Hypothermia amongst neonatal admissions in Kenya: a retrospective cohort study assessing prevalence, trends, associated factors, and its relationship with all-cause neonatal mortality.

Background: Reports on hypothermia from high-burden countries like Kenya amongst sick newborns often include few centers or relatively small sample sizes. Objectives: This 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. Methods: A 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 (<36.0°C). Factors associated with neonatal mortality, including hypothermia defined as body temperature below 36.0°C, were also explored using logistic regression. Results: A total of 58,804 newborns from newborn units in 21 study hospitals were included in the analysis. Out of these, 47,999 (82%) had their admission temperature recorded and 8,391 (17.5%) had hypothermia. Hypothermia prevalence decreased over the study period while admission temperature documentation increased. Significant associations were found between low birthweight and very low (0-3) APGAR scores with hypothermia at admission. Odds of hypothermia reduced as ambient temperature and month of participation in the Clinical Information Network (a collaborative learning health platform for healthcare improvement) increased. Hypothermia at admission was associated with 35% (OR 1.35, 95% CI 1.22, 1.50) increase in odds of neonatal inpatient death. Conclusions: A substantial proportion of newborns are admitted with hypothermia, indicating a breakdown in warm chain protocols after birth and intra-hospital transport that increases odds of mortality. Urgent implementation of rigorous warm chain protocols, particularly for low-birth-weight babies, is crucial to protect these vulnerable newborns from the detrimental effects of hypothermia.

Evaluating the documentation of vital signs following implementation of a new comprehensive newborn monitoring chart in 19 hospitals in Kenya: A time series analysis.

Multi-professional teams care for sick newborns, but nurses are the primary caregivers, making nursing care documentation essential for delivering high-quality care, fostering teamwork, and improving patient outcomes. We report on an evaluation of vital signs documentation following implementation of the comprehensive newborn monitoring chart using interrupted time series analysis and a review of filled charts. We collected post-admission vital signs (Temperature (T), Pulse (P), Respiratory Rate (R) and Oxygen Saturation (S)) documentation frequencies of 43,719 newborns with a length of stay > 48 hours from 19 public hospitals in Kenya between September 2019 and October 2021. The primary outcome was an ordinal categorical variable (no monitoring, monitoring 1 to 3 times, 4 to 7 times and 8 or more times) based on the number of complete sets of TPRS. Descriptive analyses explored documentation of at least one T, P, R and S. The percentage of patients in the no-monitoring category decreased from 68.5% to 43.5% in the post-intervention period for TPRS monitoring. The intervention increased the odds of being in a higher TPRS monitoring category by 4.8 times (p<0.001) and increased the odds of higher monitoring frequency for each vital sign, with S recording the highest odds. Sicker babies were likely to have vital signs documented in a higher monitoring category and being in the NEST360 program increased the odds of frequent vital signs documentation. However, by the end of the intervention period, nearly half of the newborns did not have a single full set of TPRS documented and there was heterogenous hospital performance. A review of 84 charts showed variable documentation, with only one chart being completed as designed. Vital signs documentation fell below standards despite increased documentation odds. More sustained interventions are required to realise the benefits of the chart and hospital-specific performance data may help customise interventions.

Pediatric prognostic models predicting inhospital child mortality in resource-limited settings: An external validation study.

Background and Aims: Prognostic models provide evidence-based predictions and estimates of future outcomes, facilitating decision-making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)-Malawi model and three other models by Lowlavaar et al. Methods: The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in-hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in-hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept. Results: The RISC-Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case-fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77-0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 -1.06), and calibration intercept was 0.81 (95% CI: 0.77-0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients  were included, with an in-hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72-0.77), the calibration slope was 0.78 (95% CI: 0.71-0.84), and the calibration intercept was 0.37 (95% CI: 0.28-0.46). All models markedly underestimated the risk of mortality. Conclusion: All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.

Recalibrating prognostic models to improve predictions of in-hospital child mortality in resource-limited settings.

BACKGROUND: In an external validation study, model recalibration is suggested once there is evidence of poor model calibration but with acceptable discriminatory abilities. We identified four models, namely RISC-Malawi (Respiratory Index of Severity in Children) developed in Malawi, and three other predictive models developed in Uganda by Lowlaavar et al. (2016). These prognostic models exhibited poor calibration performance in the recent external validation study, hence the need for recalibration. OBJECTIVE: In this study, we aim to recalibrate these models using regression coefficients updating strategy and determine how much their performances improve. METHODS: We used data collected by the Clinical Information Network from paediatric wards of 20 public county referral hospitals. Missing data were multiply imputed using chained equations. Model updating entailed adjustment of the model's calibration performance while the discriminatory ability remained unaltered. We used two strategies to adjust the model: intercept-only and the logistic recalibration method. RESULTS: Eligibility criteria for the RISC-Malawi model were met in 50,669 patients, split into two sets: a model-recalibrating set (n = 30,343) and a test set (n = 20,326). For the Lowlaavar models, 10,782 patients met the eligibility criteria, of whom 6175 were used to recalibrate the models and 4607 were used to test the performance of the adjusted model. The intercept of the recalibrated RISC-Malawi model was 0.12 (95% CI 0.07, 0.17), while the slope of the same model was 1.08 (95% CI 1.03, 1.13). The performance of the recalibrated models on the test set suggested that no model met the threshold of a perfectly calibrated model, which includes a calibration slope of 1 and a calibration-in-the-large/intercept of 0. CONCLUSIONS: Even after model adjustment, the calibration performances of the 4 models did not meet the recommended threshold for perfect calibration. This finding is suggestive of models over/underestimating the predicted risk of in-hospital mortality, potentially harmful clinically. Therefore, researchers may consider other alternatives, such as ensemble techniques to combine these models into a meta-model to improve out-of-sample predictive performance.

External validation of inpatient neonatal mortality prediction models in high-mortality settings.

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.

Evaluation of an audit and feedback intervention to reduce gentamicin prescription errors in newborn treatment (ReGENT) in neonatal inpatient care in Kenya: a controlled interrupted time series study protocol.

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.

Quantifying the indirect impact of COVID-19 pandemic on utilisation of outpatient and immunisation services in Kenya: a longitudinal study using interrupted time series analysis.

OBJECTIVE: In this study, we assess the indirect impact of COVID-19 on utilisation of immunisation and outpatient services in Kenya. DESIGN: Longitudinal study. SETTING: Data were analysed from all healthcare facilities reporting to Kenya's health information system from January 2018 to March 2021. Multiple imputation was used to address missing data, interrupted time series analysis was used to quantify the changes in utilisation of services and sensitivity analysis was carried out to assess robustness of estimates. EXPOSURE OF INTEREST: COVID-19 outbreak and associated interventions. OUTCOME MEASURES: Monthly attendance to health facilities. We assessed changes in immunisation and various outpatient services nationally. RESULTS: Before the first case of COVID-19 and pursuant intervention measures in March 2020, uptake of health services was consistent with historical levels. There was significant drops in attendance (level changes) in April 2020 for overall outpatient visits for under-fives (rate ratio, RR 0.50, 95% CI 0.44 to 0.57), under-fives with pneumonia (RR 0.43, 95% CI 0.38 to 0.47), overall over-five visits (RR 0.65, 95% CI 0.57 to 0.75), over-fives with pneumonia (RR 0.62, 95% CI 0.55 to 0.70), fourth antenatal care visit (RR 0.86, 95% CI 0.80 to 0.93), total hypertension (RR 0.89, 95% CI 0.82 to 0.96), diabetes cases (RR 0.95 95% CI, 0.93 to 0.97) and HIV testing (RR 0.97, 95% CI 0.94 to 0.99). Immunisation services, first antenatal care visits, new cases of hypertension and diabetes were not affected. The post-COVID-19 trend was increasing, with more recent data suggesting reversal of effects and health services reverting to expected levels as of March 2021. CONCLUSION: COVID-19 pandemic has had varied indirect effects on utilisation of health services in Kenya. There is need for proactive and targeted interventions to reverse these effects as part of the pandemic's response to avert non-COVID-19 indirect mortality.

Lessons from the design, development and implementation of a three-dimensional (3D) neonatal resuscitation training smartphone application: Life-saving Instruction for Emergencies (LIFE app).

Neonatal mortality remains disproportionately high in sub-Saharan Africa partly due to insufficient numbers of adequately trained and skilled front-line health workers. Opportunities for improving neonatal care may result from upskilling frontline health workers using innovative technological approaches. This practice paper describes the key steps involved in the design, development and implementation of an innovative smartphone-based training application using an agile, human-centred design approach. The Life-saving Instruction for Emergencies (LIFE) app is a three-dimension (3D) scenario-based mobile app for smartphones and is free to download. Two clinical modules are currently included with further scenarios planned. Whilst the focus of the practice paper is on the lessons learned during the design and development process, we also share key learning related to project management and sustainability plans, which we hope will help researchers working on similar projects.

Improving in-patient neonatal data quality as a pre-requisite for monitoring and improving quality of care at scale: A multisite retrospective cohort study in Kenya.

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.

Pulse oximetry adoption and oxygen orders at paediatric admission over 7 years in Kenya: a multihospital retrospective cohort study.

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 <90%). Across the same period, 8.6% (11 428/132 737) had oxygen prescribed but in 87%, pulse oximetry was either not done or the hypoxaemia threshold (SpO2 <90%) was not met. Lower chest-wall indrawing and other respiratory symptoms were associated with pulse oximetry use at admission and were also associated with oxygen prescription in the absence of pulse oximetry or hypoxaemia. CONCLUSION: The adoption of pulse oximetry recommended in international guidelines for assessing children with severe illness has been slow and erratic, reflecting system and organisational weaknesses. Most oxygen orders at admission seem driven by clinical and situational factors other than the presence of hypoxaemia. Programmes aiming to implement pulse oximetry and oxygen systems will likely need a long-term vision to promote adoption, guideline development and adherence and continuously examine impact.

Employing learning health system principles to advance research on severe neonatal and paediatric illness in Kenya.

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.

The indirect impact of COVID-19 pandemic on inpatient admissions in 204 Kenyan hospitals: An interrupted time series analysis.

The first case of severe acute respiratory coronavirus 2 (SARS-CoV-2) was identified in March 2020 in Kenya resulting in the implementation of public health measures (PHM) to prevent large-scale epidemics. We aimed to quantify the impact of COVID-19 confinement measures on access to inpatient services using data from 204 Kenyan hospitals. Data on monthly admissions and deliveries from the District Health Information Software version 2 (DHIS 2) were extracted for the period January 2018 to March 2021 stratified by hospital ownership (public or private) and adjusting for missing data using multiple imputation (MI). We used the COVID-19 event as a natural experiment to examine the impact of COVID-19 and associated PHM on use of health services by hospital ownership. We estimated the impact of COVID-19 using two approaches; Statistical process control (SPC) charts to visualize and detect changes and Interrupted time series (ITS) analysis using negative-binomial segmented regression models to quantify the changes after March 2020. Sensitivity analysis was undertaken to test robustness of estimates using Generalised Estimating Equations (GEE) and impact of national health workers strike on observed trends. SPC charts showed reductions in most inpatient services starting April 2020. ITS modelling showed significant drops in April 2020 in monthly volumes of live-births (11%), over-fives admissions for medical (29%) and surgical care (25%) with the greatest declines in the under-five's admissions (59%) in public hospitals. Similar declines were apparent in private hospitals. Health worker strikes had a significant impact on post-COVID-19 trends for total deliveries, live-births and caesarean section rate in private hospitals. COVID-19 has disrupted utilization of inpatient services in Kenyan hospitals. This might have increased avoidable morbidity and mortality due to non-COVID-19-related illnesses. The declines have been sustained. Recent data suggests a reversal in trends with services appearing to be going back to pre- COVID levels.

Evaluation of Adaptive Feedback in a Smartphone-Based Game on Health Care Providers' Learning Gain: Randomized Controlled Trial.

BACKGROUND: Although smartphone-based emergency care training is more affordable than traditional avenues of training, it is still in its infancy, remains poorly implemented, and its current implementation modes tend to be invariant to the evolving learning needs of the intended users. In resource-limited settings, the use of such platforms coupled with gamified approaches remains largely unexplored, despite the lack of traditional training opportunities, and high mortality rates in these settings. OBJECTIVE: The primary aim of this randomized experiment is to determine the effectiveness of offering adaptive versus standard feedback, on the learning gains of clinicians, through the use of a smartphone-based game that assessed their management of a simulated medical emergency. A secondary aim is to examine the effects of learner characteristics and learning spacing with repeated use of the game on the secondary outcome of individualized normalized learning gain. METHODS: The experiment is aimed at clinicians who provide bedside neonatal care in low-income settings. Data were captured through an Android app installed on the study participants' personal phones. The intervention, which was based on successful attempts at a learning task, included adaptive feedback provided within the app to the experimental arm, whereas the control arm received standardized feedback. The primary end point was completion of the second learning session. Of the 572 participants enrolled between February 2019 and July 2019, 247 (43.2%) reached the primary end point. The primary outcome was standardized relative change in learning gains between the study arms as measured by the Morris G effect size. The secondary outcomes were the participants individualized normalized learning gains. RESULTS: The effect of adaptive feedback on care providers' learning gain was found to be g=0.09 (95% CI -0.31 to 0.46; P=.47). In exploratory analysis, using normalized learning gains, when subject-treatment interaction and differential time effect was controlled for, this effect increased significantly to 0.644 (95% CI 0.35 to 0.94; P<.001) with immediate repetition, which is a moderate learning effect, but reduced significantly by 0.28 after a week. The overall learning change from the app use in both arms was large and may have obscured a direct effect of feedback. CONCLUSIONS: There is a considerable learning gain between the first two rounds of learning with both forms of feedback and a small added benefit of adaptive feedback after controlling for learner differences. We suggest that linking the adaptive feedback provided to care providers to how they space their repeat learning session(s) may yield higher learning gains. Future work might explore in more depth the feedback content, in particular whether or not explanatory feedback (why answers were wrong) enhances learning more than reflective feedback (information about what the right answers are). TRIAL REGISTRATION: Pan African Clinical Trial Registry (PACTR) 201901783811130; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=5836. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13034.

Evaluation of Adaptive Feedback in a Smartphone-Based Serious Game on Health Care Providers' Knowledge Gain in Neonatal Emergency Care: Protocol for a Randomized Controlled Trial.

BACKGROUND: Although smartphone-based clinical training to support emergency care training is more affordable than traditional avenues of training, it is still in its infancy and remains poorly implemented. In addition, its current implementations tend to be invariant to the evolving learning needs of the intended users. In resource-limited settings, the use of such platforms coupled with serious-gaming approaches remain largely unexplored and underdeveloped, even though they offer promise in terms of addressing the health workforce skill imbalance and lack of training opportunities associated with the high neonatal mortality rates in these settings. OBJECTIVE: This randomized controlled study aims to assess the effectiveness of offering adaptive versus standard feedback through a smartphone-based serious game on health care providers' knowledge gain on the management of a neonatal medical emergency. METHODS: The study is aimed at health care workers (physicians, nurses, and clinical officers) who provide bedside neonatal care in low-income settings. We will use data captured through an Android smartphone-based serious-game app that will be downloaded to personal phones belonging to the study participants. The intervention will be adaptive feedback provided within the app. The data captured will include the level of feedback provided to participants as they learn to use the mobile app, and performance data from attempts made during the assessment questions on interactive tasks participants perform as they progress through the app on emergency neonatal care delivery. The primary endpoint will be the first two complete rounds of learning within the app, from which the individuals' "learning gains" and Morris G intervention effect size will be computed. To minimize bias, participants will be assigned to an experimental or a control group by a within-app random generator, and this process will be concealed to both the study participants and the investigators until the primary endpoint is reached. RESULTS: This project was funded in November 2016. It has been approved by the Central University Research Ethics Committee of the University of Oxford and the Scientific and Ethics Review Unit of the Kenya Medical Research Institute. Recruitment and data collection began from February 2019 and will continue up to July 31, 2019. As of July 18, 2019, we enrolled 541 participants, of whom 238 reached the primary endpoint, with a further 19 qualitative interviews conducted to support evaluation. Full analysis will be conducted once we reach the end of the study recruitment period. CONCLUSIONS: This study will be used to explore the effectiveness of adaptive feedback in a smartphone-based serious game on health care providers in a low-income setting. This aspect of medical education is a largely unexplored topic in this context. In this randomized experiment, the risk of performance bias across arms is moderate, given that the active ingredient of the intervention (ie, knowledge) is a latent trait that is difficult to comprehensively control for in a real-world setting. However, the influence of any resulting bias that has the ability to alter the results will be assessed using alternative methods such as qualitative interviews. TRIAL REGISTRATION: Pan African Clinical Trials Registry PACTR201901783811130; https://pactr.samrc.ac.za/TrialDisplay. aspx?TrialID=5836. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13034.

Correction to: An exploration of mortality risk factors in non-severe pneumonia in children using clinical data from Kenya.

CORRECTION: The original article contains an omission in the Acknowledgements sub-section of the Declarations.

An exploration of mortality risk factors in non-severe pneumonia in children using clinical data from Kenya.

BACKGROUND: Childhood pneumonia is the leading infectious cause of mortality in children younger than 5 years old. Recent updates to World Health Organization pneumonia guidelines recommend outpatient care for a population of children previously classified as high risk. This revision has been challenged by policymakers in Africa, where mortality related to pneumonia is higher than in other regions and often complicated by comorbidities. This study aimed to identify factors that best discriminate inpatient mortality risk in non-severe pneumonia and explore whether these factors offer any added benefit over the current criteria used to identify children with pneumonia requiring inpatient care. METHODS: We undertook a retrospective cohort study of children aged 2-59 months admitted with a clinical diagnosis of pneumonia at 14 public hospitals in Kenya between February 2014 and February 2016. Using machine learning techniques, we analysed whether clinical characteristics and common comorbidities increased the risk of inpatient mortality for non-severe pneumonia. The topmost risk factors were subjected to decision curve analysis to explore if using them as admission criteria had any net benefit above the current criteria. RESULTS: Out of 16,162 children admitted with pneumonia during the study period, 10,687 were eligible for subsequent analysis. Inpatient mortality within this non-severe group was 252/10,687 (2.36%). Models demonstrated moderately good performance; the partial least squares discriminant analysis model had higher sensitivity for predicting mortality in comparison to logistic regression. Elevated respiratory rate (≥70 bpm), age 2-11 months and weight-for-age Z-score (WAZ) < -3SD were highly discriminative of mortality. These factors ranked consistently across the different models. For a risk threshold probability of 7-14%, there is a net benefit to admitting the patient sub-populations with these features as additional criteria alongside those currently used to classify severe pneumonia. Of the population studied, 70.54% met at least one of these criteria. Sensitivity analyses indicated that the overall results were not significantly affected by variations in pneumonia severity classification criteria. CONCLUSIONS: Children with non-severe pneumonia aged 2-11 months or with respiratory rate ≥ 70 bpm or very low WAZ experience risks of inpatient mortality comparable to severe pneumonia. Inpatient care is warranted in these high-risk groups of children.

A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory.

BACKGROUND: Audit and feedback is a common intervention for supporting clinical behaviour change. Increasingly, health data are available in electronic format. Yet, little is known regarding if and how electronic audit and feedback (e-A&F) improves quality of care in practice. OBJECTIVE: The study aimed to assess the effectiveness of e-A&F interventions in a primary care and hospital context and to identify theoretical mechanisms of behaviour change underlying these interventions. METHODS: In August 2016, we searched five electronic databases, including MEDLINE and EMBASE via Ovid, and the Cochrane Central Register of Controlled Trials for published randomised controlled trials. We included studies that evaluated e-A&F interventions, defined as a summary of clinical performance delivered through an interactive computer interface to healthcare providers. Data on feedback characteristics, underlying theoretical domains, effect size and risk of bias were extracted by two independent review authors, who determined the domains within the Theoretical Domains Framework (TDF). We performed a meta-analysis of e-A&F effectiveness, and a narrative analysis of the nature and patterns of TDF domains and potential links with the intervention effect. RESULTS: We included seven studies comprising of 81,700 patients being cared for by 329 healthcare professionals/primary care facilities. Given the extremely high heterogeneity of the e-A&F interventions and five studies having a medium or high risk of bias, the average effect was deemed unreliable. Only two studies explicitly used theory to guide intervention design. The most frequent theoretical domains targeted by the e-A&F interventions included 'knowledge', 'social influences', 'goals' and 'behaviour regulation', with each intervention targeting a combination of at least three. None of the interventions addressed the domains 'social/professional role and identity' or 'emotion'. Analyses identified the number of different domains coded in control arm to have the biggest role in heterogeneity in e-A&F effect size. CONCLUSIONS: Given the high heterogeneity of identified studies, the effects of e-A&F were found to be highly variable. Additionally, e-A&F interventions tend to implicitly target only a fraction of known theoretical domains, even after omitting domains presumed not to be linked to e-A&F. Also, little evaluation of comparative effectiveness across trial arms was conducted. Future research should seek to further unpack the theoretical domains essential for effective e-A&F in order to better support strategic individual and team goals.

Improving documentation of clinical care within a clinical information network: an essential initial step in efforts to understand and improve care in Kenyan hospitals.

In many low income countries health information systems are poorly equipped to provide detailed information on hospital care and outcomes. Information is thus rarely used to support practice improvement. We describe efforts to tackle this challenge and to foster learning concerning collection and use of information. This could improve hospital services in Kenya. We are developing a Clinical Information Network, a collaboration spanning 14 hospitals, policy makers and researchers with the goal of improving information available on the quality of inpatient paediatric care across common childhood illnesses in Kenya. Standardised data from hospitals' paediatric wards are collected using non-commercial and open source tools. We have implemented procedures for promoting data quality which are performed prior to a process of semi-automated analysis and routine report generation for hospitals in the network. In the first phase of the Clinical Information Network, we collected data on over 65 000 admission episodes. Despite clinicians' initial unfamiliarity with routine performance reporting, we found that, as an initial focus, both engaging with each hospital and providing them information helped improve the quality of data and therefore reports. The process has involved mutual learning and building of trust in the data and should provide the basis for collaborative efforts to improve care, to understand patient outcome, and to evaluate interventions through shared learning. We have found that hospitals are willing to support the development of a clinically focused but geographically dispersed Clinical Information Network in a low-income setting. Such networks show considerable promise as platforms for collaborative efforts to improve care, to provide better information for decision making, and to enable locally relevant research.

Innovating to enhance clinical data management using non-commercial and open source solutions across a multi-center network supporting inpatient pediatric care and research in Kenya.

OBJECTIVE: To share approaches and innovations adopted to deliver a relatively inexpensive clinical data management (CDM) framework within a low-income setting that aims to deliver quality pediatric data useful for supporting research, strengthening the information culture and informing improvement efforts in local clinical practice. MATERIALS AND METHODS: The authors implemented a CDM framework to support a Clinical Information Network (CIN) using Research Electronic Data Capture (REDCap), a noncommercial software solution designed for rapid development and deployment of electronic data capture tools. It was used for collection of standardized data from case records of multiple hospitals' pediatric wards. R, an open-source statistical language, was used for data quality enhancement, analysis, and report generation for the hospitals. RESULTS: In the first year of CIN, the authors have developed innovative solutions to support the implementation of a secure, rapid pediatric data collection system spanning 14 hospital sites with stringent data quality checks. Data have been collated on over 37 000 admission episodes, with considerable improvement in clinical documentation of admissions observed. Using meta-programming techniques in R, coupled with branching logic, randomization, data lookup, and Application Programming Interface (API) features offered by REDCap, CDM tasks were configured and automated to ensure quality data was delivered for clinical improvement and research use. CONCLUSION: A low-cost clinically focused but geographically dispersed quality CDM (Clinical Data Management) in a long-term, multi-site, and real world context can be achieved and sustained and challenges can be overcome through thoughtful design and implementation of open-source tools for handling data and supporting research.