Background: Climate change has shifted and will continue to alter the patterns of daytime and nighttime temperatures. However, it remains unclear whether hot days and nights, as well as cold days and nights, have varying impacts on mental disorders. Methods: Daily hospital-based data on 1.2 million outpatient visits for mental disorders were obtained from 15 Chinese cities (2011-2015). The index of hot day excess, hot night excess, cold day excess, and cold night excess were used to measure the intensity of hot/cold days and nights. Firstly, a distributed lag non-linear model was used to estimate the risk of mental disorders associated with hot/cold days and nights for each city. Secondly, city-specific estimates were pooled by multivariate meta-analysis to obtain national effect estimates. Thirdly, the attributable fraction of mental disorders was estimated for the nation, genders, age groups, heating methods, and air conditioning penetration rates. Results: Our findings revealed that both hot and cold nights were associated with a higher risk of mental disorders compared to hot and cold days. The attributable fraction of mental disorders was higher for cold (night: 25.74%; day: 19.65%) in contrast to heat (night: 16.91%; day: 7.24%). Males, the elderly, cities using central heating, and cities with low air conditioning penetration rates were vulnerable to cold nights. Conclusions: This nationwide study highlights the adverse effects and significant burden of hot and cold nights on mental disorders. It underscores the potential for mitigating these impacts through the adoption of air conditioning, particularly in southern cities across China. (c) 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

BackgroundNon-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases.MethodsData on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis.ResultsA total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45-7.42) avoided cases and USD 1.77 billion (95% CI 1.18-2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00-6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5-92.6]. Socioeconomic status and population density were effect modifiers.ConclusionsNPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

水痘是由水痘-带状疱疹病毒引起的呼吸道传染病,传染力极强,通常会引起一系列并发症,影响人群健康.全球气候的快速变化、频繁极端气象事件的发生、人口流动、空气污染物浓度变化和疫苗接种情况,均影响水痘发病率.水痘已成为世界范围内严重的公共卫生问题.本综述主要介绍国内外水痘流行病学特征,影响水痘流行的疫苗接种、气象因素、空气污染物和社会经济因素,从而为水痘防控策略的制定提供依据.

BACKGROUND:Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear. OBJECTIVE:This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China. METHODS:The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China. RESULTS:A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China. CONCLUSIONS:HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.

Background:The re-emergence of scrub typhus in the southern provinces of China in recent decades has been validated, thereby attracting the attention of public health authorities. There has been a spatial and temporal expansion of scrub typhus in Hainan Province, but the epidemiological characteristics, environmental drivers, and potential high-risk areas for scrub typhus have not yet been investigated. Objective:The aims of this study were to characterize the spatiotemporal epidemiology of scrub typhus, identify dominant environmental risk factors, and map potential risk areas in Hainan Province from 2011 to 2020. Methods:The spatiotemporal dynamics of scrub typhus in Hainan Province between 2011 and 2020 were analyzed using spatial analyses and seasonal-trend decomposition using regression (STR). The maximum entropy (MaxEnt) model was applied to determine the key environmental predictors and environmentally suitable areas for scrub typhus, and the demographic diversity of the predicted suitable zones was evaluated. Results:During 2011-2020, 3260 scrub typhus cases were recorded in Hainan Province. The number of scrub typhus cases increased continuously each year, particularly among farmers (67.61%) and individuals aged 50-59 years (23.25%) who were identified as high-risk groups. A dual epidemic peak was detected, emerging annually from April to June and from July to October. The MaxEnt-based risk map illustrated that highly suitable areas, accounting for 25.36% of the total area, were mainly distributed in the northeastern part of Hainan Province, where 75.43% of the total population lived. Jackknife tests revealed that ground surface temperature, elevation, cumulative precipitation, evaporation, land cover, population density, and ratio of dependents were the most significant environmental factors. Conclusion:In this study, we gained insights into the spatiotemporal epidemiological dynamics, pivotal environmental drivers, and potential risk map of scrub typhus in Hainan Province. These results have important implications for researchers and public health officials in guiding future prevention and control strategies for scrub typhus.

目的 通过德尔菲法确定军人应力性骨折预警指标体系及各级指标权重,为防治应力性骨折提供依据.方法 在既往研究基础上结合文献分析方法,初步编制军人应力性骨折预警指标体系框架和专家咨询表,选择20名骨科学、运动医学、体能训练和预防医学专家进行2轮德尔菲专家论证.使用肯德尔和谐系数评价结果的一致性,并采用百分权重法确定各指标权重进而建立应力性骨折预警指标体系.结果 2轮专家咨询积极系数分别为100.00％与88.24％;专家权威系数为0.90;Ⅰ级与Ⅱ级指标的协调系数检验结果均显著(P<0.05).构建的军人应力性骨折预警指标体系包括5个Ⅰ级指标和25个Ⅱ级指标.权重最高的Ⅰ级指标是"组训因素与条件(21.87％)";权重前3的Ⅱ级指标是"组训施训(6.16％)""训练环境(5.44％)"和"足弓异常(5.35％)".结论 军人应力性骨折预警指标体系具有一定的科学性和适用性,对减少军事训练伤发生与指导军事训练工作开展具有重要意义.

Background Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated. Methods A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk. Results The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus. Conclusions These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Background: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. Objective: The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB.Methods: Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM10, PM2.5, SO2, NO2, CO and O-3, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing.Results: In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 mu g/m(3) increase in NO2 in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 mu g/m(3) increase in O-3 in lag 1 week (ER:-1.2%; 95% CI:-1.8%,-0.5%), every 5 degrees C increase in average temperature (ER:-1.7%; 95% CI:-2.9%,-0.4%) and every 10% increase in average relative humidity (ER:-0.4%; 95% CI:-0.8%,-0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO2, O-3, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM2.5, SO2, sunshine duration and TB cases.Conclusion: Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

This study aims to describe the epidemiological characteristics of scrub typhus, detect the spatio-temporal patterns of scrub typhus at county level, and explore the associations between the environmental variables and scrub typhus cases in Anhui Province. Time-series analysis, spatial autocorrelation analysis, and space–time scan statistics were used to explore the characteristics and spatiotemporal patterns of the scrub typhus in Anhui Province. Negative binomial regression analysis was used to explore the association between scrub typhus and environmental variables. A total of 16,568 clinically diagnosed and laboratory-confirmed cases were reported from 104 counties of 16 prefecture-level cities. The number of female cases was higher than male cases, with a proportion of 1.32:1. And the proportion of cases over 65 years old was the highest, accounting for 33.8% of the total cases. Two primary and five secondary high-risk clusters were detected in the northwestern, northeastern, and central-eastern parts of Anhui Province. The number of cases in primary and secondary high-risk clusters accounted for 60.27% and 3.00%, respectively. Scrub typhus incidence in Anhui Province was positively correlated with the population density, normalized difference vegetation index, and several meteorological variables. The mean monthly sunshine duration with 3 lags (SSD_lag3), mean monthly ground surface temperature with 1 lag (GST_lag1), and mean monthly relative humidity with 3 lags (RHU_lag3) had the most significant association with increased cases of scrub typhus. Our findings indicate that public health interventions need to be focused on the elderly farmers in north of the Huai River in Anhui Province.

Short-term exposure to ambient high temperature (heat) could increase the risk of cardiovascular disease (CVD). However, available evidence on the burden of daytime and nighttime heat on CVD is limited and vulnerable populations remain unknown so far. We aimed to examine and differentiate the impact of daytime and nighttime heat on CVD in China. Data on daily outpatient visits for CVD were collected from 15 Chinese cities spanning multiple geographical regions, climates, and socio-economic conditions. The population-weighted temperature was used to calculate excess heat exposure in warm seasons (June-September) from 2011 to 2015. Hot day excess (HDE) and hot night excess (HNE), the sum of temperature above the heat threshold during daytime and nighttime respectively, were used to represent daytime and nighttime excess heat. A distributed lag non-linear model was employed to estimate the city-level association between HDE/HNE and daily CVD cases. The citylevel association was then pooled by multivariate meta-analysis. We further estimated the disease burden of CVD attributable to HDE and HNE by geographical regions, gender, and age. A total of 729,409 cases of CVD were included in this study. Both HDE and HNE were associated with an increased risk of CVD, with greater effects from nighttime heat (relative risk (RR): 1.38; 95% confidence interval (CI): 1.18-1.61) than daytime heat (RR: 1.10; 95% CI: 1.05-1.15). The proportion of CVD cases attributable to HNE was 15.7%, which was almost three times as high as HDE (4.6%, p for difference <0.05). Males, people living in northern cities, and those aged less than 45 years were more vulnerable to HNE. Our findings for the first time revealed an intra-day difference in the heat effect on CVD, with a greater impact from nighttime heat exposure, which should be considered to protect vulnerable populations on hot days.

Background: Hemorrhagic fever with renal syndrome (HFRS) occurs widely in Northeastern China, but the mechanism and interactions of meteorological and socio-economic factors on the transmission of HFRS are still largely unknown. Objective: We explored the effects of socioeconomic-environmental factors on the spatio-temporal variation of HFRS incidence from 2001 to 2019 in Northeastern China. Specifically, the relative importance and contribution rates (CR) of determinants of HFRS were identified by boosted regression tree and variance partitioning analysis, respectively. Structural equation models (SEMs) were used to explain the roles of climatic and socio-economic factors in the transmission of HFRS. And a negative binomial regression was used to identify the risk effect between monthly meteorological variables and HFRS with 0-6 months lags in Northeastern China. Results: Over the past decades, the high-risk areas of HFRS were mainly concentrated in the northern and eastern areas of Northeastern China. Additionally, HFRS mainly presented a decreasing trend from 2001 to 2019 in most areas of Northeastern China, but slightly increased in the cities of Daqing, Songyuan, Baicheng, and Tonghua. The temporal dynamics of the incidence of HFRS were primarily explained by the variations in population density (CR = 27.30%), climate (CR = 13.30%), and economic condition(CR = 1.90%). The spatial variations of HFRS were medicated by the climate (CR = 16.95%) and population density (CR = 9.45%) and medical health care (CR = 2.25%). The SEM models indicated that humid and warm climates were conducive to the incidence and increase of HFRS, but the improvement in education and an increase in population density reduced the transmission of HFRS. Conclusion: Climate and population density appeared to mediate the spatio-temporal variation of HFRS in Northeastern China. These findings may provide valuable empirical evidence for the management of HFRS in endemic areas.

应力性骨折是军事训练伤中的常见损伤,严重影响参训人员的训练和健康.应力性骨折发生后,若没有得到及时的治疗,可能会导致严重后果,如长期疼痛或残疾,甚至可能会导致骨折不愈合,使参训者难以重返训练.应力性骨折的发生受多种因素影响,本文简述了应力性骨折的发生现状和分布特征,并从人口学特征、生活习惯、身体形态、体能和动作能力、组训因素与条件、遗传易感性6个方面来分析其影响因素,为开展相关研究提供参考.

Background Scrub typhus has become a serious public health concern in the Asia-Pacific region including China. There were new natural foci continuously recognized and dramatically increased reported cases in mainland China. However, the epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province have yet to be investigated. Objective This study proposes to explore demographic characteristics and spatiotemporal dynamics of scrub typhus cases in Fujian province, and to detect high-risk regions between January 2012 and December 2020 at county/district scale and thereby help in devising public health strategies to improve scrub typhus prevention and control measures. Method Monthly cases of scrub typhus reported at the county level in Fujian province during 20122020 were collected from the National Notifiable Disease Surveillance System. Time-series analyses, spatial autocorrelation analyses and space-time scan statistics were applied to identify and visualize the spatiotemporal patterns of scrub typhus cases in Fujian province. The demographic differences of scrub typhus cases from high-risk and low-risk counties in Fujian province were also compared. Results A total of 11,859 scrub typhus cases reported in 87 counties from Fujian province were analyzed and the incidence showed an increasing trend from 2012 (2.31 per 100,000) to 2020 (3.20 per 100,000) with a peak in 2018 (4.59 per 100,000). There existed two seasonal peaks in June-July and September-October every year in Fujian province. A significant positive spatial autocorrelation of scrub typhus incidence in Fujian province was observed with Moran's I values ranging from 0.258 to 0.471 (P<0.001). Several distinct spatiotemporal clusters mainly concentrated in north and southern parts of Fujian province. Compared to low-risk regions, a greater proportion of cases were female, farmer, and older residents in high-risk counties. Conclusions These results demonstrate a clear spatiotemporal heterogeneity of scrub typhus cases in Fujian province, and provide the evidence in directing future researches on risk factors and effectively assist local health authorities in the refinement of public health interventions against scrub typhus transmission in the high risk regions.

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.

Background: Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health. Methods: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012-2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Nin tilde o and La Nin tilde a events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. Results: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three -character bands were also found in the associations between ST and El Nin tilde o and La Nin tilde a events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet co-herency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. Conclusion: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.

目的:分析2016-2021年北京市报告肺结核患者流动就诊情况，为北京市结核病防控提供参考依据。方法:患者数据来源于中国疾病预防控制信息系统结核病管理信息系统和标准编码管理系统，地图数据来自中国科学院地理科学与资源研究所，利用Excel 2016、SPSS 19.0、Python 3.9、ArcGIS 10.6软件分析病例的跨省流动和北京市内跨区流动情况。结果:2016-2021年北京市报告肺结核患者来自京外的占35.27%（24 307/68 926）。全国30个省（自治区、直辖市）和新疆生产建设兵团均有肺结核患者跨省流入北京市就诊。跨省来京就诊占比前5位分别为河北省（39.58%，9 620/24 307）、山西省（8.82%，2 145/24 307）、内蒙古自治区（8.66%，2 105/24 307）、黑龙江省（6.95%，1 690/24 307）和河南省（6.88%，1 672/24 307），占全部跨省流入患者的70.89%（17 232/24 307）。跨省流入患者的主要流入地区为北京市通州区（46.72%，11 356/24 307）；主要流向医院为三级甲等医院，以首都医科大学附属北京胸科医院为主（44.76%，10 880/24 307）。现住址为北京市的肺结核患者中，市内跨区就诊比例为55.06%（24 566/44 619）。北京市16个区均同时存在病例流出和流入情况，16个区的患者在市内跨区流动的就诊比例 M（ Q1， Q3）为59.30%（56.05%，65.13%）。通州区、海淀区和西城区的患者流入数多于流出数，其他13个区的患者流出数多于流入数。北京市本地患者就诊主要流向首都医科大学附属北京胸科医院（42.18%，18 822/44 619）。 结论:2016-2021年北京市报告肺结核患者的跨省流动和市内跨区流动性均较大，跨省流动主要流入北京市三级甲等医院，北京市本地患者主要流入市级结核病定点医疗机构。