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Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand

BACKGROUND: Data containing notified cases of disease are often compromised by incomplete or partial information related to individual cases. In an effort to enhance the value of information from enteric disease notifications in New Zealand, this study explored the use of Bayesian and Multiple Imput...

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Autores principales: Amene, E., Horn, B., Pirie, R., Lake, R., Döpfer, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011939/
https://www.ncbi.nlm.nih.gov/pubmed/27600394
http://dx.doi.org/10.1186/s12879-016-1784-8
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author Amene, E.
Horn, B.
Pirie, R.
Lake, R.
Döpfer, D.
author_facet Amene, E.
Horn, B.
Pirie, R.
Lake, R.
Döpfer, D.
author_sort Amene, E.
collection PubMed
description BACKGROUND: Data containing notified cases of disease are often compromised by incomplete or partial information related to individual cases. In an effort to enhance the value of information from enteric disease notifications in New Zealand, this study explored the use of Bayesian and Multiple Imputation (MI) models to fill risk factor data gaps. As a test case, overseas travel as a risk factor for infection with campylobacteriosis has been examined. METHODS: Two methods, namely Bayesian Specification (BAS) and Multiple Imputation (MI), were compared regarding predictive performance for various levels of artificially induced missingness of overseas travel status in campylobacteriosis notification data. Predictive performance of the models was assessed through the Brier Score, the Area Under the ROC Curve and the Percent Bias of regression coefficients. Finally, the best model was selected and applied to predict missing overseas travel status of campylobacteriosis notifications. RESULTS: While no difference was observed in the predictive performance of the BAS and MI methods at a lower rate of missingness (<10 %), but the BAS approach performed better than MI at a higher rate of missingness (50 %, 65 %, 80 %). The estimated proportion (95 % Credibility Intervals) of travel related cases was greatest in highly urban District Health Boards (DHBs) in Counties Manukau, Auckland and Waitemata, at 0.37 (0.12, 0.57), 0.33 (0.13, 0.55) and 0.28 (0.10, 0.49), whereas the lowest proportion was estimated for more rural West Coast, Northland and Tairawhiti DHBs at 0.02 (0.01, 0.05), 0.03 (0.01, 0.08) and 0.04 (0.01, 0.06), respectively. The national rate of travel related campylobacteriosis cases was estimated at 0.16 (0.02, 0.48). CONCLUSION: The use of BAS offers a flexible approach to data augmentation particularly when the missing rate is very high and when the Missing At Random (MAR) assumption holds. High rates of travel associated cases in urban regions of New Zealand predicted by this approach are plausible given the high rate of travel in these regions, including destinations with higher risk of infection. The added advantage of using a Bayesian approach is that the model’s prediction can be improved whenever new information becomes available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1784-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-50119392016-09-15 Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand Amene, E. Horn, B. Pirie, R. Lake, R. Döpfer, D. BMC Infect Dis Research Article BACKGROUND: Data containing notified cases of disease are often compromised by incomplete or partial information related to individual cases. In an effort to enhance the value of information from enteric disease notifications in New Zealand, this study explored the use of Bayesian and Multiple Imputation (MI) models to fill risk factor data gaps. As a test case, overseas travel as a risk factor for infection with campylobacteriosis has been examined. METHODS: Two methods, namely Bayesian Specification (BAS) and Multiple Imputation (MI), were compared regarding predictive performance for various levels of artificially induced missingness of overseas travel status in campylobacteriosis notification data. Predictive performance of the models was assessed through the Brier Score, the Area Under the ROC Curve and the Percent Bias of regression coefficients. Finally, the best model was selected and applied to predict missing overseas travel status of campylobacteriosis notifications. RESULTS: While no difference was observed in the predictive performance of the BAS and MI methods at a lower rate of missingness (<10 %), but the BAS approach performed better than MI at a higher rate of missingness (50 %, 65 %, 80 %). The estimated proportion (95 % Credibility Intervals) of travel related cases was greatest in highly urban District Health Boards (DHBs) in Counties Manukau, Auckland and Waitemata, at 0.37 (0.12, 0.57), 0.33 (0.13, 0.55) and 0.28 (0.10, 0.49), whereas the lowest proportion was estimated for more rural West Coast, Northland and Tairawhiti DHBs at 0.02 (0.01, 0.05), 0.03 (0.01, 0.08) and 0.04 (0.01, 0.06), respectively. The national rate of travel related campylobacteriosis cases was estimated at 0.16 (0.02, 0.48). CONCLUSION: The use of BAS offers a flexible approach to data augmentation particularly when the missing rate is very high and when the Missing At Random (MAR) assumption holds. High rates of travel associated cases in urban regions of New Zealand predicted by this approach are plausible given the high rate of travel in these regions, including destinations with higher risk of infection. The added advantage of using a Bayesian approach is that the model’s prediction can be improved whenever new information becomes available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1784-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-06 /pmc/articles/PMC5011939/ /pubmed/27600394 http://dx.doi.org/10.1186/s12879-016-1784-8 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Amene, E.
Horn, B.
Pirie, R.
Lake, R.
Döpfer, D.
Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title_full Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title_fullStr Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title_full_unstemmed Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title_short Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand
title_sort filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in new zealand
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011939/
https://www.ncbi.nlm.nih.gov/pubmed/27600394
http://dx.doi.org/10.1186/s12879-016-1784-8
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