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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18960

Title: Data-driven methods for imputing national-level incidence in global burden of disease studies
Authors: McDonald, Scott A.
Devleesschauwer, Brecht
Speybroeck, Niko
HENS, Niel
Praet, Nicolas
Torgerson, Paul R.
Havelaar, Arie H.
Wu, Felicia
Tremblay, Marlene
Amene, Ermias W.
Doepfer, Doerte
Issue Date: 2015
Publisher: WORLD HEALTH ORGANIZATION
Citation: BULLETIN OF THE WORLD HEALTH ORGANIZATION, 93 (4), p. 228-236
Abstract: Objective To develop transparent and reproducible methods for imputing missing data on disease incidence at national-level for the year 2005. Methods We compared several models for imputing missing country-level incidence rates for two foodborne diseases congenital toxoplasmosis and aflatoxin-related hepatocellular carcinoma. Missing values were assumed to be missing at random. Predictor variables were selected using least absolute shrinkage and selection operator regression. We compared the predictive performance of naive extrapolation approaches and Bayesian random and mixed-effects regression models. Leave-one-out cross-validation was used to evaluate model accuracy. Findings The predictive accuracy of the Bayesian mixed-effects models was significantly better than that of the naive extrapolation method for one of the two disease models. However, Bayesian mixed-effects models produced wider prediction intervals for both data sets. Conclusion Several approaches are available for imputing missing data at national level. Strengths of a hierarchical regression approach for this type of task are the ability to derive estimates from other similar countries, transparency, computational efficiency and ease of interpretation. The inclusion of informative covariates may improve model performance, but results should be appraised carefully.
Notes: [McDonald, Scott A.; Havelaar, Arie H.] Natl Inst Publ Hlth & Environm RIVM, Ctr Infect Dis Control, Bilthoven, Netherlands. [Devleesschauwer, Brecht] Univ Ghent, Fac Vet Med, Dept Virol Parasitol & Immunol, B-9820 Merelbeke, Belgium. [Speybroeck, Niko] Catholic Univ Louvain, Inst Hlth & Soc IRSS, B-1200 Brussels, Belgium. [Hens, Niel] Hasselt Univ, Ctr Stat, Diepenbeek, Belgium. [Praet, Nicolas] Inst Trop Med, Dept Biomed Sci, B-2000 Antwerp, Belgium. [Torgerson, Paul R.] Univ Zurich, Sect Vet Epidemiol, Zurich, Switzerland. [Wu, Felicia] Michigan State Univ, Dept Food Sci & Human Nutr, E Lansing, MI 48824 USA. [Tremblay, Marlene; Amene, Ermias W.; Doepfer, Doerte] UW Madison, Sch Vet Med, Food Anim Prod Med Sect, Madison, WI USA.
URI: http://hdl.handle.net/1942/18960
DOI: 10.2471/BLT.14.139972
ISI #: 000353934500014
ISSN: 0042-9686
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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