Jerome Kabakyenga

Objective: Unplanned discharges are an important risk factor for adverse outcomes in children. We aimed to assess the impact of unplanned discharge on postdischarge outcomes and to identify key clinical and socioeconomic risk factors. Methods: We conducted a secondary analysis of a multisite observational study in six Ugandan hospitals. Children aged 0–60 months admitted with suspected sepsis were enrolled (July 2017–April 2020) and followed for 6 months after discharge. Multivariable logistic regression adjusted for demographic, clinical and socioeconomic factors (including age, sex and site) identified risk factors for unplanned discharge and Cox proportional hazards regression similarly adjusted for demographic, clinical and socioeconomic factors assessed its impact on readmission and mortality. Results: Of 6189 children who survived to discharge, 5403 (87.2%) had a planned discharge and 786 (12.8%) had an unplanned discharge with no unascertained cases. Among children with planned discharge, 271 (5.1%) of 5312 died within 6 months of discharge, compared with 105 (13.4%) of 760 among children with unplanned discharge. Unplanned discharge was associated with higher mortality (HR=2.6, 95% CI 1.9 to 3.5) and readmission (HR=2.4, 95% CI 1.4 to 4.2). In analyses identifying factors associated with unplanned discharge, risk factors included malnutrition, HIV and anaemia, as well as lower maternal education, greater distance from hospital and father-led presentation. Conclusions: Unplanned discharges are common and strongly associated with postdischarge mortality and readmission. Interventions targeting children at high risk of unplanned discharge, including those facing clinical, socioeconomic and structural barriers to care, are urgently needed to improve outcomes.

Sepsis remains a major cause of preventable pediatric hospital deaths in developing countries, with progress hindered by the lack of effective risk identification tools. Early detection of children at highest risk upon hospital admission is crucial for guiding clinical care and allocating resources, particularly in resource-limited settings. Photoplethysmography, which measures blood oxygen levels, also provides objective insight into cardiovascular alterations associated with sepsis. We conducted a secondary analysis of prospectively collected data from the Smart Discharges project, involving children under five years hospitalized with suspected or confirmed infection at six Ugandan hospitals, and developed models to predict all-cause in-hospital mortality across two age groups (0–6 and 6–60 months). Mortality was 7% in younger and 4.1% in older children. Machine learning models were trained on features extracted from one-minute photoplethysmograms collected at the time of admission. The best-performing model achieved mean values for the area under the receiver operating characteristic curve of 0.70 (95% CI: 0.62–0.76) in the younger cohort and 0.67 (95% CI: 0.56–0.73) in the older cohort, with corresponding values for the area under the precision–recall curve of 0.18 (95% CI: 0.12–0.27) and 0.14 (95% CI: 0.06–0.22), respectively. Calibration within risk strata was satisfactory (Brier scores 0.06 and 0.04), and decision curve analysis showed clinical utility. Notably, the models’ predictive capacity, although moderate, was achieved with a rapid and readily available objective measurement at admission, without the need for extended monitoring. While less accurate than most existing risk scores and not a substitute for clinical judgment, these simple admission-based models may help identify high-risk children and guide targeted interventions where sophisticated diagnostics are unavailable. External validation is needed before adoption.

PurposeVerbal autopsy (VA) is widely used to infer causes of death (COD) in settings where medical certification and autopsy are unavailable. Physician-certified COD determination after review of verbal autopsy is commonly used, but it is time- and effort-intensive, and often cloaked in diagnostic uncertainty. We introduce a framework for soft-label (probabilistic) supervised VA modelling that accommodates physician diagnostic uncertainty in settings where gold-standard labels are unavailable, and compare this to a traditional hard-label (deterministic) approach.MethodsUsing pediatric post-discharge mortality VA data (N = 356) from a multisite cohort study in Uganda, we constructed soft labels (probability vectors) from primary and alternative physician-assigned causes, along with associated confidence levels. XGBoost was used to create and compare models.ResultsThe soft-label model had population-level performance, as assessed by the cause-specific mortality fraction (CSMF) accuracy, of 0.88 ± 0.04, compared with 0.78 ± 0.03 for hard labels. Individual-level performance was slightly lower with a chance-corrected concordance of 0.38 ± 0.05 vs 0.40 ± 0.06.ConclusionsBy explicitly modelling physician uncertainty, soft-label learning substantially improves population-level cause-specific mortality estimation derived from VA data. The proposed framework is well-suited for mortality surveillance and policy-relevant applications in low-resource settings.

Background: Many children under five die post hospital discharge in low-and middle-income countries (LMICs), particularly after treatment for severe infections. While some models exist, evidence on risk prediction for post-discharge mortality remains limited, with most relying solely on admission characteristics, overlooking in-hospital disease progression and discharge features. Methods: We used secondary data from prospective cohort studies in six Ugandan hospitals (2012-2021) to update models at discharge. Of 8,810 children included, 3,665 were aged P ), achieving an AUROC of 0.81 and a Brier score of 0.06. In the 6–60m cohort, the model AUROC increased by 4.4% (95% CI 2.0 – 6.9, P ), with an AUROC of 0.79 and a Brier score of 0.04. The NRI was 10.41% for children <6 months and 14.51% for those 6-60m and was achieved primarily through a reduction of false positive rates. Conclusion: Adding only three discharge characteristics to the post-discharge mortality model based on admission characteristics enhanced prediction accuracy, including model calibration, discrimination and risk stratification compared to admission-only models. Key Messages: Post-discharge mortality risk prediction models that incorporated discharge characteristics performed better than admission-only models for children under five in Uganda. The augmented model achieved stronger discrimination, improved calibration and substantial reclassification gains, primarily by reducing false positive rates. Most of the benefits of these improved models stem from accurately identifying low-risk survivors, which reduces unwarranted follow-up and enables the more effective utilization of the health system’s limited resources.

Objective: The aim of this scoping review is to identify the currently available mHealth interventions for palliative care and factors influencing their use in sub-Saharan Africa (SSA). Introduction: Although palliative care is essential for improving quality of life, its use in SSA remains low. mHealth (an interaction with a mobile device) interventions could enhance and promote palliative care in the region. Integration of mHealth into palliative care has been observed across Europe and the USA to support pain management and improve communication between health professionals and patients; however, there is little evidence on its use in low- and middle-income countries. Eligibility criteria: Eligible studies will involve key stakeholders in palliative care, such as patients with life-limiting illnesses (eg, cancer), health professionals, community volunteer workers, and community leaders. Studies reporting evidence on mHealth in the design, implementation, or evaluation of palliative care services in any country in SSA will be considered. Methods: The review will follow JBI methodology for scoping reviews and will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The following sources will be searched for published and unpublished literature: CINAHL (EBSCOhost), PubMed (Ovid), Embase (Ovid), the Cochrane Library, Google Scholar, Scopus, Web of Science, and ProQuest Dissertations and Theses Global. Study screening and selection will be conducted independently by 2 reviewers. Data will be extracted using a modified JBI data charting form, and will be presented in tabular format and narrative summary. Review registration: OSF https://osf.io/gbqcn/overview

Young people aged 15–24 years continue to be disproportionately affected by HIV globally. In sub-Saharan Africa, utilization of HIV prevention services remains suboptimal. Education plays a pivotal role in enhancing HIV prevention knowledge among youth, but its effectiveness is influenced by factors such as age, gender, and socioeconomic status. The objective of this study was to investigate how education status, age, and gender influence awareness use, and preferences of HIV prevention methods among youth in rural southwestern Uganda. A cross-sectional quantitative study was conducted in March 2022 among 216 adolescents and young adults aged 15–24 years in Rubirizi District, southwestern Uganda. Participants were recruited using stratified random sampling from three publicly funded schools and snowball sampling from local trading centers to include both school-going and non-school-going youth. Data from both sampling approaches were pooled for analysis. Data were collected using a structured questionnaire translated into the local language. Descriptive statistics and inferential analyses were performed using R software, with statistical significance set at p < 0.05. Participants had a mean age of 19.28 ± 2.32 years, consisting of 50.46% females (n = 109), and equal proportions of school-going and non-school-going youth A majority (72%) perceived themselves at low risk of HIV infection. The findings reflect pooled data from both school-going and non-school-going participants. School-going youth demonstrated higher awareness of abstinence (75% vs. 32%, p < 0.001) and prevention of mother-to-child transmission (46% vs. 25%, p = 0.002) compared to non-school-going youth. Younger individuals were more likely to practice abstinence (mean age 18.9 years vs. 19.7 years, p = 0.017). Males also had higher awareness of being faithful (59% vs. 44%, p = 0.04). Private doctors (46%) and hospitals (47%) were the most favored access points for PrEP. Younger participants expressed greater concern about daily pill-taking inconvenience (mean age 20.4 years vs. 19.2 years, p = 0.04). Radio (73%) and television (41%) were the most preferred methods for HIV information dissemination. Females preferred magazines more than males (21% vs. 10.3%, p = 0.046). Education status, age, and gender influence HIV prevention awareness through differences in access to information, risk perception, and preference of prevention methods among youth in rural Uganda. Therefore, aligning HIV prevention strategies with the specific needs and preferences of different youth subgroups can enhance awareness, risk perception, and utilization of HIV prevention methods.

Introduction: Post-discharge mortality among children following an acute illness in low-resource settings is high and demands urgent attention. We aimed to assess the impact of Smart Discharges, a mortality prevention risk-differentiated approach to peri-discharge care among children under five years admitted with suspected sepsis. Methods: We conducted a before-after two-phase study with staggered implementation at six hospitals in Uganda. During a baseline period (Phase-1), clinical prediction algorithms for post-discharge mortality were developed based on clinical and socio-demographic data collected at hospital admission. Outcomes, primarily mortality within six months of discharge, were compared against an interventional period (Phase-2). In the Smart Discharges intervention each family was given soap, a mosquito net, their risk category (low/medium/high/very high), counselling, and educational materials regardless of risk stratification, after which the intensity of recommended follow-up care was determined by age (0-6, 6-60 months) and predicted post-discharge mortality risk. Results: Overall, 13,051 children were enrolled: Phase-1, n=6,955; Phase-2, n=6,096. Characteristics were similar between groups, including mean predicted post-discharge mortality risk (6.3% Phase-1 vs. 5.9% Phase-2). With Smart Discharges, 2,331/3,891 (59.9%) medium/high/very high-risk families attending all their scheduled visits. The observed post-discharge mortality rate was 439 (6.3%) in Phase-1 vs. 296 (4.9%) in Phase-2; adjusted hazard ratio 0.77 (95%CI 0.67 to 0.90) favouring the intervention. In Phase-1, 1,313 (18.9%) children were re-admitted to hospital vs. 1,024 (16.8%) in Phase-2. Conclusion: A simple approach of risk assessment paired with education and engagement through scheduled follow-up after discharge is an appropriate strategy to improve child survival in low-resource settings.

Globally, the majority of new HIV infections are recorded in Eastern and Southern Africa, with the youth being disproportionately affected. HIV prevention is the cornerstone of controlling the spread of HIV and ending this epidemic by 2030. However, barriers to the utilization of HIV prevention services remained underexplored especially among the youth in rural settings in sub-Saharan Africa. This qualitative study, conducted between February and April 2022 in rural southwestern Uganda, explored these barriers and identified recommendations to improve the utilization of HIV prevention services among youth. We conducted six focus group discussions (with youth [15–24 years] both in and out of school), nine in-depth interviews (with teachers, health workers, and members of the village health team), and four key informant interviews (with district officials) to collect data. Thematic analysis revealed barriers at the individual level (e.g., misconceptions, fear of testing, low perceived HIV risk, confidentiality concerns), community level (e.g., stigma, lack of counseling, peer influence), and health system level (e.g., lack of youth-friendly services). Recommendations included formation of youth peer support groups, ongoing awareness campaigns, and socio-economic empowerment initiatives, particularly targeting adolescent girls and young women. National scaling of these initiatives is essential to overcoming identified barriers and reducing HIV transmission among this vulnerable population. Additionally, economic empowerment especially among adolescent girls and young women in rural areas has enormous potential to address the spread of HIV in this sub-population.

In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0–6 and 6–60 months) were conducted between 2012–2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74–0.80) for 0-6-month-olds and 0.75 (95%CI 0.72–0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.