Search

Introduction Determining aetiology of severe illness can be difficult, especially in settings with limited diagnostic resources, yet critical for providing life-saving care. Our objective was to describe the accuracy of antemortem clinical diagnoses in young children in high-mortality settings, compared with results of specific postmortem diagnoses obtained from Child Health and Mortality Prevention Surveillance (CHAMPS).Methods We analysed data collected during 2016–2022 from seven sites in Africa and South Asia. We compared antemortem clinical diagnoses from clinical records to a reference standard of postmortem diagnoses determined by expert panels at each site who reviewed the results of histopathological and microbiological testing of tissue, blood, and cerebrospinal fluid. We calculated test characteristics and 95% CIs of antemortem clinical diagnostic accuracy for the 10 most common causes of death. We classified diagnostic discrepancies as major and minor, per Goldman criteria later modified by Battle.Results CHAMPS enrolled 1454 deceased young children aged 1–59 months during the study period; 881 had available clinical records and were analysed. The median age at death was 11 months (IQR 4–21 months) and 47.3% (n=417) were female. We identified a clinicopathological discrepancy in 39.5% (n=348) of deaths; 82.3% of diagnostic errors were major. The sensitivity of clinician antemortem diagnosis ranged from 26% (95% CI 14.6% to 40.3%) for non-infectious respiratory diseases (eg, aspiration pneumonia, interstitial lung disease, etc) to 82.2% (95% CI 72.7% to 89.5%) for diarrhoeal diseases. Antemortem clinical diagnostic specificity ranged from 75.2% (95% CI 72.1% to 78.2%) for diarrhoeal diseases to 99.0% (95% CI 98.1% to 99.6%) for HIV.Conclusions Antemortem clinical diagnostic errors were common for young children who died in areas with high childhood mortality rates. To further reduce childhood mortality in resource-limited settings, there is an urgent need to improve antemortem diagnostic capability through advances in the availability of diagnostic testing and clinical skills.

Background: SARS-CoV-2 has extensively spread in cities and rural communities, and studies are needed to quantify exposure in the population. We report seroprevalence of SARS-CoV-2 in two well-characterized populations in Kenya at two time points. These data inform the design and delivery of public health mitigation measures. Methods: Leveraging on existing population based infectious disease surveillance (PBIDS) in two demographically diverse settings, a rural site in western Kenya in Asembo, Siaya County, and an urban informal settlement in Kibera, Nairobi County, we set up a longitudinal cohort of randomly selected households with serial sampling of all consenting household members in March and June 2021. Both sites included 1,794 and 1,638 participants in March and June 2021, respectively. Individual seroprevalence of SARS-CoV-2 antibodies was expressed as a percentage of the seropositive among the individuals tested, accounting for household clustering and weighted by the PBIDS age and sex distribution. Results: Overall weighted individual seroprevalence increased from 56.2% (95%CI: 52.1, 60.2%) in March 2021 to 63.9% (95%CI: 59.5, 68.0%) in June 2021 in Kibera. For Asembo, the seroprevalence almost doubled from 26.0% (95%CI: 22.4, 30.0%) in March 2021 to 48.7% (95%CI: 44.3, 53.2%) in July 2021. Seroprevalence was highly heterogeneous by age and geography in these populations—higher seroprevalence was observed in the urban informal settlement (compared to the rural setting), and children aged

Summary: Background: Most childhood deaths globally are considered preventable through high-quality clinical care, which includes adherence to clinical care recommendations. Our objective was to describe adherence to World Health Organization recommendations for the management of leading causes of death among children. Methods: We conducted a retrospective, descriptive study examining clinical data for children aged 1–59 months who were hospitalized and died in a Child Health and Mortality Prevention Surveillance (CHAMPS) catchment, December 2016–June 2021. Catchment areas included: Baliakandi and Faridpur, Bangladesh; Kersa, Haramaya, and Harar, Ethiopia; Kisumu and Siaya, Kenya; Bamako, Mali; Manhiça and Quelimane, Mozambique; Makeni, Sierra Leone; Soweto, South Africa. We reviewed medical records of those who died from lower respiratory tract infections, sepsis, malnutrition, malaria, and diarrheal diseases to determine the proportion who received recommended treatments and compared adherence by hospitalization duration. Findings: CHAMPS enrolled 460 hospitalized children who died from the leading causes (median age 12 months, 53.0% male). Median hospital admission was 31 h. There were 51.0% (n = 127/249) of children who died from lower respiratory tract infections received supplemental oxygen. Administration of intravenous fluids for sepsis (15.9%, n = 36/226) and supplemental feeds for malnutrition (14.0%, n = 18/129) were uncommon. There were 51.4% (n = 55/107) of those who died from malaria received antimalarials. Of the 80 children who died from diarrheal diseases, 76.2% received intravenous fluids. Those admitted for ≥24 h more commonly received antibiotics for lower respiratory tract infections and sepsis, supplemental feeds for malnutrition, and intravenous fluids for sepsis than those admitted

Robust data on the impact of the COVID-19 pandemic on mortality in Africa are relatively scarce. Using data from two well-characterized populations in Kenya we aimed to estimate excess mortality during the COVID-19 pandemic period. The mortality data arise from an ongoing population-based infectious disease surveillance (PBIDS) platform, which has been operational since 2006 in rural western Kenya (Asembo, Siaya County) and an urban informal settlement (Kibera, Nairobi County), Kenya. PBIDS participants were regularly visited at home (2-3 times a year) by field workers who collected demographic data, including deaths. In addition, verbal autopsy (VA) interviews for all identified deaths are conducted. We estimated all-cause and cause-specific mortality rates before and during the height of the COVID-19 pandemic, and we compared associated mortality rates between the periods using incidence rate ratios. Excess deaths during the COVID-19 period were also estimated by modelling expected deaths in the absence of COVID-19 by applying a negative binomial regression model on historical mortality data from January 2016. Overall and monthly excess deaths were determined using the P-score metric. Spearman correlation was used to assess whether there is a relationship between the generated P-score and COVID-19 positivity rate. The all-cause mortality rate was higher during the COVID-19 period compared to the pre-COVID-19 period in Asembo [9.1 (95% CI, 8.2-10.0) vs. 7.8 (95% CI, 7.3-8.3) per 1000 person-years of observation, pyo]. In Kibera, the all-cause mortality rate was slightly lower during the COVID-19 period compared to the pre-COVID-19 period [2.6 (95% CI, 2.2-3.2 per 1000 pyo) vs. 3.1; 95% CI, 2.7-3.4 per 1000 pyo)]. An increase in all-cause mortality was observed (incidence rate ratio, IRR, 1.16; 95% CI, 1.04-1.31) in Asembo, unlike in Kibera (IRR, 0.88; 95% CI, 0.71-1.09). The notable increase in mortality rate in Asembo was observed among persons aged 50 to 64 years (IRR, 2.62; 95% CI, 1.95-3.52), persons aged 65 years and above (5.47; 95% CI, 4.60-6.50) and among females (IRR, 1.25; 95% CI, 1.07-1.46). These age and gender differences were not observed in Kibera. We observed an increase in the mortality rate due to acute respiratory infection, including pneumonia (IRR, 1.45;95% CI, 1.03-2.04), and a reduction in the mortality rate due to pulmonary tuberculosis (IRR, 0.22; 95% CI, 0.05-0.87) among older children and adults in Asembo. There was no statistically significant change in mortality rates due to leading specific causes of death in Kibera. Overall, during the COVID-19 period observed deaths were higher than expected deaths in Asembo (P-score = 6.0%) and lower than expected in Kibera (P-score = -22.3%).Using well-characterized populations in the two diverse geographic locations, we demonstrate a heterogenous impact of the COVID-19 pandemic on all-cause and cause-specific mortality rates in Kenya. We observed more deaths than expected during the COVID-19 period in our rural site in western Kenya contrary to the urban site in Nairobi, the capital city in Kenya.