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Modeling tuberculosis transmission flow in China, 2010–2012

BMC Infectious Diseases volume 24, Article number: 784 (2024) Cite this article

Abstract

Background

China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China.

Methods

29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.

Results

Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows.

Conclusions

Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

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Background

Tuberculosis (TB) is a type of chronic infectious disease caused by Mycobacterium tuberculosis [1]. The disease is primarily via respiratory tract transmission and has seriously threatened human health for thousands of years [2]. China has the third largest number of TB cases in the world, and about 900,000 new cases are diagnosed each year, with the mortality rate of 1.43 per 100,000 in 2017 [3]. TB is still a leading cause of mortality and economic burden in China.

In recent decades, China has experienced rapid urbanization with the growth rate from 45.89% in 2007 to 58.52% in 2017. Economically developed regions require a large amount of labor to meet the development of diversified industries, including manufacturing and tertiary industries and modern agriculture. The average annual floating population in China is more than 200 million, which serves as the vector for the spread of related infectious diseases such as TB, SARS, H1N1 and MERS [4,5,6,7,8,9].

Regional economic difference is an important driving force for population mobility [5, 10]. Existing research indicated that in areas with poor economic conditions, there is a high TB risk [11, 12], and TB transmission has been confirmed to be associated with migration [13]. TB migration flows come from infected migrants among these floating populations. The increasing floating population across regions has a tremendous potential for spreading infectious diseases [14, 15].

Spatial interaction exists in the spatio-temporal transmission of an infectious disease [16]. The epidemiological mechanism interacting with socioeconomic factors at different spatial locations determines the variability of the geographical distribution of a disease [17]. The disparities of TB incidence were influenced by geospatial factors, population and socioeconomic heterogeneity, which will further affect the migrant population [18,19,20,21,22]. However, there is still no systematic analysis of how TB population size and socioeconomic heterogeneity affect tuberculosis transmission between the provinces in China.

Spatial interaction models have become standard tools for describing population mobility dynamics for infectious disease epidemiology [23,24,25,26,27,28]. In previous studies, spatial interactions were often used to model population movements [29,30,31]. The main advantage of these models is that they could take into account the regional characteristic factors of the origin and destination, which affect migration flows. This is in line with the actual situation of disease flow, as it is affected by many regional characteristic factors. Current studies focused on the spatial distribution characteristics and influential factors of TB spread. Understanding the spatial interaction pattern of TB migration flows is essential for clarifying the mechanisms of transmission and targeting control interventions. Therefore, the spatial interaction model was applied to model TB flow and the socioeconomic factors of origin - destination (OD) regional characteristics.

In this study, we used q-statistic to measure the spatial stratified heterogeneity of TB transmission among floating populations in China. To further understand the complexity and heterogeneity of socioeconomic factors influencing TB transmission, we applied the spatial interaction model to estimate TB migration flows in association with the potential influential factors. Finally, the results will be explained and compared with those of other studies.

Materials

Data

We used the database of 29,667 migrant TB cases from 2010 to 2012, which were diagnosed with new smear-positive pulmonary TB. Among them, 1,5640 migrant TB cases were confirmed in 2010 and 14,027 migrant TB cases in 2012. This data covered the 31 provinces of China, which have highly different geographical environments and socioeconomic conditions (Fig. 1). The data was provided by the Chinese Center for Disease Control and Prevention (CDC), and reported directly by a nationwide web-based Infectious Disease Reporting System (IDRS). Each case record in the dataset contained detailed information on age, sex, career, permanent residence, current residence, diagnosis month, results of smear microscopy diagnosis and so on.

Fig. 1
figure 1

Distribution of TB in-outflow from 2010 to 2012 in China

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Some studies have indicated that TB migration flows were influenced by various factors, such as income and social status, physical environments, working conditions, social environments, families, personal health practices and so on [32,33,34]. Generally speaking, there are relationships and differences between generic population flow and TB flow.

On one hand, TB flow is part of the generic population flow, and TB migration flow refers to infected TB migrants in the floating population. There is a commonality between generic population flow and TB flow, as they are commonly affected by the different socio-economic and geographical environmental factors of the sending and receiving regions.

On the other hand, TB flow has its specificity compared with generic population flow. TB migration flows increase the risk of TB transmission from migrants to residents and amplify their harmfulness because of the dense population and active interactions with the migrating population. In addition, short and long-distance transmissions of infectious diseases are the result of the interaction between their own epidemiological mechanisms, and socioeconomic and environmental factors, and the complexity and variability of disease spread also require factors specific to TB transmission, such as the number of TB cases and TB incidence rate.

As TB migration flow is a special kind of migration flow, the key factors affecting TB migration flow have not only the same commonality of the floating population, but also the characteristics of the disease spread. The levels of unbalanced economic development, income and job opportunities were recognized as the key determinants of population migration [35,36,37,38]. Per capita GDP, income and TB incidence were confirmed to have a relationship with the spread of infectious disease [4, 18, 20, 21, 39]; however, the quantitative relationship between them still remains unclear.

Therefore, in this study various socioeconomic indexes were employed as variables in the TB migration flow model specifications representing OD regional characteristics (Tables 1 and 2). Per capita GDP was collected from the China Statistical Yearbook. Income levels of urban and rural residents in various regions were collected from the China Rural Statistical Yearbook. In addition, other variables highly correlated with TB migration flow were also selected, such as the number of TB cases in 31 provinces of China and TB incidence rate. Their data were collected from the China Health and Family Planning Statistical Yearbook.

Table 1 Specification of the variables in the TB migration flow model
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Table 2 Descriptive variables of regional characteristics used in the TB migration flow model
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TB flow data and all potential factors were calculated at the provincial or municipal level. Figure 2 shows the relationship between TB migration flow and its proxy variables.

Fig. 2
figure 2

Relationship between TB migration flow and its proxy variables

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Methods

q-statistic

Spatial stratified heterogeneity (SSH) reflects uneven distributions of disease transmission as their environmental and socioeconomic factors have different regional characteristics [40,41,42]. SSH refers to the phenomenon that the spatial distribution of the disease and its risk determinants are more similar within a geographical region than between geographical regions. Such a spatial variation of disease transmission can be measured with the geodetector q-statistic [43]. The q-statistic is as follows:

$$\begin{gathered} q=1 - \frac{{\sum\limits_{{h=1}}^{L} {{N_h}\sigma _{h}^{2}} }}{{N{\sigma ^2}}}=1 - \frac{{SSW}}{{SST}} \hfill \\ SSW=\sum\limits_{{h=1}}^{L} {{N_h}\sigma _{h}^{2}} \hfill \\ SST=N{\sigma ^2} \hfill \\ \end{gathered}$$

where h = 1, 2,. . ., L denotes the spatial stratification of the variable y or the factor x, i.e., classification or partitioning; Nh and N are the numbers of units in layer h and the whole region; \(\sigma _{h}^{2}\) and \({\sigma ^2}\) are the variances of the y value of layer h and the whole region, respectively; The value range of q is [0,1]. The larger the value of q, the more obvious the spatial heterogeneity of y.

Spatial interaction model

Spatial interaction models of the gravity type typically rely on origin-specific, destination-specific and spatial separation factors to explain mean interaction frequencies between origins and destinations of interaction. Origin-specific factors represent the ability of regional characteristics to generate outflows, destination-specific factors characterize the attractiveness of regional characteristics to absorb inflows, and spatial separation factors characterize the method of impeding the interaction from origins to destinations [44, 45].

Spatial dependence is the key to express how spatial proximity affects TB migration flow [4, 46], which refers to the phenomenon in which TB migration flows in a given region are affected by fluctuating TB flows in neighboring regions. In this study, spatial interaction modeling epidemic flow data, combined with geographic, socioeconomic and demographic information of a country’s administrative regions, seek to explain the variation of TB migration flows at the provincial/municipal level. Estimating multiplicative spatial interaction models in their log-linearized form has long been a widely employed filtering approach [47, 48], their log-linearized form is as follows:

$$\left\{ \matrix{\left( {{I_N} - {\rho _o}{W_o})({I_N} - {\rho _d}{W_d})y = } \right. \hfill \cr \alpha {\iota _N} + c{\alpha _i} + {X_d}{\beta _d} + {X_o}{\beta _o} + {X_i}{\beta _i} + \gamma g + \varepsilon \hfill \cr LnL({\rho _d},{\rho _o},{\rho _w}) = C + \hfill \cr \ln \left| {{I_N} - {\rho _d}{W_d} - {\rho _o}{W_o} - {\rho _w}{W_w}} \right| - {N \over 2}\ln (S({\rho _d},{\rho _o},{\rho _w})) \hfill \cr {X_o} = X \otimes {I_n} \hfill \cr {X_d} = {I_n} \otimes X \hfill \cr {W_o}{\rm{ = }}W \otimes {I_N} \hfill \cr {W_d}{\rm{ = }}{I_N} \otimes W \hfill \cr Ww = {W_d} \cdot {W_o} = \left( {W \otimes {I_n}} \right) \cdot \left( {W \otimes {I_n}} \right) = W \otimes W \hfill \cr} \right.$$

where,

y represents the N-by-1 vector of TB migration flows,

Xo signifies the N-by-k matrix of k origin-specific variables,

Xd symbolizes the N-by-k matrix of k destination-specific variables,

Xi denotes the N-by-k matrix of k intraregional-specific variables,

ρo represents the spatial dependence parameters associated with the origin-based,

ρd represents the spatial dependence parameters associated with the destination-based,

ρw represents the spatial dependence parameters associated with the origin-to-destination,

g denotes the log of the geographical distance in the pairs of origin-destination locations,

Βo signifies coefficient estimates associated with the origin characteristics,

Βd signifies coefficient estimates associated with the destination characteristics,

Βi signifies coefficient estimates associated with the origin-destination characteristics,

γ symbolizes the scalar parameter of the distance g effects,

In indicates the N-by-N unit matrix,

\(\:{}_{N}\) symbolizes the N-by-1 vector of ones, and.

\(\:⨂\) indicates and Kronecker product.

The use of spatial weight matrixes is a convenient way of capturing the spatial dependence of migration flow. In the SIM, W represents geographical connectivity between the shared boundaries of n locations and measures the spatial dependence between the geo-referenced locations. Wo is the N-by-N spatial weight matrix of origin-based dependence, Wd is the N-by-N spatial weight matrix of destination-based dependence, and Ww is the N-by-N spatial weight matrix of the origin-to-destination dependence. S(ρd, ρo, ρw) represents the sum of squared errors expressed as the scalar dependence parameters alone and C denotes a constant.

Results

Spatial patterns of TB migration flow

There were two most remarkable regional clusters of TB migration flows. The largest destination and concentration areas were located in the Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast. The two most prominent regional clusters of TB migration flows appeared from the provinces with a large number of rural surplus labors to the coastal provinces with a high level of economic development.

Specifically, there were two mainstreams of TB migration flows (Figs. 3 and 4). One was TB inflows to Guangdong, which is a coastal province in south China, accounting for 34.07% of the total TB migration flows in 2010 and 35.67% in 2012, which were mainly from inland provinces in Hunan (24.01% in 2010 and 21.30% in 2012), Sichuan (17.59% in 2010 and 17.29% in 2012) and Guangxi (14.08% in 2011 and 13.35% in 2012). The other was inflows to Zhejiang, which is a coastal province in east China, accounting for 22.70% of the total TB migration flows in 2010 and 21.76% in 2012, which were mainly from inland provinces in Guizhou (32.80% in 2010 and 29.06% in 2012), Sichuan (12.81% in 2010 and 10.35% in 2012) and Yunnan (11.80% in 2010 and 11.66% in 2012).

Fig. 3
figure 3

Flow Mapping of TB migration flow in 2010 in China

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Fig. 4
figure 4

Flow Mapping of TB migration flow in 2012 in China

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TB inflow represented a typical type of interregional migration flow from different regions of origin to the same destination. The top six provinces with inflows were from developed eastern region in Guangdong, Zhejiang, Fujian, Shanghai, Jiangsu and Beijing, which accounted for 83.02% of the total TB migration flows in 2010 and 84.5% of the TB migration flows in 2012 (Additional file S1).

In addition to TB inflow, TB epidemic characteristics and their corresponding socioeconomic conditions also presented significant spatial heterogeneity, as the q-statistics for them were 0.94 (p < 0.05) and 0.89(p < 0.05), respectively. In the eastern coastal region of China, TB incidence was low, and the three provinces with the lowest incidence were in metropolitan area in Beijing, Tianjin and Shanghai, while the highest TB incidence was found in the Midwest of China. The economic index also has a similar spatial pattern. The eastern coastal region has developed economic conditions and the three provinces with the highest per capita GDP were also Beijing, Tianjin and Shanghai, while the lowest per capita GDP was also found in the middle and west of China.

In terms of the demographic characteristics of the floating population affected by TB migration flows, the predominant gender is male, with individuals aged 18–40 years comprising the majority. The workforce is primarily concentrated in four categories of occupations characterized by high levels of labor intensity. Specifically, in terms of gender, males represent the majority, approximately twice the proportion of females (Fig. 5). In 2010, males accounted for 66.2% of TB cases, while females accounted for 33.8%. In 2012, males constituted 66.7%, while females constituted 33.3%. The age group most affected by TB is the 18-40-year-old young and middle-aged population, while children, adolescents, and individuals aged 60 and above represent a smaller proportion of TB cases (Fig. 6). Specifically, in 2010, individuals aged 18–40 accounted for 67.9% of TB cases, decreasing to 66.8% in 2022. The proportion of individuals aged 40–60 in TB cases was 26.8% in 2010 and 26.9% in 2022. Children, adolescents, and individuals aged 60 and above represented 5.2% of TB cases in 2010, increasing to 6.4% in 2022. In terms of occupation, migrant workers, workers, housekeeping, housework, and unemployed individuals, as well as peasants, constitute the main occupational categories affected by TB (Fig. 7). These four categories accounted for 79.6% of TB cases in 2010 and 76.7% in 2012.

Fig. 5
figure 5

Gender distribution of TB migration flow in China

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Fig. 6
figure 6

Age distribution of TB migration flow in China

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Fig. 7
figure 7

Occupational distribution of TB migration flow in China

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Spatial dependence of TB migration flow

TB migration flow had clear spatial dependence (Table 3). The origin dependence (ρo) and destination dependence (ρd) were positively correlated, which implied that the TB inflows to the destination or outflows from the origin showed a consistent trend compared with those in their neighboring regions. Furthermore, the strength of the spatial dependence was much greater in the origin than in the destination.

Specifically, a 1% increase in TB migration flows from the origin regions in 2010 corresponded to a 0.38% increase in interregional flows in nearby regions. Moreover, 1% increase in TB migration flows of the destination locations was associated with a 0.32% increase in interregional flows within nearby locations. Whereas the origin-to-destination dependence (ρw) was negatively correlated with the TB flows. A 1% increase in ρw was associated with a 0.11% decrease in TB flows from the neighbors of a given region.

Concurrently, a 1% increase in TB migration flows from the origin regions in 2012 corresponded to a 0.66% increase in interregional flows in nearby regions. Moreover, 1% increase in TB migration flows of the destination locations was associated with a 0.47% increase in interregional flows within nearby locations. Whereas the origin-to-destination dependence (ρw) was negatively correlated with the TB flows. A 1% increase in ρw was associated with a 0.28% decrease in TB flows from the neighbors of a given region.

Additionally, spatial effects have played more significant influence on the transmission tuberculosis in 2012 than that in 2010. Specifically, the spatial effect of the origin regions increased by 18% points from 2010 to 2012, and spatial effect of the destination regions rose by 15% points during the same period. While the spatial effect between origin and destination regions decreased by 17% points from 2010 to 2012.

Table 3 Spatial dependence estimates of TB migration flows by SIM
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Determinants of TB migration flows

In this study, we found that economic development level had significant effects on TB migration flow (Table 4). Generally, the regions with high per capita GDP were associated with low TB interprovincial outflows. Furthermore, we found that this factor had different effects on TB migration flow in the origin and destination. A 10% increase in per capita GDP corresponded to a 10.2% in 2010 or 2.1% in 2012 decrease in TB outflows from the origin, while a 0.5% increase in TB inflows to the destination was associated with an 18.9% increase in TB intraprovincial flows in 2012.

The number of TB cases was found to have a positive impact on TB migration flow and was an important risk factor influencing TB migration flow. In this study, we found that a high number of TB cases were associated with increases in TB interprovincial and intraprovincial flows. A 10% increase in the number of TB cases was associated with a 2.2% in 2010 or 1.1% in 2012 increase in TB inflows to the destination provinces. By comparison, a 10% increase in the number of TB cases corresponded to a 5.2% in 2010 or 2.0% in 2012 increase in TB outflows from the provinces of origin, and a 11.5% increase in TB intraprovincial flows in 2010.

TB incidence rate as a key risk factor had different impacts on TB interprovincial flows in the origin and destination. A 10% increase in TB incidence rate was associated with a 3.9% in 2010 decrease in TB interprovincial flows from the origin, while a 7.6% increase in TB interprovincial flows to the destination. In 2012, a 10% increase in TB incidence rate was associated with a 1.6% decrease in TB interprovincial flows from the origin, while a 2.3% decrease in TB interprovincial flows to the destination.

Rural and urban income level exerted different impacts on TB migration flow in the origin and destination. The per capita net income of rural households had positive impacts on TB outflows from the origins and negative impacts on TB inflows to the destination. A 10% increase in this factor corresponded to a 14.0% in 2010 or 3.6% in 2012 increase in TB outflows from the origins. The per capita disposable income of urban households had negative impacts on TB outflows and positive impacts on TB inflows. A 10% increase in this factor corresponded to a 9.5% decrease in TB outflows from the origins, a 44.2% in 2010 or 12.8% in 2012 increase in TB inflows to the destination, while a 47.9% increase in TB intraprovincial flows in 2012.

Table 4 Coefficient estimates of TB migration flows by SIM
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Discussion

The increasing floating population across regions has a tremendous potential for spreading infectious diseases, TB migration flows can be used to reflect the details of the highly complex dynamics of the disease transmission and its spatial heterogeneity [49, 50]. In this study, we used TB flow data and the spatial interaction model of the new gravity type to model tuberculosis interactive transmission in spatially and economically heterogeneous settings. The spatial pattern of tuberculosis transmission revealed clear the regional clusters and socioeconomic factors as important factors exerted different effects on TB migration flow in the origin and destination.

Compared with the method used in the study, the gravity model has been widely used to capture the spatial interaction pattern in epidemics and account for distance, population size and other factors [23, 51, 52]. However, this approach may be insufficient for dyadic OD flows where the origin and destination regions may interact with their neighbors. Spatial interaction models of the new gravity type aim to explain the population migration variation of spatial interaction across geographic space. They focus on dyads of regions instead of individual regions and are increasingly used to understand the regional spread of an infectious disease in epidemiology [24,25,26,27,28].

TB migration inflows have become important factors that change the urban and rural distributions of the TB burden. In particular, two mainstreams of TB inflows with their destination (Guangdong or Zhejiang) have become important hubs for the interplay of infectious diseases transmission between migrants and local residents. The TB outflows were mostly from the less developed provinces in central and western China with high TB epidemics such as Hunan, Guangxi, Guizhou, Sichuan and Yunnan [53]. The formed pattern of TB migration flow was mostly in agreement with the spatial path of China’s population flows [35]. Such patterns identified with provinces with severe tuberculosis were consistent with previous studies and results of several epidemiological surveys in China [12, 19, 54,55,56]. Accordingly, China’s health system should be a major consideration in prioritizing resource assignment in high-risk areas for TB control and reducing the burden in the future. In particular, it is important to focus on the majority of the key intercity paths for TB prevention and control among migrants and local residents.

In this study, we found that economic development level had significant and different effects on TB migration flow. This factor has negative impacts on TB outflows from the origins and positive impacts on TB inflows to the destination and TB intraprovincial flows in a given region. Previous studies indicated that per capita GDP and TB incidence at the provincial level had a negative relationship [57]. Similar results were observed for other infectious diseases such as SARS and H5N1 [4, 58]. Therefore, improving the local economy is beneficial to the control of TB in the origin.

While our studies differ in methodology, data sources, objectives, and results, highlighting the spatial correlation between TB and internal migration and the establishment of TB transmission flow models, respectively [57]. The study by Liao bears some similarities to our research, as both investigations corroborate the presence of spatial correlation in TB transmission within floating populations. Building upon this premise, we proceed to quantify the spatial correlations across three distinct dimensions: origin locations, destinations, and bidirectional TB transmission flows. This endeavor serves to enhance and deepen our understanding of the spatial dynamics involved in TB dissemination. Consequently, it furnishes a robust scientific framework for the development of precise preventive and control strategies within both source and destination regions, while also facilitating judicious resource allocation. Both studies delineate a negative association between per capita GDP and TB transmission within the originating locales. However, our investigation elucidates different effects of per capita GDP on TB transmission within the source and bidirectional contexts, where a positive correlation is discerned. The principal driver underlying bidirectional TB transmission appears to be the pursuit of employment opportunities.

The number of TB cases and incidence rate were also important risk factors influencing TB migration flow in this study. A high number of TB cases and a high incidence rate were associated with an increase in TB inflows to the destination. Our finding highlighted that migration highly corresponded with TB transmission across space, which was consistent with some previous studies [59,60,61,62]. TB transmission presented an apparent regional characteristic. Geospatial clusters of TB cases reflected ongoing transmission or colocation of risk factors. This can account for TB transmission from migrants to local residents regardless of low or high endemic setting. Accordingly, it is critical to optimize effective prevention and control strategies of the TB epidemiology in this high-burden setting.

Income-related factors were found to have positive effects on TB inflows to the destinations in this study. Rural and urban income levels exerted different impacts on TB flow in the origin and destination. Income level was widely considered as a factor for tuberculosis transmission in previous studies [18, 56, 63,64,65,66]. For example, regions with low-income levels in central and western China always had high tuberculosis rates. Migration flows from the low-income regions involved a heterogeneous and vulnerable group. They had a significantly higher risk of latent TB infection than did permanent residents when their health suffered from overwork, hard life and inadequate nutrition [50, 67, 68]. Although basic free diagnosis and treatment of tuberculosis can be obtained at the inflow locations, it was difficult for migrants to receive the same treatment as the registered population in terms of employment and medical security because of the permanent hukou system in China [6, 56]. Socioeconomic interventions can be powerful for tuberculosis control. Strategies to prevent overwork, improve individual living conditions and increase social expenditure per person have been associated with decreased tuberculosis prevalence.

The tuberculosis epidemic is still serious in China, and the incidence of tuberculosis has been decreased. In recent years, the epidemic pattern of tuberculosis in cities has undergone a fundamental change because of the rapid increase in the floating population, especially the influx of migrants into the metropolis [69]. The related floating population having tuberculosis, which accounts for nearly 20% of China’s population, is a great challenge for tuberculosis control in China. The characteristics of the tuberculosis spatial pattern are becoming increasingly complicated and diverse.

This study has some limitations. TB migration flow involves a special population mobility group. This phenomenon of migration flow often occurs at various spatial scales with different socioeconomic levels. The data used in this study were unable to reflect the characteristics of small-scale TB migration flows. Therefore, a more refined scale will be a topic for future studies. In addition, poverty factors such as living standards and living conditions often cause TB to spread among low-income individuals in different regions; thus, on-the-spot investigations need to be conducted on the nutritional status and living conditions of frequently- affected populations need to be conducted for in-depth research.

Conclusions

In this study, we found that TB flows had clear spatial stratified heterogeneity and spatial autocorrelation, which influence the influx of TB to the neighboring provinces. We found that the TB flows had a statistically significant relationship with regional incidence, socioeconomic differences in regional characteristics produced different effects on TB flows in the origin and destination, and income factor played an important role among the determinants. These findings provided scientific bases for the joint and precise prevention and control of TB transmission in population inflows to provinces and their neighbors.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

TB:

Tuberculosis

SIM:

spatial interaction model

OD:

origin - destination (OD)

SSH:

spatial stratified heterogeneity

per capita GDP:

per capita gross domestic product

SARS:

Severe acute respiratory syndrome

MERS:

Middle East Respiratory Syndrome

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Acknowledgements

We thank National Science and Technology Major Project (2017ZX10201302) to provide the research material.

Funding

This research was supported by Natural Science Foundation of Henan Province (242300421361), National Science Foundation of China (42371223, 41901331) and Innovation Project of LREIS (O88RA205YA, O88RA200YA). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of the data and in writing the manuscript.

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Authors and Affiliations

  1. College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China

    Li Wang, Jiajun Qiao, Qiankun Zhu & Zhipeng Wang

  2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China

    Chengdong Xu, Maogui Hu & Jinfeng Wang

  3. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, KaiFeng, 475001, China

    Li Wang & Jiajun Qiao

  4. Chinese Center for Disease Control and Prevention, Beijing, 102206, China

    Wei Chen

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  1. Li Wang
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Contributions

LW, CDX and JJQ designed the study and drafted the manuscript. LW CDX, MGH QKZ and ZPW conducted the statistical analysis. JFW and WC guided the research. All authors contributed to the writing and modification of the manuscript. All authors read and approved the manuscript prior to publication.

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Correspondence to Jinfeng Wang or Jiajun Qiao.

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Wang, L., Xu, C., Hu, M. et al. Modeling tuberculosis transmission flow in China, 2010–2012. BMC Infect Dis 24, 784 (2024). https://doi.org/10.1186/s12879-024-09649-7

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