Social care and health outcomes in ageing: exploring measures of multimorbidity

Background The older population within developed countries is increasing, leading to increased pressure on health services. Most of this demographic have multiple conditions (multimorbidity), which is difficult to measure in a methodological context. In Scotland, efforts are being made to integrate health and social care under one body in order to provide a person-centred environment where older people with complex needs receive tailored care. In this context it is important to consider the effect of social care in conjunction with multimorbidity on health outcomes to target care provision. Objectives This study intends to determine which is the best tool for predicting both mortality and care uptake amongst older people. The effect of care on mortality in conjunction with multimorbidity is also considered. This study also attempts to derive the best predictive model for both mortality and care uptake, using additional explanatory variables such as deprivation. Methods This quantitative longitudinal study uses a linked SMR admissions and social care census dataset from 2010-2015. It considers the impact of multiple measures of multimorbidity (such as ICD-10 flagged condition indices or prescription scores) on outcomes such as mortality, receipt of informal care or admissions using nested logistic regression models with summary statistics such as the AIC, BIC, R-squared and ROC curve. Projected results Based on literature, it is hypothesised that diagnosis-based indices (such as the Charlson Index) will perform best at predicting mortality, whilst prescription-based scores (such as the Chronic Disease Score) will perform best at predicting admissions

Exploring measures of multimorbidity in predicting health and social care outcomes using administrative and survey data

Background: Multimorbidity is associated with adverse health and care outcomes, particularly in older populations. When quantifying multimorbidity, the appropriate measure varies by population, outcome under study and data available. Integrated health/social care, with a focus on the individual, improves patient satisfaction and health. In Scotland, clarity as to which measures/conditions are most strongly associated with health and care outcomes will help anticipate integrated care. Aim: To identify which multimorbidity measures, conditions and comorbidities predict health and care outcomes in an older Scottish population. Methods: Demographics, social care, admissions, and prescribing data for individuals 65+/resident in Scotland 2010-16 comprised three panel cohorts: for health (n=5,579,492), social (n=4,374,662) and informal care outcomes (n=2,449,229). Survey data linked to admissions were used for co-resident care (n=8,334). Panel logistic regression, using the receiver operating curve (ROC), identified the most predictive measures of multimorbidity for health/care. Further modelling was used to identify the strongest associated conditions/comorbidities, the impact of multimorbidity on social care by deprivation, and whether administrative outperforms survey data in predicting informal/co-resident care. Results: The Charlson Comorbidity Index (CCI) performed best (ROC >0.8) in predicting mortality, proxy measures for other health outcomes (ROC >0.7 and 0.7 and 0.8), and self-report measure (ROC >0.75) for co-resident care. Dementia is strongly associated with care, while comorbid interactions varied. An inverse effect between the relationship between multimorbidity and social care was found for local authority deprivation. Administrative data outperforms survey data at predicting informal care. Conclusions: The varying performance of multimorbidity measures highlight the importance of a wide range of data when predicting use of health and care services. A national index tailored to a Scottish population derived from both diagnosis-based and medication-based data may have better precision. This, and findings regarding individual and comorbid conditions, such as dementia, as well as macro- and micro-level effects of deprivation on the relationship between multimorbidity and care, have the potential to improve existing risk predicting algorithms within Scotland

Age-related selection bias in Parkinson's disease research : are we recruiting the right participants?

Acknowledgements We acknowledge the earlier work of Dr Kate Taylor and Dr Dominique Twelves on the previous systematic review of incidence studies in Parkinson’s disease. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors report the following funding received during the conduct of this study: Dr Macleod: fellowship funding from the Chief Scientist Office of the Scottish Government and NHS Education for Scotland; grant funding Parkinson’s UK, the Academy of Medical Sciences, NHS Grampian Endowments, the Wellcome Trust, the University of Aberdeen. Dr Henery: financial support from the University of Aberdeen Dr Nwajiugo: none Dr Scott: none Dr Caslake: grant funding from Parkinson’s UK Dr Counsell: grant funding from the Chief Scientist Office of the Scottish Government, the PSP Association, and NHS Grampian Endowments.Peer reviewedPostprin

Childhood attention-deficit hyperactivity disorder (ADHD): socio-economic inequalities in symptoms, impact, diagnosis and medication

Background Children from disadvantaged backgrounds are at greater risk of attention-deficit hyperactivity disorder (ADHD)-related symptoms, being diagnosed with ADHD, and being prescribed ADHD medications. We aimed to examine how inequalities manifest across the ‘patient journey’, from perceptions of impacts of ADHD symptoms on daily life, to the propensity to seek and receive a diagnosis and treatment. Methods We investigated four ‘stages’: (1) symptoms, (2) caregiver perception of impact, (3) diagnosis and (4) medication, in two data sets: UK Millennium Cohort Study (MCS, analytic n ~ 9,000), with relevant (parent-reported) information on all four stages (until 14 years); and a population-wide ‘administrative cohort’, which includes symptoms (child health checks) and prescriptions (dispensing records), born in Scotland, 2010–2012 (analytic n ~ 100,000), until ~6 years. We described inequalities according to maternal occupational status, with percentages and relative indices of inequality (RII). Results The prevalence of ADHD symptoms and medication receipt was considerably higher in the least compared to the most advantaged children in the administrative cohort (RIIs of 5.9 [5.5–6.4] and 8.1 [4.2–15.6]) and the MCS (3.08 [2.68–3.55], 3.75 [2.21–6.36]). MCS analyses highlighted complexities between these two stages, however, those from least advantaged backgrounds, with ADHD symptoms, were the least likely to perceive impacts on daily life (15.7% vs. average 19.5%) and to progress from diagnosis to medication (44.1% vs. average 72.5%). Conclusions Despite large inequalities in ADHD symptoms and medication, parents from the least advantaged backgrounds were less likely to report impacts of ADHD symptoms on daily life, and their children were less likely to have received medication postdiagnosis, highlighting how patient journeys differed according to socioeconomic circumstances

Association between multimorbidity and mortality in a cohort of patients admitted to hospital with COVID-19 in Scotland

Funding: BREATHE - The Health Data Research Hub for Respiratory Health, which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK (MC_PC_19004); CSO Rapid Research in Covid-19 Programme (COV/SAN/20/06); HDR UK Measuring and Understanding Multi-morbidity using Routine Data in the UK (MurMuRUK) (HDR-9006-9006; CFC0110); Medical Research Council (MR/R008345/1).Objectives We investigated the association between multimorbidity among patients hospitalised with COVID-19 and their subsequent risk of mortality. We also explored the interaction between the presence of multimorbidity and the requirement for an individual to shield due to the presence of specific conditions and its association with mortality. Design We created a cohort of patients hospitalised in Scotland due to COVID-19 during the first wave (between 28 February 2020 and 22 September 2020) of the pandemic. We identified the level of multimorbidity for the patient on admission and used logistic regression to analyse the association between multimorbidity and risk of mortality among patients hospitalised with COVID-19. Setting Scotland, UK. Participants Patients hospitalised due to COVID-19. Main outcome measures Mortality as recorded on National Records of Scotland death certificate and being coded for COVID-19 on the death certificate or death within 28 days of a positive COVID-19 test. Results Almost 58% of patients admitted to the hospital due to COVID-19 had multimorbidity. Adjusting for confounding factors of age, sex, social class and presence in the shielding group, multimorbidity was significantly associated with mortality (adjusted odds ratio 1.48, 95%CI 1.26–1.75). The presence of multimorbidity and presence in the shielding patients list were independently associated with mortality but there was no multiplicative effect of having both (adjusted odds ratio 0.91, 95%CI 0.64–1.29). Conclusions Multimorbidity is an independent risk factor of mortality among individuals who were hospitalised due to COVID-19. Individuals with multimorbidity could be prioritised when making preventive policies, for example, by expanding shielding advice to this group and prioritising them for vaccination.Publisher PDFPeer reviewe

Ethnic and social inequalities in COVID-19 outcomes in Scotland:protocol for early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II)

Introduction: Evidence from previous pandemics, and the current COVID-19 pandemic, has found that risk of infection/severity of disease is disproportionately higher for ethnic minority groups, and those in lower socioeconomic positions. It is imperative that interventions to prevent the spread of COVID-19 are targeted towards high-risk populations. We will investigate the associations between social characteristics (such as ethnicity, occupation and socioeconomic position) and COVID-19 outcomes and the extent to which characteristics/risk factors might explain observed relationships in Scotland. The primary objective of this study is to describe the epidemiology of COVID-19 by social factors. Secondary objectives are to (1) examine receipt of treatment and prevention of COVID-19 by social factors; (2) quantify ethnic/social differences in adverse COVID-19 outcomes; (3) explore potential mediators of relationships between social factors and SARS-CoV-2 infection/COVID-19 prognosis; (4) examine whether occupational COVID-19 differences differ by other social factors and (5) assess quality of ethnicity coding within National Health Service datasets. Methods and analysis: We will use a national cohort comprising the adult population of Scotland who completed the 2011 Census and were living in Scotland on 31 March 2020 (~4.3 million people). Census data will be linked to the Early Assessment of Vaccine and Anti-Viral Effectiveness II cohort consisting of primary/secondary care, laboratory data and death records. Sensitivity/specificity and positive/negative predictive values will be used to assess coding quality of ethnicity. Descriptive statistics will be used to examine differences in treatment and prevention of COVID-19. Poisson/Cox regression analyses and mediation techniques will examine ethnic and social differences, and drivers of inequalities in COVID-19. Effect modification (on additive and multiplicative scales) between key variables (such as ethnicity and occupation) will be assessed. Ethics and dissemination: Ethical approval was obtained from the National Research Ethics Committee, South East Scotland 02. We will present findings of this study at international conferences, in peer-reviewed journals and to policy-makers

Ethnic inequalities in positive SARS-CoV-2 tests, infection prognosis, COVID-19 hospitalisations, and deaths : analysis of two years of a record linked national cohort study in Scotland

Funding: Economics and Social Research Council (ESRC) ES/W000849/1, Medical Research Council (MRC) MC_UU_00022/2, Scottish Government Chief Scientist Office SPHSU17.BACKGROUND: This study aims to estimate ethnic inequalities in risk for positive SARS-CoV-2 tests, COVID-19 hospitalisations and deaths over time in Scotland. METHODS: We conducted a population-based cohort study where the 2011 Scottish Census was linked to health records. We included all individuals≥16 years living in Scotland on 1 March 2020. The study period was from 1 March 2020 to 17 April 2022. Self-reported ethnic group was taken from the census and Cox proportional hazard models estimated HRs for positive SARS-CoV-2 tests, hospitalisations and deaths, adjusted for age, sex and health board. We also conducted separate analyses for each of the four waves of COVID-19 to assess changes in risk over time. FINDINGS: Of the 4 358 339 individuals analysed, 1 093 234 positive SARS-CoV-2 tests, 37 437 hospitalisations and 14 158 deaths occurred. The risk of COVID-19 hospitalisation or death among ethnic minority groups was often higher for White Gypsy/Traveller (HR 2.21, 95% CI (1.61 to 3.06)) and Pakistani 2.09 (1.90 to 2.29) groups compared with the white Scottish group. The risk of COVID-19 hospitalisation or death following confirmed positive SARS-CoV-2 test was particularly higher for White Gypsy/Traveller 2.55 (1.81-3.58), Pakistani 1.75 (1.59-1.73) and African 1.61 (1.28-2.03) individuals relative to white Scottish individuals. However, the risk of COVID-19-related death following hospitalisation did not differ. The risk of COVID-19 outcomes for ethnic minority groups was higher in the first three waves compared with the fourth wave. INTERPRETATION: Most ethnic minority groups were at increased risk of adverse COVID-19 outcomes in Scotland, especially White Gypsy/Traveller and Pakistani groups. Ethnic inequalities persisted following community infection but not following hospitalisation, suggesting differences in hospital treatment did not substantially contribute to ethnic inequalities.Publisher PDFPeer reviewe

Investigating the uptake, effectiveness and safety of COVID-19 vaccines : protocol for an observational study using linked UK national data

Funding: This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (HDRUK2020.146). EAVE II is funded by the Medical Research Council (MC_PC_19075) and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004). BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. ConCOV is supported by the Medical Research Council (MR/V028367/1); Health Data Research UK (HDR-9006) which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK which is funded by the Economic and Social Research Council (grant ES/S007393/1).Introduction : The novel coronavirus SARS-CoV-2, which emerged in December 2019, has caused millions of deaths and severe illness worldwide. Numerous vaccines are currently under development of which a few have now been authorised for population-level administration by several countries. As of 20 September 2021, over 48 million people have received their first vaccine dose and over 44 million people have received their second vaccine dose across the UK. We aim to assess the uptake rates, effectiveness, and safety of all currently approved COVID-19 vaccines in the UK. Methods and analysis : We will use prospective cohort study designs to assess vaccine uptake, effectiveness and safety against clinical outcomes and deaths. Test-negative case–control study design will be used to assess vaccine effectiveness (VE) against laboratory confirmed SARS-CoV-2 infection. Self-controlled case series and retrospective cohort study designs will be carried out to assess vaccine safety against mild-to-moderate and severe adverse events, respectively. Individual-level pseudonymised data from primary care, secondary care, laboratory test and death records will be linked and analysed in secure research environments in each UK nation. Univariate and multivariate logistic regression models will be carried out to estimate vaccine uptake levels in relation to various population characteristics. VE estimates against laboratory confirmed SARS-CoV-2 infection will be generated using a generalised additive logistic model. Time-dependent Cox models will be used to estimate the VE against clinical outcomes and deaths. The safety of the vaccines will be assessed using logistic regression models with an offset for the length of the risk period. Where possible, data will be meta-analysed across the UK nations. Ethics and dissemination : We obtained approvals from the National Research Ethics Service Committee, Southeast Scotland 02 (12/SS/0201), the Secure Anonymised Information Linkage independent Information Governance Review Panel project number 0911. Concerning English data, University of Oxford is compliant with the General Data Protection Regulation and the National Health Service (NHS) Digital Data Security and Protection Policy. This is an approved study (Integrated Research Application ID 301740, Health Research Authority (HRA) Research Ethics Committee 21/HRA/2786). The Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub meets NHS Digital’s Data Security and Protection Toolkit requirements. In Northern Ireland, the project was approved by the Honest Broker Governance Board, project number 0064. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journals.Publisher PDFPeer reviewe