Perceptions and experiences of living with and providing care for multimorbidity: A qualitative interview study.

Background: Experiences of living with and seeking care for multimorbidity is a relatively under-researched field. By analysing experiences of people with multimorbidity, caregivers and care professionals, we can better understand the complex care needs of those with multimorbidity and identify improvements to care management. This paper reports findings from research that elicited the views of key stakeholders to inform future care practice and policy. Aim: To elicit care recipient and care provider views to understand the care needs of those living with and seeking care for multimorbidity. Method: A qualitative interview study using purposive sampling of those living with and providing care in multimorbidity. Results: Increased support to those with multimorbidity and caregivers to navigate care systems was advocated. Establishing trusting care relationships featured prominently in participants accounts. Fragmented care, inadequate coordination and poor communication between care providers, were identified as system-wide challenges. There was agreement that integrated care models were needed, which delivered personalised care, such as shared decision-making, choice in care options and accessing services, and individualised care plans. Conclusion: We found significant agreement among stakeholders on care need and management in multimorbidity. Understanding the experiences of those with multimorbidity, caregivers and care professionals, can inform future improvements in care management.

Even short periods of diabetes remission are linked to lower risk of heart attack and stroke.

The studyDambha-Miller H, Hounkpatin HO, Stuart B, Farmer A, Griffin S. Type 2 diabetes remission trajectories and variation in risk of diabetes complications: a population-based cohort study. PLoS ONE 2023;18. doi:10.1371/journal.pone.0290791To read the full NIHR Alert, go to: https://evidence.nihr.ac.uk/alert/even-short-periods-of-diabetes-remission-are-linked-to-lower-risk-of-heart-attack-and-stroke/.

Multiple long-term conditions, loneliness and social isolation: A scoping review of recent quantitative studies.

BACKGROUND: Multiple long-term conditions (MLTC), loneliness and social isolation are common in older adults. Recent studies have explored the association of MLTC with loneliness and social isolation. This scoping review aimed to map this current evidence and identify gaps in the literature. METHODS: A scoping review was conducted following the PRISMA guidelines for scoping reviews. Ovid Medline, Embase, CINAHL, The Cochrane Library, PsycInfo, and Bielefeld Academic Search Engine were searched for studies published between January 2020-April 2023. Quantitative studies, published in any language, that assessed the association of MLTC with loneliness and/or social isolation were included. RESULTS: 1827 records were identified and screened. Of these, 17 met inclusion criteria. Most studies were cross-sectional and based on older adults. Studies were conducted in Europe, the US, Canada, and low- and middle-income countries. Ten studies focused on the association between MLTC and loneliness, six assessed the association between MLTC and social isolation and one examined associations with both loneliness and social isolation. Most studies reported a significant cross-sectional association of MLTC with loneliness, but there was weaker evidence for a longitudinal association between MLTC and loneliness and an association between MLTC and social isolation. Studies were heterogenous in terms of measures and definitions of loneliness/social isolation and MLTC, confounders adjusted for, and analytical models used, making comparisons difficult. CONCLUSIONS: Further population-based longitudinal studies using consistent measures and methodological approaches are needed to improve understanding of the association of MLTC with both loneliness and social isolation.

The association between stress and multiple long-term conditions: A cohort study.

BACKGROUND: Stress is an important predictor of long-term conditions. We examine whether hair cortisol (a biomarker of stress) is associated with incidence and accumulation of multiple long-term conditions (MLTC). METHODS: We included data from 4295 individuals aged ≥50 years within the English Longitudinal Study of Ageing dataset with data on hair cortisol, sociodemographic and health behaviour variables. Cox proportional hazards models were used to quantify the association between hair cortisol at baseline and accumulation of MLTC between 2012/2013 and 2018/2019, both for individuals with and without MLTC at baseline. RESULTS: Our cohort included 1458 (34.0%) individuals who accumulated MLTC between 2012/2013 and 2018/2019. The proportion of individuals with zero, 1, and ≥ 2 conditions at baseline who accumulated MLTC were 12.0% (n = 127), 40.4% (n = 520), and 41.7% (n = 811), respectively. Higher cortisol levels were associated with higher risk of accumulation of MLTC in both unadjusted [HR:1.15(1.05-1.25)] and models adjusted for sociodemographic and health behaviours [HR:1.12(1.02-1.22)]. For individuals without MLTC at baseline, higher cortisol levels were significantly associated with higher risk of developing MLTC in unadjusted [HR: 1.20(1.05-1.36)] and adjusted models [HR: 1.16(1.02-1.32)]. CONCLUSION: The study provides the first evidence of the role of stress in the development and accumulation of MLTC. This modifiable risk factor could be targeted to reduce the risk of MLTC. However, further work is needed to better understand the mechanisms and pathways that link stress and accumulation of MLTC.

Factors associated with multimorbidity in England: an analysis of the English Longitudinal Study of Ageing.

BACKGROUND: Multimorbidity, defined as the presence of two or more long-term conditions, is a growing public health challenge, especially in terms of prevention and accumulation of long-term conditions among particular population cohorts. To date, efforts to understand multimorbidity has focused mainly on specific disease combinations, with little known about the sociodemographic factors associated with it. The study aimed to assess the factors associated with multimorbidity in England. METHODS: A cross-sectional study was conducted using the English Longitudinal Study of Ageing (ELSA), a dataset of people aged 50 years and older. The study identified ten long-term conditions from waves 2 to 9. Wave 2 to 9 were conducted between June 2004 to July 2005, May 2006 to August 2007, May 2008 to July 2009, June 2010 to July 2011, May 2012 to June 2013, June 2014 to May 2015, May 2016 to June 2017 and June 2018 to July 2019, respectively. The study included people with two or more long-term conditions. We identified the number of long-term conditions and multimorbidity, and we examined their association with age, gender, ethnicity, marital status, employment status, education, weekly contact with relative, and feeling lonely, sad or depressed using multinomial logistic regression. FINDINGS: Of 16 731 people recruited from wave 2 to wave 9, we identified 10 026 people with multimorbidity aged 50 years and older. The majority had two conditions (39%) and were female (55%), aged 50-69 years (32%), of white ethnicity (96%), married (69%) and unemployed (65·3%). The adjusted odds ratio (aOR) of having more than two long-term conditions increased with age, after adjusting for sex and ethnicity (≥5 conditions: aOR 12·89, 95% CI 2·23-3·76). Being female was associated with an increased risk of having more than two long-term conditions (≥5 conditions: aOR 1·21, 1·04-1·42). Similarly, being separated, divorced, or widowed were associated with having more than two long-term conditions (≥5 conditions: aOR 1·45, 1·21-1·74). Not owning a home was independently associated with more than two long-term conditions (≥5 conditions: aOR 1·59, 1·35-1·88). INTERPRETATION: The current analysis used only ten long-term conditions that were available in the ELSA data, so a different association might have arisen if other conditions had been considered. Our findings provide insights into which particular groups of the multimorbid population could be the target of preventive public health strategies and wider clinical and social care interventions in England to reduce the burden of multimorbidity. FUNDING: National Institute for Health and Care Research (NIHR).

Eliciting and prioritising determinants of improved care in multimorbidity: A modified online Delphi study.

Background: Multimorbidity is a major challenge to health and social care systems around the world. There is limited research exploring the wider contextual determinants that are important to improving care for this cohort. In this study, we aimed to elicit and prioritise determinants of improved care in people with multiple conditions. Methods: A three-round online Delphi study was conducted in England with health and social care professionals, data scientists, researchers, people living with multimorbidity and their carers. Results: Our findings suggest a care system which is still predominantly single condition focused. 'Person-centred and holistic care' and 'coordinated and joined up care', were highly rated determinants in relation to improved care for multimorbidity. We further identified a range of non-medical determinants that are important to providing holistic care for this cohort. Conclusions: Further progress towards a holistic and patient-centred model is needed to ensure that care more effectively addresses the complex range of medical and non-medical needs of people living with multimorbidity. This requires a move from a single condition focused biomedical model to a person-based biopsychosocial approach, which has yet to be achieved.

Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England.

BACKGROUND   : People with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes. METHODS : A cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission. RESULTS: Our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality. CONCLUSIONS: We have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level.

Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study.

Biochemical remission of type 2 diabetes is achievable through dietary changes, physical activity and subsequent weight loss. We aim to identify distinct diabetes remission trajectories in a large population-based cohort over seven-years follow-up and to examine associations between remission trajectories and diabetes complications. Group-based trajectory modelling examined longitudinal patterns of HbA1c level (adjusting for remission status) over time. Multivariable Cox models quantified the association between each remission trajectory and microvascular complications, macrovascular complications, cardiovascular (CVD) events and all-cause mortality. Four groups were assigned. Group 1 (8,112 [13.5%]; achieving HbA1c <48 mmol/mol (6.5%) followed by increasing HbA1c levels); Group 2 (6,369 [10.6%]; decreasing HbA1c levels >48 mmol/mol (6.5%)); Group 3 (36,557 [60.6%]; stable high HbA1c levels); Group 4 (9,249 [15.3%]; stable low HbA1c levels (<48mmol/mol or <6.5%)). Compared to Group 3, Groups 1 and 4 had lower risk of microvascular complications (aHRs (95% CI): 0.65 (0.61-0.70), p-value <0.001;0.59 (0.55-0.64) p-value<0.001, respectively)), macrovascular complications (aHRs (95% CI): 0.83 (0.75-0.92), p-value<0.001; 0.66 (0.61-0.71), p-value<0.001) and CVD events (aHRs (95% CI): 0.74(0.67-0.83), p-value<0.001; 0.67(0.61-0.73), p-vlaue<0.001). Risk of CVD outcomes were similar for Groups 2 and 3. Compared to Group 3, Group 1 (aHR: 0.82(95% CI: 0.76-0.89)) had lower risk of mortality, but Group 4 had higher risk of mortality (aHR: 1.11(95% CI: 1.03-1.19)). Risk of CVD outcomes vary by pattern of remission over time, with lowest risk for those in remission longer. People who achieve remission, even for shorter periods of time, continue to benefit from this lower exposure to hyperglycaemia, which may, in turn, lower the risk of CVD outcomes including mortality.

UK research data resources based on primary care electronic health records: review and summary for potential users.

BACKGROUND: The range and scope of electronic health record (EHR) data assets in the UK has recently increased, which has been mainly in response to the COVID-19 pandemic. Summarising and comparing the large primary care resources will help researchers to choose the data resources most suited to their needs. AIM: To describe the current landscape of UK EHR databases and considerations of access and use of these resources relevant to researchers. DESIGN & SETTING: Narrative review of EHR databases in the UK. METHOD: Information was collected from the Health Data Research Innovation Gateway, publicly available websites and other published data, and from key informants. The eligibility criteria were population-based open-access databases sampling EHRs across the whole population of one or more countries in the UK. Published database characteristics were extracted and summarised, and these were corroborated with resource providers. Results were synthesised narratively. RESULTS: Nine large national primary care EHR data resources were identified and summarised. These resources are enhanced by linkage to other administrative data to a varying extent. Resources are mainly intended to support observational research, although some can support experimental studies. There is considerable overlap of populations covered. While all resources are accessible to bona fide researchers, access mechanisms, costs, timescales, and other considerations vary across databases. CONCLUSION: Researchers are currently able to access primary care EHR data from several sources. Choice of data resource is likely to be driven by project needs and access considerations. The landscape of data resources based on primary care EHRs in the UK continues to evolve.

Trajectories of multiple long-term conditions and mortality in older adults: A retrospective cohort study using English Longitudinal Study of Ageing (ELSA).

Objectives: To classify older adults with MLTC into clusters based on accumulating conditions as trajectories over time, characterise clusters and quantify associations between derived clusters and all-cause mortality. Design: We conducted a retrospective cohort study using the English Longitudinal Study of Ageing (ELSA) over nine years (n=15,091 aged 50 years and older). Group-based trajectory modelling was used to classify people into MLTC clusters based on accumulating conditions over time. Derived clusters were used to quantify the associations between MLTC trajectory memberships, sociodemographic characteristics, and all-cause mortality. Results: Five distinct clusters of MLTC trajectories were identified and characterised as: "no-LTC" (18.57%), "single-LTC" (31.21%), "evolving MLTC" (25.82%), "moderate MLTC" (17.12%), and "high MLTC" (7.27%). Increasing age was consistently associated with an increased number of MLTC. Female sex (aOR = 1.13; 95%CI 1.01 to 1.27) and ethnic minority (aOR = 2.04; 95%CI 1.40 to 3.00) were associated with the "moderate MLTC" and "high MLTC" clusters, respectively. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of MLTC. All the clusters had higher all-cause mortality than the "no-LTC" cluster. Conclusions: The development of MLTC and the increase in the number of conditions over time follow distinct trajectories. These are determined by non-modifiable (age, sex, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening MLTC over time to tailor effective interventions.

Socioeconomic inequalities of Long COVID: a retrospective population-based cohort study in the United Kingdom.

OBJECTIVES: To estimate the risk of Long COVID by socioeconomic deprivation and to further examine the inequality by sex and occupation. DESIGN: We conducted a retrospective population-based cohort study using data from the ONS COVID-19 Infection Survey between 26 April 2020 and 31 January 2022. This is the largest nationally representative survey of COVID-19 in the UK with longitudinal data on occupation, COVID-19 exposure and Long COVID. SETTING: Community-based survey in the UK. PARTICIPANTS: A total of 201,799 participants aged 16 to 64 years and with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MAIN OUTCOME MEASURES: The risk of Long COVID at least 4 weeks after SARS-CoV-2 infection by index of multiple deprivation (IMD) and the modifying effects of socioeconomic deprivation by sex and occupation. RESULTS: Nearly 10% (n = 19,315) of participants reported having Long COVID. Multivariable logistic regression models, adjusted for a range of variables (demographic, co-morbidity and time), showed that participants in the most deprived decile had a higher risk of Long COVID (11.4% vs. 8.2%; adjusted odds ratio (aOR): 1.46; 95% confidence interval (CI): 1.34, 1.59) compared to the least deprived decile. Significantly higher inequalities (most vs. least deprived decile) in Long COVID existed in healthcare and patient-facing roles (aOR: 1.76; 95% CI: 1.27, 2.44), in the education sector (aOR: 1.68; 95% CI: 1.31, 2.16) and in women (aOR: 1.56; 95% CI: 1.40, 1.73) than men (aOR: 1.32; 95% CI: 1.15, 1.51). CONCLUSIONS: This study provides insights into the heterogeneous degree of inequality in Long COVID by deprivation, sex and occupation. These findings will help inform public health policies and interventions in incorporating a social justice and health inequality lens.

Factors influencing influenza, pneumococcal and shingles vaccine uptake and refusal in older adults: a population-based cross-sectional study in England.

OBJECTIVES: Uptake of influenza, pneumococcal and shingles vaccines in older adults vary across regions and socioeconomic backgrounds. In this study, we study the coverage and factors associated with vaccination uptake, as well as refusal in the unvaccinated population and their associations with ethnicity, deprivation, household size and health conditions. DESIGN, SETTING AND PARTICIPANTS: This is a cross-sectional study of adults aged 65 years or older in England, using a large primary care database. Associations of vaccine uptake and refusal in the unvaccinated with ethnicity, deprivation, household size and health conditions were modelled using multivariable logistic regression. OUTCOME MEASURE: Influenza, pneumococcal and shingles vaccine uptake and refusal (in the unvaccinated). RESULTS: This study included 2 054 463 patients from 1318 general practices. 1 711 465 (83.3%) received at least one influenza vaccine, 1 391 228 (67.7%) pneumococcal vaccine and 690 783 (53.4%) shingles vaccine. Compared with White ethnicity, influenza vaccine uptake was lower in Chinese (OR 0.49; 95% CI 0.45 to 0.53), 'Other ethnic' groups (0.63; 95% CI 0.60 to 0.65), black Caribbean (0.68; 95% CI 0.64 to 0.71) and black African (0.72; 95% CI 0.68 to 0.77). There was generally lower vaccination uptake among more deprived individuals, people living in larger household sizes (three or more persons) and those with fewer health conditions. Among those who were unvaccinated, higher odds of refusal were associated with the black Caribbean ethnic group and marginally with increased deprivation, but not associated with higher refusal in those living in large households or those with lesser health conditions. CONCLUSION: Certain ethnic minority groups, deprived populations, large households and 'healthier' individuals were less likely to receive a vaccine, although higher refusal was only associated with ethnicity and deprivation but not larger households nor healthier individuals. Understanding these may inform tailored public health messaging to different communities for equitable implementation of vaccination programmes.

Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada.

BACKGROUND: Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes. METHODS: We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the 'percentage of excess risk mediated' by these risk factors in the QResearch cohort. RESULTS: There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, 95% CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for 42.9% (South Asian) and 39.4% (Black) of the excess risk of COVID-19 death. CONCLUSION: International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues.