11±0.31
SO₂(μg/m³)	7.84±1.37	13.73±2.06	23.13±4.40	14.90±6.94
O₃(μg/m³)	92.96±4.82	100.09±1.22	104.32±1.84	99.13±5.60

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表 3 空气污染物之间的Spearman等级相关系数(r值)

Table 3. Spearman' s rank correlation coefficient between air pollutants (r_s values)

空气污染物	PM2.5	PM10	NO₂	CO	SO₂	O₃
PM2.5	1.000
PM10	0.850^a	1.000
NO₂	0.404^a	0.345^a	1.000
CO	0.809^a	0.659^a	0.673^a	1.000
SO₂	0.767^a	0.634^a	0.600^a	0.965^a	1.000
O₃	-0.576^a	-0.453^a	-0.815^a	-0.890^a	-0.852^a	1.000
注：^aP < 0.001。

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表 4 变量赋值方法

Table 4. Variable assignment description

变量	赋值方法
性别	男性=1，女性=2
状态	死亡=1，存活=2
分组	Q1组=1，Q2组=2，Q3组=3

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表 5 空气污染与恶性肿瘤患者死亡风险的Cox比例风险回归模型分析(模型1^a)

Table 5. Cox proportional risk regression model analysis of air pollution and risk of death in patients with malignancy (model 1^a)

变量	B	SE	Wald χ²	P值	HR值	95% CI
PM2.5
Q2	0.318	0.088	12.935	< 0.001	1.374	1.156~1.634
Q3	1.453	0.079	339.190	< 0.001	4.274	3.662~4.989
PM10
Q2	0.478	0.080	35.279	< 0.001	1.613	1.378~1.889
Q3	0.934	0.076	151.851	< 0.001	2.543	2.192~2.950
NO₂
Q2	-0.313	0.077	16.455	< 0.001	0.731	0.629~0.851
Q3	0.668	0.062	116.551	< 0.001	1.949	1.726~2.200
CO
Q2	-0.119	0.081	2.182	0.140	0.887	0.758~1.040
Q3	0.885	0.071	156.049	< 0.001	2.421	2.108~2.782
SO₂
Q2	-0.268	0.074	13.234	< 0.001	0.765	0.662~0.884
Q3	0.289	0.068	18.082	< 0.001	1.335	1.169~1.525
O₃
Q2	-0.310	0.062	25.337	< 0.001	0.733	0.650~0.828
Q3	0.756	0.068	122.868	< 0.001	0.470	0.411~0.537
注：^a为未调整模型。

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表 6 空气污染与恶性肿瘤患者死亡风险的Cox比例风险回归模型分析(模型2^a)

Table 6. Cox proportional risk regression model analysis of air pollution and risk of death in patients with malignancy (model 2^a)

变量	B	SE	Wald χ²	P值	HR值	95% CI
PM2.5
Q2	0.268	0.088	9.170	0.002	1.307	1.099~1.555
Q3	1.350	0.079	291.033	< 0.001	3.856	3.302~4.503
PM10
Q2	0.425	0.081	27.736	< 0.001	1.529	1.305~1.791
Q3	0.827	0.076	118.372	< 0.001	2.285	1.969~2.652
NO₂
Q2	-0.259	0.077	11.247	0.001	0.772	0.663~0.898
Q3	0.626	0.062	102.176	< 0.001	1.870	1.656~2.111
CO
Q2	-0.152	0.081	3.542	0.060	0.859	0.733~1.006
Q3	0.789	0.071	123.383	< 0.001	2.201	1.915~2.529
SO₂
Q2	-0.308	0.074	17.446	< 0.001	0.735	0.636~0.849
Q3	0.197	0.068	8.323	0.004	1.217	1.065~1.392
O₃
Q2	-0.259	0.062	17.574	< 0.001	0.772	0.684~0.871
Q3	-0.715	0.068	109.660	< 0.001	0.489	0.428~0.559
注：^a为调整性别、年龄。

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表 7 空气污染与恶性肿瘤患者死亡风险的Cox比例风险回归模型分析(模型3^a)

Table 7. Cox proportional risk regression model analysis of air pollution and risk of death in patients with malignancy (model 3^a)

变量	B	SE	Wald χ²	P值	HR值	95% CI
PM2.5
Q2	0.305	0.090	11.493	0.001	1.357	1.137~1.619
Q3	1.516	0.085	316.363	< 0.001	4.553	3.853~5.381
PM10
Q2	0.439	0.082	28.447	< 0.001	1.551	1.320~1.823
Q3	0.784	0.078	101.593	< 0.001	2.190	1.880~2.550
NO₂
Q2	0.071	0.118	0.365	0.546	1.074	0.853~1.352
Q3	1.064	0.138	59.746	< 0.001	2.899	2.213~3.796
CO
Q2	-0.107	0.081	1.736	0.188	0.898	0.766~1.054
Q3	0.930	0.081	132.868	< 0.001	2.534	2.164~2.969
SO₂
Q2	-0.289	0.074	15.226	< 0.001	0.749	0.648~0.866
Q3	0.197	0.071	7.803	0.005	1.218	1.061~1.399
O₃
Q2	-0.380	0.066	32.871	< 0.001	0.684	0.601~0.779
Q3	-1.177	0.117	100.789	< 0.001	0.308	0.245~0.388
注：^a为调整性别、年龄、人口密度、建筑密度、交叉口密度、土地利用混合度、公交站点密度和NDVI。

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