Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions).

BACKGROUND: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. OBJECTIVE: We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. METHODS: The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. RESULTS: The study will commence in October 2021 and is expected to be completed by October 2023. CONCLUSIONS: By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers "whole persons" and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34405.

Change in treatment burden among people with multimorbidity: Protocol of a follow up survey and development of efficient measurement tools for primary care.

BACKGROUND: Treatment burden is the effort required of patients to look after their health and the impact this has on their functioning and wellbeing. It is likely treatment burden changes over time as circumstances change for patients and health services. However, there are a lack of population-level studies of treatment burden change and factors associated with this change over time. Furthermore, there are currently no practical screening tools for treatment burden in time-pressured clinical settings or at population level. METHODS AND ANALYSIS: This is a three-year follow-up of a cross-sectional survey of 723 people with multimorbidity (defined as three or more long-term conditions; LTCs) registered at GP practices in in Dorset, England. The survey will repeat collection of information on treatment burden (using the 10-item Multimorbidity Treatment Burden Questionnaire (MTBQ) and a novel single-item screening tool), sociodemographics, medications, LTCs, health literacy and financial resource, as at baseline. Descriptive statistics will be used to compare change in treatment burden since the baseline survey in 2019 and associations of treatment burden change will be assessed using regression methods. Diagnostic test accuracy metrics will be used to evaluate the single-item treatment burden screening tool using the MTBQ as the gold-standard. Routine primary care data (including demographics, medications, LTCs, and healthcare usage data) will be extracted from medical records for consenting participants. A forward-stepwise, likelihood-ratio logistic regression model building approach will be employed in order to assess the utility of routine data metrics in quantifying treatment burden in comparison to self-reported treatment burden using the MTBQ. IMPACT: To the authors' knowledge, this will be the first study investigating longitudinal aspects of treatment burden. Findings will improve understanding of the extent to which treatment burden changes over time for people with multimorbidity and factors contributing to this change, as well as allowing better identification of people at risk of high treatment burden.

Patients' and kidney care team's perspectives of treatment burden and capacity in older people with chronic kidney disease: a qualitative study.

OBJECTIVE: Chronic kidney disease (CKD) is often a multimorbid condition and progression to more severe disease is commonly associated with increased management requirements, including lifestyle change, more medication and greater clinician involvement. This study explored patients' and kidney care team's perspectives of the nature and extent of this workload (treatment burden) and factors that support capacity (the ability to manage health) for older individuals with CKD. DESIGN: Qualitative semistructured interview and focus group study. SETTING AND PARTICIPANTS: Adults (aged 60+) with predialysis CKD stages G3-5 (identified in two general practitioner surgeries and two renal clinics) and a multiprofessional secondary kidney care team in the UK. RESULTS: 29 individuals and 10 kidney team members were recruited. Treatment burden themes were: (1) understanding CKD, its treatment and consequences, (2) adhering to treatments and management and (3) interacting with others (eg, clinicians) in the management of CKD. Capacity themes were: (1) personal attributes (eg, optimism, pragmatism), (2) support network (family/friends, service providers), (3) financial capacity, environment (eg, geographical distance to unit) and life responsibilities (eg, caring for others). Patients reported poor provision of CKD information and lack of choice in treatment, whereas kidney care team members discussed health literacy issues. Patients reported having to withdraw from social activities and loss of employment due to CKD, which further impacted their capacity. CONCLUSION: Improved understanding of and measures to reduce the treatment burden (eg, clear information, simplified medication, joined up care, free parking) associated with CKD in individuals as well as assessment of their capacity and interventions to improve capacity (social care, psychological support) will likely improve patient experience and their engagement with kidney care services.

The association of socioeconomic status with incidence and outcomes of acute kidney injury.

BACKGROUND: Acute kidney injury (AKI) is common and is associated with significant morbidity and mortality. Socioeconomic status may be negatively associated with AKI as some risk factors for AKI such as chronic kidney disease, diabetes and heart failure are socially distributed. This study explored the socioeconomic gradient of the incidence and mortality of AKI, after adjusting for important mediators such as comorbidities. METHODS: Linked primary care and laboratory data from two large acute hospitals in the south of England, sourced from the Care and Health Information Analytics database, were used to identify AKI cases over a 1-year period (2017-18) from a population of 580 940 adults. AKI was diagnosed from serum creatinine patterns using a Kidney Disease: Improving Global Outcomes-based definition. Multivariable logistic regression and Cox proportional hazard models adjusting for age, sex, comorbidities and prescribed medication (in incidence analyses) and AKI severity (in mortality analyses), were used to assess the association of area deprivation (using Index of Multiple Deprivation for place of residence) with AKI risk and all-cause mortality over a median (interquartile range) of 234 days (119-356). RESULTS: Annual incidence rate of first AKI was 1726/100 000 (1.7%). The risk of AKI was higher in the most deprived compared with the least deprived areas [adjusted odds ratio = 1.79, 95% confidence interval (CI) 1.59-2.01 and 1.33, 95% CI 1.03-1.72 for <65 and >65 year old, respectively] after controlling for age, sex, comorbidities and prescribed medication. Adjusted risk of mortality post first AKI was higher in the most deprived areas (adjusted hazard ratio = 1.20, 95% CI 1.07-1.36). CONCLUSIONS: Social deprivation was associated with higher incidence of AKI and poorer survival even after adjusting for the higher presence of comorbidities. Such social inequity should be considered when devising strategies to prevent AKI and improve care for AKI patients.

Delayed antibiotic prescribing for respiratory tract infections: protocol of an individual patient data meta-analysis.

INTRODUCTION: Delayed prescribing can be a useful strategy to reduce antibiotic prescribing, but it is not clear for whom delayed prescribing might be effective. This protocol outlines an individual patient data (IPD) meta-analysis of randomised controlled trials (RCTs) and observational cohort studies to explore the overall effect of delayed prescribing and identify key patient characteristics that are associated with efficacy of delayed prescribing. METHODS AND ANALYSIS: A systematic search of the databases Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL Plus and Web of Science was conducted to identify relevant studies from inception to October 2017. Outcomes of interest include duration of illness, severity of illness, complication, reconsultation and patient satisfaction. Study authors of eligible papers will be contacted and invited to contribute raw IPD data. IPD data will be checked against published data, harmonised and aggregated to create one large IPD database. Multilevel regression will be performed to explore interaction effects between treatment allocation and patient characteristics. The economic evaluation will be conducted based on IPD from the combined trial and observational studies to estimate the differences in costs and effectiveness for delayed prescribing compared with normal practice. A decision model will be developed to assess potential savings and cost-effectiveness in terms of reduced antibiotic usage of delayed prescribing and quality-adjusted life years. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Southampton Faculty of Medicine Research Ethics Committee (Reference number: 30068). Findings of this study will be published in peer-reviewed academic journals as well as General Practice trade journals and will be presented at national and international conferences. The results will have important public health implications, shaping the way in which antibiotics are prescribed in the future and to whom delayed prescriptions are issued. PROSPERO REGISTRATION NUMBER: CRD42018079400.

Does income relate to health due to psychosocial or material factors? Consistent support for the psychosocial hypothesis requires operationalization with income rank not the Yitzhaki Index.

Research on why income influences health has produced mixed findings. Many, but not all, studies suggest that the relationship between income and health is due to income indicating psychosocial position rather than the associated material benefits. The inconsistent findings may be partly due to the use of the Yitzhaki Index, a function which calculates the accumulated income shortfall for an individual relative to those with higher income, in order to represent the psychosocial position conferred by income. The current study tests whether an alternative specification - income rank - provides more consistent conclusions regarding the psychosocial effect of income on health. We used data from two nationally representative samples: 14,224 observations from 9,404 participants across three waves (2004, 2008, and 2012) of the English Longitudinal Study of Ageing (ELSA) and 29,237 observations from 8,441 individuals across seven waves (2007-2013) of the Longitudinal Internet Studies for the Social Sciences (LISS). Multilevel regression models indicated that income rank was a stronger and more consistent predictor than both the Yitzhaki Index and actual income of self-rated and objective health. The psychosocial hypothesis is more consistently supported when income rank is used to test it.