IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0209735.html
   My bibliography  Save this article

Are we validly assessing major depression disorder risk and associated factors among mothers of young children? A cross-sectional study involving home visitation programs

Author

Listed:
  • Arthur H Owora
  • Hélène Carabin
  • Tabitha Garwe
  • Michael P Anderson

Abstract

Failure to account for misclassification error accruing from imperfect case-finding instruments can produce biased estimates of suspected major depression disorder (MDD) risk factor associations. The objective of this study was to estimate the impact of misclassification error on the magnitude of measures of association between suspected risk factors and MDD assessed using the Center of Epidemiological Studies on Depression—Short Form during the prenatal and postnatal periods. Baseline data were collected from 520 mothers participating in two home visitation studies in Oklahoma City between 2010 and 2014. A Bayesian binomial latent class model was used to compare the prevalence proportion ratio (PPR) between suspected risk factors and MDD with and without adjustment for misclassification error and confounding by period of MDD symptom on-set. Adjustment for misclassification error and confounding by period of MDD on-set (prenatal vs postnatal) showed that the association between suspected risk factors and MDD is underestimated (-) and overestimated (+) differentially in different source populations of low-income mothers. The median bias in the magnitude of PPR estimates ranged between -.47 (95% Bayesian Credible Intervals [BCI]: -10.67, 1.90) for intimate partner violence to +.06 (95%BCI: -0.37, 0.47) for race/ethnicity among native-born US residents. Among recent Hispanic immigrants, bias ranged from -.77 (95%BCI: -15.31, 0.96) for history of childhood maltreatment to +.10 (95%BCI: -0.17, 0.39) for adequacy of family resources. Overall, the extent of bias on measures of association between maternal MDD and suspected risk factors is considerable without adjustment for misclassification error and is even higher for confounding by period of MDD assessment. Consideration of these biases in MDD prevention research is warranted.

Suggested Citation

  • Arthur H Owora & Hélène Carabin & Tabitha Garwe & Michael P Anderson, 2019. "Are we validly assessing major depression disorder risk and associated factors among mothers of young children? A cross-sectional study involving home visitation programs," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-18, January.
  • Handle: RePEc:plo:pone00:0209735
    DOI: 10.1371/journal.pone.0209735
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0209735
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209735&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0209735?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    3. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    4. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    5. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    6. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    7. Ralf van der Lans & Bram Van den Bergh & Evelien Dieleman, 2014. "Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit," Marketing Science, INFORMS, vol. 33(4), pages 551-566, July.
    8. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    9. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    10. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.
    11. Ockerman, Daniel H. & Goldsman, David, 1999. "Student t-tests and compound tests to detect transients in simulated time series," European Journal of Operational Research, Elsevier, vol. 116(3), pages 681-691, August.
    12. Hong, Yi & Jin, Xing, 2022. "Pricing of variance swap rates and investment decisions of variance swaps: Evidence from a three-factor model," European Journal of Operational Research, Elsevier, vol. 303(2), pages 975-985.
    13. Shofiqul Islam & Sonia Anand & Jemila Hamid & Lehana Thabane & Joseph Beyene, 2020. "A copula-based method of classifying individuals into binary disease categories using dependent biomarkers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 871-897, December.
    14. da-Silva, C.Q. & Gomes, A.E., 2011. "Bayesian inference for an item response model for modeling test anxiety," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3165-3182, December.
    15. Marie Albertine Djuikom, 2018. "Incentives to labour migration and agricultural productivity: The Bayesian perspective," WIDER Working Paper Series wp-2018-45, World Institute for Development Economic Research (UNU-WIDER).
    16. Yu, Jun, 2005. "On leverage in a stochastic volatility model," Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August.
    17. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    18. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2010. "RBCs AND DSGEs: THE COMPUTATIONAL APPROACH TO BUSINESS CYCLE THEORY AND EVIDENCE," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 113-136, February.
    19. Girondot, Marc & Dejoie, Ambre & Charpentier, Michel, 2024. "The mystery of bimodal nesting seasons in marine turtles," Ecological Modelling, Elsevier, vol. 490(C).
    20. David J. Vanness & W. Ray Kim, 2002. "Bayesian estimation, simulation and uncertainty analysis: the cost‐effectiveness of ganciclovir prophylaxis in liver transplantation," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 551-566, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0209735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.