Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China

Clinical characteristics and genomic epidemiological survey of tuberculosis in Wuzhou, China, 2022

Tuberculosis (TB) is a serious respiratory disease posing significant public health threats, such as the variation in Mycobacterium tuberculosis (M.tb) lineages and their associated drug resistance across regions. In 2022, clinical data and culture-positive TB samples were collected from the Third People's Hospital in Wuzhou, China. M.tb drug resistance and lineage were analyzed using whole-genome sequencing, while logistic regression was applied to identify factors influencing patient outcomes. Among 169 strains analyzed, an overall drug resistance rate of 23.1% was observed. Multidrug-resistant or rifampicin-resistant cases constituted 7.7% of the strains. Most strains belonged to lineage 2 (69.8%), followed by lineage 4 (27.8%). Poor treatment adherence, being aged 65 or older, and retreatment emerged as risk factors for unfavorable outcomes. This pioneering survey provides crucial insights into TB patient characteristics, drug resistance patterns, and lineage distribution in Wuzhou, laying a foundation for future targeted TB control strategies in the region.IMPORTANCEIn 2022, tuberculosis (TB) was the second leading cause of death from a single infectious agent worldwide, posing a serious threat to global health. The epidemiological characteristics of TB vary considerably from country to country, and even from region to region within a single country, due to differences in the economy, medical conditions, education, and other factors. Understanding the current status of TB epidemics in the region is important, with practical implications for local diagnosis, treatment, and control. This genomic epidemiological survey has provided a first insight into the characteristics of TB patients, drug resistance rates, prevalence lineage, and factors associated with unfavorable outcomes in Wuzhou, China.

Medical Insurance Reimbursement and the Effects of Tuberculosis Management in Guangxi Province, China: A Retrospective Cross-Sectional Study

Purpose

This study aims to compare the differences in medical insurance reimbursement for TB treatment in Guangxi and to analyze the effects of such variations, thereby contributing to the enhancement of TB care and control.

Patients and Methods

A survey was conducted across 49 randomly selected TB-designated hospitals in Guangxi using structured questionnaires and patient records. Missing data were addressed via median imputation. Non-parametric test was used to analyse and compare the differences in treatment outcomes among hospitals of different levels and types, with a P value less than 0.05 as the test criterion. Logistic regression analysis was performed to evaluate the independent effects of medical insurance reimbursement, hospital level, hospital type and service ability on TB treatment outcomes.

Results

The Urban Employee Basic Medical Insurance provided significantly higher reimbursement floors, ceilings, and rates compared to the Urban Resident Basic Medical Insurance (URBMI). Tertiary hospitals offered higher reimbursement floors for inpatient care but lower reimbursement rates compared to secondary hospitals. Despite policy reimbursement rates for TB treatment consistently exceeding 60%, the actual reimbursement rates often fell short of these benchmarks, especially in specialist hospitals and secondary care facilities. URBMI reimbursement ceiling for pulmonary TB of inpatients was positively associated with treatment success. Additionally, a lower URBMI reimbursement floor for pulmonary TB of inpatients was linked to higher disease mortality rates. Areas exhibited lower treatment success rates and higher case fatality rates shared common socioeconomic characteristics, including smaller populations, lower per capita output values, depressed production values, and lower disposable incomes among the rural population.

Conclusion

This study underscores the importance of equitable medical insurance reimbursement policies, and targeted reforms, such as raising URBMI reimbursement ceilings and enforcing real-time monitoring of actual reimbursements, are critical to mitigate disparities in TB care.

Analysis of Tuberculosis Epidemiological Distribution Characteristics in Fujian Province, China, 2005-2021: Spatial-Temporal Analysis Study

Background

Tuberculosis (TB) is a chronic infectious disease that harms human health for a long time. TB epidemiological distribution analysis can help governments to control TB in high TB incidence areas. The distribution trend of TB cases varies in different regions. The unbalanced temporal and spatial trends of pulmonary TB (PTB) risk at a fine level in Fujian Province remain unclear.

Objective

The purpose was to analyze different distribution characteristics, explore the prevalence of TB in this region, and provide a scientific basis for further guidance of TB control work in Fujian Province, China.

Methods

Prefectural-level and county-level notified PTB case data were collected in Fujian Province. A joinpoint regression model was constructed to analyze the unbalanced temporal patterns of PTB notification rates from 2005 to 2021 at prefecture-level city scales. The spatial clustering analysis and spatial autocorrelation analysis were performed to assess the inequality of the locations of PTB cases. Demographical characteristics were explored by the method of descriptive analysis.

Results

TB cases reported in Fujian showed an overall downward trend from 2005 to 2021 (in 2005: n=32,728 and in 2021: n=15,155). TB case numbers showed obvious seasonal changes. The majority of TB cases were middle-aged and older adult male patients (45 years and older; n=150,201, 42.6%). Most of the TB cases were farmers (n=166,186, 47.1%), followed by houseworkers and the unemployed (n=48,828, 13.8%) and workers (n=34,482, 9.8%). Etiologically positive TB cases continue to be the main source of TB cases (n=159,702, 45.3%). Spatially, the reported TB cases were mainly distributed in cities in southeastern Fujian, especially at the county level. TB case numbers showed 2 spatial groups; cases within each group shared similar case characteristics. In terms of geographical distribution, TB showed obvious spatial correlation, and local areas showed high aggregation.

Conclusions

The TB incidence trend decreased annually in Fujian Province. TB cases distributed commonly in the male population, middle-aged and older people, and farmers. Etiologically positive cases are still the main source of Mycobacterium tuberculosis infection. TB incidence is higher in the cities with a developed economy and large population in the southeast. TB control should be strengthened in these populations and areas, such as via early screening of cases and management of confirmed cases.

Spatio-temporal pattern and associate factors study on intestinal infectious diseases based on panel model in Zhejiang Province

BACKGROUND

Intestinal infectious diseases (IIDs) can impact the growth and development of children and weaken adults. This study aimed to establish a spatial panel model to analyze the relationship between factors such as population, economy and health resources, and the incidence of common IIDs. The objective was to provide a scientific basis for the formulation diseases prevention measures.

METHODS

Data on monthly reported cases of IIDs in each district and county of Zhejiang Province were collected from 2011 to 2021. The spatial distribution trend was plotted, and nine factors related to population, economy and health resources were selected for analysis. A spatial panel model was developed to identify statistically significant spatial patterns of influencing factors (P < 0.05).

RESULTS

The results revealed that each type of IIDs exhibited a certain level of clustering. Each IIDs had a significant radiation effect, HEV (b = 0.28, P < 0.05), bacillary dysentery (b = 0.38, P < 0.05), typhoid (b = 0.36, P < 0.05), other infectious diarrheas (OIDs) (b = 0.28, P < 0.05) and hand, foot and mouth disease (HFMD) (b = 0.39, P < 0.05), indicating that regions with high morbidity rates spread to neighboring areas. Among the population characteristics, density of population acted as a protective factor for bacillary dysentery (b=-1.81, P < 0.05), sex ratio acted as a protective factor for HFMD (b=-0.07, P < 0.05), and aging rate increased the risk of OIDs (b = 2.39, P < 0.05). Urbanization ratio posed a hazard factor for bacillary dysentery (b = 5.17, P < 0.05) and OIDs (b = 0.64, P < 0.05) while serving as a protective factor for typhoid (b=-1.61, P < 0.05) and HFMD (b=-0.39, P < 0.05). Per capita GDP was a risk factor for typhoid (b = 0.54, P < 0.05), but acted as a protective factor for OIDs (b=-0.45, P < 0.05) and HFMD (b=-0.27, P < 0.05). Additionally, the subsistence allowances ratio was a risk factor for HEV (b = 0.24, P < 0.05).

CONCLUSION

The incidence of IIDs in Zhejiang Province exhibited a certain degree of clustering, with major hotspots identified in Hangzhou, Shaoxing, and Jinhua. It would be essential to consider the spillover effects from neighboring regions and implement targeted measures to enhance disease prevention based on regional development.

Ecological-level factors associated with tuberculosis incidence and mortality: A systematic review and meta-analysis

Globally, tuberculosis (TB) is the leading infectious cause of morbidity and mortality, with the risk of infection affected by both individual and ecological-level factors. While systematic reviews on individual-level factors exist, there are currently limited studies examining ecological-level factors associated with TB incidence and mortality. This study was conducted to identify ecological factors associated with TB incidence and mortality. A systematic search for analytical studies reporting ecological factors associated with TB incidence or mortality was conducted across electronic databases such as PubMed, Embase, Scopus, and Web of Science, from each database's inception to October 30, 2023. A narrative synthesis of evidence on factors associated with TB incidence and mortality from all included studies, alongside random-effects meta-analysis where applicable, estimated the effects of each factor on TB incidence. A total of 52 articles were included in the analysis, and one study analysed two outcomes, giving 53 studies. Narrative synthesis revealed predominantly positive associations between TB incidence and factors such as temperature (10/18 studies), precipitation (4/6), nitrogen dioxide (6/9), poverty (4/4), immigrant population (3/4), urban population (3/8), and male population (2/4). Conversely, air pressure (3/5), sunshine duration (3/8), altitude (2/4), gross domestic product (4/9), wealth index (2/8), and TB treatment success rate (2/2) mostly showed negative associations. Particulate matter (1/1), social deprivation (1/1), and population density (1/1) were positively associated with TB mortality, while household income (2/2) exhibited a negative association. In the meta-analysis, higher relative humidity (%) (relative risk (RR) = 1.45, 95%CI:1.12, 1.77), greater rainfall (mm) (RR = 1.56, 95%CI: 1.11, 2.02), elevated sulphur dioxide (μg m-3) (RR = 1.04, 95% CI:1.01, 1.08), increased fine particulate matter concentration (PM2.5) (μg/ m3) (RR = 1.33, 95% CI: 1.18, 1.49), and higher population density (people/km2) (RR = 1.01,95%CI:1.01-1.02) were associated with increased TB incidence. Conversely, higher average wind speed (m/s) (RR = 0.89, 95%CI: 0.82,0.96) was associated with decreased TB incidence. TB incidence and mortality rates were significantly associated with various climatic, socioeconomic, and air quality-related factors. Intersectoral collaboration across health, environment, housing, social welfare and economic sectors is imperative for developing integrated approaches that address the risk factors associated with TB incidence and mortality.

Spatiotemporal analysis of tuberculosis in the Hunan Province, China, 2014-2022

Background

Pulmonary tuberculosis (PTB) is a major infectious disease that threatens human health. China is a high tuberculosis-burden country and the Hunan Province has a high tuberculosis notification rate. However, no comprehensive analysis has been conducted on the spatiotemporal distribution of PTB in the Hunan Province. Therefore, this study investigated the spatiotemporal distribution of PTB in the Hunan Province to enable targeted control policies for tuberculosis.

Methods

We obtained data about cases of PTB in the Hunan Province notified from January 2014 to December 2022 from the China Information System for Disease Control and Prevention. Time-series analysis was conducted to analyze the trends in PTB case notifications. Spatial autocorrelation analysis was conducted to detect the spatial distribution characteristics of PTB at a county level in Hunan Province. Space-time scan analysis was conducted to confirm specific times and locations of PTB clustering.

Results

A total of 472,826 new cases of PTB were notified in the Hunan Province during the 9-year study period. The mean PTB notification rate showed a gradual, fluctuating downward trend over time. The number of PTB notifications per month showed significant seasonal variation, with an annual peak in notifications in January or March, followed by a fluctuating decline after March, reaching a trough in November or December. Moran's I index of spatial autocorrelation revealed that the notification rate of PTB by county ranged from 0.117 to 0.317 during the study period, indicating spatial clustering. The hotspot areas of PTB were mainly concentrated in the Xiangxi Autonomous Prefecture, Zhangjiajie City, and Hengyang City. The most likely clustering region was identified in the central-southern part of the province, and a secondary clustering region was identified in the northwest part of the province.

Conclusion

This study identified the temporal trend and spatial distribution pattern of tuberculosis in the Hunan Province. PTB clustered mainly in the central-southern and northwestern regions of the province. Disease control programs should focus on strengthening tuberculosis control in these regions.

Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023

BACKGROUND

Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district.

METHODS

Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence.

RESULTS

From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016-2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014-2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations.

CONCLUSIONS

From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China

BACKGROUND

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB.

METHOD

Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA.

RESULTS

A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup.

CONCLUSION

For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis

Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.

Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

INTRODUCTION

The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis.

METHODS

The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar.

RESULTS

A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot's location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%).

CONCLUSION

There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB.

Spatial Distribution of Drug-Resistant Mycobacterium tuberculosis Infections in Rural Eastern Cape Province of South Africa

Tuberculosis (TB), an infectious airborne disease caused by Mycobacterium tuberculosis (Mtb), is a serious public health threat reported as the leading cause of morbidity and mortality worldwide. South Africa is a high-TB-burden country with TB being the highest infectious disease killer. This study investigated the distribution of Mtb mutations and spoligotypes in rural Eastern Cape Province. The Mtb isolates included were 1157 from DR-TB patients and analysed by LPA followed by spoligotyping of 441 isolates. The distribution of mutations and spoligotypes was done by spatial analysis. The rpoB gene had the highest number of mutations. The distribution of rpoB and katG mutations was more prevalent in four healthcare facilities, inhA mutations were more prevalent in three healthcare facilities, and heteroresistant isolates were more prevalent in five healthcare facilities. The Mtb was genetically diverse with Beijing more prevalent and largely distributed. Spatial analysis and mapping of gene mutations and spoligotypes revealed a better picture of distribution.

How to establishing an indicators framework for evaluating the performances in primary TB control institutions under the new TB control model? Based on a Delphi study conducted in Guangxi, China

BACKGROUND

In China, the new TB control model of trinity form had been implemented in all parts, and the comprehensively evaluation to the performances in primary TB control institutions were closely related to the working capacity and quality of TB service, but there was still no an unified evaluation indicators framework in practice and few relevant studies. The purpose of this study was to establish an indicators framework for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form in Guangxi, China.

METHODS

The Delphi method was used to establish an indicators framework for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form, and the analytic hierarchy process(AHP) was used to determine the weights of all levels of indicators, from September 2021 to December 2021 in Guangxi, China.

RESULTS

A total of 14 experts who had at least 10 years working experience and engaged in TB prevention and control and public health management from health committee, CDC, TB designated hospitals and university of Guangxi were consulted in two rounds. The average age of the experts were (43.3 ± 7.549) years old, and the effective recovery rate of the questionnaire was 100.0%. The average value of authority coefficient of experts (Cr) in the two rounds of consultation was above 0.800. The Kendall's harmony coefficient (W) of experts' opinions on the first-level indicators, the second-level indicators and the third-level indicators were 0.786, 0.201 and 0.169, respectively, which were statistically significant (P < 0.05). Finally, an indicators framework was established, which included 2 first-level indicators, 10 second-level indicators and 37 third-level indicators. The results of analytic hierarchy process (AHP) showed that the consistency test of all levels of indicators were CI < 0.10, which indicating that the weight of each indicator was acceptable.

CONCLUSION

The indicators framework established in this study was in line with the reality, had reasonable weights, and could provide a scientific evaluation tool for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form in Guangxi, China.

Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020

Background

Guizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies.

Methods

Data of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial–temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial–temporal scan statistic.

Results

Guizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014–2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial–temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020.

Conclusion

This study identified the upward trend with seasonality and spatial−temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.

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

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.

Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China

BACKGROUND

A remarkable drop in tuberculosis (TB) incidence has been achieved in China, although in 2019 it was still considered the second most communicable disease. However, TB's spatial features and risk factors in urban areas remain poorly understood. This study aims to identify the spatial differentiations and potential influencing factors of TB in highly urbanized regions on a fine scale.

METHODS

This study included 18 socioeconomic and environmental variables in the four central districts of Guangzhou, China. TB case data obtained from the Guangzhou Institute of Tuberculosis Control and Prevention. Before using Pearson correlation and a geographical detector (GD) to identify potential influencing factors, we conducted a global spatial autocorrelation analysis to select an appropriate spatial scales.

RESULTS

Owing to its strong spatial autocorrelation (Moran's I = 0.33, Z = 4.71), the 2 km × 2 km grid was selected as the spatial scale. At this level, TB incidence was closely associated with most socioeconomic variables (0.31 < r < 0.76, P < 0.01). Of five environmental factors, only the concentration of fine particulate matter displayed significant correlation (r = 0.21, P < 0.05). Similarly, in terms of q values derived from the GD, socioeconomic variables had stronger explanatory abilities (0.08 < q < 0.57) for the spatial differentiation of the 2017 incidence of TB than environmental variables (0.06 < q < 0.27). Moreover, a much larger proportion (0.16 < q < 0.89) of the spatial differentiation was interpreted by pairwise interactions, especially those (0.60 < q < 0.89) related to the 2016 incidence of TB, officially appointed medical institutions, bus stops, and road density.

CONCLUSIONS

The spatial heterogeneity of the 2017 incidence of TB in the study area was considerably influenced by several socioeconomic and environmental factors and their pairwise interactions on a fine scale. We suggest that more attention should be paid to the units with pairwise interacting factors in Guangzhou. Our study provides helpful clues for local authorities implementing more effective intervention measures to reduce TB incidence in China's municipal areas, which are featured by both a high degree of urbanization and a high incidence of TB.

A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

With 25% confirmed cases of the country's total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran's I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Interaction analysis of Mycobacterium tuberculosis between the host environment and highly mutated genes from population genetic structure comparison

We aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. tb) were distributed uniformly in cold and hot spots.The cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model.The percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, P = .022), among the elderly, people with a low BMI, and those having a history of contact with a TB patient (all P < .05). Individuals from cold spot areas had a higher frequency of out-of-town traveling (P < .05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210, and an Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (P < .001). The MDR model displayed a clear negative interaction effect of age groups with BMI (interaction entropy: -3.55%) and mutation of Rv0210 (interaction entropy: -2.39%). Through the mutations of Rv0210 and BMI had a low independent effect (interaction entropy: -1.46%).Our data suggests a statistically significant role of age, BMI and the polymorphisms of Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different populations. The characteristic strains showed a local epidemic. Strengthening genotype monitoring of strains in various regions can be used as an early warning signal of epidemic spillover.

A genome epidemiological study of mycobacterium tuberculosis in subpopulations with high and low incidence rate in Guangxi, South China

BACKGROUND

Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis (Mtb). China is the third in top 8 high TB burden countries and Guangxi is one of the high incidence areas in South China. Determine bacterial factors that affected TB incidence rate is a step toward Ending the TB epidemic.

RESULTS

Genomes of M. tuberculosis cultures from a relatively high and low incidence region in Guangxi have been sequenced. 347 of 358(96.9%) were identified as M. tuberculosis. All the strains belong to Lineage 2 and Lineage 4, except for one in Lineage 1. We found that the genetic structure of the M. tuberculosis population in each county varies enormously. Low incidence rate regions have a lower prevalence of Beijing genotypes than other regions. Four isolates which harbored mutT4-48 also had mutT2-58 mutations. It is suggested that strains from the ancestors of modern Beijing lineage is circulating in Guangxi. Strains of modern Beijing lineage (OR=2.04) were more likely to acquire drug resistances than Lineage 4. Most of the lineage differentiation SNPs are related to cell wall biosynthetic pathways.

CONCLUSIONS

These results provided a higher resolution to better understand the history of transmission of M. tuberculosis from/to South China. And the incidence rate of tuberculosis might be affected by bacterial population structure shaped by demographic history. Our findings also support the hypothesis that Modern Beijing lineage originated in South China.

Influential factors and spatial-temporal distribution of tuberculosis in mainland China

Tuberculosis (TB) is an infectious disease that threatens human safety. Mainland China is an area with a high incidence of tuberculosis, and the task of tuberculosis prevention and treatment is arduous. This paper aims to study the impact of seven influencing factors and spatial–temporal distribution of the relative risk (RR) of tuberculosis in mainland China using the spatial–temporal distribution model and INLA algorithm. The relative risks and confidence intervals (CI) corresponding to average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP were 1.018 (95% CI 1.001–1.034), 1.014 (95% CI 1.006–1.023), 1.026 (95% CI 1.014–1.039) and 1.025 (95% CI 1.011–1.040). The relative risk for average temperature and pressure were 0.956 (95% CI 0.942–0.969) and 0.767 (95% CI 0.664–0.875). Spatially, the two provinces with the highest relative risks are Xinjiang and Guizhou, and the remaining provinces with higher relative risks were mostly concentrated in the Northwest and South China regions. Temporally, the relative risk decreased year by year from 2013 to 2015. It was higher from February to May each year and was most significant in March. It decreased from June to December. Average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP had positive effects on the relative risk of tuberculosis. The average temperature and pressure had negative effects. The average wind speed had no significant effect. Mainland China should adapt measures to local conditions and develop tuberculosis prevention and control strategies based on the characteristics of different regions and time.

Spatial-temporal analysis of tuberculosis in Chongqing, China 2011-2018

BACKGROUND

China is a country with a high burden of pulmonary tuberculosis (PTB). Chongqing is in the southwest of China, where the notification rate of PTB ranks tenth in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Chongqing in order to improve TB control measures.

METHODS

A spatial-temporal analysis has been performed based on the data of PTB from 2011 to 2018, which was extracted from the National Surveillance System. The effect of TB control was measured by variation trend of pathogenic positive PTB notification rate and total TB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level.

RESULTS

A total of 188,528 cases were included in this study. A downward trend was observed in PTB between 2011 and 2018 in Chongqing. The peak of PTB notification occurred in late winter and early spring annually. By calculating the value of Global Moran's I and Local Getis's Gi*, we found that PTB was spatially clustered and some significant hot spots were detected in the southeast and northeast of Chongqing. One most likely cluster and three secondary clusters were identified by Kulldorff's scan spatial-temporal Statistic.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Chongqing. Priorities should be given to southeast and northeast of Chongqing for better TB control.

The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

BACKGROUND

At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China.

METHODS

The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed.

RESULTS

As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots.

CONCLUSION

Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

Hot and Cold Spot Areas of Household Tuberculosis Transmission in Southern China: Effects of Socio-Economic Status and Mycobacterium tuberculosis Genotypes

The aims of the study were: (1) compare sociodemographic characteristics among active tuberculosis (TB) cases and their household contacts in cold and hot spot transmission areas, and (2) quantify the influence of locality, genotype and potential determinants on the rates of latent tuberculosis infection (LTBI) among household contacts of index TB cases. Parallel case-contact studies were conducted in two geographic areas classified as "cold" and "hot" spots based on TB notification and spatial clustering between January and June 2018 in Guangxi, China, using data from field contact investigations, whole genome sequencing, tuberculin skin tests (TSTs), and chest radiographs. Beijing family strains accounted for 64.6% of Mycobacterium tuberculosis (Mtb) strains transmitted in hot spots, and 50.7% in cold spots (p-value = 0.02). The positive TST rate in hot spot areas was significantly higher than that observed in cold spot areas (p-value < 0.01). Living in hot spots (adjusted odds ratio (aOR) = 1.75, 95%, confidence interval (CI): 1.22, 2.50), Beijing family genotype (aOR = 1.83, 95% CI: 1.19, 2.81), living in the same room with an index case (aOR = 2.29, 95% CI: 1.5, 3.49), travelling time from home to a medical facility (aOR = 4.78, 95% CI: 2.96, 7.72), history of Bacillus Calmette-Guérin vaccination (aOR = 2.02, 95% CI: 1.13 3.62), and delay in diagnosis (aOR = 2.56, 95% CI: 1.13, 5.80) were significantly associated with positive TST results among household contacts of TB cases. The findings of this study confirmed the strong transmissibility of the Beijing genotype family strains and this genotype's important role in household transmission. We found that an extended traveling time from home to the medical facility was an important socioeconomic factor for Mtb transmission in the family. It is still necessary to improve the medical facility infrastructure and management, especially in areas with a high TB prevalence.