Mixed Methods EvAluation of the high-volume low-complexity Surgical hUb pRogrammE (MEASURE): a mixed methods study protocol.

INTRODUCTION: The waiting list for elective surgery in England recently reached over 7.8 million people and waiting time targets have been missed since 2010. The high-volume low complexity (HVLC) surgical hubs programme aims to tackle the backlog of patients awaiting elective surgery treatment in England. This study will evaluate the impact of HVLC surgical hubs on productivity, patient care and the workforce. METHODS AND ANALYSIS: This 4-year project consists of six interlinked work packages (WPs) and is informed by the Consolidated Framework for Implementation Research. WP1: Mapping current and future HVLC provision in England through document analysis, quantitative data sets (eg, Hospital Episodes Statistics) and interviews with national service leaders. WP2: Exploring the effects of HVLC hubs on key performance outcomes, primarily the volume of low-complexity patients treated, using quasi-experimental methods. WP3: Exploring the impact and implementation of HVLC hubs on patients, health professionals and the local NHS through approximately nine longitudinal, multimethod qualitative case studies. WP4: Assessing the productivity of HVLC surgical hubs using the Centre for Health Economics NHS productivity measure and Lord Carter's operational productivity measure. WP5: Conducting a mixed-methods appraisal will assess the influence of HVLC surgical hubs on the workforce using: qualitative data (WP3) and quantitative data (eg, National Health Service (NHS) England's workforce statistics and intelligence from WP2). WP6: Analysing the costs and consequences of HVLC surgical hubs will assess their achievements in relation to their resource use to establish value for money. A patient and public involvement group will contribute to the study design and materials. ETHICS AND DISSEMINATION: The study has been approved by the East Midlands-Nottingham Research Ethics Committee 23/EM/0231. Participants will provide informed consent for qualitative study components. Dissemination plans include multiple academic and non-academic outputs (eg, Peer-reviewed journals, conferences, social media) and a continuous, feedback-loop of findings to key stakeholders (eg, NHS England) to influence policy development. TRIAL REGISTRATION: Research registry: Researchregistry9364 (https://www.researchregistry.com/browse-the-registry%23home/registrationdetails/64cb6c795cbef8002a46f115/).

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care.

OBJECTIVE: The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined. DESIGN: A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices. SETTING: 743 hospitals in France. PARTICIPANTS: All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days. RESULTS: The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698). CONCLUSIONS: Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

Identifying models of care to improve outcomes for older people with urgent care needs: a mixed methods approach to develop a system dynamics model.

Background: We aimed to understand urgent and emergency care pathways for older people and develop a decision support tool using a mixed methods study design. Objective(s), study design, settings and participants: Work package 1 identified best practice through a review of reviews, patient, carer and professional interviews. Work package 2 involved qualitative case studies of selected urgent and emergency care pathways in the Yorkshire and Humber region. Work package 3 analysed linked databases describing urgent and emergency care pathways identifying patient, provider and pathway factors that explain differences in outcomes and costs. Work package 4 developed a system dynamics tool to compare emergency interventions. Results: A total of 18 reviews summarising 128 primary studies found that integrated social and medical care, screening and assessment, follow-up and monitoring of service outcomes were important. Forty patient/carer participants described emergency department attendances; most reported a reluctance to attend. Participants emphasised the importance of being treated with dignity, timely and accurate information provision and involvement in decision-making. Receiving care in a calm environment with attention to personal comfort and basic physical needs were key. Patient goals included diagnosis and resolution, well-planned discharge home and retaining physical function. Participants perceived many of these goals of care were not attained. A total of 21 professional participants were interviewed and 23 participated in focus groups, largely confirming the review evidence. Implementation challenges identified included the urgent and emergency care environment, organisational approaches to service development, staff skills and resources. Work package 2 involved 45 interviews and 30 hours of observation in four contrasting emergency departments. Key themes relating to implementation included: intervention-related staff: frailty mindset and behaviours resources: workforce, space, and physical environment operational influences: referral criteria, frailty assessment, operating hours, transport. context-related links with community, social and primary care organisation and management support COVID-19 pandemic. approaches to implementation service/quality improvement networks engaging staff and building relationships education about frailty evidence. The linked databases in work package 3 comprised 359,945 older people and 1,035,045 observations. The most powerful predictors of four-hour wait and transfer to hospital were age, previous attendance, out-of-hours attendance and call handler designation of urgency. Drawing upon the previous work packages and working closely with a wide range of patient and professional stakeholders, we developed an system dynamics tool that modelled five evidence-based urgent and emergency care interventions and their impact on the whole system in terms of reducing admissions, readmissions, and hospital related mortality. Limitations: Across the reviews there was incomplete reporting of interventions. People living with severe frailty and from ethnic minorities were under-represented in the patient/carer interviews. The linked databases did not include patient reported outcomes. The system dynamics model was limited to evidence-based interventions, which could not be modelled conjointly. Conclusions: We have reaffirmed the poor outcomes frequently experienced by many older people living with urgent care needs. We have identified interventions that could improve patient and service outcomes, as well as implementation tools and strategies to help including clinicians, service managers and commissioners improve emergency care for older people. Future work: Future work will focus on refining the system dynamics model, specifically including patient-reported outcome measures and pre-hospital services for older people living with frailty who have urgent care needs. Study registrations: This study is registered as PROSPERO CRD42018111461. WP 1.2: University of Leicester ethics: 17525-spc3-ls:healthsciences, WP 2: IRAS 262143, CAG 19/CAG/0194, WP 3: IRAS 215818, REC 17/YH/0024, CAG 17/CAG/0024. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme [project number 17/05/96 (Emergency Care for Older People)] and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 14. See the NIHR Journals Library website for further project information.

Many older people attending emergency care have poor outcomes; this project aimed to: describe best practice in emergency care understand how best practice might be delivered describe outcomes from emergency care, and synthesise this information in a computer simulation tool that can help teams decide which interventions might work best in their setting. The existing literature showed that holistic interventions (caring for the whole person), spanning emergency and community care, designed with the needs of older people in mind, work best. We checked these findings with front line clinicians, who agreed, but identified that implementing best practice in the emergency department was challenging. Limitations included the emergency department environment itself and the lack of staff with the right skillset. We also asked older people and their carers who had recently received emergency care what mattered. They prioritised basic needs such as comfort, communication, and timely care. They also stated that getting better, maintaining their usual level of function, and getting home safely were important outcomes. We then analysed data that linked together ambulance, emergency department, and hospital care in Yorkshire and Humber from 2011­17 for over 1 million emergency department attendances and hospital admissions. We found a novel and accurate predictor of long emergency department waits and hospital admission: the level of urgency according to the ambulance call handler. Drawing upon all the above and guided by a wide range of patient and professionals, we developed a computer model which allows emergency care teams to simulate different best practice emergency department interventions and estimate the impact on reducing admissions, readmissions, and hospital mortality. In summary, we have reaffirmed the poor outcomes experienced by many older people with urgent care needs. We have identified interventions that could improve patient and service outcomes, as well as implementation tools to help including clinicians, hospital managers and funders transform emergency care for older people.

Did the Acute Frailty Network improve outcomes for older people living with frailty? A staggered difference-in-difference panel event study.

OBJECTIVES: To evaluate whether the Acute Frailty Network (AFN) was more effective than usual practice in supporting older people living with frailty to return home from hospital sooner and healthier. DESIGN: Staggered difference-in-difference panel event study allowing for differential effects across intervention cohorts. SETTING: All English National Health Service (NHS) acute hospital sites. PARTICIPANTS: All 1 410 427 NHS patients aged 75+ with high frailty risk who had an emergency hospital admission to acute, general or geriatric medicine departments between 1 January 2012 and 31 March 2019. INTERVENTION: Membership of the AFN, a quality improvement collaborative designed to support acute hospitals in England deliver evidence-based care for older people with frailty. 66 hospital sites joined the AFN in six sequential cohorts, the first starting in January 2015, the sixth in May 2018. Usual care was delivered in the remaining 248 control sites. MAIN OUTCOME MEASURES: Length of hospital stay, in-hospital mortality, institutionalisation, hospital readmission. RESULTS: No significant effects of AFN membership were found for any of the four outcomes nor were there significant effects for any individual cohort. CONCLUSIONS: To realise its aims, the AFN might need to develop better resourced intervention and implementation strategies.

Living longer in declining health: Factors driving healthcare costs among older people.

BACKGROUND: Developed countries are facing challenges in caring for people who are living longer but with a greater morbidity burden. Such people are likely to be regular users of healthcare. OBJECTIVES: Our analytical aim is to identify factors that explain healthcare costs among: (1) people over 55 years old; (2) the top 5% and 1% high-cost users among this population; (3) those that transition into the top 5% and 1% from one year to the next; (4) those that appear in the top 5% and 1% over multiple years; and (5) those that remain in the top 5% and 1% over consecutive years. METHODS: The data covered 2011 to 2017 and comprised 1,485,170 observations for a random sample of 224,249 people aged over 55 years in the Catalan region of Spain. We analysed each person's annual healthcare costs across all public healthcare settings related to their age, gender, socio-economic status (SES), whether or not and when they died, and morbidity status, through Adjusted Morbidity Groups. RESULTS: After controlling for morbidity status, the oldest people did not have the highest costs and were less likely to be among the most costly patients. There was also only a modest impact on costs associated with SES and with dying. Healthcare costs were substantially higher for those with a neoplasm or four or more long term conditions (LTCs), costs rising with the complexity of their conditions. These morbidity indicators were also the most important factors associated with being and remaining in the top 5% or top 1% of costs. CONCLUSION: Our results suggest that age and proximity to death are poor predictors of higher costs. Rather, healthcare costs are explained mainly by morbidity status, particularly whether someone has neoplasms or multiple LTCs. Morbidity measures should be included in future studies of healthcare costs.

Examining the effect of interventions in emergency care for older people using a system dynamics decision support tool.

BACKGROUND: Rising demand for Emergency and Urgent Care is a major international issue and outcomes for older people remain sub-optimal. Embarking upon large-scale service development is costly in terms of time, energy and resources with no guarantee of improved outcomes; computer simulation modelling offers an alternative, low risk and lower cost approach to explore possible interventions. METHOD: A system dynamics computer simulation model was developed as a decision support tool for service planners. The model represents patient flow through the emergency care process from the point of calling for help through ED attendance, possible admission, and discharge or death. The model was validated against five different evidence-based interventions (geriatric emergency medicine, front door frailty, hospital at home, proactive care and acute frailty units) on patient outcomes such as hospital-related mortality, readmission and length of stay. RESULTS: The model output estimations are consistent with empirical evidence. Each intervention has different levels of effect on patient outcomes. Most of the interventions show potential reductions in hospital admissions, readmissions and hospital-related deaths. CONCLUSIONS: System dynamics modelling can be used to support decisions on which emergency care interventions to implement to improve outcomes for older people.

Factors associated with longer wait times, admission and reattendances in older patients attending emergency departments: an analysis of linked healthcare data.

BACKGROUND AND OBJECTIVE: Care for older patients in the ED is an increasingly important issue with the ageing society. To better assess the quality of care in this patient group, we assessed predictors for three outcomes related to ED care: being seen and discharged within 4 hours of ED arrival; being admitted from ED to hospital and reattending the ED within 30 days. We also used these outcomes to identify better-performing EDs. METHODS: The CUREd Research Database was used for a retrospective observational study of all 1 039 251 attendances by 368 754 patients aged 75+ years in 18 type 1 EDs in the Yorkshire and the Humber region of England between April 2012 and March 2017. We estimated multilevel logit models, accounting for patients' characteristics and contact with emergency services prior to ED arrival, time variables and the ED itself. RESULTS: Patients in the oldest category (95+ years vs 75-80 years) were more likely to have a long ED wait (OR=1.13 (95% CI=1.10 to 1.15)), hospital admission (OR=1.26 (95% CI=1.23 to 1.29)) and ED reattendance (OR=1.09 (95% CI=1.06 to 1.12)). Those who had previously attended (3+ vs 0 previous attendances) were more likely to have long wait (OR=1.07 (95% CI=1.06 to 1.08)), hospital admission (OR=1.10 (95% CI=1.09 to 1.12)) and ED attendance (OR=3.13 (95% CI=3.09 to 3.17)). Those who attended out of hours (vs not out of hours) were more likely to have a long ED wait (OR=1.33 (95% CI=1.32 to 1.34)), be admitted to hospital (OR=1.19 (95% CI=1.18 to 1.21)) and have ED reattendance (OR=1.07 (95% CI=1.05 to 1.08)). Those living in less deprived decile (vs most deprived decile) were less likely to have any of these three outcomes: OR=0.93 (95% CI=0.92 to 0.95), 0.92 (95% CI=0.90 to 0.94), 0.86 (95% CI=0.84 to 0.88). These characteristics were not strongly associated with long waits for those who arrived by ambulance. Emergency call handler designation was the strongest predictor of long ED waits and hospital admission: compared with those who did not arrive by ambulance; ORs for these outcomes were 1.18 (95% CI=1.16 to 1.20) and 1.85 (95% CI=1.81 to 1.89) for those designated less urgent; 1.37 (95% CI=1.33 to 1.40) and 2.13 (95% CI=2.07 to 2.18) for urgent attendees; 1.26 (95% CI=1.23 to 1.28) and 2.40 (95% CI=2.36 to 2.45) for emergency attendees; and 1.37 (95% CI=1.28 to 1.45) and 2.42 (95% CI=2.26 to 2.59) for those with life-threatening conditions. We identified two EDs whose patients were less likely to have a long ED, hospital admission or ED reattendance than other EDs in the region. CONCLUSIONS: Age, previous attendance and attending out of hours were all associated with an increased likelihood of exceeding 4 hours in the ED, hospital admission and reattendance among patients over 75 years. These differences were less pronounced among those arriving by ambulance. Emergency call handler designation could be used to identify those at the highest risk of long ED waits, hospital admission and ED reattendance.

Using Patient-Reported Outcome Measures to Evaluate Care for Patients With Inflammatory Chronic Rheumatic Disease.

OBJECTIVES: Few countries integrate patient-reported outcome measures (PROMs) in routine performance assessment and those that do focus on elective surgery. This study addresses the challenges of using PROMs to evaluate care in chronic conditions. We set out a modeling strategy to assess the extent to which changes over time in self-reported health status by patients with inflammatory chronic rheumatic disease are related to their biological drug therapy and rheumatology center primarily responsible for their care. METHODS: Using data from the Portuguese Register of Rheumatic Diseases, we assess health status using the Health Assessment Questionnaire-Disability Index for rheumatic patients receiving biological drugs between 2000 and 2017. We specify a fixed-effects model using the least squares dummy variables estimator. RESULTS: Patients receiving infliximab or rituximab report lower health status than those on etanercept (the most common therapy) and patients in 4 of the 26 rheumatology centers report higher health status than those at other centers. CONCLUSIONS: PROMs can be used for those with chronic conditions to provide the patient's perspective about the impact on their health status of the choice of drug therapy and care provider. Care for chronic patients might be improved if healthcare organizations monitor PROMs and engage in performance assessment initiatives on a routine basis.

The association between frailty risk and COVID-19-associated all-mortality in hospitalised older people: a national cohort study.

INTRODUCTION: Frailty has emerged as an important construct to support clinical decision-making during the COVID-19 pandemic. However, doubts remain related to methodological limitations of published studies. METHODS: Retrospective cohort study of all people aged 75 + admitted to hospital in England between 1 March 2020 and 31 July 2021. COVID-19 and frailty risk were captured using International Classification of Disease-10 (ICD-10) diagnostic codes. We used the generalised gamma model to estimate accelerated failure time, reporting unadjusted and adjusted results. RESULTS: The cohort comprised 103,561 individuals, mean age 84.1, around half female, 82% were White British with a median of two comorbidities. Frailty risk was distributed approximately 20% low risk and 40% each at intermediate or high risk. In the unadjusted survival plots, 28-day mortality was almost 50% for those with an ICD-10 code of U071 (COVID-19 virus identified), and 25-35% for those with U072 (COVID-19 virus not identified). In the adjusted analysis, the accelerated failure time estimates for those with intermediate and high frailty risk were 0.63 (95% CI 0.58-0.68) and 0.67 (95% CI 0.62-0.72) fewer days alive respectively compared to those with low frailty risk with an ICD-10 diagnosis of U072 (reference category). CONCLUSION: In older people with confirmed COVID-19, both intermediate and high frailty risk were associated with reduced survival compared to those with low frailty risk.

External validation of the Hospital Frailty Risk Score in France.

BACKGROUND: The Hospital Frailty Risk Score (HFRS) has made it possible internationally to identify subgroups of patients with characteristics of frailty from routinely collected hospital data. OBJECTIVE: To externally validate the HFRS in France. DESIGN: A retrospective analysis of the French medical information database. SETTING: 743 hospitals in Metropolitan France. SUBJECTS: All patients aged 75 years or older hospitalised as an emergency in 2017 (n = 1,042,234). METHODS: The HFRS was calculated for each patient based on the index stay and hospitalisations over the preceding 2 years. Main outcome measures were 30-day in-patient mortality, length of stay (LOS) >10 days and 30-day readmissions. Mixed logistic regression models were used to investigate the association between outcomes and HFRS score. RESULTS: Patients with high HFRS risk were associated with increased risk of mortality and prolonged LOS (adjusted odds ratio [aOR] = 1.38 [1.35-1.42] and 3.27 [3.22-3.32], c-statistics = 0.676 and 0.684, respectively), while it appeared less predictive of readmissions (aOR = 1.00 [0.98-1.02], c-statistic = 0.600). Model calibration was excellent. Restricting the score to data prior to index admission reduced discrimination of HFRS substantially. CONCLUSIONS: HFRS can be used in France to determine risks of 30-day in-patient mortality and prolonged LOS, but not 30-day readmissions. Trial registration: Reference ID on clinicaltrials.gov: ID: NCT03905629.

Association of Annual Intensive Care Unit Sepsis Caseload With Hospital Mortality From Sepsis in the United Kingdom, 2010-2016.

Importance: Sepsis is associated with a high burden of inpatient mortality. Treatment in intensive care units (ICUs) that have more experience treating patients with sepsis may be associated with lower mortality. Objective: To assess the association between the volume of patients with sepsis receiving care in an ICU and hospital mortality from sepsis in the UK. Design, Setting, and Participants: This retrospective cohort study used data from adult patients with sepsis from 231 UK ICUs between 2010 and 2016. Demographic and clinical data were extracted from the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme database. Data were analyzed from January 1, 2010, to December 31, 2016. Exposures: Annual sepsis case volume in an ICU in the year of a patient's admission. Main Outcomes and Measures: Hospital mortality after ICU admission for sepsis assessed using a mixed-effects logistic model in a 3-level hierarchical structure based on the number of individual patients nested in years nested within ICUs. Results: Among 273 001 patients included in the analysis, the median age was 66 years (interquartile range, 53-76 years), 148 149 (54.3%) were male, and 248 275 (91.0%) were White. The mean ICNARC-2018 illness severity score was 21.0 (95% CI, 20.9-21.0). Septic shock accounted for 19.3% of patient admissions, and 54.3% of patients required mechanical ventilation. The median annual sepsis volume per ICU was 242 cases (interquartile range, 177-334 cases). The study identified a significant association between the volume of sepsis cases in the ICU and mortality from sepsis; in the logistic regression model, hospital mortality was significantly lower among patients admitted to ICUs in the highest quartile of sepsis volume compared with the lowest quartile (odds ratio [OR], 0.89; 95% CI, 0.82-0.96; P = .002). With volume modeled as a restricted cubic spline, treatment in a larger ICU was associated with lower hospital mortality. A lower annual volume threshold of 215 patients above which hospital mortality decreased significantly was found; 38.8% of patients were treated in ICUs below this threshold volume. There was no significant interaction between ICU volume and severity of illness as described by the ICNARC-2018 score (ß [SE], -0.00014 [0.00024]; P = .57). Conclusions and Relevance: The findings suggest that patients with sepsis in the UK have higher odds of survival if they are treated in an ICU with a larger sepsis case volume. The benefit of a high sepsis case volume was not associated with the severity of the sepsis episode.

Securing a sustainable and fit-for-purpose UK health and care workforce.

Approximately 13% of the total UK workforce is employed in the health and care sector. Despite substantial workforce planning efforts, the effectiveness of this planning has been criticised. Education, training, and workforce plans have typically considered each health-care profession in isolation and have not adequately responded to changing health and care needs. The results are persistent vacancies, poor morale, and low retention. Areas of particular concern highlighted in this Health Policy paper include primary care, mental health, nursing, clinical and non-clinical support, and social care. Responses to workforce shortfalls have included a high reliance on foreign and temporary staff, small-scale changes in skill mix, and enhanced recruitment drives. Impending challenges for the UK health and care workforce include growing multimorbidity, an increasing shortfall in the supply of unpaid carers, and the relative decline of the attractiveness of the National Health Service (NHS) as an employer internationally. We argue that to secure a sustainable and fit-for-purpose health and care workforce, integrated workforce approaches need to be developed alongside reforms to education and training that reflect changes in roles and skill mix, as well as the trend towards multidisciplinary working. Enhancing career development opportunities, promoting staff wellbeing, and tackling discrimination in the NHS are all needed to improve recruitment, retention, and morale of staff. An urgent priority is to offer sufficient aftercare and support to staff who have been exposed to high-risk situations and traumatic experiences during the COVID-19 pandemic. In response to growing calls to recognise and reward health and care staff, growth in pay must at least keep pace with projected rises in average earnings, which in turn will require linking future NHS funding allocations to rises in pay. Through illustrative projections, we show that, to sustain annual growth in the workforce at approximately 2·4%, increases in NHS expenditure of 4% annually in real terms will be required. Above all, a radical long-term strategic vision is needed to ensure that the future NHS workforce is fit for purpose.

What is the right level of spending needed for health and care in the UK?

The health and care sector plays a valuable role in improving population health and societal wellbeing, protecting people from the financial consequences of illness, reducing health and income inequalities, and supporting economic growth. However, there is much debate regarding the appropriate level of funding for health and care in the UK. In this Health Policy paper, we look at the economic impact of the COVID-19 pandemic and historical spending in the UK and comparable countries, assess the role of private spending, and review spending projections to estimate future needs. Public spending on health has increased by 3·7% a year on average since the National Health Service (NHS) was founded in 1948 and, since then, has continued to assume a larger share of both the economy and government expenditure. In the decade before the ongoing pandemic started, the rate of growth of government spending for the health and care sector slowed. We argue that without average growth in public spending on health of at least 4% per year in real terms, there is a real risk of degradation of the NHS, reductions in coverage of benefits, increased inequalities, and increased reliance on private financing. A similar, if not higher, level of growth in public spending on social care is needed to provide high standards of care and decent terms and conditions for social care staff, alongside an immediate uplift in public spending to implement long-overdue reforms recommended by the Dilnot Commission to improve financial protection. COVID-19 has highlighted major issues in the capacity and resilience of the health and care system. We recommend an independent review to examine the precise amount of additional funds that are required to better equip the UK to withstand further acute shocks and major threats to health.

The use of linked routine data to optimise calculation of the Hospital Frailty Risk Score on the basis of previous hospital admissions: a retrospective observational cohort study.

BACKGROUND: The Hospital Frailty Risk Score (HFRS) has been widely but inconsistently applied in published studies, particularly in how diagnostic information recorded in previous hospital admissions is used in its construction. We aimed to assess how many previous admissions should be considered when constructing the HFRS and the influence of frailty risk on long length of stay, in-hospital mortality, and 30-day readmission. METHODS: This is a retrospective observational cohort study of patients aged 75 years or older who had at least one emergency admission to any of 49 hospital sites in the Yorkshire and Humber region of England, UK. We constructed multiple versions of the HFRS for each patient, each form incorporating diagnostic data from progressively more previous admissions in its construction within a 1-year or 2-year window. We assessed the ability of each form of the HFRS to predict long length of stay (>10 days), in-hospital death, and 30-day readmission. FINDINGS: Between April 1, 2013, and March 31, 2017, 282 091 patients had 675 155 hospital admissions. Regression analyses assessing the different constructions of HFRS showed that the form constructed with diagnostic information recorded in the current and previous two admissions within the preceding 2 years performed best for predicting all three outcomes. Under this construction, 263 432 (39·0%) of 674 615 patient admissions were classified as having low frailty risk, for whom 33 333 (12·7%) had a long length of stay, 10 145 (3·9%) died in hospital, and 45 226 (17·2%) were readmitted within 30 days. By contrast with those patients with low frailty risk, for those with intermediate frailty risk, the probability was 2·5-times higher (95% CI 2·4 to 2·6) for long length of stay, 2·17-times higher (2·1 to 2·2) for in-hospital death, and 0·7% higher (0·5 to 1) for readmission. For patients with high frailty risk, the probability was 4·3-times higher (4·2 to 4·5) for long length of stay, 2·48-times higher (2·4 to 2·6) for in-hospital death, and -1% (-1·2 to -0·5) lower for readmission than those with low frailty risk. The intermediate and high frailty risk categories were more important predictors of long length of stay than any of the other rich set of control variables included in our analysis. These categories also proved to be important predictors of in-hospital mortality, with only the Charlson Comorbidity Index offering greater predictive power. INTERPRETATION: We recommend constructing the HFRS with diagnostic information from the current admission and from the previous two admissions in the preceding 2 years. This HFRS form was a powerful predictor of long length of stay and in-hospital mortality, but less so of emergency readmissions. FUNDING: National Institute of Health Research.

Evaluating integrated care for people with complex needs.

OBJECTIVES: As part of the Vanguard programme, two integrated care models were introduced in South Somerset for people with complex care needs: the Complex Care Team and Enhanced Primary Care. We assessed their impact on a range of utilization measures and mortality. METHODS: We used monthly individual-level linked primary and secondary care data from April 2014 to March 2018 to assess outcomes before and after the introduction of the care models. The analysis sample included 564 Complex Care Team and 841 Enhanced Primary Care cases that met specific criteria. We employed propensity score methods to identify out-of-area control patients and difference-in-differences analysis to isolate the care models' impact. RESULTS: We found no evidence of significantly reduced utilization in any of the Complex Care Team or Enhanced Primary Care cohorts. The death rate was significantly lower only for those in the first Enhanced Primary Care cohort. CONCLUSIONS: The integrated care models did not significantly reduce utilization nor consistently reduce mortality. Future research should test longer-term outcomes associated with the new models of care and quantify their contribution in the context of broader initiatives.