Spatial-temporal analysis of Tuberculosis in Chongqing, China 2011-2018 Ya Yu Chongqing Institute of Tuberculosis Control and Prevention Bo Wu Chongqing Institute of Tuberculosis Control and Prevention Chengguo Wu Chongqing Institute of Tuberculosis Control and Prevention Qingya Wang Chongqing Institute of Tuberculosis Control and Prevention Daiyu Hu Chongqing Institute of Tuberculosis Control and Prevention Wei Chen ( [email protected] ) Chinese Center for Disease Control and Prevention https://orcid.org/0000-0001-5649-081X Research article Keywords: Epidemiology, Spatial Analysis, Tuberculosis Posted Date: July 14th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-18695/v3 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on July 22nd, 2020. See the published version at https://doi.org/10.1186/s12879-020-05249-3. Page 1/21 Abstract Background: China is a country with a high burden of pulmonary tuberculosis (PTB). Chongqing is in the southwest of China, where the notication rate of PTB ranks tenth in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Chongqing in order to improve TB control measures. Methods: A spatial-temporal analysis has been performed based on the data of PTB from 2011 to 2018, which was extracted from the National Surveillance System. The effect of TB control was measured by variation trend of pathogenic positive PTB notication rate and total TB notication rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level. Results: A total of 188,528 cases were included in this study. A downward trend was observed in PTB between 2011 and 2018 in Chongqing. The peak of PTB notication occurred in late winter and early spring annually. By calculating the value of Global Moran's I and Local Getis’s Gi*, we found that PTB was spatially clustered and some signicant hot spots were detected in the southeast and northeast of Chongqing. One most likely cluster and three secondary clusters were identied by Kulldorff’s scan spatial-temporal Statistic. Conclusions: This study identied seasonal patterns and spatial-temporal clusters of PTB cases in Chongqing. Priorities should be given to southeast and northeast of Chongqing for better TB control. Background Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB). It is one of the top 10 causes of death[1]. China contributed 9% of total cases in the world in 2017, ranking the second place after India[1]. In the 1990s, a TB control project, including directly observed treatment and short-course (DOTS) strategy, was implemented in 13 provinces of China[2]. Then the strategy had been expanded to the whole country by 2005[3]. After more than ten years efforts, China has reached the 2005 global tuberculosis control targets[4]. It is the only country with a high TB burden that has made the achievements[5]. But TB remains a major public health problem in China, especially in the relatively poor northern and western areas [6,7]. Chongqing is located in the southwest of China with 39 districts/counties, and 14 of them are national- level poor districts and counties[8]. In 2018, about 20,000 TB cases were reported in Chongqing with the overall notication rate of 73.4 cases per 100,000 population, which was higher than the average of the whole country[9]. A previous study of Chongqing pointed out that distribution of TB was uneven across the city[10], and a certain degree of seasonality was observed[11]. In recent years, spatial-temporal analysis has been widely used to describe the distribution characteristics and transmission patterns of tuberculosis in China[7,12,13] and other countries[14-18]. These studies demonstrated that TB has a highly complex dynamics and is spatially heterogeneous at provincial, national, and international levels during certain periods of time[19]. Few studies have been conducted in Chongqing to explore the spatial epidemiology at the county level. In order to improve TB control Page 2/21 measures, we conducted Geographical Information System (GIS) based spatial-temporal scan statistic in Chongqing from 2011 to 2018. Methods Study setting Chongqing is a mountainous city located in southwest China, with 27 districts and 12 counties. It covers an area of 82,400 square kilometers[20], of which 76% are mountains, 22% hills, 2% valleys and plains. The main stream of the Yangtze River runs through the city from west to east, with a ow of 665 kilometers. In 2018, there are 31 million permanent residents in Chongqing, of whom about 20 million were urban population, accounting for 65.5 percent of total residents. The gross domestic product (GDP) per capita was RMB 65,933 Yuan in 2018[21], which was 1.9 times higher than that of 2011. Data collection and management The Chongqing TB surveillance data from January 2011 to December 2018 was extracted from the National Surveillance System for Notiable Infectious Disease, which is established and operated by the Chinese Center for Disease Control and Prevention (CCDC).This surveillance system covers all counties of 32 provinces, autonomous regions and municipalities of China, and collects data of all TB cases reported by hospitals. We obtained these TB data from Chongqing Institute of Tuberculosis Control and Prevention, and were permitted to use by CCDC. Each case contains demographic information as well as medical information. Each case is identied by the unique identity card (ID) number to avoid duplicate reports. In order to protect patients’ privacy, asterisk was used to block the name and ID number of all cases when the data were extracted. The effect of TB control was measured by the variation trend of pathogenic positive PTB notication rate and total TB notication rate. Pathogenic positive PTB includes smear positive (SS+) cases and smear negative but culture positive (S-C+) cases. With the improvement of national infectious disease surveillance system, the rifampicin resistant tuberculosis (RR-TB) information has been added to the system since July 1, 2017 [22], so pathogenic positive PTB in 2017 and 2018 also included RR-TB cases. The overall PTB included: 1) pathogenic positive PTB; 2) pathogenic negative PTB, referring to those PTB patients whose smear and culture were both negative; and 3) PTB without pathogenic examination, referring to those PTB patients who had slight symptom and could not cough up with sputum. Each enrolled case was geocoded by current address. Then them were matched to the county-level polygon maps of the geographic information (Geographic database from China CDC) at a 1:1,000,000 scale as the layer’s attribute table by the same identied number. The longitude and latitude coordinates of the central point for each district and county were located though the Google geocoding service and the toolbox of Geoprocessing in ArcGIS v.10 (ESRI Inc, Redlands, CA, USA). The coordinate information Page 3/21 was used for spatial-temporal analysis. This geocoding process has been widely applied in previous studies [7,19,23,24], and we used a similar approach. The annual population data of each administrative district from 2011 to 2018 were obtained from the Chongqing Statistical Yearbook and the Basic Information System for Disease Prevention and Control. Statistics analysis Time Series and Descriptive Analysis The epidemiological characteristics of TB cases were analyzed at provincial level. TB cases reported from 2011 to 2018 were aggregated by age, gender, occupation and date of onset for TB notication rate analysis. Comparison between different demographic groups was carried out by Chi-square test or Fisher’s exact test. The trend of the notication rate was tested using Cochran-Armitage test. All statistical analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA). The difference was considered signicant if P-value was less than 0.05. The temporal patterns were examined by looking at the onset month of all TB cases. The time series included 96 months from January 2011 to December 2018 and was examined using EXCEL2016. Spatial Autocorrelation Analysis Spatial autocorrelation analysis is a spatial statistical method that can reveal the regional structure of spatial variables. It can verify whether an element attribute value is associated with an attribute value at an adjacent space point. It mainly includes global autocorrelation analysis and local autocorrelation analysis. Global Moran's I values[25] calculated by ArcGIS v.10 software (ESRI Inc., Redlands, CA, USA) was used to identify spatial autocorrelation and detect the spatial distribution pattern of TB in Chongqing, China. The range of Moran I value is between -1 and 1. A positive Moran I value indicates that a positive correlation exists, and the larger the value, the more obvious the tendency to cluster is, while a negative Moran I value indicates that a negative correlation exists, showing a discrete distribution. There is no spatial clustering when the value is zero, meaning that the data are randomly distributed[26]. Both Z-score and P-value are used to evaluate the signicance of Moran’s I[27]. * Local Getis’s Gi statistic[28] is used to identify the local level of spatial autocorrelation and determine * locations of clusters or hotspots. Value of Gi in this study was calculated using ArcGIS v.10 software (ESRI Inc., Redlands, CA, USA). Page 4/21 Spatial-temporal Scan Statistic Kulldorff’s spatial-temporal scan statistical analysis was used to identify the spatial, temporal and clusters of PTB across different counties geographically and in different time period. SaTScanTM version 9.1.1 software (Kulldorff, Boston, MA, USA ) was used based on the Poisson probability model[29].