Kenya's experience implementing event-based surveillance during the COVID-19 pandemic

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.

In-hospital mortality risk stratification in children aged under 5 years with pneumonia with or without pulse oximetry: A secondary analysis of the Pneumonia REsearch Partnership to Assess WHO REcommendations (PREPARE) dataset

OBJECTIVES

We determined the pulse oximetry benefit in pediatric pneumonia mortality risk stratification and chest-indrawing pneumonia in-hospital mortality risk factors.

METHODS

We report the characteristics and in-hospital pneumonia-related mortality of children aged 2-59 months who were 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% confidence interval [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 [adjusted odds ratio (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).

CONCLUSION

Children with a pulse oximetry measurement had a lower CFR. Many children hospitalized with chest-indrawing pneumonia were hypoxemic and one in 10 died. Young age and moderate malnutrition were risk factors for in-hospital chest-indrawing pneumonia-related mortality. Pulse oximetry should be integrated in pneumonia hospital care for children under 5 years.

Assembling a global database of child pneumonia studies to inform WHO pneumonia management algorithm: Methodology and applications

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 285 839 children with pneumonia (244 323 in the hospital and 41 516 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 285 839 episodes, 280 998 occurred in children 0-59 months old, of which 129 584 (46%) were 2-11 months of age and 152 730 (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.

Derivation and validation of a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality in 20 countries

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.

External validation of the RISC, RISC-Malawi, and PERCH clinical prediction rules to identify risk of death in children hospitalized with pneumonia

BACKGROUND

Existing scores to identify children at risk of hospitalized pneumonia-related mortality lack broad external validation. Our objective was to externally validate three such risk scores.

METHODS

We applied the Respiratory Index of Severity in Children (RISC) for HIV-negative children, the RISC-Malawi, and the Pneumonia Etiology Research for Child Health (PERCH) scores to hospitalized children in the Pneumonia REsearch Partnerships to Assess WHO REcommendations (PREPARE) data set. The PREPARE data set includes pooled data from 41 studies on pediatric pneumonia from across the world. We calculated test characteristics and the area under the curve (AUC) for each of these clinical prediction rules.

RESULTS

The RISC score for HIV-negative children was applied to 3574 children 0-24 months and demonstrated poor discriminatory ability (AUC = 0.66, 95% confidence interval (CI) = 0.58-0.73) in the identification of children at risk of hospitalized pneumonia-related mortality. The RISC-Malawi score had fair discriminatory value (AUC = 0.75, 95% CI = 0.74-0.77) among 17 864 children 2-59 months. The PERCH score was applied to 732 children 1-59 months and also demonstrated poor discriminatory value (AUC = 0.55, 95% CI = 0.37-0.73).

CONCLUSIONS

In a large external application of the RISC, RISC-Malawi, and PERCH scores, a substantial number of children were misclassified for their risk of hospitalized pneumonia-related mortality. Although pneumonia risk scores have performed well among the cohorts in which they were derived, their performance diminished when externally applied. A generalizable risk assessment tool with higher sensitivity and specificity to identify children at risk of hospitalized pneumonia-related mortality may be needed. Such a generalizable risk assessment tool would need context-specific validation prior to implementation in that setting.

Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

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 (I²>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.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3181-y) contains supplementary material, which is available to authorized users.

Epidemiological and virological characteristics of influenza B: results of the Global Influenza B Study

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 ≈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.

Correction: Temporal Patterns of Influenza A and B in Tropical and Temperate Countries: What Are the Lessons for Influenza Vaccination?

[This corrects the article DOI: 10.1371/journal.pone.0152310.].