Evidence map›Paper›PMID 40390052›Full record

ArticleBMC health services research2025

Impact of the COVID-19 pandemic on continuity of medical treatment for patients with chronic diseases in Japan: a retrospective cohort analysis.

Tomoki Ishikawa, Akihito Kako, Jumpei Sato, Junko Hattori, Hiromasa Yoshimoto, Masaru Kitsuregawa, Kazuo Goda, Yoshimitsu Takahashi, Takeo Nakayama, Naohiro Mitsutake

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

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

10 authors.

Tomoki IshikawaInstitute for Health Economics and Policy, Association for Health Economics Research and Social Insurance and Welfare, 3F Tokyu Toranomon Bldg., 1-21-19, Toranomon, Minato-ku, Tokyo, 105-0001, Japan. tomoki.ishikawa@ihep.jp.
Akihito KakoInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Jumpei SatoInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Junko HattoriInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Hiromasa YoshimotoInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Masaru KitsuregawaInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Kazuo GodaInstitute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Yoshimitsu TakahashiDepartment of Implementation Science in Public Health, Kyoto University School of Public Health, Kyoto, Japan.
Takeo NakayamaDepartment of Health Informatics, Graduate School of Medicine & School of Public Health, Kyoto University, Kyoto, Japan.
Naohiro MitsutakeInstitute for Health Economics and Policy, Association for Health Economics Research and Social Insurance and Welfare, 3F Tokyu Toranomon Bldg., 1-21-19, Toranomon, Minato-ku, Tokyo, 105-0001, Japan.

Funding

Japan Agency for Medical Research and Development JP18lk1010033h0001Progress of the next Cross-ministerial Strategic Innovation Promotion Program on "Integrated Health Care System(C-1)" JPJ012425
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has raised concerns about continuity of care for chronic diseases such as diabetes, hypertension, and dyslipidemia. Although studies have documented declines in healthcare utilization, few have assessed individual-level patterns of treatment interruption.

methodsWe conducted a retrospective cohort analysis using administrative claims data (2018-2021) from a Prefecture, Japan. Eligible patients were diagnosed with and prescribed medications for diabetes mellitus, hypertension, or dyslipidemia in January of 2019 or 2020 and followed until December of each year. The primary outcome was the time-to- interruption of physician visits, defined as a gap of at least 60 days between follow-up visits. We used Cox proportional hazards regression, adjusted for age, sex, comorbidities, and prior healthcare utilization, to compare the risk of treatment interruption in 2020 (COVID-19 group) vs. 2019 (pre-pandemic group). Sensitivity analyses were performed with thresholds of 30 and 90 days. Logistic regression examined whether pandemic-related interruptions were associated with the likelihood of returning to treatment within one year.

resultsA total of 410,911 patients met the inclusion criteria. Across all three chronic conditions, the hazard of interruption was significantly higher in 2020 than in 2019; adjusted HRs are 1.26 (95% CI = 1.17-1.36) for diabetes, 1.39 (95%CI = 1.31-1.47) for hypertension, and 1.24 (95%CI = 1.17-1.32) for dyslipidemia, respectively. No significant difference in resuming care was observed between the 2020 and 2019 interruptions (OR = 0.89; 95% CI = 0.81-1.09). Sensitivity analyses showed consistent findings, despite slight variations in hazard ratios for different gap thresholds.

conclusionsThe COVID-19 pandemic was associated with an increased risk of treatment interruption in patients with chronic diseases, underscoring the need for targeted strategies to maintain care continuity during public health emergencies. While older patients and those with multimorbidity showed earlier interruptions, the overall patterns persisted across disease groups, emphasizing the importance of preemptive measures and patient outreach to prevent delayed or forgone care.

Indexed as

Continuity of Patient CareCOVID-19AdultAgedAged, 80 and overChronic DiseaseDiabetes MellitusDyslipidemiasFemaleHumansHypertensionJapanMaleMiddle AgedPandemicsRetrospective StudiesAdministrative claims dataChronic disease managementContinuity of careCOVID‑19Treatment interruption

Identifiers

PMID40390052
PMCPMC12087208

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.