Modelling spatio-temporal patterns of disease risk for data with misalignment and measurement errors: an application on measles and HIV prevalence data in Namibia select="/dri:document/dri:meta/dri:pageMeta/dri:metadata[@element='title']/node()"/>

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dc.contributor.author Ntirampeba, Dismas
dc.date.accessioned 2018-05-14T07:18:47Z
dc.date.available 2018-05-14T07:18:47Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/11070/2219
dc.description A dissertation submitted in fulfilment of the requirement for the Degree of Doctor of Philosophy in Science (Statistics) en_US
dc.description.abstract Disease mapping has important applications in public health because it enables the identification of areas which are at high risk of particular health problems. It helps visualising the spatial pattern of the disease distribution, which is of interest to the health sector as it enables the sector to plan, evaluate and redesign prevention and control strategies, and also make important policy decisions particularly for geographically targeted intervention in resource poor settings. Analyses of spatial disease patterns are generally based on data of a single disease and they are often fraught with challenges that include lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This thesis focused on the development and extension of statistical models with particular interest to dealing with misalignment, measurement errors and jointly modeling of data from multiple sources. The first objective was to estimate and map the risk of measles at a sub-region level (i.e. constituency level) in Namibia using data obtained at the regional level. Direct inferences at constituency level made on basis of the original level of aggregation may lead to an inferential problem known as a misalignment in the statistical literature. Using measles data from Namibia for the period 2005-2014, both multi-step and direct approaches were applied to correct the misalignment. The multi-step approach model provided a relatively better model. The second objective was to fit a spatio-temporal model while dealing with misalignment and measurement error, again applied to measles data aggregated at regional level over the period 2005 to 2014. Again this leads to a spatial misalignment problem if the purpose is to make decisions at constituency level. Moreover, data on risk factors of measles were not available each year between 2005 and 2014. Thus, assuming that covariates were constant through the study period would induce measurement errors which might have effects on the analysis results. The multi-step approach was further extended to include temporal effects and account for measurement errors. Consequently, spatio-temporal models, which included Bernardinelli and Knorr-Held approaches, and classical measurement error models were adopted. Comparison of the results obtained from the nave method (i.e. modelling that ignored errors in covariates) and those from the approach that accounts for measurement errors showed that the latter modelling approach performed better than the former. The study showed a spatio-temporal variation of the measles risk over the 2009-2014 period. The third objective of this study was to develop a joint spatial model for HIV prevalence, using two sources (i.e. 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15-49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS)), which would enable the estimation at any location of the constituency or district level while dealing with misalignment in data. The shared component modelling approach was adopted through the use of stochastic partial differential equations (SPDE). The bivariate modelling approach developed allowed to combine two data sources that are available at different spatial levels in a single model and it catered for a specification of different spatial processes through the link function. Findings revealed that health districts and constituencies in the northern part of Namibia were highly associated with HIV infection. Also, the study showed that the place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia. Finally, it was shown that the prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. In conclusion, results showed that the multi-step approach may be used to deal with misalignment. Moreover, introducing the error model proved to be a useful approach to correct for measurement errors in data and improve inferences in situations where mismeasured values in covariates are encountered instead of native analyses that ignore the presence of errors in measurements. Lastly, the thesis showed that the prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. en_US
dc.language.iso en en_US
dc.publisher University of Namibia en_US
dc.subject Data en_US
dc.subject HIV en_US
dc.subject Measles en_US
dc.subject.lcsh Medicine, Preventive
dc.subject.lcsh Health risk assessment
dc.subject.lcsh Self-examination, Medical
dc.subject.lcsh Preventive health services
dc.title Modelling spatio-temporal patterns of disease risk for data with misalignment and measurement errors: an application on measles and HIV prevalence data in Namibia en_US
dc.type Thesis en_US


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