By using the geographical detector and other spatial analysis tool, a series of specific urban elements, as well as their pairwise interactions, were identified for the spatial heterogeneity of this epidemic across the four central districts in Guangzhou, from which some notable findings were achieved. This study would provide some useful clues for local authorities making more targeted interventions on this disease in Guangzhou and similar municipal regions of China.
Occupational difference of the cases was an obvious characteristic of TB prevalence. It has been reported that the farmers and workers accounted for the largest proportion of TB cases in some regions of China (e.g., north-east Yunnan Province, and Xi'an) [30–32]. On the contrary, these occupations failed to rank the first in some highly urbanized regions (e.g., Guangzhou and Foshan) while other occupations (i.e., the household and unemployed) possessed relatively higher percentages [33, 34], which was also observed in the central part of Guangzhou for the household and unemployed occupation (33.11%), the retired population (23.88%), as well as the business service (15.12%). However, the TB prevalence in this region was remarkably featured by its higher percentages of retired (61.49%), business (56.45%), catering service (47.33%), students (43.18%), and cadre’s (42.68%) TB cases in the corresponding occupations in the whole city, which may be owed to its functional attributes (e.g., residential, commercial, educational, and service) [7, 21]. Meanwhile, this epidemic in the central region was also characterized by its slightly higher percentages of TB cases in different age groups, especially in the 60~ years old population (34.46%) due to the increasingly aging population [35, 36]. It can be thus seen that the TB prevalence in the four central districts possessed its own unique epidemiological characteristics in addition to those in the whole city. Accordingly, we suggest that the features of TB epidemic closely related to the social and economic status should be heavily considered in order to draw up regional intervention plans (e.g., adequate propaganda and education on these specific occupations and population) for containing this disease.
Previous studies have already pointed out that the dominant influencing factors on the prevalence of some infectious diseases tended to change due to the varying research units [37, 38]. In our study, the 2km × 2km grid was chosen as the appropriate spatial scale, on which the spatial clustering distribution of TB prevalence across the central region were clearly distinguished, especially in the western part of Tianhe District and the junction area between Haizhu, Liwan, and Yuexiu districts owing to the grids with relatively higher incidence rates. Moreover, the spatial relation between the TB incidence rates and most of the selected factors were also easily observed so that the detection of potential influences on the TB prevalence’s spatial differentiations was sufficiently conducted for identifying the specific urban elements in this study area. In a word, the choice of an appropriate spatial scale is fundamental for the identifications of spatial differentiation of the TB epidemic, as well as its influencing factors in the target region.
It has been revealed that the local TB prevalence was often determined by some socioeconomic indices including the population at risk of spreading this disease, population density and mobility, traffic system, economic status, and medical level at fine scales [6, 11, 15, 16]. Similar findings were obtained in our study that three socioeconomic variables (i.e., the incidence rate in the previous year (2016), the counts of officially appointed medical institutions, and the number of bus stops) posed larger impacts on the spatial differentiation of this epidemic across the central region of Guangzhou. There was a fourfold increase of transmission risk from some TB patients to their close contacts causing high exposure of the susceptible population to this disease , which may be a reasonable explanation for the strongest interpreting ability of TB incidence rate in the previous year. Another possible interpretation is that the recurrence of previous TB cases after treatment due to the increasing drug resistance of M. tuberculosis was very likely to increase the prevalence [39, 40], especially in the regions with high incidence rates in the previous year. As far as the count of officially appointed medical institutions is concerned, its heavy influences on the TB epidemic were probably correlated with being representative places for gathering various patients, including potential TB patients and susceptible people with low immunity . Besides, the number of bus stops termed for public transportation condition was another non-neglected influencing factor for the TB epidemic because higher probability of the contacts among individuals and population mobility tended to be increased by the convenient public traffic system . In general, the selection of these potential variables in this study was reasonable for identifying the dominant factors of TB epidemic in the central region. Then, we cautiously suggest that the treatment of current TB cases, as well as more effective methods dealing with the drug resistance, need to be considered first for reducing their potential impacts on the next year’s TB prevalence, and that more resources should be well allocated for implementing the hospital infection management and reinforcing the propaganda and education for the individuals who often visits the hospitals or takes the buses.
In comparison to the individuals, their explanatory abilities were very likely to be extremely enhanced by their pairwise interactions[41, 42]. Rasam et al., and Ge et al., also found that the interactions, between public traffic system, population density, and urban functional areas, posed much higher abilities of accounting for the spatial differentiations of TB prevalence than that of each individual [16, 15]. Our study obtained similar findings that the explanatory ability of each factor interpreting the spatial heterogeneity of TB epidemic in the central region of Guangzhou was extremely enhanced due to the pairwise interactions. In particular, the contributions of these weaker factors(q < 0.2) tended to be significantly enhanced while interacting with bus stops, officially appointed hospitals (i.e., Hosp, Hosp11, and Hosp12), and the incidence rate in previous year. Among these individuals, UV, termed for the widely distributed units with crowded population in the low buildings clustered in the study area , is a typical urban element posing impacts on the transmission of M. tuberculosis and TB infection [1, 43]. In general, the pairwise interactions made great contributions to the interpretation of the TB epidemic’s spatial differentiations across the four central districts. We intensively recommend that these weaker elements must be comprehensively considered as necessary targets for making effective interventions, and that comprehensive prevention and control measures should be meticulously implemented in these regions with these paired elements for containing this disease in Guangzhou.
A few limitations should be mentioned here. First, although public transportation termed by bus stops and subway stations were included in this study, the mobility of the population was not adequately considered due to the difficulty of collecting the dynamics of population movement, which might be addressed in the future through obtaining and processing either cell phone data or public transportation smart cards. Second, although the geographical detector tool was employed to identify the specific influencing elements on this disease, it failed to explore the strength of pairwise interactions and its difference, for which an appropriate indicator need be developed in this tool so as to quantify the interactions. Finally, the TB case data in ten years or more longer period should be obtained in the future so as to further consolidate the current findings based on only one year’s data.