Characteristics of children readmitted with severe pneumonia in Kenyan hospitals.

BACKGROUND: Pneumonia is a leading cause of childhood morbidity and mortality. Hospital re-admission may signify missed opportunities for care or undiagnosed comorbidities. METHODS: We conducted a retrospective cohort study including children aged ≥ 2 months-14 years hospitalised with severe pneumonia between 2013 and 2021 in a network of 20 primary referral hospitals in Kenya. Severe pneumonia was defined using the 2013 World Health Organization criteria, and re-admission was based on clinical documentation from individual patient case notes. We estimated the prevalence of re-admission, described clinical management practices, and modelled risk factors for re-admission and inpatient mortality. RESULTS: Among 20,603 children diagnosed with severe pneumonia, 2,274 (11.0%, 95% CI 10.6-11.5) were readmitted. Re-admission was independently associated with age (12-59 months vs. 2-11 months: adjusted odds ratio (aOR) 1.70, 1.54-1.87; >5 years vs. 2-11 months: aOR 1.85, 1.55-2.22), malnutrition (weight-for-age-z-score (WAZ) <-3SD vs. WAZ> -2SD: aOR 2.05, 1.84-2.29); WAZ - 2 to -3 SD vs. WAZ> -2SD: aOR 1.37, 1.20-1.57), wheeze (aOR 1.17, 1.03-1.33) and presence of a concurrent neurological disorder (aOR 4.42, 1.70-11.48). Chest radiography was ordered more frequently among those readmitted (540/2,274 [23.7%] vs. 3,102/18,329 [16.9%], p < 0.001). Readmitted patients more frequently received second-line antibiotics (808/2,256 [35.8%] vs. 5,538/18,173 [30.5%], p < 0.001), TB medication (69/2,256 [3.1%] vs. 298/18,173 [1.6%], p < 0.001), salbutamol (530/2,256 [23.5%] vs. 3,707/18,173 [20.4%], p = 0.003), and prednisolone (157/2,256 [7.0%] vs. 764/18,173 [4.2%], p < 0.001). Inpatient mortality was 2,354/18,329 (12.8%) among children admitted with a first episode of severe pneumonia and 269/2,274 (11.8%) among those who were readmitted (adjusted hazard ratio (aHR) 0.93, 95% CI 0.82-1.07). Age (12-59 months vs. 2-11 months: aHR 0.62, 0.57-0.67), male sex (aHR 0.81, 0.75-0.88), malnutrition (WAZ <-3SD vs. WAZ >-2SD: aHR 1.87, 1.71-2.05); WAZ - 2 to -3 SD vs. WAZ >-2SD: aHR 1.46, 1.31-1.63), complete vaccination (aHR 0.74, 0.60-0.91), wheeze (aHR 0.87, 0.78-0.98) and anaemia (aHR 2.14, 1.89-2.43) were independently associated with mortality. CONCLUSIONS: Children readmitted with severe pneumonia account for a substantial proportion of pneumonia hospitalisations and deaths. Further research is required to develop evidence-based approaches to screening, case management, and follow-up of children with severe pneumonia, prioritising those with underlying risk factors for readmission and mortality.

Implementing adaptive e-learning for newborn care in Tanzania: an observational study of provider engagement and knowledge gains.

INTRODUCTION: To improve healthcare provider knowledge of Tanzanian newborn care guidelines, we developed adaptive Essential and Sick Newborn Care (aESNC), an adaptive e-learning environment. The objectives of this study were to (1) assess implementation success with use of in-person support and nudging strategy and (2) describe baseline provider knowledge and metacognition. METHODS: 6-month observational study at one zonal hospital and three health centres in Mwanza, Tanzania. To assess implementation success, we used the Reach, Efficacy, Adoption, Implementation and Maintenance framework and to describe baseline provider knowledge and metacognition we used Howell's conscious-competence model. Additionally, we explored provider characteristics associated with initial learning completion or persistent activity. RESULTS: aESNC reached 85% (195/231) of providers: 75 medical, 53 nursing and 21 clinical officers; 110 (56%) were at the zonal hospital and 85 (44%) at health centres. Median clinical experience was 4 years (IQR 1-9) and 45 (23%) had previous in-service training for both newborn essential and sick newborn care. Efficacy was 42% (SD ±17%). Providers averaged 78% (SD ±31%) completion of initial learning and 7% (SD ±11%) of refresher assignments. 130 (67%) providers had ≥1 episode of inactivity >30 day, no episodes were due to lack of internet access. Baseline conscious-competence was 53% (IQR: 38%-63%), unconscious-incompetence 32% (IQR: 23%-42%), conscious-incompetence 7% (IQR: 2%-15%), and unconscious-competence 2% (IQR: 0%-3%). Higher baseline conscious-competence (OR 31.6 (95% CI 5.8 to 183.5)) and being a nursing officer (aOR: 5.6 (95% CI 1.8 to 18.1)), compared with medical officer, were associated with initial learning completion or persistent activity. CONCLUSION: aESNC reach was high in a population of frontline providers across diverse levels of care in Tanzania. Use of in-person support and nudging increased reach, initial learning and refresher assignment completion, but refresher assignment completion remains low. Providers were often unaware of knowledge gaps, and lower baseline knowledge may decrease initial learning completion or activity. Further study to identify barriers to adaptive e-learning normalisation is needed.

Prevalence and risk factors for long COVID and post-COVID-19 condition in Africa: a systematic review.

BACKGROUND: An improved estimation of the clinical sequelae of SARS-CoV-2 infection is crucial in African countries, where the subject has received little attention despite more than 12 million reported cases and evidence that many more people were infected. We reviewed the evidence on prevalence, associated risk factors for long COVID, and systemic or sociocultural determinants of reporting long COVID. METHODS: We conducted a systematic review, searching PubMed, the Living OVerview of Evidence platform, and grey literature sources for publications from Dec 1, 2019, to Nov 23, 2022. We included articles published in English, French, Spanish, or Portuguese that reported on any study type in Africa with participants of any age who had symptoms for 4 weeks or more after an acute SARS-CoV-2 infection. We excluded secondary research, comments, and correspondence. Screening and data extraction were performed by two reviewers. Summary estimates were extracted, including sociodemographic factors, medical history, prevalence of persistent symptoms, and symptoms and associated factors. Results were analysed descriptively. The study was registered on the Open Science Framework platform. FINDINGS: Our search yielded 294 articles, of which 24 peer-reviewed manuscripts were included, reporting on 9712 patients from eight African countries. Only one study exclusively recruited children, and one other study included children as part of their study population. Studies indicated moderate to low risk of bias. Prevalence of long COVID varied widely, from 2% in Ghana to 86% in Egypt. Long COVID was positively associated with female sex, older age, non-Black ethnicity, low level of education, and the severity of acute infection and underlying comorbidity. HIV and tuberculosis were not identified as risk factors. Factors influencing reporting included absence of awareness, inadequate clinical data and diagnostics, and little access to health-care services. INTERPRETATION: In Africa, research on long COVID is scarce, particularly among children, who represent the majority of the population. However, existing studies show a substantial prevalence across settings, emphasising the importance of vaccination and other prevention strategies to avert the effects of long COVID on individual wellbeing, the increased strain on health systems, and the potential negative effects on economically vulnerable populations. At a global level, including African countries, tools for research on long COVID need to be harmonised to maximise the usefulness of the data collected. FUNDING: None.

SARS-CoV-2 seroprevalence and implications for population immunity: Evidence from two Health and Demographic Surveillance System sites in Kenya, February-December 2022.

BACKGROUND: We sought to estimate SARS-CoV-2 antibody seroprevalence within representative samples of the Kenyan population during the third year of the COVID-19 pandemic and the second year of COVID-19 vaccine use. METHODS: We conducted cross-sectional serosurveys among randomly selected, age-stratified samples of Health and Demographic Surveillance System (HDSS) residents in Kilifi and Nairobi. Anti-spike (anti-S) immunoglobulin G (IgG) serostatus was measured using a validated in-house ELISA and antibody concentrations estimated with reference to the WHO International Standard for anti-SARS-CoV-2 immunoglobulin. RESULTS: HDSS residents were sampled in February-June 2022 (Kilifi HDSS N = 852; Nairobi Urban HDSS N = 851) and in August-December 2022 (N = 850 for both sites). Population-weighted coverage for ≥1 doses of COVID-19 vaccine were 11.1% (9.1-13.2%) among Kilifi HDSS residents by November 2022 and 34.2% (30.7-37.6%) among Nairobi Urban HDSS residents by December 2022. Population-weighted anti-S IgG seroprevalence among Kilifi HDSS residents increased from 69.1% (65.8-72.3%) by May 2022 to 77.4% (74.4-80.2%) by November 2022. Within the Nairobi Urban HDSS, seroprevalence by June 2022 was 88.5% (86.1-90.6%), comparable with seroprevalence by December 2022 (92.2%; 90.2-93.9%). For both surveys, seroprevalence was significantly lower among Kilifi HDSS residents than among Nairobi Urban HDSS residents, as were antibody concentrations (p < 0.001). CONCLUSION: More than 70% of Kilifi residents and 90% of Nairobi residents were seropositive for anti-S IgG by the end of 2022. There is a potential immunity gap in rural Kenya; implementation of interventions to improve COVID-19 vaccine uptake among sub-groups at increased risk of severe COVID-19 in rural settings is recommended.

Symptom prevalence and secondary attack rate of SARS-CoV-2 in rural Kenyan households: A prospective cohort study.

BACKGROUND: We estimated the secondary attack rate of SARS-CoV-2 among household contacts of PCR-confirmed cases of COVID-19 in rural Kenya and analysed risk factors for transmission. METHODS: We enrolled incident PCR-confirmed cases and their household members. At baseline, a questionnaire, a blood sample, and naso-oropharyngeal swabs were collected. Household members were followed 4, 7, 10, 14, 21 and 28 days after the date of the first PCR-positive in the household; naso-oropharyngeal swabs were collected at each visit and used to define secondary cases. Blood samples were collected every 1-2 weeks. Symptoms were collected in a daily symptom diary. We used binomial regression to estimate secondary attack rates and survival analysis to analyse risk factors for transmission. RESULTS: A total of 119 households with at least one positive household member were enrolled between October 2020 and September 2022, comprising 503 household members; 226 remained in follow-up at day 14 (45%). A total of 43 secondary cases arose within 14 days of identification of the primary case, and 81 household members remained negative. The 7-day secondary attack rate was 4% (95% CI 1%-10%), the 14-day secondary attack rate was 28% (95% CI 17%-40%). Of 38 secondary cases with data, eight reported symptoms (21%, 95% CI 8%-34%). Antibody to SARS-CoV-2 spike protein at enrolment was not associated with risk of becoming a secondary case. CONCLUSION: Households in our setting experienced a lower 7-day attack rate than a recent meta-analysis indicated as the global average (23%-43% depending on variant), and infection is mostly asymptomatic in our setting.

Development of pediatric acute care education (PACE): An adaptive electronic learning (e-learning) environment for healthcare providers in Tanzania.

Globally, inadequate healthcare provider (HCP) proficiency with evidence-based guidelines contributes to millions of newborn, infant, and child deaths each year. HCP guideline proficiency would improve patient outcomes. Conventional (in person) HCP in-service education is limited in 4 ways: reach, scalability, adaptability, and the ability to contextualize. Adaptive e-learning environments (AEE), a subdomain of e-learning, incorporate artificial intelligence technology to create a unique cognitive model of each HCP to improve education effectiveness. AEEs that use existing internet access and personal mobile devices may overcome limits of conventional education. This paper provides an overview of the development of our AEE HCP in-service education, Pediatric Acute Care Education (PACE). PACE uses an innovative approach to address HCPs' proficiency in evidence-based guidelines for care of newborns, infants, and children. PACE is novel in 2 ways: 1) its patient-centric approach using clinical audit data or frontline provider input to determine content and 2) its ability to incorporate refresher learning over time to solidify knowledge gains. We describe PACE's integration into the Pediatric Association of Tanzania's (PAT) Clinical Learning Network (CLN), a multifaceted intervention to improve facility-based care along a single referral chain. Using principles of co-design, stakeholder meetings modified PACE's characteristics and optimized integration with CLN. We plan to use three-phase, mixed-methods, implementation process. Phase I will examine the feasibility of PACE and refine its components and protocol. Lessons gained from this initial phase will guide the design of Phase II proof of concept studies which will generate insights into the appropriate empirical framework for (Phase III) implementation at scale to examine effectiveness.

Feasibility of an Adaptive E-Learning Environment to Improve Provider Proficiency in Essential and Sick Newborn Care in Mwanza, Tanzania.

Introduction: To improve healthcare provider knowledge of Tanzanian newborn care guidelines, we developed adaptive Essential and Sick Newborn Care (aESNC), an adaptive e-learning environment (AEE). The objectives of this study were to 1) assess implementation success with use of in-person support and nudging strategy and 2) describe baseline provider knowledge and metacognition. Methods: 6-month observational study at 1 zonal hospital and 3 health centers in Mwanza, Tanzania. To assess implementation success, we used the RE-AIM framework and to describe baseline provider knowledge and metacognition we used Howell's conscious-competence model. Additionally, we explored provider characteristics associated with initial learning completion or persistent activity. Results: aESNC reached 85% (195/231) of providers: 75 medical, 53 nursing, and 21 clinical officers; 110 (56%) were at the zonal hospital and 85 (44%) at health centers. Median clinical experience was 4 years [IQR 1,9] and 45 (23%) had previous in-service training for both newborn essential and sick newborn care. Efficacy was 42% (SD±17%). Providers averaged 78% (SD±31%) completion of initial learning and 7%(SD±11%) of refresher assignments. 130 (67%) providers had ≥1 episode of inactivity >30 day, no episodes were due to lack of internet access. Baseline conscious-competence was 53% [IQR:38-63%], unconscious-incompetence 32% [IQR:23-42%], conscious-incompetence 7% [IQR:2-15%], and unconscious-competence 2% [IQR:0-3%]. Higher baseline conscious-competence (OR 31.6 [95%CI:5.8, 183.5) and being a nursing officer (aOR: 5.6 [95%CI:1.8, 18.1]), compared to medical officer) were associated with initial learning completion or persistent activity. Conclusion: aESNC reach was high in a population of frontline providers across diverse levels of care in Tanzania. Use of in-person support and nudging increased reach, initial learning, and refresher assignment completion, but refresher assignment completion remains low. Providers were often unaware of knowledge gaps, and lower baseline knowledge may decrease initial learning completion or activity. Further study to identify barriers to adaptive e-learning normalization is needed.

Clinical epidemiology of COVID-19 among hospitalized children in rural western Kenya.

The epidemiology of pediatric COVID-19 in sub-Saharan Africa and the role of fecal-oral transmission in SARS-CoV-2 are poorly understood. Among children and adolescents in Kenya, we identify correlates of COVID-19 infection, document the clinical outcomes of infection, and evaluate the prevalence and viability of SARS-CoV-2 in stool. We recruited a prospective cohort of hospitalized children aged two months to 15 years in western Kenya between March 1 and June 30 2021. Children with SARS-CoV-2 were followed monthly for 180-days after hospital discharge. Bivariable logistic regression analysis was used to identify the clinical and sociodemographics correlates of SARS-CoV-2 infection. We also calculated the prevalence of SARS-CoV-2 detection in stool of confirmed cases. Of 355 systematically tested children, 55 (15.5%) were positive and were included in the cohort. The commonest clinical features among COVID-19 cases were fever (42/55, 76%), cough (19/55, 35%), nausea and vomiting (19/55, 35%), and lethargy (19/55, 35%). There were no statistically significant difference in baseline sociodemographic and clinical characteristics between SARS-CoV-2 positive and negative participants. Among positive participants, 8/55 (14.5%, 95%CI: 5.3%-23.9%) died; seven during the inpatient period. Forty-nine children with COVID-19 had stool samples or rectal swabs available at baseline, 9 (17%) had PCR-positive stool or rectal swabs, but none had SARS-CoV-2 detected by culture. Syndromic identification of COVID-19 is particularly challenging among children as the presenting symptoms and signs mirror other common pediatric diseases. Mortality among children hospitalized with COVID-19 was high in this cohort but was comparable to mortality seen with other common illnesses in this setting. Among this small set of children with COVID-19 we detected SARS-CoV-2 DNA, but were not able to culture viable SARs-CoV-2 virus, in stool. This suggests that fecal transmission may not be a substantial risk in children recently diagnosed and hospitalized with COVID-19 infection.

Serum immunoglobulin G and mucosal immunoglobulin A antibodies from prepandemic samples collected in Kilifi, Kenya, neutralize SARS-CoV-2 in vitro.

OBJECTIVES: Many regions of Africa have experienced lower COVID-19 morbidity and mortality than Europe. Pre-existing humoral responses to endemic human coronaviruses (HCoV) may cross-protect against SARS-CoV-2. We investigated the neutralizing capacity of SARS-CoV-2 spike reactive and nonreactive immunoglobulin (Ig)G and IgA antibodies in prepandemic samples. METHODS: To investigate the presence of pre-existing immunity, we performed enzyme-linked immunosorbent assay using spike antigens from reference SARS-CoV-2, HCoV HKU1, OC43, NL63, and 229E using prepandemic samples from Kilifi in coastal Kenya. In addition, we performed neutralization assays using pseudotyped reference SARS-CoV-2 to determine the functionality of the identified reactive antibodies. RESULTS: We demonstrate the presence of HCoV serum IgG and mucosal IgA antibodies, which cross-react with the SARS-CoV-2 spike. We show pseudotyped reference SARS-CoV-2 neutralization by prepandemic serum, with a mean infective dose 50 of 1: 251, which is 10-fold less than that of the pooled convalescent sera from patients with COVID-19 but still within predicted protection levels. The prepandemic naso-oropharyngeal fluid neutralized pseudo-SARS-CoV-2 at a mean infective dose 50 of 1: 5.9 in the neutralization assay. CONCLUSION: Our data provide evidence for pre-existing functional humoral responses to SARS-CoV-2 in Kilifi, coastal Kenya and adds to data showing pre-existing immunity for COVID-19 from other regions.

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21.

INTRODUCTION: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity. METHODS: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection. RESULTS: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi. CONCLUSIONS: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya.

BACKGROUND: A few studies have assessed the epidemiological impact and the cost-effectiveness of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. METHODS: We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (>18 years) population prioritising roll-out in those over 50-years (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at US$7 per dose and vaccine delivery costs of US$3.90-US$6.11 per dose. The cost-effectiveness threshold was US$919.11. FINDINGS: Slow roll-out at 30% coverage largely targets those over 50 years and resulted in 54% fewer deaths (8132 (7914-8373)) than no vaccination and was cost saving (incremental cost-effectiveness ratio, ICER=US$-1343 (US$-1345 to US$-1341) per disability-adjusted life-year, DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757-872) and 5% (282 (251-317) but was not cost-effective, using Kenya's cost-effectiveness threshold (US$919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=US$-1607 (US$-1609 to US$-1604) per DALY averted) compared with slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. INTERPRETATION: With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective.

Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study.

Background: Detailed understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) regional transmission networks within sub-Saharan Africa is key for guiding local public health interventions against the pandemic. Methods: Here, we analysed 1139 SARS-CoV-2 genomes from positive samples collected between March 2020 and February 2021 across six counties of Coastal Kenya (Mombasa, Kilifi, Taita Taveta, Kwale, Tana River, and Lamu) to infer virus introductions and local transmission patterns during the first two waves of infections. Virus importations were inferred using ancestral state reconstruction, and virus dispersal between counties was estimated using discrete phylogeographic analysis. Results: During Wave 1, 23 distinct Pango lineages were detected across the six counties, while during Wave 2, 29 lineages were detected; 9 of which occurred in both waves and 4 seemed to be Kenya specific (B.1.530, B.1.549, B.1.596.1, and N.8). Most of the sequenced infections belonged to lineage B.1 (n = 723, 63%), which predominated in both Wave 1 (73%, followed by lineages N.8 [6%] and B.1.1 [6%]) and Wave 2 (56%, followed by lineages B.1.549 [21%] and B.1.530 [5%]). Over the study period, we estimated 280 SARS-CoV-2 virus importations into Coastal Kenya. Mombasa City, a vital tourist and commercial centre for the region, was a major route for virus imports, most of which occurred during Wave 1, when many Coronavirus Disease 2019 (COVID-19) government restrictions were still in force. In Wave 2, inter-county transmission predominated, resulting in the emergence of local transmission chains and diversity. Conclusions: Our analysis supports moving COVID-19 control strategies in the region from a focus on international travel to strategies that will reduce local transmission. Funding: This work was funded by The Wellcome (grant numbers: 220985, 203077/Z/16/Z, 220977/Z/20/Z, and 222574/Z/21/Z) and the National Institute for Health and Care Research (NIHR), project references: 17/63/and 16/136/33 using UK Aid from the UK government to support global health research, The UK Foreign, Commonwealth and Development Office. The views expressed in this publication are those of the author(s) and not necessarily those of the funding agencies.

Measuring and monitoring child health and wellbeing: recommendations for tracking progress with a core set of indicators in the Sustainable Development Goals era.

Although great improvements in child survival were achieved in the past two decades, progress has been uneven within and across countries, and the COVID-19 pandemic threatens to reverse previous advances. Demographic and epidemiological transitions around the world have resulted in shifts in the causes and distribution of child death and diseases, and many children are living with short-term and long-term chronic illnesses and disabilities. These changes, plus global threats such as pandemics, transnational and national security issues, and climate change, mean that regular monitoring of child health and wellbeing is essential if we are to achieve the Sustainable Development Goals. This Health Policy describes the three-phased process undertaken by the Child Health Accountability Tracking technical advisory group (CHAT) to develop a core set of indicators on child health and wellbeing for global monitoring purposes, and presents CHAT's research recommendations to address data gaps. CHAT reached consensus on 20 core indicators specific to the health sector, which include 11 impact-level indicators and nine outcome-level indicators that cover the topics of: acute conditions and prevention; health promotion and child development; and chronic conditions, disabilities, injuries, and violence against children. An additional six indicators (three impact and three outcome) that capture information on child health issues such as malaria and HIV are recommended; however, these indicators are only relevant to high-burden regions. CHAT's four research priorities will require investments in health information systems and measurement activities. These investments will help to increase data on children aged 5-9 years; develop standard metadata and data collection processes to enable cross-country comparisons and progress assessments over time; reach a global consensus on essential interventions and associated indicators for monitoring emerging priority areas such as child development, chronic conditions, disabilities, and injuries; and implement strategies to increase the uptake of data on child health to improve evidence-based planning, programming, and advocacy efforts.

Unmet need for COVID-19 vaccination coverage in Kenya.

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

Malaria hospitalisation in East Africa: age, phenotype and transmission intensity.

BACKGROUND: Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. METHODS: Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2-10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. RESULTS: 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2-10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2-10 < 5%), five low-moderate transmission (PfPR2-10 5-9%), 20 moderate transmission (PfPR2-10 10-29%) and 12 high transmission (PfPR2-10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69-85%) and rare among children aged 10-14 years (0.7-5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines. CONCLUSIONS: Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2-23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.