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Abstract Background The emergence of COVID-19 precipitated containment policies (e.g., lockdowns, school closures, etc.). These policies disrupted healthcare, potentially eroding gains for Sustainable Development Goals including for neonatal mortality. Our analysis aimed to evaluate indirect effects of COVID-19 containment policies on neonatal admissions and mortality in 67 neonatal units across Kenya, Malawi, Nigeria, and Tanzania between January 2019 and December 2021. Methods The Oxford Stringency Index was applied to quantify COVID-19 policy stringency over time for Kenya, Malawi, Nigeria, and Tanzania. Stringency increased markedly between March and April 2020 for these four countries (although less so in Tanzania), therefore defining the point of interruption. We used March as the primary interruption month, with April for sensitivity analysis. Additional sensitivity analysis excluded data for March and April 2020, modelled the index as a continuous exposure, and examined models for each country. To evaluate changes in neonatal admissions and mortality based on this interruption period, a mixed effects segmented regression was applied. The unit of analysis was the neonatal unit (n = 67), with a total of 266,741 neonatal admissions (January 2019 to December 2021). Results Admission to neonatal units decreased by 15% overall from February to March 2020, with half of the 67 neonatal units showing a decline in admissions. Of the 34 neonatal units with a decline in admissions, 19 (28%) had a significant decrease of ≥ 20%. The month-to-month decrease in admissions was approximately 2% on average from March 2020 to December 2021. Despite the decline in admissions, we found no significant changes in overall inpatient neonatal mortality. The three sensitivity analyses provided consistent findings. Conclusion COVID-19 containment measures had an impact on neonatal admissions, but no significant change in overall inpatient neonatal mortality was detected. Additional qualitative research in these facilities has explored possible reasons. Strengthening healthcare systems to endure unexpected events, such as pandemics, is critical in continuing progress towards achieving Sustainable Development Goals, including reducing neonatal deaths to less than 12 per 1000 live births by 2030.

Abstract Preterm birth (

BackgroundProgress in neonatal care has resulted in a 51% decrease in global neonatal mortality rates from 1990 to 2017. Enhanced survival will put pressure on health care systems to provide appropriate post-discharge, follow-up care but the scale of need for such care is poorly defined.MethodsWe conducted a retrospective cohort study of newborns discharged from 23 public hospital neonatal units (NBUs) in Kenya between January 2018 and June 2023 to identify initial follow-up needs. We first determined pragmatic follow-up categories based on survivors’ clinical conditions and morbidities. We then used individual phenotypes of individual babies to assign them to needing one or more forms of specialized clinical follow-up. We use descriptive statistics to estimate proportions of those with specific needs and patterns of need.FindingsAmong 136,249/159,792 (85.3%) neonates discharged, around one-third (33%) were low birth weight (

Abstract Background Thirty million small and sick newborns worldwide require inpatient care each year. Many receive antibiotics for clinically diagnosed infections without blood cultures, the current ‘gold standard’ for neonatal infection detection. Low neonatal blood culture use hampers appropriate antibiotic use, fuelling antimicrobial resistance (AMR) which threatens newborn survival. This study analysed the gap between blood culture use and antibiotic prescribing in hospitals implementing with Newborn Essential Solutions and Technologies (NEST360) in Kenya, Malawi, Nigeria, and Tanzania. Methods Inpatient data from every newborn admission record (July 2019–August 2022) were included to describe hospital-level blood culture use and antibiotic prescription. Health Facility Assessment data informed performance categorisation of hospitals into four tiers: (Tier 1) no laboratory, (Tier 2) laboratory but no microbiology, (Tier 3) neonatal blood culture use 50%. Results A total of 144,146 newborn records from 61 hospitals were analysed. Mean hospital antibiotic prescription was 70% (range = 25–100%), with 6% mean blood culture use (range = 0–56%). Of the 10,575 blood cultures performed, only 24% (95%CI 23–25) had results, with 10% (10–11) positivity. Overall, 40% (24/61) of hospitals performed no blood cultures for newborns. No hospitals were categorised as Tier 1 because all had laboratories. Of Tier 2 hospitals, 87% (20/23) were District hospitals. Most hospitals could do blood cultures (38/61), yet the majority were categorised as Tier 3 (36/61). Only two hospitals performed > 50% blood cultures for newborns on antibiotics (Tier 4). Conclusions The two Tier 4 hospitals, with higher use of blood cultures for newborns, underline potential for higher blood culture coverage in other similar hospitals. Understanding why these hospitals are positive outliers requires more research into local barriers and enablers to performing blood cultures. Tier 3 facilities are missing opportunities for infection detection, and quality improvement strategies in neonatal units could increase coverage rapidly. Tier 2 facilities could close coverage gaps, but further laboratory strengthening is required. Closing this culture gap is doable and a priority for advancing locally-driven antibiotic stewardship programmes, preventing AMR, and reducing infection-related newborn deaths.

BackgroundReports on hypothermia from high-burden countries like Kenya amongst sick newborns often include few centers or relatively small sample sizes.ObjectivesThis study endeavored to describe: (i) the burden of hypothermia on admission across 21 newborn units in Kenya, (ii) any trend in prevalence of hypothermia over time, (iii) factors associated with hypothermia at admission, and (iv) hypothermia's association with inpatient neonatal mortality.MethodsA retrospective cohort study was conducted from January 2020 to March 2023, focusing on small and sick newborns admitted in 21 NBUs. The primary and secondary outcome measures were the prevalence of hypothermia at admission and mortality during the index admission, respectively. An ordinal logistic regression model was used to estimate the relationship between selected factors and the outcomes cold stress (36.0°C–36.4°C) and hypothermia (

Background One in five newborns in Nairobi County, Kenya, may require inpatient neonatal care. We sought to examine referrals to and from three busy first-level referral public hospitals in Nairobi and what infrastructure and systems are available to support neonatal transport from these first-referral level hospitals to the main tertiary care center. Methods Patient-level data of newborns over 12 months were retrospectively extracted from routinely collected patient data and examined to characterize those referred into and out of three newborn units in the study hospitals. Structural assessments using a checklist completed during hospital visits were used to describe hospitals’ readiness to support newborn referral and transport. Results Five percent (398/7720) of the cohort studied were either referrals into study hospitals (68%, 272/398) or referrals out (32%, 126/398). Among 397 (99%) and 268 (67%) with sex and gestation documented respectively, 63% (251) were male and 44% (118) were preterm infants (

Abstract Background Missed nursing care undermines nursing standards of care and minimising this phenomenon is crucial to maintaining adequate patient safety and the quality of patient care. The concept is a neglected aspect of human resource for health thinking, and it remains understudied in low-income and middle-income country (LMIC) settings which have 90% of the global nursing workforce shortages. Our objective in this review was to document the prevalence of missed nursing care in LMIC, identify the categories of nursing care that are most missed and summarise the reasons for this. Methods We conducted a systematic review searching Medline, Embase, Global Health, WHO Global index medicus and CINAHL from their inception up until August 2021. Publications were included if they were conducted in an LMIC and reported on any combination of categories, reasons and factors associated with missed nursing care within in-patient settings. We assessed the quality of studies using the Newcastle Ottawa Scale. Results Thirty-one studies met our inclusion criteria. These studies were mainly cross-sectional, from upper middle-income settings and mostly relied on nurses’ self-report of missed nursing care. The measurement tools used, and their reporting were inconsistent across the literature. Nursing care most frequently missed were non-clinical nursing activities including those of comfort and communication. Inadequate personnel numbers were the most important reasons given for missed care. Conclusions Missed nursing care is reported for all key nursing task areas threatening care quality and safety. Data suggest nurses prioritise technical activities with more non-clinical activities missed, this undermines holistic nursing care. Improving staffing levels seems a key intervention potentially including sharing of less skilled activities. More research on missed nursing care and interventions to tackle it to improve quality and safety is needed in LMIC. PROSPERO registration number: CRD42021286897.

Abstract Background Data from High Income Countries have now linked low nurse staff to patient ratios to poor quality patient care. Adequately staffing hospitals is however still a challenge in resource-constrained Low-middle income countries (LMICs) and poor staff-to-patient ratios are largely taken as a norm. This in part relates to limited evidence on the relationship between staffing and quality of patient care in these settings and also an absence of research on benefits that might occur from improving hospital staff numbers in LMICs. This study will determine the effect on the quality of patient care of prospectively adding extra nursing staff to newborn units in a resource constrained LMIC setting and describe the relationship between staffing and quality of care. Methods This prospective workforce intervention study will involve a multi-method approach. We will conduct a before and after study in newborn units of 4 intervention hospitals and a single time-point comparison in 4 non-intervention hospitals to determine if there is a change in the level of missed nursing care, a process measure of the quality of patient care. We will also determine the effect of our intervention on routinely collected quality indicators using interrupted time series analysis. Using three nurse staffing metrics (Total nursing hours, nursing hours per patient day and nursing hours per patient per shift), we will describe the relationship between staffing and the quality of patient care. Discussion There is an urgent need for the implementation of staffing policies in resource constrained LMICs that are guided by relevant contextual data. To the best of our knowledge, this is the first study to evaluate the prospective addition of nursing staff in resource-constrained care settings. Our findings are likely to provide the much-needed evidence for better staffing in these settings. Trial registration This study was retrospectively registered in the Pan African Clinical Trial Registry ( https://pactr.samrc.ac.za/Default.aspx?Logout=True ) database on the 10th of June 2022 with a unique identification number-PACTR202206477083141.

Abstract Background Two neonatal mortality prediction models, the Neonatal Essential Treatment Score (NETS) which uses treatments prescribed at admission and the Score for Essential Neonatal Symptoms and Signs (SENSS) which uses basic clinical signs, were derived in high-mortality, low-resource settings to utilise data more likely to be available in these settings. In this study, we evaluate the predictive accuracy of two neonatal prediction models for all-cause in-hospital mortality. Methods We used retrospectively collected routine clinical data recorded by duty clinicians at admission from 16 Kenyan hospitals used to externally validate and update the SENSS and NETS models that were initially developed from the data from the largest Kenyan maternity hospital to predict in-hospital mortality. Model performance was evaluated by assessing discrimination and calibration. Discrimination, the ability of the model to differentiate between those with and without the outcome, was measured using the c-statistic. Calibration, the agreement between predictions from the model and what was observed, was measured using the calibration intercept and slope (with values of 0 and 1 denoting perfect calibration). Results At initial external validation, the estimated mortality risks from the original SENSS and NETS models were markedly overestimated with calibration intercepts of − 0.703 (95% CI − 0.738 to − 0.669) and − 1.109 (95% CI − 1.148 to − 1.069) and too extreme with calibration slopes of 0.565 (95% CI 0.552 to 0.577) and 0.466 (95% CI 0.451 to 0.480), respectively. After model updating, the calibration of the model improved. The updated SENSS and NETS models had calibration intercepts of 0.311 (95% CI 0.282 to 0.350) and 0.032 (95% CI − 0.002 to 0.066) and calibration slopes of 1.029 (95% CI 1.006 to 1.051) and 0.799 (95% CI 0.774 to 0.823), respectively, while showing good discrimination with c-statistics of 0.834 (95% CI 0.829 to 0.839) and 0.775 (95% CI 0.768 to 0.782), respectively. The overall calibration performance of the updated SENSS and NETS models was better than any existing neonatal in-hospital mortality prediction models externally validated for settings comparable to Kenya. Conclusion Few prediction models undergo rigorous external validation. We show how external validation using data from multiple locations enables model updating and improving their performance and potential value. The improved models indicate it is possible to predict in-hospital mortality using either treatments or signs and symptoms derived from routine neonatal data from low-resource hospital settings also making possible their use for case-mix adjustment when contrasting similar hospital settings.

Abstract Background Medication errors are likely common in low- and middle-income countries (LMICs). In neonatal hospital care where the population with severe illness has a high mortality rate, around 14.9% of drug prescriptions have errors in LMICs settings. However, there is scant research on interventions to improve medication safety to mitigate such errors. Our objective is to improve routine neonatal care particularly focusing on effective prescribing practices with the aim of achieving reduced gentamicin medication errors. Methods We propose to conduct an audit and feedback (A&F) study over 12 months in 20 hospitals with 12 months of baseline data. The medical and nursing leaders on their newborn units had been organised into a network that facilitates evaluating intervention approaches for improving quality of neonatal care in these hospitals and are receiving basic feedback generated from the baseline data. In this study, the network will (1) be expanded to include all hospital pharmacists, (2) include a pharmacist-only professional WhatsApp discussion group for discussing prescription practices, and (3) support all hospitals to facilitate pharmacist-led continuous medical education seminars on prescription practices at hospital level, i.e. default intervention package. A subset of these hospitals (n = 10) will additionally (1) have an additional hospital-specific WhatsApp group for the pharmacists to discuss local performance with their local clinical team, (2) receive detailed A&F prescription error reports delivered through mobile-based dashboard, and (3) receive a PDF infographic summarising prescribing performance circulated to the clinicians through the hospital-specific WhatsApp group, i.e. an extended package. Using interrupted time series analysis modelling changes in prescribing errors over time, coupled with process fidelity evaluation, and WhatsApp sentiment analysis, we will evaluate the success with which the A&F interventions are delivered, received, and acted upon to reduce prescribing error while exploring the extended package’s success/failure relative to the default intervention package. Discussion If effective, these theory-informed A&F strategies that carefully consider the challenges of LMICs settings will support the improvement of medication prescribing practices with the insights gained adapted for other clinical behavioural targets of a similar nature. Trial registration PACTR, PACTR202203869312307 . Registered 17th March 2022.

Abstract Background Audit of facility-based care provided to small and sick newborns is a quality improvement initiative that helps to identify the modifiable gaps in newborn care (BMC Pregnancy Childbirth 14: 280, 2014). The aim of this work was to identify literature on modifiable factors in the care of newborns in the newborn units in health facilities in low-middle-income countries (LMICs). We also set out to design a measure of the quality of the perinatal and newborn audit process. Methods The scoping review was conducted using the methodology outlined by Arksey and O’Malley and refined by Levac et al, (Implement Sci 5:1-9, 2010). We reported our results using the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. We identified seven factors to ensure a successful audit process based on World Health Organisation (WHO) recommendations which we subsequently used to develop a quality of audit process score. Data sources We conducted a structured search using PubMed, CINAHL, EMBASE, LILACS, POPLINE and African Index Medicus. Study selection Studies published in English between 1965 and December 2019 focusing on the identification of modifiable factors through clinical or mortality audits in newborn care in health facilities from LMICs. Data extraction We extracted data on the study characteristics, modifiable factors and quality of audit process indicators. Results A total of six articles met the inclusion criteria. Of these, four were mortality audit studies and two were clinical audit studies that we used to assess the quality of the audit process. None of the studies were well conducted, two were moderately well conducted, and four were poorly conducted. The modifiable factors were divided into three time periods along the continuum of newborn care. The period of newborn unit care had the highest number of modifiable factors, and in each period, the health worker related modifiable factors were the most dominant. Conclusion Based on the significant number of modifiable factors in the newborn unit, a neonatal audit tool is essential to act as a structured guide for auditing newborn unit care in LMICs. The quality of audit process guide is a useful method of ensuring high quality audits in health facilities.

Clinical audits are an important intervention that enables health workers to reflect on their practice and identify and act on modifiable gaps in the care provided. To effectively audit the quality of care provided to the small and sick newborns, the clinical audit process must use a structured tool that comprehensively covers the continuum of newborn care from immediately after birth to the period of newborn unit care. The objective of the study was to co-design a newborn clinical audit tool that considered the key principles of a Human Centred Design approach. A three-step Human Centred Design approach was used that began by (1) understanding the context, the users and the available audit tools through literature, focus group discussions and a consensus meeting that was used to develop a prototype audit tool and its implementation guide, (2) the prototype audit tool was taken through several cycles of reviewing with users on real cases in a high volume newborn unit and refining it based on their feedback, and (3) the final prototype tool and the implementation guide were then tested in two high volume newborn units to determine their usability. Several cycles of evaluation and redesigning of the prototype audit tool revealed that the users preferred a comprehensive tool that catered to human factors such as reduced free text for ease of filling, length of the tool, and aesthetics. Identified facilitators and barriers influencing the newborn clinical audit in Kenyan public hospitals informed the design of an implementation guide that builds on the strengths and overcomes the barriers. We adopted a Human Centred Design approach to developing a newborn clinical audit tool and an implementation guide that we believe are comprehensive and consider the characteristics of the context of use and the user requirements.

Objective To identify nurse staffing and patient care outcome literature in published systematic reviews and map out the evidence gaps for low/middle-income countries (LMICs).Methods We included quantitative systematic reviews on nurse staffing levels and patient care outcomes in regular ward settings published in English. We excluded qualitative reviews or reviews on nursing skill mix. We searched the Cochrane Register of Systematic Reviews, the Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, Medline, Embase and Cumulative Index to Nursing and Allied Health Literature from inception until July 2021. We used the A Measurement Tool to Assess Systematic Reviews -2 (AMSTAR-2) criteria for risk of bias assessment and conducted a narrative synthesis.Results From 843 papers, we included 14 in our final synthesis. There were overlaps in primary studies summarised across reviews, but overall, the reviews summarised 136 unique primary articles. Only 4 out of 14 reviews had data on LMIC publications and only 9 (6.6%) of 136 unique primary articles were conducted in LMICs. Only 8 of 23 patient care outcomes were reported from LMICs. Less research was conducted in contexts with staffing levels that are typical of many LMIC contexts.Discussion Our umbrella review identified very limited data for nurse staffing and patient care outcomes in LMICs. We also identified data from high-income countries might not be good proxies for LMICs as staffing levels where this research was conducted had comparatively better staffing levels than the few LMIC studies. This highlights a critical need for the conduct of nurse staffing research in LMIC contexts.Limitations We included data on systematic reviews that scored low on our risk of bias assessment because we sought to provide a broad description of the research area. We only considered systematic reviews published in English and did not include any qualitative reviews in our synthesis.PROSPERO registration number CRD42021286908.

ObjectiveMultidrug-resistant organisms (MDRO), especially carbapenem-resistant organisms (CRO), represent a threat for newborns. This study investigates the colonization prevalence of these pathogens in a newborn unit at a Kenyan tertiary hospital in an integrated approach combining routine microbiology, whole genome sequencing (WGS) and hospital surveillance data.MethodsThe study was performed in the Kenyatta National Hospital (KNH) in 2019 over a four-month period and included 300 mother-baby pairs. A total of 1,097 swabs from newborns (weekly), mothers (once) and the hospital environment were taken. Routine clinical microbiology methods were applied for surveillance. Of the 288 detected MDRO, 160 isolates were analyzed for antimicrobial resistance genes and phylogenetic relatedness using whole genome sequencing (WGS) and bioinformatic analysis.ResultsIn maternal vaginal swabs, MDRO detection rate was 15% (n=45/300), including 2% CRO (n=7/300). At admission, MDRO detection rate for neonates was 16% (n=48/300), including 3% CRO (n=8/300) with a threefold increase for MDRO (44%, n=97/218) and a fivefold increase for CRO (14%, n=29/218) until discharge. Among CRO, K. pneumoniae harboring blaNDM-1 (n=20) or blaNDM-5 (n=16) were most frequent. WGS analysis revealed 20 phylogenetically related transmission clusters (including five CRO clusters). In environmental samples, the MDRO detection rate was 11% (n=18/164), including 2% CRO (n=3/164).ConclusionOur study provides a snapshot of MDRO and CRO in a Kenyan NBU. Rather than a large outbreak scenario, data indicate several independent transmission events. The CRO rate among newborns attributed to the spread of NDM-type carbapenemases is worrisome.

Background: Adequate staffing is key to the delivery of nursing care and thus to improved inpatient and health service outcomes. Several systematic reviews have addressed the relationship between nurse staffing and these outcomes. Most primary studies within each systematic review are likely to be from high-income countries which have different practice contexts to low and middle-income countries (LMICs), although this has not been formally examined. We propose conducting an umbrella review to characterise the existing evidence linking nurse staffing to key outcomes and explicitly aim to identify evidence gaps in nurse staffing research in LMICs. Methods and analysis: This protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P). Literature searching will be conducted across Ovid Medline, Embase and EBSCO Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. Two independent reviewers will conduct searching and data abstraction and discordance will be handled by discussion between both parties. The risk of bias of the individual studies will be performed using the AMSTAR-2. Ethics and dissemination: Ethical permission is not required for this review as we will make use of already published data. We aim to publish the findings of our review in peer-reviewed journals. PROSPERO registration number: CRD42021286908

Background: Missed nursing care (care left undone or task incompletion) is viewed as an important early predictor of adverse patient care outcomes and is a useful indicator to determine the quality of patient care. Available systematic reviews on missed nursing care are based mainly on primary studies from developed countries, and there is limited evidence on missed nursing care from low-middle income countries (LMICs). We propose conducting a systematic review to identify the magnitude of missed nursing care and document factors and reasons associated with this phenomenon in LMIC settings. Methods and analysis: This protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P). We will conduct literature searching across the Ovid Medline, Embase and EBSCO Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, from inception to 2021. Two independent reviewers will conduct searches and data abstraction, and discordance will be handled by discussion between both parties. The risk of bias of the individual studies will be determined using the Newcastle-Ottawa Scale (NOS). Ethics and dissemination: Ethical permission is not required for this review as we will make use of already published data. We aim to publish the findings of our review in peer-reviewed journals PROSPERO registration number: CRD42021286897 (27th October 2021)

The objectives of this study were to (1)explore the quality of clinical data generated from hospitals providing in-patient neonatal care participating in a clinical information network (CIN) and whether data improved over time, and if data are adequate, (2)characterise accuracy of prescribing for basic treatments provided to neonatal in-patients over time. This was a retrospective cohort study involving neonates ≤28 days admitted between January 2018 and December 2021 in 20 government hospitals with an interquartile range of annual neonatal inpatient admissions between 550 and 1640 in Kenya. These hospitals participated in routine audit and feedback processes on quality of documentation and care over the study period. The study's outcomes were the number of patients as a proportion of all eligible patients over time with (1)complete domain-specific documentation scores, and (2)accurate domain-specific treatment prescription scores at admission, reported as incidence rate ratios. 80,060 neonatal admissions were eligible for inclusion. Upon joining CIN, documentation scores in the monitoring, other physical examination and bedside testing, discharge information, and maternal history domains demonstrated a statistically significant month-to-month relative improvement in number of patients with complete documentation of 7.6%, 2.9%, 2.4%, and 2.0% respectively. There was also statistically significant month-to-month improvement in prescribing accuracy after joining the CIN of 2.8% and 1.4% for feeds and fluids but not for Antibiotic prescriptions. Findings suggest that much of the variation observed is due to hospital-level factors. It is possible to introduce tools that capture important clinical data at least 80% of the time in routine African hospital settings but analyses of such data will need to account for missingness using appropriate statistical techniques. These data allow exploration of trends in performance and could support better impact evaluation, exploration of links between health system inputs and outcomes and scrutiny of variation in quality and outcomes of hospital care.

Background: Adequate staffing is key to the delivery of nursing care and thus to improved inpatient and health service outcomes. Several systematic reviews have addressed the relationship between nurse staffing and these outcomes. Most primary studies within each systematic review are likely to be from high-income countries which have different practice contexts to low and middle-income countries (LMICs), although this has not been formally examined. We propose conducting an umbrella review to characterise the existing evidence linking nurse staffing to key outcomes and explicitly aim to identify evidence gaps in nurse staffing research in LMICs. Methods and analysis: This protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P). Literature searching will be conducted across Ovid Medline, Embase and EBSCO Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. Two independent reviewers will conduct searching and data abstraction and discordance will be handled by discussion between both parties. The risk of bias of the individual studies will be performed using the AMSTAR-2. Ethics and dissemination: Ethical permission is not required for this review as we will make use of already published data. We aim to publish the findings of our review in peer-reviewed journals. PROSPERO registration number: CRD42021286908

Objectives To characterise adoption and explore specific clinical and patient factors that might influence pulse oximetry and oxygen use in low-income and middle-income countries (LMICs) over time; to highlight useful considerations for entities working on programmes to improve access to pulse oximetry and oxygen.Design A multihospital retrospective cohort study.Settings All admissions (n=132 737) to paediatric wards of 18 purposely selected public hospitals in Kenya that joined a Clinical Information Network (CIN) between March 2014 and December 2020.Outcomes Pulse oximetry use and oxygen prescription on admission; we performed growth-curve modelling to investigate the association of patient factors with study outcomes over time while adjusting for hospital factors.Results Overall, pulse oximetry was used in 48.8% (64 722/132 737) of all admission cases. Use rose on average with each month of participation in the CIN (OR: 1.11, 95% CI 1.05 to 1.18) but patterns of adoption were highly variable across hospitals suggesting important factors at hospital level influence use of pulse oximetry. Of those with pulse oximetry measurement, 7% (4510/64 722) had hypoxaemia (SpO2

Objectives To examine the prevalence of dehydration without diarrhoea among admitted children aged 1–59 months and to describe fluid management practices in such cases.Design A multisite observational study that used routine in-patient data collected prospectively between October 2013 and December 2018.Settings Study conducted in 13 county referral hospitals in Kenya.Participants Children aged 1–59 months with admission or discharge diagnosis of dehydration but had no diarrhoea as a symptom or diagnosis. Children aged

Background Most of the deaths among neonates in low-income and middle-income countries (LMICs) can be prevented through universal access to basic high-quality health services including essential facility-based inpatient care. However, poor routine data undermines data-informed efforts to monitor and promote improvements in the quality of newborn care across hospitals.Methods Continuously collected routine patients’ data from structured paper record forms for all admissions to newborn units (NBUs) from 16 purposively selected Kenyan public hospitals that are part of a clinical information network were analysed together with data from all paediatric admissions ages 0–13 years from 14 of these hospitals. Data are used to show the proportion of all admissions and deaths in the neonatal age group and examine morbidity and mortality patterns, stratified by birth weight, and their variation across hospitals.Findings During the 354 hospital months study period, 90 222 patients were admitted to the 14 hospitals contributing NBU and general paediatric ward data. 46% of all the admissions were neonates (aged 0–28 days), but they accounted for 66% of the deaths in the age group 0–13 years. 41 657 inborn neonates were admitted in the NBUs across the 16 hospitals during the study period. 4266/41 657 died giving a crude mortality rate of 10.2% (95% CI 9.97% to 10.55%), with 60% of these deaths occurring on the first-day of admission. Intrapartum-related complications was the single most common diagnosis among the neonates with birth weight of 2000 g or more who died. A threefold variation in mortality across hospitals was observed for birth weight categories 1000–1499 g and 1500–1999 g.Interpretation The high proportion of neonatal deaths in hospitals may reflect changing patterns of childhood mortality. Majority of newborns died of preventable causes (>95%). Despite availability of high-impact low-cost interventions, hospitals have high and very variable mortality proportions after stratification by birth weight.

We have worked to develop a Clinical Information Network (CIN) in Kenya as an early form of learning health systems (LHS) focused on paediatric and neonatal care that now spans 22 hospitals. CIN’s aim was to examine important outcomes of hospitalisation at scale, identify and ultimately solve practical problems of service delivery, drive improvements in quality and test interventions. By including multiple routine settings in research, we aimed to promote generalisability of findings and demonstrate potential efficiencies derived from LHS. We illustrate the nature and range of research CIN has supported over the past 7 years as a form of LHS. Clinically, this has largely focused on common, serious paediatric illnesses such as pneumonia, malaria and diarrhoea with dehydration with recent extensions to neonatal illnesses. CIN also enables examination of the quality of care, for example that provided to children with severe malnutrition and the challenges encountered in routine settings in adopting simple technologies (pulse oximetry) and more advanced diagnostics (eg, Xpert MTB/RIF). Although regular feedback to hospitals has been associated with some improvements in quality data continue to highlight system challenges that undermine provision of basic, quality care (eg, poor access to blood glucose testing and routine microbiology). These challenges include those associated with increased mortality risk (eg, delays in blood transfusion). Using the same data the CIN platform has enabled conduct of randomised trials and supports malaria vaccine and most recently COVID-19 surveillance. Employing LHS principles has meant engaging front-line workers, clinical managers and national stakeholders throughout. Our experience suggests LHS can be developed in low and middle-income countries that efficiently enable contextually appropriate research and contribute to strengthening of health services and research systems.

In low and middle-income countries (LMIC) general hospitals are important for delivering some key acute care services. Neonatal care is emblematic of these acute services as averting deaths requires skilled care over many days from multiple professionals with at least basic equipment. However, hospital care is often of poor quality and large-scale change is needed to improve outcomes. In this manuscript we aim to show how we have drawn upon our understanding of contexts of care in Kenyan general hospital NBUs, and on social and behavioural theories that offer potential mechanisms of change in these settings, to develop an initial programme theory guiding a large scale change intervention to improve neonatal care and outcomes. Our programme theory is an expression of our assumptions about what actions will be both useful and feasible. It incorporates a recognition of our strengths and limitations as a research-practitioner partnership to influence change. The steps we employ represent the initial programme theory development phase commonly undertaken in many Realist Evaluations. However, unlike many Realist Evaluations that develop initial programme theories focused on pre-existing interventions or programmes, our programme theory informs the design of a new intervention that we plan to execute. Within this paper we articulate briefly how we propose to operationalise this new intervention. Finally, we outline the quantitative and qualitative research activities that we will use to address specific questions related to the delivery and effects of this new intervention, discussing some of the challenges of such study designs. We intend that this research on the intervention will inform future efforts to revise the programme theory and yield transferable learning.

In low and middle-income countries (LMIC) general hospitals are important for delivering some key acute care services. Neonatal care is emblematic of these acute services as averting deaths requires skilled care over many days from multiple professionals with at least basic equipment. However, hospital care is often of poor quality and large-scale change is needed to improve outcomes. However, achieving large scale change in health systems remains challenging. To set the scene we first characterise the problems of hospital newborn units (NBU) in Kenya. We then combine our understanding of theory and context with reflection on our own position as an embedded research group with no formal authority to help us propose a feasible intervention strategy linked to in initial programme theory. We explain this programme theory and suggest how within a multi-level clinical professional network leaders at the ward or mid-level of hospital hierarchies are key potential change agents. In support of our programme theory we briefly outline and seek to integrate key ideas drawn from a wider set of theories. We propose how an intervention might be developed and employed in a phased approach to create the ownership, relationships and momentum that will be needed to achieve change at scale. Finally, we discuss the implications of such a strategy for our research design that is based on a prospective, in-depth case study that includes quantitative and qualitative data collection linked to specific sub-studies. We suggest using Realistic Evaluation to integrate our findings and develop an updated programme theory that should inform future large-scale change efforts before briefly discussing some of the challenges of evaluating a network as an intervention

Objectives To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).Design Systematic review of peer-reviewed journals.Data sources MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteria We included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesis This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.Results Our search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.Conclusion This review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration number CRD42018088599.

Introduction: In low- and middle-income countries (LMICs) where healthcare resources are often limited, making decisions on appropriate treatment choices is critical in ensuring reduction of paediatric deaths as well as instilling proper utilisation of the already constrained healthcare resources. Well-developed and validated prognostic models can aid in early recognition of potential risks thus contributing to the reduction of mortality rates. The aim of the planned systematic review is to identify and appraise the methodological rigor of multivariable prognostic models predicting in-hospital paediatric mortality in LMIC in order to identify statistical and methodological shortcomings deserving special attention and to identify models for external validation. Methods and analysis: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols. A search of articles will be conducted in MEDLINE, Google Scholar, and CINAHL (via EbscoHost) from inception to 2019 without any language restriction. We will also perform a search in Web of Science to identify additional reports that cite the identified studies. Data will be extracted from relevant articles in accordance with the Cochrane Prognosis Methods’ guidance; the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. Methodological quality assessment will be performed based on prespecified domains of the Prediction study Risk of Bias Assessment Tool. Ethics and dissemination: Ethical permission will not be required as this study will use published data. Findings from this review will be shared through publication in peer-reviewed scientific journals and, presented at conferences. It is our hope that this study will contribute to the development of robust multivariable prognostic models predicting in-hospital paediatric mortality in low- and middle-income countries. Registration: PROSPERO ID CRD42018088599; registered on 13 February 2018.

Abstract Background Effective coverage requires that those in need can access skilled care supported by adequate resources. There are, however, few studies of effective coverage of facility-based neonatal care in low-income settings, despite the recognition that improving newborn survival is a global priority. Methods We used a detailed retrospective review of medical records for neonatal admissions to public, private not-for-profit (mission) and private-for-profit (private) sector facilities providing 24×7 inpatient neonatal care in Nairobi City County to estimate the proportion of small and sick newborns receiving nationally recommended care across six process domains. We used our findings to explore the relationship between facility measures of structure and process and estimate effective coverage. Results Of 33 eligible facilities, 28 (four public, six mission and 18 private), providing an estimated 98.7% of inpatient neonatal care in the county, agreed to partake. Data from 1184 admission episodes were collected. Overall performance was lowest (weighted mean score 0.35 [95% confidence interval or CI: 0.22–0.48] out of 1) for correct prescription of fluid and feed volumes and best (0.86 [95% CI: 0.80–0.93]) for documentation of demographic characteristics. Doses of gentamicin, when prescribed, were at least 20% higher than recommended in 11.7% cases. Larger (often public) facilities tended to have higher process and structural quality scores compared with smaller, predominantly private, facilities. We estimate effective coverage to be 25% (estimate range: 21–31%). These newborns received high-quality inpatient care, while almost half (44.5%) of newborns needed care but did not receive it and a further 30.4% of newborns received an inadequate service. Conclusions Failure to receive services and gaps in quality of care both contribute to a shortfall in effective coverage in Nairobi City County. Three-quarters of small and sick newborns do not have access to high-quality facility-based care. Substantial improvements in effective coverage will be required to tackle high neonatal mortality in this urban setting with high levels of poverty.

Abstract Background Treatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings. Methods We conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies. Results Three thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75–0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study). Conclusion Existing neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status. Systematic review registration PROSPERO CRD42016034205

Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortality to 12 per 1,000 or lower by 2030. There is a paucity of data on neonatal in-hospital mortality in Kenya that is poorly captured in the existing health information system. Better measurement of neonatal mortality in facilities may help promote improvements in the quality of health care that will be important to achieving SDG 3 in countries such as Kenya. Methods: This was a cohort study using routinely collected data from a large urban neonatal unit in Nairobi, Kenya. All the patients admitted to the unit between April 2014 to December 2015 were included. Clinical characteristics are summarised descriptively, while the competing risk method was used to estimate the probability of in-hospital mortality considering discharge alive as the competing risk. Results: A total of 9,115 patients were included. Most were males (966/9115, 55%) and the majority (6287/9115, 69%) had normal birthweight (2.5 to 4 kg). Median length of stay was 2 days (range, 0 to 98 days) while crude mortality was 9.2% (839/9115). The probability of in-hospital death was higher than discharge alive for birthweight less than 1.5 kg with the transition to higher probability of discharge alive observed after the first week in birthweight 1.5 to

Regular assessment of quality of care allows monitoring of progress towards system goals and identifies gaps that need to be addressed to promote better outcomes. We report efforts to initiate routine assessments in a low-income country in partnership with government.A cross-sectional survey undertaken in 22 'internship training' hospitals across Kenya that examined availability of essential resources and process of care based on review of 60 case-records per site focusing on the common childhood illnesses (pneumonia, malaria, diarrhea/dehydration, malnutrition and meningitis).Availability of essential resources was 75% (45/61 items) or more in 8/22 hospitals. A total of 1298 (range 54-61) case records were reviewed. HIV testing remained suboptimal at 12% (95% CI 7-19). A routinely introduced structured pediatric admission record form improved documentation of core admission symptoms and signs (median score for signs 22/22 and 8/22 when form used and not used respectively). Correctness of penicillin and gentamicin dosing was above 85% but correctness of prescribed intravenous fluid or oral feed volumes for severe dehydration and malnutrition were 54% and 25% respectively. Introduction of Zinc for diarrhea has been relatively successful (66% cases) but use of artesunate for malaria remained rare. Exploratory analysis suggests considerable variability of the quality of care across hospitals.Quality of pediatric care in Kenya has improved but can improve further. The approach to monitoring described in this survey seems feasible and provides an opportunity for routine assessments across a large number of hospitals as part of national efforts to sustain improvement. Understanding variability across hospitals may help target improvement efforts.