To describe (1) the proportion of deaths that were in recently hospitalised children and (2) causes of mortality among deceased children aged 0–59 months with preceding hospitalisations who enrolled in a mortality surveillance programme.

Descriptive study using prospectively collected data.

Eight Child Health and Mortality Prevention Surveillance (CHAMPS) community and healthcare sites in sub-Saharan Africa and South Asia.

Deaths among children aged 0–59 months enrolled in CHAMPS 2016–2023.

None.

Deaths with antecedent hospitalisations within 180 days of death. Causes of death determined by expert panels who reviewed clinical data and histopathologic and microbiologic results from postmortem minimally invasive tissue sampling.

CHAMPS enrolled 8548 deaths; we excluded 3688 neonates who died before discharge or ≤24 hours of birth and 482 with unclear information on antecedent hospitalisations. Out of the 4378 remaining deaths, 16.7% (95% CI 15.7% to 17.9%) were deaths that occurred within 180 days of a hospitalisation (n=733/4378). Of these, 55.7% (95% CI 52.0% to 59.3%) occurred outside healthcare facilities. Among included deaths with minimally invasive tissue sampling completed (n=337), lower respiratory tract infections (41.2%, 95% CI 36.0% to 46.7%), sepsis (39.8%, 95% CI 34.5% to 45.2%) and undernutrition (n=92, 27.3%, 95% CI 22.7% to 32.4%) were most common causes of death among cases with antecedent hospitalisations. The greatest proportion of deaths with antecedent hospital admissions occurred among cases aged 1–11 months (48.0%, 95% CI 44.4% to 51.7%), compared with those aged 0–1 months (21.7%, 95% CI 18.8% to 24.9%) and those aged 1–5 years (30.3%, 95% CI 27.0% to 33.8%). Moreover, the greatest proportion of deaths with antecedent hospital admissions occurred among infants/children with weight-for-age Z-score of

We observed a high proportion of deaths with antecedent hospitalisations within 180 days among young children across eight sites in sub-Saharan Africa and Asia. Among those deaths, children aged 1–11 months and undernourished infants were over-represented, suggesting early follow-up as a potential point to focus targeted support and future research.

The Puerto Rico Department of Health (PRDH) seeks to identify dengue epidemics as early as possible with high specificity.

Development and prospective application of an early warning system for dengue epidemics using routine historical surveillance data. A weekly intercept-only negative binomial regression model was fitted using historical probable and confirmed dengue data. A range of threshold definitions was explored using three model-estimated percentiles of weekly dengue case counts.

Dengue is endemic in Puerto Rico with irregular occurrence of large epidemics with substantial impact on health burden and health systems. Probable and confirmed dengue data are routinely collected from all hospitals and private clinics.

A total of 86 282 confirmed or probable dengue virus cases were reported from 1 January 1986 to 30 June 2024, with an annual mean of 2212 cases (median: 1533; range: 40–10 356).

The model was fitted retrospectively to mimic real-time epidemic detection and assessed based on sensitivity and specificity of epidemic detection.

The 75th percentile threshold aligned best with historical epidemic classifications, balancing false alarms and missed detections. This model provides a robust method for defining thresholds, accounting for skewed data, using all historical data and improving on traditional methods like endemic channels.

In March 2024, PRDH declared a public health emergency due to an early, out-of-season surge in cases that exceeded the epidemic alert threshold developed in this study. This real-time application highlights the value of these thresholds to support dengue epidemic detection and public health response. Integrating thresholds with other tools and strategies can enhance epidemic preparedness and management.