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A hierarchical model for analyzing multisite individual‐level disease surveillance data from multiple systems

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Listed:
  • Yuzi Zhang
  • Howard H. Chang
  • Qu Cheng
  • Philip A. Collender
  • Ting Li
  • Jinge He
  • Justin V. Remais

Abstract

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost‐effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual‐level covariate‐dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two‐stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias‐corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

Suggested Citation

  • Yuzi Zhang & Howard H. Chang & Qu Cheng & Philip A. Collender & Ting Li & Jinge He & Justin V. Remais, 2023. "A hierarchical model for analyzing multisite individual‐level disease surveillance data from multiple systems," Biometrics, The International Biometric Society, vol. 79(2), pages 1507-1519, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1507-1519
    DOI: 10.1111/biom.13647
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Robert M. Dorazio & J. Andrew Royle, 2003. "Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals," Biometrics, The International Biometric Society, vol. 59(2), pages 351-364, June.
    3. Dean T. Jamison & Joel G. Breman & Anthony R. Measham & George Alleyne & Mariam Claeson & David B. Evans & Prabhat Jha & Ann Mills & Philip Musgrove, 2006. "Disease Control Priorities in Developing Countries, Second Edition," World Bank Publications - Books, The World Bank Group, number 7242.
    4. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
    5. A. Farcomeni, 2016. "A general class of recapture models based on the conditional capture probabilities," Biometrics, The International Biometric Society, vol. 72(1), pages 116-124, March.
    6. Fodé Tounkara & Louis‐Paul Rivest, 2015. "Mixture regression models for closed population capture–recapture data," Biometrics, The International Biometric Society, vol. 71(3), pages 721-730, September.
    7. Lönnroth, Knut & Jaramillo, Ernesto & Williams, Brian G. & Dye, Christopher & Raviglione, Mario, 2009. "Drivers of tuberculosis epidemics: The role of risk factors and social determinants," Social Science & Medicine, Elsevier, vol. 68(12), pages 2240-2246, June.
    8. Brent A. Coull & Alan Agresti, 1999. "The Use of Mixed Logit Models to Reflect Heterogeneity in Capture-Recapture Studies," Biometrics, The International Biometric Society, vol. 55(1), pages 294-301, March.
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