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There are limited data on outcomes of SARS-CoV-2 infection among infants (<1 year of age). In the absence of approved vaccines for infants, understanding characteristics associated with hospitalization and severe disease from COVID-19 in this age group will help inform clinical management and public health interventions. The objective of this study was to describe the clinical manifestations, disease severity, and characteristics associated with hospitalization among infants infected with the initial strains of SARS-CoV-2. This is a national, prospective study of infants with SARS-CoV-2 from April 8.sup.th 2020 to May 31.sup.st 2021 using the infrastructure of the Canadian Paediatric Surveillance Program. Infants <1 year of age with microbiologically confirmed SARS-CoV-2 infection from both inpatients and outpatients seen in clinics and emergency departments were included. Cases were classified as either: 1) Non-hospitalized patient with SARS-CoV-2 infection; 2) COVID-19-related hospitalization; or 3) non-COVID-19-related hospitalization (e.g., incidentally detected SARS-CoV-2). Case severity was defined as asymptomatic, outpatient care, mild (inpatient care), moderate or severe disease. Multivariable logistic regression was performed to identify characteristics associated with hospitalization. A total of 531 cases were reported, including 332 (62.5%) non-hospitalized and 199 (37.5%) hospitalized infants. Among hospitalized infants, 141 of 199 infants (70.9%) were admitted because of COVID-19-related illness, and 58 (29.1%) were admitted for reasons other than acute COVID-19. Amongst all cases with SARS-CoV-2 infection, the most common presenting symptoms included fever (66.5%), coryza (47.1%), cough (37.3%) and decreased oral intake (25.0%). In our main analysis, infants with a comorbid condition had higher odds of hospitalization compared to infants with no comorbid conditions (aOR = 4.53, 2.06-9.97), and infants <1 month had higher odds of hospitalization then infants aged 1-3 months (aOR = 3.78, 1.97-7.26). In total, 20 infants (3.8%) met criteria for severe disease. We describe one of the largest cohorts of infants with SARS-CoV-2 infection. Overall, severe COVID-19 in this age group was found to be uncommon. Comorbid conditions and younger age were associated with COVID-19-related hospitalization amongst infants.

Risk factors for severe outcomes of SARS-CoV-2 infection are not well established in children. We sought to describe pediatric hospital admissions associated with SARS-CoV-2 infection in Canada and identify risk factors for more severe disease. We conducted a national prospective study using the infrastructure of the Canadian Paediatric Surveillance Program (CPSP). Cases involving children who were admitted to hospital with microbiologically confirmed SARS-CoV-2 infection were reported from Apr. 8 to Dec. 31 2020, through weekly online questionnaires distributed to the CPSP network of more than 2800 pediatricians. We categorized hospital admissions as related to COVID-19, incidental, or for social or infection control reasons and determined risk factors for disease severity in hospital. Among 264 hospital admissions involving children with SARS-CoV-2 infection during the 9-month study period, 150 (56.8%) admissions were related to COVID-19 and 100 (37.9%) were incidental infections (admissions for other reasons and found to be positive for SARS-CoV-2 on screening). Infants (37.3%) and adolescents (29.6%) represented most cases. Among hospital admissions related to COVID-19, 52 (34.7%) had critical disease, 42 (28.0%) of whom required any form of respiratory or hemodynamic support, and 59 (39.3%) had at least 1 underlying comorbidity. Children with obesity, chronic neurologic conditions or chronic lung disease other than asthma were more likely to have severe or critical COVID-19. Among children who were admitted to hospital with SARS-CoV-2 infection in Canada during the early COVID-19 pandemic period, incidental SARS-CoV-2 infection was common. In children admitted with acute COVID-19, obesity and neurologic and respiratory comorbidities were associated with more severe disease.

While children have experienced less severe coronavirus disease (COVID-19) after SARS-CoV-2 infection than adults, the cause of this remains unclear. The objective of this study was to describe the humoral immune response to COVID-19 in child vs. adult household contacts, and to identify predictors of the response over time. In this prospective cohort study, children with a positive SARS-CoV-2 polymerase chain reaction (PCR) test (index case) were recruited along with their adult household contacts. Serum IgG antibodies against SARS-CoV-2 S1/S2 spike proteins were compared between children and adults at 6 and 12 months after infection. A total of 91 participants (37 adults and 54 children) from 36 families were enrolled. Overall, 78 (85.7%) participants were seropositive for anti-S1/S2 IgG antibody at 6 months following infection; this was higher in children than in adults (92.6% vs. 75.7%) (p = 0.05). Significant predictors of a lack of SARS-CoV-2 seropositivity were age ≥ 25 vs. < 12 years (odds ratio [OR] = 0.23, p = 0.04), presence of comorbidities (vs. none, adjusted OR = 0.23, p = 0.03), and immunosuppression (vs. immunocompetent, adjusted OR = 0.17, p = 0.02).

Background Ongoing high neonatal mortality rates (NMRs) represent a global challenge. In 2021, of the 5 million deaths reported worldwide for children under five years of age, 47% were newborns. Pakistan has one of the five highest national NMRs in the world, with an estimated 39 neonatal deaths per 1,000 live births. Reducing newborn deaths requires sustainable, evidence-based, and cost-effective interventions that can be integrated within existing community healthcare infrastructure across regions with high NMR. Methods This pragmatic, community-based, parallel-arm, open-label, cluster randomized controlled trial aims to estimate the effect of Lady Health Workers (LHWs) providing an integrated newborn care kit (iNCK) with educational instructions to pregnant women in their third trimester, compared to the local standard of care in Gilgit-Baltistan, Pakistan, on neonatal mortality and other newborn and maternal health outcomes. The iNCK contains a clean birth kit, 4% chlorhexidine topical gel, sunflower oil emollient, a ThermoSpot™ temperature monitoring sticker, a fleece blanket, a click-to-heat reusable warmer, three 200 μg misoprostol tablets, and a pictorial instruction guide and diary. LHWs are also provided with a handheld scale to weigh the newborn. The primary study outcome is neonatal mortality, defined as a newborn death in the first 28 days of life. Discussion This study will generate policy-relevant knowledge on the effectiveness of integrating evidence-based maternal and newborn interventions and delivering them directly to pregnant women via existing community health infrastructure, for reducing neonatal mortality and morbidity, in a remote, mountainous area with a high NMR. Trial registration NCT04798833, March 15, 2021.

Control of pneumonia and diarrhea mortality in India requires understanding of their etiologies. We combined time series analysis of seasonality, climate region, and clinical syndromes from 243,000 verbal autopsies in the nationally representative Million Death Study. Pneumonia mortality at 1 month-14 years was greatest in January (Rate ratio (RR) 1.66, 99% CI 1.51–1.82; versus the April minimum). Higher RRs at 1–11 months suggested respiratory syncytial virus (RSV) etiology. India’s humid subtropical region experienced a unique summer pneumonia mortality. Diarrhea mortality peaked in July (RR 1.66, 1.48–1.85) and January (RR 1.37, 1.23–1.48), while deaths with fever and bloody diarrhea (indicating enteroinvasive bacterial etiology) showed little seasonality. Combining mortality at ages 1–59 months with prevalence surveys, we estimate 40,600 pneumonia deaths from Streptococcus pneumoniae , 20,700 from RSV, 12,600 from influenza, and 7200 from Haemophilus influenzae type b and 24,700 diarrheal deaths from rotavirus occurred in 2015. Careful mortality studies can elucidate etiologies and inform vaccine introduction.

During the perinatal period, women in low- and middle-income countries experience high rates of common mental disorders (CMDs). We aimed to estimate CMD prevalence at 6 and 12 months postpartum in Rahim Yar Khan (RYK), Pakistan, and identify factors associated with postpartum mental health. We conducted secondary analysis of a longitudinal birth cohort study, which was nested within the control arm of a community-based, cluster-randomized trial that enrolled pregnant women in their third trimester (n = 2,122). Mental health was assessed using the Self-Reporting Questionnaire. Factors associated with postpartum mental health were explored using mixed-effects linear regression, and associations between preconception, antenatal and postpartum CMDs were assessed using robust Poisson regression. The prevalence of CMDs was 16% and 17% at 6 and 12 months postpartum, respectively. Women who reported that their husbands were unhappy had poorer postpartum mental health, whereas high social support was associated with improved postpartum mental health. History of antenatal CMDs was associated with increased risk of CMDs at 6 and 12 months postpartum (adjusted risk ratio = 2.60 and 1.90, 95% confidence interval: 1.69–4.01 and 1.40–2.58, respectively). Mothers with identified risk factors may benefit from targeted mental health support during the perinatal period.

BackgroundViral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromic surveillance tools, including the WHO Severe Acute Respiratory Infection definition, demonstrate limited diagnostic accuracy in children whose symptom profiles vary widely. This study aimed to develop a machine learning (ML) model to predict microbiologically confirmed vRTIs in hospitalised children and to evaluate performance across age groups and viral pathogens.MethodsWe conducted a retrospective cross-sectional study of 2050 paediatric patients (<18 years) admitted with acute respiratory infections to two tertiary paediatric hospitals in Canada. Predictors included age, sex, hospital transfer status, chronic comorbidity status and 22 presenting symptoms. The primary outcome was microbiologically confirmed vRTI, determined by multiplex PCR or rapid antigen testing. Six ML algorithms were trained and the best-performing model, identified by area under the receiver operating characteristic curve (auROC), was tested on age subgroups, viral pathogens and sites.ResultsAmong 2050 patients (median (IQR) age 2.4 (0.8–5.2) years), 1831 (89.3%) tested positive, most commonly for respiratory syncytial virus (RSV) (38.7%) and enterovirus/rhinovirus (32.8%). Logistic regression with L2 regularisation demonstrated the best performance (auROC, 0.754; 95% CI 0.697 to 0.808; sensitivity, 69.2%; specificity, 69.9%), with greatest performance among children <1 year (auROC, 0.763) and RSV cases (auROC, 0.727).ConclusionsAn ML-based logistic regression model using admission data accurately predicted paediatric vRTIs, outperforming traditional syndromic surveillance definitions, especially among infants <1 year. By integrating ML models into hospital electronic medical records, healthcare systems can achieve enhanced respiratory virus surveillance, faster outbreak detection, greater diagnostic efficiency and improved pandemic preparedness.

IntroductionRobust estimates of COVID-19 prevalence in settings with limited capacity for SARS-CoV-2 molecular and serologic testing are scarce. We aimed to describe the epidemiology of confirmed and probable COVID-19 in Gilgit-Baltistan, and to develop a symptom-based predictive model to identify infected but undiagnosed individuals with COVID-19.MethodsWe conducted a cross-sectional survey in 10 257 randomly selected households in Gilgit-Baltistan from June to August 2021. Data regarding SARS-CoV-2 testing, healthcare worker (HCW) diagnoses, symptoms and outcomes since March 2020 were self-reported by households. ‘Confirmed/probable’ infection was defined as a positive test, HCW COVID-19 diagnosis or HCW pneumonia diagnosis with COVID-19-positive contact. Robust Poisson regression was conducted to assess differences in symptoms, outcomes and SARS-CoV-2 testing rates. We developed a symptom-based machine learning model to differentiate confirmed/probable infections from those with negative tests. We applied this model to untested respondents to estimate the total prevalence of SARS-CoV-2 infection.ResultsData were collected for 77 924 people. Overall, 314 (0.5%) had confirmed/probable infections, 3263 (4.4%) had negative tests and 74 347 (95.1%) were untested. Children were tested less often than adults (adjusted prevalence ratio (aPR) 0.08, 95% CI 0.06 to 0.12 for ages 1–4 years vs 30–39 years), while males were tested more often than females (aPR 1.51, 95% CI 1.40 to 1.63). In the predictive model, area under the receiver operating characteristic curve was 0.92 (95% CI 0.90 to 0.93). We estimate there were 8–17 total SARS-CoV-2 infections for each positive test (8–17:1). The ratio of estimated to confirmed cases was higher for ages 1–4 years (211–480:1), 5–9 years (80–185:1) and for females (13–25:1).ConclusionsFrom March 2020 to August 2021, the majority of SARS-CoV-2 infections in Gilgit-Baltistan went unconfirmed, particularly among women and children. Predictive models which incorporate self-reported symptoms may improve understanding of the burden of disease in settings lacking diagnostic capacity.

In 2018, Pakistan had the world's highest neonatal mortality rate. Within Pakistan, most neonatal deaths occur in rural areas where access to health facilities is limited, and robust vital registration systems are lacking. To improve newborn survival, there is a need to better understand the causes of neonatal death in high burden settings and engage caregivers in the promotion of newborn health. To describe the causes of neonatal death in a rural area in Pakistan and to estimate the effect of an integrated neonatal care kit (iNCK) on cause-specific neonatal mortality. We analyzed data from a community-based, cluster-randomized controlled trial of 5286 neonates in Rahim Yar Khan (RYK), Punjab, Pakistan between April 2014 and August 2015. In intervention clusters, Lady Health Workers (LHW) delivered the iNCK and education on its use to pregnant women while control clusters received the local standard of care. The iNCK included interventions to prevent and identify signs of infection, identify low birthweight (LBW), and identify and manage hypothermia. Verbal autopsies were attempted for all deaths. The primary outcome was cause-specific neonatal mortality. Verbal autopsies were conducted for 84 (57%) of the 147 reported neonatal deaths. The leading causes of death were infection (44%), intrapartum-related complications (26%) and prematurity/LBW (20%). There were no significant differences in neonatal mortality due to prematurity/LBW (RR 0.43; 95% CI 0.15-1.24), infection (RR 1.10; 95% CI 0.58-2.10) or intrapartum-related complications (RR 1.04; 95% CI 0.0.45-2.41) among neonates who died in the intervention arm compared to those who died in the control arm. The major causes of neonatal deaths in RYK, Pakistan mirror the global landscape of neonatal deaths. The iNCK did not significantly reduce any cause-specific neonatal mortality.

•·       COVID-19 has disrupted physicians’ immunization services for children in Ontario.•A large proportion of appointments have shifted from in-person to virtual visits.•Barriers include parents’ concerns of contracting COVID-19, and lack of PPE.•Solutions include dedicated settings for vaccination and parental education. The COVID-19 pandemic has a worldwide impact on all health services, including childhood immunizations. In Canada, there is limited data to quantify and characterize this issue. We conducted a descriptive, cross-sectional study by distributing online surveys to physicians across Ontario. The survey included three sections: provider characteristics, impact of COVID-19 on professional practice, and impact of COVID-19 on routine childhood immunization services. Multivariable logistic regression identified factors associated with modification of immunization services. A total of 475 respondents answered the survey from May 27th to July 3rd 2020, including 189 family physicians and 286 pediatricians. The median proportion of in-person visits reported by physicians before the pandemic was 99% and dropped to 18% during the first wave of the pandemic in Ontario. In total, 175 (44.6%) of the 392 respondents who usually provide vaccination to children acknowledged a negative impact caused by the pandemic on their immunization services, ranging from temporary closure of their practice (n = 18; 4.6%) to postponement of vaccines in certain age groups (n = 103; 26.3%). Pediatricians were more likely to experience a negative impact on their immunization services compared to family physicians (adjusted odds ratio [aOR] = 2.64, 95% CI: 1.48–4.68), as well as early career physicians compared to their more senior colleagues (aOR = 2.69, 95% CI: 1.30–5.56), whereas physicians from suburban settings were less impacted than physicians from urban settings (aOR = 0.62, 95% CI: 0.39–0.99). Some of the proposed solutions to decreased immunization services included assistance in accessing personal protective equipment, dedicated centers or practices for vaccination, universal centralized electronic immunization records and education campaigns for parents. COVID-19 has caused substantial modifications to pediatric immunization services across Ontario. Strategies to mitigate barriers to immunizations during the pandemic need to be implemented in order to avoid immunity gaps that could lead to an eventual increase in vaccine preventable diseases.