ABSTRACT
Introduction: NICE guidance (2020) for Long Covid calls for a comprehensive assessment and, where appropriate, referral to rehabilitation. Our aim was to explore characteristics of rehabilitation programmes (RP) for patients in the UK. Method(s): Rehabilitation specific information was extracted from a survey of sites taking part in the PHOSP-COVID study (https://www.phosp.org/), Feb 2020-Mar 2021. Data included professions and services involved, content, outcome measures and model used. Data was recorded on JISC and exported to Excel. Result(s): 32/45 sites had access to RP, half was covid specific RP. 22 provided content information. Most RP were delivered by the same organisation providing post-hospitalisation review, 19/22 used more than one model of delivery. Models included face to face, virtual, digital platforms and home-based models. Decisions on how to deliver RP depended on available staff, service pressures, safety and patient preference. RPs delivered most often by Allied Health Professionals and existing Pulmonary Rehabilitation services (16 services), 3 single profession. Not all RPs matched outcome measures to components delivered, Figure1. Conclusion(s): 2/3rds of sites were able to access either covid or non-covid specific MDT rehabilitation. There is heterogeneity in the assessment and content of rehabilitation delivered, as well as the model of delivery.
ABSTRACT
Introduction: For patients with Long Covid, NICE guidance 2020 suggests a comprehensive clinical assessment alongside referral to rehabilitation where appropriate. We aimed to describe and categorise UK healthcare pathways post-hospital discharge for COVID-19 as a first step towards understanding clinical and cost-effectiveness. Method(s): We surveyed hospital sites taking part in the PHOSP-COVID study (www.phosp.org). The online survey topics included availability of proactive follow-up, patient access, involvement of multidisciplinary and specialty teams, investigations, and access to rehabilitation. Data was recorded on JISC. The classification of services was coproduced by patients, clinicians, and policy makers at a consensus event. Result(s): 45/64 (70%) hospitals completed the survey and 37/45 had a proactive follow-up service. Figure 1 shows the classification of clinical assessment and mapping of rehabilitation services from the consensus event. Conclusion(s): In the UK, there is heterogeneity in patient access and intensity of assessment services available posthospitalisation for survivors of Covid-19. At site level, the intensity of assessment provided did not fully map to the intensity of rehabilitation provided.
ABSTRACT
BACKGROUND: Social prescribing encourages health-care and other professionals to refer patients to a link worker, who will develop a personalised plan to improve the patient's health and well-being. We explore the feasibility of evaluating the service. OBJECTIVE: The objective was to answer the following research questions. (1) What are the most important evaluation questions that an impact study could investigate? (2) What data are already available at a local or national level and what else would be needed? (3) Are there sites delivering at a large enough scale and in a position to take part in an impact study? (4) How could the known challenges to evaluation (e.g. information governance and identifying a control group) be addressed? DATA SOURCES: Data sources included MEDLINE ALL (via Ovid), searched from inception to 14 February 2019, and the first 100 hits of a Google (Google Inc., Mountain View, CA, USA) search. REVIEW METHODS: Rapid systematic review - electronic searches up to February 2019. Studies included any study design or outcomes. Screening was conducted by one reviewer;eligibility assessment and data extraction were undertaken by two reviewers. Data were synthesised narratively. Qualitative interviews - data from 25 participants in different regions of England were analysed using a pragmatic framework approach across 12 areas including prior data collection, delivery sites, scale and processes of current service delivery, and known challenges to evaluation. Views of key stakeholders (i.e. patients and academics) were captured. RESULTS: Rapid systematic review - 27 out of 124 studies were included. We identified outcomes and highlighted research challenges. Important evaluation questions included identification of the most appropriate (1) outcomes and (2) methods for dealing with heterogeneity. Qualitative interviews - social prescribing programmes are holistic in nature, covering domains such as social isolation and finance. Service provision is heterogeneous. The follow-on services that patients access are often underfunded or short term. Available data - there was significant heterogeneity in data availability, format and follow-up. Data were collected using a range of tools in ad hoc databases across sites. Non-attendance data were frequently not captured. Service users are more deprived and vulnerable than the overall practice population. Feasibility and potential limitations of an evaluation - current data collection is limited in determining the effectiveness of the link worker social prescribing model;therefore, uniform data collection across sites is needed. Standardised outcomes and process measures are required. Cost-utility analysis could provide comparative values for assessment alongside other NHS interventions. LIMITATIONS: This was a rapid systematic review that did not include a systematic quality assessment of studies. COVID-19 had an impact on the shape of the service. We were not able to examine the potential causal mechanisms in any detail. CONCLUSIONS: We describe possible future research approaches to determine effectiveness and cost-effectiveness evaluations;all are limited in their application. (1) Evaluation using currently available, routinely collected health-care, costing and outcomes data. (2) Evaluative mixed-methods research to capture the complexity of social prescribing through understanding heterogeneous service delivery across comparative settings. Cost-effectiveness evaluation using routinely available costing and outcomes data to supplement qualitative data. (3) Interventional evaluative research, such as a cluster randomised controlled trial focused on the link worker model. Cost-effectiveness data collected as part of the trial. FUTURE WORK: Mature data are currently not available. There needs to be an agreement across schemes on the key outcomes that need to be measured, harmonisation of data collection, and follow-up referrals (how and when). FUNDING: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research;Vol. 10, No. 29. See the NIHR Journals Library website for further project information.
ABSTRACT
Introduction: The COVID-19 pandemic has deteriorated sleep health in the United States (U.S.) and worldwide. Most studies that have examined the association between COVID-19 and sleep outcomes have used a non-probability sampling with potential sampling bias and limited generalizability. We examined the association between diagnosed COVID-19 and sleep health in a large representative sample of civilian adults aged ≥18 years in the U.S. Methods: This study was based on data from the 2020 National Health Interview Survey (NHIS) of adults (n=17,636). Sleep health was captured by self-reported sleep quantity [(very short (≤ 4 hours), short (5-6 hours), healthy (7-8 hours), or long (≥9 hours)] and sleep complaints (trouble falling and staying asleep;with responses ranging from never to every day) in the past 30 days. To account for correlated residuals among the endogenous sleep outcomes, generalized structural equation modeling (GSEM) was conducted with COVID-19 diagnosis as the predictor of interest. Other covariates (age, sex, race/ethnicity, education, employment, poverty level, marital status, birthplace, health insurance, region of residence, metropolitan areas, number of children and adults in the household, obesity, and sleep medication) were included in the models. NHIS complex probability sampling design was accounted for in descriptive and GSEM analyses. Results: About 4.2% of adults had a positive COVID-19 diagnosis. Among them, 3.1% had very short sleep, 24.2% had short sleep, 59.9% had healthy sleep, and 12.8% had long sleep;37.0% had trouble falling some days, 10.9% most days, and 6.5% every day;and 33.7% had trouble staying asleep some days, 13.9% most days, and 6.6% every day. Findings from GSEM revealed that a history of COVID-19 almost doubled the odds of having short sleep (OR: 1.9;95% CI: 1.1-3.4;p=0.032). No significant associations were found between COVID-19 and the other sleep outcomes. Conclusion: Individuals with a COVID-19 diagnosis were more likely to report very short sleep, although they did not exhibit a greater likelihood of reporting more sleep complaints. Further research using longitudinal national data and examining environmental factors are needed to determine causality.
ABSTRACT
Introduction: Long-term exposure to pandemics like COVID-19 may increase psychological distress (e.g., peri-traumatic and post-traumatic distress) and sleep problems. Little is known about the effects of COVID-19 on peritraumatic distress, a well-documented risk factor for post-traumatic stress disorders (PTSD). The aim of this study was to investigate the association between COVID-19 risk perception and peritraumatic distress, and whether this relationship is moderated by sleep quality among individuals located in NY. Methods: We examined data from 541 individuals (69% were female, mean age (SD) = 40.9 (15.3)] recruited online during summer and fall 2020 in New York for the NYU-COVID-19 Mental Health Study. Data were gathered on sociodemographic, COVID-19 risk perception (yes or no items), peri-traumatic distress measured by Peritraumatic Distress Inventory (PDI), and sleep quality measured by the Pittsburg Sleep Quality Index (PSQI). Descriptive, regression analysis and interaction terms were conducted using SPSS v. 25 to examine associations between COVID-19 risk perception with symptoms of peritraumatic distress and sleep quality. Results: Of the 541 participants, 311(57.5%) reported they felt at risk for contracting COVID-19. PSQI was positively correlated with PDI (r =.38, p =0.01). An independent sample t student test indicated, on average, that the symptoms of PDI [(mean (SD)=27.3 (7.63), t = 7.07, n =307)] and PSQI [mean(SD)=10.62(3.57), t=4.31 n=311)] of our participants who felt at risk for contracting the COVID-19 significantly exceeded those who did not [(PDI mean(SD)=22.7(7.13), n =228);PSQI (mean(SD) =9.25(3.72), n=229]. Results of multiple linear regression analysis shown that COVID-19 risk perception was the strongest predictor of PDI [B(t) = -.630(12.7);p < .001]. Furthermore, the interaction effect of PSQI scores and COVID-19 risk perception revealed that sleep quality significantly reduced the association between COVID-19 risk perception and PDI [B(t) = .319(5.71);p <.001], such that poorer sleep and feeling at risk of contracting COVID-19 resulted in more severe PDI scores. Conclusion: COVID-19 risk perception was associated with peritraumatic distress and poorer sleep quality, and sleep quality attenuated this relationship.