Variation in process quality measures of diabetes care by region and institution in Japan during 2015–2016: An observational study of nationwide claims data

Sugiyama, Takehiro; Imai, Kenjiro; Ihana‐Sugiyama, Noriko; Tanaka, Hirokazu; Yanagisawa-Sugita, Ayako; Sasako, Takayoshi; Higashi, Takahiro; Okamura, Tomonori; Yamauchi, Toshimasa; Ueki, Kohjiro; Ohsugi, Mitsuru; Kadowaki, Takashi

doi:10.1016/j.diabres.2019.05.029

Cited by 29 publications

(30 citation statements)

References 22 publications

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“…Insurance claims databases have been increasingly used for studies to identify risk factors for cardiovascular disease and to compare the effectiveness of drugs in treating cardiovascular disease 6‐8 . Recently, studies using claims data have increased in the field of diabetes in Japan 9‐17 . In Japan claims data are recorded electronically using unique master codes 18 .…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Japanese claims data in identifying diabetes‐related complications

Fujihara

Yamada-Harada

Matsubayashi

et al. 2021

Pharmacoepidemiology and Drug

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Purpose To evaluate the accuracy of various claims‐based definitions of diabetes‐related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis). Methods We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes‐related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims‐based definition associated with diabetes‐related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD‐10) codes, procedure codes and medication codes were calculated. Results DPC‐based definitions had higher sensitivity, specificity, and PPV than ICD‐10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963–1.000 and 0.905–0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD‐10 codes + medication were better for heart failure than the ICD‐10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes‐related complications using ICD‐10 codes only. Conclusion The DPC‐based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD‐10 codes with medication codes for heart failure could accurately identify these diabetes‐related complications from claims databases.

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Section: Introductionmentioning

confidence: 99%

Accuracy of Japanese claims data in identifying diabetes‐related complications

Fujihara

Yamada-Harada

Matsubayashi

et al. 2021

Pharmacoepidemiology and Drug

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“…Therefore, regular visits to ophthalmologists are important. However, a recent survey found that fewer than half of Japanese patients with diabetes had had an ophthalmologic consultation within 1 year [10]. The reasons for inadequate visits to ophthalmologists included long waiting times on the day of the visit, high examination costs, and the inability to undergo an examination near home [11].…”

Section: Discussionmentioning

confidence: 99%

“…Sugiyama et al reported that 46.5% of 4,154,452 outpatients who were treated with regular antidiabetic medications, underwent a retinopathy examination at least annually, according to the National Database of Health Insurance Claims from 2015 to 2016 [10]. The ophthalmologic consultation rate within 1 year was 48% for patients with diabetes who were evaluated at an Internal Medicine Department, which was almost the same as the receipt-based result of Sugiyama et al [10]. The frequency of ophthalmologic visits was correlated negatively with age, HbA1c, duration of diabetes, and severity of diabetes.…”

Section: Discussionmentioning

confidence: 99%

Remote screening of diabetic retinopathy using ultra-widefield retinal imaging

Kato

Fujishima²,

Takami

et al. 2021

Diabetes Research and Clinical Practice

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To study the possibility of constructing a remote interpretation system for retinal images.Methods: An ultra-widefield (UWF) retinal imaging device was installed in the internal medicine department specializing in diabetes to obtain fundus images of patients with diabetes.Remote interpretation was conducted at Nagoya City University using a cloud server. The medical data, severity of retinopathy, and frequency of ophthalmologic visits were analyzed.Results: Four hundred ninety-nine patients (mean age, 62.5 ± 13.4 years) were included. The duration of diabetes in 240 (48.1%) patients was less than 10 years and 433 (86.7%) patients had a hemoglobin (Hb) A1c below 8%. Regarding the retinopathy severity, 360 (72.1%) patients had no diabetic retinopathy (NDR), 63 (12.6%) mild nonproliferative retinopathy (NPDR), 38 (7.64%) moderate NPDR, 13 (2.6%) severe NPDR, and 25 (5.0%) PDR. Two hundred forty-one (48.3%) patients had an ophthalmologic consultation within 1 year, 104 (20.8%) had no history of an ophthalmologic consultation. DR was not present in 86 (82.7%) patients who had never had an ophthalmologic examination, 30 (78.9%) patients with severe NPDR or PDR had had an ophthalmologic visit within 1 year. The frequency of ophthalmic visits was correlated negatively with age, diabetes duration, HbA1c, and severity of retinopathy. Conclusion:Remote interpretation of DR using UWF retinal imaging was useful for retinopathy screening. During the COVID-19 pandemic, a remote screening system that can ensure compulsory social distancing and reduce the number of ophthalmic visits can be a safe system for patients and clinicians.

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“…We have previously used the National Database to create retrospective sample cohorts of > 100 million individuals with a very small selection bias, and thus, good generalizability 9 The National Database contains health data on an extremely large population, which enables epidemiological studies of relatively rare conditions to be conducted with good statistical power. The National Database has been used to obtain real-world health-related evidence of global relevance [10][11][12][13][14] Using a large national database, this population-based study aimed to determine whether there is an association between the onset of type 1 diabetes and a diagnosis of influenza. To this end, we used a self-controlled case series method that enabled us to evaluate the effects of specific environmental factors on the onset of type 1 diabetes.…”

Section: Introductionmentioning

confidence: 99%

Association between influenza and the incidence rate of new‐onset type 1 diabetes in Japan

Nishioka

Noda

Okada

et al. 2021

J of Diabetes Invest

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Aim: This study aimed to determine whether there is an association between influenza and new-onset type 1 diabetes. Materials and methods: This population-based retrospective cohort study used data from the National Database of Health Insurance Claims and Specific Health Check-ups of Japan. Influenza was defined based on drug prescriptions and the onset of type 1 diabetes was defined using specific medical codes indicating a diagnosis of type 1 diabetes. The incidence rate ratio of new-onset type 1 diabetes within 180 days after an influenza diagnosis was calculated and it was compared with that at other times using Poisson regression and generalized estimating equations. Sensitivity analyses were performed to confirm the robustness of this finding. Results: The data of 10,400 patients with new-onset type 1 diabetes were analyzed, including 2,196 (952 male 1,244 female) patients diagnosed with influenza between 1 September 2014 and 31 August 2017. Although only patients with type 1 diabetes were included, adjusted analysis showed that individuals had a 1.3-fold (95% confidence interval: 1.15-1.46) higher risk of developing type 1 diabetes in the first 180 days after influenza diagnosis than that at other times. Conclusions: In this Japanese population-based cohort, the risk of new-onset type 1 diabetes may increase after the diagnosis of influenza. These results, which must be confirmed in other populations, suggest that influenza may be a causal factor for new-onset type 1 diabetes. The molecular mechanisms underlying the potential etiological relationship between influenza and type 1 diabetes should be elucidated.

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Variation in process quality measures of diabetes care by region and institution in Japan during 2015–2016: An observational study of nationwide claims data

Cited by 29 publications

References 22 publications

Accuracy of Japanese claims data in identifying diabetes‐related complications

Accuracy of Japanese claims data in identifying diabetes‐related complications

Remote screening of diabetic retinopathy using ultra-widefield retinal imaging

Association between influenza and the incidence rate of new‐onset type 1 diabetes in Japan

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