Abstract
Background
In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group.
Methods
Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects.
Results
77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects.
Conclusion
Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
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Background
Tuberculosis (TB) is one of the deadliest infectious diseases, causing substantial concern globally. The single agent of this disease, Mycobacterium Tuberculosis (MTB), is transmitted by the respiratory tract causing lesions in nearly all tissues and organs [1]. In 2022, an estimated 10.6 million people worldwide fell ill with TB, with an incidence of 133 cases per 100,000 people and 1.3 million deaths [1]. Although continuous efforts had been implemented in high burden countries, China, as one of 30 high burden countries, still accounts for 7.1% of global cases [1]. Among the affected population, the elderly group had contributed to a substantial part [2]. Along with aging, influencing factors such as the decreased immune function, cognitive deficiency, and insufficient social and family care might also lead to increased morbidity and PTB transmission risk in this special group [3, 4]. Thus, increasing attentions was directed on lowering the reactivation of latent pulmonary TB (PTB) infection and preventing new infections among the elderly.
Increasing evidence demonstrated that the dominated drivers of PTB development not only included personal factors such as gender, malnutrition, Acquired Immune Deficiency Syndrome (AIDS), diabetics, smoking and alcohol consumption, but were also associated with environmental factors and spatial locations [5,6,7]. For environmental factors, available research showed that atmospheric pollutants such as PM2.5 and SO2 were associated with notification rate of PTB, partly even with a lag effect [8, 9]. It was possible that atmospheric pollutants may affect the susceptibility of TB in individual level by the possible mechanism of inducing damage in the tracheobronchial mucosa, and triggering systemic immune responses through inhibition of the synthesis and secretion of inflammatory mediators [8]. Besides, meteorological factor such as humidity can influence the transmission of MTB in the environment, thereby altering the risk of infection in the population [6]. However, the spillover effect caused by environmental factors is denoted that these factors not only affect the local epidemic but also impact surrounding regions, which was explored for TB onset in limited literature. In addition, the pattern of communicable diseases generally showed a diversity in spatial distribution. Thus, spatiotemporal analysis has been widely used in epidemiological research to identify temporal and spatial clusters of infectious diseases. However, among the elderly, the spillover effect of environmental factors and the spatial characteristics of PTB onset remain unclear [10, 11].
Our study aimed to analyze and determine the spatial and temporal distribution characteristics and risk clustering of the elderly PTB in Zhejiang Province, eastern China, as well as to identify environmental factors that have direct and spillover effects on notification rate of PTB. These findings may provide important empirical evidence for health policy formulation and public health resource allocation.
Methods
Study area
Zhejiang Province is located in the eastern coastal region of China and has a total area of 100,000 km2 and 11 cities like Hangzhou, Ningbo, Wenzhou, Jiaxing, Huzhou, Shaoxing, **hua, Quzhou, Zhoushan, Taizhou, and Lishui [25]. In addition, due to mass X-ray screening of people aged over 60 or 65 years, especially among the elderly with no symptoms, active cases were identified earlily and given standardized treatment regimes. This also would help decrease local PTB epidemics in the general population.
In the Spatial Durbin Model, our study provided strong evidence that meteorological factors were vital factors affecting the occurrence of PTB among the elderly. There was a significant positive association between annual mean precipitation and PTB occurrence, which was consistent with the results of Qin T et al. [26]. The spillover effect of precipitation was approximately twice as significant as its direct effect, possibly due to Zhejiang Province’s coastal location, where frequent air currents aid in the formation and dispersal of droplets and suspended particles related to TB [26]. These particles can spread in all directions with the airflow, significantly impacting neighboring areas. Additionally, TB can be transmitted through various mediums, including surface water and groundwater formed by precipitation [27]. It could cause a longer distance of dissemination in space, leading to the expansion of spatial spillover effects. Furthermore, areas with higher tuberculosis notification rate often exhibited spatial clustering, further intensifying the impact of precipitation-induced spatial spillover effects [28]. Moreover, the results indicated that the high annual mean temperature could reduce the tuberculosis occurrence in local region and had a significantly negative spillover effect. It was suggested that high temperatures may stimulate the immune system’s response, leading to increased inflammation and production of immune effector molecules, thereby enhancing the ability to clear TB. The increase in local temperature reduces outdoor gatherings and activities among the elderly [29], potentially inhibiting transmission in the external environment. Hence, the increased local temperature exerts an indirect influence on surrounding areas through the spatial spillover effects. Also, our study found different effects between air pollutants and the risk of PTB occurrence among elderly. SO2 has no direct effect on the notification rate of PTB. Previous study found no significant correlation between SO2 and the risk of TB when long-term exposure or exposure to abnormally high concentrations of pollutants was ignored [30, 31], which is consistent with our findings. However, SO2 has a notable negative spatial spillover effect on the health of the elderly. One potential explanation is that low-level SO2 exhibits antimicrobial properties by reacting with enzymes and proteins within the cell membranes of microorganisms, thereby disrupting their structure and function, leading to the inhibition of microbial growth and reproduction [32]. Therefore, considering the distance-decay-effect of SO2, short-term exposure to low-level SO2 exhibited an protective effect on the elderly in the surrounding region during the diffusion process [33]. Exposure to PM2.5 may increase the risk of PTB among the elderly in the local population which is consistent with previous studies [34]. Interestingly, there was no spatial spillover effect between PM2.5 and the tuberculosis occurrence in our study. This may be attributed to the “Low-low” distribution of air pollutants in Zhejiang province, as well as the special geographical location and meteorological conditions near the sea, which help dissipate PM2.5 [35]. Additionally, greenspace can contribute to the dispersal of PM2.5 through deposition and filtration [36], thereby reducing the negative impact of PM2.5 on the health of the elderly in the surrounding counties.
Despite its strengths, this study had some limitations. First, like other surveillance data, some PTB cases among the elderly may not be notified owing to a delay in seeking health care or not visiting medical institutions. Underestimation of the PTB notification rate in this specific population was unavoidable. Second, in 2019 and 2021, the administrative regions of several counties in Zhejiang Province had changed, and we integrated adjacent regions as a whole, which might ignore the spatial-temporal correlation within the integrated region. Third, the environmental factors obtained in this study were from the value of annual mean between 2010 and 2020, which may have affected the further analysis in 2021 and ignored the lag effect, leading to potential bias.
Conclusion
Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Decreasing environmental pollution levels, such as PM2.5, and enhancing the diversity of greenspace would be beneficial in controlling PTB occurrence while the low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
Data availability
All data and materials were included in this paper. The corresponding author (Bin Chen)can provide data upon reasonable request after all studies and sub-studies have been completed.
Abbreviations
- TB:
-
Tuberculosis
- PTB:
-
Pulmonary Tuberculosis
- TBIMS:
-
Tuberculosis Information Management System
- MTB:
-
Mycobacterium Tuberculosis
- DOTS:
-
Directly observed treatment and short-course
- LISA:
-
Local indications of spatial autocorrelation
- LLR:
-
Log-likelihood ratio
- RR:
-
Relative risk
- CI:
-
Confidence interval
- CLCD:
-
China land cover dataset
- GEE:
-
Google Earth Engine
- PLAND:
-
Percentage of greenspace
- SHAPE_AM:
-
Complexity of the shape of greenspace
- PM2.5 :
-
Particulate matter with diameter of less than 2.5 μm
- SO2 :
-
Sulfur dioxide
- CLCD:
-
China land cover dataset
- GEE:
-
The Google Earth Engine
- GDP:
-
Gross Domestic Product
- SDM:
-
Spatial Durbin Model
- SLM:
-
Spatial Lag Model
- LM test:
-
Lagrange multiplier test
- SEM:
-
Spatial Error Model
- ZJCDC:
-
Zhejiang Provincial Center for Disease Control and Prevention
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Acknowledgements
Not applicable.
Funding
This study was supported by the National-Zhejiang Health commission Major S&T Project (Grant No. WKJ-ZJ-2118), and the National Natural Science Foundation of China [No. 42371252], Zhejiang Provincial Medical and Health Project (2021KY618 and 2020KY520).
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DL and LW contributed equally to this paper. BC, BX and KL shared joint correspondence in this work. BC, BX, KL and LMcontributed to the study design and manuscript revision. DL and LWcontributed to the data extraction, analysis, and paper writing. BC, KL, BX, SC, YZ, WW and QWcontributed to the fund acquisition and manuscript editing. MZ and YWcontributed to the data analysis and paper finalization. All authors read and approved the final manuscript.
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The use of PTB data in this study was approved by the Ethics Committee of the Zhejiang Provincial Center for Disease Control and Prevention (ZJCDC). As surveillance data was used, the requirement for informed consent was waived. Access to the original data was granted by correspondence, and all personal information was anonymized before data processing. Furthermore, we strictly adhered to the People’s Republic of China Law of Prevention and Treatment of Infectious Diseases.
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Luo, D., Wang, L., Zhang, M. et al. Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China. BMC Public Health 24, 209 (2024). https://doi.org/10.1186/s12889-024-17644-5
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DOI: https://doi.org/10.1186/s12889-024-17644-5