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

Alene, Kefyalew Addis; Viney, Kerri; McBryde, Emma S.; Clements, Archie C. A.

doi:10.4081/gh.2017.575

Cited by 17 publications

(25 citation statements)

References 54 publications

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“…For example, a number of studies used the age prevalence of tuberculin-skin test positivity to measure risks of infection from household and community exposure [ 30 , 31 ]. Previous studies have suggested that areas with high childhood tuberculosis case notification rates may correspond to areas of active transmission [ 32 – 34 ]; however, only one included covariates to account for potential non-transmission explanations of the spatial distribution of child cases [ 34 ]. Thus, our analysis is the first to provide molecular and epidemiological evidence to corroborate inferences of local tuberculosis transmission with attempts to control for unobserved, spatially heterogeneous, non-transmission factors that may explain the distribution of child cases (such as risk factors for progression of infection, migration of infected individuals into the area, and/or local diagnostic capacity).…”

Section: Discussionmentioning

confidence: 99%

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

Gunasekera

Zelner

Becerra

et al. 2020

BMC Med

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Background: Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission because the population distribution of covariates affecting susceptibility and disease progression may confound the relationship between tuberculosis incidence and transmission. Child cases of tuberculosis and other endemic infectious disease have been observed to provide a signal of their transmission intensity. We assessed whether local overrepresentation of child cases in tuberculosis notification data corresponds to areas where recent transmission events are concentrated. Methods: We visualized spatial clustering of children < 5 years old notified to Peru's National Tuberculosis Program from two districts of Lima, Peru, from 2005 to 2007 using a log-Gaussian Cox process to model the intensity of the point-referenced child cases. To identify where clustering of child cases was more extreme than expected by chance alone, we mapped all cases from the notification data onto a grid and used a hierarchical Bayesian spatial model to identify grid cells where the proportion of cases among children < 5 years old is greater than expected. Modeling the proportion of child cases allowed us to use the spatial distribution of adult cases to control for unobserved factors that may explain the spatial variability in the distribution of child cases. We compare where young children are overrepresented in case notification data to areas identified as transmission hotspots using molecular epidemiological methods during a prospective study of tuberculosis transmission conducted from 2009 to 2012 in the same setting. Results: Areas in which childhood tuberculosis cases are overrepresented align with areas of spatial concentration of transmission revealed by molecular epidemiologic methods.

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Section: Discussionmentioning

confidence: 99%

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

Gunasekera

Zelner

Becerra

et al. 2020

BMC Med

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“…Geographic variability exists for multiple health conditions in Ethiopia, particularly infectious diseases [8, 28–30]. We previously demonstrated that rates of paediatric TB and multidrug-resistant TB vary across districts in Ethiopia [8, 9]. However, to our knowledge, no previous published study has assessed spatial variability in TB treatment outcomes in Ethiopia.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial epidemiological studies have assessed the incidence rates of TB in some parts of Ethiopia [4–9]. However, to date, no spatial epidemiological analyses of TB treatment outcomes have been reported in Ethiopia.…”

Section: Introductionmentioning

confidence: 99%

Mapping tuberculosis treatment outcomes in Ethiopia

et al. 2019

Self Cite

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Background Tuberculosis (TB) is the leading cause of death from an infectious disease in Ethiopia, killing more than 30 thousand people every year. This study aimed to determine whether the rates of poor TB treatment outcome varied geographically across Ethiopia at district and zone levels and whether such variability was associated with socioeconomic, behavioural, health care access, or climatic conditions. Methods A geospatial analysis was conducted using national TB data reported to the health management information system (HMIS), for the period 2015–2017. The prevalence of poor TB treatment outcomes was calculated by dividing the sum of treatment failure, death and loss to follow-up by the total number of TB patients. Binomial logistic regression models were computed and a spatial analysis was performed using a Bayesian framework. Estimates of parameters were generated using Markov chain Monte Carlo (MCMC) simulation. Geographic clustering was assessed using the Getis-Ord Gi* statistic, and global and local Moran’s I statistics. Results A total of 223,244 TB patients were reported from 722 districts in Ethiopia during the study period. Of these, 63,556 (28.5%) were cured, 139,633 (62.4%) completed treatment, 6716 (3.0%) died, 1459 (0.7%) had treatment failure, and 12,200 (5.5%) were lost to follow-up. The overall prevalence of a poor TB treatment outcome was 9.0% (range, 1–58%). Hot-spots and clustering of poor TB treatment outcomes were detected in districts near the international borders in Afar, Gambelia, and Somali regions and cold spots were detected in Oromia and Amhara regions. Spatial clustering of poor TB treatment outcomes was positively associated with the proportion of the population with low wealth index (OR: 1.01; 95%CI: 1.0, 1.01), the proportion of the population with poor knowledge about TB (OR: 1.02; 95%CI: 1.01, 1.03), and higher annual mean temperature per degree Celsius (OR: 1.15; 95% CI: 1.08, 1.21). Conclusions This study showed significant spatial variation in poor TB treatment outcomes in Ethiopia that was related to underlying socioeconomic status, knowledge about TB, and climatic conditions. Clinical and public health interventions should be targeted in hot spot areas to reduce poor TB treatment outcomes and to achieve the national End-TB Strategy targets. Electronic supplementary material The online version of this article (10.1186/s12879-019-4099-8) contains supplementary material, which is available to authorized users.

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“…Transmission patterns via genotyping of notified cases and health-seeking behaviors through additional primary data collection as used in similar spatial analysis studies may help our understanding of the results. [91,92]…”

Section: Discussionmentioning

confidence: 99%

Optimizing community screening for tuberculosis: Spatial analysis of localized case finding from door-to-door screening for TB in an urban district of Ho Chi Minh City, Viet Nam

Vo¹,

Vu²,

Nguyen³

et al. 2018

PLoS ONE

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BackgroundTuberculosis (TB) is the deadliest infectious disease globally. Current case finding approaches may miss many people with TB or detect them too late.Data and methodsThis study was a retrospective, spatial analysis of routine TB surveillance and cadastral data in Go Vap district, Ho Chi Minh City. We geocoded TB notifications from 2011 to 2015 and calculated theoretical yields of simulated door-to-door screening in three concentric catchment areas (50m, 100m, 200m) and three notification window scenarios (one, two and four quarters) for each index case. We calculated average yields, compared them to published reference values and fit a GEE (Generalized Estimating Equation) linear regression model onto the data.ResultsThe sample included 3,046 TB patients. Adjusted theoretical yields in 50m, 100m and 200m catchment areas were 0.32% (95%CI: 0.27,0.37), 0.21% (95%CI: 0.14,0.29) and 0.17% (95%CI: 0.09,0.25), respectively, in the baseline notification window scenario. Theoretical yields in the 50m-catchment area for all notification window scenarios were significantly higher than a reference yield from literature. Yield was positively associated with treatment failure index cases (beta = 0.12, p = 0.001) and short-term inter-province migrants (beta = 0.06, p = 0.022), while greater distance to the DTU (beta = -0.02, p<0.001) was associated with lower yield.ConclusionsThis study is an example of inter-departmental collaboration and application of repurposed cadastral data to progress towards the end TB objectives. The results from Go Vap showed that the use of spatial analysis may be able to identify areas where targeted active case finding in Vietnam can help improve TB case detection.

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

Cited by 17 publications

References 54 publications

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

Mapping tuberculosis treatment outcomes in Ethiopia

Optimizing community screening for tuberculosis: Spatial analysis of localized case finding from door-to-door screening for TB in an urban district of Ho Chi Minh City, Viet Nam

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