Worldwide Spatial and Temporal Distribution of Tuberculosis (TB)

Woldeyohannes, Solomon Meseret; Abera, Sisay Yifru

doi:10.4172/2155-6113.1000452

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…Therefore, TB interventions may not be able to use the “ one size fits all ” approach across different districts. Districts with high disease rates as a result of the social determinants of disease will require more focussed attention than areas with a low risk [ 21 ]. Areas where hotspots and clusters of TB cases were detected may be used as priority areas for targeted control to achieve high impact [ 44 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although these studies have provided valuable insights in understanding risk factors of TB occurrence, there is limited application of spatial analysis particularly in detecting hotspots and cold spots of TB especially in Zimbabwe. This is despite the fact that GIS-based spatial analysis combined with spatial statistics are indispensable tools for supporting surveillance and control of most diseases in the world [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

et al. 2021

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Introduction Accurate 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. Methods In 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. Results Results 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 recommendations Results 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).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

et al. 2021

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“…Tuberculosis (TB) is an infectious disease caused by bacteria of the Mycobacterium tuberculosis complex transmitted via airborne spread of droplet nuclei produced by patients with infectious pulmonary or laryngeal TB (Sia and Wieland, 2011). The worldwide distribution of the disease, which varies by time period and place (Tiwari et al, 2006;Touray et al, 2010;Li et al, 2013;Nana et al, 2014;Dangisso et al, 2015b;Woldeyohannes and Abera, 2015), is partly determined by ecological level factors such as socio-economic (Leung et al, 2004;Nana et al, 2014), demographic (Feske et al, 2011), climatic (Li et al, 2014) and other environmental factors (Sun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal transmission and socio-climatic factors related to paediatric tuberculosis in north-western Ethiopia

Alene¹,

Viney²,

McBryde

et al. 2017

Geospat Health

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The burden of tuberculosis (TB) in children reflects continuing and recent transmission within a population. This study aimed to identify spatiotemporal and socio-climatic factors associated with paediatric TB in north-western Ethiopia. Multivariate Poisson regression models were computed using a Bayesian framework. Estimates of parameters were generated using Markov chain Monte Carlo simulation. A total of 2,240 children aged under 15 years diagnosed with TB during the years 2013- 2016 were included in the analysis. The annual TB incidence rates were 44 and 28 per 100,000 children, for children aged under 15 and 5 years, respectively. Spatial clustering of TB was observed in the border area of north-western Ethiopia. The spatio-temporal transmission of childhood TB was found to be associated with district level socio-climatic factors such as urbanisation [relative risk (RR): 1.8; 95% credible interval (CrI): 1.2, 2.6], lower educational status (RR: 1.5; 95% CrI: 1.0, 2.1), a high percentage of internal migration (RR: 1.3; 95% CrI: 1.0, 1.6), high temperature (RR: 1.3; 95% CrI: 1.0, 1.7) and high rainfall (RR: 1.5; 95% CrI: 1.1, 2.0). We conclude that interventions targeting hotspot districts with a high proportion of childhood TB are important to reduce TB transmission in northwest Ethiopia.

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“…As a highly successful pathogen, MTBC exhibits remarkable adaptation to its human host, in uencing between-host selection of the bacterial population and population-level epidemiologic dynamics (6), (7). This in uence is primarily determined by spatial proximity and the likelihood of contact between hosts, with transmission probability decreasing as the distance between hosts increases (8), (9), (10).…”

Section: Introductionmentioning

confidence: 99%

Mapping the threads: Geospatial Exploration of Shared Modifiable Tuberculosis Disease Risks in a clinic-based population

Asare-Baah,

Luong,

Afriyie-Mensah

et al. 2024

Preprint

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Background In emerging economies experiencing rapid sociodemographic transitions and historically high tuberculosis (TB) prevalence, effective TB control requires acknowledging the evolving socio-behavioral characteristics of diverse patient populations shaping community-level TB risk. This study aimed to explore the spatial distribution and clustering of shared modifiable clinical and social risk factors for TB in a clinic-based population in Accra, Ghana.Methods We prospectively enrolled new and previously treated TB patients between June 2022 and July 2023. At diagnosis, patients provided informed consent to collect their residential coordinates and completed a questionnaire assessing their demographic and modifiable clinical and social risks for TB. We used geospatial scan statistics to describe the spatial distribution of cases and PERMANOVA to examine the correlation between spatial proximity and shared socio-behavioral risks, with a 1.5 square kilometer threshold defining significant residential proximity.Results The study population (N = 150) was predominantly male (68.0%) and of working age (80.0% aged 25–64 years), with half the sample engaged in unskilled labor (51.3%). Approximately one-third reported heavy alcohol (36.0%) and recreational drug use (26.7%) in the past year. Fifteen percent were HIV-positive, of whom more than 80% were diagnosed at the time of TB diagnosis. Local Moran's I statistics revealed spatial clusters of TB cases in separate sections of the study area. Unskilled labor, recreational drug use, and a history of cough in patients’ social contacts were significantly associated with residential proximity, explaining 1.26% of the variance in our model (F = 1.89, R^2 = 1.3%, p = 0.004).Conclusions Shared modifiable risks, including unskilled labor, recreational drug use, and close contact with TB, exhibited spatial clustering, suggesting their potential to enhance TB disease progression and transmission in this setting. Targeted interventions addressing these socio-behavioral risks within identified hotspots may improve TB control efforts.

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Worldwide Spatial and Temporal Distribution of Tuberculosis (TB)

Cited by 8 publications

References 20 publications

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

Spatiotemporal transmission and socio-climatic factors related to paediatric tuberculosis in north-western Ethiopia

Mapping the threads: Geospatial Exploration of Shared Modifiable Tuberculosis Disease Risks in a clinic-based population

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