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Objective: To derive and validate internally a novel risk assessment tool to identify young children at risk for all-cause mortality ≤60 days of discharge from hospitals in sub-Saharan Africa., Study Design: We performed a prospective observational cohort study of children aged 1-59 months discharged from Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia (2019-2022). Caregivers received telephone calls up to 60 days after discharge to ascertain participant vital status. We collected socioeconomic, demographic, clinical, and anthropometric data during hospitalization. Candidate variables with P < .20 in bivariate analyses were included in a multivariable logistic regression model with best subset selection to identify risk factors for the outcome. We internally validated our tool using bootstrapping with 500 repetitions., Results: There were 1933 young children enrolled in the study. The median (IQR) age was 11 (4, 23) months and 58.7% were males. In total, 67 (3.5%) died during follow-up. Ten variables contributed to our tool (total possible score 82). Cancer (aOR 10.6, 95% CI 2.58, 34.6), pedal edema (aOR 6.94, 95% CI 1.69, 22.6), and leaving against medical advice (aOR 6.46, 95% CI 2.46, 15.3) were most predictive of post-discharge mortality. Our risk assessment tool demonstrated good discriminatory value (optimism corrected area under the receiver operating characteristic curve 0.77), high precision, and sufficient calibration., Conclusions: After validation, this tool may be used to identify young children at risk for post-discharge mortality to direct resources for follow-up of high-risk children., Competing Interests: Declaration of Competing Interest C.P.D is a member of the Editorial Board of the Journal of Pediatrics but played no role in the handling of this manuscript. The other authors declare no conflicts of interest. This study was funded by the National Institutes of Health (K24 DK104676 and P30 DK040561 to CPD, K24 AT009893 to CRM, and K23HL173694 to CAR), the Boston Children's Hospital Global Health Program to CAR, the Palfrey Fund for Child Health Advocacy to CAR, and the Emory Pediatric Research Alliance Junior Faculty Focused Award to CAR. This work was presented, in part, as a platform presentation at the American Academy of Pediatrics National Conference & Exhibition in October 2023 in Washington DC., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Researchers and healthcare providers have paid little attention to morbidity and unplanned healthcare encounters for children following hospital discharge in low- and middle-income countries. Our objective was to compare symptoms and unplanned healthcare encounters among children aged <5 years who survived with those who died within 60 days of hospital discharge through follow-up phone calls., Methods: We conducted a secondary analysis of a prospective observational cohort of children aged <5 years discharged from neonatal and paediatric wards of two national referral hospitals in Dar es Salaam, Tanzania and Monrovia, Liberia. Caregivers of enrolled participants received phone calls 7, 14, 30, 45, and 60 days after hospital discharge to record symptoms, unplanned healthcare encounters, and vital status. We used logistic regression to determine the association between reported symptoms and unplanned healthcare encounters with 60-day post-discharge mortality., Results: A total of 4243 participants were enrolled and had 60-day vital status available; 138 (3.3%) died. For every additional symptom ever reported following discharge, there was a 35% greater likelihood of post-discharge mortality (adjusted odds ratio [aOR] 1.35, 95% confidence interval [CI] 1.10 to 1.66; p=0.004). The greatest survival difference was noted for children who had difficulty breathing (2.1% among those who survived vs 36.0% among those who died, p<0.001). Caregivers who took their child home from the hospital against medical advice during the initial hospitalisation had over eight times greater odds of post-discharge mortality (aOR 8.06, 95% CI 3.87 to 16.3; p<0.001) and those who were readmitted to a hospital had 3.42 greater odds (95% CI 1.55 to 8.47; p=0.004) of post-discharge mortality than those who did not seek care when adjusting for site, sociodemographic factors, and clinical variables., Conclusion: Surveillance for symptoms and repeated admissions following hospital discharge by healthcare providers is crucial to identify children at risk for post-discharge mortality., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: The immediate period after hospital discharge carries a large burden of childhood mortality in sub-Saharan Africa. Our objective was to derive and internally validate a risk assessment tool to identify neonates discharged from the neonatal ward at risk for 60-day post-discharge mortality., Methods: We conducted a prospective observational cohort study of neonates discharged from Muhimbili National Hospital in Dar es Salaam, Tanzania, and John F Kennedy Medical Centre in Monrovia, Liberia. Research staff called caregivers to ascertain vital status up to 60 days after discharge. We conducted multivariable logistic regression analyses with best subset selection to identify socioeconomic, demographic, clinical, and anthropometric factors associated with post-discharge mortality. We used adjusted log coefficients to assign points to each variable and internally validated our tool with bootstrap validation with 500 repetitions., Results: There were 2344 neonates discharged and 2310 (98.5%) had post-discharge outcomes available. The median (IQR) age at discharge was 8 (4, 15) days; 1238 (53.6%) were male. In total, 71 (3.1%) died during follow-up (26.8% within 7 days of discharge). Leaving against medical advice (adjusted OR [aOR] 5.62, 95% CI 2.40 to 12.10) and diagnosis of meconium aspiration (aOR 6.98, 95% CI 1.69 to 21.70) conferred the greatest risk for post-discharge mortality. The risk assessment tool included nine variables (total possible score=63) and had an optimism corrected area under the receiver operating characteristic curve of 0.77 (95% CI 0.75 to 0.80). A score of ≥6 was most optimal (sensitivity 68.3% [95% CI 64.8% to 71.5%], specificity 72.1% [95% CI 71.5% to 72.7%])., Conclusions: A small number of factors predicted all-cause, 60-day mortality after discharge from neonatal wards in Tanzania and Liberia. After external validation, this risk assessment tool may facilitate clinical decision making for eligibility for discharge and the direction of resources to follow-up high risk neonates., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: There are no validated clinical decision aids to identify neonates and young children at risk of hospital readmission or postdischarge mortality in sub-Saharan Africa, leaving the decision to discharge a child to a clinician's impression. Our objective was to determine the precision of clinician impression to identify neonates and young children at risk for readmission and postdischarge mortality., Methods: We conducted a survey study nested in a prospective observational cohort of neonates and children aged 1-59 months followed 60 days after hospital discharge from Muhimbili National Hospital in Dar es Salaam, Tanzania or John F. Kennedy Medical Center in Monrovia, Liberia. Clinicians who discharged each enrolled patient were surveyed to determine their perceived probability of the patient's risk of 60-day hospital readmission or postdischarge mortality. We calculated the area under the precision-recall curve (AUPRC) to determine the precision of clinician impression for both outcomes., Results: Of 4247 discharged patients, 3896 (91.7%) had available clinician surveys and 3847 (98.7%) had 60-day outcomes available: 187 (4.8%) were readmitted and 120 (3.1%) died within 60 days of hospital discharge. Clinician impression had poor precision in identifying neonates and young children at risk of hospital readmission (AUPRC: 0.06, 95% CI: 0.04 to 0.08) and postdischarge mortality (AUPRC: 0.05, 95% CI: 0.03 to 0.08). Patients for whom clinicians attributed inability to pay for future medical treatment as the reason for risk for unplanned hospital readmission had 4.76 times the odds hospital readmission (95% CI: 1.31 to 17.25, p=0.02)., Conclusions: Given the poor precision of clinician impression alone to identify neonates and young children at risk of hospital readmission and postdischarge mortality, validated clinical decision aids are needed to aid in the identification of young children at risk for these outcomes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Over half of the 5 million annual deaths among children aged 0-59 months occur in sub-Saharan Africa. The period immediately after hospitalisation is a vulnerable time in the life of a child in sub-Saharan Africa as postdischarge mortality rates are as high as 1%-18%. Identification of neonates and children who are at highest risk for postdischarge mortality may allow for the direction of interventions to target patients at highest risk., Methods and Analysis: The Predicting Post-Discharge Mortality study is a prospective, observational study being conducted at Muhimbili National Hospital (Dar es Salaam, Tanzania) and John F. Kennedy Medical Center (Monrovia, Liberia). The aim is to derive and validate two, age population specific, clinical prediction rules for the identification of neonates (n=2000) and children aged 1-59 months (n=2000) at risk for all-cause mortality within 60 days of discharge from the neonatal intensive care unit or paediatric ward. Caregivers of participants will receive phone calls 7, 14, 30, 45 and 60 days after discharge to assess vital status. Candidate predictor variables will include demographic, anthropometric and clinical factors. Elastic net regression will be used to derive the clinical prediction rules. Bootstrapped selection with repetitions will be used for internal validation. Planned secondary analyses include the external validation of existing clinical prediction models, determination of clinicians' ability to identify neonates and children at risk of postdischarge mortality at discharge, analysis of factors associated with hospital readmission and unplanned clinic visits and description of health-seeking behaviours in the postdischarge period., Ethics and Dissemination: This study received ethical clearance from the Tanzania National Institute of Medical Research, Muhimbili University of Health and Allied Sciences, the John F. Kennedy Medical Center Institutional Review Board, and the Boston Children's Hospital Institutional Review Board. Findings will be disseminated at scientific conferences and as peer-reviewed publications., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)