Abstract
Background
Migrants are the key population for tuberculosis (TB) transmission in China. However, it remains unknown how many migrants have received TB education and through what means.
Objectives
To identify the rate and methods of TB education among migrants in China by using nationally representative data.
Method
This study used secondary data analysis. The data were derived from the China Migrants Dynamic Survey 2014–2017. A total sample of 745,926 migrants was included in the following analysis. Information on TB education was collected through a self-report questionnaire. We used hierarchical logistic regression models to explore the relationship between the independent variables and the receipt of TB education.
Results
Only 30.4% (n = 226,458) received TB education. Among all age-groups, participants between 65 and 69 years old had the highest TB education rate (33.4%). Bulletin boards (86.5%–91%), media (73% to 86.7%), and books/magazines (59.2%–67.4%) were the most common ways for migrants to receive TB education.
Conclusions
Our study showed the rates of TB education in each region of China and indicated the significant disparity among the seven regions. Traditional media, off-line medical consultation, community advocacy, and bulletin boards should be the primary methods of delivering TB education. TB education campaigns targeting migrants with a low socioeconomic status should be actively promoted.
According to the 2019 Global Tuberculosis Report from the World Health Organization, 866,000 tuberculosis (TB) cases and 66,000 multidrug-resistant TB (MDR-TB) cases were reported in China in 2018 (Annabel et al., 2019). Although China had already achieved the Millennium Development Goal of reducing TB incidence by 50% by the end of 2015, it was still one of the highest burden countries for TB, MDR-TB, and TB/HIV in the 2016 to 2020 period (L. Wang et al., 2017; Y. Guo & Huang, 2016). Additionally, the incidence rates of TB/HIV, TB/chronic disease comorbidity, and MDR-TB have all increased in the past 5 years (S. Zhu et al., 2017). These facts indicate that it will be a serious challenge for China to achieve the End TB target by 2035. Additional actions, including preventive education and active case finding, should be taken between 2020 and 2030 to control TB prevalence.
Migrant are the key vulnerable population for TB in China. Previous studies have shown that migrants, who do not have local household registration status, account for 60% to 80% of all new TB cases in major cities in China, and these percentages are much higher than those in other countries within the same period (X. Li et al., 2017; Yang et al., 2018). Additionally, this population is also a crucial vector driving the local incidence of TB in urban areas in China. Local TB transmission dynamics indicate that the migrant-residence-migrant transmission mode in destination cities was the dominant mechanism contributing to increasing numbers of cases in urban regions in China (Yang et al., 2018). Migrants face a series of risk factors for TB transmission, including overcrowded living conditions, low socioeconomic status, lack of access to local health services, low spending capacity, and forced displacement (Boudville et al., 2020; Castelli & Sulis, 2017; Sotgiu et al., 2017). Among the risk factors, limited knowledge about TB is the most crucial and easily changed aspect associated with TB and MDR-TB prevalence.
Previous studies have shown that TB education rates differ by population and region in China. Chen’s and Zou’s studies found that only 41.5% of residents in Guizhou Province and 44.1% of residents in Anqing City in Anhui Province had basic knowledge of TB (Chen et al., 2016; Zou et al., 2020). Zhao et al.’s (2013) study showed that 24.1% to 34.1% of undergraduate medical students from Southwest China had basic knowledge regarding TB or had received TB-related health education. Although migrants are the key population for TB transmission in China, it remains unknown how many migrants have received TB education and through what means. It is necessary to answer these questions using a uniform questionnaire in a nationally representative sample, an approach that has the most value for policymakers.
Therefore, to address the knowledge gap, this study aims to identify the rate and methods of TB education among migrants in China by using nationally representative data. The findings of this study will serve as empirical evidence and benchmarks to help both local and national governments to develop policies regarding TB education among migrants in China.
Method
Study Population
Individual-level data regarding migrants’ sociodemographic variables and information on TB education were derived from the China Migrants Dynamic Survey (CMDS) 2014–2017, which is a nationwide representative survey conducted annually. The CMDS applied multistage random sampling with the probability proportional to size method and collected representative demographic- and health care service–related data from migrants in China. Further details about the CMDS can be found in previous studies (M. Guo et al., 2019; Z. Zhu et al., 2019). Individuals who left their place of household registration for over 1 month were included in the analysis. Individuals with missing information on TB education were excluded. Finally, a total sample of 745,926 migrants was included in the following analysis.
Measures
TB Education Rate and Methods
Information on TB education was collected through a self-report questionnaire. Participants were asked, “Did you receive any TB education after moving to your current city?” If participants answered “yes,” they were further asked, “How did you receive the education?” Potential choices included lecture, book/magazine, media, face-to-face consultation, community advocacy, bulletin board, online medication consultation, and SMS/WeChat message.
Individual-Level Variables
We included four sociodemographic variables, four socioeconomic variables, and four migrant-related variables as individual-level covariates. Sociodemographic variables included age (continuous), gender (male or female), ethnicity (Han or minority), and marital status (married or otherwise). Socioeconomic variables included education level (<middle school, high school or equivalent, college, or >college), monthly income (continuous), having a health record (yes or no), and having basic medical insurance (yes or no). Migrant-related variables included reasons for migration (work or study, moving with family, or otherwise), migration duration (continuous), long-term residence inclination (yes or no), and type of household (urban or rural).
Regional-Level Variables
We used four regional-level variables, including destination region (east-coast, central, northwest, southwest, west-Tibet, west-Uyghur, and northeast), prevalence of TB (continuous), density of medical professionals (continuous), and density of migrants (continuous). Regions were categorized by geographic distribution and economic growth data from the Chinese government (National Health and Family Planning Commission of China, 2015). The data on TB prevalence in each province/region were derived from China’s Notifiable Infectious Disease Reporting in 2014–2017 (Chinese Center for Disease Control and Prevention, 2015–2018). The data on medical professional density and migrant density were collected from the Statistical Yearbook of China 2014–2018 (National Bureau of Statistics of China, 2014–2018).
Statistical Analysis
SPSS 22.0 (IBM, Armonk, NY) was used for the descriptive analysis and logistic regression models. Weighted percentages, means, and standard deviations were used to describe participants’ characteristics and TB education methods. Weighted logistic regression models were used to assess the association among age, region, and the receipt of TB education. Age, region, and other covariates were entered into three models. Model 1 included two main variables, age and region. Model 2 (crude adjusted model) added gender, ethnicity, marital status, education attainment, lg (monthly income), health record status, and health insurance status. Model 3 (fully adjusted model) further included reasons for migration, length of migration, type of household, and long-term living preference. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and two-tailed p values less than .05 were considered statistically significant. Log-likelihood (−2 LL) was the goodness-of-fit index for the logistic regression models.
We used HLM 7.0 to conduct hierarchical logistic regression modeling (HLM) to explore the relationship between the individual-level and regional-level variables and the receipt of TB education. HLM can capture regional differences beyond individual demographic factors when analyzing nested data. Age, 12 individual-level covariates, and three regional-level covariates were entered into three models. A null model was used to verify the hierarchical structure of the data. The random coefficient model included all individual-level variables with a random effect. The intercepts-and-slopes-as-outcome model further included three regional-level variables. Log-likelihood (−2 LL) was used to measure the goodness of fit for all models.
Results
Sample Characteristics
Table 1 shows the distributions of the demographic, socioeconomic, and migrant-related characteristics of the participants. Among the 745,926 migrants, most were from the east-coast region (n = 311,980, 41.8%), followed by the central (n = 125,985, 16.9%), southwest (n = 106,993, 14.3%), northeast (n = 54,999, 7.4%), west-Uyghur (n = 33,996, 4.6%), and west-Tibet (n = 33,979, 4.6%) regions. The majority of participants were female (n = 389,985, n = 52.3%), aged 19 to 35 years (n = 399,526, 53.6%), Han Chinese (n = 685,140, 91.9%), married (n = 589,562, 79.0%), and educated at a middle school level or less (n = 476,641, 63.9%). The average monthly income was 3,257.9 (SD = 2721.9) CNY (Chinese yuan renminbi). Most of the participants migrated to the destination city for work or study (n = 559,638, 75.0%) and had a long-term residence inclination (n = 474,475, 63.6%). Migrants in the east-coast region were relatively younger (34.5 ± 9.9 years) and had a higher education level (college degree and above: n = 50,829, 16.3%) than the migrants in the other regions. They also had a higher monthly income (3565.9 ± 3078.7 CNY) and a lower rate of having a health record (n = 60,601, 19.4%).
Distributions of Characteristics by Region Among Migrants in the Migrants Population Dynamic Monitoring Survey (China Migrants Dynamic Survey) 2014–2017 (n = 745,926).
Note. Individual data are n (%) or M ± SD. TB = tuberculosis.
Prevalence of TB, density of medical professionals, and density of migrants are regional data.
TB Education Among Migrants
The results showed that only 30.4% (n = 226,458) of all participants received TB education (Table 1). The east-coast region had the lowest education rate of all the regions (n = 69,932, 22.4%), followed by the northeast (n = 13,946, 25.4%), central (n = 38,725, 30.7%), and northwest (n = 24,696, 31.7%) regions. West-Tibet (n = 16,106, 47.4%), west-Uyghur (n = 15,275, 44.9%), and the southwest region (n = 47,778, 44.7%) had a higher proportion of migrants receiving TB education but still less than 50%. Figure 1a shows the TB education rate in provinces across China. Guizhou Province (n = 9,313, 51.7%) in the southwest region and Qinghai Province (n = 9,141, 50.8%) in the west-Tibet region had the highest TB education rates. Figure 1b shows a map of TB prevalence by province. Xingjiang Province in the west-Uyghur region (186.6 cases per 100,000 population), Guizhou Province in the southwest region (128.6 cases per 100,000 population), and Tibet in the west-Tibet region (120.8 cases per 100,000 population) were the top three provinces in terms of TB prevalence.
Proportion of receiving tuberculosis (TB) education and prevalence of TB by province: (a) Proportion of receiving TB education by province (%); (b) Prevalence of TB by province (total number per 100,000 population).
Figure 2a shows the proportion of participants who had received TB education by age. Among all age-groups, participants between 65 and 69 years old were the most likely to have received TB education (33.4%), followed by those between 70 and 74 years old and between 60 and 64 years old. Figure 2b shows the proportion of TB education methods by age. Bulletin boards (86.5%–91%), media (73%–86.7%), and books/magazines (59.2%–67.4%) were the most common ways for migrants to receive TB education in all age-groups. Only less than 50% of participants received TB education via face-to-face consultation (27.9%–47.3%), SMS/WeChat (10.9%–45.2%), and online medical consultation (10.5%–37.5%). Participants over 40 years old more commonly received TB education via off-line methods (lecture, community advocacy, and face-to-face consultation) than online methods (SMS/WeChat and online medical consultation).
Proportion of receiving tuberculosis (TB) education and methods by age: (a) Proportion of receiving TB education by age (%); (b) Proportion of education methods by age (%).
The results showed that 30.5% (n = 108,666) male participants and 30.2% (n = 117,792) female participants received TB education (χ2 = 9.296, p = .002). Compared with female participants, male participants more commonly received TB education via lecture (χ2 = 3.949, p = .047), book/magazine (χ2 = 252.523, p = .000), community advocacy (χ2 = 58.919, p = .000), and bulletin board (χ2 = 10.156, p = .001). Female participants more commonly received TB education via online medical consultation (χ2 = 180.384, p = .000), SMS/WeChat (χ2 = 171.313, p = .000), and face-to-face consultation (χ2 = 161.392, p = .000). Compared with migrants educated at the high school level or below, migrants educated at the college level or above more commonly received TB education via all methods except media (χ2 = 1.040, p = .309) and face-to-face consultation (χ2 = 1.260, p = .264).
Variables Associated With the Probability of Receiving TB Education
Table 2 shows the results regarding the associations between age and the seven regions and the receipt of TB education. In the fully adjusted model (Model 3) controlling for gender, ethnicity, marital status, education level, lg (monthly income), health record status, health insurance status, reason for migration, length of migration, type of household, and long-term living intention, older participants were more likely to have received TB education (OR = 0.998, 95% CI [0.998, 0.999], p = .000). Participants in the east-coast region were less likely to have received TB education than those in other regions (OR = 1.110, 95% CI [1.086, 1.134], p = .000).
Associations Between Age and Region and Receiving Tuberculosis Education Among Migrants in the Migrants Population Dynamic Monitoring Survey (China Migrants Dynamic Survey) 2014–2017 (n = 745,926).
Note. OR = odds ratio; 95% CI = 95% confidence interval.
Model 2, crude-adjusted model: adjusting for gender, ethnicity, marital status, education attainment, lg (monthly income), health record status, and health insurance status. bModel 3, fully adjusted model: added reasons of migration, length of migration, type of household, and long-term living intention. cWeighted odds ratio and 95% confidence interval.
Table 3 shows the results of HLMs for the receipt of TB education. The null model showed that the data were hierarchical and suitable for establishing multilevel logistic regression models (intraclass correlation coefficient [ICC] = 3.29, design effect [DEFF] < 2). Random coefficient modeling showed, after the 12 individual-level variables were added, that participants who were older (OR = 0.999, 95% CI [0.999, 0.999]), minorities (OR = 0.924, 95% CI [0.917, 0.932]), not married (OR = 0.980, 95% CI [0.973-0.986]), educated at the college level or above (OR = 0.972, 95% CI [0.965, 0.979]), had a health record (OR = 1.125, 95% CI [1.119, 1.131]), had basic medical insurance (OR = 1.072, 95% CI [1.064, 1.080]), lived in an urban household (OR = 0.977, 95% CI [0.971, 0.983]), had a longer migration duration (OR = 0.999, 95% CI [0.999, 0.999]), had a long-term living preference over 5 years (OR = 1.029, 95% CI [1.024, 1.034]), and migrated for work or study (OR = 0.987, 95% CI [0.981, 0.992]) were more likely to receive TB education. In the intercepts-and-slopes-as-outcome model, we further included three regional-level variables. Reason for migration was no longer associated with the receipt of TB education (OR = 0.996, 95% CI [0.991, 1.001]). We further found that individuals with a higher monthly income (OR = 0.984, 95% CI [0.975, 0.993]) and who were living in regions with a higher prevalence of TB (OR = 0.998, 95% CI [0.998, 0.998]) and a lower density of migrants (OR = 1.000, 95% CI [1.000, 1.000]) were more likely to receive TB education.
Hierarchical Linear Models of Receiving TB Education Among Migrants in the Migrants Population Dynamic Monitoring Survey (China Migrants Dynamic Survey) 2014–2017 (n = 745,926).
Note. TB = tuberculosis; OR = odds ratio; 95% CI = 95% confidence interval; LL = log-likelihood.
Discussion
This is the first study investigating the prevalence of TB education and education methods among migrants in China using a nationally representative sample. Our study showed the rates of TB education in each region of China and indicated the significant disparity among the seven regions. We also found an age disparity in methods of TB education receipt. Bulletin boards, media, and books/magazines were the most common methods for all age-groups, and migrants over 40 years old were more likely to receive education via off-line methods than their younger counterparts.
Our study found that the average TB education rate among migrants was less than 35%, which is far below the target of the 13th Five-year National Plan for TB Control set by the Chinese government (Compilation and Translation Bureau, Central Committee of the Communist Party of China, 2015; P. X. Lu et al., 2020). This plan requires an overall TB education rate of at least 85%, and migrants are the target population of the education campaigns. Our findings were consistent with previous studies conducted in certain small areas. Chen et al. (2016) conducted a cross-sectional survey of 10,237 residents in Guizhou Province and found that the overall TB education rate was 41.5%. Zou et al.’s (2020) study showed that 44.07% of respondents in Anhui Province received TB education and had basic knowledge of the disease. Although the Chinese Center for Disease Control and Prevention has promoted TB education on a large scale since 2005 and further provided operational guideline toolkits to strengthen education among migrants, awareness of TB has not sustainably improved (Houben et al., 2016; Z. Wang et al., 2019). Indeed, the TB education rate among migrants was lower than that for HIV and other chronic diseases (M. Guo et al., 2019; Z. Zhu et al., 2019). The results of these studies all indicated the need for further implementation of China’s TB education campaigns among migrants in order for the requirements of the National Plan to be met, especially in the east-coast region, where there is a high density of migrants. Although the TB education rate matches the TB prevalence, it is still necessary to make efforts to launch additional TB education campaigns in regions with a high density of migrants.
We also found that migrants with higher education levels and a better economic status had more access to TB education. This finding is indicative of the socioeconomic inequity among migrants seeking health care services in China and is in line with previous studies (Fan et al., 2020; Y. Wang et al., 2020). There may be several reasons why this inequity affects migrants’ receipt of TB education in China. First, due to the large economic disparity in China, the primary goal of migrants with a lower socioeconomic status who moved from rural to urban areas was to seek job opportunities that allowed them to earn more money. Thus, accessing health care services was not their priority, and having online or off-line consultations with health care professionals was considered time-consuming (Chang, 2019; Y. Wang et al., 2020). Second, migrants with lower socioeconomic status had higher internal mobility, which affected their ability to access local health education (L. Lu et al., 2017). Migrants with a lower socioeconomic status, especially rural-to-urban migrants, moved to their destination cities mainly to pursue employment opportunities. However, in China, low-skill jobs without contracts generally do not last more than 1 year (Cheng et al., 2020; Shao et al., 2016). Therefore, migrants were likely to move frequently, which was a barrier to obtaining access to local health care services. This may partially explain why migrants with longer migration durations and who preferred to stay in their destination cities long term were more likely to receive TB education. Therefore, the results of our study indicated that TB education campaigns targeting low socioeconomic groups should be actively promoted among migrants in China. More bulletin boards regarding TB education should be set up near the main working places of migrants, such as construction sites and factories.
Regarding the methods of TB education, we found that online and off-line methods varied among age-groups. Older migrants were more likely to receive TB education via off-line methods. This finding was similar to that in our previous study exploring HIV education methods among migrants (Z. Zhu et al., 2019). Unlike with HIV and other sexually transmitted diseases, elderly people are the high-risk group for TB. In China, elderly people were more likely to be underdiagnosed and untreated for TB, which has contributed to the high TB incidence and high risk of developing TB in the past 5 years (Cheng et al., 2020). Since 2013, the National Health Commission of the People’s Republic of China has been promoting health knowledge among internal migrants by disseminating education through a combination of mobile and off-line health education campaigns (Huynh, 2016; J. Li et al., 2017; P. X. Lu et al., 2020). However, disseminating basic TB knowledge via mobile and other online social platforms on a large scale results in a failure to reach older migrants, leading to a low TB education rate. Therefore, we suggest that traditional media, off-line medical consultations, community advocacy, and bulletin boards should be the primary methods for delivering TB education to older migrants. Other new technology-based methods, such as mobile messaging and online-/AI-based consultations, can also be used among older migrants as a supplement.
Although this is the first study using nationally representative data to explore the TB education rate and methods among migrants in China, it also has several limitations. First, we used only one self-report question to investigate whether migrants had received TB education. Reporting bias may have been present, which could affect the credibility of the responses. Second, we included only eight education methods widely used in China. Other methods, including exhibits, audiovisual products, and brochures, were not included in this study.
Conclusion
Our study generated new evidence that the prevalence of TB education among migrants is low in China. Off-line medical consultations, community advocacy, and bulletin boards are the most common methods for migrants receiving TB education. We suggest that TB education campaigns among migrants be implemented on a large scale. Such campaigns targeting migrants with a low socioeconomic status should be actively promoted. Traditional media, off-line medical consultations, community advocacy, and bulletin boards, the most common methods, should be the primary methods of delivering TB education to migrants, and new technology-based methods can be used as a supplement. TB education methods should be tailored to different age-groups.
Footnotes
Acknowledgements
The authors thank Professor Bei Wu, New York University Rory Meyers College of Nursing, for offering valuable advice.
Authors’ Note
This study was approved by the Institutional Review Board of the School of Nursing, Fudan University (IRB#TYSA2016-3-1).
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by Shanghai Soft Science Research Program (20692190300), Shanghai Sailing Program (20YF1401800), and the Ministry of Education of Humanities and Social Science Project (20YJCZH254). The funders had no involvement in or influence on this study.
