Faculty Bibliography

INTRODUCTION/BACKGROUND:'Multimorbidity' describes the presence of two or more long-term conditions, which can include communicable, non-communicable diseases, and mental disorders. The rising global burden from multimorbidity is well documented, but trial evidence for effective interventions in low-/middle-income countries (LMICs) is limited. Selection of appropriate outcomes is fundamental to trial design to ensure cross-study comparability, but there is currently no agreement on a core outcome set (COS) to include in trials investigating multimorbidity specifically in LMICs. Our aim is to develop international consensus on two COSs for trials of interventions to prevent and treat multimorbidity in LMIC settings. METHODS AND ANALYSIS/UNASSIGNED:Following methods recommended by the Core Outcome Measures in Effectiveness Trials initiative, the development of these two COSs will occur in parallel in three stages: (1) generation of a long list of potential outcomes for inclusion; (2) two-round online Delphi surveys and (3) consensus meetings. First, to generate an initial list of outcomes, we will conduct a systematic review of multimorbidity intervention and prevention trials and interviews with people living with multimorbidity and their caregivers in LMICs. Outcomes will be classified using an outcome taxonomy. Two-round Delphi surveys will be used to elicit importance scores for these outcomes from people living with multimorbidity, caregivers, healthcare professionals, policy makers and researchers in LMICs. Finally, consensus meetings including all of these stakeholders will be held to agree outcomes for inclusion in the two COSs. ETHICS AND DISSEMINATION/UNASSIGNED:The study has been approved by the Research Governance Committee of the Department of Health Sciences, University of York, UK (HSRGC/2020/409/D:COSMOS). Each participating country/research group will obtain local ethics board approval. Informed consent will be obtained from all participants. We will disseminate findings through peer-reviewed open access publications, and presentations at global conferences selected to reach a wide range of LMIC stakeholders. PROSPERO REGISTATION NUMBER/UNASSIGNED:CRD42020197293.

Objective/UNASSIGNED:This study aimed at determining the various types of home-based remedies, mode of administration, prevalence of use, and their relevance in reducing the risk of infection, hospital admission, severe disease, and death. Methods/UNASSIGNED:The study design is an open cohort of all participants who presented for testing for COVID-19 at the Infectious Disease Treatment Centre (Tamale) and were followed up for a period of six weeks. A nested case-control study was designed. Numerical data were analysed using STATA version 14, and qualitative data were thematically analysed. Results/UNASSIGNED: = 2)). Participants who practiced any form of home-based therapy were protected from SARS-CoV-2 infection (OR = 0.28 (0.20-0.39)), severe/critical COVID-19 (OR = 0.15 (0.05-0.48)), hospital admission (OR = 0.15 (0.06-0.38)), and death (OR = 0.31 (0.07-1.38)). Analysis of the various subgroups of the home-based therapies, however, demonstrated that not all the home-based remedies were effective. Steam inhalation and herbal baths were associated with 26.6 (95% CI = 6.10-116.24) and 2.7 (95% CI = 0.49-14.78) times increased risk of infection, respectively. However, change in diet (AOR = 0.01 (0.00-0.13)) and physical exercise (AOR = 0.02 (0.00-0.26)) remained significantly associated with a reduced risk of infection. We described results of thematic content analysis regarding the common ingredients in the drinks, diets, and other home-based methods administered. Conclusion/UNASSIGNED:Almost a third of persons presenting for COVID-19 test were involved in some form of home-based remedy to prevent COVID-19. Steam inhalation and herbal baths increased risk of COVID-19 infection, while physical exercise and dietary changes were protective against COVID-19 infection and hospital admission. Future protocols might consider inclusion of physical activity and dietary changes based on demonstrated health gains.

BACKGROUND:Environmental exposures account for a growing proportion of global mortality. Large cohort studies are needed to characterize the independent impact of environmental exposures on mortality in low-income settings. METHODS:We collected data on individual and environmental risk factors for a multiethnic cohort of 50,045 individuals in a low-income region in Iran. Environmental risk factors included: ambient fine particular matter air pollution; household fuel use and ventilation; proximity to traffic; distance to percutaneous coronary intervention (PCI) center; socioeconomic environment; population density; local land use; and nighttime light exposure. We developed a spatial survival model to estimate the independent associations between these environmental exposures and all-cause and cardiovascular mortality. FINDINGS:Several environmental factors demonstrated associations with mortality after adjusting for individual risk factors. Ambient fine particulate matter air pollution predicted all-cause mortality (per μg/m3, HR 1.20, 95% CI 1.07, 1.36) and cardiovascular mortality (HR 1.17, 95% CI 0.98, 1.39). Biomass fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.23, 95% CI 0.99, 1.53) and cardiovascular mortality (HR 1.36, 95% CI 0.99, 1.87). Kerosene fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.09, 95% CI 0.97, 1.23) and cardiovascular mortality (HR 1.19, 95% CI 1.01, 1.41). Distance to PCI center predicted all-cause mortality (per 10km, HR 1.01, 95% CI 1.004, 1.022) and cardiovascular mortality (HR 1.02, 95% CI 1.004, 1.031). Additionally, proximity to traffic predicted all-cause mortality (HR 1.13, 95% CI 1.01, 1.27). In a separate validation cohort, the multivariable model effectively predicted both all-cause mortality (AUC 0.76) and cardiovascular mortality (AUC 0.81). Population attributable fractions demonstrated a high mortality burden attributable to environmental exposures. INTERPRETATION:Several environmental factors predicted cardiovascular and all-cause mortality, independent of each other and of individual risk factors. Mortality attributable to environmental factors represents a critical opportunity for targeted policies and programs.

More than 500 million people worldwide live with cardiovascular disease (CVD). Health systems today face fundamental challenges in delivering optimal care due to ageing populations, healthcare workforce constraints, financing, availability and affordability of CVD medicine, and service delivery. Digital health technologies can help address these challenges. They may be a tool to reach Sustainable Development Goal 3.4 and reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030. Yet, a range of fundamental barriers prevents implementation and access to such technologies. Health system governance, health provider, patient and technological factors can prevent or distort their implementation. World Heart Federation (WHF) roadmaps aim to identify essential roadblocks on the pathway to effective prevention, detection, and treatment of CVD. Further, they aim to provide actionable solutions and implementation frameworks for local adaptation. This WHF Roadmap for digital health in cardiology identifies barriers to implementing digital health technologies for CVD and provides recommendations for overcoming them.

Non-adherence to antihypertensive medications is a major cause of uncontrolled hypertension, leading to cardiovascular morbidity and mortality. Ensuring consistent medication possession is crucial in addressing non-adherence. Community-based medication delivery is a strategy that may improve medication possession, adherence, and blood pressure (BP) reduction. Our program in Kenya piloted a community medication delivery program, coupled with blood pressure monitoring and adherence evaluation. Between September 2019 and March 2020, patients who received hypertension care from our chronic disease management program also received community-based delivery of antihypertensive medications. We calculated number of days during which each patient had possession of medications and analyzed the relationship between successful medication delivery and self-reported medication adherence and BP. A total of 128 patient records (80.5% female) were reviewed. At baseline, mean systolic blood pressure (SBP) was 155.7 mmHg and mean self-reported adherence score was 2.7. Sixty-eight (53.1%) patients received at least 1 successful medication delivery. Our pharmacy dispensing records demonstrated that medication possession was greater among patients receiving medication deliveries. Change in self-reported medication adherence from baseline worsened in patients who did not receive any medication delivery (+0.5), but improved in patients receiving 1 delivery (-0.3) and 2 or more deliveries (-0.8). There was an SBP reduction of 1.9, 6.1, and 15.5 mmHg among patients who did not receive any deliveries, those who received 1 delivery, and those who received 2 or more medication deliveries, respectively. Adjusted mixed-effect model estimates revealed that mean SBP reduction and self-reported medication adherence were improved among individuals who successfully received medication deliveries, compared to those who did not. A community medication delivery program in western Kenya was shown to be implementable and enhanced medication possession, reduced SBP, and significantly improved self-reported adherence. This is a promising strategy to improve health outcomes for patients with uncontrolled hypertension that warrants further investigation.

BACKGROUND:Digital mental health interventions are being used more than ever for the prevention and treatment of psychological problems. Optimizing the implementation aspects of digital mental health is essential to deliver the program to populations in need, but there is a lack of validated implementation outcome measures for digital mental health interventions. OBJECTIVE:The primary aim of this study is to develop implementation outcome scales of digital mental health for different levels of stakeholders involved in the implementation process: users, providers, and managers or policy makers. The secondary aim is to validate the developed scale for users. METHODS:We developed English and Japanese versions of the implementation outcome scales for digital mental health (iOSDMH) based on the literature review and panel discussions with experts in implementation research and web-based psychotherapy. The study developed acceptability, appropriateness, feasibility, satisfaction, and harm as the outcome measures for users, providers, and managers or policy makers. We conducted evidence-based interventions via the internet using UTSMeD, a website for mental health information (N=200). Exploratory factor analysis (EFA) was conducted to assess the structural validity of the iOSDMH for users. Satisfaction, which consisted of a single item, was not included in the EFA. RESULTS:The iOSDMH was developed for users, providers, and managers or policy makers. The iOSDMH contains 19 items for users, 11 items for providers, and 14 items for managers or policy makers. Cronbach α coefficients indicated intermediate internal consistency for acceptability (α=.665) but high consistency for appropriateness (α=.776), feasibility (α=.832), and harm (α=.777) of the iOSDMH for users. EFA revealed 3-factor structures, indicating acceptability and appropriateness as close concepts. Despite the similarity between these 2 concepts, we inferred that acceptability and appropriateness should be used as different factors, following previous studies. CONCLUSIONS:We developed iOSDMH for users, providers, and managers. Psychometric assessment of the scales for users demonstrated acceptable reliability and validity. Evaluating the components of digital mental health implementation is a major step forward in implementation science.

INTRODUCTION:In ED patients with acute drug overdose involving prescription medication and/or substances of abuse, severe QTc prolongation (> 500 ms) is predictive of adverse cardiovascular events (ACVE), defined as myocardial injury, ventricular dysrhythmia, shock, or cardiac arrest. However, it is unclear whether delayed severe QTc prolongation (dsQTp) is a risk factor for ACVE and if specific clinical factors are associated with occurrence of dsQTp. METHODS:A secondary analysis of a prospective cohort of consecutive adult ED patients with acute drug overdose was performed on patients with initial QTc < 500 ms. The predictor variable, dsQTp, was defined as initial QTc < 500 ms followed by repeat QTc ≥ 500 ms. The primary outcome was occurrence of ACVE. Multivariable logistic regression was performed to test whether dsQTp was an independent predictor of ACVE and to derive clinical factors associated with dsQTp. RESULTS:Of 2311 patients screened, 1648 patients were included. The dsQTp group (N = 27) was older than the control group (N = 1621) (51.6 vs 40.2, p < 0.001) and had a higher number of drug exposures (2.92 vs 2.16, p = 0.003). Following adjustment for age, sex, race/ethnicity, number of exposures, serum potassium, and opioid exposure, dsQTp remained an independent predictor of ACVE (aOR: 12.44, p < 0.0001). Clinical factors associated with dsQTp were age > 45 years and polydrug (≥ 3) overdoses. CONCLUSION:In this large secondary analysis of ED patients with acute drug overdose, dsQTp was an independent risk factor for in-hospital occurrence of ACVE.

BACKGROUND:Human-centered design (HCD) is an increasingly recognized approach for engaging stakeholders and developing contextually appropriate health interventions. As a component of the ongoing STRENGTHS study (Strengthening Referral Networks for Management of Hypertension Across the Health System), we report on the process and outcomes of utilizing HCD to develop the implementation strategy prior to a cluster-randomized controlled trial. METHODS:We organized a design team of 15 local stakeholders to participate in an HCD process to develop implementation strategies. We tested prototypes for acceptability, appropriateness, and feasibility through focus group discussions (FGDs) with various community stakeholder groups and a pilot study among patients with hypertension. FGD transcripts underwent content analysis, and pilot study data were analyzed for referral completion and reported barriers to referral. Based on this community feedback, the design team iteratively updated the implementation strategy. During each round of updates, the design team reflected on their experience through FGDs and a Likert-scale survey. RESULTS:The design team developed an implementation strategy consisting of a combined peer navigator and a health information technology (HIT) package. Overall, community participants felt that the strategy was acceptable, appropriate, and feasible. During the pilot study, 93% of referrals were completed. FGD participants felt that the implementation strategy facilitated referral completion through active peer engagement; enhanced communication between clinicians, patients, and health administrators; and integrated referral data into clinical records. Challenges included referral barriers that were not directly addressed by the strategy (e.g. transportation costs) and implementation of the HIT package across multiple health record systems. The design team reflected that all members contributed significantly to the design process, but emphasized the need for more transparency in how input from study investigators was incorporated into design team discussions. CONCLUSIONS:The adaptive process of co-creation, prototyping, community feedback, and iterative redesign aligned our implementation strategy with community stakeholder priorities. We propose a new framework of human-centered implementation research that promotes collaboration between community stakeholders, study investigators, and the design team to develop, implement, and evaluate HCD products for implementation research. Our experience provides a feasible and replicable approach for implementation research in other settings. TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov, NCT02501746 , registration date: July 17, 2015.