Evidence map›Paper›PMID 37576956›Full record

ArticleFrontiers in endocrinology2023

Status of screening and preventive efforts against diabetic kidney disease between 2013 and 2018: analysis using an administrative database from Kyoto-city, Japan.

Yukiko Tateyama, Tomonari Shimamoto, Manako K Uematsu, Shotaro Taniguchi, Norihiro Nishioka, Keiichi Yamamoto, Hiroshi Okada, Yoshimitsu Takahashi, Takeo Nakayama, Taku Iwami

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In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 17% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
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  3. Article
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  5. Review
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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 at 4 institutions in 1 country.

Yukiko TateyamaDepartment of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan.
Tomonari ShimamotoDepartment of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan.
Manako K UematsuHealthtech Laboratory, Inc., Kyoto, Japan.
Shotaro TaniguchiMiyazaki Prefectural Health Foundation, Miyazaki, Japan.
Norihiro NishiokaDepartment of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan.
Keiichi YamamotoTranslational Research Institute for Medical Innovation, Osaka Dental University, Hirakata, Japan.
Hiroshi OkadaDepartment of Endocrinology and Metabolism, Kyoto Prefectural University of Medicine, Graduate School of Medical Science, Kyoto, Japan.
Yoshimitsu TakahashiDepartment of Health Informatics, Kyoto University School of Public Health, Kyoto, Japan.
Takeo NakayamaDepartment of Health Informatics, Kyoto University School of Public Health, Kyoto, Japan.
Taku IwamiDepartment of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan.
Kyoto University · JPKyoto Prefectural University of Medicine · JPMiyazaki Prefectural Hospital · JPOsaka Dental University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Japan has the second highest prevalence of dialysis use in the world. Approximately 40% of patients who begin dialysis have diabetic kidney disease (DKD). Local governments and medical facilities are required to provide preventive measures against worsening diabetes mellitus (DM). However, the percentage of patients with DM who receive such screening or interventions for DKD is unclear. This study aimed to reveal to what extent screening for DKD and preventive measures against worsening DKD are performed in patients with DM, using an administrative database in a municipality. Methods: This was a cross-sectional study that used the Kyoto-city's administrative medical and long-term care database. Patients with a diagnosis of DM and receiving antidiabetic medication between 2013 and 2018 were defined as patients with DM and included. Patients with DKD were defined as those diagnosed with diabetic nephropathy or those with chronic kidney disease. We described the characteristics of patients with DM, diabetic complications, and extent of DKD screenings and preventive efforts against worsening of DM by fiscal year. Results: Across fiscal years, 25.8% to 27.5% of patient with DM had DKD. More than 3% of patients were on dialysis due DM in each fiscal year; approximately 15% started receiving dialysis that year. The percentage of patients who were regularly prescribed antidiabetic medication and received glycosylated hemoglobin testing ranged from 64.0% to 67.2% and from 30.6% to 36.5%, respectively. Urine microalbuminuria testing at least once a year occurred in 9.3% to 10.0%. The percentage of patients who received nutritional guidance ranged from 19.0% to 21.0%. Approximately 1% of patients received guidance for preventing DM from progressing to a disease that requires dialysis each fiscal year. Conclusion: This study from Japan, where a super-aging society has developed, using an administrative database in a municipality covering most of the elderly population clearly demonstrated an evidence-practice gap in efforts to prevent worsening of DKD. Strengthening cooperation between government and medical facilities and support for providing preventive measures against DKD are urgently needed.

Indexed as

Diabetes ComplicationsDiabetes MellitusDiabetic NephropathiesAgedCross-Sectional StudiesHumansHypoglycemic AgentsJapanHypoglycemic Agentsadministrative databasediabetes mellitusdiabetic kidney diseasepreventive efforts against diabetic kidney diseasescreening test

Identifiers

PMID37576956
PMCPMC10413551
OpenAlexW4385299668

What OpenQuestion holds

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Registered trials

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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.