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Otiende VA, Achia TN, Mwambi HG. Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya. PLoS One 2020; 15:e0234456. [PMID: 32614847 PMCID: PMC7332062 DOI: 10.1371/journal.pone.0234456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 05/27/2020] [Indexed: 11/25/2022] Open
Abstract
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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Affiliation(s)
- Verrah A. Otiende
- Department of Mathematical Sciences, Pan African University Institute of Basic Sciences Technology and Innovation, Nairobi, Kenya
- * E-mail: ,
| | - Thomas N. Achia
- School of Mathematics, Statistics & Computer Science, University of KwaZulu Natal, Pietermaritzburg, South Africa
| | - Henry G. Mwambi
- School of Mathematics, Statistics & Computer Science, University of KwaZulu Natal, Pietermaritzburg, South Africa
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Otiende V, Achia T, Mwambi H. Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya. BMC Infect Dis 2019; 19:902. [PMID: 31660883 PMCID: PMC6819548 DOI: 10.1186/s12879-019-4540-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 10/09/2019] [Indexed: 02/01/2023] Open
Abstract
Background Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels. Methods We analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012–2018). Using spatiotemporal poisson regression models within the Integrated Nested Laplace Approach (INLA) paradygm, we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model. Results Of the total 608,312 TB case notifications, 194,129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters (pD) comparisons, the spatiotemporal model allowing space-time interaction was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR) = 5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio (RR = 5.81e−04, Cr. I = (1.06e−04, 3.18e−03). The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years. Conclusions We identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients.
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Affiliation(s)
- Verrah Otiende
- Department of Mathematical Sciences, Pan African University Institute of Basic Sciences Technology and Innovation, Nairobi, Kenya.
| | - Thomas Achia
- School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Henry Mwambi
- School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
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Behdenna A, Lembo T, Calatayud O, Cleaveland S, Halliday JEB, Packer C, Lankester F, Hampson K, Craft ME, Czupryna A, Dobson AP, Dubovi EJ, Ernest E, Fyumagwa R, Hopcraft JGC, Mentzel C, Mzimbiri I, Sutton D, Willett B, Haydon DT, Viana M. Transmission ecology of canine parvovirus in a multi-host, multi-pathogen system. Proc Biol Sci 2019; 286:20182772. [PMID: 30914008 PMCID: PMC6452066 DOI: 10.1098/rspb.2018.2772] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/27/2019] [Indexed: 12/25/2022] Open
Abstract
Understanding multi-host pathogen maintenance and transmission dynamics is critical for disease control. However, transmission dynamics remain enigmatic largely because they are difficult to observe directly, particularly in wildlife. Here, we investigate the transmission dynamics of canine parvovirus (CPV) using state-space modelling of 20 years of CPV serology data from domestic dogs and African lions in the Serengeti ecosystem. We show that, although vaccination reduces the probability of infection in dogs, and despite indirect enhancement of population seropositivity as a result of vaccine shedding, the vaccination coverage achieved has been insufficient to prevent CPV from becoming widespread. CPV is maintained by the dog population and has become endemic with approximately 3.5-year cycles and prevalence reaching approximately 80%. While the estimated prevalence in lions is lower, peaks of infection consistently follow those in dogs. Dogs exposed to CPV are also more likely to become infected with a second multi-host pathogen, canine distemper virus. However, vaccination can weaken this coupling, raising questions about the value of monovalent versus polyvalent vaccines against these two pathogens. Our findings highlight the need to consider both pathogen- and host-level community interactions when seeking to understand the dynamics of multi-host pathogens and their implications for conservation, disease surveillance and control programmes.
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Affiliation(s)
- Abdelkader Behdenna
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Tiziana Lembo
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | | | - Sarah Cleaveland
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Jo E. B. Halliday
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Craig Packer
- Ecology Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA
| | - Felix Lankester
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA
| | - Katie Hampson
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Meggan E. Craft
- Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN 55108, USA
| | - Anna Czupryna
- Lincoln Park Zoo, Chicago, IL 60614, USA
- Department of Ecology and Evolution, University of Illinois, Chicago, IL 60607, USA
| | - Andrew P. Dobson
- Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Edward J. Dubovi
- Animal Health Diagnostic Center, College of Veterinary Medicine, Cornell University, Ithaca, NY 14851, USA
| | - Eblate Ernest
- Tanzania Wildlife Research Institute, Arusha, Tanzania
| | - Robert Fyumagwa
- Conservation Areas and Species Diversity Programme, South Africa Country Office, International Union for the Conservation of Nature, Pretoria, South Africa
| | - J. Grant C. Hopcraft
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Christine Mentzel
- Conservation Areas and Species Diversity Programme, South Africa Country Office, International Union for the Conservation of Nature, Pretoria, South Africa
| | | | - David Sutton
- MSD Animal Health, Walton Manor, Walton, Milton Keynes MK7 7AJ, UK
| | - Brian Willett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G6 1QH, UK
| | - Daniel T. Haydon
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Mafalda Viana
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
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Houben RMGJ, Dowdy DW, Vassall A, Cohen T, Nicol MP, Granich RM, Shea JE, Eckhoff P, Dye C, Kimerling ME, White RG. How can mathematical models advance tuberculosis control in high HIV prevalence settings? Int J Tuberc Lung Dis 2015; 18:509-14. [PMID: 24903784 PMCID: PMC4436821 DOI: 10.5588/ijtld.13.0773] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Existing approaches to tuberculosis (TB) control have been no more than partially successful in areas with high human immunodeficiency virus (HIV) prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing those strategies that are most likely to bring public health and economic benefits. In this paper, we present an overview of past and recent contributions of TB modelling in this key area, and suggest a way forward through a modelling research agenda that supports a more effective response to the TB-HIV epidemic, based on expert discussions at a meeting convened by the TB Modelling and Analysis Consortium. The research agenda identified high-priority areas for future modelling efforts, including 1) the difficult diagnosis and high mortality of TB-HIV; 2) the high risk of disease progression; 3) TB health systems in high HIV prevalence settings; 4) uncertainty in the natural progression of TB-HIV; and 5) combined interventions for TB-HIV. Efficient and rapid progress towards completion of this modelling agenda will require co-ordination between the modelling community and key stakeholders, including advocates, health policy makers, donors and national or regional finance officials. A continuing dialogue will ensure that new results are effectively communicated and new policy-relevant questions are addressed swiftly.
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Affiliation(s)
- R M G J Houben
- TB Modelling Group, TB Centre, and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
| | - D W Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - A Vassall
- Department of Global Health and Development, LSHTM, London, UK
| | - T Cohen
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - M P Nicol
- Division of Medical Microbiology and Institute of Infectious Diseases and Molecular Medicine, University of Cape Town and National Health Laboratory Service, South Africa
| | - R M Granich
- Joint United Nations Programme on HIV/AIDS, World Health Organization (WHO), Geneva, Switzerland
| | - J E Shea
- Oxford-Emergent Tuberculosis Consortium, Wokingham, UK
| | - P Eckhoff
- Intellectual Ventures Laboratory, Bellevue, Washington, USA
| | - C Dye
- HIV, TB Malaria and Neglected Tropical Diseases Cluster, WHO, Geneva, Switzerland
| | - M E Kimerling
- Bill and Melinda Gates Foundation, Seattle, Washington, USA
| | - R G White
- TB Modelling Group, TB Centre, and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
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Impact of isoniazid preventive therapy for HIV-infected adults in Rio de Janeiro, Brazil: an epidemiological model. J Acquir Immune Defic Syndr 2014; 66:552-8. [PMID: 24853308 DOI: 10.1097/qai.0000000000000219] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND The potential epidemiological impact of isoniazid preventive therapy (IPT), delivered at levels that could be feasibly scaled up among people living with HIV (PLHIV) in modern, moderate-burden settings, remains uncertain. METHODS We used routine surveillance and implementation data from a cluster-randomized trial of IPT among HIV-infected clinic patients with good access to antiretroviral therapy in Rio de Janeiro, Brazil, to populate a parsimonious transmission model of tuberculosis (TB)/HIV. We modeled IPT delivery as a constant process capturing a proportion of the eligible population every year. We projected feasible reductions in TB incidence and mortality in the general population and among PLHIV specifically at the end of 5 years after implementing an IPT program. RESULTS Data on time to IPT fit an exponential curve well, suggesting that IPT was delivered at a rate covering 20% (95% confidence interval: 16% to 24%) of the 2500 eligible individuals each year. By the end of year 5 after modeled program rollout, IPT had reduced TB incidence by 3.0% [95% uncertainty range (UR): 1.6% to 7.2%] in the general population and by 15.6% (95% UR: 15.5% to 36.5%) among PLHIV. Corresponding reductions in TB mortality were 4.0% (95% UR: 2.2% to 10.3%) and 14.3% (14.6% to 33.7%). Results were robust to wide variations in parameter values on sensitivity analysis. CONCLUSIONS TB screening and IPT delivery can substantially reduce TB incidence and mortality among PLHIV in urban, moderate-burden settings. In such settings, IPT can be an important component of a multi-faceted strategy to feasibly reduce the burden of TB in PLHIV.
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Monitoring linked epidemics: the case of tuberculosis and HIV. PLoS One 2010; 5:e8796. [PMID: 20098716 PMCID: PMC2808389 DOI: 10.1371/journal.pone.0008796] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2009] [Accepted: 12/28/2009] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The tight epidemiological coupling between HIV and its associated opportunistic infections leads to challenges and opportunities for disease surveillance. METHODOLOGY/PRINCIPAL FINDINGS We review efforts of WHO and collaborating agencies to track and fight the TB/HIV co-epidemic, and discuss modeling--via mathematical, statistical, and computational approaches--as a means to identify disease indicators designed to integrate data from linked diseases in order to characterize how co-epidemics change in time and space. We present R(TB/HIV), an index comparing changes in TB incidence relative to HIV prevalence, and use it to identify those sub-Saharan African countries with outlier TB/HIV dynamics. R(TB/HIV) can also be used to predict epidemiological trends, investigate the coherency of reported trends, and cross-check the anticipated impact of public health interventions. Identifying the cause(s) responsible for anomalous R(TB/HIV) values can reveal information crucial to the management of public health. CONCLUSIONS/SIGNIFICANCE We frame our suggestions for integrating and analyzing co-epidemic data within the context of global disease monitoring. Used routinely, joint disease indicators such as R(TB/HIV) could greatly enhance the monitoring and evaluation of public health programs.
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