A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

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.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions.

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

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.

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.

Deep learning identifies synergistic drug combinations for treating COVID-19.

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

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.

A multiplexable assay for screening antibiotic lethality against drug-tolerant bacteria.

Antibiotic screens typically rely on growth inhibition to characterize compound bioactivity-an approach that cannot be used to assess the bactericidal activity of antibiotics against bacteria in drug-tolerant states. To address this limitation, we developed a multiplexed assay that uses metabolism-sensitive staining to report on the killing of antibiotic-tolerant bacteria. This method can be used with diverse bacterial species and applied to genome-scale investigations to identify therapeutic targets against tolerant pathogens.

Development and validation of a novel patient-reported outcome measure in people with episodic migraine and chronic migraine: The Activity Impairment in Migraine Diary.

OBJECTIVE: To evaluate the content validity and psychometric properties of the Activity Impairment in Migraine Diary (AIM-D). BACKGROUND: Measuring treatment effects on migraine impairment requires a psychometrically sound patient-reported outcome (PRO) measure developed consistent with U.S. Food and Drug Administration guidance. METHODS: The AIM-D was created from concepts that emerged during qualitative interviews with five clinicians experienced in treating migraine and concept elicitation (CE) interviews with 40 adults with episodic migraine (EM) or chronic migraine (CM). The initial version was refined based on three waves of cognitive interviews with 38 adults with EM or CM and input from a panel of clinical and measurement experts. The AIM-D was psychometrically evaluated using data from 316 adults with EM or CM who participated in a 13-week prospective observational study. Study participants completed PRO assessments including the AIM-D and a daily headache diary. Exploratory and confirmatory factor analysis were used to determine the factor structure. The reliability, validity, and responsiveness of the AIM-D were assessed. Additional PRO measures including the Patient Global Impression - Severity (PGI-S), Migraine Specific Quality of Life Questionnaire, Version 2.1 Role Function-Restrictive domain, and Headache Impact Test were used for psychometric evaluation of the AIM-D. RESULTS: Based on CE interviews with adults with migraine and input from an expert panel, activity impairment was identified as the target in the preliminary conceptual framework, which had two domains: performance of daily activities (PDAs) and physical impairment (PI). Revision of the draft AIM-D through multiple rounds of cognitive interviews and expert panel meetings resulted in a content valid 11-item version. Exploratory factor analysis supported both one- and two-domain structures for the AIM-D, which were further supported by confirmatory factor analysis (factor loadings all >0.90). The AIM-D domains (PDA and PI) and total score showed high internal consistency reliability (Cronbach's alpha 0.95-0.97), acceptable test-retest reliability for weekly average scores (intraclass correlation coefficient >0.60 for participants with no change in PGI-S between baseline and week 2), and good convergent and known-groups validity. There was evidence of responsiveness based on changes in PGI-S score and monthly migraine days. CONCLUSION: The AIM-D is a content valid and psychometrically sound measure designed to evaluate activity impairment and is suitable for use in clinical trials of preventive treatments for EM or CM.

The effects of multi-disciplinary integrated care on healthcare utilization: Evidence from a natural experiment in the UK.

Better integration is a priority for most international health systems. However, multiple interventions are often implemented simultaneously, making evaluation difficult and providing limited evidence for policy makers about specific interventions. We evaluate a common integrated care intervention, multi-disciplinary group (MDG) meetings for discussion of high-risk patients, introduced in one socio-economically deprived area in the UK in spring 2015. Using data from multiple waves of the national GP Patient Survey and Hospital Episode Statistics, we estimate its effects on primary and secondary care utilization and costs, health status and patient experience. We use triple differences, exploiting the targeting at people aged 65 years and over, parsing effects from other population-level interventions implemented simultaneously. The intervention reduced the probability of visiting a primary care nurse by three percentage points and decreased length of stay by 1 day following emergency care admission. However, since planned care use increased, overall costs were unaffected. MDG meetings are presumably fulfilling public health objectives by decreasing length of stay and detecting previously unmet needs. However, the effect of MDGs on health system cost is uncertain and health remains unchanged. Evaluations of specific integrated care interventions may be more useful to public decision makers facing budget constraints.

The relative effects of non-pharmaceutical interventions on wave one Covid-19 mortality: natural experiment in 130 countries.

BACKGROUND: Non-pharmaceutical interventions have been implemented around the world to control Covid-19 transmission. Their general effect on reducing virus transmission is proven, but they can also be negative to mental health and economies, and transmission behaviours can also change voluntarily, without mandated interventions. Their relative impact on Covid-19 attributed mortality, enabling policy selection for maximal benefit with minimal disruption, is not well established due to a lack of definitive methods. METHODS: We examined variations in timing and strictness of nine non-pharmaceutical interventions implemented in 130 countries and recorded by the Oxford COVID-19 Government Response Tracker (OxCGRT): 1) School closing; 2) Workplace closing; 3) Cancelled public events; 4) Restrictions on gatherings; 5) Closing public transport; 6) Stay at home requirements ('Lockdown'); 7) Restrictions on internal movement; 8) International travel controls; 9) Public information campaigns. We used two time periods in the first wave of Covid-19, chosen to limit reverse causality, and fixed country policies to those implemented: i) prior to first Covid-19 death (when policymakers could not possibly be reacting to deaths in their own country); and, ii) 14-days-post first Covid-19 death (when deaths were still low, so reactive policymaking still likely to be minimal). We then examined associations with daily deaths per million in each subsequent 24-day period, which could only be affected by the intervention period, using linear and non-linear multivariable regression models. This method, therefore, exploited the known biological lag between virus transmission (which is what the policies can affect) and mortality for statistical inference. RESULTS: After adjusting, earlier and stricter school (- 1.23 daily deaths per million, 95% CI - 2.20 to - 0.27) and workplace closures (- 0.26, 95% CI - 0.46 to - 0.05) were associated with lower Covid-19 mortality rates. Other interventions were not significantly associated with differences in mortality rates across countries. Findings were robust across multiple statistical approaches. CONCLUSIONS: Focusing on 'compulsory', particularly school closing, not 'voluntary' reduction of social interactions with mandated interventions appears to have been the most effective strategy to mitigate early, wave one, Covid-19 mortality. Within 'compulsory' settings, such as schools and workplaces, less damaging interventions than closing might also be considered in future waves/epidemics.

Exploring the Experience of Living with and Managing Presbyopia.

SIGNIFICANCE: Presbyopia typically occurs around 40 years of age and affects approximately one-quarter of the global population. Up to October 2021, there were no approved pharmacotherapies for presbyopia, and common treatments, such as glasses, can have disadvantages for individuals' health-related quality of life. PURPOSE: This study aimed to document the experience of living with and managing presbyopia, identify perspectives on treatment options, and determine whether there is an unmet need in the treatment landscape. METHODS: Coded transcripts of concept elicitation (CE; n = 20) and cognitive debriefing (n = 20) interviews with presbyopic individuals, originally conducted for development of patient-reported outcome instruments, were reanalyzed to identify salient concepts describing participants' experiences with presbyopia treatments. Qualitative ranking exercises assessed participants' preferences for a potential pharmacotherapy vs. existing treatments. RESULTS: Because most concepts were identified with the CE interviews, data reflect CE findings unless otherwise noted. Average age across CE/cognitive debriefing interviews was 49.4 years; a vast majority of participants used glasses for presbyopia treatment. Four themes related to treatment with glasses were identified with the interviews: inconvenience during daily activities, negative physical sensations around the eyes/head, limitations, and undesirable impacts on daily life (e.g., psychosocial). Most commonly, participants reported inconveniences related to forgetting glasses and psychosocial impacts (e.g., feeling/looking older). Strained/tired eyes and limited ability to see at varying distances were also reported. Among participants with near-vision glasses who provided data, two-thirds expressed interest in alternative treatments. In addition, almost three-quarters of the participants ranked hypothetical eye drops as their first or second preferred option, vs. reading glasses, contact lenses, magnifying glasses, and surgery. CONCLUSIONS: This study explored the experience of living with and managing presbyopia and identified limitations and negative impacts of current treatments. Pharmacological development (e.g., eye drops) may fulfill an unmet need in the presbyopia treatment landscape.

Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study.

BACKGROUND: Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs. METHODS AND FINDINGS: We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions. CONCLUSIONS: Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study.

BACKGROUND: To estimate excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics. METHODS: Daily number of deaths in all residential and nursing homes in England notified to the Care Quality Commission (CQC) from 1 January 2017 to 7 August 2020. Care home-level data linked with CQC care home register to identify home characteristics: client type (over 65s/children and adults), ownership status (for-profit/not-for-profit; branded/independent) and size (small/medium/large). Excess deaths computed as the difference between observed and predicted deaths using local authority fixed-effect Poisson regressions on pre-pandemic data. Fixed-effect logistic regressions were used to model odds of experiencing COVID-19 suspected/confirmed deaths. RESULTS: Up to 7 August 2020, there were 29,542 (95% CI 25,176 to 33,908) excess deaths in all care homes. Excess deaths represented 6.5% (95% CI 5.5 to 7.4%) of all care home beds, higher in nursing (8.4%) than residential (4.6%) homes. 64.7% (95% CI 56.4 to 76.0%) of the excess deaths were confirmed/suspected COVID-19. Almost all excess deaths were recorded in the quarter (27.4%) of homes with any COVID-19 fatalities. The odds of experiencing COVID-19 attributable deaths were higher in homes providing nursing services (OR 1.8, 95% CI 1.6 to 2.0), to older people and/or with dementia (OR 5.5, 95% CI 4.4 to 6.8), amongst larger (vs. small) homes (OR 13.3, 95% CI 11.5 to 15.4) and belonging to a large provider/brand (OR 1.2, 95% CI 1.1 to 1.3). There was no significant association with for-profit status of providers. CONCLUSIONS: To limit excess mortality, policy should be targeted at care homes to minimise the risk of ingress of disease and limit subsequent transmission. Our findings provide specific characteristic targets for further research on mechanisms and policy priority.

Racial and socioeconomic disparities in multimorbidity and associated healthcare utilisation and outcomes in Brazil: a cross-sectional analysis of three million individuals.

BACKGROUND: Evidence is limited on racial/ethnic group disparities in multimorbidity and associated health outcomes in low- and middle-income countries hampering effective policies and clinical interventions to address health inequalities. METHODS: This study assessed race/ethnic and socioeconomic disparities in the prevalence of multimorbidity and associated healthcare utilisation, costs and death in Rio de Janeiro, Brazil. A cross-sectional analysis was carried out of 3,027,335 individuals registered with primary healthcare (PHC) services. Records included linked data to hospitalisation, mortality, and welfare-claimant (Bolsa Família) records between 1 Jan 2012 and 31 Dec 2016. Logistic and Poisson regression models were carried out to assess the likelihood of multimorbidity (two or more diagnoses out of 53 chronic conditions), PHC use, hospital admissions and mortality from any cause. Interactions were used to assess disparities. RESULTS: In total 13,509,633 healthcare visits were analysed identifying 389,829 multimorbid individuals (13%). In adjusted regression models, multimorbidity was associated with lower education (Adjusted Odds Ratio (AOR): 1.26; 95%CI: 1.23,1.29; compared to higher education), Bolsa Família receipt (AOR: 1.14; 95%CI: 1.13,1.15; compared to non-recipients); and black race/ethnicity (AOR: 1.05; 95%CI: 1.03,1.06; compared to white). Multimorbidity was associated with more hospitalisations (Adjusted Rate Ratio (ARR): 2.75; 95%CI: 2.69,2.81), more PHC visits (ARR: 3.46; 95%CI: 3.44,3.47), and higher likelihood of death (AOR: 1.33; 95%CI: 1.29,1.36). These associations were greater for multimorbid individuals with lower educational attainment (five year probability of death 1.67% (95%CI: 1.61,1.74%) compared to 1.13% (95%CI: 1.02,1.23%) for higher education), individuals of black race/ethnicity (1.48% (95%CI: 1.41,1.55%) compared to 1.35% (95%CI: 1.31,1.40%) for white) and individuals in receipt of welfare (1.89% (95%CI: 1.77,2.00%) compared to 1.35% (95%CI: 1.31,1.38%) for non-recipients). CONCLUSIONS: The prevalence of multimorbidity and associated hospital admissions and mortality are greater in individuals with black race/ethnicity and other deprived socioeconomic groups in Rio de Janeiro. Interventions to better prevent and manage multimorbidity and underlying disparities in low- and middle-income country settings are needed.

The patient at the centre: evidence from 17 European integrated care programmes for persons with complex needs.

BACKGROUND: As the prevalence of multi-morbidity increases in ageing societies, health and social care systems face the challenge of providing adequate care to persons with complex needs. Approaches that integrate care across sectors and disciplines have been increasingly developed and implemented in European countries in order to tackle this challenge. The aim of the article is to identify success factors and crucial elements in the process of integrated care delivery for persons with complex needs as seen from the practical perspective of the involved stakeholders (patients, professionals, informal caregivers, managers, initiators, payers). METHODS: Seventeen integrated care programmes for persons with complex needs in 8 European countries were investigated using a qualitative approach, namely thick description, based on semi-structured interviews and document analysis. In total, 233 face-to-face interviews were conducted with stakeholders of the programmes between March and September 2016. Meta-analysis of the individual thick description reports was performed with a focus on the process of care delivery. RESULTS: Four categories that emerged from the overarching analysis are discussed in the article: (1) a holistic view of the patient, considering both mental health and the social situation in addition to physical health, (2) continuity of care in the form of single contact points, alignment of services and good relationships between patients and professionals, (3) relationships between professionals built on trust and facilitated by continuous communication, and (4) patient involvement in goal-setting and decision-making, allowing patients to adapt to reorganised service delivery. CONCLUSIONS: We were able to identify several key aspects for a well-functioning integrated care process for complex patients and how these are put into actual practice. The article sets itself apart from the existing literature by specifically focussing on the growing share of the population with complex care needs and by providing an analysis of actual processes and interpersonal relationships that shape integrated care in practice, incorporating evidence from a variety of programmes in several countries.

Development and Validation of the FSIQ-RMS: A New Patient-Reported Questionnaire to Assess Symptoms and Impacts of Fatigue in Relapsing Multiple Sclerosis.

OBJECTIVES: A new patient-reported outcome (PRO) instrument to measure fatigue symptoms and impacts in relapsing multiple sclerosis (RMS) was developed in a qualitative stage, followed by psychometric validation and migration from paper to an electronic format. METHODS: Adult patients with relapsing-remitting multiple sclerosis (RRMS) were interviewed to elicit fatigue-related symptoms and impacts. A draft questionnaire was debriefed in cognitive interviews with further RRMS patients, and revised. Content confirmation interviews were conducted with patients with progressive-relapsing multiple sclerosis (PRMS) and relapsing secondary-progressive multiple sclerosis (RSPMS). Psychometric analyses used data from adult patients with different RMS subtypes and matched non-RMS controls in a multicenter, observational study. After item reduction, the final instrument was migrated to a smartphone (eDiary) and usability was confirmed in interviews with additional adult RMS patients. RESULTS: The qualitative stage included 37 RRMS, 5 PRMS, and 5 RSPMS patients. Saturation of concepts was reached during concept elicitation. Cognitive interviews confirmed that participants understood the instructions, items, and response options of the instrument-named FSIQ-RMS-as intended. Psychometric validation included 164 RMS and 74 control patients. Internal consistency and test-retest reliability were demonstrated. The symptoms domain discriminated along the RMS symptom-severity continuum and between patients and controls. Patients were able to attribute fatigue-related symptoms to RMS. Usability and conceptual equivalence of the eDiary were confirmed (n = 10 participants). CONCLUSIONS: With 7 symptom items and 13 impact items (in 3 impacts subdomains: physical, cognitive and emotional, and coping) after item reduction, the FSIQ-RMS is a comprehensive, valid, and reliable measure of fatigue-related symptoms and impacts in RMS patients.

Effects of non-medical health coaching on multimorbid patients in primary care: a difference-in-differences analysis.

BACKGROUND: Health systems, globally, are attempting to strengthen primary care to promote a population-health management approach to care provision, incentivising prevention and self-management. This paper evaluates the "Enhanced Primary Care" model implemented in a geographical region in England. Enhanced Primary Care introduces a new non-medical role, health coaches, to the traditional primary care team to provide additional support for patients with chronic conditions. We evaluate effects of health coaching on patient outcomes using a quasi-experiment. METHODS: We estimate the programme's effects on health status (EQ-5D-5L, physical functioning, psychological wellbeing, and resilience), health behaviour (smoking habit), experience of care (person-centeredness and continuity of care), and health care (primary care) utilisation using data from 3.5 million respondents to the national GP Patient Surveys between 2013 and 2017. We use a weighted difference-in-differences design to compare changes in outcomes over time between intervention practices and comparable control practices in the rest of England. We conduct our main analysis on multimorbid patients and additional analysis on all patients to assess population-level effects. RESULTS: For multimorbid patients, we find reductions in psychological wellbeing (short and medium term) of -0.0174 (95% confidence interval -0.0283 to -0.0065), relative difference -2% from the pre-intervention mean; and person-centeredness (short term) of -0.0356 (-0.0530 to -0.0183), -4%. We find no significant effects on other outcome measures. For population-level effects, in the short term we find reductions in primary care utilisation of -0.0331 (-0.0448 to -0.0214), -5%. All other outcomes are not consistently statistically significant. CONCLUSIONS: Our results show that there is very little effect of health coaching on patient experience and outcomes in the short-to-medium term (up to 14 months). Introduction of Enhanced Primary Care was associated with slightly lower psychological wellbeing and person-centeredness amongst multimorbid patients (it might be initially difficult for patients to adjust to the model). However, it was also associated with a decline in primary care visits at the population-level (potentially freeing up practitioner time for more complex patients). The results raise important questions regarding primary care workforce changes advocated in the NHS Long Term Plan, and the time horizon of any benefits of prevention strategies.

Implementing a national diabetes prevention programme in England: lessons learned.

BACKGROUND: Type 2 diabetes mellitus is preventable through lifestyle intervention. Diabetes prevention programmes (DPPs) aim to deliver prevention-based behaviour change interventions to reduce incidence. Such programmes vary from usual primary care in terms of where, how, and by whom they are delivered. Implementation is therefore likely to face new commissioning, incentive and delivery challenges. We report on the implementation of a national DPP in NHS England, and identify lessons learned in addressing the implementation challenges. METHODS: In 2017/18, we conducted 20 semi-structured telephone interviews covering 16 sampled case sites with the designated lead(s) responsible for local implementation of the programme. Interviews explored the process of implementation, including organisation of the programme, expectations and attitudes to the programme, funding, target populations and referral and clinical pathways. We drew on constant comparative methods to analyse the data and generate over-arching themes. We complemented our qualitative data with a survey focused on variation in the financial incentives used across sites to ensure usual primary care services recruited patients to new providers. RESULTS: We identified five over-arching areas of learning for implementing this large-scale programme: 1) managing new providers; 2) promoting awareness of services; 3) recruiting patients; 4) incentive payments; and 5) mechanisms for sharing learning. In general, tensions appeared to be caused by a lack of clear roles/responsibilities between hierarchical actors, and lack of communication. Both local sites and the national NHS coordination team gained experience through learning by doing. Initial tensions with roles and expectations have been worked out during implementation. CONCLUSIONS: Implementing a national disease prevention programme is a major task, and one that will be increasingly faced by health systems globally as they aim to adjust to demand pressures. We provide practical learning opportunities for the wider uptake and sustainability of prevention programmes. Future implementers might wish to define clear responsibilities for each actor prior to implementation, ensure early engagement with new providers, offer mechanisms/forums for sharing learning, generate evidence and provide advice on incentive payments, and prioritise public and professional awareness of the programme.