IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i23p7133-d1292520.html
   My bibliography  Save this article

Long- and Short-Term Trends in Outpatient Attendance by Speciality in Japan: A Joinpoint Regression Analysis in the Context of the COVID-19 Pandemic

Author

Listed:
  • Asuka Takeda

    (Department of Health Crisis Management, National Institute of Public Health, Wako-shi, Saitama 3510197, Japan)

  • Yuichi Ando

    (Department of Health Promotion, National Institute of Public Health, Wako-shi, Saitama 3510197, Japan)

  • Jun Tomio

    (Department of Health Crisis Management, National Institute of Public Health, Wako-shi, Saitama 3510197, Japan)

Abstract

The COVID-19 pandemic resulted in a decline in outpatient attendance. Therefore, this study aimed to clarify long- and short-term clinic attendance trends by speciality in Japan between 2009 and 2021. A retrospective observational study of Japan’s claims between 2009 and 2021 was conducted using the Estimated Medical Expenses Database. The number of monthly outpatient claims in clinics was used as a proxy indicator for monthly outpatient attendance, and specialities were categorised into internal medicine, paediatrics, surgery, orthopaedics, dermatology, obstetrics and gynaecology, ophthalmology, otolaryngology, and dentistry. The annually summarised age-standardised proportions and the percentage of change were calculated. Joinpoint regression analysis was used to evaluate long-term secular trends. The data set included 4,975,464,894 outpatient claims. A long-term statistically significant decrease was observed in outpatient attendance in internal medicine, paediatrics, surgery, ophthalmology, and otolaryngology during the pandemic. From March 2020 to December 2021, which includes the COVID-19 pandemic period, outpatient attendance in paediatrics, surgery, and otolaryngology decreased in all months compared with that of the corresponding months in 2019. For some specialities, the impact of the pandemic was substantial, even in the context of long-term trends. Speciality-specific preparedness is required to ensure essential outpatient services in future public health emergencies.

Suggested Citation

  • Asuka Takeda & Yuichi Ando & Jun Tomio, 2023. "Long- and Short-Term Trends in Outpatient Attendance by Speciality in Japan: A Joinpoint Regression Analysis in the Context of the COVID-19 Pandemic," IJERPH, MDPI, vol. 20(23), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:23:p:7133-:d:1292520
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/23/7133/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/23/7133/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Callie L. Brown & Kimberly Montez & Jane Blakely Amati & Kristina Simeonsson & John D. Townsend & Colin J. Orr & Deepak Palakshappa, 2021. "Impact of COVID-19 on Pediatric Primary Care Visits at Four Academic Institutions in the Carolinas," IJERPH, MDPI, vol. 18(11), pages 1-9, May.
    2. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
    3. Georgina Pujolar & Aida Oliver-Anglès & Ingrid Vargas & María-Luisa Vázquez, 2022. "Changes in Access to Health Services during the COVID-19 Pandemic: A Scoping Review," IJERPH, MDPI, vol. 19(3), pages 1-31, February.
    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. Yana Melnykov & Marcus Perry, 2024. "On Robust Change Point Detection and Estimation in Multisubject Studies," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 827-879, August.
    2. Yann Guédon, 2013. "Exploring the latent segmentation space for the assessment of multiple change-point models," Computational Statistics, Springer, vol. 28(6), pages 2641-2678, December.
    3. Debi P Bal & Badri N Rath, 2019. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India - A Reassessment," Economics Bulletin, AccessEcon, vol. 39(1), pages 592-604.
    4. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    5. Kurozumi, Eiji & Tuvaandorj, Purevdorj, 2011. "Model selection criteria in multivariate models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 164(2), pages 218-238, October.
    6. Fryzlewicz, Piotr, 2020. "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection," LSE Research Online Documents on Economics 103430, London School of Economics and Political Science, LSE Library.
    7. Lee Jaeeun & Chen Jie, 2019. "A penalized regression approach for DNA copy number study using the sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(4), pages 1-14, August.
    8. Neil Kellard & Denise Osborn & Jerry Coakley & Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 741-762, September.
    9. Arnaud Dufays & Elysee Aristide Houndetoungan & Alain Coën, 2022. "Selective Linear Segmentation for Detecting Relevant Parameter Changes [Risks and Portfolio Decisions Involving Hedge Funds]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 762-805.
    10. Giacomo Pietro Vigezzi & Paola Bertuccio & Andrea Amerio & Cristina Bosetti & Davide Gori & Luca Cavalieri d’Oro & Licia Iacoviello & David Stuckler & Alberto Zucchi & Silvano Gallus & Anna Odone & Lo, 2022. "Older Adults’ Access to Care during the COVID-19 Pandemic: Results from the LOckdown and LifeSTyles (LOST) in Lombardia Project," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    11. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
    12. Lu Shaochuan, 2020. "Bayesian multiple changepoints detection for Markov jump processes," Computational Statistics, Springer, vol. 35(3), pages 1501-1523, September.
    13. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
    14. Harris, David & Kew, Hsein & Taylor, A.M. Robert, 2020. "Level shift estimation in the presence of non-stationary volatility with an application to the unit root testing problem," Journal of Econometrics, Elsevier, vol. 219(2), pages 354-388.
    15. Brennen T. Fagan & Marina I. Knight & Niall J. MacKay & A. Jamie Wood, 2020. "Change point analysis of historical battle deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 909-933, June.
    16. Guédon, Yann & Legave, Jean Michel, 2008. "Analyzing the time-course variation of apple and pear tree dates of flowering stages in the global warming context," Ecological Modelling, Elsevier, vol. 219(1), pages 189-199.
    17. Picard, F. & Lebarbier, E. & Budinskà, E. & Robin, S., 2011. "Joint segmentation of multivariate Gaussian processes using mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1160-1170, February.
    18. Brigida, Matt & Pratt, William R., 2017. "Fake news," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 564-573.
    19. Jaromír Antoch & Daniela Jarušková, 2013. "Testing for multiple change points," Computational Statistics, Springer, vol. 28(5), pages 2161-2183, October.
    20. Coilín ÓhAiseadha & Gerry A. Quinn & Ronan Connolly & Awwad Wilson & Michael Connolly & Willie Soon & Paul Hynds, 2023. "Unintended Consequences of COVID-19 Non-Pharmaceutical Interventions (NPIs) for Population Health and Health Inequalities," IJERPH, MDPI, vol. 20(7), pages 1-40, March.

    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:gam:jijerp:v:20:y:2023:i:23:p:7133-:d:1292520. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.