Potential deprescribing indications for antidepressants between 2012 and 2019: repeated cross-sectional analysis in two Scottish health boards.

BACKGROUND: Antidepressants have a pivotal role in the treatment of many psychiatric disorders, but there are concerns about long-term use and adverse effects. The objectives of this study were (1) to examine time trends in antidepressant use, (2) to estimate the prevalence of long-term and potential high-risk antidepressant use, and (3) to examine patient characteristics associated with potential deprescribing indications (PDIs) (i.e., simultaneous long-term and potential high-risk antidepressant use). METHODS: Repeated population-based cross-sectional study for all 609,299 people aged ≥ 18 years resident in the Tayside or Fife regions of Scotland. The prevalence of antidepressant use was examined on June 30th (index date) of each year from 2012 to 2019, while the prevalence of long-term and potential high-risk use as well as PDIs was assessed and compared on the same dates in 2012 and 2019. Binary logistic regression modeling was used to examine patient characteristics associated with PDIs. RESULTS: Antidepressant use increased by 27% from 12.0 to 15.3% among adult residents between 2012 and 2019. While the proportion of antidepressants users dispensed ≥ 1 antidepressant for > 2 years increased from 54.3 to 61.9% between 2012 and 2019, the proportion of antidepressant users triggering ≥ 1 indicator of potential high-risk use decreased slightly from 37.9 to 34.7%. In 2019, potential high-risk use most commonly related to indicators targeting fall risk (16.0%), cardiovascular risks (14.1%), insomnia (10.6%), and risk of orthostatic hypotension (8.6%). More than 1 in 4 (25.8%) antidepressant users had PDIs. The main risk factors associated with PDIs included increasing age (65-79, adjusted OR 14.12; 95% CI, 13.15-15.17), increasing number of drugs taken concomitantly (≥ 15 drugs, adjusted OR 7.37; 95% CI, 6.71-8.10), use of tricyclic antidepressants (≥ 50 mg) (adjusted OR 5.49; 95% CI, 5.02-6.01), and concomitant use of ≥ 2 antidepressants (adjusted OR 5.52; 95% CI, 5.20-5.85). CONCLUSIONS: Long-term and potential high-risk use of antidepressants is widespread, and potential deprescribing indications (PDIs) are increasing, suggesting the need for a critical review of their ongoing use by clinicians. If deemed necessary, future deprescribing interventions may use the criteria applied here for identification of patients with PDIs and for evaluating intervention effectiveness.

Analysis of the impact of COVID-19 on Scotland's care-homes from March 2020 to October 2021: national linked data cohort analysis.

BACKGROUND: The impact of the COVID-19 pandemic on long-term care residents remains of wide interest, but most analyses focus on the initial wave of infections. OBJECTIVE: To examine change over time in: (i) The size, duration, classification and pattern of care-home outbreaks of COVID-19 and associated mortality and (ii) characteristics associated with an outbreak. DESIGN: Retrospective observational cohort study using routinely-collected data. SETTING: All adult care-homes in Scotland (1,092 homes, 41,299 places). METHODS: Analysis was undertaken at care-home level, over three periods. Period (P)1 01/03/2020-31/08/2020; P2 01/09/2020-31/05/2021 and P3 01/06/2021-31/10/2021. Outcomes were the presence and characteristics of outbreaks and mortality within the care-home. Cluster analysis was used to compare the pattern of outbreaks. Logistic regression examined care-home characteristics associated with outbreaks. RESULTS: In total 296 (27.1%) care-homes had one outbreak, 220 (20.1%) had two, 91 (8.3%) had three, and 68 (6.2%) had four or more. There were 1,313 outbreaks involving residents: 431 outbreaks in P1, 559 in P2 and 323 in P3. The COVID-19 mortality rate per 1,000 beds fell from 45.8 in P1, to 29.3 in P2, and 3.5 in P3. Larger care-homes were much more likely to have an outbreak, but associations between size and outbreaks were weaker in later periods. CONCLUSIONS: COVID-19 mitigation measures appear to have been beneficial, although the impact on residents remained severe until early 2021. Care-home residents, staff, relatives and providers are critical groups for consideration and involvement in future pandemic planning.

Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.

INTRODUCTION: Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults. METHODS: We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool. RESULTS: Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/c statistic 0.70-0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent. CONCLUSIONS: The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination.

New horizons in systems engineering and thinking to improve health and social care for older people.

Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.

New models of health and social care for people in later life: mapping of innovation in services in two regions of the United Kingdom using a mixed method approach.

BACKGROUND: Innovation for reforming health and social care is high on the policy agenda in the United Kingdom in response to the growing needs of an ageing population. However, information about new innovations of care being implemented is sparse. METHODS: We mapped innovations for people in later life in two regions, North East England and South East Scotland. Data collection included discussions with stakeholders (n = 51), semi-structured interviews (n = 14) and website searches that focused on technology, evaluation and health inequalities. We analysed qualitative data using framework and thematic analyses. Quantitative data were analysed descriptively. RESULTS: One hundred eleven innovations were identified across the two regions. Interviewees reported a wide range of technologies that had been rapidly introduced during the COVID-19 pandemic and many remained in use. Digital exclusion of certain groups of older people was an ongoing concern. Innovations fell into two groups; system-level ones that aimed to alleviate systems pressures such as preventing hospital (re)admissions, and patient-level ones which sought to enhance health and wellbeing directly. Interviewees were aware of the importance of health inequalities but lacked data to monitor the impact of innovations on these, and evaluation was challenging due to lack of time, training, and support. Quantitative findings revealed that two thirds of innovations (n = 74, 67%) primarily focused on the system level, whilst a third (n = 37, 33%) primarily focused on the patient-level. Overall, over half (n = 65, 59%) of innovations involved technologies although relatively few (n = 12, 11%) utilised advanced technologies. Very few (n = 16, 14%) focused on reducing health inequalities, and only a minority of innovations (n = 43, 39%) had undergone evaluation (most of which were conducted by the service providers themselves). CONCLUSIONS: We found a wide range of innovative care services being developed for people in later life, yet alignment with key policy priorities, such as addressing health inequalities, was limited. There was a strong focus on technology, with little consideration for the potential to widen the health inequality gap. The absence of robust evaluation was also a concern as most innovations were implemented without support to monitor effectiveness and/or without plans for sustainability and spread.

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).

The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.

BACKGROUND: Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. METHODS AND FINDINGS: We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. CONCLUSIONS: In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

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.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service.

BACKGROUND: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Assessing Representativeness of Randomised Controlled Trials using Serious Adverse Events.

Context: 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. Objectives: We explore an approach assessing trial representativeness by comparing rates of trial Serious Adverse Events (SAEs: most of which reflect hospitalisations/deaths) to rates of hospitalisation/death in routine care (which, in a trial setting, would be SAEs be definition). Study design: Secondary analysis of trial and routine healthcare data. Dataset and population: 483 trials (n=636,267) from clinicaltrials.gov across 21 index conditions. A routine care comparison was identified from SAIL databank (n=2.3M). Instrument: SAIL data were used to derive the expected rate of hospitalisations/deaths by age, sex and index condition. Outcomes: We calculated the expected number of SAEs for each trial compared to the observed number of SAEs (observed/expected SAE ratio). We then re-calculated the observed/expected SAE ratio additionally accounting for comorbidity count in 125 trials for which we accessed individual participant data. Results: For 12/21 index conditions the observed/expected SAE ratio was <1, indicating fewer SAEs in trials than expected given community rates of hospitalisations and deaths. A further 6/21 had point estimates <1 but the 95% CI included the null. The median observed/expected SAE ratio was 0.60 (95% CI 0.56-0.65; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.88 (0.59-1.33; IBD). Higher comorbidity count was associated with SAEs/hospitalisations and deaths across index conditions. For most trials, the observed/expected ratio was attenuated but remained <1 after additionally accounting for comorbidity count. Conclusion: 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 applicability of trial findings to older populations in whom multimorbidity and frailty are common.

External validation of the QLifetime cardiovascular risk prediction tool: population cohort study.

BACKGROUND: Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. METHODS: Independent external validation of QRiskLifetime using Clinical Practice Research Datalink data, examining discrimination and calibration in the whole population and stratified by age, and reclassification compared to QRISK3. Since lifetime CVD risk is unobservable, performance was evaluated at 10-years' follow-up, and lifetime performance inferred in terms of performance for in the different age-groups from which lifetime predictions are derived. RESULTS: One million, two hundreds sixty thousand and three hundreds twenty nine women and 1,223,265 men were included in the analysis. Discrimination was excellent in the whole population (Harrell's-C = 0.844 in women, 0.808 in men), but moderate to poor stratified by age-group (Harrell's C in people aged 30-44 0.714 for both men and women, in people aged 75-84 0.578 in women and 0.556 in men). Ten-year CVD risk was under-predicted in the whole population, and in all age-groups except women aged 45-64, with worse under-prediction in older age-groups. Compared to those at highest QRISK3 estimated 10-year risk, those with highest lifetime risk were younger (mean age: women 50.5 vs. 71.3 years; men 46.3 vs. 63.8 years) and had lower systolic blood pressure and prevalence of treated hypertension, but had more family history of premature CVD, and were more commonly minority ethnic. Over 10-years, the estimated number needed to treat (NNT) with a statin to prevent one CVD event in people with QRISK3 ≥ 10% was 34 in women and 37 in men, compared to 99 and 100 for those at highest lifetime risk. CONCLUSIONS: QRiskLifetime underpredicts 10-year CVD risk in nearly all age-groups, so is likely to also underpredict lifetime risk. Treatment based on lifetime risk has considerably lower medium-term benefit than treatment based on 10-year risk.

Single and combined use of fall-risk-increasing drugs and fracture risk: a population-based case-control study.

BACKGROUND: while many drug groups are associated with falls in older people, less is known about absolute increases in risk and how these risks vary across different groups of drugs or individuals. METHOD AND DESIGN: we conducted a population based nested case-control study among people aged ≥65 years in the Scottish regions of Tayside and Fife. Cases were individuals hospitalised with a fracture between 2010 and 2020, to whom we matched up to 10 controls. We examined relative and absolute risks of drug groups known as 'Fall-Risk-Increasing Drugs' (FRIDs), alone and in combination, and among younger and older (≥75 years) adults. Adjusting for previous hospitalisations, drug use and laboratory data, we used conditional logistic regression to quantify associations between drug exposures and outcomes. We conducted four sensitivity analyses to test the robustness of our findings. RESULTS: the cohort comprised 246,535 people aged ≥65 years, of whom 18,456 suffered an incident fracture. Fracture risks were significantly increased for most FRIDs examined. Absolute risks were much larger among older vs younger people and both relative and absolute risks increased with the number of FRIDs combined. Overall, the highest absolute increase in risk were found in people aged ≥75 years for selective serotonin reuptake inhibitors (number needed to harm 53), tricyclic antidepressants (NNH 81), antipsychotics (NNH 75) and use of three or more FRIDs (NNH ≤66). CONCLUSION: patients aged ≥75 years prescribed antidepressants or antipsychotics or taking three or more drugs that increase risk of falls may benefit most from deprescribing interventions.

Complex interventions for improving independent living and quality of life amongst community-dwelling older adults: a systematic review and meta-analysis.

BACKGROUND: community-based complex interventions for older adults have a variety of names, including Comprehensive Geriatric Assessment, but often share core components such as holistic needs assessment and care planning. OBJECTIVE: to summarise evidence for the components and effectiveness of community-based complex interventions for improving older adults' independent living and quality of life (QoL). METHODS: we searched nine databases and trial registries to February 2022 for randomised controlled trials comparing complex interventions to usual care. Primary outcomes included living at home and QoL. Secondary outcomes included mortality, hospitalisation, institutionalisation, cognitive function and functional status. We pooled data using risk ratios (RRs) or standardised mean differences (SMDs) with 95% confidence intervals (CIs). RESULTS: we included 50 trials of mostly moderate quality. Most reported using holistic assessment (94%) and care planning (90%). Twenty-seven (54%) involved multidisciplinary care, with 29.6% delivered mainly by primary care teams without geriatricians. Nurses were the most frequent care coordinators. Complex interventions increased the likelihood of living at home (RR 1.05; 95% CI 1.00-1.10; moderate-quality evidence) but did not affect QoL. Supported by high-quality evidence, they reduced mortality (RR 0.86; 95% CI 0.77-0.96), enhanced cognitive function (SMD 0.12; 95% CI 0.02-0.22) and improved instrumental activities of daily living (ADLs) (SMD 0.11; 95% CI 0.01-0.21) and combined basic/instrumental ADLs (SMD 0.08; 95% CI 0.03-0.13). CONCLUSIONS: complex interventions involving holistic assessment and care planning increased the chance of living at home, reduced mortality and improved cognitive function and some ADLs.

Examining the social networks of older adults receiving informal or formal care: a systematic review.

PURPOSE: To address the care needs of older adults, it is important to identify and understand the forms of care support older adults received. This systematic review aims to examine the social networks of older adults receiving informal or formal care and the factors that influenced their networks. METHODS: A systematic review was conducted by searching six databases from inception to January 31, 2023. The review included primary studies focusing on older adults receiving long-term care, encompassing both informal and formal care. To assess the risk of bias in the included studies, validated appraisal tools specifically designed for different study types were utilized. Network analysis was employed to identify the grouping of study concepts, which subsequently formed the foundation for describing themes through narrative synthesis. RESULTS: We identified 121 studies relating to the formal and informal care of older adults' networks. A variety of social ties were examined by included studies. The most commonly examined sources of care support were family members (such as children and spouses) and friends. Several factors were consistently reported to influence the provision of informal care, including the intensity of networks, reciprocity, and geographical proximity. In terms of formal care utilization, older age and poor health status were found to be associated with increased use of healthcare services. Additionally, physical limitations and cognitive impairment were identified as factors contributing to decreased social engagement. CONCLUSION: This review found that older people were embedded within a diverse network. The findings of this review emphasize the importance of recognizing and incorporating the diversity of social networks in care plans and policies to enhance the effectiveness of interventions and improve the overall well-being of older adults.

Half a century of the inverse care law: A comparison of general practitioner job satisfaction and patient satisfaction in deprived and affluent areas of Scotland.

BACKGROUND AND AIMS: The 'inverse care law', first described in 1971, results from a mismatch of healthcare need and healthcare supply in deprived areas. GPs in such areas struggle to cope with the high levels of demand resulting in shorter consultations and poorer patient outcomes. We compare recent national GP and patient satisfaction data to investigate the ongoing existence of this disparity in Scotland. METHODS AND RESULTS: Secondary analysis of cross-sectional national surveys (2017/2018) on upper and lower deprivation quintiles. GP measures; job satisfaction, job stressors, positive and negative job attributes. Patient measures; percentage positive responses per practice on survey questions on access and consultation quality. GPs in high deprivation areas reported lower job satisfaction and positive job attributes, and higher job stressors and negative job attributes compared with GPs in low deprivation areas. Patients living in high deprivation areas reported lower satisfaction with access and consultation quality than patients in low deprivation areas. These differences in GP and patient satisfaction persisted after adjusting for confounding variables. CONCLUSIONS: Lower GP work satisfaction in deprived areas was mirrored by lower patient satisfaction. These findings add to the evidence that the inverse care law persists in Scotland, over 50 years after it was first described.

Predictive performance of a competing risk cardiovascular prediction tool CRISK compared to QRISK3 in older people and those with comorbidity: population cohort study.

BACKGROUND: Recommended cardiovascular disease (CVD) prediction tools do not account for competing mortality risk and over-predict incident CVD in older and multimorbid people. The aim of this study was to derive and validate a competing risk model (CRISK) to predict incident CVD and compare its performance to that of QRISK3 in UK primary care. METHODS: We used UK linked primary care data from the Clinical Practice Research Datalink (CPRD) GOLD to identify people aged 25-84 years with no previous CVD or statin treatment split into derivation and validation cohorts. In the derivation cohort, we derived models using the same covariates as QRISK3 with Fine-Gray competing risk modelling alone (CRISK) and with Charlson Comorbidity score (CRISK-CCI) as an additional predictor of non-CVD death. In a separate validation cohort, we examined discrimination and calibration compared to QRISK3. Reclassification analysis examined the number of patients recommended for treatment and the estimated number needed to treat (NNT) to prevent a new CVD event. RESULTS: The derivation and validation cohorts included 989,732 and 494,865 women and 946,784 and 473,392 men respectively. Overall discrimination of CRISK and CRISK-CCI were excellent and similar to QRISK3 (for women, C-statistic = 0.863/0.864/0.863 respectively; for men 0.833/0.819/0.832 respectively). CRISK and CRISK-CCI calibration overall and in younger people was excellent. CRISK over-predicted in older and multimorbid people although performed better than QRISK3, whilst CRISK-CCI performed the best. The proportion of people reclassified by CRISK-CCI varied by QRISK3 risk score category, with 0.7-9.7% of women and 2.8-25.2% of men reclassified as higher risk and 21.0-69.1% of women and 27.1-57.4% of men reclassified as lower risk. Overall, CRISK-CCI recommended fewer people for treatment and had a lower estimated NNT at 10% risk threshold. Patients reclassified as higher risk were younger, had lower SBP and higher BMI, and were more likely to smoke. CONCLUSIONS: CRISK and CRISK-CCI performed better than QRISK3. CRISK-CCI recommends fewer people for treatment and has a lower NNT to prevent a new CVD event compared to QRISK3. Competing risk models should be recommended for CVD primary prevention treatment recommendations.

Patient outcomes following AKI and AKD: a population-based cohort study.

BACKGROUND: Acute kidney injury (AKI) is common and associated with adverse outcomes as well as important healthcare costs. However, evidence examining the epidemiology of acute kidney disease (AKD)-recently defined as AKI persisting between 7 and 90 days-remains limited. The aims of this study were to establish the rates of early AKI recovery, progression to AKD and non-recovery; examine risk factors associated with non-recovery and investigate the association between recovery timing and adverse outcomes, in a population-based cohort. METHODS: All adult residents of Tayside & Fife, Scotland, UK, with at least one episode of community or hospital-managed AKI using KDIGO creatinine-based definition during the period 1 January 2010 to 31 December 2018 were identified. Logistic regression was used to examine factors associated with non-recovery, and Cox modelling was used to establish associations between AKI recovery timing and risks of mortality and development of de novo CKD. RESULTS: Over 9 years, 56,906 patients with at least one AKI episode were identified with 18,773 (33%) of these progressing to AKD. Of those progressing to AKD, 5059 (27%) had still not recovered at day 90 post AKI diagnosis. Risk factors for AKD included: increasing AKI severity, pre-existing cancer or chronic heart failure and recent use of loop diuretics. Compared with early AKI recovery, progression to AKD was associated with increased hazard of 1-year mortality and de novo CKD (HR = 1.20, 95% CI 1.13 to 1.26 and HR = 2.21, 95% CI 1.91 to 2.57 respectively). CONCLUSIONS: These findings highlight the importance of early AKI recognition and management to avoid progression to AKD and long-term adverse outcomes.

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.

Feedback of Antibiotic Prescribing in Primary Care (FAPPC) trial: results of a real-world cluster randomized controlled trial in Scotland, UK.

OBJECTIVES: To evaluate the effect of general practice-level prescribing feedback on antibiotic prescribing in a real-world pragmatic cluster randomized controlled trial. METHODS: Three hundred and forty general practices in four territorial Health Boards in NHS Scotland were randomized in Quarter 1, 2016 to receive four quarterly antibiotic-prescribing feedback reports or not, from Quarter 2, 2016 to Quarter 1, 2017. Reports included different clinical topics, benchmarking against national and health board rates, and behavioural messaging with improvement actions. The primary outcome was total antibiotic prescribing rate. There were 16 secondary prescribing outcomes and 5 hospital admission outcomes (potential adverse effects of reduced prescribing). The main evaluation timepoint was 1 year after the final report (Quarter 1, 2018), with an additional evaluation in the quarter after the final report (Quarter 2, 2017). Routine administrative NHS data were used to generate the feedback reports and analyse the effects. RESULTS: Total antibiotic prescribing rates were lower at the main evaluation timepoint in both intervention (1.83 versus baseline 1.93 prescriptions/1000 patients/day) and control (1.90 versus baseline 1.98) practices, with no evidence of intervention effect [adjusted rate ratio (ARR) 0.98 (95% CI 0.94-1.02; P = 0.35)]. At the additional timepoint, adjusted total antibiotic prescribing rates were 1.67 and 1.73 prescriptions/1000 patients/day, with evidence of a small intervention effect, ARR 0.99 (0.98-1.00; P = 0.03). CONCLUSIONS: This well-designed, practice-level antibiotic-prescribing feedback had limited evidence of additional effects in the context of decreasing antibiotic prescribing and an established national stewardship programme.

Developing an AKI Consensus Definition for Database Research: Findings From a Scoping Review and Expert Opinion Using a Delphi Process.

RATIONALE & OBJECTIVE: The KDIGO (Kidney Disease: Improving Global Outcomes) definition of acute kidney injury (AKI) is frequently used in studies to examine the epidemiology of AKI. This definition is variably interpreted and applied to routinely collected health care data. The aim of this study was to examine this variation and to achieve consensus in how AKI should be defined for research using routinely collected health care data. SOURCES OF EVIDENCE AND STUDY DESIGN: Scoping review via searching Medline and EMBASE for studies using health care data to examine AKI by using the KDIGO creatinine-based definition. An international panel of experts formed to participate in a modified Delphi process to attempt to generate consensus about how AKI should be defined when using routinely collected laboratory data. CHARTING METHODS AND ANALYTICAL APPROACH: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) extension for scoping reviews was followed. For the Delphi process, 2 rounds of questions were distributed via internet-based questionnaires to all participants with a prespecified cutoff of 75% agreement used to define consensus. RESULTS: The scoping review found 174 studies that met the inclusion criteria. The KDIGO definition was inconsistently applied, and the methods for application were poorly described. We found 58 (33%) of papers did not provide a definition of how the baseline creatinine value was determined, and only 34 (20%) defined recovery of kidney function. Of 55 invitees to the Delphi process, 35 respondents participated in round 1, and 25 participated in round 2. Some consensus was achieved in areas related to how to define the baseline creatinine value, which patients should be excluded from analysis of routinely collected laboratory data, and how persistent chronic kidney disease or nonrecovery of AKI should be defined. LIMITATIONS: The Delphi panel members predominantly came from the United Kingdom, the United States, and Canada, and there were low response rates for some questions in round 1. CONCLUSIONS: The current methods for defining AKI using routinely collected data are inconsistent and poorly described in the available literature. Experts could not achieve consensus for many aspects of defining AKI and describing its sequelae. The KDIGO guidelines should be extended to include a standardized definition for how AKI should be defined when using routinely collected data.