Understanding ethnic inequalities in the design and implementation of digital health interventions for cardiometabolic disease: a qualitative study.

BACKGROUND: The use of digital health interventions (DHIs), such as apps and wearable devices, for prevention and management of cardiometabolic disease, has been accelerated by the impact of COVID-19 on health-care services. Digital inequalities disproportionately affect those most at risk of wider health inequalities (e.g., older age, minority ethnicity, and lower household income) and might intersect with populations with higher cardiometabolic disease risk such as South Asians in the UK. We wanted to understand how those involved in DHI implementation perceive and address these inequalities, to help develop recommendations to reduce the risk of DHI implementation exacerbating existing health inequalities. METHODS: For this qualitative study we used a purposive sampling strategy, whereby focus groups and semi-structured interviews were done online between April 7 and Dec 8, 2022, with stakeholders, including health-care professionals (n=15); technology developers and digital experts (n=10); those in strategy, evaluation, or policy roles (n=15); and charities (n=4). Discussions covered barriers and facilitators to inclusive design and implementation of DHIs, with focus dependent on expertise. Findings from a qualitative study with South Asian patients have been reported separately. Audio recordings were transcribed and coded using reflexive thematic analysis. Participants provided written consent and the study received NHS Health Research Authority approval from London - Brent Research Ethics Committee (IRAS 261047). FINDINGS: Participants had a good understanding of barriers to DHI use for cardiometabolic disease faced by South Asians, highlighting the need to design for language, culture, and diet. Many emphasised the link between digital exclusion and socioeconomic deprivation, across all ethnic groups in the UK. The potential for DHIs in improving patient outcomes was recognised; however, equity concerns included unequal patient access, lack of data and resources to target support, and need for quality evidence to recommend and commission digital tools. A range of solutions for improving equity were suggested such as resourcing support for digital upskilling, community engagement, and the role of regulation in embedding improved design and evaluation of DHIs available through health-care services. INTERPRETATION: This study reflects the experiences of professionals interested in (digital) health inequalities. However, challenges to equitable digital health implementation and use are well described. Our findings present multi-sectoral responsibilities and opportunities for action. FUNDING: National Institute for Health and Care Research (NIHR).

The Use of Digital Health Interventions for Cardiometabolic Diseases Among South Asian and Black Minority Ethnic Groups: Realist Review.

BACKGROUND: Digital health interventions (DHIs) for the prevention and management of cardiometabolic diseases have become increasingly common. However, there is limited evidence for the suitability of these approaches in minority ethnic populations, who are at an increased risk of these diseases. OBJECTIVE: This study aimed to investigate the use of DHIs for cardiovascular disease and type 2 diabetes among minority ethnic populations in countries with a majority of White, English-speaking populations, focusing on people who identified as South Asian, Black, or African American. METHODS: A realist methodology framework was followed. A literature search was conducted to develop context-mechanism-outcome configurations, including the contexts in which DHIs work for the target minority ethnic groups, mechanisms that these contexts trigger, and resulting health outcomes. After systematic searches, a qualitative analysis of the included studies was conducted using deductive and inductive coding. RESULTS: A total of 15 studies on the uptake of DHIs for cardiovascular disease or diabetes were identified, of which 13 (87%) focused on people with an African-American background. The review found evidence supporting the use of DHIs in minority ethnic populations when specific factors are considered in implementation and design, including patients' beliefs, health needs, education and literacy levels, material circumstances, culture, social networks, and wider community and the supporting health care systems. CONCLUSIONS: Our context-mechanism-outcome configurations provide a useful guide for the future development of DHIs targeted at South Asian and Black minority ethnic populations, with specific recommendations for improving cultural competency and promoting accessibility and inclusivity of design.

Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study.

BACKGROUND: Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms. OBJECTIVE: This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe. METHODS: Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital's health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS). RESULTS: Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively. CONCLUSIONS: This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings.

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.

Introduction: Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS. Methods: Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-of-the-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree. Results: Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects. Conclusion: Human-centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

Digital health, cardiometabolic disease and ethnicity: an analysis of United Kingdom government policies from 2010 to 2022.

Recent health policies in the United Kingdom (UK) and internationally have focussed on digitisation of healthcare. We examined UK policies for evidence of government action addressing health inequalities and digital health, using cardiometabolic disease as an exemplar. Using a systematic search methodology, we identified 87 relevant policy documents published between 2010 and 2022. We found increasing emphasis on digital health, including for prevention, diagnosis and management of cardiometabolic disease. Several policies also focused on tackling health inequalities and improving digital access. The COVID-19 pandemic amplified inequalities. No policies addressed ethnic inequalities in digital health for cardiometabolic disease, despite high prevalence in minority ethnic communities. Our findings suggest that creating opportunities for digital inclusion and reduce longer-term health inequalities, will require future policies to focus on: the heterogeneity of ethnic groups; cross-sectoral disadvantages which contribute to disease burden and digital accessibility; and disease-specific interventions which lend themselves to culturally tailored solutions.

Development and Pilot Implementation of Neotree, a Digital Quality Improvement Tool Designed to Improve Newborn Care and Survival in 3 Hospitals in Malawi and Zimbabwe: Cost Analysis Study.

Background: Two-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. Objective: We present results from a cost analysis of a pilot implementation of Neotree in 3 hospitals in Malawi and Zimbabwe. Methods: We 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. Results: Total 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). Conclusions: Neotree 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.

Prevalence and risk factors of stroke in Bangladesh: A nationwide population-based survey.

Background: A paucity of high-quality epidemiological survey on stroke in Bangladesh emphasizes the need for a drastic effort at the national level to study the burden of stroke in Bangladesh. Therefore, this community survey was conducted with to estimate the prevalence of stroke and its associated common risk factors among Bangladeshi population. Methods: This was a population-based cross-sectional study, carried out in 8 administrative divisions and 64 districts to estimate the prevalence of stroke throughout the country. The study adopted a two-stage cluster random sampling approach. The calculated sample size was 25,287. A semi-structured questionnaire was used to identify suspected stroke patients who were subsequently confirmed by consultant neurologists. Result: In the first stage, a total number of 25,287 respondents were interviewed throughout the country. Interviewers identified 561 respondents as suspected stroke through the Questionnaire for Verifying Stroke Free Status (QVSFS) system in 64 districts. Of the 25,287 respondents 13,878 (54.9%) were male and 11,409 (45.1%) were female. Mean age was 39.9 years. In the second stage, all suspected stroke cases (561) were further examined by neurologists and finally 288 patients were confirmed as stroke which provided a prevalence of 11.39 per 1000 population. The highest stroke prevalence (14.71 per thousand) were found in Mymensingh division and lowest (7.62 per thousand) found in Rajshahi division. The stroke prevalence varied in different age groups. It was 30.10 per thousand in the age group of >60 years and 4.60 in the age group below 40 years. The prevalence of stroke among male was twice that of female (13.62 versus 8.68 per thousand). The prevalence was slightly higher in rural areas (11.85 versus 11.07). About 50.4% respondents had some idea about stroke.Out of a total of 288 cases, 79.7% (213) patients had an ischemic stroke, 15.7% (42) had hemorrhagic, and 4.6% (12) were diagnosed as subarachnoid hemorrhage. The majority of the stroke patients had hypertension (79.2%), followed by dyslipidemia (38.9%), tobacco use in any form (37.2%), diabetes (28.8%), ischemic heart disease (20.1%). Conclusion: We have found a stroke prevalence of 11.39 per 1000 population, the highest being in the Mymensingh division. The prevalence was much higher in the elderly and male population. More than three fourth had an ischemic stroke. Hypertension, dyslipidemia, tobacco use, diabetes, ischemic heart disease are the most common risk factors observed among stroke patients.

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: 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.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings.

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.

Identifying Data Sharing and Reuse with Scholix: Potentials and Limitations.

The Scholexplorer API, based on the Scholix (Scholarly Link eXchange) framework, aims to identify links between articles and supporting data. This quantitative case study demonstrates that the API vastly expanded the number of datasets previously known to be affiliated with University of Bath outputs, allowing improved monitoring of compliance with funder mandates by identifying peer-reviewed articles linked to at least one unique dataset. Availability of author names for research outputs increased from 2.4% to 89.2%, which enabled identification of ten articles reusing non-Bath-affiliated datasets published in external repositories in the first phase, giving valuable evidence of data reuse and impact for data producers. Of these, only three were formally cited in the references. Further enhancement of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies would be beneficial. The adoption of standardized data citations by journals will be critical to creating links in a more systematic manner.

Is useful research data usually shared? An investigation of genome-wide association study summary statistics.

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.