Impact of the COVID-19 pandemic on type 2 diabetes care and factors associated with care disruption in Kenya and Tanzania

BACKGROUND

The COVID-19 pandemic affected healthcare delivery globally, impacting care access and delivery of essential services.

OBJECTIVES

We investigated the pandemic's impact on care for patients with type 2 diabetes and factors associated with care disruption in Kenya and Tanzania.

METHODS

A cross-sectional study was conducted among adults diagnosed with diabetes pre-COVID-19. Data were collected in February-April 2022 reflecting experiences at two time-points, three months before and the three months most affected by the COVID-19 pandemic. A questionnaire captured data on blood glucose testing, changes in medication prescription and access, and healthcare provider access.

RESULTS

We recruited 1000 participants (500/country). Diabetes care was disrupted in both countries, with 34.8% and 32.8% of the participants reporting change in place and frequency of testing in Kenya, respectively. In Tanzania, 12.4% and 17.8% reported changes in location and frequency of glucose testing, respectively. The number of health facility visits declined, 14.4% (p < 0.001) in Kenya and 5.6% (p = 0.001) in Tanzania. In Kenya, there was a higher likelihood of severe care disruption among insured patients (adjusted odds ratio [aOR] 1.56, 95% confidence interval [CI][1.05-2.34]; p = 0.029) and a lower likelihood among patients residing in rural areas (aOR, 0.35[95%CI, 0.22-0.58]; p < 0.001). Tanzania had a lower likelihood of severe disruption among insured patients (aOR, 0.51[95%CI, 0.33-0.79]; p = 0.003) but higher likelihood among patients with low economic status (aOR, 1.81[95%CI, 1.14-2.88]; p = 0.011).

CONCLUSIONS

COVID-19 disrupted diabetes care more in Kenya than Tanzania. Health systems and emergency preparedness should be strengthened to ensure continuity of service provision for patients with diabetes.

Management of antipsychotics in primary care: Insights from healthcare professionals and policy makers in the United Kingdom

INTRODUCTION

Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review. We explore perceptions of healthcare professionals and managers/directors of policy regarding reasons for increasing prevalence and management of antipsychotics in primary care.

METHODS

Qualitative study using semi-structured interviews with 11 general practitioners (GPs), 8 psychiatrists, and 11 managers/directors of policy in the United Kingdom. Data was analysed using thematic analysis.

RESULTS

Respondents reported competency gaps that impaired ability to manage patients prescribed antipsychotic medications, arising from inadequate postgraduate training and professional development. GPs lacked confidence to manage antipsychotic medications alone; psychiatrists lacked skills to address cardiometabolic risks and did not perceive this as their role. Communication barriers, lack of integrated care records, limited psychology provision, lowered expectation towards patients with serious mental illness by professionals, and pressure to discharge from hospital resulted in patients in primary care becoming 'trapped' on antipsychotics, inhibiting opportunities to deprescribe. Organisational and contractual barriers between services exacerbate this risk, with socioeconomic deprivation and lack of access to non-pharmacological interventions driving overprescribing. Professionals voiced fears of censure if a catastrophic event occurred after stopping an antipsychotic. Facilitators to overcome these barriers were suggested.

CONCLUSIONS

People prescribed antipsychotics experience a fragmented health system and suboptimal care. Several interventions could be taken to improve care for this population, but inadequate availability of non-pharmacological interventions and socioeconomic factors increasing mental distress need policy change to improve outcomes. The role of professionals' fear of medicolegal or regulatory censure inhibiting antipsychotic deprescribing was a new finding in this study.

The influence of socioeconomic status on the association between unhealthy lifestyle factors and adverse health outcomes: a systematic review

Background

Combinations of lifestyle factors (LFs) and socioeconomic status (SES) are independently associated with cardiovascular disease (CVD), cancer, and mortality. Less advantaged SES groups may be disproportionately vulnerable to unhealthy LFs but interactions between LFs and SES remain poorly understood. This review aimed to synthesise the available evidence for whether and how SES modifies associations between combinations of LFs and adverse health outcomes.

Methods

Systematic review of studies that examine associations between combinations of >3 LFs (eg.smoking/physical activity/diet) and health outcomes and report data on SES (eg.income/education/poverty-index) influences on associations. Databases (PubMed/EMBASE/CINAHL), references, forward citations, and grey-literature were searched from inception to December 2021. Eligibility criteria were analyses of prospective adult cohorts that examined all-cause mortality or CVD/cancer mortality/incidence.

Results

Six studies (n=42,467-399,537; 46.5-56.8 years old; 54.6-59.3% women) of five cohorts were included. All examined all-cause mortality; three assessed CVD/cancer outcomes. Four studies observed multiplicative interactions between LFs and SES, but in opposing directions. Two studies tested for additive interactions; interactions were observed in one cohort (UK Biobank) and not in another (National Health and Nutrition Examination Survey (NHANES)). All-cause mortality HRs (95% confidence intervals) for unhealthy LFs (versus healthy LFs) from the most advantaged SES groups ranged from 0.68 (0.32-1.45) to 4.17 (2.27-7.69). Equivalent estimates from the least advantaged ranged from 1.30 (1.13-1.50) to 4.00 (2.22-7.14). In 19 analyses (including sensitivity analyses) of joint associations between LFs, SES, and all-cause mortality, highest all-cause mortality was observed in the unhealthiest LF-least advantaged suggesting an additive effect.

Conclusions

Limited and heterogenous literature suggests that the influence of SES on associations between combinations of unhealthy LFs and adverse health could be additive but remains unclear. Additional prospective analyses would help clarify whether SES modifies associations between combinations of unhealthy LFs and health outcomes.

Registration

Protocol is registered with PROSPERO (CRD42020172588;25 June 2020).

Factors associated with COVID-19 vaccine uptake among people with type 2 diabetes in Kenya and Tanzania: a mixed-methods study

BACKGROUND

People with type 2 diabetes (T2D) are at increased risk of poor outcomes from COVID-19. Vaccination can improve outcomes, but vaccine hesitancy remains a major challenge. We examined factors influencing COVID-19 vaccine uptake among people with T2D in two sub-Saharan Africa countries that adopted different national approaches to combat COVID-19, Kenya and Tanzania.

METHODS

A mixed-methods study was conducted in February-March 2022, involving a survey of 1000 adults with T2D (500 Kenya; 500 Tanzania) and 51 in-depth interviews (21 Kenya; 30 Tanzania). Determinants of COVID-19 vaccine uptake were identified using a multivariate logistic regression model, while thematic content analysis explored barriers and facilitators.

RESULTS

COVID-19 vaccine uptake was lower in Tanzania (26%) than in Kenya (75%), which may reflect an initial political hesitancy about vaccines in Tanzania. People with college/university education were four times more likely to be vaccinated than those with no education (Kenya AOR=4.25 (95% CI 1.00 to 18.03), Tanzania AOR=4.07 (1.03 to 16.12)); and people with health insurance were almost twice as likely to be vaccinated than those without health insurance (Kenya AOR=1.70 (1.07 to 2.70), Tanzania AOR=1.81 (1.04 to 3.13)). Vaccine uptake was higher in older people in Kenya, and among those with more comorbidities and higher socioeconomic status in Tanzania. Interviewees reported that wanting protection from severe illness promoted vaccine uptake, while conflicting information, misinformation and fear of side-effects limited uptake.

CONCLUSION

COVID-19 vaccine uptake among people with T2D was suboptimal, particularly in Tanzania, where initial political hesitancy had a negative impact. Policy-makers must develop strategies to reduce fear and misconceptions, especially among those who are less educated, uninsured and younger.

Epidemiology of multimorbidity in low-income countries of sub-Saharan Africa: Findings from four population cohorts

We investigated prevalence and demographic characteristics of adults living with multimorbidity (≥2 long-term conditions) in three low-income countries of sub-Saharan Africa, using secondary population-level data from four cohorts; Malawi (urban & rural), The Gambia (rural) and Uganda (rural). Information on; measured hypertension, diabetes and obesity was available in all cohorts; measured hypercholesterolaemia and HIV and self-reported asthma was available in two cohorts and clinically diagnosed epilepsy in one cohort. Analyses included calculation of age standardised multimorbidity prevalence and the cross-sectional associations of multimorbidity and demographic/lifestyle factors using regression modelling. Median participant age was 29 (Inter quartile range-IQR 22-38), 34 (IQR25-48), 32 (IQR 22-53) and 37 (IQR 26-51) in urban Malawi, rural Malawi, The Gambia, and Uganda, respectively. Age standardised multimorbidity prevalence was higher in urban and rural Malawi (22.5%;95% Confidence intervals-CI 21.6-23.4%) and 11.7%; 95%CI 11.1-12.3, respectively) than in The Gambia (2.9%; 95%CI 2.5-3.4%) and Uganda (8.2%; 95%CI 7.5-9%) cohorts. In multivariate models, females were at greater risk of multimorbidity than males in Malawi (Incidence rate ratio-IRR 1.97, 95% CI 1.79-2.16 urban and IRR 2.10; 95%CI 1.86-2.37 rural) and Uganda (IRR- 1.60, 95% CI 1.32-1.95), with no evidence of difference between the sexes in The Gambia (IRR 1.16, 95% CI 0.86-1.55). There was strong evidence of greater multimorbidity risk with increasing age in all populations (p-value <0.001). Higher educational attainment was associated with increased multimorbidity risk in Malawi (IRR 1.78; 95% CI 1.60-1.98 urban and IRR 2.37; 95% CI 1.74-3.23 rural) and Uganda (IRR 2.40, 95% CI 1.76-3.26), but not in The Gambia (IRR 1.48; 95% CI 0.56-3.87). Further research is needed to study multimorbidity epidemiology in sub-Saharan Africa with an emphasis on robust population-level data collection for a wide variety of long-term conditions and ensuring proportionate representation from men and women, and urban and rural areas.

Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role?

BACKGROUND

In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility.

METHODS

The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination.

RESULTS

Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots).

DISCUSSION

A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.

Social connection and mortality in UK Biobank: a prospective cohort analysis

BACKGROUND

Components of social connection are associated with mortality, but research examining their independent and combined effects in the same dataset is lacking. This study aimed to examine the independent and combined associations between functional and structural components of social connection and mortality.

METHODS

Analysis of 458,146 participants with full data from the UK Biobank cohort linked to mortality registers. Social connection was assessed using two functional (frequency of ability to confide in someone close and often feeling lonely) and three structural (frequency of friends/family visits, weekly group activities, and living alone) component measures. Cox proportional hazard models were used to examine the associations with all-cause and cardiovascular disease (CVD) mortality.

RESULTS

Over a median of 12.6 years (IQR 11.9-13.3) follow-up, 33,135 (7.2%) participants died, including 5112 (1.1%) CVD deaths. All social connection measures were independently associated with both outcomes. Friends/family visit frequencies < monthly were associated with a higher risk of mortality indicating a threshold effect. There were interactions between living alone and friends/family visits and between living alone and weekly group activity. For example, compared with daily friends/family visits-not living alone, there was higher all-cause mortality for daily visits-living alone (HR 1.19 [95% CI 1.12-1.26]), for never having visits-not living alone (1.33 [1.22-1.46]), and for never having visits-living alone (1.77 [1.61-1.95]). Never having friends/family visits whilst living alone potentially counteracted benefits from other components as mortality risks were highest for those reporting both never having visits and living alone regardless of weekly group activity or functional components. When all measures were combined into overall functional and structural components, there was an interaction between components: compared with participants defined as not isolated by both components, those considered isolated by both components had higher CVD mortality (HR 1.63 [1.51-1.76]) than each component alone (functional isolation 1.17 [1.06-1.29]; structural isolation 1.27 [1.18-1.36]).

CONCLUSIONS

This work suggests (1) a potential threshold effect for friends/family visits, (2) that those who live alone with additional concurrent markers of structural isolation may represent a high-risk population, (3) that beneficial associations for some types of social connection might not be felt when other types of social connection are absent, and (4) considering both functional and structural components of social connection may help to identify the most isolated in society.

Protocol for the development and validation of a patient reported measure (PRM) of treatment burden in stroke

Background

Treatment burden is the workload of healthcare for people with long-term conditions and the impact on wellbeing. A validated measure of treatment burden after stroke is needed. We aim to adapt a patient-reported measure (PRM) of treatment burden in multimorbidity, PETS (Patient Experience with Treatment and Self-Management version 2.0), to create a stroke-specific measure, PETS-stroke. We aim to examine content validity, construct validity, reliability and feasibility in a stroke survivor population.

Methods

1) Adaptation of 60-item PETS to PETS-stroke using a taxonomy of treatment burden. 2) Content validity testing through cognitive interviews that will explore the importance, relevance and clarity of each item. 3) Evaluation of scale psychometric properties through analysis of data from stroke survivors recruited via postal survey (n=340). Factor structure will be tested with confirmatory factor analysis and Cronbach's alpha will be used to index internal consistency. Construct validity will be tested against: The Stroke Southampton Self-Management Questionnaire; The Satisfaction with Stroke Care Measure; and The Shortened Stroke Impact Scale. We will explore known-groups validity by exploring the association between treatment burden, socioeconomic deprivation and multimorbidity. Test-retest reliability will be examined via re-administration after 2 weeks. Acceptability and feasibility of use will be explored via missing data rates and telephone interviews with 30 participants.

Conclusions

We aim to create a validated PRM of treatment burden after stroke. PETS-stroke is designed for use as an outcome measure in clinical trials of stroke treatments and complex interventions to ascertain if treatments are workable for patients in the context of their everyday lives.

Treatment burden for people experiencing homelessness with a recent non-fatal overdose: a questionnaire study

BACKGROUND

People experiencing homelessness (PEH) who have problem drug use have complex medical and social needs, with barriers to accessing services and treatments. Their treatment burden (workload of self-management and impact on wellbeing) remains unexplored.

AIM

To investigate treatment burden in PEH with a recent non-fatal overdose using a validated questionnaire, the Patient Experience with Treatment and Self-management (PETS).

DESIGN AND SETTING

The PETS questionnaire was collected as part of a pilot randomised control trial (RCT) undertaken in Glasgow, Scotland; the main outcome is whether this pilot RCT should progress to a definitive RCT.

METHOD

An adapted 52-item, 12-domain PETS questionnaire was used to measure treatment burden. Greater treatment burden was indicated by higher PETS scores.

RESULTS

Of 128 participants, 123 completed PETS; mean age was 42.1 (standard deviation [SD] 8.4) years, 71.5% were male, and 99.2% were of White ethnicity. Most (91.2%) had >5 chronic conditions, with an average of 8.5 conditions. Mean PETS scores were highest in domains focusing on the impact of self-management on wellbeing: physical and mental exhaustion (mean 79.5, SD 3.3) and role and social activity limitations (mean 64.0, SD 3.5) Scores were higher than those observed in studies of patients who are not homeless.

CONCLUSION

In a socially marginalised patient group at high risk of drug overdose, the PETS showed a very high level of treatment burden and highlights the profound impact of self-management work on wellbeing and daily activities. Treatment burden is an important person-centred outcome to help compare the effectiveness of interventions in PEH and merits inclusion in future trials as an outcome measure.

Ubuntu as a mediator in coping with multimorbidity treatment burden in a disadvantaged rural and urban setting in South Africa

BACKGROUND

People living with multimorbidity in economically precarious circumstances in low- and middle-income countries (LMICs) experience a high workload trying to meet self-management demands. However, in countries such as South Africa, the availability of social networks and support structures may improve patient capacity, especially when networks are governed by cultural patterns linked to the Pan-African philosophy of Ubuntu, which promotes solidarity through humanness and human dignity. We explore the mediating role Ubuntu plays in people's ability to self-manage HIV/NCD multimorbidity in underprivileged settings in urban and rural South Africa.

METHODS

We conducted semi-structured interviews with 30 patients living with HIV/NCD multimorbidity between February-April 2022. Patients attended public health clinics in Gugulethu, Cape Town and Bulungula, Eastern Cape. We analysed interviews using framework analysis, using the Cumulative Complexity Model (CuCoM) and Burden of Treatment Theory (BoTT) as frameworks through which to conceptualise the data.

RESULTS

Despite facing economic hardship, people with multimorbidity in South Africa were able to cope with their workload. They actively used and mobilized family relations and external networks that supported them financially, practically, and emotionally, allowing them to better self-manage their chronic conditions. Embedded in their everyday life, patients, often unconsciously, embraced Ubuntu and its core values, including togetherness, solidarity, and receiving Imbeko (respect) from health workers. This enabled participants to share their treatment workload and increase self-management capacity.

CONCLUSION

Ubuntu is an important mediator for people living with multimorbidity in South Africa, as it allows them to navigate their treatment workload and increase their social capital and structural resilience, which is key to self-management capacity. Incorporating Ubuntu and linked African support theories into current treatment burden models will enable better understandings of patients' collective support and can inform the development of context-specific social health interventions that fit the needs of people living with chronic conditions in African settings.

Development and piloting of a primary school-based salt reduction programme: Formative work and a process evaluation in rural and urban Malawi

Excess salt intake is a major modifiable risk factor for cardiovascular disease. Promoting salt reduction as part of routine school-health programming may be a pragmatic way to address this risk factor early in the life course but has not been tested in sub-Saharan Africa (SSA). Here we describe the formative work with stakeholders and process evaluation of pilot work to develop a school-based salt reduction programme for children aged 11-14 years, in preparation for a cluster-randomised trial in rural/urban Malawi. Collection of observational data and documentary evidence (meeting minutes/field notes) from the earliest key stakeholder engagement with Malawi Ministries of Health, Education, Local Government and Rural Development and Malawi Institute of Education, and non-governmental stakeholders; and a series of semi-structured interviews and focus groups (with head teachers (n = 2); teachers (n = 4); parents (n = 30); and learners (n = 40)). Data was analysed thematically and conceptualised through a Normalization Process Theory lens. Formative work illustrated a range of administrative, technical, and practical issues faced during development of the programme; including allocation of stakeholder roles and responsibilities, harmonisation with pre-existing strategies and competing priorities, resources required for programme development, and design of effective teaching materials. While participants were positive about the programme, the process evaluation identified features to be refined including perceived challenges to participation, recommended adaptations to the content and delivery of lessons, and concerns related to quantity/quality of learning resources provided. This study demonstrates the importance of comprehensive, sustained, and participatory stakeholder engagement in the development of a novel school health programme in SSA; and highlights the factors that were critical to successfully achieving this. We also demonstrate the value of detailed process evaluation in informing development of the programme to ensure that it was feasible and relevant to the context prior to evaluation through a cluster-randomised trial.

Adaptation and content validation of a patient-reported measure of treatment burden for use in stroke survivors: the patient experience with treatment and self-management in stroke (PETS-stroke) measure

PURPOSE

Stroke survivors often live with significant treatment burden yet our ability to examine this is limited by a lack of validated measurement instruments. We aimed to adapt the 60-item, 12-domain Patient Experience with Treatment and Self-Management (PETS) (version 2.0, English) patient-reported measure to create a stroke-specific measure (PETS-stroke) and to conduct content validity testing with stroke survivors.

MATERIALS AND METHODS

Step 1 - Adaptation of PETS to create PETS-stroke: a conceptual model of treatment burden in stroke was utilised to amend, remove or add items. Step 2 - Content validation: Fifteen stroke survivors in Scotland were recruited through stroke groups and primary care. Three rounds of five cognitive interviews were audio recorded and transcribed. Framework analysis was used to explore importance/relevance/clarity of PETS-stroke content. COSMIN reporting guidelines were followed.

RESULTS

The adapted PETS-stroke had 34 items, spanning 13 domains; 10 items unchanged from PETS, 6 new and 18 amended. Interviews (n = 15) resulted in further changes to 19 items, including: instructions; wording; item location; answer options; and recall period.

CONCLUSIONS

PETS-stroke has content that is relevant, meaningful and comprehensible to stroke survivors. Content validity and reliability testing are now required. The validated tool will aid testing of tailored interventions to lessen treatment burden.IMPLICATIONS FOR REHABILITATIONTreatment burden is reported by stroke survivors but no stroke-specific measure of treatment burden exists.We adapted an existing measure of treatment burden for use in multimorbid patients (PETS) to create a stroke specific version (PETS-stroke).The items in PETS-stroke are relevant and meaningful to people with stroke.Further testing will examine construct validity, reliability, and useability.This measure will be useful in future RCTs to measure treatment burden and to identify stroke patients who are at high risk of treatment burden.

Understanding the impacts of the COVID-19 pandemic on the care experiences of people with mental-physical multimorbidity: protocol for a mixed methods study

BACKGROUND

Primary care and other health services have been disrupted during the COVID-19 pandemic, yet the consequences of these service disruptions on patients' care experiences remain largely unstudied. People with mental-physical multimorbidity are vulnerable to the effects of the pandemic, and to sudden service disruptions. It is thus essential to better understand how their care experiences have been impacted by the current pandemic. This study aims to improve understanding of the care experiences of people with mental-physical multimorbidity during the pandemic and identify strategies to enhance these experiences.

METHODS

We will conduct a mixed-methods study with multi-phase approach involving four distinct phases. Phase 1 will be a qualitative descriptive study in which we interview individuals with mental-physical multimorbidity and health professionals in order to explore the impacts of the pandemic on care experiences, as well as their perspectives on how care can be improved. The results of this phase will inform the design of study phases 2 and 3. Phase 2 will involve journey mapping exercises with a sub-group of participants with mental-physical multimorbidity to visually map out their care interactions and experiences over time and the critical moments that shaped their experiences. Phase 3 will involve an online, cross-sectional survey of care experiences administered to a larger group of people with mental disorders and/or chronic physical conditions. In phase 4, deliberative dialogues will be held with key partners to discuss and plan strategies for improving the delivery of care to people with mental-physical multimorbidity. Pre-dialogue workshops will enable us to synthesize an prepare the results from the previous three study phases.

DISCUSSION

Our study results will generate much needed evidence of the positive and negative impacts of the COVID-19 pandemic on the care experiences of people with mental-physical multimorbidity and shed light on strategies that could improve care quality and experiences.

EXPERTS II - How are patient and caregiver participation in health and social care shaped by experienced burden of treatment and social inequalities? Protocol for a qualitative synthesis

Background

The workload health and social care service users and caregivers take on, and their capacity to do this work is important. It may play a key part in shaping the implementation of innovations in health service delivery and organisation; the utilisation and satisfaction with services; and the outcomes of care. Previous research has often focused on experiences of a narrow range of long-term conditions, and on factors that shape adherence to self-care regimes.

Aims

With the aim of deriving policy and practice implications for service redesign, this evidence synthesis will extend our understanding of service user and caregiver workload and capacity by comparing how they are revealed in qualitative studies of lived experience of three kinds of illness trajectories: long-term conditions associated with significant disability (Parkinson's disease, schizophrenia); serious relapsing remitting disease (Inflammatory Bowel Disease, bipolar disorder); and rapidly progressing acute disease (brain cancer, early onset dementia).

Methods

We will review and synthesise qualitative studies of lived experience of participation in health and social care that are shaped by interactions between experienced treatment burdens, social inequalities and illness trajectories. The review will involve:   1.  Construction of a theory-informed coding manual; systematic search of bibliographic databases to identify, screen and quality assess full-text papers.   2.  Analysis of papers using manual coding techniques, and text mining software; construction of taxonomies of service user and caregiver work and capacity.   3.  Designing a model of core components and identifying common factors across conditions, trajectories, and contexts.   4.  Work with practitioners, and a Patient and Public Involvement (PPI) group, to explore the validity of the models produced; to develop workload reduction strategies; and to consider person-centred service design.

Dissemination

We will promote workload reduction models to support service users and caregivers and produce policy briefs and peer-reviewed publications for practitioners, policy-makers, and researchers.

Psychological impact of lung cancer screening using a novel antibody blood test followed by imaging: the ECLS randomized controlled trial

BACKGROUND

The Early CDT®-Lung antibody blood test plus serial computed tomography scans for test-positives (TPGs) reduces late-stage lung cancer presentation. This study assessed the psychological outcomes of this approach.

METHODS

Randomized controlled trial (n = 12 208) comparing psychological outcomes 1-12 months post-recruitment in a subsample (n = 1032) of TPG, test-negative (TNG) and control groups (CG).

RESULTS

Compared to TNG, TPG had lower positive affect (difference between means (DBM), 3 months (3m: -1.49 (-2.65, - 0.33)), greater impact of worries (DBM 1m: 0.26 (0.05, 0.47); 3m: 0.28 (0.07, 0.50)), screening distress (DBM 1m: 3.59 (2.28, 4.90); 3m: 2.29 (0.97, 3.61); 6m: 1.94 (0.61, 3.27)), worry about tests (odds ratio (OR) 1m: 5.79 (2.66, 12.63) and more frequent lung cancer worry (OR 1m: 2.52 (1.31, 4.83); 3m: 2.43 (1.26, 4.68); 6m: 2.87 (1.48, 5.60)). Compared to CG, TPG had greater worry about tests (OR 1m: 3.40 (1.69, 6.84)). TNG had lower negative affect (log-transformed DBM 3m: -0.08 (-0.13, -0.02)), higher positive affect (DBM 1m: 1.52 (0.43, 2.61); 3m: 1.43 (0.33, 2.53); 6m: 1.27 (0.17, 2.37)), less impact of worries (DBM 3m: -0.27 (-0.48, -0.07)) and less-frequent lung cancer worry (OR 3m: 0.49 (0.26, 0.92)).

CONCLUSIONS

Negative psychological effects in TPG and positive effects in TNG were short-lived and most differences were small.

Frailty in randomised controlled trials for dementia or mild cognitive impairment measured via the frailty index: prevalence and prediction of serious adverse events and attrition

BACKGROUND

Frailty and dementia have a bidirectional relationship. However, frailty is rarely reported in clinical trials for dementia and mild cognitive impairment (MCI) which limits assessment of trial applicability. This study aimed to use a frailty index (FI)-a cumulative deficit model of frailty-to measure frailty using individual participant data (IPD) from clinical trials for MCI and dementia. Moreover, the study aimed to quantify the prevalence of frailty and its association with serious adverse events (SAEs) and trial attrition.

METHODS

We analysed IPD from dementia (n = 1) and MCI (n = 2) trials. An FI comprising physical deficits was created for each trial using baseline IPD. Poisson and logistic regression were used to examine associations with SAEs and attrition, respectively. Estimates were pooled in random effects meta-analysis. Analyses were repeated using an FI incorporating cognitive as well as physical deficits, and results compared.

RESULTS

Frailty could be estimated in all trial participants. The mean physical FI was 0.14 (SD 0.06) and 0.14 (SD 0.06) in the MCI trials and 0.24 (SD 0.08) in the dementia trial. Frailty prevalence (FI > 0.24) was 6.9%/7.6% in MCI trials and 48.6% in the dementia trial. After including cognitive deficits, the prevalence was similar in MCI (6.1% and 6.7%) but higher in dementia (75.4%). The 99th percentile of FI (0.31 and 0.30 in MCI, 0.44 in dementia) was lower than in most general population studies. Frailty was associated with SAEs: physical FI IRR = 1.60 [1.40, 1.82]; physical/cognitive FI IRR = 1.64 [1.42, 1.88]. In a meta-analysis of all three trials, the estimated association between frailty and trial attrition included the null (physical FI OR = 1.17 [0.92, 1.48]; physical/cognitive FI OR = 1.16 [0.92, 1.46]), although higher frailty index values were associated with attrition in the dementia trial.

CONCLUSION

Measuring frailty from baseline IPD in dementia and MCI trials is feasible. Those living with more severe frailty may be under-represented. Frailty is associated with SAEs. Including only physical deficits may underestimate frailty in dementia. Frailty can and should be measured in future and existing trials for dementia and MCI, and efforts should be made to facilitate inclusion of people living with frailty.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials

BACKGROUND

People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD).

METHODS AND FINDINGS

We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities.

CONCLUSIONS

Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Baseline characteristics of people experiencing homelessness with a recent drug overdose in the PHOENIx pilot randomised controlled trial

BACKGROUND

Drug-related deaths in Scotland are the highest in Europe. Half of all deaths in people experiencing homelessness are drug related, yet we know little about the unmet health needs of people experiencing homelessness with recent non-fatal overdose, limiting a tailored practice and policy response to a public health crisis.

METHODS

People experiencing homelessness with at least one non-fatal street drug overdose in the previous 6 months were recruited from 20 venues in Glasgow, Scotland, and randomised into PHOENIx plus usual care, or usual care. PHOENIx is a collaborative assertive outreach intervention by independent prescriber NHS Pharmacists and third sector homelessness workers, offering repeated integrated, holistic physical, mental and addictions health and social care support including prescribing. We describe comprehensive baseline characteristics of randomised participants.

RESULTS

One hundred and twenty-eight participants had a mean age of 42 years (SD 8.4); 71% male, homelessness for a median of 24 years (IQR 12-30). One hundred and eighteen (92%) lived in large, congregate city centre temporary accommodation. A quarter (25%) were not registered with a General Practitioner. Participants had overdosed a mean of 3.2 (SD 3.2) times in the preceding 6 months, using a median of 3 (IQR 2-4) non-prescription drugs concurrently: 112 (87.5%) street valium (benzodiazepine-type new psychoactive substances); 77 (60%) heroin; and 76 (59%) cocaine. Half (50%) were injecting, 50% into their groins. 90% were receiving care from Alcohol and Drug Recovery Services (ADRS), and in addition to using street drugs, 90% received opioid substitution therapy (OST), 10% diazepam for street valium use and one participant received heroin-assisted treatment. Participants had a mean of 2.2 (SD 1.3) mental health problems and 5.4 (SD 2.5) physical health problems; 50% received treatment for physical or mental health problems. Ninety-one per cent had at least one mental health problem; 66% had no specialist mental health support. Participants were frail (70%) or pre-frail (28%), with maximal levels of psychological distress, 44% received one or no daily meal, and 58% had previously attempted suicide.

CONCLUSIONS

People at high risk of drug-related death continue to overdose repeatedly despite receiving OST. High levels of frailty, multimorbidity, unsuitable accommodation and unmet mental and physical health care needs require a reorientation of services informed by evidence of effectiveness and cost-effectiveness. Trial registration UK Clinical Trials Registry identifier: ISRCTN 10585019.

The impact of persistent precarity on patients' capacity to manage their treatment burden: A comparative qualitative study between urban and rural patients with multimorbidity in South Africa

Background

People living with multimorbidity in low-and middle-income countries (LMICs) experience a high workload trying to meet the demands of self-management. In an unequal society like South Africa, many people face continuous economic uncertainty, which can impact on their capacity to manage their illnesses and lead to poor health outcomes. Using precariousness - the real and perceived impact of uncertainty - as a lens, this paper aims to identify, characterise, and understand the workload and capacity associated with self-management amongst people with multimorbidity living in precarious circumstances in urban and rural South Africa.

Methods

We conducted qualitative semi-structured interviews with 30 patients with HIV and co-morbidities between February and April 2021. Patients were attending public clinics in Cape Town (Western Cape) and Bulungula (Eastern Cape). Interviews were transcribed and data analysed using qualitative framework analysis. Burden of Treatment Theory (BoTT) and the Cumulative Complexity Model (CuCoM) were used as theoretical lenses through which to conceptualise the data.

Results

People with multimorbidity in rural and urban South Africa experienced multi-faceted precariousness, including financial and housing insecurity, dangerous living circumstances and exposure to violence. Women felt unsafe in their communities and sometimes their homes, whilst men struggled with substance use and a lack of social support. Older patients relied on small income grants often shared with others, whilst younger patients struggled to find stable employment and combine self-management with family responsibilities. Precariousness impacted access to health services and information and peoples' ability to buy healthy foods and out-of-pocket medication, thus increasing their treatment burden and reducing their capacity.

Conclusion

This study highlights that precariousness reduces the capacity and increases treatment burden for patients with multimorbidity in low-income settings in South Africa. Precariousness is both accumulative and cyclic, as financial insecurity impacts every aspect of peoples' daily lives. Findings emphasise that current models examining treatment burden need to be adapted to accommodate patients' experiences in low-income settings and address cumulative precariousness. Understanding treatment burden and capacity for patients in LMICs is a crucial first step to redesign health systems which aim to improve self-management and offer comprehensive person-centred care.

Protocol for a pilot randomised controlled trial to evaluate integrated support from pharmacist independent prescriber and third sector worker for people experiencing homelessness: the PHOENIx community pharmacy study

BACKGROUND

People experiencing homelessness (PEH) have complex health and social care needs and most die in their early 40 s. PEH frequently use community pharmacies; however, evaluation of the delivery of structured, integrated, holistic health and social care intervention has not been previously undertaken in community pharmacies for PEH. PHOENIx (Pharmacy Homeless Outreach Engagement Non-medical Independent prescribing Rx) has been delivered and tested in Glasgow, Scotland, by NHS pharmacist independent prescribers and third sector homelessness support workers offering health and social care intervention in low threshold homeless drop-in venues, emergency accommodation and emergency departments, to PEH. Building on this work, this study aims to test recruitment, retention, intervention adherence and fidelity of community pharmacy-based PHOENIx intervention.

METHODS

Randomised, multi-centre, open, parallel-group external pilot trial. A total of 100 PEH aged 18 years and over will be recruited from community pharmacies in Glasgow and Birmingham. PHOENIx intervention includes structured assessment in the community pharmacy of health, housing, benefits and activities, in addition to usual care, through weekly visits lasting up to six months. A primary outcome is whether to proceed to a definitive trial based on pre-specified progression criteria. Secondary outcomes include drug/alcohol treatment uptake and treatment retention; overdose rates; mortality and time to death; prison/criminal justice encounters; healthcare utilisation; housing tenure; patient-reported measures and intervention acceptability. Analysis will include descriptive statistics of recruitment and retention rates. Process evaluation will be conducted using Normalisation Process Theory. Health, social care and personal resource use data will be identified, measured and valued.

DISCUSSION

If the findings of this pilot study suggest progression to a definitive trial, and if the definitive trial offers positive outcomes, it is intended that PHOENIx will be a publicly funded free-to-access service in community pharmacy for PEH. The study results will be shared with wider stakeholders and patients in addition to dissemination through medical journals and scientific conferences.

TRIAL REGISTRATION

International Clinical Trial Registration ISRCTN88146807. Approved protocol version 2.0 dated July 19, 2022.

The influence of socioeconomic status on the association between unhealthy lifestyle factors and adverse health outcomes: a systematic review

Background: Combinations of lifestyle factors (LFs) and socioeconomic status (SES) are independently associated with cardiovascular disease (CVD), cancer, and mortality. Less advantaged SES groups may be disproportionately vulnerable to unhealthy LFs but interactions between LFs and SES remain poorly understood. This review aimed to synthesise the available evidence for whether and how SES modifies associations between combinations of LFs and adverse health outcomes. Methods: Systematic review of studies that examine associations between combinations of >3 LFs and health outcomes and report data on SES influences on associations. Databases (PubMed/EMBASE/CINAHL), references, forward citations, and grey-literature were searched from inception to December 2021. Eligibility criteria were analyses of prospective adult cohorts that examined all-cause mortality or CVD or cancer mortality/incidence. Results: Six studies (n=42,467–399,537; 46.5–56.8 years old; 54.6–59.3% women) of five cohorts were included. All examined all-cause mortality; three assessed CVD/cancer outcomes. Four studies observed multiplicative interactions between LFs and SES, but in opposing directions. Two studies tested for additive interactions; interactions were observed in one cohort (UK Biobank) and not in another (NHANES). All-cause mortality HRs (95% CIs) for unhealthy LFs (versus healthy LFs) from the most advantaged SES groups ranged from 0.68 (0.32–1.45) to 4.17 (2.27–7.69). Equivalent estimates from the least advantaged ranged from 1.30 (1.13–1.50) to 4.00 (2.22–7.14). In 19 analyses (including sensitivity analyses) of joint associations between LFs, SES, and all-cause mortality, highest all-cause mortality was observed in the unhealthiest LF-least advantaged suggesting an additive effect. Conclusions: Limited and heterogenous literature suggests that the influence of SES on associations between combinations of unhealthy LFs and adverse health could be additive but remains unclear. Additional prospective analyses would help clarify whether SES modifies associations between combinations of unhealthy LFs and health outcomes. Registration: Protocol is registered with PROSPERO (CRD42020172588; 25 June 2020).

Potential impact of NICE guidelines on referrals from primary care to nephrology: a primary care database and prospective research study

BACKGROUND

National Institute for Health and Care Excellence 2021 guidelines on chronic kidney disease (CKD) recommend the use of the Kidney Failure Risk Equation (KFRE), which includes measurement of albuminuria. The equation to calculate estimated glomerular filtration rate (eGFR) has also been updated.

AIM

To investigate the impact of the use of KFRE and the updated eGFR equation on CKD diagnosis (eGFR <60 mL/min/1.73 m2) in primary care and potential referrals to nephrology.

DESIGN AND SETTING

Primary care database (Secure Anonymised Information Linkage Databank [SAIL]) and prospective cohort study (UK Biobank) using data available between 2013 and 2020.

METHOD

CKD diagnosis rates were assessed when using the updated eGFR equation. Among people with eGFR 30-59 mL/min/1.73 m2 the following groups were identified: those with annual albuminuria testing and those who met nephrology referral criteria because of: a) accelerated eGFR decline or significant albuminuria; b) eGFR decline <30 mL/ min/1.73 m2 only; and c) KFRE >5% only. Analyses were stratified by ethnicity in UK Biobank.

RESULTS

Using the updated eGFR equation resulted in a 1.2-fold fall in new CKD diagnoses in the predominantly White population in SAIL, whereas CKD prevalence rose by 1.9-fold among Black participants in UK Biobank. Rates of albuminuria testing have been consistently below 30% since 2015. In 2019, using KFRE >5% identified 182/61 721 (0.3%) patients at high risk of CKD progression before their eGFR declined and 361/61 721 (0.6%) low-risk patients who were no longer eligible for referral. Ethnic groups 'Asian' and 'other' had disproportionately raised KFREs.

CONCLUSION

Application of KFRE criteria in primary care will lead to referral of more patients at elevated risk of kidney failure (particularly among minority ethnic groups) and fewer low-risk patients. Albuminuria testing needs to be expanded to enable wider KFRE implementation.

Association between risk, duration and cause of hospitalisations in people with rheumatoid arthritis and multimorbidity in the UK Biobank and Scottish Early Rheumatoid Arthritis (SERA) cohorts: Longitudinal observational study

OBJECTIVES

To investigate association between presence of multimorbidity in people with established and early rheumatoid arthritis (RA) and risk, duration and cause of hospitalisations.

DESIGN

Longitudinal observational study.

SETTING

UK Biobank, population-based cohort recruited between 2006 and 2010, and the Scottish Early Rheumatoid Arthritis (SERA), inception cohort recruited between 2011 and 2015. Both linked to mortality and hospitalisation data.

PARTICIPANTS

4757 UK Biobank participants self-reporting established RA; 825 SERA participants with early RA meeting the 2010 ACR/EULAR classification criteria. Participants stratified by number of long-term conditions (LTCs) in addition to RA (RA only, RA + 1 LTC and RA + ≥ 2 LTCs) and matched to five non-RA controls.

MAIN OUTCOME MEASURES

Number and duration of hospitalisations and their causes. Incidence rate ratios (IRR) and 95% confidence intervals (CI) calculated using negative binomial regression models.

RESULTS

Participants with RA + ≥ 2 LTCs experienced higher hospitalisation rates compared to those with RA alone (UK Biobank: IRR 2.10, 95% CI 1.91 to 2.30; SERA: IRR 1.74, 95% CI 1.23 to 2.48). Total duration of hospitalisation in RA + ≥ 2 LTCs was also higher (UK Biobank: IRR 2.48, 95% CI 2.17 to 2.84; SERA: IRR 1.90, 95% CI 1.07 to 3.38) than with RA alone. Rate and total duration of hospitalisations was higher in UK Biobank RA participants than non-RA controls with equivalent number of LTCs. Hospitalisations for respiratory infection were higher in early RA than established RA and were the commonest cause of hospital admission in early RA.

CONCLUSIONS

Participants with established or early RA with multimorbidity experienced a higher rate and duration of hospitalisations than those with RA alone and with non-RA matched controls.

Prevalence, measurement, and implications of frailty in stroke survivors: An analysis of three global aging cohorts

BACKGROUND

Our understanding of the relationship between frailty and stroke, beyond the acute phase of stroke, is limited. We aimed to estimate the prevalence of frailty in stroke survivors using differing methods of assessment and describe relationships with stroke outcomes.

METHODS

We used data from three international population surveys (American Health and Retirement Survey/English Longitudinal Study of Ageing/Survey for Health and Retirement in Europe) of aging. Frailty status was assessed using the Fried frailty phenotype, a 40-item frailty index (FI) and the clinical frailty scale (CFS). We created estimates of frailty prevalence and assessed association of frailty with outcomes of mortality/hospital admission/recurrent stroke at 2 years follow-up using logistic regression models adjusted for age/sex. Additional analyses explored effects of adding cognitive measures to frailty assessments and of missing grip strength data.

FINDINGS

Across 9617 stroke survivors, using the frailty phenotype, 23.8% (n = 2094) identified as frail; with CFS, 30.1% (n = 2906) were moderately or severely frail; using FI, 22.7% (n = 2147) had moderate frailty and 31.9% (n = 3021) had severe frailty. Frailty was associated with increased risk of mortality/hospitalization/recurrent stroke using all three measures. Adding cognitive variables to the FI produced minimal difference in prevalence of frailty. People with physical frailty (phenotype or CFS) plus cognitive impairment had a greater risk of mortality than people with an equivalent level of frailty but no cognitive impairment. Excluding people unable to provide grip strength underestimated frailty prevalence.

INTERPRETATION

Frailty is common in stroke and associated with poor outcomes, regardless of measure used. Adding cognitive variables to frailty phenotype/CFS measures identified those at greater risk of poor outcomes. Physical and cognitive impairments in stroke survivors do not preclude frailty assessment.

The selfBACK artificial intelligence-based smartphone app can improve low back pain outcome even in patients with high levels of depression or stress

BACKGROUND

selfBACK provides individually tailored self-management support for low back pain (LBP) via an artificial intelligence-based smartphone app. We explore whether those with depressive/stress symptoms can benefit from this technology.

METHODS

Secondary analysis of the selfBACK randomized controlled trial (n = 461). Participants with LBP were randomized to usual care (n = 229), or usual care plus selfBACK (n = 232).

PRIMARY OUTCOME

LBP-related disability (Roland-Morris Disability Questionnaire, RMDQ) over 9 months.

SECONDARY OUTCOMES

global perceived effect (GPE)/pain self-efficacy (PSEQ)/satisfaction/app engagement. Baseline depressive symptoms were measured using the patient health questionnaire (PHQ-8) and stress with the perceived stress scale (PSS). Outcomes stratified by baseline PHQ-8/PSS scores to assess associations across the whole cohort, and intervention versus control groups.

RESULTS

Participants with higher levels of depressive/stress symptoms reported more baseline LBP-related disability (RMDQ 3.1; 1.6 points higher in most vs least depressed/stressed groups respectively); lower self-efficacy (PSEQ 8.1; 4.6 points lower in most vs least depressive/stressed groups respectively). LBP-related disability improved over time; relative risk of improvement in those with greatest depressive/stress symptoms versus nil symptom comparators at 9 months: 0.8 (95% CI: 0.6 to 1.0) and 0.8 (95% CI: 0.7 to 1.0) respectively. No evidence that different baseline levels of depressive/perceived stress symptoms are associated with different RMDQ/GPE/PSEQ outcomes. Whilst participants with higher PHQ-8/PSS were less likely to be satisfied or engage with the app, there was no consistent association among PHQ-8/PSS level, the intervention and outcomes.

CONCLUSIONS

The selfBACK app can improve outcomes even in those with high levels of depressive/stress symptoms and could be recommended for patients with LBP.

SIGNIFICANCE

We have demonstrated that an app supporting the self-management of LBP is helpful, even in those with higher levels of baseline depression and stress symptoms. selfBACK offers an opportunity to support people with LBP and provides clinicians with an additional tool for their patients, even those with depression or high levels of stress. This highlights the potential for digital health interventions for chronic pain.

1310 FRAILTY, LONELINESS AND SOCIAL ISOLATION IN THE UK BIOBANK COHORT

Background

Three challenges for ageing populations are frailty (a state of reduced physiological reserve), social isolation (objective lack of social connections), and loneliness (subjective experience of feeling alone). These are associated with adverse outcomes. This study aims to examine how frailty in combination with loneliness or social isolation is associated with all-cause mortality and hospitalisation rate using data from UK Biobank, a large population-based research cohort.

Methods

502,456 UK Biobank participants were recruited 2006-2010. Baseline data assessed frailty (via two measures: Fried frailty phenotype, Rockwood frailty index), social isolation, and loneliness. Adjusted cox-proportional hazards models assessed association between frailty in combination with loneliness or social isolation and all-cause mortality. Negative binomial regression models assessed hospitalisation rate.

Findings

Frailty, social isolation, and loneliness are common in UK Biobank (frail as per frailty phenotype 3.38%, frail as per frailty index 4.68%, social isolation 9.04%, loneliness 4.75%). Social isolation/loneliness were more common in frailty/pre-frailty. Frailty is associated with increased mortality regardless of social isolation/loneliness. Hazard ratios for frailty (frailty phenotype) were 3.38 (3.11-3.67) with social isolation and 2.89 (2.75-3.05) without social isolation, 2.94 (2.64-3.27) with loneliness and 2.9 (2.76-3.04) without loneliness. Social isolation was associated with increased mortality at all levels of frailty; loneliness only in robust/pre-frail. Frailty was also associated with hospitalisation regardless of social isolation/loneliness. Incidence rate ratios for frailty (frailty phenotype) were 3.93 (3.66-4.23) with social isolation and 3.75 (3.6-3.9) without social isolation, 4.42 (4.04-4.83) with loneliness and 3.69 (3.55-3.83) without loneliness. At all levels frailty, social isolation/loneliness are associated with increased hospitalisation Results were similar using the frailty index definition.

Conclusion

Social isolation is relevant at all levels frailty. Risk of loneliness is more pronounced in those who are robust or pre-frail. Proactive identification of loneliness, regardless of physical health status may provide opportunities for intervention.

1311 FRAILTY IN RANDOMISED CONTROLLED TRIALS FOR DEMENTIA OR MILD COGNITIVE IMPAIRMENT

Background

Frailty and dementia have a bidirectional relationship. However, frailty is rarely reported in clinical trials for dementia and mild cognitive impairment (MCI) which limits assessment of trial applicability. This study aims to use a frailty index (FI) to measure frailty using individual participant data (IPD) from clinical trials for MCI and dementia and to quality the prevalence of frailty and its association with serious adverse events (SAEs) and trial attrition.

Methods

We analysed IPD from three dementia (n=1) and MCI (n=2) trials. An FI comprising physical deficits was created for each trial using baseline IPD. Poisson and logistic regression were used to examine associations with SAEs and attrition, respectively. Estimates were pooled in random effects meta-analysis. Analyses were repeated using an FI incorporating cognitive as well as physical deficits, and results compared.

Results

The mean physical FI was 0.13 and 0.14 in the MCI trials and 0.25 in the dementia trial. Frailty prevalence (FI&gt;0.24) was 5.1%, 5.4% in MCI trials and 55.6% in dementia. After including cognitive deficits, prevalence was similar in MCI (4.6% and 4.9%) but higher in dementia (80.7%). 99th percentile of FI (0.29 in MCI, 0.44 in dementia) was lower than in most general population studies. Frailty was associated with SAEs (physical FI IRR = 1.63 [1.43, 1.87]; physical/cognitive FI IRR = 1.67 [1.45, 1.93]). Frailty was not associated with trial attrition (physical FI OR = 1.18 [0.92, 1.53]; physical/cognitive FI OR = 1.17 [0.92, 1.49]).

Conclusion

Measuring frailty from IPD in dementia and MCI trials is feasible. Severe frailty may be under-represented. Frailty is associated with clinically significant outcomes. Including only physical deficits may underestimate frailty in dementia. Frailty can and should be measured in trials for dementia and MCI, and efforts should be made to facilitate inclusion of people living with frailty.

Systematic review and meta-analysis of prevalence, trajectories, and clinical outcomes for frailty in COPD

This systematic review synthesised measurement and prevalence of frailty in COPD and associations between frailty and adverse health outcomes. We searched Medline, Embase and Web of Science (1 January 2001-8 September 2021) for observational studies in adults with COPD assessing frailty prevalence, trajectories, or association with health-related outcomes. We performed narrative synthesis and random-effects meta-analyses. We found 53 eligible studies using 11 different frailty measures. Most common were frailty phenotype (n = 32), frailty index (n = 5) and Kihon checklist (n = 4). Prevalence estimates varied by frailty definitions, setting, and age (2.6-80.9%). Frailty was associated with mortality (5/7 studies), COPD exacerbation (7/11), hospitalisation (3/4), airflow obstruction (11/14), dyspnoea (15/16), COPD severity (10/12), poorer quality of life (3/4) and disability (1/1). In conclusion, frailty is a common among people with COPD and associated with increased risk of adverse outcomes. Proactive identification of frailty may aid risk stratification and identify candidates for targeted intervention.

Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study

PURPOSE

We aimed to classify individuals with RA and ≥2 additional long-term conditions (LTCs) and describe the association between different LTC classes, number of LTCs and adverse health outcomes.

METHODS

We used UK Biobank participants who reported RA (n=5,625) and employed latent class analysis (LCA) to create classes of LTC combinations for those with ≥2 additional LTCs. Cox-proportional hazard and negative binomial regression were used to compare the risk of all-cause mortality, major adverse cardiac events (MACE), and number of emergency hospitalisations over an 11-year follow-up across the different LTC classes and in those with RA plus one additional LTC. Persons with RA without LTCs were the reference group. Analyses were adjusted for demographic characteristics, smoking, BMI, alcohol consumption and physical activity.

RESULTS

A total of 2,566 (46%) participants reported ≥2 LTCs in addition to RA. This involved 1,138 distinct LTC combinations of which 86% were reported by ≤2 individuals. LCA identified 5 morbidity-classes. The distinctive condition in the class with the highest mortality was cancer (class 5; HR 2.66 95%CI (1.91-3.70)). The highest MACE (HR 2.95 95%CI (2.11-4.14)) and emergency hospitalisations (rate ratio 3.01 (2.56-3.54)) were observed in class 3 which comprised asthma, COPD & CHD. There was an increase in mortality, MACE and emergency hospital admissions within each class as the number of LTCs increased.

CONCLUSIONS

The risk of adverse health outcomes in RA varied with different patterns of multimorbidity. The pattern of multimorbidity should be considered in risk assessment and formulating management plans in patients with RA.

Correlations between comorbidities in trials and the community: An individual-level participant data meta-analysis

Background

People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community.

Methods

Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition.

Results

Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively.

Conclusions

Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials.

Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) - protocol for a research collaboration

Background

Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as 'early onset'). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled 'MELD-B' to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions.

Aim

Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses.

Design

We will develop deeper understanding of 'burdensomeness' and 'complexity' through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential 'preventable moments', defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout.

Multimorbidity and Blood Pressure Control in Patients Attending Primary Care in Canada

BACKGROUND

There has been conflicting evidence on the association between multimorbidity and blood pressure (BP) control. This study aimed to investigate this associations in people with hypertension attending primary care in Canada, and to assess whether individual long-term conditions are associated with BP control.

METHODS

This was a cross-sectional study in people with hypertension attending primary care in Toronto between January 1, 2017 and December 31, 2019. Uncontrolled BP was defined as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. A list of 11 a priori selected chronic conditions was used to define multimorbidity. Multimorbidity was defined as having ≥1 long-term condition in addition to hypertension. Logistic regression models were used to estimate the association between multimorbidity (or individual long-term conditions) with uncontrolled BP.

RESULTS

A total of 67 385 patients with hypertension were included. They had a mean age of 70, 53.1% were female, 80.6% had multimorbidity, and 35.7% had uncontrolled BP. Patients with multimorbidity had lower odds of uncontrolled BP than those without multimorbidity (adjusted OR = 0.72, 95% CI 0.68-0.76). Among the long-term conditions, diabetes (aOR = 0.73, 95%CI 0.70-0.77), heart failure (aOR = 0.81, 95%CI 0.73-0.91), ischemic heart disease (aOR = 0.74, 95%CI 0.69-0.79), schizophrenia (aOR = 0.79, 95%CI 0.65-0.97), depression/anxiety (aOR = 0.91, 95%CI 0.86-0.95), dementia (aOR = 0.87, 95%CI 0.80-0.95), and osteoarthritis (aOR = 0.89, 95%CI 0.85-0.93) were associated with a lower likelihood of uncontrolled BP.

CONCLUSION

We found that multimorbidity was associated with better BP control. Several conditions were associated with better control, including diabetes, heart failure, ischemic heart disease, schizophrenia, depression/anxiety, dementia, and osteoarthritis.

Using qualitative study designs to understand treatment burden and capacity for self-care among patients with HIV/NCD multimorbidity in South Africa: A methods paper

Background

Low- and middle-income countries (LMICs), including South Africa, are currently experiencing multiple epidemics: HIV and the rising burden of non-communicable diseases (NCDs), leading to different patterns of multimorbidity (the occurrence of two or more chronic conditions) than experienced in high income settings. These adversely affect health outcomes, increase patients' perceived burden of treatment, and impact the workload of self-management. This paper outlines the methods used in a qualitative study exploring burden of treatment among people living with HIV/NCD multimorbidity in South Africa.

Methods

We undertook a comparative qualitative study to examine the interaction between individuals' treatment burden (self-management workload) and their capacity to take on this workload, using the dual lenses of Burden of Treatment Theory (BoTT) and Cumulative Complexity Model (CuCoM) to aid conceptualisation of the data. We interviewed 30 people with multimorbidity and 16 carers in rural Eastern Cape and urban Cape Town between February-April 2021. Data was analysed through framework analysis.

Findings

This paper discusses the methodological procedures considered when conducting qualitative research among people with multimorbidity in low-income settings in South Africa. We highlight the decisions made when developing the research design, recruiting participants, and selecting field-sites. We also explore data analysis processes and reflect on the positionality of the research project and researchers.

Conclusion

This paper illustrates the decision-making processes conducting this qualitative research and may be helpful in informing future research aiming to qualitatively investigate treatment burden among patients in LMICs.

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity

Objective

Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity.

Method

This work was conducted as part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) study. The conceptualisation of multimorbidity lifecourse determinant domains was shaped by a review of existing research evidence and policy, and co-produced with public involvement via two workshops.

Results

Early-life risk factors incorporate personal, social, economic, behavioural and environmental factors, and the key domains discussed in research evidence, policy, and with public contributors included adverse childhood experiences, socioeconomics, the social and physical environment, and education. Policy recommendations more often focused on individual-level factors as opposed to the wider determinants of health discussed within the research evidence. Some domains highlighted through our co-production process with public contributors, such as religion and spirituality, health screening and check-ups, and diet, were not adequately considered within the research evidence or policy.

Conclusions

This co-produced conceptualisation can inform research directions using primary and secondary data to investigate the early-life characteristics of population groups at risk of future multimorbidity, as well as policy directions to target public health prevention scenarios of early-onset multimorbidity.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis

BACKGROUND

Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life.

METHODS AND FINDINGS

Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631.

CONCLUSIONS

Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity.

TRIAL REGISTRATION

A prespecified protocol was registered on PROSPERO (CRD42018048202).

Associations between a weighted health behaviour score, socioeconomic status, and all-cause mortality in UK Biobank cohort: a prospective analysis

BACKGROUND

Unhealthy behaviours are associated with disproportionate mortality among socioeconomically deprived populations. Previous studies exploring that disproportionate harm do not examine weighted scores, or examine few behaviours. We aimed to create an extended weighted health behaviour score and examine the effect of socioeconomic status on the association between score and all-cause mortality.

METHODS

Data was sourced from the UK Biobank population cohort, recruited in 2006-10. The main exposures included in the analysis were 11 health behaviours (baseline self-report): smoking status, alcohol consumption, physical activity, time spent watching television, sleep duration, added salt in diet, social isolation, intake of red meat, intake of processed meat, intake of oily fish, and intake of fruit and vegetables. Behaviours were classified as healthy or unhealthy according to national guidelines or latest evidence. Socioeconomic deprivation was measured with the Townsend deprivation index. Cox proportional hazard models of health behaviour data were prospectively linked to death registries to examine associations between health behaviours and all-cause mortality. Models were adjusted for demographics and health at baseline. Mortality associated with each behaviour alone was used to determine score weighting. For sensitivity analysis, we explored associations between weighted lifestyle score and all-cause mortality stratified by sex and ethnicity.

FINDINGS

The analysis included 229 107 participants with complete data. Median age was 53 years (IQR 47-60) for 119 634 (52·2%) women and 54 years (47-60) for 109 473 (47·8%) men. Over a median follow-up of 11·9 years (IQR 11.1-12.6), 9379 (4·1%) participants died. Compared with having no unhealthy behaviours, each behaviour was positively associated with all-cause mortality. Smoking (hazard ratio [HR] 2·47 [95% CI 2·25-2·70]) and social isolation (1·69 [1·54-1·86]) were associated with notably higher mortality. A weighted score was created by ascribing one point to each 40% increment in risk (four points for smoking, two points for social isolation, and one points for each of all other behaviours). A dose-response increment for all-cause mortality HR was noted with each additional point of weighted score. Associations were stronger in more deprived tertiles. With least deprived and lowest score as reference, HRs for highest scores were 2·22 (95% CI 1·72-2·86) in the least deprived and 4·10 (3·62-4·65) in the most deprived. An additive interaction between sex and lifestyle score for all-cause mortality was suggested by the data; men had slightly higher HRs at each level of the lifestyle score. However, a statistical test for interaction on a multiplicative scale was not significant. No evidence was found of interaction (either additive or multiplicative) between ethnicity and lifestyle score.

INTERPRETATION

An extended weighted health behaviour score has strong associations with mortality, and associations are stronger in more deprived participants. Weighted health behaviour scores that account for socioeconomic deprivation could convey personalised risk and inform healthy living policy. Further work with adequate numbers of participants from minority ethnic groups is required to make more accurate estimates of mortality associated with a weighted health behaviour score in these populations.

FUNDING

HMEF is supported by a Medical Research Council Clinical Research Training Fellowship (grant number MR/T001585/1), which covered the costs of accessing the data herein.

The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function

BACKGROUND

Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes.

METHODS

Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006-2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006-2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m²). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)).

RESULTS

Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m², most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m², clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m², in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31-3.07) and MACE HR is 4.18 (CI 3.65-4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22-3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m², in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62-2.46) and MACE HR is 4.09 (CI 3.39-4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18-1.61) and MACE HR is 1.58 (CI 1.42-1.76).

CONCLUSIONS

Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.

Which combinations of health behaviours are associated with highest risk? An exploration of the UK Biobank population cohort

BACKGROUND

Combinations of unhealthy behaviours are associated with greater mortality than single behaviours, but some combinations might have stronger associations than others. High-risk combinations might be more prevalent among socioeconomically deprived populations. We examined associations between combinations of 11 unhealthy behaviours and mortality and explored socioeconomic distributions of high-risk combinations.

METHODS

We used the UK Biobank population cohort (n=502 459; aged 37-73 years) recruited between 2006 and 2010. Analysis included 229 197 participants with complete data. Main exposures were any combination of smoking, alcohol, physical activity, television time, sleep, added salt, social isolation, intake of red meat, processed meat, oily fish, and fruit and vegetables (each classified as healthy or unhealthy via guidelines or latest evidence). Townsend index was used to explore socioeconomic distribution. Cox proportional hazard models were used to examine associations between behaviours and all-cause mortality. Models were adjusted for demographic, health, and socioeconomic factors.

FINDINGS

Over a median follow-up of 11·6 years, 9739 (4·2%) of 229 197 participants died. From 716 unique combinations, 77 (11%) were associated with mortality with hazard ratios (HRs) greater than that for smoking alone (2·31 [95% CI 2·11-2·53]); HRs ranged from 9·44 to 2·34. Of these 77 high-risk combinations, smoking featured in 61 (79%), low fruit and vegetables in 45 (58%), and low oily fish in 41 (53%). All combinations featuring social isolation (18 [23%] of 77) had HRs greater than 3·00. Participants with high-risk combinations had greater deprivation scores than those with no unhealthy behaviours. Median deprivation scores of the ten highest risk combinations ranged from -2·0 to 2·1, whereas for participants with no unhealthy behaviours the score was -2·5. Examining women and men separately resulted in similar findings. Examination of ethnic differences was severely limited by small numbers of participants in minority ethnic groups.

INTERPRETATION

Many unique unhealthy behaviour combinations are strongly associated with mortality and high-risk combinations are more prevalent among more deprived populations than among more affluent populations. Exploring unique combinations of a wide range of health behaviours can identify high-risk populations. Future work with adequately sampled minority ethnic groups is required to examine high-risk combinations by ethnicity. Supporting healthy living in deprived populations, including tackling structural barriers to health, could address a wide range of health behaviours in combination.

FUNDING

UK Medical Research Council.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

BACKGROUND

The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care.

METHODS

This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity.

RESULTS

For 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55-0.64; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.87 (0.58-1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most.

CONCLUSIONS

Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

One size does not fit all: Participants' experiences of the selfBACK app to support self-management of low back pain-a qualitative interview study

BACKGROUND

Low back pain (LBP) is one of the most common reasons for disability globally. Digital interventions are a promising means of supporting people to self-manage LBP, but implementation of digital interventions has been suboptimal. An artificial intelligence-driven app, selfBACK, was developed to support self-management of LBP as an adjunct to usual care. To better understand the process of implementation from a participant perspective, we qualitatively explored factors influencing embedding, integrating, and sustaining engagement with the selfBACK app, and the self-perceived effects, acceptability, and satisfaction with the selfBACK app.

METHODS

Using a qualitative interview study and an analytic framework approach underpinned by Normalization Process Theory (NPT), we investigated the experiences of patients who participated in the selfBACK randomized controlled trial (RCT). Interviews focused on the motivation to participate in the RCT, experiences of using the selfBACK app, and views about future intended use and potential of using digital health interventions for self-management of LBP. Participants were purposively sampled to represent diversity in age, sex, and implementation reflected by a proxy measure of number of app-generated self-management plans during the first three months of RCT participation.

RESULTS

Twenty-six participants aged 21-78, eleven females and fifteen men, with two to fourteen self-management plans, were interviewed between August 2019 and April 2020. A broad range of factors influencing implementation of selfBACK within all constructs of NPT were identified. Key facilitating factors were preferences and beliefs favoring self-management, a friendly, motivational, and reassuring supporter, tailoring and personalization, convenience and ease of use, trustworthiness, perceiving benefits, and tracking achievements. Key impeding factors were preferences and beliefs not favoring self-management, functionality issues, suboptimal tailoring and personalization, insufficient time or conflicting life circumstances, not perceiving benefits, and insufficient involvement of health care practitioners. Self-perceived effects on pain and health, behavior/attitude, and gaining useful knowledge varied by participant.

CONCLUSIONS

The high prevalence of LBP globally coupled with the advantages of providing help through an app offers opportunities to help countless people. A range of factors should be considered to facilitate implementation of self-management of LBP or similar pain conditions using digital health tools.

Frailty in people with rheumatoid arthritis: a systematic review of observational studies

Background: Frailty, an age-related decline in physiological reserve, is an increasingly important concept in the management of chronic diseases. The implications of frailty in people with rheumatoid arthritis are not well understood. We undertook a systematic review to assess prevalence of frailty in people with rheumatoid arthritis, and the relationship between frailty and disease activity or clinical outcomes. Methods: We searched four electronic databases (January 2001 to April 2021) for observational studies assessing the prevalence of frailty (any frailty measure) in adults (≥18 years) with rheumatoid arthritis, or analysing the relationship between frailty and disease activity or clinical outcomes (e.g. quality of life, hospitalisation or mortality) in people with rheumatoid arthritis. Study quality was assessed using an adapted Newcastle-Ottawa Scale. Screening, quality assessment and data extraction were performed independently by two reviewers. We used narrative synthesis. Results: We identified 17 analyses, from 14 different populations. 15/17 were cross-sectional. Studies used 11 different measures of frailty. Frailty prevalence ranged from 10% (frailty phenotype) to 36% (comprehensive rheumatologic assessment of frailty) in general adult populations with rheumatoid arthritis. In younger populations (<60 or <65 years) prevalence ranged from 2.4% (frailty phenotype) to 19.9% (Kihon checklist) while in older populations (>60 or >65) prevalence ranged from 31.2% (Kihon checklist) to 55% (Geriatric 8 tool). Frailty was cross-sectionally associated with higher disease activity (10/10 studies), lower physical function (7/7 studies) and longer disease duration (2/5 studies), and with hospitalization and osteoporotic fractures (1/1 study, 3.7 years follow-up). Conclusion: Frailty is common in rheumatoid arthritis, including those aged <65 years, and is associated with a range of adverse features. However, these is heterogeneity in how frailty is measured. We found few longitudinal studies making the impact of frailty on clinical outcomes over time and the extent to which frailty is caused by rheumatoid arthritis unclear.

Effect of age, sex, and morbidity count on trial attrition: meta-analysis of individual participant level data from phase 3/4 industry funded clinical trials

Objectives

To estimate the association between individual participant characteristics and attrition from randomised controlled trials.

Design

Meta-analysis of individual participant level data (IPD).

Data sources

Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).

Eligibility criteria for selecting studies

Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).

Main outcome measures

Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.

Results

In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.

Conclusions

Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Multimorbidity

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank

BACKGROUND

Frailty, a state of reduced physiological reserve, is common in people with chronic obstructive pulmonary disease (COPD). Frailty can occur at any age; however, the implications in younger people (eg, aged <65 years) with COPD are unclear. We assessed the prevalence of frailty in UK Biobank participants with COPD; explored relationships between frailty and forced expiratory volume in 1 second (FEV1) and quantified the association between frailty and adverse outcomes.

METHODS

UK Biobank participants (n=3132, recruited 2006-2010) with COPD aged 40-70 years were analysed comparing two frailty measures (frailty phenotype and frailty index) at baseline. Relationship with FEV1 was assessed for each measure. Outcomes were mortality, major adverse cardiovascular event (MACE), all-cause hospitalisation, hospitalisation with COPD exacerbation and community COPD exacerbation over 8 years of follow-up.

RESULTS

Frailty was common by both definitions (17% frail using frailty phenotype, 28% moderate and 4% severely frail using frailty index). The frailty phenotype, but not the frailty index, was associated with lower FEV1. Frailty phenotype (frail vs robust) was associated with mortality (HR 2.33; 95% CI 1.84 to 2.96), MACE (2.73; 1.66 to 4.49), hospitalisation (incidence rate ratio 3.39; 2.77 to 4.14) hospitalised exacerbation (5.19; 3.80 to 7.09) and community exacerbation (2.15; 1.81 to 2.54), as was frailty index (severe vs robust) (mortality (2.65; 95% CI 1.75 to 4.02), MACE (6.76; 2.68 to 17.04), hospitalisation (3.69; 2.52 to 5.42), hospitalised exacerbation (4.26; 2.37 to 7.68) and community exacerbation (2.39; 1.74 to 3.28)). These relationships were similar before and after adjustment for FEV1.

CONCLUSION

Frailty, regardless of age or measure, identifies people with COPD at risk of adverse clinical outcomes. Frailty assessment may aid risk stratification and guide-targeted intervention in COPD and should not be limited to people aged >65 years.

963 THE IMPACT OF AGE, SEX AND MORBIDITY COUNT ON EARLY TERMINATION: A META-ANALYSIS OF INDIVIDUAL PATIENT DATA FROM CLINICAL TRIALS

Introduction

Multimorbidity is found in around half of people with any long-term condition but is substantially less common in randomised controlled trials (‘trials’). Multimorbidity may diminish a participant’s ability to complete a trial. However, empirical estimates of the association between individual patient characteristics and early termination are lacking.

Method

Individual patient-level data were obtained from Phase 3/4 trials contained within two clinical trial repositories. Eligible trials for inclusion were identified according to pre-specified criteria (PROSPERO CRD42018048202). Within each trial, the association between morbidity count and early termination (failure for any reason to complete the final trial visit) was estimated in logistic regression models, adjusting for age and sex. These estimates were meta-analysed in Bayesian linear models, with partial pooling across index conditions and drug classes. Using these estimates, the impact of morbidity count on early termination was modelled for a set of notional trials.

Results

In 92 trials across 20 index conditions and 17 drug classes, the mean morbidity count ranged from 0.3–2.7. Neither age nor sex was associated with early termination (OR 1.04, 95% CI 0.98–1.11; OR 1.00, 95% PI 0.95–1.07 respectively). Morbidity count was associated with early termination (OR per additional morbidity: 1.11, 95% CI: 1.07 to 1.14). There was no evidence of non-linearity in the association between morbidity count and early termination, and there was minimal variation across drug classes and index conditions. For a notional trial with high level of early termination in individuals without multimorbidity, doubling the mean morbidity count from 1 to 2 increases risk of early termination from 29% to 31%.

Conclusion

Multimorbidity, irrespective of age and sex, is associated with a relatively modest increased odds of early termination of trial participation. The benefit of increased generalisability of trials by including patients with multimorbidity appears likely to outweigh the disadvantages of lower retention.

Translational framework for implementation evaluation and research: Protocol for a qualitative systematic review of studies informed by Normalization Process Theory (NPT) [version 1; peer review: 2 approved]

BACKGROUND

Normalization Process Theory (NPT) identifies mechanisms that have been demonstrated to play an important role in implementation processes. It is now widely used to inform feasibility, process evaluation, and implementation studies in healthcare and other areas of work. This qualitative synthesis of NPT studies aims to better understand how NPT explains observed and reported implementation processes, and to explore the ways in which its constructs explain the implementability, enacting and sustainment of complex healthcare interventions.

METHODS

We will systematically search Scopus, PubMed and Web of Science databases and use the Google Scholar search engine for citations of key papers in which NPT was developed. This will identify English language peer-reviewed articles in scientific journals reporting (a) primary qualitative or mixed methods studies; or, (b) qualitative or mixed methods evidence syntheses in which NPT was the primary analytic framework. Studies may be conducted in any healthcare setting, published between June 2006 and 31 December 2021. We will perform a qualitative synthesis of included studies using two parallel methods: (i) directed content analysis based on an already developed coding manual; and (ii) unsupervised textual analysis using Leximancer® topic modelling software.

OTHER

We will disseminate results of the review using peer reviewed publications, conference and seminar presentations, and social media (Facebook and Twitter) channels. The primary source of funding is the National Institute for Health Research ARC North Thames. No human subjects or personal data are involved and no ethical issues are anticipated.

The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort

BACKGROUND

Infection with SARS-CoV-2 virus (COVID-19) impacts disadvantaged groups most. Lifestyle factors are also associated with adverse COVID-19 outcomes. To inform COVID-19 policy and interventions, we explored effect modification of socioeconomic-status (SES) on associations between lifestyle and COVID-19 outcomes.

METHODS

Using data from UK-Biobank, a large prospective cohort of 502,536 participants aged 37-73 years recruited between 2006 and 2010, we assigned participants a lifestyle score comprising nine factors. Poisson regression models with penalised splines were used to analyse associations between lifestyle score, deprivation (Townsend), and COVID-19 mortality and severe COVID-19. Associations between each exposure and outcome were examined independently before participants were dichotomised by deprivation to examine exposures jointly. Models were adjusted for sociodemographic/health factors.

RESULTS

Of 343,850 participants (mean age > 60 years) with complete data, 707 (0.21%) died from COVID-19 and 2506 (0.76%) had severe COVID-19. There was evidence of a nonlinear association between lifestyle score and COVID-19 mortality but limited evidence for nonlinearity between lifestyle score and severe COVID-19 and between deprivation and COVID-19 outcomes. Compared with low deprivation, participants in the high deprivation group had higher risk of COVID-19 outcomes across the lifestyle score. There was evidence for an additive interaction between lifestyle score and deprivation. Compared with participants with the healthiest lifestyle score in the low deprivation group, COVID-19 mortality risk ratios (95% CIs) for those with less healthy scores in low versus high deprivation groups were 5.09 (1.39-25.20) and 9.60 (4.70-21.44), respectively. Equivalent figures for severe COVID-19 were 5.17 (2.46-12.01) and 6.02 (4.72-7.71). Alternative SES measures produced similar results.

CONCLUSIONS

Unhealthy lifestyles are associated with higher risk of adverse COVID-19, but risks are highest in the most disadvantaged, suggesting an additive influence between SES and lifestyle. COVID-19 policy and interventions should consider both lifestyle and SES. The greatest public health benefit from lifestyle focussed COVID-19 policy and interventions is likely to be seen when greatest support for healthy living is provided to the most disadvantaged groups.

Experiences of multimorbidity in urban and rural Malawi: An interview study of burdens of treatment and lack of treatment

Multimorbidity (presence of ≥2 long term conditions (LTCs)) is a growing global health challenge, yet we know little about the experiences of those living with multimorbidity in low- and middle-income countries (LMICs). We therefore explore: 1) experiences of men and women living with multimorbidity in urban and rural Malawi including their experiences of burden of treatment and 2) examine the utility of Normalization Process Theory (NPT) and Burden of Treatment Theory (BOTT) for structuring analytical accounts of these experiences. We conducted in depth, semi-structured interviews with 32 people in rural (n = 16) and urban settings (n = 16); 16 males, 16 females; 15 under 50 years; and 17 over 50 years. Data were analysed thematically and then conceptualised through the lens of NPT and BOTT. Key elements of burden of treatment identified included: coming to terms with and gaining an understanding of life with multimorbidity; dealing with resulting disruptions to family life; the work of seeking family and community support; navigating healthcare systems; coping with lack of continuity of care; enacting self-management advice; negotiating medical advice; appraising treatments; and importantly, dealing with the burden of lack of treatments/services. Poverty and inadequate healthcare provision constrained capacity to deal with treatment burden while supportive social and community networks were important enabling features. Greater access to health information/education would lessen treatment burden as would better resourced healthcare systems and improved standards of living. Our work demonstrates the utility of NPT and BOTT for aiding conceptualisation of treatment burden issues in LMICs but our findings highlight that 'lack' of access to treatments or services is an important additional burden which must be integrated in accounts of treatment burden in LMICs.

Frailty in rheumatoidrmdopen-2021-002111 arthritis and its relationship with disease activity, hospitalisation and mortality: a longitudinal analysis of the Scottish Early Rheumatoid Arthritis cohort and UK Biobank

OBJECTIVE

To assess the prevalence of frailty in rheumatoid arthritis (RA) and its association with baseline and longitudinal disease activity, all-cause mortality and hospitalisation.

PARTICIPANTS

People with RA identified from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort (newly diagnosed, mean age 58.2 years) and UK Biobank (established disease identified using diagnostic codes, mean age 59 years). Frailty was quantified using the frailty index (both datasets) and frailty phenotype (UK Biobank only). Disease activity was assessed using Disease Activity Score in 28 joints (DAS28) in SERA. Associations between baseline frailty and all-cause mortality and hospitalisation was estimated after adjusting for age, sex, socioeconomic status, smoking and alcohol, plus DAS28 in SERA.

RESULTS

Based on the frailty index, frailty was common in SERA (12% moderate, 0.2% severe) and UK Biobank (20% moderate, 3% severe). In UK Biobank, 23% were frail using frailty phenotype. Frailty index was associated with DAS28 in SERA, as well as age and female sex in both cohorts. In SERA, as DAS28 lessened over time with treatment, mean frailty index also decreased. The frailty index was associated with all-cause mortality (HR moderate/severe frailty vs robust 4.14 (95% CI 1.49 to 11.51) SERA, 1.68 (95% CI 1.26 to 2.13) UK Biobank) and unscheduled hospitalisation (incidence rate ratio 2.27 (95% CI 1.45 to 3.57) SERA 2.74 (95% CI 2.29 to 3.29) UK Biobank). In UK Biobank, frailty phenotype also associated with mortality and hospitalisation.

CONCLUSION

Frailty is common in early and established RA and associated with hospitalisation and mortality. Frailty in RA is dynamic and, for some, may be ameliorated through controlling disease activity in early disease.

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BACKGROUND

Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.

METHODS AND FINDINGS

These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.

CONCLUSIONS

In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.