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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.
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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
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Ding C, Ji Z, Zheng L, Jin X, Ruan B, Zhang Y, Li L, Xu K. Population-based active screening strategy contributes to the prevention and control of tuberculosis. Zhejiang Da Xue Xue Bao Yi Xue Ban 2022; 51:669-678. [PMID: 36915974 PMCID: PMC10262001 DOI: 10.3724/zdxbyxb-2022-0426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/10/2022] [Indexed: 12/24/2022]
Abstract
Despite the achievements obtained worldwide in the control of tuberculosis in recent years, many countries and regions including China still face challenges such as low diagnosis rate, high missed diagnosis rate, and delayed diagnosis of the disease. The discovery strategy of tuberculosis in China has changed from "active discovery by X-ray examination" to "passive discovery by self-referral due to symptoms", and currently the approach is integrated involving self-referral due to symptoms, active screening, and physical examination. Active screening could help to identify early asymptomatic and untreated cases. With the development of molecular biology and artificial intelligence-assisted diagnosis technology, there are more options for active screening among the large-scale populations. Although the implementation cost of a population-based active screening strategy is high, it has great value in social benefits, and active screening in special populations can obtain better benefits. Active screening of tuberculosis is an important component of the disease control. It is suggested that active screening strategies should be optimized according to the specific conditions of the regions to ultimately ensure the benefit of the tuberculosis control.
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Affiliation(s)
- Cheng Ding
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
| | - Zhongkang Ji
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
| | - Lin Zheng
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
| | - Xiuyuan Jin
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
| | - Bing Ruan
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
| | - Ying Zhang
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
- 2. Jinan Microecological Biomedicine Shandong Laboratory, Jinan 250117, China
| | - Lanjuan Li
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
- 2. Jinan Microecological Biomedicine Shandong Laboratory, Jinan 250117, China
| | - Kaijin Xu
- 1. The First Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
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Gilmour B, Xu Z, Bai L, Alene KA, Clements ACA. The impact of ethnic minority status on tuberculosis diagnosis and treatment delays in Hunan Province, China. BMC Infect Dis 2022; 22:90. [PMID: 35081919 PMCID: PMC8790941 DOI: 10.1186/s12879-022-07072-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) continues to be a major public health challenge in China. Understanding TB management delays within the context of China's unique ethnic diversity may be of value in tackling the disease. This study sought to evaluate the impact of ethnic minority status on TB diagnosis and treatment delays. METHODS This retrospective cohort study was conducted on patients diagnosed with TB in Hunan Province, China between 2013 and 2018. Diagnosis delay was defined as the time interval between the onset of symptoms and the date of diagnosis. Treatment delay was defined as the time interval between diagnosis and treatment commencement. Univariable and multivariable logistic regression models were used to identify factors associated with TB diagnosis and treatment delay, including ethnic minority status. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were calculated to assess the strength of association between the dependant and independent variables. RESULTS A total of 318,792 TB patients were included in the study with a mean age of 51.7 years (SD 17.7). The majority of patients were male (72.6%) and Han ethnicity (90.6%). The odds of experiencing diagnosis delay (> 21 days) were significantly higher for Tujia (AOR: 1.46, 95% CI: 1.41, 1.51), Miao (AOR: 1.31, 95% CI: 1.26, 1.37), Dong (AOR: 1.97, 95% CI: 1.85, 2.11), Yao (AOR: 1.27, 95% CI: 1.17, 1.37), and Bai (AOR: 1.45, 95% CI: 1.22, 1.74) ethnic minorities compared to the Han majority. The odds of experiencing treatment delay (> 15 days) were significantly lower for five of the seven ethnic minority groups relative to the Han majority: Tujia (AOR 0.92, 95% CI 0.88, 0.96), Miao (AOR 0.74, 95% CI 0.70, 0.79), Dong (AOR 0.87, 95% CI 0.81, 0.95), Yao (AOR 0.20, 95% CI 0.17, 0.24) and 'other' (ethnic minorities that individually represented < 0.1% of the patient population) (AOR 0.70, 955 CI 0.51, 0.97). CONCLUSIONS This study shows ethnic minority status to be a significant risk factor in diagnosis delay, but for it to reduce the odds of treatment delay. Further research is required to determine the underlying causes of diagnosis delay within ethnic minority populations.
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Affiliation(s)
- Beth Gilmour
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.
| | - Zuhui Xu
- Xiangya School of Public Health, Central South University, Changsha, China.,TB Control Institute of Hunan Province, Changsha, China
| | - Liqiong Bai
- TB Control Institute of Hunan Province, Changsha, China
| | - Kefyalew Addis Alene
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.,Telethon Kids Institute, Nedlands, WA, Australia
| | - Archie C A Clements
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.,Telethon Kids Institute, Nedlands, WA, Australia
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Total, Patient and System Diagnostic Delays for Pulmonary Bacilliferous Tuberculosis in the Six Diagnostic and Treatment Centers in the Five Health Districts of the Central Region, Burkina Faso, 2018. J Epidemiol Glob Health 2022; 12:124-132. [PMID: 34978709 PMCID: PMC8907367 DOI: 10.1007/s44197-021-00027-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction Long diagnosis delay contributes significantly to the failure to eradicate tuberculosis. The objective of this study was to evaluate the total, patient and system delays in diagnosis of pulmonary bacilliferous in the six tuberculosis Diagnostic and Treatment Centers in the five health districts of the central region in Burkina Faso. Methods A descriptive cross-sectional study was conducted among 384 microscopy-positive pulmonary tuberculosis patients in 2018 to address this objective. It concerned the socio-demographic, clinical, microbiological characteristics, and referral location/pathway characteristics of the patients. We then calculated the different delays. The “patient” (time from first symptoms to first consultation), “system” (time from first consultation to first diagnosis) and total (time from first symptoms to diagnosis) median diagnostic delay were estimated. Results The median “total”, “patient” and “system” diagnostic times were 37, 21 and 7 days, respectively. Of the 384 patients surveyed, 158 patients or 41.25% of patients had a long total diagnostic delay (> 45 days). The number of patients with a long system diagnostic delay was 125 patients (32.55%; p < 0.001) and those with a long patient diagnostic delay were 105 patients (27.34%; p < 0.001). Conclusion The total diagnosis delay of pulmonary tuberculosis was long for almost half of the patients. Awareness of the signs of tuberculosis among patients and caregivers, and consultation in a health center must be intensified to help considerably reduce these delays. Supplementary Information The online version contains supplementary material available at 10.1007/s44197-021-00027-z.
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Teo AKJ, Singh SR, Prem K, Hsu LY, Yi S. Duration and determinants of delayed tuberculosis diagnosis and treatment in high-burden countries: a mixed-methods systematic review and meta-analysis. Respir Res 2021; 22:251. [PMID: 34556113 PMCID: PMC8459488 DOI: 10.1186/s12931-021-01841-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 09/08/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Thirty countries with the highest tuberculosis (TB) burden bear 87% of the world's TB cases. Delayed diagnosis and treatment are detrimental to TB prognosis and sustain TB transmission in the community, making TB elimination a great challenge, especially in these countries. Our objective was to elucidate the duration and determinants of delayed diagnosis and treatment of pulmonary TB in high TB-burden countries. METHODS We conducted a systematic review and meta-analysis of quantitative and qualitative studies by searching four databases for literature published between 2008 and 2018 following PRISMA guidelines. We performed a narrative synthesis of the covariates significantly associated with patient, health system, treatment, and total delays. The pooled median duration of delay and effect sizes of covariates were estimated using random-effects meta-analyses. We identified key qualitative themes using thematic analysis. RESULTS This review included 124 articles from 14 low- and lower-middle-income countries (LIC and LMIC) and five upper-middle-income countries (UMIC). The pooled median duration of delays (in days) were-patient delay (LIC/LMIC: 28 (95% CI 20-30); UMIC: 10 (95% CI 10-20), health system delay (LIC/LMIC: 14 (95% CI 2-28); UMIC: 4 (95% CI 2-4), and treatment delay (LIC/LMIC: 14 (95% CI 3-84); UMIC: 0 (95% CI 0-1). There was consistent evidence that being female and rural residence was associated with longer patient delay. Patient delay was also associated with other individual, interpersonal, and community risk factors such as poor TB knowledge, long chains of care-seeking through private/multiple providers, perceived stigma, financial insecurities, and poor access to healthcare. Organizational and policy factors mediated health system and treatment delays. These factors included the lack of resources and complex administrative procedures and systems at the health facilities. We identified data gaps in 11 high-burden countries. CONCLUSIONS This review presented the duration of delays and detailed the determinants of delayed TB diagnosis and treatment in high-burden countries. The gaps identified could be addressed through tailored approaches, education, and at a higher level, through health system strengthening and provision of universal health coverage to reduce delays and improve access to TB diagnosis and care. PROSPERO registration: CRD42018107237.
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Affiliation(s)
- Alvin Kuo Jing Teo
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore, #10-01, 12 Science Drive 2, Singapore, 117549, Singapore.
| | - Shweta R Singh
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Kiesha Prem
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Li Yang Hsu
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Siyan Yi
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
- KHANA Center for Population Health Research, Phnom Penh, Cambodia
- Center for Global Health Research, Touro University California, Vallejo, USA
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