Ethnic differences in metabolic syndrome in high-income countries: A systematic review and meta-analysis.

This review aimed to systematically quantify the differences in Metabolic Syndrome (MetS) prevalence across various ethnic groups in high-income countries by sex, and to evaluate the overall prevalence trends from 1996 to 2022. We conducted a systematic literature review using MEDLINE, Web of Science Core Collection, CINAHL, and the Cochrane Library, focusing on studies about MetS prevalence among ethnic groups in high-income countries. We pooled 23 studies that used NCEP-ATP III criteria and included 147,756 healthy participants aged 18 and above. We calculated pooled prevalence estimates and 95% confidence intervals (CI) using both fixed-effect and random-effect intercept logistic regression models. Data were analysed for 3 periods: 1996-2005, 2006-2009, and 2010-2021. The pooled prevalence of MetS in high-income countries, based on the NCEP-ATP III criteria, was 27.4% over the studied period, showing an increase from 24.2% in 1996-2005 to 31.9% in 2010-2021, with men and women having similar rates. When stratified by ethnicity and sex, ethnic minority women experienced the highest prevalence at 31.7%, while ethnic majority women had the lowest at 22.7%. Notably, MetS was more prevalent in ethnic minority women than men. Among ethnic minorities, women had a higher prevalence of MetS than men, and the difference was highest in Asians (about 15 percentage points). Among women, the prevalence of MetS was highest in Asians (41.2%) and lowest in Blacks/Africans (26.7%). Among men, it was highest in indigenous minority groups (34.3%) and lowest among in Blacks/Africans (19.8%). MetS is increasing at an alarming rate in high-income countries, particularly among ethnic minority women. The burden of MetS could be effectively reduced by tailoring interventions according to ethnic variations and risk profiles.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study.

OBJECTIVES: High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine. DESIGN: A cross-sectional epidemiological study was employed. SETTING: This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires. PARTICIPANTS: This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria. OUTCOME MEASURES: We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics. RESULTS: Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively. CONCLUSION: A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

Comparative serum lipid and immunohematological values among adult pulmonary tuberculosis and tuberculosis lymphadenitis cases and their association with sputum bacilli load and time to culture positivity in Northwestern Ethiopia.

BACKGROUND: The serum lipid and immunohematological values of tuberculosis lymphadenitis (TBLN) patients is poorly documented relative to pulmonary tuberculosis (PTB) cases. Therefore, the aim of this study was to investigate the serum lipid and immunohematological values of patients with TBLN in comparison with PTB (PTB) patients. METHODS: An institution-based comparative cross-sectional study was conducted in Northwest Ethiopia from March to December 2021. The study participants were bacteriologically confirmed PTB (n = 82) and TBLN (n = 94) cases with no known comorbidity and whose ages was greater than 18 years and with no current pregnancy. Independent sample t-test, one-way ANOVA, box plot, and correlation matrix were used to analyze the data. RESULTS: The body mass index (BMI), CD4 + T cell count, and high-density lipoprotein-Cholesterol (HDL-C) values were significantly higher among TBLN cases compared with PTB cases. Additionally, the total white blood cell (WBC) count, hemoglobin (Hb), total Cholesterol (CHO) and creatinine (Cr) values were relatively higher among TBLN than PTB (P > 0.05). On the reverse, the platelet count and triacylglycerol (TAG) values were relatively higher among PTB than in TBLN cases. While the mean days of culture positivity were 11.6 days for TBLN, the mean days of culture positivity were 14.0 days for PTB. Anemia and serum lipid values showed no correlation with sputum bacilli load and time to culture positivity. CONCLUSION: Tuberculous lymphadenitis patients were well-endowed with serum lipid, immunological and nutritional status compared with PTB cases. Hence, the high incidence rate of TBLN in Ethiopia could not be explained by low peripheral immunohematological values, malnutrition, Anemia, and dyslipidemia. Further study for identifying the predictors for TBLN in Ethiopia is highly desirable.

In-depth evaluation of machine learning methods for semi-automating article screening in a systematic review of mechanistic literature.

We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for training (70%) and testing (30%) the algorithms. The remaining 60% (n = 9857) were used as a validation set. We evaluated down- and up-sampling methods and compared unigram, bigram, and singular value decomposition (SVD) approaches. For each approach, Naïve Bayes, Support Vector Machines (SVM), regularized logistic regressions, neural networks, random forest, Logit boost, and XGBoost were implemented using simple term frequency or Tf-Idf feature representations. Performance was evaluated using sensitivity, specificity, precision and area under the Curve. We combined predictions of the best-performing algorithms (Youden Index ≥0.3 with sensitivity/specificity≥70/60%). In a down-sample unigram approach, Naïve Bayes, SVM/quanteda text models with Tf-Idf, and linear SVM e1071 package with Tf-Idf achieved >90% sensitivity at specificity >65%. Combining the predictions of the 10 best-performing algorithms improved the performance to reach 95% sensitivity and 64% specificity in the validation set. Crude screening burden was reduced by 61% (5979) (adjusted: 80.3%) with 5% (27) false negativity rate. All the other approaches yielded relatively poorer performances. The down-sampling unigram approach achieved good performance in our data. Combining the predictions of algorithms improved sensitivity while screening burden was reduced by almost two-third. Implementing machine learning approaches in title/abstract screening should be investigated further toward refining these tools and automating their implementation.

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24-2.69)], birth order [AOR = 0.22, 95% CI (0.11-0.46)], television ownership [AOR = 6.83, 95% CI (2.52-18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26-2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47-3.21)], age at first birth [AOR = 1.96, 95% CI (1.31-2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55-4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.

Corrigendum: Popular Diabetes Apps and the Impact of Diabetes App Use on Self-Care Behaviour: A Survey Among the Digital Community of Persons With Diabetes on Social Media.

[This corrects the article DOI: 10.3389/fendo.2019.00135.].

Popular Diabetes Apps and the Impact of Diabetes App Use on Self-Care Behaviour: A Survey Among the Digital Community of Persons With Diabetes on Social Media.

Introduction: This study aimed to identify popular diabetes applications (apps) and to investigate the association of diabetes app use and other factors with cumulative self-care behaviour. Methods: From November 2017 to March 2018, we conducted a web-based survey with persons 18 years of age and above. We recruited respondents via diabetes Facebook groups, online patient-forums and targeted Facebook advertisements (ads). Data on participants' demographic, clinical, and self-management characteristics, as well as on self-care behaviour and characteristics of the diabetes apps use were collected. Self-care behaviour was measured using a licensed version of the Summary of Diabetes Self-care Activities (SDSCA) questionnaire. The cumulative self-care score was calculated by summing up scores for "general diet," "specific diet," "exercise," "blood glucose testing," "foot care" and "smoking." To identify popular diabetes apps, users were requested to list all apps they use for diabetes self-management. Two sample t-test and multiple linear regression stratified by type of diabetes were performed to examine associations between app use and self-care behaviour, by controlling for key confounders. Results: One thousand fifty two respondents with type 1 and 630 respondents with type 2 diabetes mellitus (DM) entered the survey. More than half, 549 (52.2%), and one third, 210 (33.3%), of respondents with type 1 and 2 DM, respectively, reported using diabetes apps for self-management. "mySugr" and continuous glucose monitoring apps, such as "Dexcom," "Freestyle Libre," and "Xdrip+" were some of the most popular diabetes apps. In both respondent groups, the cumulative self-care behaviour score was significantly higher among diabetes app users (compared to non-users) and scores for three individual self-care components, namely "blood glucose monitoring," "general diet," and "physical activity" were significantly higher among diabetes app users than among non-users. After adjusting for confounding factors, diabetes app use increased the cumulative self-care score by 1.08 (95%CI: 0.46-1.7) units among persons with type 1 DM and by 1.18 (95%CI: 0.26-2.09) units among persons with type 2 DM, respectively. Conclusion: For both, persons with type 1 and type 2 diabetes, using diabetes apps for self-management was positively associated with self-care behaviour. Our findings suggest that apps can support changes in lifestyle and glucose monitoring in these populations.

The Role of Continuous Glucose Monitoring, Diabetes Smartphone Applications, and Self-Care Behavior in Glycemic Control: Results of a Multi-National Online Survey.

Background: This study investigated the determinants (with a special emphasis on the role of diabetes app use, use of continuous glucose monitoring (CGM) device, and self-care behavior) of glycemic control of type 1 and type 2 diabetes mellitus (DM). Methods: A web-based survey was conducted using diabetes Facebook groups, online patient-forums, and targeted Facebook advertisements (ads). Demographic, CGM, diabetes app use, and self-care behavior data were collected. Glycemic level data were categorized into hyperglycemia, hypoglycemia, and good control. Multinomial logistic regression stratified by diabetes type was performed. Results: The survey URL was posted in 78 Facebook groups and eight online forums, and ten targeted Facebook ads were conducted yielding 1854 responses. Of those owning smartphones (n = 1753, 95%), 1052 (62.6%) had type 1 and 630 (37.4%) had type 2 DM. More than half of the type 1 respondents (n = 549, 52.2%) and one third the respondents with type 2 DM (n = 210, 33.3%) reported using diabetes apps. Increased odds of experiencing hyperglycemia were noted in persons with type 1 DM with lower educational status (Adjusted Odds Ratio (AOR) = 1.7; 95% Confidence Interval (CI): 1.21⁻2.39); smokers (1.63, 95% CI: 1.15⁻2.32), and high diabetes self-management concern (AOR = 2.09, 95% CI: 1.15⁻2.32). CGM use (AOR = 0.66, 95% CI: 0.44⁻1.00); "general diet" (AOR = 0.86, 95% CI: 0.79⁻0.94); and "blood glucose monitoring" (AOR = 0.88, 95%CI: 0.80⁻0.97) self-care behavior reduced the odds of experiencing hyperglycemia. Hypoglycemia in type 1 DM was reduced by using CGM (AOR = 0.24, 95% CI: 0.09⁻0.60), while it was increased by experiencing a high diabetes self-management concern (AOR = 1.94, 95% CI: 1.04⁻3.61). Hyperglycemia in type 2 DM was increased by age (OR = 1.02, 95% CI: 1.00⁻1.04); high self-management concern (AOR = 2.59, 95% CI: 1.74⁻3.84); and poor confidence in self-management capacity (AOR = 3.22, 2.07⁻5.00). Conversely, diabetes app use (AOR = 0.63, 95% CI: 0.41⁻0.96) and "general diet" self-care (AOR = 0.84, 95% CI: 0.75⁻0.94), were significantly associated with the reduced odds of hyperglycemia. Conclusion: Diabetes apps, CGM, and educational interventions aimed at reducing self-management concerns and enhancing dietary self-care behavior and self-management confidence may help patients with diabetes to improve glycemic control.

Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes.

AIMS: Pooling the effect sizes of randomized controlled trials (RCTs) from continuous outcomes, such as glycated hemoglobin level (HbA1c), is an important method in evidence syntheses. However, due to challenges related to baseline imbalances and pre/post correlations, simple analysis of change scores (SACS) and simple analysis of final values (SAFV) meta-analyses result in under- or overestimation of effect estimates. This study was aimed to compare pooled effect sizes estimated by Analysis of Covariance (ANCOVA), SACS, and SAFV meta-analyses, using the example of RCTs of digital interventions with HbA1c as the main outcome. MATERIALS AND METHODS: Three databases were systematically searched for RCTs published from 1993 through June 2017. Two reviewers independently assessed titles and abstracts using predefined eligibility criteria, assessed study quality, and extracted data, with disagreements resolved by arbitration from a third reviewer. RESULTS: ANCOVA, SACS, and SAFV resulted in pooled HbA1c mean differences of -0.39% (95% CI: [-0.51, -0.26]), -0.39% (95% CI: [-0.51, -0.26]), and -0.34% (95% CI: [-0.48-0.19]), respectively. Removing studies with both high baseline imbalance (≥±0.2%) and pre/post correlation of ≥±0.6 resulted in a mean difference of -0.39% (95% CI: [-0.53, -0.26]), -0.40% (95% CI: [-0.54, -0.26]), and -0.33% (95% CI: [-0.48, -0.18]) with ANCOVA, SACS, and SAFV meta-analyses, respectively. Substantial heterogeneity was noted. Egger's test for funnel plot symmetry did not indicate evidence of publication bias for all methods. CONCLUSION: By all meta-analytic methods, digital interventions appear effective in reducing HbA1c in type 2 diabetes. The effort to adjust for baseline imbalance and pre/post correlation using ANCOVA relies on the level of detail reported from individual studies. Reporting detailed summary data and, ideally, access to individual patient data of intervention trials are essential.

Effectiveness of Digital Interventions for Improving Glycemic Control in Persons with Poorly Controlled Type 2 Diabetes: A Systematic Review, Meta-analysis, and Meta-regression Analysis.

BACKGROUND: Digital interventions may assist patients with type 2 diabetes in improving glycemic control. We aimed to synthesize effect sizes of digital interventions on glycated hemoglobin (HbA1c) levels and to identify effective features of digital interventions targeting patients with poorly controlled type 2 diabetes. MATERIALS AND METHODS: MEDLINE, ISI Web of Science, and PsycINFO were searched for randomized controlled trials (RCTs) comparing the effects of digital interventions with usual care. Two reviewers independently assessed studies for eligibility and determined study quality, using the Cochrane Risk of Bias Assessment Tool. The Behavioral Change Technique Taxonomy V1 (BCTTv1) was used to identify BCTs used in interventions. Mean HbA1c differences were pooled using analysis of covariance to adjust for baseline differences and pre-post correlations. To examine effective intervention features and to evaluate differences in effect sizes across groups, meta-regression and subgroup analyses were performed. RESULTS: Twenty-three arms of 21 RCTs were included in the meta-analysis (n = 3787 patients, 52.6% in intervention arms). The mean HbA1c baseline differences ranged from -0.2% to 0.64%. The pooled mean HbA1c change was statistically significant (-0.39 {95% CI: [-0.51 to -0.26]} with substantial heterogeneity [I2 statistic, 80.8%]) and a significant HbA1c reduction was noted for web-based interventions. A baseline HbA1c level above 7.5%, ß = -0.44 (95% CI: [-0.81 to -0.06]), the BCTs "problem solving," ß = -1.30 (95% CI: [-2.05 to -0.54]), and "self-monitoring outcomes of behavior," ß = -1.21 (95% CI: [-1.95 to -0.46]) were significantly associated with reduced HbA1c levels. CONCLUSIONS: Digital interventions appear effective for reducing HbA1c levels in patients with poorly controlled type 2 diabetes.

Characterizing Active Ingredients of eHealth Interventions Targeting Persons With Poorly Controlled Type 2 Diabetes Mellitus Using the Behavior Change Techniques Taxonomy: Scoping Review.

BACKGROUND: The behavior change technique taxonomy v1 (BCTTv1; Michie and colleagues, 2013) is a comprehensive tool to characterize active ingredients of interventions and includes 93 labels that are hierarchically clustered into 16 hierarchical clusters. OBJECTIVE: The aim of this study was to identify the active ingredients in electronic health (eHealth) interventions targeting patients with poorly controlled type 2 diabetes mellitus (T2DM) and relevant outcomes. METHODS: We conducted a scoping review using the BCTTv1. Randomized controlled trials (RCTs), studies with or pre-post-test designs, and quasi-experimental studies examining efficacy and effectiveness of eHealth interventions for disease management or the promotion of relevant health behaviors were identified by searching PubMed, Web of Science, and PsycINFO. Reviewers independently screened titles and abstracts for eligibility using predetermined eligibility criteria. Data were extracted following a data extraction sheet. The BCTTv1 was used to characterize active ingredients of the interventions reported in the included studies. RESULTS: Of the 1404 unique records screened, 32 studies fulfilled the inclusion criteria and reported results on the efficacy and or or effectiveness of interventions. Of the included 32 studies, 18 (56%) were Web-based interventions delivered via personal digital assistant (PDA), tablet, computer, and/or mobile phones; 7 (22%) were telehealth interventions delivered via landline; 6 (19%) made use of text messaging (short service message, SMS); and 1 employed videoconferencing (3%). Of the 16 hierarchical clusters of the BCTTv1, 11 were identified in interventions included in this review. Of the 93 individual behavior change techniques (BCTs), 31 were identified as active ingredients of the interventions. The most common BCTs identified were instruction on how to perform behavior, adding objects to the environment, information about health consequences, self-monitoring of the outcomes and/or and prefers to be explicit to avoid ambiguity. Response: Checked and avoided of a certain behavior Author: Please note that the journal discourages the use of parenthesis to denote either and/or and prefers to be explicit to avoid ambiguity. Response: Checked and avoided "and/or" and prefers to be explicit to avoid ambiguity. Response: Checked and avoided, and feedback on outcomes of behavior. CONCLUSIONS: Our results suggest that the majority of BCTs employed in interventions targeting persons with T2DM revolve around the promotion of self-regulatory behavior to manage the disease or to assist patients in performing health behaviors necessary to prevent further complications of the disease. Detailed reporting of the BCTs included in interventions targeting this population may facilitate the replication and further investigation of such interventions.

Knowledge of Abortion Legislation Towards Induced Abortion Among Female Preparatory School Students in Dabat District, Ethiopia.

More than 32% of maternal mortality reported in Ethiopia is due to unsafe abortions. Women's knowledge of abortion legislation is a key determinant of the utilization of safe abortion services. The objective of this study was to determine knowledge of abortion legislation and associated factors among female students of Dabat Preparatory School. A quantitative school-based cross-sectional study was conducted from May 25-June 7/2014. Data were collected from 234 randomly selected female students using structured and pre-tested questionnaire. Majority of the participants, 147 (62.8%) know that the law in Ethiopia allows safe and legal abortion under certain circumstances. Ninety-seven (41.5%) of them have poor knowledge towards the legality of induced abortion in Ethiopia. Higher family income (OR=2.63, 95% CI=1.22-5.63), knowing the place where safely induced abortion can be performed (2.51, 95%CI=1.31-4.81) and current use of contraceptive (OR=2.3, 95% CI, 1.1-4.81) are significantly associated with knowledge of the abortion legislation. Student's knowledge of the legal status of abortion is still low. School-based health education about abortion legislation and where it can be safely performed is essential.