Association between mandatory health examination attendance and diabetes treatment initiation among employees being treated for hypertension

Yamana, Hayato; Ono, Sachiko; Okada, Akira; Jo, Taisuke; Yasunaga, Hideo

doi:10.1002/1348-9585.12183

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From those who satisfied the inclusion and who did not satisfy the exclusion criteria, the first observation for each individual was obtained to remove the effects of homogeneity in the same individual or those from health guidance. This guidance was performed by a team of trained registered nurses or physicians after prior checkups, particularly among those with abnormal results 23 …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic ability using fatty liver and metabolic markers for metabolic‐associated fatty liver disease stratified by metabolic/glycemic abnormalities

Okada

Yamada

Kimura

et al. 2022

J of Diabetes Invest

Self Cite

View full text Add to dashboard Cite

Aims/Introduction Although several noninvasive predictive markers for fatty liver and metabolic markers have been used for fatty liver prediction, whether such markers can also predict metabolic‐associated fatty liver disease (MAFLD) remains unclear. We aimed to examine the ability of existing fatty liver or metabolic markers to predict MAFLD. Materials and Methods Participants in a high‐volume center in Tokyo were classified into groups with and without MAFLD, based on the presence of metabolic abnormalities and fatty liver diagnosed through abdominal ultrasonography, between 2008 and 2018. The diagnostic abilities of three fatty liver markers: fatty liver index (FLI), hepatic steatosis index (HSI), and lipid accumulation product (LAP), and three common metabolic markers: waist‐to‐height ratio (WHR), body mass index (BMI), and waist circumference (WC), for predicting MAFLD, were evaluated. Analyses stratified by MAFLD subtypes were performed. Results Of 92,374 individuals, 19,392 (36.1%) had MAFLD. The diagnostic performances for MAFLD prediction, measured as c‐statistics, for FLI, HSI, LAP, WHR, BMI, and WC were 0.906, 0.892, 0.878, 0.844, 0.877, and 0.878, respectively. Optimal cutoff values for diagnosing MAFLD for FLI, HSI, LAP, WHR, BMI, and WC were 20.3, 32.7, 20.0, 0.49, 22.9, and 82.1, respectively. Analyses stratified by MAFLD subtypes, based on BMI and metabolic/glycemic abnormalities, suggested that FLI and HSI had acceptable (c‐statistics >0.700) diagnostic abilities throughout all the analyses. Conclusions All six markers were excellent predictors of MAFLD in diagnosing among the general population, with FLI and HSI particularly useful among all sub‐populations.

show abstract

Section: Methodsmentioning

confidence: 99%

“…This guidance was performed by a team of trained registered nurses or physicians after prior checkups, particularly among those with abnormal results. 23 This study was approved by the Institutional Review Board of the University of Tokyo (no. 2020264NI) and St Luke's International Hospital (no.…”

Section: Data Sourcementioning

confidence: 99%

Diagnostic ability using fatty liver and metabolic markers for metabolic‐associated fatty liver disease stratified by metabolic/glycemic abnormalities

Okada

Yamada

Kimura

et al. 2022

J of Diabetes Invest

Self Cite

View full text Add to dashboard Cite

show abstract

Educational attainment affects the diagnostic time in type 2 diabetes mellitus and the mortality risk of those enrolled in the diabetes pay-for-performance program

Liao,

Tsai,

Chiu

et al. 2023

Health Policy

View full text Add to dashboard Cite

A Machine Learning–Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program

et al. 2022

View full text Add to dashboard Cite

OBJECTIVE Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We aimed to develop a machine-learning model for predicting people’s failure to attend a follow-up visit. RESEARCH DESIGN AND METHODS We conducted a retrospective cohort study of adults with newly screened diabetes at a national screening program using a large Japanese insurance claims database (JMDC, Tokyo, Japan). We defined failure to attend a follow-up visit for diabetes care as no physician consultation during the 6 months after the screening. The candidate predictors were patient demographics, comorbidities, and medication history. In the training set (randomly selected 80% of the sample), we developed two models (previously reported logistic regression model and Lasso regression model). In the test set (remaining 20%), prediction performance was examined. RESULTS We identified 10,645 patients, including 5,450 patients who failed to attend follow-up visits for diabetes care. The Lasso regression model using four predictors had a better discrimination ability than the previously reported logistic regression model using 13 predictors (C-statistic: 0.71 [95% CI 0.69–0.73] vs. 0.67 [0.65–0.69]; P < 0.001). The four selected predictors in the Lasso regression model were lower frequency of physician visits in the previous year, lower HbA1c levels, and negative history of antidyslipidemic or antihypertensive treatment. CONCLUSIONS The developed machine-learning model using four predictors had a good predictive ability to identify patients who failed to attend a follow-up visit for diabetes care after a screening program.

show abstract

Association between mandatory health examination attendance and diabetes treatment initiation among employees being treated for hypertension

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 3 publications

References 18 publications

Diagnostic ability using fatty liver and metabolic markers for metabolic‐associated fatty liver disease stratified by metabolic/glycemic abnormalities

Diagnostic ability using fatty liver and metabolic markers for metabolic‐associated fatty liver disease stratified by metabolic/glycemic abnormalities

Educational attainment affects the diagnostic time in type 2 diabetes mellitus and the mortality risk of those enrolled in the diabetes pay-for-performance program

A Machine Learning–Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program

Contact Info

Product

Resources

About