SummaryBackgroundThe Xpert MTB/RIF assay is an automated molecular test that has improved the detection of tuberculosis and rifampicin resistance, but its sensitivity is inadequate in patients with paucibacillary disease or HIV. Xpert MTB/RIF Ultra (Xpert Ultra) was developed to overcome this limitation. We compared the diagnostic performance of Xpert Ultra with that of Xpert for detection of tuberculosis and rifampicin resistance.MethodsIn this prospective, multicentre, diagnostic accuracy study, we recruited adults with pulmonary tuberculosis symptoms presenting at primary health-care centres and hospitals in eight countries (South Africa, Uganda, Kenya, India, China, Georgia, Belarus, and Brazil). Participants were allocated to the case detection group if no drugs had been taken for tuberculosis in the past 6 months or to the multidrug-resistance risk group if drugs for tuberculosis had been taken in the past 6 months, but drug resistance was suspected. Demographic information, medical history, chest imaging results, and HIV test results were recorded at enrolment, and each participant gave at least three sputum specimen on 2 separate days. Xpert and Xpert Ultra diagnostic performance in the same sputum specimen was compared with culture tests and drug susceptibility testing as reference standards. The primary objectives were to estimate and compare the sensitivity of Xpert Ultra test with that of Xpert for detection of smear-negative tuberculosis and rifampicin resistance and to estimate and compare Xpert Ultra and Xpert specificities for detection of rifampicin resistance. Study participants in the case detection group were included in all analyses, whereas participants in the multidrug-resistance risk group were only included in analyses of rifampicin-resistance detection.FindingsBetween Feb 18, and Dec 24, 2016, we enrolled 2368 participants for sputum sampling. 248 participants were excluded from the analysis, and 1753 participants were distributed to the case detection group (n=1439) and the multidrug-resistance risk group (n=314). Sensitivities of Xpert Ultra and Xpert were 63% and 46%, respectively, for the 137 participants with smear-negative and culture-positive sputum (difference of 17%, 95% CI 10 to 24); 90% and 77%, respectively, for the 115 HIV-positive participants with culture-positive sputum (13%, 6·4 to 21); and 88% and 83%, respectively, across all 462 participants with culture-positive sputum (5·4%, 3·3 to 8·0). Specificities of Xpert Ultra and Xpert for case detection were 96% and 98% (−2·7%, −3·9 to −1·7) overall, and 93% and 98% for patients with a history of tuberculosis. Xpert Ultra and Xpert performed similarly in detecting rifampicin resistance.InterpretationFor tuberculosis case detection, sensitivity of Xpert Ultra was superior to that of Xpert in patients with paucibacillary disease and in patients with HIV. However, this increase in sensitivity came at the expense of a decrease in specificity.FundingGovernment of Netherlands, Government of Australia, Bill & Melinda Gates Foundati...

In response to coronavirus disease-2019 pandemic (COVID-19), the government of Uganda instituted movement restrictions to curb disease spread. However, this affected accessibility to medical services in a setting where the healthcare system is not equipped to handle most healthcare needs of the populace outside hospital premises. This gap led to the prominence and unprecedented rise in the use of digital health technologies to deliver health information and services at a distance (telehealth) during the COVID-19 outbreak. The use of telehealth modalities including tele-consultation, tele-psychiatry, call centers and mobile phone health information dissemination increased. The COVID-19 pandemic augmented the rising role of digital health technologies as a much needed aspect of medical service delivery in our times. However, the efficacy and impact on clinical outcomes across various healthcare thematic areas need to be explored further and more evidence generated.

Background: Trauma is an increasingly important cause of disease globally. Half of this trauma is from road traffic injuries with motorcycles contributing 21-58%. Low protective gear use, lack of regulation and weak traffic law enforcement contribute to unsafe nature of commercial motorcycles also known as "boda boda" in Uganda. Objectives: To determine the prevalence of protective gear use, the occurrence of head injury and the relationship between the two among commercial motorcycle riders in Kampala. Methods: Following ethical approval we recruited consecutive consenting participants to this analytical cross-sectional study. Data was collected using pretested interviewer administered questionnaires, double entered in Epidata and analyzed with STATA. Proportions and means were used to summarize data. Odds ratios were calculated for association between wearing helmets and occurrence and severity of head injury. Results: All 328 participants recruited were male. Of these, 18.6% used Protective gear and 71.1 % sustained head injury. Helmets protected users from head injury (OR 0.43, 95% CI, 0.23-0.8) and significantly reduced its severity when it occurred. Conclusion: Protective gear use was low, with high occurrence of head injury among commercial motorcycle riders in Uganda. More effective strategies are needed to promote protective gear use among Uganda's commercial motorcycle riders.

23 Introduction: Artificial intelligence (AI) in healthcare has gained momentum with advances in 24 affordable technology that has potential to help in diagnostics, predictive healthcare and 25 personalized medicine. In pursuit of applying universal non-biased AI in healthcare, it is 26 essential that data from different settings (gender, age and ethnicity) is represented. We present 27 findings from beta-testing an AI-powered dermatological algorithm called Skin Image Search, by 28 online dermatology company First Derm on Fitzpatrick 6 skin type (dark skin) dermatological 29 conditions. Methods: 123 dermatological images selected from a total of 173 images 30 retrospectively extracted from the electronic database of a Ugandan telehealth company, The 31 Medical Concierge Group (TMCG) after getting their consent. Details of age, gender and 32 dermatological clinical diagnosis were analyzed using R on R studio software to assess the 33 diagnostic accuracy of the AI app along disease diagnosis and body part. Predictability levels of 34 the AI app was graded on a scale of 0 to 5, where 0-no prediction made and 1-5 demonstrating 35 reducing correct prediction. Results: 76 (62%) of the dermatological images were from females 36 and 47 (38%) from males. The 5 most reported body parts were; genitals (20%), trunk (20%), 37 lower limb (14.6%), face (12%) and upper limb (12%) with the AI app predicting a diagnosis in 38 62% of image body parts uploaded. Overall diagnostic accuracy of the AI app was low at 17% 39 (21 out of 123 predictable images) with varying predictability levels correctness i.e. 1-8.9%, 2-40 2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with 41 dermatitis (80%). Conclusion: There is a need for diversity in the image datasets used when 42 training dermatology algorithms for AI applications in clinical decision support as a means to 43 increase accuracy and thus offer correct treatment across skin types and geographies. 44

Introduction majority of alcohol use pattern studies among university students are from developed countries. Information about the different alcohol use patterns and their correlates among university students in sub-Saharan Africa is limited. The aim of this study was to examine the prevalence and cardinal demographic and psychosocial factors associated with specific alcohol use patterns among Ugandan university students. Methods a cross section study conducted over 5-months among university students using a standardized socio-demographic questionnaire screened for alcohol use problems, depression symptoms and academic stress using the alcohol use disorders identification test (AUDIT), self-reporting questionnaire (SRQ-20) and the higher education stress inventory (HESI) respectively. Multivariate multinomial regression models were used to determine factors independently associated with a specific alcohol use pattern with low-risk drinkers as the reference group. Results a thousand out of 1200 students completed all study requirements for which 60% were males; median age was 22.3 (SD=2.36). The prevalence estimates of any alcohol use, low-risk drinking, heavy episodic drinking and alcohol misuse were 31%, 17.3%, 4.5% and 8.9% respectively. In comparison to low-risk drinkers, students reporting heavy episodic drinking were more likely to report high levels of academic stress (P-value <0.10). Those with alcohol misuse were more likely to be males and with significant depression symptoms (P-value ≤0.05). Non-alcohol users were more likely to report high levels of academic stress (P-value ≤0.05). Conclusion the prevalence of maladaptive alcohol use patterns is high among Ugandan university students. Integrating peer led psychological interventions into student health services is desperately needed.