Understanding the patterns of HIV/AIDS epidemics is crucial to tracking and monitoring the progress of prevention and control efforts in countries. We provide a comprehensive assessment of the levels and trends of HIV/AIDS incidence, prevalence, mortality, and coverage of antiretroviral therapy (ART) for 1980-2017 and forecast these estimates to 2030 for 195 countries and territories. We determined a modelling strategy for each country on the basis of the availability and quality of data. For countries and territories with data from population-based seroprevalence surveys or antenatal care clinics, we estimated prevalence and incidence using an open-source version of the Estimation and Projection Package-a natural history model originally developed by the UNAIDS Reference Group on Estimates, Modelling, and Projections. For countries with cause-specific vital registration data, we corrected data for garbage coding (ie, deaths coded to an intermediate, immediate, or poorly defined cause) and HIV misclassification. We developed a process of cohort incidence bias adjustment to use information on survival and deaths recorded in vital registration to back-calculate HIV incidence. For countries without any representative data on HIV, we produced incidence estimates by pulling information from observed bias in the geographical region. We used a re-coded version of the Spectrum model (a cohort component model that uses rates of disease progression and HIV mortality on and off ART) to produce age-sex-specific incidence, prevalence, and mortality, and treatment coverage results for all countries, and forecast these measures to 2030 using Spectrum with inputs that were extended on the basis of past trends in treatment scale-up and new infections. Global HIV mortality peaked in 2006 with 1·95 million deaths (95% uncertainty interval 1·87-2·04) and has since decreased to 0·95 million deaths (0·91-1·01) in 2017. New cases of HIV globally peaked in 1999 (3·16 million, 2·79-3·67) and since then have gradually decreased to 1·94 million (1·63-2·29) in 2017. These trends, along with ART scale-up, have globally resulted in increased prevalence, with 36·8 million (34·8-39·2) people living with HIV in 2017. Prevalence of HIV was highest in southern sub-Saharan Africa in 2017, and countries in the region had ART coverage ranging from 65·7% in Lesotho to 85·7% in eSwatini. Our forecasts showed that 54 countries will meet the UNAIDS target of 81% ART coverage by 2020 and 12 countries are on track to meet 90% ART coverage by 2030. Forecasted results estimate that few countries will meet the UNAIDS 2020 and 2030 mortality and incidence targets. Despite progress in reducing HIV-related mortality over the past decade, slow decreases in incidence, combined with the current context of stagnated funding for related interventions, mean that many countries are not on track to reach the 2020 and 2030 global targets for reduction in incidence and mortality. With a growing population of people living with HIV, it will continue to be a major threat to public health for years to come. The pace of progress needs to be hastened by continuing to expand access to ART and increasing investments in proven HIV prevention initiatives that can be scaled up to have population-level impact. Bill & Melinda Gates Foundation, National Institute of Mental Health of the US National Institutes of Health (NIH), and the National Institute on Aging of the NIH.

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. “mySugar” 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.

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 [ I ² statistic, 80.8%]) and a significant HbA1c reduction was noted for web-based interventions. A baseline HbA1c level above 7.5%, β = −0.44 (95% CI: [−0.81 to −0.06]), the BCTs “problem solving,” β = −1.30 (95% CI: [−2.05 to −0.54]), and “self-monitoring outcomes of behavior,” β = −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.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Maternal mortality and morbidity are the priority agenda for sub-Saharan Africa including Ethiopia. Uterine rupture is the leading cause of maternal and fetal death in developing countries. Limited evidence is available on the magnitude of uterine rupture; maternal and fetal outcomes of uterine rupture and factors associated with maternal death secondary to uterine rupture in Ethiopia. This study aimed to assess the magnitude of uterine rupture; maternal and fetal outcome of uterine rupture and factors associated with maternal death secondary to uterine rupture in Debremarkos Referral Hospital, Northwest Ethiopia. An institutional-based cross-sectional study was conducted in December 2015 in Debremarkos referral hospital, Northwest Ethiopia. A total of 242 records of mothers with uterine rupture at Debremarkos referral Hospital during the year 2011–2014 were included in the study. Secondary data was collected from the records of mothers admitted for the management of uterine rupture. Descriptive statistics were performed to characterize the study population. Bivariate and multivariable logistic regression model was fitted to identify factors associated with maternal death secondary to uterine rupture. Odds ratio with 95% confidence interval was computed to determine the level of significance. A total of 10,379 deliveries were attended A total of 242 uterine rupture cases were included in this study. The magnitude of uterine rupture was 2.44% (1 in 41 deliveries). Sixteen (6.6%) mothers died from uterine rupture. Fourteen (5.8%) had experienced Vesico Vaginal Fistula. The majority of the mothers, 72% (176), admitted for uterine rupture stayed in hospital for 6–10 days. Fetal outcome was grave, 98.3% (238) were stillborn. Place of labor [Adjusted odds ratio (AOR): 6.92, 95% confidence interval (CI): (1.16, 33.74)], occurrence of hypo volume shock [AOR: 3.48, 95% CI: (1.01, 11.96)] and postoperative severe anemia [AOR: 0.092, 95% CI: (0.01, 0.956)] were significantly associated with maternal death secondary to uterine rupture. The magnitude of uterine rupture was high in the study area. Initiation of labor at health institutions, early treatment of hypo-volumia and prevention of postoperative anemia is recommended to decrease maternal death secondary to uterine rupture.

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.