Sexual behaviours and their associated factors among young people in the Dodowa Health and Demographic Surveillance Site (DHDSS) in Ghana.

OBJECTIVE: This paper describes sexual behaviours and their associated factors among young people. DESIGN: The study design is cross-sectional. SETTING: Dodowa Health and Demographic Surveillance Site (DHDSS) in Ghana's Shai-Osudoku and Ningo Prampram districts. PARTICIPANTS: Young people aged 10 to 24 years, median age 17 years. OUTCOME MEASURES: Self-reported to have ever had sex, non-use of a condom at last sex, and ever been pregnant or gotten someone pregnant. RESULTS: Of the 1689 young people; 42% reported having ever had sex, not using a condom at last sexual activity (64%), and ever been pregnant or gotten someone pregnant (41%). The proportion of non-use of condoms at last sex was high across all age groups but was highest (93%) in a small proportion of 10 to 14-year-olds who have ever had sex. Higher proportions of females than males; were reported to have ever had sex (46%), not using a condom at their last sex (66%) and ever been pregnant or getting someone pregnant (56%). Age group (20 to 24), females, primary or junior high school, living alone and lower household socio-economic status were risk factors associated with all three outcome measures. CONCLUSION: Risky sexual behaviour is high among young people in the Dodowa HDSS. Therefore, interventions that promote safer sexual practices and help young people make timely decisions on their sexual and reproductive health care needs are required. FUNDING: No funding was obtained for this paper

Effect of Removing Direct Payment for Health Care on Utilisation and Health Outcomes in Ghanaian Children: A Randomised Controlled Trial

Evelyn Ansah and colleagues report on whether removing user fees has an impact on health care-seeking behavior and health outcomes in households with children in Ghana

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes : a systematic review and meta-analysis of randomised controlled trials

Background Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the different modes is not currently available. Therefore, this study aimed to compare the effectiveness of SMS, smartphone application, and website-based interventions on improving glycaemia in adults with type 2 diabetes and report on their reach, uptake, and feasibility. Methods In this systematic review and meta-analysis, we searched CINAHL, Cochrane Central, Embase, MEDLINE, and PsycInfo on May 25, 2022, for randomised controlled trials (RCTs) that examined the effectiveness of digital health interventions in reducing glycated haemoglobin A1c (HbA1c) in adults with type 2 diabetes, published in English from Jan 1, 2009. Screening was carried out using Covidence, and data were extracted following Cochrane's guidelines. The primary endpoint assessed was the change in the mean (and 95% CI) plasma concentration of HbA1c at 3 months or more. Cochrane risk of bias 2 was used to assess risk of bias. Data on reach, uptake, and feasibility were summarised narratively and data on HbA1c reduction were synthesised in a meta-analysis. Grading of Recommendations, Assessment, Development, and Evaluation criteria was used to evaluate the level of evidence. The study was registered with PROSPERO, CRD42021247845. Findings Of the 3236 records identified, 56 RCTs from 24 regions (n=11 486 participants), were included in the narrative synthesis, and 26 studies (n=4546 participants) in the meta-analysis. 20 studies used SMS as the primary mode of delivery of the digital health intervention, 25 used smartphone applications, and 11 implemented interventions via websites. Smartphone application interventions reported higher reach compared with SMS and website-based interventions, but website-based interventions reported higher uptake compared with SMS and smartphone application interventions. Effective interventions, in general, included people with greater severity of their condition at baseline (ie, higher HbA1c) and administration of a higher dose intensity of the intervention, such as more frequent use of smartphone applications. Overall, digital health intervention group participants had a –0·30 (95% CI –0·42 to –0·19) percentage point greater reduction in HbA1c, compared with control group participants. The difference in HbA1c reduction between groups was statistically significant when interventions were delivered through smartphone applications (–0·42% [–0·63 to –0·20]) and via SMS (–0·37% [–0·57 to –0·17]), but not when delivered via websites (–0·09% [–0·64 to 0·46]). Due to the considerable heterogeneity between included studies, the level of evidence was moderate overall. Interpretation Smartphone application and SMS interventions, but not website-based interventions, were associated with better glycaemic control. However, the studies' heterogeneity should be recognised. Considering that both smartphone application and SMS interventions are effective for diabetes management, clinicians should consider factors such as reach, uptake, patient preference, and context of the intervention when deciding on the mode of delivery of the intervention. Nine in ten people worldwide own a feature phone and can receive SMS and four in five people have access to a smartphone, with numerous smartphone applications being available for diabetes management. Clinicians should familiarise themselves with this modality of programme delivery and encourage people with type 2 diabetes to use evidence-based applications for improving their self-management of diabetes. Future research needs to describe in detail the mediators and moderators of the effectiveness and implementation of SMS and smartphone application interventions, such as the optimal dose, frequency, timing, user interface, and communication mode to both further improve their effectiveness and to increase their reach, uptake, and feasibility