A primary care research agenda for multiple long-term conditions: a Delphi study.

BACKGROUND: Multiple long-term conditions (MLTC), also known as multimorbidity, has been identified as a priority research topic globally. Research priorities from the perspectives of patients and research funders have been described. Although most care for MLTC is delivered in primary care, the priorities of academic primary care have not been identified. AIM: To identify and prioritise the academic primary care research agenda for MLTC. DESIGN AND SETTING: This was a three-phase study with primary care MLTC researchers from the UK and other high-income countries. METHOD: The study consisted of: an open-ended survey question, a face-to-face workshop to elaborate questions with researchers from the UK and Ireland, and a two-round Delphi consensus survey with international multimorbidity researchers. RESULTS: Twenty-five primary care researchers responded to the initial open-ended survey and generated 84 potential research questions. In the subsequent workshop discussion (n = 18 participants), this list was reduced to 31 questions. The longlist of 31 research questions was included in round 1 of the Delphi; 27 of the 50 (54%) round 1 invitees and 24 of the 27 (89%) round 2 invitees took part in the Delphi. Ten questions reached final consensus. These questions focused broadly on addressing the complexity of the patient group with development of new models of care for multimorbidity, and methods and data development. CONCLUSION: These high-priority research questions offer funders and researchers a basis on which to build future grant calls and research plans. Addressing complexity in this research is needed to inform improvements in systems of care and for disease prevention.

How do hospitals respond to payment unbundling for diagnostic imaging of suspected cancer patients?

Payments for some diagnostic scans undertaken in outpatient settings were unbundled from Diagnosis Related Group based payments in England in April 2013 to address under-provision. Unbundled scans attracted additional payments of between £45 and £748 directly following the reform. We examined the effect on utilization of these scans for patients with suspected cancer. We also explored whether any detected effects represented real increases in use of scans or better coding of activity. We applied difference-in-differences regression to patient-level data from Hospital Episodes Statistics for 180 NHS hospital Trusts in England, between April 2010 and March 2018. We also explored heterogeneity in recorded use of scans before and after the unbundling at hospital Trust-level. Use of scans increased by 0.137 scans per patient following unbundling, a 134% relative increase. This increased annual national provider payments by £79.2 million. Over 15% of scans recorded after the unbundling were at providers that previously recorded no scans, suggesting some of the observed increase in activity reflected previous under-coding. Hospitals recorded substantial increases in diagnostic imaging for suspected cancer in response to payment unbundling. Results suggest that the reform also encouraged improvements in recording, so the real increase in testing is likely lower than detected.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Discovery of antibiotics that selectively kill metabolically dormant bacteria.

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Validation of the novel Eosinophilic Esophagitis Impact Questionnaire.

BACKGROUND: Eosinophilic esophagitis (EoE) has a detrimental effect on health-related quality of life (HRQOL). The Eosinophilic Esophagitis Impact Questionnaire (EoE-IQ) is a novel patient-reported outcome (PRO) measure assessing the impact of EoE on HRQOL. To assess suitability of the EoE-IQ, its measurement properties were evaluated. METHODS: Using baseline and week 24 data from the pivotal, randomized, placebo-controlled, multinational phase 3 R668-EE-1774 trial (NCT03633617) of dupilumab, we evaluated EoE-IQ's measurement properties (including reliability, construct and known-groups validity, and ability to detect change) and established the threshold for change in scores that can be considered clinically meaningful. RESULTS: The analysis population comprised 239 adults and adolescents with EoE. Mean age was 28.1 (standard deviation, 13.14) years; 63.6% were male, and 90.4% were White. Reliability estimates for the EoE-IQ average score exceeded acceptable thresholds for patients who were stable as indicated by ratings of Patient Global Impression of Severity (PGIS) and Change (PGIC) (intraclass correlation coefficients, 0.75 and 0.81). Construct validity correlations with other EoE-specific PRO scores were moderate at baseline (|r|= 0.44-0.60) and moderate to strong at week 24 (|r|= 0.61-0.72). In known-groups analysis, EoE-IQ average score discriminated among groups of patients at varying EoE severity levels defined by PGIS scores. A ≥ 0.6-point reduction in EoE-IQ average score (where scores range from 1 to 5, with higher scores indicating worse HRQOL) from baseline to week 24 can be considered clinically meaningful. CONCLUSIONS: The EoE-IQ's measurement properties are acceptable, making it a valid, reliable measure of the HRQOL impacts of EoE among adults and adolescents. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03633617. Registered August 14, 2018, https://clinicaltrials.gov/study/NCT03633617 .

How do family doctors respond to reduced waiting times for cancer diagnosis in secondary care?

Reducing waiting times is a priority in public health systems. Efforts of healthcare providers to shorten waiting times could be negated if they simultaneously induce substantial increases in demand. However, separating out the effects of changes in supply and demand on waiting times requires an exogenous change in one element. We examine the impact of a pilot programme in some English hospitals to shorten waiting times for urgent diagnosis of suspected cancer on family doctors' referrals. We examine referrals from 6,666 family doctor partnerships to 145 hospitals between 1st April 2012 and 31st March 2019. Five hospitals piloted shorter waiting times initiatives in 2017. Using continuous difference-in-differences regression, we exploit the pilot as a 'supply shifter' to estimate the effect of waiting times on referral volumes for two suspected cancer types: bowel and lung. The proportion of referred patients breaching two-week waiting times targets for suspected bowel cancer fell by 3.9 percentage points in pilot hospitals in response to the policy, from a baseline of 4.8%. Family doctors exposed to the pilot increased their referrals (demand) by 10.8%. However, the pilot was not successful for lung cancer, with some evidence that waiting times increased, and a corresponding reduction in referrals of -10.5%. Family doctor referrals for suspected cancer are responsive at the margin to waiting times. Healthcare providers may struggle to achieve long-term reductions in waiting times if supply-side improvements are offset by increases in demand.

Analysing changes to the flow of public funding within local health and care systems: An adaptation of the System of Health Accounts framework to a local health system in England.

Financial flows relating to health care are routinely analysed at national and international level. They have rarely been systematically analysed at local level, despite sub-national variation due to population needs and decisions enacted by local organisations. We illustrate an adaptation of the System of Health Accounts framework to map the flow of public health and care funding within local systems, with an application for Greater Manchester (GM), an area in England which agreed a health and social care devolution deal with the central government in 2016. We analyse how financial flows changed in GM during the four years post-devolution, and whether spending was aligned with local ambitions to move towards prevention of ill-health and integration of health and social care. We find that GM decreased spending on public health by 15%, and increased spending on general practice by 0.1% in real terms. The share of total local expenditure paid to NHS Trusts for general and acute services increased from 70.3% to 71.6%, while that for community services decreased from 11.7% to 10.3%. Results suggest that GM may have experienced challenges in redirecting resources towards their goals. Mapping financial flows at a local level is a useful exercise to examine whether spending is aligned with system goals and highlight areas for further investigation.

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.

Applications of machine learning in microbial natural product drug discovery.

INTRODUCTION: Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development. AREAS COVERED: This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery. EXPERT OPINION: Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

Patient-reported migraine-specific quality of life, activity impairment and headache impact with once-daily atogepant for preventive treatment of migraine in a randomized, 52-week trial.

BACKGROUND: Atogepant is an oral, small-molecule, calcitonin gene-related peptide receptor antagonist for the preventive treatment of episodic migraine. METHODS: In this 52-week, multicenter, randomized, open-label trial, adults with 4-14 monthly migraine days received atogepant 60 mg once-daily or standard care. Health outcome endpoints collected from participants randomized to atogepant included change from baseline in Migraine-Specific Quality of Life Questionnaire version 2.1 (MSQ v2.1) Role Function-Restrictive (RFR), Role Function-Preventive (RFP) and Emotional Function (EF) domain scores, change in Activity Impairment in Migraine-Diary (AIM-D) Performance of Daily Activities (PDA) and Physical Impairment (PI) domain scores, and change in Headache Impact Test-6 (HIT-6) total score. RESULTS: Of 744 randomized participants, 521 received atogepant 60 mg in the modified intent-to-treat population. Least-squares mean changes from baseline in MSQ-RFR score were 30.02 (95% confidence interval = 28.16-31.87) at week 12 and 34.70 (95% confidence interval = 32.74-36.66) at week 52. Improvements were also observed in other MSQ domains, AIM-D PDA, PI and HIT-6 total scores. A ≥5-point improvement from baseline in HIT-6 score was observed in 59.9% of participants at week 4 and 80.8% of participants at week 52. CONCLUSION: Over 52 weeks, atogepant 60 mg once-daily was associated with sustained improvements in quality of life and reductions in activity impairment and headache impact.Trial Registration: NCT03700320.

Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis?

BACKGROUND: Many complex public health evidence gaps cannot be fully resolved using only conventional public health methods. We aim to familiarise public health researchers with selected systems science methods that may contribute to a better understanding of complex phenomena and lead to more impactful interventions. As a case study, we choose the current cost-of-living crisis, which affects disposable income as a key structural determinant of health. METHODS: We first outline the potential role of systems science methods for public health research more generally, then provide an overview of the complexity of the cost-of-living crisis as a specific case study. We propose how four systems science methods (soft systems, microsimulation, agent-based and system dynamics models) could be applied to provide more in-depth understanding. For each method, we illustrate its unique knowledge contributions, and set out one or more options for studies that could help inform policy and practice responses. RESULTS: Due to its fundamental impact on the determinants of health, while limiting resources for population-level interventions, the cost-of-living crisis presents a complex public health challenge. When confronted with complexity, non-linearity, feedback loops and adaptation processes, systems methods allow a deeper understanding and forecasting of the interactions and spill-over effects common with real-world interventions and policies. CONCLUSIONS: Systems science methods provide a rich methodological toolbox that complements our traditional public health methods. This toolbox may be particularly useful in early stages of the current cost-of-living crisis: for understanding the situation, developing solutions and sandboxing potential responses to improve population health.

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

A typology of integrated care policies in the care home sector: A policy document analysis.

Background: Health and social care systems in many countries have begun to trial and adopt "integrated" approaches. Yet, the significant role care homes play within the health and social care system is often understated. A key first step to identifying the care home integration interventions that are most (cost-)effective is the ability to precisely identify and record what has been implemented, where, and when-a "policy map." Methods: To address gaps relating to the identification and recording of (cost-)effective integrated care home interventions, we developed a new typology tool. We conducted a policy mapping exercise in a devolved region of England-Greater Manchester (GM). Specifically, we carried out systematic policy documentary searches and extracted a range of qualitative data relating to integrated health and social care initiatives in the GM region for care homes. The data were then classified according to existing national ambitions for England as well as a generic health systems framework to illustrate gaps in existing recording tools and to iteratively develop a novel approach. Results: A combined total of 124 policy documents were identified and screened, in which 131 specific care home integration initiatives were identified. Current initiatives emphasized monitoring quality in care homes, workforce training, and service delivery changes (such as multi-disciplinary teams). There was comparatively little emphasis on financing or other incentive changes to stimulate provider behavior for the care home setting. We present a novel typology for capturing and comparing care home integration policy initiatives, largely conceptualizing which part of the system or specific transition point the care home integration is targeting, or whether there is a broader cross-cutting system intervention being enacted, such as digital or financial interventions. Conclusions: Our typology builds on the gaps in current frameworks, including previous lack of specificity to care homes and lack of adaptability to new and evolving initiatives internationally. It could provide a useful tool for policymakers to identify gaps in the implementation of initiatives within their own areas, while also allowing researchers to evaluate what works most effectively and efficiently in future research based on a comprehensive policy map.

Antibiotic discovery in the artificial intelligence era.

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.

OBJECTIVE: Outpatient no-shows have important implications for costs and the quality of care. Predictive models of no-shows could be used to target intervention delivery to reduce no-shows. We reviewed the effectiveness of predictive model-based interventions on outpatient no-shows, intervention costs, acceptability, and equity. MATERIALS AND METHODS: Rapid systematic review of randomized controlled trials (RCTs) and non-RCTs. We searched Medline, Cochrane CENTRAL, Embase, IEEE Xplore, and Clinical Trial Registries on March 30, 2022 (updated on July 8, 2022). Two reviewers extracted outcome data and assessed the risk of bias using ROB 2, ROBINS-I, and confidence in the evidence using GRADE. We calculated risk ratios (RRs) for the relationship between the intervention and no-show rates (primary outcome), compared with usual appointment scheduling. Meta-analysis was not possible due to heterogeneity. RESULTS: We included 7 RCTs and 1 non-RCT, in dermatology (n = 2), outpatient primary care (n = 2), endoscopy, oncology, mental health, pneumology, and an magnetic resonance imaging clinic. There was high certainty evidence that predictive model-based text message reminders reduced no-shows (1 RCT, median RR 0.91, interquartile range [IQR] 0.90, 0.92). There was moderate certainty evidence that predictive model-based phone call reminders (3 RCTs, median RR 0.61, IQR 0.49, 0.68) and patient navigators reduced no-shows (1 RCT, RR 0.55, 95% confidence interval 0.46, 0.67). The effect of predictive model-based overbooking was uncertain. Limited information was reported on cost-effectiveness, acceptability, and equity. DISCUSSION AND CONCLUSIONS: Predictive modeling plus text message reminders, phone call reminders, and patient navigator calls are probably effective at reducing no-shows. Further research is needed on the comparative effectiveness of predictive model-based interventions addressed to patients at high risk of no-shows versus nontargeted interventions addressed to all patients.

Effect of Atogepant for Preventive Migraine Treatment on Patient-Reported Outcomes in the Randomized, Double-blind, Phase 3 ADVANCE Trial.

BACKGROUND AND OBJECTIVES: The oral calcitonin gene-related peptide receptor antagonist atogepant is indicated for the preventive treatment of episodic migraine. We evaluated changes in patient-reported outcomes with atogepant in adults with migraine. METHODS: In this phase 3, 12-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group trial (ADVANCE), adults with 4-14 migraine days per month received atogepant (10, 30, or 60 mg) once daily or placebo. Secondary endpoints included changes from baseline in Migraine-Specific Quality-of-Life Questionnaire (MSQ) version 2.1 Role Function-Restrictive (RFR) domain at week 12 and mean monthly Activity Impairment in Migraine-Diary (AIM-D) Performance of Daily Activities (PDA) and Physical Impairment (PI) domains across the 12-week treatment period. Exploratory endpoints included change in MSQ Role Function-Preventive (RFP) and Emotional Function (EF) domains; AIM-D total scores; and change in Headache Impact Test (HIT)-6 scores. RESULTS: Of 910 participants randomized, 873 comprised the modified intent-to-treat population (atogepant: 10 mg [n = 214]; 30 mg [n = 223]; and 60 mg [n = 222]; placebo [n = 214]). All atogepant groups demonstrated significantly greater improvements vs placebo in MSQ RFR that exceeded minimum clinically meaningful between-group difference (3.2 points) at week 12 (least-square mean difference [LSMD] vs placebo: 10 mg [9.9]; 30 mg [10.1]; 60 mg [10.8]; all p < 0.0001). LSMDs in monthly AIM-D PDA and PI scores across the 12-week treatment period improved significantly for the atogepant 30 (PDA: -2.54; p = 0.0003; PI: -1.99; and p = 0.0011) and 60 mg groups (PDA: -3.32; p < 0.0001; PI: -2.46; p < 0.0001), but not for the 10 mg group (PDA: -1.19; p = 0.086; PI: -1.08; p = 0.074). In exploratory analyses, atogepant 30 and 60 mg were associated with nominal improvements in MSQ RFP and EF domains, other AIM-D outcomes, and HIT-6 scores at the earliest time point (week 4) and throughout the 12-week treatment period. Results varied for atogepant 10 mg. DISCUSSION: Atogepant 30 and 60 mg produced significant improvements in key patient-reported outcomes including MSQ-RFR scores and both AIM-D domains. Nominal improvements also occurred for other MSQ domains and HIT-6, reinforcing the beneficial effects of atogepant as a new treatment for migraine prevention. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT03777059. Submitted: December 13, 2018; First patient enrolled: December 14, 2018. CLINICALTRIALS: gov/ct2/show/NCT03777059. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that daily atogepant is associated with improvements in health-related quality-of-life measures in patients with 4-14 migraine days per month.

Psychometric performance of the Primary Mitochondrial Myopathy Symptom Assessment (PMMSA) in a randomized, double-blind, placebo-controlled crossover study in subjects with mitochondrial disease.

BACKGROUND: The Primary Mitochondrial Myopathy Symptom Assessment (PMMSA) is a 10-item patient-reported outcome (PRO) measure designed to assess the severity of mitochondrial disease symptoms. Analyses of data from a clinical trial with PMM patients were conducted to evaluate the psychometric properties of the PMMSA and to provide score interpretation guidelines for the measure. METHODS: The PMMSA was completed as a daily diary for approximately 14 weeks by individuals in a Phase 2 randomized, placebo-controlled crossover trial evaluating the safety, tolerability, and efficacy of subcutaneous injections of elamipretide in patents with mitochondrial disease. In addition to the PMMSA, performance-based assessments, clinician ratings, and other PRO measures were also completed. Descriptive statistics, psychometric analyses, and score interpretation guidelines were evaluated for the PMMSA. RESULTS: Participants (N = 30) had a mean age of 45.3 years, with the majority of the sample being female (n = 25, 83.3%) and non-Hispanic white (n = 29, 96.6%). The 10 PMMSA items assessing a diverse symptomology were not found to form a single underlying construct. However, four items assessing tiredness and muscle weakness were grouped into a "general fatigue" domain score. The PMMSA Fatigue 4 summary score (4FS) demonstrated stable test-retest scores, internal consistency, correlations with the scores produced by reference measures, and the ability to differentiate between different global health levels. Changes on the PMMSA 4FS were also related to change scores produced by the reference measures. PMMSA severity scores were higher for the symptom rated as "most bothersome" by each subject relative to the remaining nine PMMSA items (most bothersome symptom mean = 2.88 vs. 2.18 for other items). Distribution- and anchor-based evaluations suggested that reduction in weekly scores between 0.79 and 2.14 (scale range: 4-16) may represent a meaningful change on the PMMSA 4FS and reduction in weekly scores between 0.03 and 0.61 may represent a responder for each of the remaining six non-fatigue items, scored independently. CONCLUSIONS: Upon evaluation of its psychometric properties, the PMMSA, specifically the 4FS domain, demonstrated strong reliability and construct-related validity. The PMMSA can be used to evaluate treatment benefit in clinical trials with individuals with PMM. Trial registration ClinicalTrials.gov identifier, NCT02805790; registered June 20, 2016; https://clinicaltrials.gov/ct2/show/NCT02805790 .

Defining Pooled' Place-Based' Budgets for Health and Social Care: A Scoping Review.

Introduction: Current descriptions of pooled budgets in the literature pose challenges to good quality evaluation of their contribution to integrated care. Addressing this gap is increasingly important given the shift from early models of integrated care targeting segments of the population, to more recent approaches that aim to target 'places', broader geographically defined populations. This review draws on the current international evidence to describe practical examples of pooled health and social care budgets, highlighting specific place-based approaches. Methods: We initially conducted a scoping review, a systematic database search ('Medline', 'Embase', 'Econ Lit' and 'Google Scholar') complemented by further snowballing for academic and 'grey literature' publications (1995 - 2020). Results were analysed thematically according to budget characteristics and macro-environment, with additional specific case studies. Results: Thirty-six primary studies were included, describing ten broad models of pooled budgets across seven countries. Most budgets targeted specific sub-populations rather than an entire geographically defined population. Specific budget structures varied and were generally under-described. The closest place-based models were for small populations and implemented in a national health system, or insurance-based with natural geographical boundaries. Conclusion: Despite their increasing relevance in the current political debate, pooled place-based budgets are still at an early stage of implementation and research. Adequate description is required for future meta-analysis of effectiveness on outcomes.

Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews.

Closer integration of health and social care is a policy priority in many countries. The COVID-19 pandemic has reinforced the necessity of joining up health and social care systems, especially in care home settings. However, the meaning and perceived importance of integration for residents' and carers' experience is unclear and we do not know whether it has changed during the pandemic. Using unique data from on-line care home service reviews, we combined multiple methods. We used Natural Language Processing with supervised machine learning to construct a measure of sentiment for care home residents' and their relatives' (measured by AFINN score). Difference-in-difference analysis was used to examine whether experiencing integrated care altered these sentiments by comparing changes in sentiment in reviews related to integration (containing specific terms) to those which were not. Finally, we used network analysis on post-estimation results to assess which specific attributes stakeholders focus on most when detailing their most/least positive experiences of health and care integration in care homes, and whether these attributes changed over the pandemic. Reviews containing integration words were more positive than reviews unrelated to integration in the pre-pandemic period (about 2.3 points on the AFINN score) and remained so during the first year of the pandemic. Overall positive sentiment increased during the COVID-19 period (average by +1.1 points), mainly in reviews mentioning integration terms at the beginning of the first (+2.17, p-value 0.175) and second waves (+3.678, p-value 0.027). The role of care home staff was pivotal in both positive and negative reviews, with a shift from aspects related to care in pre-pandemic to information services during the pandemic, signalling their importance in translating integrated needs-based paradigms into policy and practice.