Edited by: Yun Qian, Shanghai Jiao Tong University, China
Reviewed by: Yanbin Kuang, Shanghai JiaoTong University, China; Shuyuan Wang, Shanghai Jiaotong University, China
This article was submitted to Infectious Diseases – Surveillance, Prevention and Treatment, a section of the journal Frontiers in Public Health
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Tuberculosis is a communicable disease, the leading cause of death ranking above HIV/AIDS. Globally, an estimate of 10 million people fell ill with tuberculosis (TB), and 1.4 million people died from this disease in 2019 (
In China, the migrant populations are mostly internal migrants (henceforth called migrants). In 2019, the number of migrants in China reached 236 million, accounting for one-sixth of the whole population (
The early detection and timely treatment of migrant patients with TB are essential for effective TB control and are emphasized in the “End TB strategy” of the WHO (
This is a retrospective study of the diagnostic delay of migrant patients with TB as compared with local patients with TB over 5 years (2015–2019). The data was collected from the patient records exported from the Tuberculosis Information Management System of China (TBIMS) (TB special reporting system version 2.0).
This study was conducted in Cangnan County of Wenzhou City, Zhejiang Province. Zhejiang is one of the most developed provinces in China and a key destination for migrants. Wenzhou is a city under the jurisdiction of Zhejiang Province. In 2019, Wenzhou had a total gross domestic product (GDP) exceeding the US $100 billion, ranking third out of 11 cities in Zhejiang province, with an average per capita GDP of $10,000 (
This study used the routine electronic practice data that were collected and recorded by TB health providers during TB consultation at the time of TB registration. This study selected the data of patients with TB registered from January 1, 2015, to December 31, 2019. The data was exported from TBIMS to Microsoft Excel (Microsoft, Redmond, Washington, United States) by the staff in the Center for Disease Control and Prevention (CDC) of Zhejiang Province and Cangnan. The data covers the demographics of patients with TB such as age, sex, occupation, patient source, level of the hospital for initial TB diagnosis, and household registration (migrant or local resident); clinical characteristics such as smear sputum results, cavity, TB severity (e.g., with large cavities or lesions in more than two lung lobes), treatment category (new or retreated cases), treatment outcomes; and health service-related information such as date of onset of TB symptoms, date of first health-care visit, and date of confirmed TB diagnosis.
In this study, patients with TB who did not have household registration in Cangnan County or who lived in Cangnan County for <6 months at the time of registration was classified as “migrant” in the TBIMS. In this study, we analyzed the total diagnostic delay, patient delay, and health system delay of migrant patients with TB as compared with local patients with TB. The total diagnostic delay (henceforth called total delay) is the sum of the patient delay and health system delay. The patient delay and health system delay is defined as the interval between the onset of TB symptoms and the first visit to a health facility, and the interval between the first visit to a health facility and confirmed TB diagnosis in the TB designated hospital, respectively (
The data were analyzed using SPSS 21.0 (SPSS, Inc., Chicago, United States). The demographics, clinical symptoms, and delay in TB diagnosis for the migrant and local patients with TB were depicted using descriptive statistics. The continuous variables were presented by the median and interquartile range (IQR), while the categorical variables were presented by counts and proportions. The delay in the TB diagnosis (including delay times and delay proportion) between the migrant and local patients with TB was compared using a Mann-Whitney U test and Chi-square test. In addition, we estimated the probability of the migrant patients with TB being diagnosed at different time intervals (e.g., 1*14 days) based on three diagnostic delay categories, as compared with the local residents, further testifying and comparing the diagnostic delay among these two cohorts. Hence, Kaplan-Meier survival curves were drawn to record the median times of total delay, patient delay, and health system across 5 years. The difference in these delay curves between migrant and local patients with TB was examined using a Log-rank test. A
A total of 2,487 TB cases were reported between 2015 and 2019 in Cangnan County, Zhejiang Province, including 539 migrants (22%) and 1,948 residents (78%). The migrant patients with TB were significantly younger than the local patients with TB (39 vs. 49 years,
Demographics, clinical characteristics of migrant and local patients with TB from 2015 to 2019.
Age (Median, IQR) | 29 (22–49) | 36 (25–52) | 44 (31–56) | 42 (26–54) | 41 (28–54) | 39 (26–53) | 49 33–61) | 46 (30–60) | 51 (37–65) | 49 (31–63) | 52 (35–65) | 49 (33–63) | 0.000 |
Age >45 | 26 (29) | 40 (40) | 4 5 (44) | 47 (40) | 53 (41) | 211 (39) | 265 (55) | 206 (51) | 246 (64) | 203 (56) | 194 (61) | 1,114 (57) | 0.000 |
Male | 61 (69) | 71 (70) | 69 (68) | 85 (73) | 94 (72) | 380 (71) | 349 (73) | 295 (73) | 287 (74) | 259 (72) | 242 (76) | 1,432 (74) | 0.164 |
Farmer | 43 (48) | 48 (48) | 34 (33) | 27 (23) | 38 (30) | 190 (35) | 432 (90) | 340 (85) | 309 (80) | 207 (57) | 217 (69) | 1,505 (77) | 0.000 |
Smear positive | 40 (45) | 32 (32) | 41 (40) | 35 (30) | 35 (27) | 183 (34) | 174 (36) | 113 (28) | 136 (35) | 146 (40) | 114 (36) | 683 (35) | 0.632 |
Cavity | 39 (44) | 37 (37) | 37 (36) | 31 (27) | 34 (27) | 178 (33) | 182 (38) | 153 (38) | 156 (40) | 136 (38) | 134 (42) | 761 (39) | 0.012 |
Severe cases | 38 (43) | 33 (33) | 33 (32) | 17 (15) | 22 (17) | 143 (27) | 172 (36) | 150 (37) | 155 (40) | 127 (35) | 130 (41) | 734 (38) | 0.000 |
Patient source |
0.000 | ||||||||||||
Symptomatic visits | 44 (49) | 77 (76) | 73 (72) | 80 (70) | 39 (30) | 313 (68) | 111 (23) | 97 (24) | 60 (16) | 32 (9) | 25 (8) | 325 (17) | |
Referral | 41 (46) | 23 (23) | 24 (24) | 22 (19) | 34 (26) | 144 (32) | 368 (77) | 305 (76) | 323 (84) | 329 (91) | 291 (92) | 1,616 (83) | |
Treatment category | 0.945 | ||||||||||||
New | 76 (85) | 80 (79) | 90 (88) | 108 (92) | 118 (91) | 472 (88) | 425 (89) | 353 (88) | 332 (86) | 311 (86) | 287 (91) | 1,708 (88) | |
Retreated | 13 (15) | 21 (21) | 12 (12) | 9 (8) | 12 (9) | 67 (12) | 55 (11) | 49 (12) | 55 (14) | 51 (14) | 30 (9) | 240 (12) | |
Level of hospital for initial TB diagnosis | |||||||||||||
County | 48 (54) | 36 (36) | 36 (35) | 29 (25) | 38 (29) | 187 (35) | 479 (99) | 400 (99) | 386 (99) | 361 (99) | 315 (99) | 1,941 (99) | 0.000 |
Prefectural and above | 41 (46) | 65 (64) | 66 (65) | 88 (75) | 92 (71) | 352 (65) | 1 (1) | 2 (1) | 1 (1) | 1 (1) | 2 (1) | 7 (1) | |
Treatment outcomes |
0.000 | ||||||||||||
Treatment success | 80 (90) | 76 (75) | 88 (86) | 99 (90) | 7 (70) | 350 (85) | 470 (98) | 395 (98) | 367 (95) | 331 (95) | 13 (62) | 1,576 (96) | |
Cured | 36 (40) | 20 (20) | 37 (36) | 29 (26) | 1 (10) | 123 (30) | 167 (35) | 110 (27) | 122 (32) | 132 (38) | 1 (5.0) | 532 (32) | |
Treatment completed | 44 (49) | 56 (55) | 51 (50) | 70 (64) | 6 (60) | 227 (55) | 303 (63) | 285 (71) | 245 (63) | 199 (57) | 12 (57) | 1,044 (64) | |
Unfavorable outcomes | 9 (10) | 25 (25) | 14 (14) | 11 (10) | 3 (30) | 62 (15) | 10 (2.0) | 7 (2.0) | 20 (5.0) | 17 (5.0) | 8 (38) | 62 (4.0) | |
Lost to follow-up | 5 (5.6) | 12 (12) | 8 (7.8) | 4 (3.6) | 0 (0.0) | 29 (7.0) | 0 (0.0) | 0 (0.0) | 7 (1.8) | 3 (0.9) | 0 (0.0) | 10 (0.6) | |
Treatment failed | 1 (1.1) | 2 (2.0) | 1 (1.0) | 0 (0.0) | 0 (0.0) | 4 (1.0) | 6 (1.3) | 1 (0.2) | 6 (1.6) | 4 (1.1) | 3 (14) | 20 (1.2) | |
Diagnostic change | 0 (0.0) | 5 (5.0) | 3 (2.9) | 4 (3.6) | 1 (10) | 13 (3.2) | 2 (0.4) | 3 (0.7) | 2 (0.5) | 7 (2.0) | 2 (9.5) | 16 (1.0) | |
Transfer to MDR | 0 (0.0) | 2 (2.0) | 1 (1.0) | 2 (1.8) | 1 (10) | 6 (1.5) | 1 (0.2) | 1 (0.2) | 0 (0.0) | 2 (0.6) | 1 (5.0) | 5 (0.3) | |
Adverse reactions | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1 (0.9) | 0 (0.0) | 1 (0.2) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
Died | 1 (1.1) | 1 (1.0) | 0 (0.0) | 0 (0.0) | 1 (10) | 3 (0.7) | 1 (0.2) | 2 (0.5) | 4 (1.0) | 0 (0.0) | 0 (0.0) | 7 (0.4) | |
Other | 2 (2.2) | 3 (3.0) | 1 (1.0) | 0 (0.0) | 0 (0.0) | 6 (1.5) | 0 (0.0) | 0 (0.0) | 1 (0.3) | 1 (0.3) | 2 (9.5) | 4 (0.2) |
The median (IQR) total delay for the migrant patients with TB was 30 (11–64) days, significantly longer than the 9 (4–17) days for the local patients with TB over 5 years (
The total delay of migrant and local patients with TB from 2015 to 2019.
2015 | 29 (10–52) | 9 (4–19) | 0.000 | 45 (51) | 63 (13) | 0.000 |
2016 | 30 (12–60) | 8 (4–16) | 0.000 | 53 (53) | 47 (12) | 0.000 |
2017 | 38 (16–78) | 10 (4–21) | 0.000 | 61 (60) | 63 (16) | 0.000 |
2018 | 49 (21–105) | 8 (3–18) | 0.000 | 83 (71) | 53 (15) | 0.000 |
2019 | 17 (7–33) | 9 (3–14) | 0.000 | 38 (29) | 31 (10) | 0.000 |
2015–2019 | 30 (11–64) | 9 (4–17) | 0.000 | 280 (52) | 257 (13) | 0.000 |
The median (IQR) patient delay for the migrant patients with TB was 13 (
Patient delay of migrant and local patients with TB from 2015 to 2019.
2015 | 13(6–31) | 9 (4–19) | 0.001 | 41 (46) | 156 (33) | 0.013 |
2016 | 17 (6–35) | 8 (4–16) | 0.000 | 54 (53) | 112 (28) | 0.000 |
2017 | 20 (6–53) | 10 (4–21) | 0.000 | 55 (54) | 143 (37) | 0.002 |
2018 | 28 (8–59) | 8 (3–17) | 0.000 | 68 (58) | 106 (29) | 0.000 |
2019 | 4 (0–15) | 9 (3–14) | 0.000 | 33 (25) | 70 (22) | 0.451 |
2015–2019 | 13(4–34) | 9 (4–17) | 0.000 | 251 (47) | 587 (30) | 0.000 |
The median (IQR) health system delay for the migrant patients with TB was 9 (0–25) days, which is significantly longer than the 0 (0–0) days for the local patients with TB over 5 years
Health system delay of migrant and local patients with TB from 2015 to 2019.
2015 | 1 (0–21) | 0 (0–0) | 0.000 | 34 (38) | 2 (0.4) | 0.000 |
2016 | 11 (0–23) | 0 (0–0) | 0.000 | 46 (46) | 2 (0.5) | 0.000 |
2017 | 16 (0–29) | 0 (0–0) | 0.000 | 53 (52) | 1 (0.3) | 0.000 |
2018 | 15 (0–42) | 0 (0–0) | 0.000 | 60 (51) | 3 (0.8) | 0.000 |
2019 | 5 (0–15) | 0 (0–0) | 0.000 | 35 (27) | 2 (0.6) | 0.000 |
2015–2019 | 9 (0–25) | 0 (0–0) | 0.000 | 228 (42) | 10 (0.5) | 0.000 |
The log-rank test showed that the total delay curves significantly differed between the migrant and local patients with TB over 5 years (Log-rank test χ2 = 268.409,
Total delay curves between migrant and local patients with TB from 2015 to 2019.
The log-rank test showed that the patient delay curves significantly differed between the migrant and local patients with TB over 5 years (Log-rank test χ2 = 78.135,
Patient delay curves between migrant and local patients with TB from 2015 to 2019.
The log-rank test showed that the health system delay curves significantly differed between the migrants and local patients with TB over 5 years (Log-rank test χ2 = 1,169.030,
Health system delay curves between migrant and local patients with TB from 2015 to 2019.
In this study, we found that migrant patients with TB tended to be younger, non-farmer, have less severe conditions, receive an initial diagnosis at prefectural and above-level hospitals. The median total delay, patient delay, and health system delay for migrant patients with TB were 30, 13, and 9 days, respectively, as compared with 9, 9, and 0 days for local patients with TB. Compared with local patients with TB, migrant patients with TB had a higher proportion of patients with a total delay of >28 days, patient delay of >14 days, and health system delay of >14 days. The survival curves of delay showed that the longer the time interval was, the more likely the migrant patients with TB were to be diagnosed.
Migrants remain one of the most important vulnerable subgroups in the context of the TB epidemic. Tuberculosis control in migrants is pivotal for countries to progress toward TB elimination in accordance with the WHO END TB strategy (
Similar to the previous studies (
The main purpose of this study is to examine the delayed diagnosis of migrant patients with TB as compared with local patients with TB. In this study, we reported a median total delay of 30 days for the migrant patients with TB, which is between the 21 days reported by Zhou et al. in the counties of Shandong and 46 days reported by Xiao et al. in the counties of Zhejiang (
Our study reported a median patient delay of 13 days and a proportion of 47% with patient delay >14 days for migrant patients with TB. Previous studies have reported a median patient delay ranging from 10 to 21 days and a proportion ranging from 39 to 68% with patient delay >14 days for migrant patients with TB in China (
Our study reported a median health system delay of 9 days and a proportion of 42% with health system delay >14 days for migrant patients with TB. This finding is consistent with the results from previous studies, which reported a median health system delay ranging from 8 to 11 days and a proportion ranging from 27 to 45% with health system delay >14 days for migrant patients with TB in China (
Very few studies have employed survival analysis into the research of delay in TB diagnosis among patients with TB. The study of Chen et al. seemed to be the first study to analyze the diagnostic delay of patients with TB using survival analysis in China (
Our study has several limitations. First, this study was only conducted in only one county of China, hence the generalizability of our findings is limited. However, with a larger sample size including all registered TB cases from 2015 to 2019, our study provides solid data on the diagnostic delay for migrant patients with TB as compared with local patients with TB. Second, the time of onset of TB symptoms and first visit to a health facility was based on the self-reported information of patients with TB during clinical consultations, thus recall biases might exist. Third, the diagnosis of TB is sometimes difficult due to the confusion between TB and other diseases like lung cancer. In this case, the delay may be prolonged, in addition to the delay caused by the diagnostic process itself as it takes 2 days for three samples of smear sputum to be tested before TB confirmation. In other words, our analysis of health systems delay may dilute the diagnostic process when patients arrive at the TB clinic. Future studies should make differences between provider delay (from the first contact of the health facilities to the visit of the TB clinic) and confirmation delay (from the first visit of the TB clinic to the confirmation of TB). Finally, our study discloses the patterns of differences in diagnostic delays and time points between migrant and local patients for 5 years. Previous studies have suggested that factors such as socio-demographic and clinical characteristics could also influence the patient and health systems delay (
Diagnosis is significantly delayed among migrant patients with TB. Our study highlights the importance of early screening and diagnosis for TB especially among migrants, to improve access and ensure better management for all patients with TB.
The data analyzed in this study is subject to the following licenses/restrictions: Information in our database is confidential. Requests to access these datasets should be directed to Guanyang Zou,
The studies involving human participants were reviewed and approved by Zhejiang Provincial Center for Disease Control and Prevention. Written informed consent for participation was not provided by the participants' legal guardians/next of kin because: This is a patient record review study with data exported from China's Tuberculosis Information Management System. Data recorded in the system and used for this study are collected during TB registration and consultation.
GZ, WX, and LZ conceived and designed the study. BC and DH participated in data collection and analysis. GZ and WX were major contributors in writing the manuscript. OC and XW provided constructive suggestions on the study. All authors contributed to the article and approved the submitted version.
This study was supported by the National Social Science Foundation of China (Grant Number 20&ZD122) and the Zhejiang Provincial Science and Public Welfare Project (LGF19H260004). The funding source was not involved in the design of the study and the collection, analysis and interpretation of data, and writing the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
We would like to thank Dr. Keven Bermudez for his help in editing and improving the English language of this manuscript.
Tuberculosis
World Health Organization
China's Tuberculosis information management system
Gross Domestic Products
Center for Disease Prevention and Control
Interquartile Range.