Analysis on spatial-temporal distribution characteristics of smear positive pulmonary tuberculosis in China, 2004-2015

被引:19
作者
Mao, Qiang [1 ]
Zeng, Chenghui [1 ]
Zheng, Dacheng [1 ]
Yang, Yahong [2 ]
机构
[1] First Peoples Hosp Jingmen, Dept Med Records Stat, Jingmen 448000, Peoples R China
[2] Gansu Prov Peoples Hosp, Dept Infect Management, Lanzhou 730000, Gansu, Peoples R China
关键词
Smear positive PTB; Spatial auto-correlation; Spatial-temporal scanning; HEALTH SYSTEM; SEASONALITY; PROVINCE; TRENDS; POLICY;
D O I
10.1016/j.ijid.2019.02.038
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background: In China, tuberculosis (TB) is still a major infectious disease threatening people's health. Smear positive pulmonary TB is one of the most common infectious forms of TB and it might easily cause the outbreak in some areas. With a better understanding of the spatial-temporal variations of smear positive PTB, we would reach the targets for TB prevention and controlling, identify high-risk areas and periods. Thus, the aim of this study was to investigate the spatial-temporal variations of smear positive PTB. Methods: Provincial level data of reported smear positive PTB monthly cases and incidence from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health of China. Purely spatial-temporal descriptive analysis was used to characterize the distribution patterns of smear positive PTB. The global spatial auto-correlation statistics (Moran's I) and the local indicators of spatial association (LISA) were conducted to identify the spatial auto-correlation and high risk areas of smear positive PTB cases. Furthermore, the space-time scan statistic was adopted to detect the spatial-temporal clusters in different periods. Results: A total of 4,711,571 smear positive PTB cases were notified in China with an average annual incidence of 29.59/100,000. The proportion of male in different age groups were obviously higher than that of women. The largest number of cases was reported in the 20-24 years age group. Time-series analysis indicated that monthly incidence appeared a clearly seasonality and periodicity, which the seasonal peaks occurred in January and March. Smear positive PTB cases had a positive global spatial auto-correlation in 2013-2015 (Moran's I = 0.186, P = 0.046). Spatial clusters were identified in four periods, located in the different regions. The time period of 2004-2006, the most likely spatial-temporal cluster (RR = 1.69, P < 0.001) was mainly located in Hubei, Hunan, Jiangxi and Anhui of central China, clustering in the time frame from January 2005 to June 2006. During 2007-2009, the most likely spatial-emporal cluster (RR = 5.65, P < 0.001) was located in Guizhou, clustering in the time frame from January to December 2009. The spatial-temporal clustering in the years 2010-2012 showed the most likely cluster (RR = 1.44, P < 0.001) was distributed in Anhui, Hunan, Hubei, Jiangxi and Guangdong with the time frame from January 2010 to June 2011. During 2013-2015, the most likely cluster (RR = 1.86, P < 0.001) was detected in Hunan, Hubei, Jiangxi and Guangdong from February 2013 to June 2014. Conclusions: This study identified the spatial-temporal patterns of smear positive PTB in China and demonstrated the capability and utility of the spatial-temporal approach in epidemiology. The results of this study would contribute to estimating the high risk periods and areas, and to providing more useful information for policy-making. (c) 2019 Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:S36 / S44
页数:9
相关论文
共 44 条
  • [1] Al-Jebouri M. M., 2014, British Journal of Medicine and Medical Research, V4, P2546
  • [2] GeoDa:: An introduction to spatial data analysis
    Anselin, L
    Syabri, I
    Kho, Y
    [J]. GEOGRAPHICAL ANALYSIS, 2006, 38 (01) : 5 - 22
  • [3] LOCAL INDICATORS OF SPATIAL ASSOCIATION - LISA
    ANSELIN, L
    [J]. GEOGRAPHICAL ANALYSIS, 1995, 27 (02) : 93 - 115
  • [4] Pulmonary tuberculosis space-time clustering and spatial variation in temporal trends in Portugal, 2000-2010: an updated analysis
    Areias, C.
    Briz, T.
    Nunes, C.
    [J]. EPIDEMIOLOGY AND INFECTION, 2015, 143 (15) : 3211 - 3219
  • [5] A hybrid seasonal prediction model for tuberculosis incidence in China
    Cao, Shiyi
    Wang, Feng
    Tam, Wilson
    Tse, Lap Ah
    Kim, Jean Hee
    Liu, Junan
    Lu, Zuxun
    [J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2013, 13
  • [6] Spatial analysis of patients with multi-drug resistant pulmonary tuberculosis between 2009 and 2012 in Eastern China
    Chen, W.
    Liu, Z.
    Wang, X.
    Wang, W.
    [J]. INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2016, 45 : 221 - 221
  • [7] Cliff AD, 1973, TRENDS ECOL EVOL, V14, P19
  • [8] Genotypic and Spatial Analysis of Mycobacterium tuberculosis Transmission in a High-Incidence Urban Setting
    Correa Ribeiro, Fabiola Karla
    Pan, William
    Bertolde, Adelmo
    Vinhas, Solange Alves
    Peres, Renata Lyrio
    Riley, Lee
    Palaci, Moises
    Maciel, Ethel Leonor
    [J]. CLINICAL INFECTIOUS DISEASES, 2015, 61 (05) : 758 - 766
  • [9] Tuberculosis
    Dheda, Keertan
    Barry, Clifton E., III
    Maartens, Gary
    [J]. LANCET, 2016, 387 (10024) : 1211 - 1226
  • [10] Strategies for halting the rise of multidrug resistant TB epidemics: assessing the effect of early case detection and isolation
    Espindola, Aquino L.
    Varughese, Marie
    Laskowski, Marek
    Shoukat, Affan
    Heffernan, Jane M.
    Moghadas, Seyed M.
    [J]. INTERNATIONAL HEALTH, 2017, 9 (02): : 80 - 90