Prevalence of type 1 and type 2 diabetes among US pediatric population in the MarketScan Multi-State Database, 2002 to 2016

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Developing a risk assessment tool for insulin resistance in European adolescents: the HELENA-IR score

OBJECTIVES

To develop and validate an easy-to-use screening tool for identifying adolescents at high-risk for insulin resistance (IR).

METHODS

Α total of 1,053 adolescents (554 females), aged 12.5 to 17.5 years with complete data on glucose and insulin levels were included. Body mass index (BMI), fat mass index (FMI) and the homeostasis model assessment for insulin resistance (HOMA-IR) were calculated. VO₂max was predicted using 20 m multi-stage fitness test. The population was randomly separated into two cohorts for the development (n=702) and validation (n=351) of the index, respectively. Factors associated with high HOMA-IR were identified by Spearman correlation in the development cohort; multiple logistic regression was performed for all identified independent factors to develop a score index. Finally, receiver operating characteristic (ROC) analysis was performed in the validation cohort and was used to define the cut-off values that could identify adolescents above the 75th and the 95th percentile for HOMA-IR.

RESULTS

BMI and VO₂max significantly identified high HOMA-IR in males; and FMI, TV watching and VO₂max in females. The HELENA-IR index scores range from 0 to 29 for males and 0 to 43 for females. The Area Under the Curve, sensitivity and specificity for identifying males above the 75th and 95th of HOMA-IR percentiles were 0.635 (95%CI: 0.542-0.725), 0.513 and 0.735, and 0.714 (95%CI: 0.499-0.728), 0.625 and 0.905, respectively. For females, the corresponding values were 0.632 (95%CI: 0.538-0.725), 0.568 and 0.652, and 0.708 (95%CI: 0.559-0.725), 0.667 and 0.617, respectively. Simple algorithms were created using the index cut-off scores.

CONCLUSIONS

Paediatricians or physical education teachers can use easy-to-obtain and non-invasive measures to apply the HELENA-IR score and identify adolescents at high risk for IR, who should be referred for further tests.

Trends in Prevalence of Type 1 and Type 2 Diabetes in Children and Adolescents in the US, 2001-2017

IMPORTANCE

Changes in the prevalence of youth-onset diabetes have previously been observed.

OBJECTIVE

To estimate changes in prevalence of type 1 and type 2 diabetes in youths in the US from 2001 to 2017.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional observational study, individuals younger than 20 years with physician-diagnosed diabetes were enumerated from 6 areas in the US (4 geographic areas, 1 health plan, and select American Indian reservations) for 2001, 2009, and 2017.

EXPOSURES

Calendar year.

MAIN OUTCOMES AND MEASURES

Estimated prevalence of physician-diagnosed type 1 and type 2 diabetes overall and by race and ethnicity, age, and sex.

RESULTS

Among youths 19 years or younger, 4958 of 3.35 million had type 1 diabetes in 2001, 6672 of 3.46 million had type 1 diabetes in 2009, and 7759 of 3.61 million had type 1 diabetes in 2017; among those aged 10 to 19 years, 588 of 1.73 million had type 2 diabetes in 2001, 814 of 1.85 million had type 2 diabetes in 2009, and 1230 of 1.85 million had type 2 diabetes in 2017. The estimated type 1 diabetes prevalence per 1000 youths for those 19 years or younger increased significantly from 1.48 (95% CI, 1.44-1.52) in 2001 to 1.93 (95% CI, 1.88-1.98) in 2009 to 2.15 (95% CI, 2.10-2.20) in 2017, an absolute increase of 0.67 per 1000 youths (95%, CI, 0.64-0.70) and a 45.1% (95% CI, 40.0%-50.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic White (0.93 per 1000 youths [95% CI, 0.88-0.98]) and non-Hispanic Black (0.89 per 1000 youths [95% CI, 0.88-0.98]) youths. The estimated type 2 diabetes prevalence per 1000 youths aged 10 to 19 years increased significantly from 0.34 (95% CI, 0.31-0.37) in 2001 to 0.46 (95% CI, 0.43-0.49) in 2009 to 0.67 (95% CI, 0.63-0.70) in 2017, an absolute increase of 0.32 per 1000 youths (95% CI, 0.30-0.35) and a 95.3% (95% CI, 77.0%-115.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic Black (0.85 per 1000 youths [95% CI, 0.74-0.97]) and Hispanic (0.57 per 1000 youths [95% CI, 0.51-0.64]) youths.

CONCLUSIONS AND RELEVANCE

In 6 areas of the US from 2001 to 2017, the estimated prevalence of diabetes among children and adolescents increased for both type 1 and type 2 diabetes.

Country-Specific Prevalence and Incidence of Youth-Onset Type 2 Diabetes: A Narrative Literature Review

BACKGROUND

With increased awareness of type 2 diabetes (T2D) in children and adolescents, an overview of country-specific differences in epidemiology data is needed to develop a global picture of the disease development.

SUMMARY

This study examined country-specific prevalence and incidence data of youth-onset T2D published between 2008 and 2019, and searched for national guidelines to expand the understanding of country-specific similarities and differences. Of the 1,190 articles and 17 congress abstracts identified, 58 were included in this review. Our search found the highest reported prevalence rates of youth-onset T2D in China (520 cases/100,000 people) and the USA (212 cases/100,000) and lowest in Denmark (0.6 cases/100,000) and Ireland (1.2 cases/100,000). However, the highest incidence rates were reported in Taiwan (63 cases/100,000) and the UK (33.2 cases/100,000), with the lowest in Fiji (0.43 cases/100,000) and Austria (0.6 cases/100,000). These differences in epidemiology data may be partly explained by variations in the diagnostic criteria used within studies, screening recommendations within national guidelines and race/ethnicity within countries. Key Messages: Our study suggests that published country-specific epidemiology data for youth-onset T2D are varied and scant, and often with reporting inconsistencies. Finding optimal diagnostic criteria and screening strategies for this disease should be of high interest to every country.

TRIAL REGISTRATION

Not applicable.

Views of People with Diabetes Regarding Their Experiences of the Facilitators and Barriers in Type 1 Diabetes Inpatient Care: An Interpretative Phenomenological Analysis

BACKGROUND

The aim of this study was to comprehend how people with diabetes view their experiences of the possible barriers and facilitators in inpatient care for type 1 diabetes from non-specialized nurses.

DESIGN

An interpretative phenomenology analysis (IPA) was conducted.

METHODS

The sample consisted of people with type 1 diabetes 1 (n = 24) who use the services of the state hospitals in Cyprus. The data were collected in two phases: firstly, focus groups with people with diabetes (n = 2) were conducted and analysed, and then individual semi-structured interviews with people with diabetes (n = 12) were conducted.

RESULTS

It is evident from the findings that people with diabetes experienced several barriers in diabetes inpatient care, which is concerning since this can have adverse effects on patients' outcomes. No facilitators were reported.

CONCLUSION

Significant results were found in relation to the barriers to diabetes inpatient care. Crucially, the findings demonstrate that all these factors can negatively affect the quality of care of patients with diabetes, and most of these factors are related not only to diabetes care but also generally to all patients who receive inpatient care. Interestingly, no participant reported any facilitators to their care, which further affected the negative perceptions of the care received.

Targeting Gut Microbiota for the Prevention and Management of Diabetes Mellitus by Dietary Natural Products

Diabetes mellitus is one of the biggest public health concerns worldwide, which includes type 1 diabetes mellitus, type 2 diabetes mellitus, gestational diabetes mellitus, and other rare forms of diabetes mellitus. Accumulating evidence has revealed that intestinal microbiota is closely associated with the initiation and progression of diabetes mellitus. In addition, various dietary natural products and their bioactive components have exhibited anti-diabetic activity by modulating intestinal microbiota. This review addresses the relationship between gut microbiota and diabetes mellitus, and discusses the effects of natural products on diabetes mellitus and its complications by modulating gut microbiota, with special attention paid to the mechanisms of action. It is hoped that this review paper can be helpful for better understanding of the relationships among natural products, gut microbiota, and diabetes mellitus.