Spatial pattern of tuberculosis cases in Jos Metropolis, Nigeria in the period 2019-2022.

In Nigeria, Plateau State, with its capital Jos Metropolis (JM), is one of 14 high Tuberculosis (TB) burden states with an increasing TB occurrence.There are no reported spatio-temporal studies from JM, therefore, it is not known how TB spatio-temporal clusters may impact TB transmission in JM.Spatio-temporal patterns of TB cases in JM were analysed in this study.Identifying such patterns may help towards the development of TB prevention and control strategies in the JM.Coordinates from Polling Unit Locator (PUL) at the Independent National Electoral Commission (INEC) website were used to geolocate TB cases to their nearest Polling Units within an Electoral Ward (EW).SaTScan software was used to identify space-time TB clusters through a retrospective space-time analysis based on the discrete Poisson model.The TB cases mapped were 4,897 over the study period (2019)(2020)(2021)(2022).Three significant (primary) spatiotemporal TB clusters were identified, with one of them occurring within the 2021-2022 time period.Thirteen EWs were present within the primary cluster (LLR = 436.95,RR = 2.68 and P-value = 0.001).The clusters were located in the central areas of the northern parts of the JM.These spatio-temporal TB clusters can potentially serve as a source of further spread of TB infection and disease to other locations in JM.Due to the space-time nature of these clusters, timely and targeted interventions, particularly in the affected areas, are required to limit and prevent the further spread of TB within the Jos Metropolis.

Use of spatial statistics and monitoring data to identify clustering of bovine tuberculosis in Argentina

A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015

Context:Stratification of social determinants leads to clustering of low socioeconomic communities, which then leads to spatio-temporal tuberculosis (TB) clusters. While previous studies have investigated spatio-temporal TB clusters, few have reported on the dynamics of them and the characteristics of social determinants.Aims:To investigate the spatio-temporal dynamics of TB clusters in Bandar Lampung, Indonesia, from 2015 to 2016, and to identify the characteristics of population density and percentage of poverty of the clusters.Settings and Design:A cross-sectional study was performed to analyze the spatio-temporal dynamics of TB clusters. The sample consisted of 705 TB patients (2015) and 1134 TB patients (2016), registered in 30 community health centers in Bandar Lampung, IndonesiaSubjects and Methods:Geographical coordinates of the TB patients' residence were collected using Geographical Positioning System. Secondary data, consisting of population density and the percentage of poverty, were obtained from the subdistrict office in the region under investigation.Statistical Analysis:Data were analyzed with space–time permutation model using SaTScan software.Results:Spatio-temporal dynamics of TB clusters were found in 2015 and 2016, including the number of significant clusters, TB cases within the clusters, as well as locations and sizes of the clusters. All the clusters were found to have similar social determinant characteristics: medium–high population density and low–medium percentage of poverty.Conclusions:TB control programs in countries with a high TB burden and low social determinants should consider the spatio-temporal dynamics of the TB cluster and its social determinant characteristics for a better TB's intervention.

Tuberculosis (TB) is responsible for a substantial public health burden worldwide, with a heterogeneous geographical distribution. Sub-Saharan Africa accounts for almost one-quarter of the global burden of TB. This is associated with high HIV-prevalence, low socio-demographic development, and poor living and working conditions. Identification of high-risk populations and locations within countries with a high burden of TB has the potential to improve the cost-effectiveness of TB control strategies.Two groups of 30 countries account for almost 90 per cent of the global burden of TB disease and TB and HIV co-infection. Ethiopia is in both groups, with both high-TB and TB and HIV co-infection. However, information is limited to the geographical distribution of high-risk areas which could inform targeted intervention. The overall aim of the researches in this Thesis is to enhance the TB control program in Ethiopia by providing more detailed knowledge of the distribution of TB, and the contributory role of HIV, low sociodemographic development and poor living and working conditions. This will provide sustained TB control in Ethiopia and enable enhanced strategic implementation of the “End TB strategy.”The first part (Chapters 1-3) of this Thesis highlights the available evidence on the geographical variation of TB and the burden of TB and HIV co-infection. Chapter 4 presents a systematic review of published studies on the effects of altitude and temperature on TB notification. This review indicated that studies examining the correlations between altitude and temperature on TB notification are limited in number. Despite low study power, the report demonstrated that living in low-altitude and high-temperature settings may increase TB risk.Chapter 5 presents evidence from a systematic review and meta-analysis conducted to estimate the prevalence of HIV in patients diagnosed with TB in sub-Saharan Africa. The prevalence of HIV in diagnosed TB patients (HIV/TB) showed substantial heterogeneity with an overall prevalence estimate of HIV/TB of 31.8 per cent. Heterogeneity in HIV/TB between studies was mainly attributable to geographic region and HIV prevalence. The Eastern and Southern sub-Saharan African region had a higher prevalence of HIV/TB (34.4 per cent) compared to Western and Central sub-Saharan Africa (27.3 per cent). However, the prevalence decreased more in the Eastern and Southern sub-Saharan African region than Western and Central sub-Saharan African between 2000 and 2010. This study suggests that collaborative TB and HIV activities need to be strengthened and sustained to achieve an end to the TB epidemic.Based on evidence from previous chapters, Chapters 6 and 7 present the spatial distributions of TB and HIV, profiling the sociodemographic and environmental determinants in the Amhara region of Ethiopia, using separate TB and HIV cluster detection methods. These studies demonstrated the spatial heterogeneity of both diseases. Both district-level TB notifications, reported between 2014 and 2017, and HIV infection rates, between 2015 and 2017, were spatially clustered in the border areas of the Amhara region of Ethiopia. Regression analyses demonstrated that the most important factor associated with both TB and HIV clustering was the proportion of seasonal migrant populations in the district. Additional factors associated with high notifications of TB were the proportion of people living in urban areas, crowding, the percentage of males, people living with HIV (PLHIV) /1000 population, access to health care, and the use of charcoal for cooking. Living at low altitude was also associated with TB clustering, which was consistent with the review findings in Chapter 4. Low educational status was also associated with HIV high-risk areas.Chapter 8 presents the epidemiology of TB and HIV co-infection in Ethiopia. This chapter elucidates the progress of the implementation of collaborative TB and HIV activities and the consequent impact on the national TB control program. Findings from sentinel surveillance of TB and HIV co-infection data, from 76 health facilities in Ethiopia, suggest that collaborative TB and HIV services were either not uniform or not consistently implemented between 2010 and 2015. However, encouragingly, intensified TB case finding in PLHIV and the screening of HIV patients for TB diagnosis increased. This Thesis provides more detailed evidence on the distribution of TB and associated HIV infection in Ethiopia, using geo-spatial tools and modelling. It shows that TB and HIV infection are geographically heterogeneous and co-clustered in the Northwest border areas of Ethiopia, likely influenced by the proportion of seasonal migrants in common. This information provides the basis to enhance the strategic implementation of TB control program in Ethiopia. It also highlights the need to strengthen integrated TB and HIV management, to address social determinants of TB, and to deal with issues of population movement to control and prevent HIV and TB in Ethiopia, and thus achieve the “End-TB” goal.

Diabetes is a severe metabolic disorder affecting human health worldwide, with increasing prevalence in low- and middle-income countries. Gaps in knowledge regarding factors that lead to diabetes and its association with tuberculosis (TB) endemicity at the national scale still exist, mainly because of the lack of large-scale dual testing and appropriate evaluation methods. To identify locations in India where diabetes prevalence is concentrated, examine the association of diabetes with sociodemographic and behavioral covariates, and uncover where high regional TB endemicity overlaps with diabetes. This cross-sectional study included 803 164 men aged 15 to 54 years and women aged 15 to 49 years who participated in the Demographic Health Survey (2015-2016), carried out by the India Ministry of Health and Family Welfare using a 2-stage clustered sampling, which included a diabetes estimation component. The survey was conducted from January 2015 to December 2016, and data analysis was conducted from July 2018 to January 2019. Self-reported diabetes status. Self-reported diabetes status was used to estimate the association of covariates, including educational level, sex, age, religion, marital status, alcohol use, tobacco use, obesity status, and household socioeconomic level, with diabetes prevalence. Additionally, regional tuberculosis endemicity level, estimated using the India TB report for 2014 from the Revised National TB Program, was included to evaluate the national extent of the spatial overlap of diabetes and TB. Among 803 164 sampled individuals (691 982 [86.2%] women; mean [SD] age, 30.09 [9.97] years), substantial geographic variation in diabetes prevalence in India was found, with a concentrated burden at the southern coastline (cluster 1, Andhra Pradesh and Telangana: prevalence, 3.01% [1864 of 61 948 individuals]; cluster 2, Tamil Nadup and Kerala: prevalence, 4.32% [3429 of 79 435 individuals]; cluster 3, east Orissa: prevalence, 2.81% [330 of 11 758 individuals]; cluster 4, Goa: prevalence, 4.43% [83 of 1883 individuals]). Having obesity and overweight (odds ratio [OR], 2.44; 95% CI, 2.18-2.73; P < .001; OR, 1.66; 95% CI, 1.52-1.82; P < .001, respectively), smoking tobacco (OR, 3.04; 95% CI, 1.66-5.56; P < .001), and consuming alcohol (OR, 2.01; 95% CI, 1.37-2.95; P < .001) were associated with increased odds of diabetes. Regional TB endemicity and diabetes spatial distributions showed that there is a lack of consistent geographical overlap between these 2 diseases (eg, TB cluster 4: 60 213 TB cases; 186.79 diabetes cases in 20 183.88 individuals; 0.93% diabetes prevalence; TB cluster 8: 47 381 TB cases; 180.53 diabetes cases in 22 449.18 individuals; 0.80% diabetes prevalence; TB cluster 9: 37 620 TB cases, 601.45 diabetes cases in 12 879.36 individuals; 4.67% diabetes prevalence). In this study, identifying spatial clusters of diabetes on the basis of a nationally representative survey suggests that India may face different levels of disease severity, and each region might need to implement control strategies that are more appropriate for its unique epidemiologic context.

To identify spatiotemporal patterns and clusters of tuberculosis (TB) in Brazil between 2001 and 2023, assess the impact of the COVID-19 pandemic on TB trends, and provide recommendations for targeted interventions. This ecological study analyzed secondary data from Brazil's Notifiable Diseases Information System (known as SINAN), which covers all confirmed TB cases during the study period. Three main indicators were analyzed: TB incidence and two treatment outcomes - cure and loss to follow up. Spatiotemporal cluster analysis was conducted using the Emerging Hot Spot Analysis tool in ArcGIS Pro 2.8 (Esri, Redlands, CA, USA), based on the Getis-Ord Gi* statistic. An average of 74 057 TB cases were reported annually. TB incidence declined until 2016 but increased afterward, peaking in 2023. Cure rates declined after 2016, especially following the COVID-19 pandemic, while the rates of loss to follow up increased. Hot spots for incidence and loss to follow up were concentrated in the North, Southeast, and Central-West regions. The South region showed cold spots for cure and loss to follow up. These spatial trends revealed persistent regional disparities in TB outcomes that are closely related to indicators associated with socioeconomic status and access to health care. TB continues to present critical public health challenges in Brazil, particularly since the COVID-19 pandemic. Spatiotemporal analysis revealed significant regional clusters of TB burden. Strengthening surveillance systems and improving early diagnosis and treatment adherence strategies, especially in high-burden regions, are essential to mitigate the post-pandemic resurgence of TB and to achieve the World Health Organization's goal of eliminating TB by 2035.

Objective: To explore the tuberculosis (TB) clustering areas and changing tendency in China from 2013 to 2018, so as to provide evidence for improving the control and prevention programs on TB. Methods: Spatial distribution of TB in China from 2013 to 2018 was studied using data from TB information management system of Chinese disease prevention and control information system. The SaTScan 9.6 software was used to detect and analyze the TB clusters and relative risks. Likelihood ratio and P value under the scan windows were used as the main index to define the clusters. Results: The TB registration rate declined in China from 2013 to 2018. High-risk clusters were mainly located in western regions of Xinjiang, Tibet, Shaanxi, Sichuan, Chongqing, Yunnan, Guizhou, Guangxi provinces and central and eastern regions of Inner Mongolia, Hubei, Hunan, Jiangxi, Guangdong and Hainan provinces. The number of counties with TB clusters declined slowly, from 1 112 in 2013 to 806 in 2018. In comparison with areas other than the clusters, the RR of TB in the clusters remained at high levels in southern Xinjiang and western Tibet from 2013 to 2018 (RR>3.0). Conclusions: The number of the counties with TB clustering declined slowly from 2013 to 2018, and most of them were located in western and central China. Early detection and health education should be focused on TB patients to prevent the spread of TB. Management programs set for TB patients should be strengthened, especially in the western parts of the country where TB clusters and high relative risks existed.

Sadly, despite a previous warning of a cattle tuberculosis (TB) crisis (Hancox 1998a,b), there has been a steady worsening of the situation with a very real risk to public health. Bovine TB is now back to 1960s levels in Britain, from a low point in the mid‐1970s of 89 herds and 600 cases to 872 new herds and 7000 cases in 1999. TB has spread from tiny south‐west ‘hot spots’ to an area from Cornwall up to Cheshire/Derbyshire (distribution maps in Krebs 1997). Irish incidence has gone back to 1970s levels with over 45 000 cases last year. Over the last 30 years attention has focused to such an extent on badgers (Meles meles L.) as the ‘main source’ of further herd outbreaks, that amidst the confused debate over this ‘highly complex and emotive issue’, it is quite extraordinary that the importance of cattle‐to‐cattle transmission is seriously questioned (Griffin and Dolan 1995; O'Reilly and Daborn 1995; Gardner 1999; Anonymous 2000; Bourne 2000; Morrison et al. 2000), and this despite a century's experience of cattle TB schemes in over a hundred countries to the contrary! Out of five main schemes the widespread success of ‘area eradication’ rests on a robust scientific base as the most cost‐effective and sustainable means of minimizing or preventing transmission within the cattle population. But whereas early on, most test reactors are lesioned and infectious, late on most ‘reactors’ do not have ‘tubercle’ lesions and are not infectious because they do not have TB, hence the confusion (Francis 1947).

Background: Tuberculosis (TB) was the worldwide leading cause of mortality from a single infectious agent before the coronavirus disease 2019 (COVID-19) pandemic. The incidence of TB infections has continually declined since 2000, but the COVID-19 pandemic has reversed this trend. In 2020, global health officials reported a 21% drop in documented cases relative to TB cases in 2019. Although previous studies evaluated the impact of the COVID-19 pandemic on global TB cases, we are not aware of reports that compared U.S. and global TB cases during the COVID-19 pandemic. Objective: To analyze prepandemic and pandemic volumes of TB cases within the United States and compare findings with global TB volumes. Methods: This descriptive study used data from the Centers for Disease Control and Prevention to compare reported TB cases in the United States in 2019 and 2020. TB cases from the United States were compared with data about global TB cases. Results: The COVID-19 pandemic was associated with decreased TB testing and cases in the United States. The five states with the highest number of TB cases remained the same in 2019 and 2020, and included California, Texas, New York, Florida, and New Jersey. In these states, TB predominantly occurred in non‐U.S.-born residents and most patients solely presented with pulmonary manifestations. In the United States, the most substantial risk factor for TB was diabetes mellitus. Conclusion: The COVID-19 pandemic decreased access to TB services and discouraged patients from seeking TB care, which inadvertently disrupted international and U.S. TB surveillance systems. Given the decline in documented TB cases, leaders may need to anticipate an increase in TB cases and begin to aggressively reallocate resources to improve TB detection and care to mitigate the recent changes.

IntroductionAccurate mapping of spatial heterogeneity in tuberculosis (TB) cases is critical for achieving high impact control as well as guide resource allocation in most developing countries. The main aim of this study was to explore the spatial patterns of TB occurrence at district level in Zimbabwe from 2015 to 2018 using GIS and spatial statistics as a preamble to identifying areas with elevated risk for prioritisation of control and intervention measures.MethodsIn this study Getis-Ord Gi* statistics together with SaTscan were used to characterise TB hotspots and clusters in Zimbabwe at district level from 2015 to 2018. GIS software was used to map and visualise the results of cluster analysis.ResultsResults show that TB occurrence exhibits spatial heterogeneity across the country. The TB hotspots were detected in the central, western and southern part of the country. These areas are characterised by artisanal mining activities as well as high poverty levels.Conclusions and recommendationsResults of this study are useful to guide TB control programs and design effective strategies which are important in achieving the United Nations Sustainable Development goals (UNSDGs).

ObjectivesChina is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.MethodsSpatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran’s I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011. ResultsTwo indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran’s I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.ConclusionThe total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.

Despite significant advancements in tuberculosis (TB) diagnostics, treatment, and prevention, the disease remains a major global public health challenge, with only limited reductions in incidence rates observed. In recent years, whole-genome sequencing (WGS) of Mycobacterium tuberculosis isolates has become a key approach for studying various aspects of TB, providing high-resolution insights into strain phylogenetic diversity, drug resistance mechanisms, disease transmission dynamics, and recurrence causes. Although WGS offers a promising alternative to widely implemented molecular and culture-based M. tuberculosis analysis methods, further improvements in sequencing data analysis strategies are needed to fully unlock its potential for addressing critical scientific, epidemiological, and clinical challenges in TB control. This doctoral thesis aimed to apply WGS to M. tuberculosis isolates from Latvian patients to address unresolved epidemiologically and clinically relevant issues impacting TB control and propose improvements for current WGS data analysis strategies. This research focused on two key subsets of patients with TB: those who experienced recurrent TB episodes and those suspected of being part of the same TB clusters based on epidemiological data and conventional genotyping results. The studied population included adults and children with pulmonary or extrapulmonary TB. M. tuberculosis isolates were subjected to WGS for genetic-variant-based strain genotyping, phylogenetic analyses, determination of the causes of recurrent TB episodes, identification of epidemiologically relevant TB clusters, delineation of transmission chains, detection of mixed-strain infections, and WGS-based drug susceptibility testing (DST). Relevant clinical and epidemiological data and results from previous M. tuberculosis isolate analyses were retrieved from patients’ medical records. Conventional genotyping (spoligotyping and insertion sequence 6110 restriction fragment length polymorphism analysis) and phenotypic DST results were compared with WGS findings. Patients’ diagnoses, sputum smear microscopy results, specimen collection timelines, contact tracing and geospatial data were integrated to interpret the WGS results according to the analysis objective. WGS-assigned lineages and sub-lineages generally agreed with the spoligotyping results of the M. tuberculosis isolates studied, with both methods revealing significant variability in M. tuberculosis genotypes in Latvia, even within specific TB patient subsets. The isolates within the two most prevalent M. tuberculosis genotype families in Latvia, Beijing and LAM, exhibited greater genetic relatedness than isolates from other genotype families, with varying drug resistance distribution frequencies observed among different M. tuberculosis spoligotypes. Distinguishing between the two possible causes of TB recurrence (endogenous reactivation and exogenous reinfection) was more precise when performed as a part of the TB cluster transmission chain delineation. This approach integrated WGS data with relevant clinical and epidemiological information, as identifying pairwise single nucleotide variant (SNV) distances and analysing differing SNVs between isolates proved insufficient. Using genetic distance thresholds of 12 SNVs for identifying epidemiologically relevant TB clusters and 5 SNVs for inferring recent transmission events proved effective in a low-to-moderate TB incidence setting. However, the close genetic relatedness of isolates belonging to Beijing and LAM genotype families, which are highly prevalent in Latvia, should be considered during TB cluster identification to avoid the risk of false clustering. Limitations in TB transmission chain delineation included unidentified active TB cases, variability in M. tuberculosis mutation rate, low genetic variability of the pathogen, and tight specimen collection timeline of genetically identical isolates. The proposed integrated approach significantly complemented epidemiological investigation and conventional genotyping results, enabling more accurate identification of source cases and evaluation of index cases. However, its application may not be necessary for all epidemiologically relevant cases. Lastly, discordances between phenotypic and WGS-based DST among the studied isolates were attributed to the technical complexities of phenotypic tests, the variable effects of genetic variants on the minimal inhibitory concentration of drugs, and insufficient data on drug-resistance-conferring variants. While WGS-based DST cannot fully replace pDST, combining these methods provides the most accurate assessment of drug resistance patterns. To conclude, this research provided valuable insights into the TB transmission dynamics in Latvia, the genetic diversity of M. tuberculosis genotypes circulating in the country, and the distribution of drug-resistance-conferring variants among these genotypes. It also highlighted challenges in phenotypic and WGS-based DST, evaluated different WGS-based strategies for determining the causes of recurrent TB, and proposed an integrative approach for delineating TB transmission chains. These findings support the potential of integrating WGS into routine practice as part of a comprehensive local TB surveillance program. The Doctoral Thesis was developed Latvian Biomedical Research and Study Centre, Laboratory of Molecular Microbiology. Defence: at the public session of the Promotion Council of Basic Medicine on 28 May 2025 at 14.00 in the Hippocrates Lecture Theatre, 16 Dzirciema Street, Rīgas Stradiņš University and remotely via online platform Zoom. European Regional Development Fund project No 1.1.1.1/20/A/046 “Whole genome sequencing of tuberculosis for routine clinical use in Latvia: evidence-based design and concept evaluation” and European Union’s Recovery and Resilience Mechanism project No 5.2.1.1.i.0/2/24/I/CFLA/001 “Consolidation of the Latvian Institute of Organic Synthesis and the Latvian Biomedical Research and Study Centre”. 3.1 Basic Medicine; Sub-Sector – Clinical Pharmacy. Keywords: tuberculosis; Mycobacterium tuberculosis; whole-genome sequencing; genotyping; drug resistance; drug susceptibility testing; transmission; endogenous reactivation; exogenous reinfection.

Birmingham, United Kingdom, 2010-2014. To investigate predictors for clustering of tuberculosis (TB) cases and cluster size and to evaluate the impact of cluster investigation using social network data. Retrospective observational cohort study. Prioritised cases linked using 24-locus mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) were interviewed using a social network approach to find epidemiological links. Of 2055 TB cases notified, 56% could be typed. Clustering was associated with younger age, UK birth, Black Caribbean ethnicity, social risk factors, pulmonary TB and negative human immunodeficiency virus status. Only UK birth and presence of more than one social risk factor were associated with larger cluster size, while drug resistance was associated with smaller cluster size. Social network data from 139/431 clustered cases found new epidemiological links in 11/19 clusters with ⩾5 members (undirected median network density 0.09, interquartile range 0.05-0.4). Ninety-eight additional contacts were assessed, with one case of active TB and 24 with latent tuberculous infection diagnosed. A social network approach increased knowledge of likely transmission events, but few additional TB cases were diagnosed. Obtaining social network data for all typed and untyped TB cases may improve contact tracing and reduce unexpected transmission detected from molecular data.

Risk factors associated with large clusters of tuberculosis patients determined by whole-genome sequencing in a high-tuberculosis-burden country