2017—2021年贵州省毕节市新发尘肺病的时空分布及聚集性分析

杨绍芳; 杨军红; 何文蕾; 贺瑶瑶; 唐彪

doi:10.16462/j.cnki.zhjbkz.2023.10.018

2017—2021年贵州省毕节市新发尘肺病的时空分布及聚集性分析

doi: 10.16462/j.cnki.zhjbkz.2023.10.018

贵州省疾病预防控制中心，贵阳 550004

基金项目:

贵州省疾病预防控制中心科学基金课题 2022-E-1

详细信息

通讯作者:
杨军红，E-mail: 274054930@qq.com

中图分类号: R135
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- 被引次数: 0
出版历程
- 收稿日期: 2023-02-23
- 修回日期: 2023-07-12
- 网络出版日期: 2023-10-23
- 刊出日期: 2023-10-10

Analysis of the spatiotemporal distribution and clustering on newly reported pneumoconiosis cases in Bijie City, Guizhou Province from 2017 to 2021

Guizhou Center for Disease Control and Prevention, Guiyang 550004, China

Funds:

Science Foundation of Guizhou Center for Disease Control and Prevention 2022-E-1

More Information

Corresponding author: YANG Junhong, E-mail: 274054930@qq.com

摘要

摘要: 目的分析贵州省毕节市新发尘肺病发病的空间流行特征，为职业病防治工作提供参考。方法应用ArcGIS 10.2、Matlab R2011a和GeoDa 0.9.5-i软件对毕节市2017—2021年新发尘肺病例数进行时空分布、空间自相关、热点分析，并将4个影响因子作为自变量，新发尘肺病例数作为因变量建立空间自回归模型，呈现该市新发尘肺病例数的时空分布和聚集性特征。结果毕节市2017—2021年共报告新发尘肺病1 487例，发病人数呈逐年下降趋势(R_s < 0)。全局空间自相关结果表明，新发尘肺病例数空间分布呈非随机分布，存在一定聚集性。热点分析结果显示，2017—2020年毕节市新发尘肺病例数热点区域为金沙县，2021年度为织金县，2017—2021年累计新发尘肺病例数热点区域为金沙县，差异均有统计学意义(均P < 0.05)；金沙县、织金县与周围区域新发尘肺病例数分布呈高-高相邻模式。空间自回归模型结果显示，工业增加值、第二产业增加值、煤矿企业数量等3个影响因子差异有统计学意义，其中工业增加值最为明显(Z=2.574, P=0.010)。结论贵州省毕节市新发尘肺病例数呈逐年下降趋势，其中金沙县、织金县及邻近区域可作为重点防控区域，工业行业为尘肺病防控的重点行业。
- 毕节市 /
- 新发尘肺病 /
- 时空分布 /
- 空间自回归模型
Abstract: Objective To analyze the spatial and temporal characteristics of newly reported pneumoconiosis cases in Bijie City, Guizhou Province, aiming to provide references for the prevention and control of occupational disease. Methods The spatial and temporal distribution analysis, spatial autocorrelation analysis, hot spot analysis and spatial autoregressive analysis between four impact factors and newly reported pneumoconiosis cases (2017-2021) in Bijie City were conducted using ArcGIS 10.2, Matlab R2011a and GeoDa 0.9.5-i software. Results From 2017 to 2021, a total of 1 487 new cases of pneumoconiosis were reported in Bijie City, with an annual decrease in the number of cases (R_s < 0). The global spatial autocorrelation analysis showed a non-random spatial distribution and spatial clustering pattern for the newly reported pneumoconiosis cases. Hotspot analysis revealed that the hotspot area of the number of newly reported pneumoconiosis cases from 2017 to 2020 located in Jinsha County, and in Zhijin County at 2021, and the hotspot area of the cumulative number of newly reported pneumoconiosis cases from 2017 to 2021 located in Jinsha County. All hotspot areas were statistically significant (P < 0.05). There was a high-high adjacent distribution pattern of newly reported pneumoconiosis cases in Jinsha County, Zhijin County and surrounding areas. The results of spatial autoregressive model showed that the industrial added value, secondary sector of the economy added value and the number of coal mining enterprises were statistically significant related to the occurrence of newly reported pneumoconiosis cases in Bijie City, and the most statistically significant influencing factor was the industrial added value(Z=2.574, P=0.010). Conclusion The number of newly reported pneumoconiosis cases in Bijie City, Guizhou Province, has been decreasing annually. Jinsha, Zhijin County and the adjacent areas can be taken as the key prevention and control areas of pneumoconiosis, and the industrial sector should be the focus of pneumoconiosis prevention and control efforts.
- Bijie City /
- Newly reported pneumoconiosis /
- The spatial and temporal distribution /
- Spatial autoregressive model

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图 1 2017—2021年毕节市新发尘肺病例数空间分布

1. 威宁彝族回族苗族自治县; 2. 赫章县; 3. 七星关区; 4. 大方县; 5. 金沙县; 6. 黔西市; 7. 纳雍县; 8. 织金县。

Figure 1. Spatial distribution of newly reported pneumoconiosis cases in Bijie City from 2017 to 2021

1. WeiningYi, HuiandMiao Autonomous Count; 2. Hezhang County; 3. Qi Xing Guan District; 4. Dafang County; 5. Jinsha County; 6. Qianxi City; 7. Nayong County; 8. Zhijin County.

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图 2 毕节市新发尘肺病例数热点分布图

1. 威宁彝族回族苗族自治县; 2. 赫章县; 3. 七星关区; 4. 大方县; 5. 金沙县; 6. 黔西市; 7. 纳雍县; 8. 织金县。

Figure 2. The hot spot distribution diagram of newly reported pneumoconiosis cases in Bijie City

1. WeiningYi, HuiandMiao Autonomous Count; 2. Hezhang County; 3. Qi Xing Guan District; 4. Dafang County; 5. Jinsha County; 6. Qianxi City; 7. Nayong County; 8. Zhijin County.

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表 1 2017—2021年毕节市新发尘肺病例数的地区分布

Table 1. Geographical distribution of newly reported pneumoconiosis cases in Bijie City from 2017 to 2021

地区Areas	病例数[人数(占比/%)] Cases [Number of people (proportion /%)]
地区Areas	2017年Year	2018年Year	2019年Year	2020年Year	2021年Year	合计Total
七星关区Qi Xing Guan District	7(1.35)	4(0.90)	4(1.26)	3(1.94)	1(1.85)	19(1.28)
大方县Dafang County	83(15.99)	51(11.54)	57(17.98)	26(16.77)	4(7.41)	221(14.86)
金沙县Jinsha County	235(45.28)	237(53.62)	164(51.74)	70(45.16)	11(20.37)	717(48.22)
织金县Zhijin County	60(11.56)	58(13.12)	38(11.99)	22(14.19)	26(48.15)	204(13.72)
纳雍县Nayong County	43(8.29)	14(3.17)	9(2.84)	15(9.68)	4(7.41)	85(5.72)
黔西市Qianxi City	87(16.76)	73(16.52)	45(14.20)	19(12.26)	6(11.11)	230(15.47)
赫章县Hezhang County	1(0.19)	4(0.90)	0(0)	0(0)	2(3.70)	7(0.47)
威宁彝族回族苗族自治县Weining Yi, Hui and Miao Autonomous Count	3(0.58)	1(0.23)	0(0)	0(0)	0(0)	4(0.27)
合计Total	519(100.00)	442(100.00)	317(100.00)	155(100.00)	54(100.00)	1 487(100.00)

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表 2 Daniel趋势检验结果

Table 2. The results of Daniel trend test

地区Area	年份Year	R_s值value	趋势Trend	wp值value	显著性Significance
七星关区Qi Xing Guan District	2017—2021	-0.850	下降descend	0.900	不显著Non-significant
大方县Dafang County		-0.900	下降descend	0.900	不显著Non-significant
金沙县Jinsha County		-0.900	下降descend	0.900	不显著Non-significant
织金县Zhijin County		-0.900	下降descend	0.900	不显著Non-significant
纳雍县Nayong County		-0.700	下降descend	0.900	不显著Non-significant
黔西市Qianxi City		-1.000	下降descend	0.900	显著Significant
赫章县Hezhang County		-0.350	下降descend	0.900	不显著Non-significant
威宁彝族回族苗族自治县Weining Yi, Hui and Miao Autonomous Count		-1.450	下降descend	0.900	显著Significant
毕节市全域Whole area of Bijie City		-1.000	下降descend	0.900	显著Significant
毕节市全域Whole area of Bijie City	2017—2020	-1.000	下降descend	1.000	不显著Non-significant

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表 3 毕节市2017—2021年新发尘肺病例数全局空间自相关分析

Table 3. Global spatial autocorrelation analysis of newly reported pneumoconiosis cases in Bijie City from 2017 to 2021

年份Year	Moran′s I值value	预期指数Expectations	方差Variance	Z值value	P值value
2017	0.030	-0.143	0.006	2.252	0.024
2018	0.006	-0.143	0.005	2.034	0.042
2019	0.003	-0.143	0.006	1.948	0.051
2020	-0.040	-0.143	0.005	1.398	0.162
2021	-0.056	-0.143	0.006	1.157	0.247
合计Total	0.018	-0.143	0.005	2.133	0.033

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表 4 毕节市2017—2021年新发尘肺病例数热点分析

Table 4. Hot spot analysis of newly reported pneumoconiosis cases in Bijie City from 2017 to 2021

年份Year	地区Area	Z值value	P值value	显著性水平Significance level
2017	金沙县Jinsha County	2.361	0.018	Hot Spot-95%
2018	金沙县Jinsha County	2.471	0.013	Hot Spot-95%
2019	金沙县Jinsha County	2.419	0.016	Hot Spot-95%
2020	金沙县Jinsha County	2.369	0.018	Hot Spot-95%
2021	织金县Zhijin County	2.422	0.015	Hot Spot-95%
合计Total	金沙县Jinsha County	2.410	0.016	Hot Spot-95%

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表 5 空间自回归模型拟合度指标

Table 5. Fitting indexes of spatial autoregressive models

空间自回归模型Spatial autoregressive model	对数似然估计值Log likelihood	AIC值value	SC值value
一阶空间自回归模型Ordinary least squares	-37.477	80.953	81.192
空间滞后模型Spatial lag model	-37.050	82.099	82.417
空间误差模型Spatial error model	-37.229	80.457	80.696
注：AIC, 赤池信息准则; SC, 施瓦茨信息准则。 Note：AIC, Akaike Information Criterion; SC, Schwarz criterion.

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表 6 毕节市各区县新发尘肺病例数单因素及多因素空间自回归分析指标

Table 6. Univariate and multivariate spatial autoregressive analysis of newly reported pneumoconiosis cases in different districts and counties of Bijie City

影响因素Impact factors	单因素空间误差模型Single factor spatial error model					多因素空间误差模型Multi-factor spatial error model
影响因素Impact factors	回归系数Coefficient	Z值value	P值value	异方差性诊断值B-P test value	PROB值value	回归系数Coefficient	Z值value	P值value	95% CI
地区生产总值Gross domestic product	-0.113	-0.722	0.470	0.145	0.704
工业增加值Industry	1.408	3.223	0.001	5.425	0.020	0.976	2.492	0.013	(0.209~1.744)
第二产业增加值Secondary industry	1.056	2.771	0.006	7.692	0.006
煤矿企业数量The number of coal mining enterprises	3.320	2.590	0.010	4.918	0.027	2.121	2.311	0.021	(0.322~3.919)

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