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

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

Most patients are diagnosed as having diabetes only after experiencing diabetes complications. Educational attainment might have a positive relationship with diabetes prognosis. The diabetes pay-for-performance (P4P) program-providing comprehensive, continuous medical care-has improved diabetes prognosis in Taiwan. This retrospective cohort study investigated how educational attainment affects the presence of diabetes complications at diabetes diagnosis and mortality risk in patients with diabetes enrolled in the P4P program. From the National Health Insurance Research Database, we identified patients aged >45 years who had received a new diagnosis of type 2 diabetes during 2002-2015; they were followed up until the end of 2017. We next used logistic regression analysis to explore whether the patients with different educational attainments had varied diabetic complication risks at diabetes diagnosis. The Cox proportional hazard model was employed to examine the association of different educational attainments in people with diabetes with mortality risk after their enrollment in the P4P program. The results indicated that as educational attainment increased, the risk of diabetes complications at type 2 diabetes diagnosis decreased gradually. When type 2 diabetes with different educational attainments joined the P4P program, high school education had the highest effect on reducing mortality risk; however, those with ≤ 6th grade education had the lowest impact.

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

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.

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

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.