| Background:Tuberculosis is an infectious disease that poses a significant threat to human health,with pulmonary infections being the most common form.Today,tuberculosis has emerged as a major global public health challenge,posing a tremendous threat to people’s lives and well-being.In 2021 alone,there were a staggering 10.6 million new cases of tuberculosis worldwide,and in China,the number of tuberculosis patients accounts for 7.4%of the global total,ranking third in the world and among the 30 countries with a high burden of tuberculosis.Undoubtedly,tuberculosis is a deadly disease that surpasses our imagination in terms of its harm and its high degree of transmissibility.If neglected,it can cause more harm and even fatalities.Therefore,it is imperative that we take effective measures to protect people’s health.Previous studies have indicated significant regional disparities in the occurrence and mortality of tuberculosis,influenced by local natural conditions,population size,and climate change.However,these studies have often focused on large spatial scales and lacked systematic spatial epidemiological analysis,making it challenging to develop targeted local control measures.Practical evidence shows that even within a single county,there can be significant differences in the incidence rates between two neighboring villages.This suggests that there may exist some degree of clustering even at the smallest spatial scale,possibly due to specific geographical environmental factors.To better understand the spatial epidemiological characteristics of tuberculosis and its geographical risk factors,it is crucial to shift our focus to smaller research scopes and engage in more in-depth discussions.This will help in formulating more effective regional tuberculosis prevention,control,and intervention measures to reduce the incidence rates in high-burden areas.The significance of this research lies in revealing the geographical distribution patterns of tuberculosis,providing scientific evidence for governments and health departments,and promoting early prevention,screening,and timely interventions for tuberculosis.Objective:In this study,we focused on Pingyi County,Shandong Province,and utilized the infectious disease surveillance system of the Disease Prevention and Control Center,along with the framework of geographic information systems.We applied spatial epidemiology and spatial statistics methods to systematically analyze the spatial structure and spatial heterogeneity of tuberculosis at a small-scale level,specifically at the village level.Additionally,we employed geospatial detectors and geographically weighted regression models to investigate the impact of geographical environmental factors on tuberculosis incidence rates.The objective of these studies is to furnish scientific grounds for formulating targeted regional tuberculosis prevention and control policies.Methods:(1)During the period of 2013-2022,the incidence data of tuberculosis in Pingyi County were obtained from the Infectious Disease Monitoring Information System of Pingyi County Center for Disease Control and Prevention.Based on descriptive epidemiological methods,a comprehensive analysis was conducted to explore the spatial distribution characteristics of newly reported tuberculosis cases in Pingyi County from 2013 to 2022.In order to gain a more comprehensive understanding of the periodicity and seasonality of tuberculosis incidence,this study employed the HP filtering method and seasonal index method for analysis.Furthermore,using the natural breakpoint method,thematic maps of tuberculosis incidence rates and relative risk maps were generated to reveal the distribution patterns and trends of tuberculosis across different regions.(2)Global and local spatial autocorrelation analyses of tuberculosis incidence rates in Pingyi County were conducted using Moran’s Ⅰ and Getis-Ord Gi*indices.Spatiotemporal scanning based on the Poisson distribution was employed to detect high-risk areas and clusters of tuberculosis incidence in Pingyi County.(3)The geographic detector model was applied to identify the dominant factors in the geographic environment that are associated with tuberculosis incidence.Additionally,a geographically weighted regression model was constructed based on the dominant factors and tuberculosis incidence rates to further explore the localized effects of geographic environmental factors on tuberculosis incidence in the area.Results:(1)General Epidemiological Characteristics of Tuberculosis in Pingyi County:During the study period(2013-2022),the cumulative reported tuberculosis cases in Pingyi County were statistically analyzed,revealing a total of 4,676 cases.In terms of gender distribution,the number of male cases was significantly higher than that of females,with a ratio of 2.92:1.Furthermore,the annual average reported incidence rate was 52.13 per 100,000 population,showing a decreasing trend over the years.Regarding mortality cases,there were a total of 19 deaths during the study period,with males accounting for the majority and a male-to-female ratio of 2.8:1.The annual average reported mortality rate was 0.29 per 100,000 population.In terms of age,the incidence exhibited a bimodal distribution,with the age groups of 20-29 and 60-64 showing the highest peaks.In terms of occupational distribution,the majority of cases were farmers,followed by unemployed individuals and students,constituting 83.02%,7.16%,and 2.61%of the total,respectively,accounting for a combined proportion of 92.79%.The incidence demonstrated clear periodicity and seasonality,with a peak in December and a trough in February.Geographically,the disease was predominantly concentrated in Pingyi County’s streets,as well as the towns of Baitai and Baiyan.(2)Spatial Epidemiological Characteristics of Tuberculosis in Pingyi County:During the periods of 2013-2016 and 2018-2019,there was a significant positive spatial correlation in the incidence of tuberculosis in Pingyi County,indicating clear spatial clustering characteristics.Over the study period from 2013 to 2022,a global Moran’s I index analysis was conducted on the tuberculosis cases in Pingyi County,revealing a gradual decline in the results.Moreover,the hotspots of disease shifted from several major clusters to a dispersed pattern across different areas,with the degree of clustering weakening each year.By performing spatiotemporal scan analysis on the tuberculosis incidence rates in Pingyi County from 2013 to 2022,six statistically significant cluster areas were identified.Among these clusters,Pingyi Street was determined as the most likely primary cluster area,with a clustering timeframe from January 2013 to December 2017.The relative risk within this area was 2.874,with a logarithmic likelihood ratio of 85.403.The main distribution of tuberculosis clusters in Pingyi County was observed in Pingyi Street,Baiyan Town,Zhengcheng Town,Linjian Town,Baitai Town,and Berlin Town.These areas highly corresponded to the hotspot regions identified through local spatial autocorrelation analysis.(3)The results of the geographic detector model revealed that average annual relative humidity,annual sunshine hours,annual precipitation,average annual temperature,average wind speed,carbonic oxide(CO),nitrogen dioxide(NO2),ozone(O3),inhalable particulate matter(PM10),altitude,population density,and per capita Gross Domestic Product(GDP)had an impact on tuberculosis incidence.Through factor detection,we found that NO2 concentration was the main factor contributing to spatial clustering and differentiation of tuberculosis,explaining 20.3%of the variance.Additionally,through the study of interaction detectors,we discovered that the combination of any two factors had a greater impact on the spatial distribution of tuberculosis incidence.The interaction between sunshine hours and CO showed the highest explanatory power at 44%.The results of geographically weighted regression demonstrated significant spatial heterogeneity in the impact of geographic risk factors on tuberculosis incidence in Pingyi County.In statistically significant areas(P<0.10),annual precipitation,PM 10,population density,GDP,and average annual relative humidity showed a positive correlation with tuberculosis incidence rate,while NO2 concentration showed a negative correlation.However,the associations of CO and O3 with tuberculosis incidence rate varied in different regions.Conclusions:(1)Tuberculosis incidence in Pingyi County is higher among males than females,with a higher prevalence in middle-aged and elderly individuals.Farmers,homemakers,and unemployed individuals are at higher risk for tuberculosis in Pingyi County.The average incidence rate of tuberculosis in Pingyi County is 52.13 per 100,000 population,and it has been decreasing annually,with the highest peak occuring in December.(2)There is evident spatial autocorrelation in the tuberculosis incidence rate in Pingyi County,indicating the presence of high-risk hotspots.The clustering areas are primarily distributed in the urban areas of Pingyi County and remote rural regions,with a decreasing level of clustering each year.Tuberculosis in Pingyi County exhibits significant spatiotemporal distribution characteristics,with six spatiotemporal clusters identified.(3)Environmental factors,including precipitation,relative humidity,sunshine hours,temperature,wind speed,carbonic oxide(CO),nitrogen dioxide(NO2),ozone(O3),inhalable particulate matter(PM 10),altitude,population density,and Gross Domestic Product(GDP),have an impact on tuberculosis incidence.Among them,precipitation,humidity,PM 10,population density,and GDP show a positive correlation with tuberculosis incidence rate,while NO2 shows a negative correlation.However,the associations of CO and O3 with tuberculosis incidence rate vary in different regions. |
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