安徽省2016―2019年食源性疾病与气温的关系分析

朱建胜; 孟灿; 徐粒子; 赵云霞; 林超; 苏虹

doi:10.16462/j.cnki.zhjbkz.2023.05.017

安徽省2016―2019年食源性疾病与气温的关系分析

doi: 10.16462/j.cnki.zhjbkz.2023.05.017

朱建胜¹,
孟灿³,
徐粒子³,
赵云霞²,
林超²,
苏虹^4, ,

1.
241000 芜湖，芜湖市疾病预防控制中心主任室
2.
241000 芜湖，芜湖市疾病预防控制中心公共卫生科
3.
230601 合肥，安徽省疾病预防控制中心, 安徽省公共卫生研究院食品营养与学校卫生科
4.
230032 合肥，安徽医科大学公共卫生学院流行病与卫生统计学系

基金项目:

国家自然科学基金 81773518

详细信息

通讯作者:
苏虹，E-mail：suhong5151@sina.com

中图分类号: R181.3;R155.3
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- 文章访问数: 148
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- 被引次数: 0
出版历程
- 收稿日期: 2022-04-14
- 修回日期: 2022-10-06
- 刊出日期: 2023-05-10

Analysis of the association between ambient temperature and foodborne diseases in Anhui Province, 2016-2019

1.
Office of Center Director, Wuhu Center for Disease Control and Prevention, Wuhu 241000, China
2.
Department of Pulic Health, Wuhu Center for Disease Control and Prevention, Wuhu 241000, China
3.
Department of Food Nutrition and School Health, Anhui Provincial Center for Disease Control and Prevention, Public Health Research Institute of Anhui Province, Hefei 230601, China
4.
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230032, China

Funds:

National Natural Science Foundation of China 81773518

More Information

Corresponding author: SU Hong，E-mail：suhong5151@sina.com

摘要

摘要: 目的分析安徽省食源性疾病与气温的关系，探讨滞后效应及识别易感人群。方法收集2016―2019年安徽省各市食源性疾病监测数据以及同期气象数据。采用类Poisson回归的广义线性模型分析各市日均气温和食源性疾病的关系，然后采用Meta分析合并效应值。结果研究期间，安徽省共填报348 958例食源性疾病病例，年均发病率0.13%。日均气温与食源性疾病发病呈线性关系，且存在一定的滞后效应。单日滞后中，其效应在当天(lag0)最大，效应值为1.009 6(95% CI：1.004 7~1.019 0)，即日均气温每增加1 ℃，当日食源性疾病发生风险增加1.009 6倍。到滞后第3天及以后其效应无统计学意义。累积滞后中，lag05对应的RR值最大，为1.019 9(95% CI: 1.012 6~1.027 2)。亚组分析结果显示， < 65岁年龄组较≥65岁年龄组更易受到影响。结论气温升高会增加食源性疾病的发病风险，且存在滞后效应，应加强对易感人群的预防。
- 食源性疾病 /
- 气温 /
- 分布特征
Abstract: Objective To examine the effect of ambient temperature on foodborne diseases in Anhui Province, identify lag effects, and pinpoint vulnerable populations. Methods Foodborne disease surveillance data and meteorological data from all Cities in Anhui Province from 2016 to 2019 were collected. A generalized linear model based on quasi-Poisson regression was used to analyze the potential association between mean temperature and foodborne diseases in each city. Meta-analysis was then applied to pool the estimated city-specific effects. Results Between 2016 and 2019, 348 958 cases of foodborne diseases were reported in Anhui Province, with an annual incidence rate of 0.13%. Mean ambient temperature exhibited a linear effect on foodborne diseases incidence and revealed a delayed effect. In single lag effect, the maximum effect occurred at lag0 with a corresponding RR of 1.009 6 (95% CI: 1.004 7-1.019 0), indicating that a 1 ℃ temperature increase would raise the risk of foodborne diseases by 1.009 6-time on the current day. As the lag day lengthened, the effect diminished gradually, becoming statistically insignificant on the third lag day. For cumulative lag effects, the maximum effect was at lag05 1.019 9(95% CI: 1.012 6-1.027 2). Subgroup analysis showed that individuals less than 65 years old were more susceptible than those aged 65 or older. Conclusions Ambient temperature can increase the risk of foodborne disease, with a lag effect observed. Prevention program on foodborne disease should be focusing on susceptible individuals.
- Foodborne diseases /
- Temperature /
- Distribution characteristic

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图 1 安徽省2016―2019年食源性疾病发病地区分布图

Figure 1. Distribution map of foodborne diseases between 2016-2019 in Anhui Province

下载: 全尺寸图片幻灯片

图 2 2016―2019年安徽省食源性疾病发病及每日MT的时序图

Figure 2. Time distribution of foodborne diseases and MT in Anhui Province, 2016-2019

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图 3 安徽省日均气温对食源性疾病发病影响的滞后效应

Figure 3. The lag effect of the association between average daily temperature and incidence of foodborne diseases in Anhui Province

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图 4 安徽省各城市日均气温(lag05)与食源性疾病发病的关系

Figure 4. The association between the daily average temperature (lag05) and foodborne diseases in Anhui Province

下载: 全尺寸图片幻灯片

表 1 安徽省2016―2019年各市食源性疾病发病数及气象因素基本情况[M(IQR)]

Table 1. Basic characteristics of number of foodborne diseases and meteorological factors in cities of Anhui Province, 2016-2019 [M(IQR)]

城市	日发病数	MT(℃)	平均RH(%)	平均WS(m/s)	平均RF(mm)	平均P(hpa)
阜阳市	28.0(37.0)	17.1(17.0)	75.0(20.0)	2.1(1.3)	0.0(0.1)	1 013.4(16.1)
安庆市	17.0(14.0)	18.1(15.4)	76.0(21.0)	2.5(1.8)	0.0(1.3)	1 010.8(14.6)
滁州市	16.0(16.0)	17.0(16.2)	78.0(16.0)	1.6(1.0)	0.0(0.3)	1 013.3(14.9)
合肥市	16.0(21.0)	17.4(15.8)	77.0(17.0)	1.9(1.5)	0.0(0.9)	1 012.3(15.7)
芜湖市	14.0(11.0)	17.6(15.6)	78.0(18.0)	2.4(1.4)	0.0(0.0)	1 011.4(15.1)
蚌埠市	12.0(18.0)	16.8(16.8)	77.0(17.0)	2.3(1.4)	0.0(0.2)	1 013.7(16.0)
六安市	11.0(10.0)	17.5(15.8)	75.0(22.2)	1.7(0.9)	0.0(1.3)	1 007.7(15.5)
宿州市	11.0(20.0)	16.6(14.7)	74.0(21.0)	1.9(1.0)	0.0(2.2)	1 014.4(16.2)
马鞍山市	10.0(9.0)	17.6(15.1)	75.0(23.0)	2.5(1.5)	0.0(0.0)	1 009.4(14.5)
宣城市	10.0(11.0)	17.7(15.6)	77.0(10.0)	2.4(1.3)	0.0(1.1)	1 014.0(15.0)
亳州市	9.0(13.0)	17.2(17.8)	70.0(21.0)	2.3(1.2)	0.0(0.0)	1 012.6(16.4)
铜陵市	9.0(11.0)	18.0(15.3)	79.0(18.0)	2.3(1.5)	0.0(1.0)	1 014.6(15.8)
黄山市	8.0(8.0)	16.1(15.4)	90.0(23.0)	4.8(2.8)	0.2(3.9)	999.4(3.9)
池州市	7.0(7.0)	17.8(15.6)	82.0(15.0)	2.0(1.2)	0.0(1.3)	1 010.7(14.7)
淮南市	7.0(14.0)	16.7(16.4)	82.0(16.0)	2.1(1.4)	0.0(0.2)	1 014.4(15.6)
淮北市	6.0(9.0)	16.7(18.2)	71.0(23.0)	1.5(0.9)	0.0(0.1)	1 014.0(16.0)

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表 2 气象因素间Spearman相关性分析

Table 2. Spearman correlation analysis between meteorological factors

气象因素	MT	RH	P	RF	WS
MT	1.00	0.03	-0.84	-0.05	-0.02
RH		1.00	-0.18	0.62	0.03
P			1.00	-0.12	-0.08
RF				1.00	0.16
WS					1.00
注: 所有r均有统计学意义(均有P < 0.05)。

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表 3 气温与食源性疾病关系敏感性分析

Table 3. Sensitivity analysis of the association between ambient temperature and foodborne diseases

分析指标	RR值(95% CI)	P值
控制变量
P	1.017 0(1.007 1~1.026 8)	0.643
改变参数
υ=2	1.019 4(1.012 1~1.026 6)	0.924
υ=4	1.019 2(1.011 7~1.026 7)	0.895
υ=5	1.016 3(1.008 1~1.024 4)	0.519

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