Dataset Information

A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

ABSTRACT:

Background

Reported tuberculosis (TB) incidence globally continues to be heavily influenced by expert opinion of case detection rates and ecological estimates of disease duration. Both approaches are recognised as having substantial variability and inaccuracy, leading to uncertainty in true TB incidence and other such derived statistics.

Methods

We developed Bayesian binomial mixture geospatial models to estimate TB incidence and case detection rate (CDR) in Ethiopia. In these models the underlying true incidence was formulated as a partially observed Markovian process following a mixed Poisson distribution and the detected (observed) TB cases as a binomial distribution, conditional on CDR and true incidence. The models use notification data from multiple areas over several years and account for the existence of undetected TB cases and variability in true underlying incidence and CDR. Deviance information criteria (DIC) were used to select the best performing model.

Results

A geospatial model was the best fitting approach. This model estimated that TB incidence in Sheka Zone increased from 198 (95% Credible Interval (CrI) 187, 233) per 100,000 population in 2010 to 232 (95% CrI 212, 253) per 100,000 population in 2014. The model revealed a wide discrepancy between the estimated incidence rate and notification rate, with the estimated incidence ranging from 1.4 (in 2014) to 1.7 (in 2010) times the notification rate (CDR of 71% and 60% respectively). Population density and TB incidence in neighbouring locations (spatial lag) predicted the underlying TB incidence, while health facility availability predicted higher CDR.

Conclusion

Our model estimated trends in underlying TB incidence while accounting for undetected cases and revealed significant discrepancies between incidence and notification rates in rural Ethiopia. This approach provides an alternative approach to estimating incidence, entirely independent of the methods involved in current estimates and is feasible to perform from routinely collected surveillance data.

SUBMITTER: Shaweno D

PROVIDER: S-EPMC5625624 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

Shaweno Debebe D Trauer James M JM Denholm Justin T JT McBryde Emma S ES

BMC infectious diseases 20171002 1

<h4>Background</h4>Reported tuberculosis (TB) incidence globally continues to be heavily influenced by expert opinion of case detection rates and ecological estimates of disease duration. Both approaches are recognised as having substantial variability and inaccuracy, leading to uncertainty in true TB incidence and other such derived statistics.<h4>Methods</h4>We developed Bayesian binomial mixture geospatial models to estimate TB incidence and case detection rate (CDR) in Ethiopia. In these mod ...[more]

PMID: 28969585

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Project description:IntroductionTuberculosis (TB), a major public health concern in Ethiopia, is distributed heterogeneously across the country. Mapping TB prevalence at national and subnational levels can provide information for designing and implementing control strategies. Data for spatial analysis can be obtained through systematic review of the literature, and spatial prediction can be done by meta-analysis of published data (geospatial meta-analysis). Geospatial meta-analysis can increase the power of spatial analytic models by making use of all available data. It can also provide a means for spatial prediction where new survey data in a given area are sparse or not available. In this report, we present a protocol for a geospatial meta-analysis to investigate the spatial patterns of TB prevalence in Ethiopia.Methods and analysisTo conduct this study, a national TB prevalence survey, supplemented with data from a systematic review of published reports, will be used as the source of TB prevalence data. Systematic searching will be conducted in PubMed, Scopus and Web of Science for studies published up to 15 April 2020 to identify all potential publications reporting TB prevalence in Ethiopia. Data for covariates for multivariable analysis will be obtained from different, readily available sources. Extracted TB survey and covariate data will be georeferenced to specific locations or the centroids of small administrative areas. A binomial logistic regression model will be fitted to TB prevalence data using both fixed covariate effects and random geostatistical effects based on the approach of model-based geostatistics. Markov Chain Monte Carlo simulation will be conducted to obtained posterior parameter estimations, including spatially predicted prevalence.Ethics and disseminationEthical approval will not be required for this study as it will be based on deidentified, aggregate published data. The final report of this review will be disseminated through publication in a peer-reviewed scientific journal and will also be presented at relevant conferences.

Project description:IntroductionRotavirus causes substantial morbidity and mortality every year, particularly among under-five children. Despite Rotavirus immunization preventing severe diarrheal disease in children, the vaccination coverage remains inadequate in many African countries including Ethiopia. Measuring rotavirus immunization coverage in a lower geographic area can provide information for designing and implementing a targeted immunization campaign. This study aimed to investigate the spatial distributions of rotavirus immunization coverage in Ethiopia.MethodsRotavirus immunization coverage data were obtained from the recent Ethiopian Demographic and Health Survey (EDHS 2019). Covariate data were assembled from different publicly available sources. A Bayesian geostatistics model was used to estimate the national rotavirus immunization coverage at a pixel level and to identify factors associated with the spatial clustering of immunization coverages.ResultThe national rotavirus immunization coverage in Ethiopia was 52.3% (95% CI: 50.3, 54.3). The immunization coverage varied substantially at the sub-national level with spatial clustering of low immunization coverage observed in the Eastern, Southeastern, and Northeastern parts of Ethiopia. The spatial clustering of the rotavirus immunization coverage was positively associated with altitude of the area [mean regression coefficient (β): 0.38; 95% credible interval (95% CrI): 0.18, 0.58] and negatively associated with travel time to the nearest cities in minutes [mean regression coefficient (β): - 0.45; 95% credible interval (95% CrI): (- 0.73, - 0.18)] and distance to the nearest health facilities [mean regression coefficient (β): - 0.71908; 95% credible interval (95% CrI): (- 1.07, - 0.37)].ConclusionsThis study found that the rotavirus immunization coverage varied substantially at sub-national and local levels in Ethiopia. The spatial clustering of rotavirus immunization coverage was associated with geographic and healthcare access factors such as altitude, distance to health facilities, and travel time to the nearest cities. The immunization program should be strengthened in Ethiopia, especially in the Eastern, Southeastern, and Northeastern parts of the Country. Outreach immunization services should be also implemented in areas with low coverage.

Project description:BackgroundIn 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia.MethodsWe used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015.ResultsPodoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

Dataset Information

A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

Background

Methods

Results

Conclusion

Publications

A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

Similar Datasets

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