Event-based surveillance (EBS) systems have been implemented globally to support early warning surveillance across human, animal, and environmental health in diverse settings, including at the community level, within health facilities, at border points of entry, and through media monitoring of internet-based sources. EBS systems should be evaluated periodically to ensure that they meet the objectives related to the early detection of health threats and to identify areas for improvement in the quality, efficiency, and usefulness of the systems. However, to date, there has been no comprehensive framework to guide the monitoring and evaluation of EBS systems; this absence of standardisation has hindered progress in the field. The Africa Centres for Disease Control and Prevention and US Centers for Disease Control and Prevention have collaborated to develop an EBS monitoring and evaluation indicator framework, adaptable to specific country contexts, that uses measures relating to input, activity, output, outcome, and impact to map the processes and expected results of EBS systems. Through the implementation and continued refinement of these indicators, countries can ensure the early detection of health threats and improve their ability to measure and describe the impacts of EBS systems, thus filling the current evidence gap regarding their effectiveness.

Event-based surveillance (EBS) can be implemented in most settings for the detection of potential health threats by recognition and immediate reporting of predefined signals. Such a system complements existing case-based and sentinel surveillance systems. With the emergence of the COVID-19 pandemic in early 2020, the Kenya Ministry of Health (MOH) modified and expanded an EBS system in both community and health facility settings for the reporting of COVID-19-related signals. Using an electronic reporting tool, m-Dharura, MOH recorded 8790 signals reported, with 3002 (34.2%) verified as events, across both community and health facility sites from March 2020 to June 2021. A subsequent evaluation found that the EBS system was flexible enough to incorporate the addition of COVID-19-related signals during a pandemic and maintain high rates of reporting from participants. Inadequate resources for follow-up investigations to reported events, lack of supportive supervision for some community health volunteers and lack of data system interoperability were identified as challenges to be addressed as the EBS system in Kenya continues to expand to additional jurisdictions.

OBJECTIVES: We determined pulse oximetry benefit in pediatric pneumonia mortality-risk stratification and chest indrawing pneumonia in-hospital mortality risk factors. METHODS: We report characteristics and in-hospital pneumonia-related mortality of children 2-59-months-old included in the Pneumonia Research Partnership to Assess WHO Recommendations dataset. We developed multivariable logistic regression models of chest indrawing pneumonia to identify mortality risk factors. RESULTS: Among 285,839 children, 164,244 (57·5%) from hospital-based studies were included. Pneumonia case fatality risk (CFR) without pulse oximetry measurement was higher than with measurement (5·8%, 95% CI 5·6-5·9% vs 2·1%, 95% CI 1·9-2·4%). One in five children with chest indrawing pneumonia was hypoxemic (19·7%, 95% CI 19·0-20·4%) and the hypoxemic CFR was 10·3% (95% CI 9·1%-11·5%). Other mortality risk factors were younger age (either 2-5 months (aOR 9·94, 95% CI 6·67-14·84) or 6-11 months (aOR 2·67, 95% CI 1·71-4·16)), moderate malnutrition (aOR 2·41, 95% CI 1·87-3·09), and female sex (aOR 1·82, 95% CI 1·43-2·32). CONCLUSIONS: Children with a pulse oximetry measurement had a lower CFR. Many children hospitalized with chest indrawing pneumonia were hypoxemic and one in ten died. Young age and moderate malnutrition were risk factors for in-hospital chest indrawing pneumonia-related mortality. Pulse oximetry should be integrated in under-five pneumonia hospital care.

BACKGROUND: The existing World Health Organization (WHO) pneumonia case management guidelines rely on clinical symptoms and signs for identifying, classifying, and treating pneumonia in children up to 5 years old. We aimed to collate an individual patient-level data set from large, high-quality pre-existing studies on pneumonia in children to identify a set of signs and symptoms with greater validity in the diagnosis, prognosis, and possible treatment of childhood pneumonia for the improvement of current pneumonia case management guidelines. METHODS: Using data from a published systematic review and expert knowledge, we identified studies meeting our eligibility criteria and invited investigators to share individual-level patient data. We collected data on demographic information, general medical history, and current illness episode, including history, clinical presentation, chest radiograph findings when available, treatment, and outcome. Data were gathered separately from hospital-based and community-based cases. We performed a narrative synthesis to describe the final data set. RESULTS: Forty-one separate data sets were included in the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) database, 26 of which were hospital-based and 15 were community-based. The PREPARE database includes 285839 children with pneumonia (244323 in the hospital and 41516 in the community), with detailed descriptions of clinical presentation, clinical progression, and outcome. Of 9185 pneumonia-related deaths, 6836 (74%) occurred in children <1 year of age and 1317 (14%) in children aged 1-2 years. Of the 285839 episodes, 280998 occurred in children 0-59 months old, of which 129584 (46%) were 2-11 months of age and 152730 (54%) were males. CONCLUSIONS: This data set could identify an improved specific, sensitive set of criteria for diagnosing clinical pneumonia and help identify sick children in need of referral to a higher level of care or a change of therapy. Field studies could be designed based on insights from PREPARE analyses to validate a potential revised pneumonia algorithm. The PREPARE methodology can also act as a model for disease database assembly.

INTRODUCTION: Existing risk assessment tools to identify children at risk of hospitalised pneumonia-related mortality have shown suboptimal discriminatory value during external validation. Our objective was to derive and validate a novel risk assessment tool to identify children aged 2-59 months at risk of hospitalised pneumonia-related mortality across various settings. METHODS: We used primary, baseline, patient-level data from 11 studies, including children evaluated for pneumonia in 20 low-income and middle-income countries. Patients with complete data were included in a logistic regression model to assess the association of candidate variables with the outcome hospitalised pneumonia-related mortality. Adjusted log coefficients were calculated for each candidate variable and assigned weighted points to derive the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) risk assessment tool. We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool. RESULTS: A total of 27 388 children were included in the analysis (mean age 14.0 months, pneumonia-related case fatality ratio 3.1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0.83, 95% CI 0.81 to 0.84). CONCLUSIONS: The PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset. After external validation, this tool may be implemented in various settings to identify children at risk of hospitalised pneumonia-related mortality.

BACKGROUND: Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases). METHODS: For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted meta-regression and sub-group analyses to explore causes of between-estimates heterogeneity. RESULTS: The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1)pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I(2)>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play. CONCLUSIONS: These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.

INTRODUCTION: Determining the optimal time to vaccinate is important for influenza vaccination programmes. Here, we assessed the temporal characteristics of influenza epidemics in the Northern and Southern hemispheres and in the tropics, and discuss their implications for vaccination programmes. METHODS: This was a retrospective analysis of surveillance data between 2000 and 2014 from the Global Influenza B Study database. The seasonal peak of influenza was defined as the week with the most reported cases (overall, A, and B) in the season. The duration of seasonal activity was assessed using the maximum proportion of influenza cases during three consecutive months and the minimum number of months with ≥80% of cases in the season. We also assessed whether co-circulation of A and B virus types affected the duration of influenza epidemics. RESULTS: 212 influenza seasons and 571,907 cases were included from 30 countries. In tropical countries, the seasonal influenza activity lasted longer and the peaks of influenza A and B coincided less frequently than in temperate countries. Temporal characteristics of influenza epidemics were heterogeneous in the tropics, with distinct seasonal epidemics observed only in some countries. Seasons with co-circulation of influenza A and B were longer than influenza A seasons, especially in the tropics. DISCUSSION: Our findings show that influenza seasonality is less well defined in the tropics than in temperate regions. This has important implications for vaccination programmes in these countries. High-quality influenza surveillance systems are needed in the tropics to enable decisions about when to vaccinate.

We estimate the proportion of patients hospitalized for suspected dengue that tested positive for influenza virus in El Salvador during the 2012 influenza season. We tested specimens from 321 hospitalized patients: 198 patients with SARI and 123 patients with suspected dengue. Among 121 hospitalized suspected dengue (two co-infected excluded) patients, 28% tested positive for dengue and 19% positive for influenza; among 35 with suspected dengue and respiratory symptoms, 14% were positive for dengue and 39% positive for influenza. One percent presented co-infection between influenza and dengue. Clinicians should consider the diagnosis of influenza among patients with suspected dengue during the influenza season.

INTRODUCTION: Literature on influenza focuses on influenza A, despite influenza B having a large public health impact. The Global Influenza B Study aims to collect information on global epidemiology and burden of disease of influenza B since 2000. METHODS: Twenty-six countries in the Southern (n = 5) and Northern (n = 7) hemispheres and intertropical belt (n = 14) provided virological and epidemiological data. We calculated the proportion of influenza cases due to type B and Victoria and Yamagata lineages in each country and season; tested the correlation between proportion of influenza B and maximum weekly influenza-like illness (ILI) rate during the same season; determined the frequency of vaccine mismatches; and described the age distribution of cases by virus type. RESULTS: The database included 935 673 influenza cases (2000-2013). Overall median proportion of influenza B was 22.6%, with no statistically significant differences across seasons. During seasons where influenza B was dominant or co-circulated (>20% of total detections), Victoria and Yamagata lineages predominated during 64% and 36% of seasons, respectively, and a vaccine mismatch was observed in approximately 25% of seasons. Proportion of influenza B was inversely correlated with maximum ILI rate in the same season in the Northern and (with borderline significance) Southern hemispheres. Patients infected with influenza B were usually younger (5-17 years) than patients infected with influenza A. CONCLUSION: Influenza B is a common disease with some epidemiological differences from influenza A. This should be considered when optimizing control/prevention strategies in different regions and reducing the global burden of disease due to influenza.

BACKGROUND: The annual number of hospital admissions and in-hospital deaths due to severe acute lower respiratory infections (ALRI) in young children worldwide is unknown. We aimed to estimate the incidence of admissions and deaths for such infections in children younger than 5 years in 2010. METHODS: We estimated the incidence of admissions for severe and very severe ALRI in children younger than 5 years, stratified by age and region, with data from a systematic review of studies published between Jan 1, 1990, and March 31, 2012, and from 28 unpublished population-based studies. We applied these incidence estimates to population estimates for 2010, to calculate the global and regional burden in children admitted with severe ALRI in that year. We estimated in-hospital mortality due to severe and very severe ALRI by combining incidence estimates with case fatality ratios from hospital-based studies. FINDINGS: We identified 89 eligible studies and estimated that in 2010, 11.9 million (95% CI 10.3-13.9 million) episodes of severe and 3.0 million (2.1-4.2 million) episodes of very severe ALRI resulted in hospital admissions in young children worldwide. Incidence was higher in boys than in girls, the sex disparity being greatest in South Asian studies. On the basis of data from 37 hospital studies reporting case fatality ratios for severe ALRI, we estimated that roughly 265,000 (95% CI 160,000-450,000) in-hospital deaths took place in young children, with 99% of these deaths in developing countries. Therefore, the data suggest that although 62% of children with severe ALRI are treated in hospitals, 81% of deaths happen outside hospitals. INTERPRETATION: Severe ALRI is a substantial burden on health services worldwide and a major cause of hospital referral and admission in young children. Improved hospital access and reduced inequities, such as those related to sex and rural status, could substantially decrease mortality related to such infection. Community-based management of severe disease could be an important complementary strategy to reduce pneumonia mortality and health inequities. FUNDING: WHO.