Evidence map›Paper›PMID 38481745›Full record

ArticleJournal of general and family medicine2024

Treatment interruption in hypertensive patients during the COVID-19 pandemic: An interrupted time series analysis using prescription data in Okayama, Japan.

Naoko Nakamura, Toshiharu Mitsuhashi, Naomi Matsumoto, Shunsaku Hayase, Takashi Yorifuji

Abstract read
In one paragraph

Article in Journal of general and family medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Naoko NakamuraDepartment of Epidemiology Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Okayama Japan.ORCID https://orcid.org/0000-0001-6512-8396
Toshiharu MitsuhashiCenter for Innovative Clinical Medicine Okayama University Hospital Okayama Japan.
Naomi MatsumotoDepartment of Epidemiology Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Okayama Japan.
Shunsaku HayaseAcademic Affairs Division Okayama University Okayama Japan.
Takashi YorifujiDepartment of Epidemiology Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Okayama Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic has impacted healthcare behaviors, leading to fewer pediatric visits in Japan and potentially fewer visits by adult patients. However, existing Japanese studies on treatment interruptions have generally relied on questionnaire-based methods. In this study, we assessed the impact of the pandemic on antihypertensive treatment interruption using real-world prescription data. Methods: We conducted an interrupted time series analysis using the National Health Insurance Database in Okayama Prefecture, Japan. Participants included individuals aged 40-69 years with at least one antihypertensive prescription between 2018 and 2020. Treatment interruption was defined as a 3-month or longer gap in prescriptions after medication depletion. We used segmented Poisson regression with models unadjusted and adjusted for seasonality and over-dispersion to assess monthly treatment interruptions before and after Japan's April 2020 emergency. Results: During the study period, 23.0% of 55,431 participants experienced treatment interruptions. Cyclical fluctuations in interruptions were observed. The crude analysis indicated a 1.2-fold increase in treatment interruptions following the pandemic; however, the adjusted models showed no significant changes. Even among higher-risk groups, such as women, younger adults, and those with shorter prescriptions, no significant alterations were observed. Conclusion: We found no significant impact of the COVID-19 pandemic on antihypertensive treatment interruption in Okayama Prefecture. The less severe outbreak in the area or increased use of telemedicine and extended prescriptions may have contributed to treatment continuity. Further research is needed using a more stable and comprehensive database, broader regional data, and detailed prescription records to validate and extend our findings.

Indexed as

antihypertensive agentsCOVID‐19health behaviorinterrupted time series analysisprescription drugstreatment interruption

Identifiers

PMID38481745
PMCPMC10927915

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.