Analysis of spatial distribution characteristics of pulmonary tuberculosis cases in Hefei City, 2009—2020

doi:10.19982/j.issn.1000-6621.20220159

Abstract

Abstract:

Objective: To analyze the spatial distribution characteristics of pulmonary tuberculosis (PTB) cases in Hefei City from 2009 to 2020. Methods: The information of 42681 PTB patients who were recorded in The TB Management Information System (a sub-system of the China Disease Prevention and Control Information System) in Hefei City from 2009 to 2020 was obtained. A case spatial distribution map of the street/township level was drawn with GeoDa and Arcgis 10.8 spatial analysis software, to explore the spatial distribution pattern and cold/hot spot areas of PTB. Results: A total of 42681 PTB cases were notified in Hefei City from 2009 to 2020, the incidence decreased from 57.96/100000 (4195/7237966) in 2009 to 31.04/100000 (2908/9369881) in 2020 and showed a fluctuating decreasing trend ($\chi _{\text{trend}}^{2}$=12.531,P<0.001). The result of global spatial autocorrelation analysis showed that there was spatial autocorrelation of PTB incidence in Hefei City from 2009 to 2014, the Moran’s I values were all greater than 0, P values were all <0.05. The spatial distribution from 2015 to 2020 trended to be random. Local autocorrelation analysis showed that the high-high clustered incidences were mainly concentrated in Changfeng County and Feidong County. Low-low clustered areas were mainly concentrated in the main urban areas (Luyang District, Baohe District). The analysis of cold/hot spot showed that there were two hot spots, which were distributed in Yijing township of Changfeng County and Yandian Township of Feixi County. Conclusion: The PTB epidemic in Hefei City has a certain degree of aggregation, so different prevention and control measures should be taken according to characteristics of different counties, districts, streets and towns, and the prevention and control efforts should be strengthened in the high-high clustered streets and hot spot areas.

Key words: Tuberculosis,pulmonary, Space-time clustering, Data interpretation,statistical, Hefei

CLC Number:

R521

Nie Tingyue, Chen Wei, Zhang Jieying, Cao Hong, Qian Bing, Gu Yingqiang, Liu Xuxiang. Analysis of spatial distribution characteristics of pulmonary tuberculosis cases in Hefei City, 2009—2020[J]. Chinese Journal of Antituberculosis, 2022, 44(9): 947-953. doi: 10.19982/j.issn.1000-6621.20220159

Figures/Tables 5

References 30

[1]	张正斌, 王丽娜, 鲁周琴, 等. 2011-2019年武汉市肺结核疫情特征及空间聚集性分析. 公共卫生与预防医学, 2020, 31(6):51-55. doi: 10.3969/j.issn.1006-2483.2020.06.012. doi: 10.3969/j.issn.1006-2483.2020.06.012
[2]	赖铿, 吴桂锋, 雷宇, 等. 2014-2018年广州市肺结核发病空间分布特征分析. 中国防痨杂志, 2020, 42(4):327-334. doi: 10.3969/j. issn.1000-6621.2020.04.006. doi: 10.3969/j. issn.1000-6621.2020.04.006
[3]	庞艳, 吴成果, 漆莉, 等. 2014-2019年重庆市学生肺结核时空特征流行病学分析. 疾病监测, 2021, 36(2):167-171. doi: 10.3784/jbjc.202009220328. doi: 10.3784/jbjc.202009220328
[4]	陈文明, 周庆荣, 王晓萌, 等. 空间流行病学与分子流行病学结合分析在肺结核研究中的应用进展. 中华流行病学杂志, 2016, 37(12):1683-1686. doi: 10.3760/cma.j.issn.0254-6450.2016.12.024. doi: 10.3760/cma.j.issn.0254-6450.2016.12.024
[5]	李志东, 郭秀梅. 基于生态足迹偏离份额的合肥市自然资本利用研究. 合肥工业大学学报(社会科学版), 2021, 35(4):28-40. doi: 10.3969/j.issn.1008-3634.2021.04.004. doi: 10.3969/j.issn.1008-3634.2021.04.004
[6]	周晓农. 空间流行病学. 北京: 科学出版社, 2009.
[7]	Wang Q, Guo L, Wang J, et al. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013-2016. Trop Med Int Health,2019, 24(9): 1104-1113. doi: 10.1111/tmi.13289. doi: 10.1111/tmi.13289 URL
[8]	He WC, Ju K, Gao YM, et al. Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011-2017: a spatial analysis. Infect Dis Poverty, 2020, 9(1):159. doi: 10.1186/s40249-020-00778-0. doi: 10.1186/s40249-020-00778-0 URL
[9]	周银发, 林淑芳, 戴志松, 等. 2011-2020年福建省肺结核发病时空特征分析. 中国防痨杂志, 2021, 43(8):853-856. doi: 10.3969/j.issn.1000-6621.2021.08.019. doi: 10.3969/j.issn.1000-6621.2021.08.019
[10]	何秋平, 郭亮永, 朱荣健, 等. 2010-2019年南宁市农村地区结核病空间分布特征分析. 公共卫生与预防医学, 2022, 33(1):48-52. doi: 10.3969/j.issn.1006-2483.2022.01.010. doi: 10.3969/j.issn.1006-2483.2022.01.010
[11]	Scholze AR, Dália Alves J, Berra TZ, et al. The burden of alcohol, tobacco and others drugs among incarcerated population diagnosed with tuberculosis: time trends and spatial determinants in Southern Brazil. BMC Public Health, 2022, 22(1):999. doi: 10.1186/s12889-022-13408-1. doi: 10.1186/s12889-022-13408-1 URL
[12]	Gwitira I, Karumazondo N, Shekede MD, et al. Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018. PLoS One, 2021, 16(4): e0249523. doi: 10.1371/journal.pone.0249523. doi: 10.1371/journal.pone.0249523 URL
[13]	杨丽佳, 李涛, 陈伟. 2013-2018年中国肺结核空间聚集性分析. 中华流行病学杂志, 2020, 41(11):1843-1847. doi: 10.3760/cma.j.cn112338-20200114-00029. doi: 10.3760/cma.j.cn112338-20200114-00029
[14]	任正洪. 2005-2011年我国肺结核发病的时间流行病学特征及趋势. 中国卫生统计, 2013, 30(2):158-161.
[15]	艾克旦, 商越, 王兆芬, 等. 青海省南部2009-2019年肺结核时空分布特征演变研究. 中国预防医学杂志, 2021, 22(8):582-586. doi: 10.16506/j.1009-6639.2021.08.003. doi: 10.16506/j.1009-6639.2021.08.003
[16]	Zeng MC, Jia QJ, Huang YY. The Spatiotemporal Dynamic Distributions of New Tuberculosis in Hangzhou, China. Biomed Environ Sci, 2020, 33(4):277-281. doi: 10.3967/bes2020.038. doi: 10.3967/bes2020.038
[17]	Li XX, Wang LX, Zhang H, et al. Seasonal variations in notification of active tuberculosis cases in China, 2005-2012. PLoS One, 2013, 8(7): e68102. doi: 10.1371/journal.pone.0068102. doi: 10.1371/journal.pone.0068102
[18]	张慧, 赵雁林. 强化多部门合作机制深入推进我国肺结核防治. 中国防痨杂志, 2022, 44(2):115-119. doi: 10.19982/j. issn.1000-6621.20210723. doi: 10.19982/j. issn.1000-6621.20210723
[19]	彭巧君, 周燕燕, 罗晓莉, 等. 维吾尔族肺结核患者肺结核防治核心信息知晓情况及影响因素分析. 中国社会医学杂志, 2019, 36(6):611-614. doi: 10.3969/j. issn.1673-5625.2019.06. 015. doi: 10.3969/j. issn.1673-5625.2019.06. 015
[20]	李建志. 把传染病发病率作为考核指标弊大于利. 中国公共卫生, 1992, 8(12):531.
[21]	安徽省人民政府. 关于《长江三角洲城市群发展规划安徽实施方案》的解读. 皖政办〔2016〕76号.2016-12-19.
[22]	黄传霞, 张浩. 关于G60科创走廊八城市深度参与、推动实施高质量发展的考察报告. 中共合肥市委党校学报, 2019(2):17-21,28.
[23]	李玲. 安徽省肺结核流行特征与患者直接经济负担调查研究. 合肥:安徽医科大学, 2020. doi: 10.26921/d.cnki.ganyu.2020.001261. doi: 10.26921/d.cnki.ganyu.2020.001261
[24]	安徽省人民政府. 安徽省肺结核防治规划(2011-2015年)的通知. 皖政办〔2012〕19号.2012-03-23.
[25]	曹红, 张欢. 2011-2013年合肥市结核分枝杆菌与人类免疫缺陷病毒感染双向筛查情况分析. 中国防痨杂志, 2014, 36(7):542-545. doi: 10.3969/j.issn.1000-6621.2014.07.007. doi: 10.3969/j.issn.1000-6621.2014.07.007
[26]	尹婷, 刘宏, 张茂镕, 等. 昆明市“三位一体”结核病防治模式运行效果分析. 现代预防医学, 2020, 47(17):3154-3157.
[27]	李娇. “三位一体”管理模式在石棉县肺结核预防控制中的应用研究. 成都:西南医科大学, 2019.
[28]	黄小婵, 袁薇, 杨敬源, 等. 贵州省2015-2018年涂阳肺结核流行及空间分布特征分析. 现代预防医学, 2021, 48(20):3673-3677,3682.
[29]	Im C, Kim Y. Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016. PLoS One, 2021, 16(8):e0255727. doi: 10.1371/journal.pone.0255727. doi: 10.1371/journal.pone.0255727
[30]	Lipman M, McQuaid CF, Abubakar I, et al. The impact of COVID-19 on global tuberculosis control. Indian J Med Res, 2021, 153(4):404-408. doi: 10.4103/ijmr.IJMR_326_21. doi: 10.4103/ijmr.IJMR_326_21 pmid: 34380784

年份	发病例数	常住人口 (名)	发病率 (/10万)
2009	4195	7237966	57.96
2010	3931	7450698	52.76
2011	3695	7520000	49.14
2012	3728	7570000	49.25
2013	3858	7610000	50.70
2014	3845	7582726	50.71
2015	3622	7963767	45.48
2016	3326	7863714	42.30
2017	3193	8205044	38.91
2018	3322	8088000	41.07
2019	3058	8189000	37.34
2020	2908	9369881	31.04
合计	42681	94650796	45.09

年份	1月	2月	3月	4月	5月	6月	7月	8月	9月	10月	11月	12月
2009	208	337	345	367	330	400	342	352	366	417	337	394
2010	304	232	400	427	374	326	330	297	311	306	371	253
2011	240	185	384	303	365	275	262	279	286	268	395	453
2012	189	242	373	341	379	402	278	279	234	277	370	364
2013	261	211	361	388	357	317	376	291	364	274	329	329
2014	335	204	334	344	357	339	322	324	416	305	285	280
2015	310	225	379	367	329	334	297	296	289	271	227	298
2016	222	207	315	287	307	275	289	309	296	221	302	296
2017	248	190	308	280	282	287	268	249	286	243	259	293
2018	258	138	289	302	265	309	336	313	300	287	261	264
2019	244	176	242	274	288	288	277	252	227	263	227	300
2020	174	115	232	255	274	289	262	265	306	240	259	237
合计	2993	2462	3962	3935	3907	3841	3639	3506	3681	3372	3622	3761

年份	Moran’s I值	Z值	P值
2009	0.529	10.643	0.001
2010	0.301	6.180	0.001
2011	0.235	4.809	0.001
2012	0.342	6.994	0.001
2013	0.235	4.875	0.001
2014	0.146	3.216	0.002
2015	0.041	1.093	0.125
2016	0.022	0.741	0.219
2017	-0.001	0.405	0.405
2018	-0.015	-0.181	0.447
2019	0.035	0.968	0.162
2020	-0.007	0.802	0.459