IDEAS home Printed from https://ideas.repec.org/p/zbw/i4rdps/222.html

A Comment on "Calvert et al. (2023): Changes in Preterm Birth and Stillbirth during COVID-19 Lockdowns in 26 Countries"

Author

Listed:
  • Dyroff, Philipp
  • Miller, Robert

Abstract

Calvert et al. (2023) meta-analyzed effect estimates from interrupted time series (ITS) analyses of changes in preterm birth- and stillbirth rates following the first four months lockdown in various countries. Evidence for small relative reductions was reported regarding preterm birth rates in high income countries, which was not countered by an increase in stillbirth rates. This comment attempts to (recreate) reproduce these findings in population-based samples. To check robustness, effect size estimates were additionally obtained after accounting for for serial autocorrelation. Due to restricted data access, the data for reproduction were not identical to those analyzed by the original study, but were extracted from the provided time-series plots of birth rates using a Web Plot Digitizer (Rohatgi, 2024). Our results show very similar effect size estimates with ITS analysis conducted using Poisson regression and their subsequent random-effects meta-analysis. Despite of the methodological constraints arising from a lack of data openness, our reproductive analyses provide reasonable indications for the robustness of "Changes in preterm birth rate and stillbirth during COVID-19 lockdowns in 26 countries" by Calvert et al. (2023).

Suggested Citation

  • Dyroff, Philipp & Miller, Robert, 2025. "A Comment on "Calvert et al. (2023): Changes in Preterm Birth and Stillbirth during COVID-19 Lockdowns in 26 Countries"," I4R Discussion Paper Series 222, The Institute for Replication (I4R).
    • Handle: RePEc:zbw:i4rdps:222
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/315049/1/I4R-DP222.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 13, pages 659-711, Elsevier.
    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. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    2. Pablo Pincheira B., 2014. "Predictive Evaluation of Sectoral and Total Employment Based on Entrepreneurial Confidence Indicators," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 17(1), pages 66-87, April.
    3. Nicolás Chanut & Mario Marcel C. & Carlos A. Medel V., 2019. "Can economic perception surveys improve macroeconomic forecasting in Chile?," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 034-097, December.
    4. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    5. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    6. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    7. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    8. Pablo M. Pincheira & Carlos A. Medel, 2015. "Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 2-29, January.
    9. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545, arXiv.org, revised Aug 2025.
    10. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    11. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.
    12. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 65-126.
    13. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.
    14. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, November.
    15. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    16. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    17. Pincheira, Pablo, 2013. "A Bunch of Models, a Bunch of Nulls and Inference about Predictive Ability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 26-43, October.
    18. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    19. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.
    20. Dzikowski, Daniel & Jentsch, Carsten, 2025. "Structural periodic vector autoregressions," Journal of Econometrics, Elsevier, vol. 252(PA).

    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:zbw:i4rdps:222. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.i4replication.org/ .

    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.