1
|
Fan M, Liu Y, Liu K, Liu X, Li Y, Li T, Zhang C, Zhang H, Cheng J. Health system delay and risk factors in pulmonary tuberculosis diagnosis before and during the COVID-19 epidemic: a multi-center survey in China. Front Public Health 2025; 13:1526774. [PMID: 40078758 PMCID: PMC11896862 DOI: 10.3389/fpubh.2025.1526774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2024] [Accepted: 02/10/2025] [Indexed: 03/14/2025] Open
Abstract
Background Understanding health system delay (HSD) in pulmonary tuberculosis (PTB) diagnosis aids in tailoring interventions for case detection and curbing transmission. However, recent nationwide studies on HSD in PTB diagnosis have been scarce. This study assesses HSD and its risk factors in China, taking into account the impact of the COVID-19 epidemic. Methods Patients diagnosed with PTB between 2019 and 2022 were selected using a multistage stratified clustering method. A semi-structured questionnaire was employed to assess HSD, which was defined as the interval between the patient's initial visit to a health facility and the definitive PTB diagnosis. The HSD was then compared between 2019 (before the epidemic) and 2020-2022 (during the epidemic). Factors associated with long health system delay (LHSD, defined as HSD > 14 days) were examined using both univariate and multivariate analyses with chi-square tests and binary logistic regression, respectively. Results In total, 958 patients with PTB were analyzed: 478 before and 480 during the epidemic. The HSD was 14 (interquartile range, 7-30) days for all patients, and the HSD before and during the epidemic also shared this value. A total of 199 patients (20.8%) had LHSD. LHSD was more prevalent in patients presenting solely with cough and expectoration (Odds ratio [OR]: 1.482, 95% confidence interval [CI]: 1.015-2.162) and those visiting ≥2 health facilities before definitive diagnosis (2 health facilities: OR = 2.469, 95%CI: 1.239-4.920; ≥3 health facilities: OR = 8.306, 95%CI: 4.032-17.111). Additionally, patients with negative bacteriological results were independently associated with higher LHSD risk (OR = 1.485, 95%CI: 1.060-2.080). Conclusion In China, HSD in PTB diagnosis remains relatively low and is primarily mediated by factors associated with health providers. No significant impact on HSD from the COVID-19 epidemic has been found. Implementing targeted training programs to enhance health providers' awareness of chronic respiratory symptoms and maintain vigilance for PTB; strengthening presumptive PTB identification capabilities at grassroots health facilities, and promoting the use of Mycobacterium tuberculosis (MTB) bacteriological technologies are recommended to shorten the HSD.
Collapse
Affiliation(s)
- Mingkuan Fan
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Medical College of Xiangyang Polytechnic, Xiangyang, Hubei, China
| | - Yushu Liu
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kui Liu
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Xiaoqiu Liu
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuhong Li
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tao Li
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Canyou Zhang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Zhang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Cheng
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| |
Collapse
|
2
|
Huang R, Kartsonaki C, Turnbull I, Pei P, Chen Y, Liu J, Du H, Sun D, Yang L, Barnard M, Lv J, Yu C, Chen J, Li L, Chen Z, Bragg F. Incidence and mortality rates of 14 site-specific infectious diseases in 10 diverse areas of China: findings from China Kadoorie Biobank, 2006-2018. Int J Infect Dis 2024; 147:107169. [PMID: 39002770 DOI: 10.1016/j.ijid.2024.107169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/20/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024] Open
Abstract
BACKGROUND Infectious diseases remain a major global health concern, including in China, with an estimated >10 million cases of infectious disease in 2019. We describe the burden of site-specific infectious diseases among Chinese adults. METHODS From 2004 to 2008, the prospective China Kadoorie Biobank enrolled 512,726 adults aged 30-79 years from 10 diverse areas (5 rural, 5 urban) of China. During the 12 years of follow-up, 101,673 participants were hospitalized for any infectious disease. Descriptive analyses examined standardized incidence, mortality and case fatality of infections. FINDINGS The incidence of any infectious disease was 1856 per 100,000 person-years; respiratory tract infections (1069) were most common. The infectious disease mortality rate was 31.8 per 100,000 person-years (20.3 and 9.4 for respiratory and non-respiratory infections, respectively) and case fatality was 2.2% (2.6% and 1.6% for respiratory and non-respiratory infections, respectively). Infectious disease incidence and mortality rates were higher at older ages and in rural areas. There were no clear sex differences in infectious disease incidence rates, but mortality and case fatality rates were twice as high in men as in women. INTERPRETATION Infectious diseases were common in Chinese adults. The observed burden of, and disparities in, site-specific infections can inform targeted prevention efforts. FUNDING Kadoorie Foundation, Wellcome Trust, MRC, BHF, CR-UK, MoST, NNSF.
Collapse
Affiliation(s)
- Rui Huang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK.
| | - Iain Turnbull
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Pei Pei
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Jingchao Liu
- Suzhou Centre of Disease Prevention and Control, Suzhou, China
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Dianjianyi Sun
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Maxim Barnard
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Jun Lv
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Junshi Chen
- National Centre for Food Safety Risk Assessment, Beijing, China
| | - Liming Li
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK; Health Data Research UK Oxford, University of Oxford, Oxford, UK
| |
Collapse
|
3
|
Li Y, Luo D, Zheng Y, Liu K, Chen S, Zhang Y, Wang W, Wu Q, Ling Y, Zhou Y, Chen B, Jiang J. Spatiotemporal distribution and risk factors for patient and diagnostic delays among groups with tuberculous pleurisy: an analysis of 5-year surveillance data in eastern China. Front Public Health 2024; 12:1461854. [PMID: 39314789 PMCID: PMC11416949 DOI: 10.3389/fpubh.2024.1461854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 08/29/2024] [Indexed: 09/25/2024] Open
Abstract
Objective To understand and analyze the factors relating to patient and diagnostic delays among groups with tuberculous pleurisy (TP), and its spatiotemporal distribution in Zhejiang Province. Methods Data of all tuberculous pleurisy patients were collected from the existing Tuberculosis Information Management System. A time interval of > 2 weeks between first symptom onset and visit to the designated hospital was considered a patient delay, and a time interval of > 2 weeks between the first visit and a confirmed TP diagnosis was considered a diagnostic delay. Univariate and multivariate logistic regression analyses were used to explore factors influencing patient and diagnostic delays in patients with TP. Spatial autocorrelation and spatiotemporal scan analyses were used to identify hot spots and risk clusters, respectively. Results In total, 10,044 patients with TP were included. The median time and interquartile range for patients seeking medical care and diagnosis were 15 (7-30) and 1 (0-8) days, respectively. The results showed that people aged > 65 years, retirees, and residents of Jinhua, Lishui, and Quzhou were positively correlated with patient delay, whereas retreatment patients, houseworkers, unemployed people, and residents of Zhoushan or Ningbo were positively correlated with diagnostic delay. Additionally, high-risk clusters of patient delays were observed in the midwestern Zhejiang Province. The most likely clusters of TP diagnostic delays were found in southeast Zhejiang Province. Conclusion In summary, patient delay of TP in Zhejiang province was shorter than for pulmonary tuberculosis in China, while the diagnostic delay had no difference. Age, city, occupation, and treatment history were related to both patient and diagnostic delays in TP. Interventions in central and western regions of Zhejiang Province should be initiated to improve the early detection of TP. Additionally, the allocation of health resources and accessibility of health services should be improved in the central and eastern regions of Zhejiang Province.
Collapse
Affiliation(s)
- Yang Li
- School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Dan Luo
- School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yi Zheng
- Department of Tuberculosis Control and Prevention, Jiaxing Nanhu District Center for Disease Control and Prevention, Jiaxing, Zhejiang, China
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yuxiao Ling
- School of Public Health, Health Science Center, Ningbo University, Ningbo, Zhejiang, China
| | - Yiqing Zhou
- School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Jianmin Jiang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| |
Collapse
|
4
|
Spatial-temporal analysis of pulmonary tuberculosis in Hubei Province, China, 2011-2021. PLoS One 2023; 18:e0281479. [PMID: 36749779 PMCID: PMC9904469 DOI: 10.1371/journal.pone.0281479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Pulmonary tuberculosis (PTB) is an infectious disease of major public health problem, China is one of the PTB high burden counties in the word. Hubei is one of the provinces having the highest notification rate of tuberculosis in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Hubei province for targeted intervention on TB epidemics. METHODS The data on PTB cases were extracted from the National Tuberculosis Information Management System correspond to population in 103 counties of Hubei Province from 2011 to 2021. The effect of PTB control was measured by variation trend of bacteriologically confirmed PTB notification rate and total PTB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level of Hubei. RESULTS A total of 436,955 cases were included in this study. The total PTB notification rate decreased significantly from 81.66 per 100,000 population in 2011 to 52.25 per 100,000 population in 2021. The peak of PTB notification occurred in late spring and early summer annually. This disease was spatially clustering with Global Moran's I values ranged from 0.34 to 0.63 (P< 0.01). Local spatial autocorrelation analysis indicated that the hot spots are mainly distributed in the southwest and southeast of Hubei Province. Using the SaTScan 10.0.2 software, results from the staged spatial-temporal analysis identified sixteen clusters. CONCLUSIONS This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Hubei province. High-risk areas in southwestern Hubei still exist, and need to focus on and take targeted control and prevention measures.
Collapse
|
5
|
Cao Y, Li M, Haihambo N, Zhu Y, Zeng Y, Jin J, Qiu J, Li Z, Liu J, Teng J, Li S, Zhao Y, Zhao X, Wang X, Li Y, Feng X, Han C. Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021. Front Public Health 2022; 10:903025. [PMID: 36033737 PMCID: PMC9402928 DOI: 10.3389/fpubh.2022.903025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/19/2022] [Indexed: 01/22/2023] Open
Abstract
Background Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government. Method In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis. Results We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter. Conclusion Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.
Collapse
Affiliation(s)
- Yanxiang Cao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Yuyao Zhu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yimeng Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jianhua Jin
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jinyi Qiu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhirui Li
- Baoding First Central Hospital, Baoding, China
| | - Jiaxin Liu
- Department of Psychology, University of Washington, Washington, SA, United States
| | - Jiayi Teng
- School of Psychology, Philosophy and Language Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Sixiao Li
- Faculty of Arts, Humanities and Cultures, School of Music, University of Leeds, Leeds, United Kingdom
| | - Yanan Zhao
- China Academy of Chinese Medical Sciences, Institute of Acupuncture and Moxibustion, Beijing, China
| | - Xixi Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xuemei Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yaqiong Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiaoyang Feng
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
| | - Chuanliang Han
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, China
| |
Collapse
|
6
|
Nejadghaderi SA, Saghazadeh A, Rezaei N. Health Care Policies and COVID-19 Prevalence: Is There Any Association? INTERNATIONAL JOURNAL OF HEALTH SERVICES 2021; 52:9-22. [PMID: 33686893 DOI: 10.1177/0020731421993940] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected almost all countries and territories. As of December 6, 2020, the United States of America and India have the highest prevalence. Each country has implemented different strategies to control and reduce the spread of disease. Here, the association between prevalence number and health policies is evaluated by comparing 2 groups of countries: (1) Italy, the United States of America, Germany, Spain, and India with a higher prevalence than a linear trend line; and (2) Singapore and China with a lower or equal prevalence than linear forecasts. A rapid overview revealed that many countries have similar strategies for controlling COVID-19, including the suspension of air travel, the lockdown on the cities with the most cases detected, active case findings, monitoring of close contacts, and raising public awareness. Also, they used a gradual and phased plan to reopen activities. So, the difference between countries in the burden of COVID-19 can be attributable to the strict mode and nonstrict mode of implementation of strategies. Limitations at the national levels call for systemic rather than regional strategies.
Collapse
Affiliation(s)
- Seyed A Nejadghaderi
- Systematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,School of Medicine, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amene Saghazadeh
- Systematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Research Center for Immunodeficiencies, Children's Medical Center, 48439Tehran University of Medical Sciences, Tehran, Iran
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Children's Medical Center, 48439Tehran University of Medical Sciences, Tehran, Iran.,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| |
Collapse
|
7
|
The association between internal migration and pulmonary tuberculosis in China, 2005-2015: a spatial analysis. Infect Dis Poverty 2020; 9:5. [PMID: 32063228 PMCID: PMC7025414 DOI: 10.1186/s40249-020-0621-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/07/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Internal migration places individuals at high risk of contracting tuberculosis (TB). However, there is a scarcity of national-level spatial analyses regarding the association between TB and internal migration in China. In our research, we aimed to explore the spatial variation in cases of sputum smear-positive pulmonary TB (SS + PTB) in China; and the associations between SS + PTB, internal migration, socioeconomic factors, and demographic factors in the country between 2005 and 2015. METHODS Reported cases of SS + PTB were obtained from the national PTB surveillance system database; cases were obtained at the provincial level. Internal migration data were extracted from the national population sampling survey and the census. Spatial autocorrelations were explored using the global Moran's statistic and local indicators of spatial association. The spatial temporal analysis was performed using Kulldorff's scan statistic. Fixed effects regression was used to explore the association between SS + PTB and internal migration. RESULTS A total of 4 708 563 SS + PTB cases were reported in China between 2005 and 2015, of which 3 376 011 (71.7%) were male and 1 332 552 (28.3%) were female. There was a trend towards decreasing rates of SS + PTB notifications between 2005 and 2015. The result of global spatial autocorrelation indicated that there were significant spatial correlations between SS + PTB rate and internal migration each year (2005-2015). Spatial clustering of SS + PTB cases was mainly located in central and southern China and overlapped with the clusters of emigration. The proportions of emigrants and immigrants were significantly associated with SS + PTB. Per capita GDP and education level were negatively associated with SS + PTB. The internal migration flow maps indicated that migrants preferred neighboring provinces, with most migrating for work or business. CONCLUSIONS This study found a significant spatial autocorrelation between SS + PTB and internal migration. Both emigration and immigration were statistically associated with SS + PTB, and the association with emigration was stronger than that for immigration. Further, we found that SS + PTB clusters overlapped with emigration clusters, and the internal migration flow maps suggested that migrants from SS + PTB clusters may influence the TB epidemic characteristics of neighboring provinces. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migrants.
Collapse
|
8
|
Chen J, Wang J, Wang M, Liang R, Lu Y, Zhang Q, Chen Q, Niu B. Retrospect and Risk Analysis of Foot-and-Mouth Disease in China Based on Integrated Surveillance and Spatial Analysis Tools. Front Vet Sci 2020; 6:511. [PMID: 32039251 PMCID: PMC6986238 DOI: 10.3389/fvets.2019.00511] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and seriously affects the development of animal husbandry. It is necessary to defend the spread of FMD. To explore the distribution characteristics and transmission of FMD between 2010 and 2017 in China, Global Moran's I test and Getis-Ord Gi index were used to analyze the spatial cluster. A space-time permutation scan statistic was applied to analyze the spatio-temporal pattern. GIS-based method was employed to create a map representing the distribution pattern, directional trend, and hotspots for each outbreak. The number of cases was defined as the number of animals with FMD for the above analysis. We also constructed a phylogenetic tree to compare the homology and variation of FMD virus (FMDV) to provide a clue for the potential development of an effective vaccine. The results indicated that the FMD outbreaks in China had obvious time patterns and clusters in space and space-time, with the outbreaks concentrated in the first half of each year. The outbreaks of FMD decreased each year from 2010 with an obvious downward trend of hotspots. Spatial analysis revealed that the distribution of FMD outbreaks in 2010, 2015, and 2017 exhibited a clustered pattern. Space-time scanning revealed that the spatio-temporal clusters were centered in Guangdong, Tibet and the junction of Wuhan, Jiangxi, Anhui. Comparison of the spatial analysis and space-time analysis of FMD outbreaks revealed that Guangdong was the same cluster of the two in 2010. In addition, the directional trend analysis indicated that the FMD transmission was oriented northwest-southeast. The findings demonstrated that FMDV in China can be divided into three pedigrees and the homology of these strains is very high while comparing the first FMDV strain with the others. The data provide a basis for the effective monitoring and prevention of FMD, and for the development of an FMD vaccine in China.
Collapse
Affiliation(s)
- Jiahui Chen
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Jianying Wang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Minjia Wang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Ruirui Liang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Yi Lu
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Qiang Zhang
- Tech Ctr Anim Plant & Food Inspect & Quarantine, Shanghai Customs, Shanghai, China
| | - Qin Chen
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| | - Bing Niu
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, Shanghai, China
| |
Collapse
|
9
|
Zhu B, Fu Y, Liu J, He R, Zhang N, Mao Y. Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China. BMC Health Serv Res 2018; 18:957. [PMID: 30541543 PMCID: PMC6292090 DOI: 10.1186/s12913-018-3737-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 11/19/2018] [Indexed: 01/21/2023] Open
Abstract
Background Health workforce misdistribution leads to severe inequity and low-efficiency in health services in the developing countries. Targeting at China, this research aims to reveal, visualize and compare the geographical distribution patterns of different subtypes of urban and rural health workforce and identify the priority regions for health workforce planning and allocation policies designing. Methods The health workforce density (workforce-to-population ratio) is adopted to represent the accessibility to health workforce in each geographical unit. Besides a descriptive geography of health workforce as a whole, the local indicators of spatial association (LISA) are used to explore the spatial clusters of different subtypes of health workforce, which are visualized by geographical tools. Results Results reveal that regional disparities and spatial clusters exist in China’s health workforce distribution, with different types of workforce exhibiting relatively different spatial distribution characteristics. Besides, huge urban-rural disparities are found in the distribution of health workforce in China. Unexpectedly but intriguingly, most of the high-high and high-low cluster area of urban health workforce are concentrated in the western China (Xinjiang, Xizang etc.), indicating the relative abundant stock of urban health workforce in these units, while the low-low and low-high cluster area of different types of urban health workforce are mainly distributed in middle China. Regarding the rural health workforce, there is an obvious and similar low-low and low-high clustering pattern in western provinces (Sichuan, Yunnan) for the licensed doctors, pharmacists, technologists, which play a critical role in health services delivery. Conclusions Different types of health workforce displayed distinct spatial distribution patterns, while the misdistribution of rural health workforce imposed more challenges to the Chinese health sector due to its poorer stock and more disadvantaged positions of backward regions (i.e., low-low and low-high cluster area). Subtype-specific and region-oriented health workforce planning and allocation policies are suggested to be made, aiming at the urban and rural health workforce respectively, by prioritizing the identified low-low and low-high cluster areas. Electronic supplementary material The online version of this article (10.1186/s12913-018-3737-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Bin Zhu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China.,Department of Public Policy, City University of Hong Kong, Hong Kong, 999077, China
| | - Yang Fu
- College of Management, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong, China
| | - Jinlin Liu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Rongxin He
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China.
| |
Collapse
|