Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the “clinical whitespace” between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.

As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models.

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.

Background: In Ethiopia, orthopaedic services are limited, and many injured children undergo traditional bonesetting (TBS) despite its association with limb- and life-threatening complications. We sought to identify the risk factors for amputation and a prolonged hospitalization of >7 days in children who presented to hospitals after undergoing TBS. Methods: Over a 15-month period, we prospectively enrolled children who presented to 8 Ethiopian hospitals after undergoing TBS. Separately for each outcome (amputation and prolonged hospitalization), we used multivariable logistic regression to evaluate associations between the outcome and 16 covariates, including demographic and injury characteristics, parent or guardian preference for TBS, and TBS topical treatments and immobilization methods. Results: We enrolled 460 children (mean age, 9.0 ± 4.0 years; 75% male) representing 8 Ethiopian regions and diverse demographic and socioeconomic backgrounds. Elbow injuries (194 patients; 42.2%) and closed fractures and/or dislocations (364 patients; 79.1%) were most common. TBS treatments included topical inorganic (190 patients; 41.3%) or organic (82 patients; 17.8%) material application and rigid (166 patients; 36.1%) or soft (182 patients; 39.6%) immobilization. Twenty-six children (5.7%) underwent an amputation, and 102 (22.2%) had a prolonged hospitalization. The odds of amputation were higher for children from rural communities (adjusted odds ratio [AOR], 6.71; 95% confidence interval [CI], 2.01 to 22.41) and for children with only non-osseous injuries (AOR, 5.76; 95% CI, 1.56 to 21.28). The odds of prolonged hospitalization were higher for children who were 11 to 17 years old (AOR, 2.77; 95% CI, 1.18 to 6.50) and for children with open fractures with a grade of ≥2 (AOR, 4.52; 95% CI, 1.33 to 15.28) but were lower for children from households with secondary education or higher (AOR, 0.40; 95% CI, 0.21 to 0.79). TBS with rigid immobilization increased the odds of amputation (AOR, 5.84; 95% CI, 1.74 to 19.60) and prolonged hospitalization (AOR, 2.20; 95% CI, 1.02 to 4.73). TBS organic topical treatment (with mud, leaves, or butter) increased the odds of amputation (AOR, 3.88; 95% CI, 1.40 to 10.73). Conclusions: For children who underwent TBS prior to hospital presentation, rigid splinting by bonesetters increased the odds of amputation and prolonged hospitalization. TBS organic topical treatments also increased the odds of amputation. Training bonesetters to avoid these dangerous practices may prevent devastating complications for children in Ethiopia. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Vanzacaftor/tezacaftor/deutivacaftor (VNZ/TEZ/D-IVA) was shown to provide greater restoration of cystic fibrosis transmembrane conductance regulator (CFTR) function than prior CFTR modulators in people with cystic fibrosis (CF) aged ≥6 years. Here, we assessed the impact of VNZ/TEZ/D-IVA on health-related quality of life (HRQoL) in children, adolescents, and adults with CF. Post-hoc analyses were conducted using VNZ/TEZ/D-IVA Phase 3 studies in adolescents and adults with F508del/minimal function variant (F/MF) genotypes (SKYLINE 102) or F/F and other responsive genotypes (SKYLINE 103) and children aged 6-11 years across all responsive genotypes (RIDGELINE 105). For participants aged ≥14 years, CF Questionnaire-Revised-8 Dimensions (CFQ-R-8D) utility scores, a CF-specific, preference-based scoring algorithm based on CFQ-R data, were compared for VNZ/TEZ/D-IVA and ELX/TEZ/IVA treatment through Week 52. For children aged 6-11 years, absolute changes in CFQ-R non-respiratory domain (CFQ-R non-RD) scores from baseline, following an ELX/TEZ/IVA run-in period, were assessed through Week 24. CFQ-R-8D utility scores were higher in adolescents and adults given VNZ/TEZ/D-IVA compared to ELX/TEZ/IVA (pooled 0.017; nominal P = 0.01); participants with F/MF genotypes had larger changes (0.031; nominal P = 0.004) compared to non-F/MF genotypes (0.008; nominal P = 0.35). Children treated with VNZ/TEZ/D-IVA had improvements in 6 out of 7 CFQ-R non-RD scores, ranging from 3.7 [95 % CI: 1.0, 6.3] points in emotional functioning to 7.2 [95 % CI: 3.8, 10.5] points in social functioning. VNZ/TEZ/D-IVA was associated with higher HRQoL in adolescents and adults aged ≥14 years compared to ELX/TEZ/IVA, with largest changes in participants with F/MF genotypes, and in children aged 6-11 years compared to ELX/TEZ/IVA baseline. These results highlight potential HRQoL benefits from greater CFTR function restoration with VNZ/TEZ/D-IVA.