Spatial analysis and influencing factors of pulmonary tuberculosis among students in Nanning, during 2012–2018

Abstract

Background Economically underdeveloped areas in western China are hotspots of tuberculosis, especially among students. However, the related spatial and temporal patterns and influencing factors are still unclear and there are few studies to analyze the causes of pulmonary tuberculosis in students from the perspective of space. Methods We collected data regarding the reported incidence of pulmonary tuberculosis (PTB) among students at township level in Nanning, from 2012 to 2018. The reported incidence of pulmonary tuberculosis among students in Nanning was analyzed using spatial autocorrelation and spatial scan statistical analysis to depict hotspots of PTB incidence and spatial and temporal clustering. Spatial panel data of the reported incidence rates and influencing factors at district and county levels in Nanning were collected from 2015 to 2018. Then, we analyzed the spatial effects of incidence and influencing factors using the spatial Durbin model to explore the mechanism of each influencing factor in areas with high disease prevalence under spatial effects. Results From 2012 to 2018, 1609 cases of PTB were reported among students in Nanning, with an average annual reported incidence rate of 14.84/100,000. Through the Joinpoint regression model, We observed a steady trend in the percentage of cases reported each year (P>0.05). There was spatial autocorrelation between the annual reported incidence and the seven-years average reported incidence from 2012 to 2018. The high-incidence area was distributed in the junction of six urban areas and spread to the periphery, with the junction at the center. The population of college students, per capita financial expenditure on health, per capita gross domestic product, and the number of health technicians per 1,000 population were all influencing factors in the reported incidence of PTB among students. Conclusion We identified spatial clustering of the reported incidence of PTB among students in Nanning, mainly located in the urban center and its surrounding areas. The clustering gradually decreased from the urban center to the surrounding areas. Spatial effects influenced the reported incidence of PTB. The population density of college students, per capita health financial expenditure, gross domestic product (GDP) per capita, and the number of health technicians per 1,000 were all influencing factors in the reported incidence of PTB among students.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021

“…Several studies shown that urbanization [ 37 ], the number of health physicians [ 13 ], population density [ 30 ], and number of beds in medical institution [ 13 , 30 ] are associated with the TB incidence. We explored the ten factors from demographic, medical and health resource, and economic aspects.…”

“…Population, population density and urbanization rate were negatively related to TB incidence, which is consistent with the previous study [ 30 ]. The PLADs with better development level and economic situations are usually with higher population density and urbanization rate, leading to the higher accessibility for medical services and better living standards [ 13 , 30 ]. The number of medical and health institutions, number of health technicians per 10,000 population were negatively related to TB incidence, indicating higher accessibility for medical services contribute to the TB control.…”

“…The selection of fixed-effect and random-effect models was determined by the objective of this study and Hausman test. We focused on the analysis in 31 PLADs and did not extrapolate the results, so we chose the fixed effects model [ 13 ]. The Akaike information criterion ( AIC ), BIC , and R 2 were compared between time fixed, individual fixed and two-way fixed SDM to select suitable model.…”

“…A study employing DLNM found temperature, relative humidity, and wind speed playing crucial roles in TB incidence with delayed and non-linear effects in Urumqi, China [ 10 ]. Additionally, spatial panel data models have been employed to analyze the relationship between socioeconomic factors and TB incidence, accounting for spatial dependence and heterogeneity [ 13 , 14 ]. However, all these studies have been conducted in different geographic locations which focus on the relationship of someone factors with TB incidence.…”

Spatial Effect of Ecological Environmental Factors on Mumps in China during 2014–2018

“…Spatial econometric models have been widely used in tuberculosis, hand, foot and mouth disease (HFMD), AIDS and other infectious diseases [ 19 , 20 , 21 , 22 ], but there is still a knowledge gap in the study of mumps. Therefore, this paper aims to use spatial autocorrelation analysis to discover the clustering pattern of mumps and then further detect the spatial association and spillover effect of mumps with meteorological conditions, pollutants, health resources, and economic and social factors by using the Spatial Durbin Panel Model (SDPM) in the spatial econometric models.…”