Dataset Information

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

ABSTRACT:

Introduction

Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of-the-art software are used to improve bedside postnatal care and to provide insights into population health trends, to impact wider policy and practice.

Methods and analysis

This is a mixed methods (1) intervention codevelopment and optimisation and (2) pilot implementation evaluation (including economic evaluation) study. Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Over the 2-year study period clinical and demographic newborn data will be collected via Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation and measures of cost, newborn care quality and usability. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies.

Ethics and dissemination

This is a Wellcome Trust funded project (215742_Z_19_Z). Research ethics approvals have been obtained: Malawi College of Medicine Research and Ethics Committee (P.01/20/2909; P.02/19/2613); UCL (17123/001, 6681/001, 5019/004); Medical Research Council Zimbabwe (MRCZ/A/2570), BRTI and JREC institutional review boards (AP155/2020; JREC/327/19), Sally Mugabe Hospital Ethics Committee (071119/64; 250418/48). Results will be disseminated via academic publications and public and policy engagement activities. In this study, the care for an estimated 15 000 babies across three sites will be impacted.

Trial registration number

NCT0512707; Pre-results.

SUBMITTER: Wilson E

PROVIDER: S-EPMC9258512 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

Wilson Emma E Gannon Hannah H Chimhini Gwendoline G Fitzgerald Felicity F Khan Nushrat N Lorencatto Fabiana F Kesler Erin E Nkhoma Deliwe D Chiyaka Tarisai T Haghparast-Bidgoli Hassan H Lakhanpaul Monica M Cortina Borja Mario M Stevenson Alexander G AG Crehan Caroline C Sassoon Yali Y Hull-Bailey Tim T Curtis Kristina K Chiume Msandeni M Chimhuya Simbarashe S Heys Michelle M

BMJ open 20220705 7

<h4>Introduction</h4>Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of- ...[more]

PMID: 35790332

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Project description:BackgroundTwo-thirds of the 2.4 million newborn deaths that occurred in 2020 within the first 28 days of life might have been avoided by implementing existing low-cost evidence-based interventions for all sick and small newborns. An open-source digital quality improvement tool (Neotree) combining data capture with education and clinical decision support is a promising solution for this implementation gap.ObjectiveWe present results from a cost analysis of a pilot implementation of Neotree in 3 hospitals in Malawi and Zimbabwe.MethodsWe combined activity-based costing and expenditure approaches to estimate the development and implementation cost of a Neotree pilot in 1 hospital in Malawi, Kamuzu Central Hospital (KCH), and 2 hospitals in Zimbabwe, Sally Mugabe Central Hospital (SMCH) and Chinhoyi Provincial Hospital (CPH). We estimated the costs from a provider perspective over 12 months. Data were collected through expenditure reports, monthly staff time-use surveys, and project staff interviews. Sensitivity and scenario analyses were conducted to assess the impact of uncertainties on the results or estimate potential costs at scale. A pilot time-motion survey was conducted at KCH and a comparable hospital where Neotree was not implemented.ResultsTotal cost of pilot implementation of Neotree at KCH, SMCH, and CPH was US $37,748, US $52,331, and US $41,764, respectively. Average monthly cost per admitted child was US $15, US $15, and US $58, respectively. Staff costs were the main cost component (average 73% of total costs, ranging from 63% to 79%). The results from the sensitivity analysis showed that uncertainty around the number of admissions had a significant impact on the costs in all hospitals. In Malawi, replacing monthly web hosting with a server also had a significant impact on the costs. Under routine (nonresearch) conditions and at scale, total costs are estimated to fall substantially, up to 76%, reducing cost per admitted child to as low as US $5 in KCH, US $4 in SMCH, and US $14 in CPH. Median time to admit a baby was 27 (IQR 20-40) minutes using Neotree (n=250) compared to 26 (IQR 21-30) minutes using paper-based systems (n=34), and the median time to discharge a baby was 9 (IQR 7-13) minutes for Neotree (n=246) compared to 3 (IQR 2-4) minutes for paper-based systems (n=50).ConclusionsNeotree is a time- and cost-efficient tool, comparable with the results from limited similar mHealth decision-support tools in low- and middle-income countries. Implementation costs of Neotree varied substantially between the hospitals, mainly due to hospital size. The implementation costs could be substantially reduced at scale due to economies of scale because of integration to the health systems and reductions in cost items such as staff and overhead. More studies assessing the impact and cost-effectiveness of large-scale mHealth decision-support tools are needed.

Project description:BACKGROUND:Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. METHODS AND FINDINGS:This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3-40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4-40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. CONCLUSIONS:We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02738892.

Project description:ObjectiveSurgical informed consent is essential prior to caesarean section, but potentially compromised by insufficient communication. We assessed the association between a multicomponent intervention and women's recollection of information pertaining to informed consent for caesarean section in a low-resource setting, thereby contributing to respectful maternity care.DesignPre-post implementation survey, conducted from January to June 2018, surveying women prior to discharge.SettingRural 150-bed mission hospital in Southern Malawi.ParticipantsA total of 160 postoperative women were included: 80 preimplementation and 80 postimplementation.InterventionBased on observed deficiencies and input from local stakeholders, a multicomponent intervention was developed, consisting of a standardised checklist, wall poster with a six-step guide and on-the-job communication training for health workers.Primary and secondary outcome measuresIndividual components of informed consent were: indication, explanation of procedure, common complications, implications for future pregnancies and verbal enquiry of consent, which were compared preintervention and postintervention using χ2 test. Generalised linear models were used to analyse incompleteness scores and recollection of the informed consent process.ResultsThe proportion of women who recollected being informed about procedure-related risks increased from 25/80 to 47/80 (OR 3.13 (95% CI 1.64 to 6.00)). Recollection of an explanation of the procedure changed from 44/80 to 55/80 (OR 1.80 (0.94 to 3.44)), implications for future pregnancy from 25/80 to 47/80 (1.69 (0.89 to 3.20)) and of consent enquiry from 67/80 to 73/80 (OR 2.02 (0.73 to 5.37)). After controlling for other variables, incompleteness scores postintervention were 26% lower (Exp(β)=0.74; 95% CI 0.57 to 0.96). Recollection of common complications increased with 0.25 complications (β=0.25; 95% CI 0.01 to 0.49). Recollection of the correct indication did not differ significantly.ConclusionRecollection of informed consent for caesarean section changed significantly in the postintervention group. Obtaining informed consent for caesarean section is one of the essential components of respectful maternity care.

Project description:BackgroundMillions of newborns die annually from preventable causes, with the highest rates occurring in Africa. Reducing neonatal mortality requires investment to scale hospital care, which includes providing hospitals with appropriate technology to care for small and sick newborns. Expensive medical devices designed for high-resource settings often fail to withstand conditions in low-resource hospitals, including humidity, dust, frequent user turnover, complex maintenance, lack of stable power, or difficulty sourcing expensive consumables. Rigorous evaluation protocols are needed to identify effective, affordable, rugged, and easy-to-use medical devices appropriate for quality hospital-based newborn care in low-resource hospitals.MethodsWe developed an evidence-based technology review process to identify medical devices suitable for small and sick newborn care in low-resource hospitals. The eight-step process consists of: identifying devices needed for effective newborn care; defining Target Product Profiles (TPPs); identifying commercially-available products that may meet TPPs; conducting desk research to evaluate technologies against TPPs; performing technical performance verification testing under laboratory conditions; verifying technical performance after exposure to heat, humidity, dust, and power loss; performing usability evaluations with nurses, and qualifying devices that pass all steps. Devices were purchased, installed, and monitored in newborn wards across Kenya, Malawi, Nigeria, and Tanzania.ResultsOf 271 devices considered, only 45 (16.6%) met corresponding TPPs based on desk research. Thirty-nine were purchased and evaluated in the laboratory; five (12.8%) failed to meet TPPs. Thirty-four products passing laboratory evaluation underwent short-term environmental testing; only one (2.9%) device failed. Thirty-seven products underwent usability testing with 127 clinicians; surprisingly, 14 (37.8%) failed to meet TPPs. Twenty-three products passed all evaluations, and 2457 devices were installed across 65 newborn wards in Kenya, Malawi, Nigeria, and Tanzania. Continuous device monitoring reported minimal device failures, with failed devices typically returned to service within two days, resulting in an average uptime (service days divided by days installed) of 99%.ConclusionAn evidence-based device selection process can improve procurement of effective, affordable, rugged, usable newborn care devices for low-resource hospitals, and feedback to manufacturers can improve device quality. Similar processes could be adapted beyond newborn care to identify medical devices suitable for implementation in any low-resource setting.

Project description:BackgroundMaternal and neonatal mortality rates remain high in many low-income and middle-income countries. Different approaches for the improvement of birth outcomes have been used in community-based interventions, with heterogeneous effects on survival. We assessed the effects of women's groups practising participatory learning and action, compared with usual care, on birth outcomes in low-resource settings.MethodsWe did a systematic review and meta-analysis of randomised controlled trials undertaken in Bangladesh, India, Malawi, and Nepal in which the effects of women's groups practising participatory learning and action were assessed to identify population-level predictors of effect on maternal mortality, neonatal mortality, and stillbirths. We also reviewed the cost-effectiveness of the women's group intervention and estimated its potential effect at scale in Countdown countries.FindingsSeven trials (119,428 births) met the inclusion criteria. Meta-analyses of all trials showed that exposure to women's groups was associated with a 37% reduction in maternal mortality (odds ratio 0.63, 95% CI 0.32-0.94), a 23% reduction in neonatal mortality (0.77, 0.65-0.90), and a 9% non-significant reduction in stillbirths (0.91, 0.79-1.03), with high heterogeneity for maternal (I(2)=58.8%, p=0.024) and neonatal results (I(2)=64.7%, p=0.009). In the meta-regression analyses, the proportion of pregnant women in groups was linearly associated with reduction in both maternal and neonatal mortality (p=0.026 and p=0.011, respectively). A subgroup analysis of the four studies in which at least 30% of pregnant women participated in groups showed a 55% reduction in maternal mortality (0.45, 0.17-0.73) and a 33% reduction in neonatal mortality (0.67, 0.59-0.74). The intervention was cost effective by WHO standards and could save an estimated 283,000 newborn infants and 41,100 mothers per year if implemented in rural areas of 74 Countdown countries.InterpretationWith the participation of at least a third of pregnant women and adequate population coverage, women's groups practising participatory learning and action are a cost-effective strategy to improve maternal and neonatal survival in low-resource settings.FundingWellcome Trust, Ammalife, and National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for Birmingham and the Black Country programme.

Project description:BackgroundRecent reviews have highlighted the need for participatory research to design and evaluate inclusive, community-based interventions that address the diverse needs of people with lived experience of psychosis, within and beyond the health sector. The SUCCEED Africa consortium aims to co-produce a 6-year programme of research across four countries in West (Sierra Leone, Nigeria) and Southeast Africa (Zimbabwe and Malawi). This protocol describes the pilot study in which SUCCEED's intervention, research tools and processes will be tested on a small scale in each country in preparation for future evaluation research.MethodsThe SUCCEED intervention comprises peer support, case management and livelihood activities for people with lived experience of psychosis. The pilot uses a before-and-after study design investigating change in subjective quality of life in adults diagnosed with a primary psychotic disorder or another mental disorder with psychotic symptoms who are offered the SUCCEED intervention over a 4-month period. Nested within this study are the following: a baseline assessment of the feasibility, acceptability and face validity of the selected measurement tool and validity of proxy versus self-completion; and a multi-method process evaluation examining key process indicators and implementation, service and client-level outcomes. Methods include the following: baseline cognitive interviews; semi-structed observation and routine monitoring and evaluation of service delivery; endline interviews and focus group discussions; and a comparison of provider competencies at endline. At each of the four pilot sites, participants will include the following: ten people with lived experience of psychosis, recruited from either health services or community settings using purposive sampling to maximise variation; up to ten adult family members (one per participant with lived experience) involved in their care; the peer support worker, community support worker and supervisor responsible for delivering the intervention; and the data collectors. Recruitment will take place in July and August 2023.DiscussionTo the best of our knowledge, this will be the first study of a community-based intervention incorporating lay-delivered case management, formal peer support and livelihoods activities for people with lived experience of psychosis in sub-Saharan Africa. Findings will be relevant not only to SUCCEED but also to others interested in promoting rights-based approaches to community mental health in low-resource settings.Trial registrationUS National Library of Medicine (ClinicalTrials.gov), Protocol reference ID 28346. Initially registered retrospectively July 20/2023: In review.

Project description:BackgroundAlthough the incidence of acute myocardial infarction (AMI) is rising in sub-Saharan Africa, the uptake of evidence-based care for the diagnosis and treatment of AMI is limited throughout the region. In Tanzania, studies have revealed common misdiagnosis of AMI, infrequent administration of aspirin, and high short-term mortality rates following AMI.ObjectiveThis study aims to evaluate the implementation and efficacy outcomes of an intervention, the Multicomponent Intervention to Improve Acute Myocardial Infarction Care (MIMIC), which was developed to improve the delivery of evidence-based AMI care in Tanzania.MethodsThis single-arm pilot trial will be conducted in the emergency department (ED) at a referral hospital in northern Tanzania. The MIMIC intervention will be implemented by the ED staff for 1 year. Approximately 400 adults presenting to the ED with possible AMI symptoms will be enrolled, and research assistants will observe their care. Thirty days later, a follow-up survey will be administered to assess mortality and medication use. The primary outcome will be the acceptability of the MIMIC intervention, which will be measured by the Acceptability of Intervention Measurement (AIM) instrument. Acceptability will further be assessed via in-depth interviews with key stakeholders. Secondary implementation outcomes will include feasibility and fidelity. Secondary efficacy outcomes will include the following: the proportion of participants who receive electrocardiogram and cardiac biomarker testing, the proportion of participants with AMI who receive aspirin, 30-day mortality among participants with AMI, and the proportion of participants with AMI taking aspirin 30 days following enrollment.ResultsImplementation of MIMIC began on September 1, 2023. Enrollment is expected to be completed by September 1, 2024, and the first results are expected to be published by December 31, 2024.ConclusionsThis study will be the first to evaluate an intervention for improving AMI care in sub-Saharan Africa. If MIMIC is found to be acceptable, the findings from this study will inform a future cluster-randomized trial to assess effectiveness and scalability.Trial registrationClinicalTrials.gov NCT04563546; https://clinicaltrials.gov/study/NCT04563546.International registered report identifier (irrid)DERR1-10.2196/59917.

Dataset Information

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

Introduction

Methods and analysis

Ethics and dissemination

Trial registration number

Publications

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

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