Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya

Otiende, Verrah A; Achia, Thomas; Mwambi, Henry

doi:10.1186/s12879-019-4540-z

Cited by 25 publications

(24 citation statements)

References 42 publications

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“…Studies conducted in Ethiopia have shown that TB/HIV co-infected individuals have a greater risk of psychosocial problems, low quality of life, and poorer physical health than HIV infected individuals without active TB [11–13]. The prevalence of TB/HIV co-infection has been found to vary widely in Ethiopia [14, 15] and other countries [16–18], partially because of differences in health care access and other socio-demographic factors such as wealth index and literacy rate.…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

et al. 2019

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BackgroundTuberculosis (TB) and human immunodeficiency virus (HIV) are the leading causes of infectious-disease-related deaths in Ethiopia, but little is known about their spatial distribution across the country. The aim of this study was to investigate the spatial patterns of TB and HIV co-infection in Ethiopia at the district level.MethodsWe conducted an ecological study using TB and HIV data reported from all regions of Ethiopia through the national Health Management Information System (HMIS), between June 2015 and June 2017. Spatial clustering was assessed using Moran’s I statistic and Getis-Ord statistic. Spatial binomial regression models were constructed separately for the prevalence of TB among people living with HIV and for the prevalence of HIV among TB patients, with and without spatial components using a Bayesian approach.ResultsA total of 1,830,880 HIV and 192,359 TB patients were included in the analysis. The prevalence of HIV among TB patients was 7.34%; hotspots were observed in districts located in Amhara, Afar, and Gambela regions, and cold spots were observed in Oromiya and Southern Nations, Nationalities, and People (SNNP) regions. The prevalence of TB among people living with HIV varied from 0.7% in Oromia region to 14.5% in Afar region. Hotspots of TB prevalence among people living with HIV were observed in districts located in Gambela, Afar, Somali, and Oromiya regions; whereas the cold spots were observed in districts located in Amhara and Tigray regions. The ecological-level factors associated with the prevalence of TB among people living with HIV were low wealth index (OR: 1.49; 95% CrI: 1.05, 2.05), low adult literacy rate (OR: 0.67; 95% CrI: 0.46, 0.94), and distance to an international border (OR: 0.61; 95% CrI: 0.40, 0.91). The factors associated with the prevalence of HIV among TB patients were poor health care access (OR: 0.76; 95% CrI: 0.59, 0.95), low wealth index (OR: 1.31; 95% CrI: 1.01, 1.67), and low adult literacy rate (OR: 1.37; 95% CrI: 1.03, 1.78).ConclusionOur study provides evidence for geographic clustering of TB/HIV co-infection in Ethiopia. Health care access, proximity to international borders, and demographic factors such as low wealth index and adult literacy were significantly associated with the prevalence of TB/HIV co-infection.

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Section: Introductionmentioning

confidence: 99%

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

et al. 2019

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“…17 Bayesian modeling framework combines the likelihood function for the data and the prior distributions for the parameters resulting in a distribution known as the posterior distribution. 15,[17][18][19] The posterior distribution is defined as P θjy ð Þ, (i.e. probability distribution of the parameters given that the data which is proportional to the product of the likelihood function) while the prior distribution is defined as:…”

Section: Hierarchical Space Modelmentioning

confidence: 99%

“…Hence, the purpose of this study was to model the spatiotemporal risk pattern of TB in Ghana, using Bayesian hierarchical and space-time models discussed in previous literature. [12][13][14][15][16]…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal characteristics of Tuberculosis in Ghana

2022

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Background: The number of Tuberculosis (TB) cases or deaths is declining, however, the rate of decline is not adequate to meet the World Health Organization's (WHO's) mitigation. TB remains a public health problem in Ghana with a significant economic and health burden on its citizens and health care system. Consequently, there is a need for further studies about the disease aimed at accelerating the rate of decline in cases. Methods: The spatio-temporal characteristics of TB in Ghana using Bayesian spatial and spatio-temporal regression models was analysed in this study. Data were obtained from Ghana National Tuberculosis Programme (NTP) for the 10 regions of Ghana, collected over a six-year period. The study also examines some baseline predictors of TB infections to ascertain their effects on the TB risk across the ten regions in Ghana. Results: Hot-spots of TB cases are observed in the Upper East, Upper West, Volta, Western, and Central regions and low risk in the Northern, Ashanti, Greater Accra, Brong Ahafo, Eastern and Western regions. The results indicated a clustering of risk between neighboring regions. TB cure rate, TB success rate, knowledge about TB, awareness that TB is airborne, HIV prevalence, percentage of literacy, and high income are important predictors of detection for this disease across the ten regions of Ghana. Conclusion: Most regions in Ghana have similar TB risks. A substantial reduction in TB cases requires measures that will increase detection, success and cure rates, awareness, knowledge about how this disease spreads as well adequate health facilities with easy access.

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“…Kenya suffers from the dual epidemics ranking 4 th and 15 th globally in the high disease burden for HIV and TB respectively [31][32][33]. In 2013, Kenya reported more than 35% of the notified TB to be HIV infected compared to the global 13%.…”

Section: Plos Onementioning

confidence: 99%

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

Otiende¹,

Achia

Mwambi

2020

PLoS ONE

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The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

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Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya

Cited by 25 publications

References 42 publications

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

Spatial patterns of tuberculosis and HIV co-infection in Ethiopia

Spatio-temporal characteristics of Tuberculosis in Ghana

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

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