United Nations Children's Fund Zambia

ObjectiveTo examine prevalence of novel newborn types among 541 285 live births in 23 countries from 2000 to 2021.DesignDescriptive multi‐country secondary data analysis.SettingSubnational, population‐based birth cohort studies (n = 45) in 23 low‐ and middle‐income countries (LMICs) spanning 2000–2021.PopulationLiveborn infants.MethodsSubnational, population‐based studies with high‐quality birth outcome data from LMICs were invited to join the Vulnerable Newborn Measurement Collaboration. We defined distinct newborn types using gestational age (preterm [PT], term [T]), birthweight for gestational age using INTERGROWTH‐21st standards (small for gestational age [SGA], appropriate for gestational age [AGA] or large for gestational age [LGA]), and birthweight (low birthweight, LBW [<2500 g], nonLBW) as ten types (using all three outcomes), six types (by excluding the birthweight categorisation), and four types (by collapsing the AGA and LGA categories). We defined small types as those with at least one classification of LBW, PT or SGA. We presented study characteristics, participant characteristics, data missingness, and prevalence of newborn types by region and study.ResultsAmong 541 285 live births, 476 939 (88.1%) had non‐missing and plausible values for gestational age, birthweight and sex required to construct the newborn types. The median prevalences of ten types across studies were T+AGA+nonLBW (58.0%), T+LGA+nonLBW (3.3%), T+AGA+LBW (0.5%), T+SGA+nonLBW (14.2%), T+SGA+LBW (7.1%), PT+LGA+nonLBW (1.6%), PT+LGA+LBW (0.2%), PT+AGA+nonLBW (3.7%), PT+AGA+LBW (3.6%) and PT+SGA+LBW (1.0%). The median prevalence of small types (six types, 37.6%) varied across studies and within regions and was higher in Southern Asia (52.4%) than in Sub‐Saharan Africa (34.9%).ConclusionsFurther investigation is needed to describe the mortality risks associated with newborn types and understand the implications of this framework for local targeting of interventions to prevent adverse pregnancy outcomes in LMICs.

The coronavirus disease 2019 (COVID-19) pandemic has been closely tied with what has been called an infodemic, a “second disease” that occurs when massive information volumes (particularly with a high prevalence of false information) hinder the public health response. In this context, social listening, the process of monitoring and analyzing conversations to inform strategic activities both online and offline, becomes an even more essential component of risk communication and engagement strategies. In the Eastern and Southern Africa region, the United Nations Children's Fund (UNICEF) and partners in the response have activated their capacity to gather insights on the information needs of the populations served to better inform and engage with local communities. We describe the social listening approach taken at the Eastern and Southern Africa regional level to respond to COVID-19 and highlight efforts by the Comoros, Kenya, Madagascar, Malawi, and Zambia UNICEF country offices to implement digital and nondigital social listening to inform risk communication and community engagement. The analysis highlights channels leveraged, types of data monitored, and provides examples of social listening data use, as well as early challenges and lessons learned.

BackgroundThere is considerable evidence that health systems, in so far as they ensure access to healthcare, promote population health even independent of other determinants. Access to child health services remains integral to improving child health outcomes. Cognisant that improvements in child health have been unevenly distributed, it is imperative that health services and research focus on the disadvantaged groups. Children residing in urban slums are known to face a health disadvantage that is masked by the common view of an urban health advantage. Granted increasing urbanisation rates and proliferation of urban slums resulting from urban poverty, the health of under-five children in slums remains a public health imperative in Malawi. We explored determinants of healthcare-seeking from a biomedical health provider for childhood symptoms of fever, cough with fast breathing and diarrhoea in three urban slums of Lilongwe, Malawi.MethodsThis was a population-based cross-sectional study involving 543 caregivers of under-five children. Data on childhood morbidity and healthcare seeking in three months period were collected using face-to-face interviews guided by a validated questionnaire. Data were entered in CS-Pro 5.0 and analysed in SPSS version 20 using descriptive statistics and logistic regression analyses.Results61% of caregivers sought healthcare albeit 53% of them sought healthcare late. Public health facilities constituted the most frequently used health providers. Healthcare was more likely to be sought: for younger than older under-five children (AOR = 0.54; 95% CI: 0.30–0.99); when illness was perceived to be severe (AOR = 2.40; 95% CI: 1.34–4.30); when the presenting symptom was fever (AOR = 1.77; 95% CI: 1.10–2.86). Home management of childhood illness was negatively associated with care-seeking (AOR = 0.54; 95% CI: 0.36–0.81) and timely care-seeking (AOR = 0.44; 95% CI: 0.2–0.74). Caregivers with good knowledge of child danger signs were less likely to seek care timely (AOR = 0.57; 95% CI: 0.33–0.99).ConclusionsEven in the context of geographical proximity to healthcare services, caregivers in urban slums may not seek healthcare or when they do so the majority may not undertake timely healthcare care seeking. Factors related to the child, the type of illness, and the caregiver are central to the healthcare decision making dynamics. Improving access to under-five child health services therefore requires considering multiple factors.

ABSTRACT Despite evidence for the substantial benefits of school readiness among children in low- and middle-income countries (LMICs), most lack access to any pre-primary education at all, let alone high- quality preprimary education. In this article, we discuss obstacles and example of solutions to providing universal access to high-quality preprimary education in LMICs. We address (1) the need for a vision among policymakers, educators and families of what high-quality preprimary education should look like and how it could be provided in their context, and the motivation to make the vision a reality; (2) human resources, including motivated, skilled and qualified teachers as the center of a broad support system at national, regional and local levels; (3) material resources and infrastructure, including culturally appropriate play materials and child-friendly spaces; and (4) stable financing that allows for flexible adaptations of the model to different local contexts. For each element, we provide examples of these challenges and how they can be addressed, with a geographical focus on West Africa, the Middle East, Southern and Eastern Asia.

In response to the growing HIV/AIDS and other health-related issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to provide prevention strategies, health case advice, and counseling support to vulnerable population. Due to a rapid increase in U-Report usage (up to 300% in last 3 years), plus approximately 1,000 new registrations each day, the volume of messages has thus continued to increase, which made it impossible for the team at UNICEF to process them in a timely manner. In this paper, we present a platform designed to perform automatic classification of short messages (SMS) in real-time to help UNICEF categorize and prioritize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intelligence that seeks to resolve the information overload issue by introducing processing of large-scale data at high-speed while maintaining a high classification accuracy. The system has recently been tested in conjunction with UNICEF in Zambia to classify short messages received via the U-Report platform on various health related issues. The system is designed to enable UNICEF make sense of a large volume of short messages in a timely manner. In terms of evaluation, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness.