Suchergebnisse

A good understanding of age, when individuals develop individual conditions and subsequently become multimorbid, would provide evidence to support better prevention, detection, and management of multiple long-term conditions. The study aimed to assess the age at onset, incidence, and prevalence of 31 long-term conditions and multimorbidity in Fife and Tayside, Scotland by sex and socioeconomic status.
We linked routinely collected health records of 431,772 patients aged 25 years or higher on January 1st, 2000 till December 31st, 2018 from several database. Age of first report of the 31 Elixhauser long-term conditions was assessed.
Median age at the onset of conditions within the population ranged from 39 years for AIDS/HIV to 73 years for fluid and electrolyte disorders and 75 years for renal failure. This was mostly delayed among the least deprived than most deprived people, up to 14 years for psychoses and AIDS/HIV. People with the highest deprivation in both sexes had earlier onset of all conditions except for blood loss anaemia where most deprived males had three years delay. Median age at the onset of multimorbidity and complex multimorbidity was 67 years (68 for females vs 66 for males; 68 for most deprived vs 73 for least deprived) and 71 years (73 for females vs 70 for males; 73 for most deprived vs 78 for least deprived) respectively. The yearly incidence of both multimorbidity and complex multimorbidity rose steadily from 1.6% and 0.2% in 2000, peaked in 2015 (3.2% vs 1.4%), declined to 2.9% and 1.3% respectively in 2018 while the prevalence grew from 1.6% and 0.2% to 27.2% and 10.1% over the same period.
A number of conditions had earlier onset while others were delayed. The onset of conditions is generally about five years earlier among most deprived people than the least deprived just as multimorbidity and complex multimorbidity is higher and earlier among the most deprived. This information could help drive health system policy, management, intervention, and allocation of resources.

IntroductionIntegration of health and social care services is a potential solution to improving care despite budgetary constraints and increased demand for services. Little is known about how having two-or-more long-term conditions (multimorbidity) and socioeconomic status affect social care use, or how all these factors affect unscheduled health care use.
Objectives and ApproachThe project aims to describe the demographic, geographic, and socioeconomic differences in the receipt of social care for over 65s in Scotland and how multimorbidity status influences amounts of social care received. Additional analyses will consider the influence receipt of social care has on use of unscheduled health care services and mortality.
Social Care Survey (SCS) data collected by the Scottish Government is linked to administrative health and mortality records. Linkage includes; prescribing information service and USC data which records episodes of A & E attendance, emergency admission to hospital, GP out-of-hours attendance, Scottish Ambulance Service use, and NHS24 contact
ResultsThe cohort includes 1.1million individuals over the age of 65 (54.8% Female), of which 274,011 (24.2%) people died during the study period.
The linkage rate of the SCS to records with a CHI number in the National Records of Scotland population spine was 90.5%, with one local authority removed for very low linkage rates and the remaining 31 with rates between 76.7% and 97.9%.
As of February 2018, all requested data has been transferred to the National Safe Haven and data cleaning and analysis has begun. Significant results are expected to have been produced by August 2018.
Conclusion/ImplicationsThis research will help understand if receipt of social care is equitably distributed among the population of Scotland after allowing for multimorbidity and socioeconomic status. Understanding the influence health status has on social care receipt and the influence social care has on unscheduled healthcare use has important implications for policy development.

BackgroundThe Social Care Survey (SCS) is collected annually by the Scottish Government (SG) from all of Scotland's 32 local authorities. In addition to other information, it details the amount of home care delivered to all individuals in receipt of this service during a specified census week. How well this census represents care delivered throughout the year is unknown.
ObjectivesUsing complete data from one local authority area:1. Quantify the annual proportion of individuals receiving care during the census week.2. Describe differences in type and duration of care packages received during/not during the census week.
MethodsA data sharing agreement was completed with Renfrewshire council to transfer all social care data for the years 2006-2016 to a safe haven environment. A time series was created identifying how much care each individual received at weekly intervals during the study period allowing comparison of counts of individuals at any given week with total counts for financial years. Differences in packages received during/not during the census week in each financial year were compared using the Mann-Whitney U test.
Findings41,002 packages of home care for 10,130 individuals over the age of 65 (64% female), were analysed. The proportion of people receiving care in the census week varied from 54.1% to 64.8% of the total number of individuals receiving care in a given financial year.Approximately half of packages received during the census week were of similar duration and intensity (weekly hours delivered) as packages not captured by the census. The remainder tended to be of much longer duration (>1 year) and less intensity.
ConclusionsThe SCS represents approximately 60% of individuals receiving home care in the Renfrewshire council area and a representative sample of packages of care delivered.

Background: The Social Care Survey (SCS) is collected annually by the Scottish Government (SG) from all of Scotland's 32 local authorities. In addition to other information, it details the amount of home care delivered to all individuals in receipt of this service during a specified census week. How well this census represents care delivered throughout the year is unknown. Objectives: Using complete data from one local authority area: 1. Quantify the annual proportion of individuals receiving care during the census week. 2. Describe differences in type and duration of care packages received during/not during the census week. Methods: A data sharing agreement was completed with Renfrewshire council to transfer all social care data for the years 2006-2016 to a safe haven environment. A time series was created identifying how much care each individual received at weekly intervals during the study period allowing comparison of counts of individuals at any given week with total counts for financial years. Differences in packages received during/not during the census week in each financial year were compared using the Mann-Whitney U test. Findings: 41,002 packages of home care for 10,130 individuals over the age of 65 (64% female), were analysed. The proportion of people receiving care in the census week varied from 54.1% to 64.8% of the total number of individuals receiving care in a given financial year. Approximately half of packages received during the census week were of similar duration and intensity (weekly hours delivered) as packages not captured by the census. The remainder tended to be of much longer duration (>1 year) and less intensity. Conclusions: The SCS represents approximately 60% of individuals receiving home care in the Renfrewshire council area and a representative sample of packages of care delivered.

IntroductionMany high-income countries are reorganising and integrating health and social care (long-term care) services. However, little evidence exists showing how these services interact. Demographic changes and austerity measures have led to increased demand for social care services at the same time as the availability of formal and informal services has declined. The aim of this study was to identify risks for unplanned hospital admission taking account of social care receipt, multimorbidity and sociodemographic status.
Objectives and ApproachThis retrospective, observational study included all individuals over the age of 65 in Scotland in financial year (Apr-March) 2015/16 (n = 879,027). The main outcome was time to first unplanned hospital admission. Kaplan-Meier curves were plotted to compare survival rates. Cox proportional hazards regression models were used to determine risk factors for unplanned admission. Explanatory variables included: receipt of social care, multimorbidity, socioeconomicposition, age, and sex.
ResultsMultivariate analysis showed that, after adjusting for: age, sex, multimorbidity, socioeconomic position, and any past unplanned admissions, receipt of social care was associated with a two-fold increased risk of having an unplanned admission compared to those without care (HR 1.98 95%CI 1.95-2.00). Increasing age, severity of multimorbidity, and lower socioeconomic position were also all associated with increasing risks of unplanned admission.
Conclusion / ImplicationsOur results show those in current receipt of social care are also more likely to use unplanned hospital care compared with those that do not receive care. Current provision of social care services is unlikely to lead to reductions in unplanned hospital admission.

Multimorbidity, the presence of two or more long-term conditions, is associated with numerous negative outcomes. Little is known, however, about the relationship between multimorbidity and social care use (also known as long-term care). This is important as many developed countries seek to integrate health and social care services as a means of improving quality of services at the same time as making efficiency savings.
The aim of the study was to assess the relationship between receipt of formal social care services, sociodemographic variables, and two proxy measures of multimorbidity.
For this retrospective population-based observational study including all individuals over the age of 65 in Scotland in financial years 2014/15 and 2015/16 (n= 975,265), we linked the Scottish Social Care Survey (which holds information on those receiving social care from local authorities) to administrative NHS community prescribing and demographic records. The main outcome binary measure was presence or not in the social care survey. Two proxy measures of multimorbidity were derived from prescribing data. We used logistic regression models and report Average Marginal Effects (AME).
Fifteen percent of all individuals received formal social care services. After adjustment for other modelled factors, multimorbidity showed an increasing magnitude of effect on receipt of social care (AME 16.4% (95% CI 16.2-16.6%) increased probability for 9+ medicines compared to 0-2 medicines, AME 18.8% (95% CI 18.5-19.1%) increased probability for 6+ BNF chapters compared to 0 BNF chapters). The magnitude of effect of deprivation on social care receipt was lower (AME 5.1% (95% CI 4.8-5.4%) increased probability in the most deprived area decile compared to the most affluent) after adjusting for multimorbidity and other demographic factors.
This is one of the first studies to demonstrate an association between multimorbidity and receipt of social care. Increasing prevalence of multimorbidity has greater societal implications than previously described.

ABSTRACT
ObjectivesThe poster will showcase the Training & Capacity Building Programme (TCBP) established by the Farr network that is available to PhD students and Early Career Researchers (ECRs) both from within and outside the network.
ApproachThe authors are using a mixed-methods approach to present the current state of the art that has motivated the structure of the TCBP and how the programme has been received by students, academic and industry leaders.
Since 2014, the Farr network education leads have worked on a unique education programme that aims to equip students with key professional and methodological skills required by the roles they are likely to take in their future careers.
The content of the programme has been informed by current research in the fields of data linkage and analysis of big datasets as well as the immediate experience of researchers, and healthcare and industry leads working actively in the field.
We have focussed on developing a community of practice through the provision of an environment that has enabled students to share experiences and knowledge and to start building their own networks of collaboration across the Farr centres.
ResultsThe Farr network of PhD students comprises of approximately 66 students from a variety of backgrounds including bioinformatics, computer science, epidemiology, mathematics, psychology and statistics.
The Farr network hosted 2 main events in 2015 that provided the foundation for 34 PhD students to come together to develop skills in written and oral communication, Public and Patient Involvement (PPI), problem-solving, requirements gathering, data visualisation and spatial data analysis.
Formal feedback collected from each event suggests that the programme is received well by students who see it as a set of activities that complement their specialised PhD related education. Feedback from supervisors and future employers also suggests that this programme has facilitated the development of skills that are useful for their PhD and employment.
ConclusionThe Farr network has laid the foundation for the community of practice around the analysis of real-world health datasets across the UK, and will continue to cultivate this through the enrichment of the programme.

BackgroundA deeper understanding of the clustering of multimorbidity (2+) (MM) conditions by age, sex, and deprivation could help prepare health systems for adequate and effective preventive intervention. We identified the commonest conditions and their clusters contributing to MM stratified age, sex, and deprivation.
MethodsRetrospective cohort of 431,772 patients aged >25 years, registered with a general practice in Fife/Tayside, Scotland and alive in January 2000 were followed till December 2019. We developed Multimorbidity e-Cohort for Fife/Tayside (MCFT), Scotland. MCFT combined anonymised Scottish demographic dataset, prescribing information systems records from 2009 and secondary care: hospital admission and day-case records, mental health inpatient and accident and emergency attendances from 2000 from the Scottish Safe Havens Health Informatics Centre. Elixhauser comorbidity classifications were used to identify the presence of 31 conditions. Besides descriptive statistics, dissimilarity algorithm in association rule mining techniques were used for the analysis.
Results33.6% and 13.3% had MM and complex MM (4+) respectively, which varied across age, sex and deprivation of the patients. The commonest condition among <50 years with MM are alcohol abuse (32%), chronic pulmonary disease and depression (24% each) compared with uncomplicated hypertension (57%), and cardiac arrhythmia (40%) among those aged 80+ years. We identified four unique clusters among multimorbid individuals (a) liver disease, alcohol, other neurological disorders and depression; (b) weight loss, Rheumatoid Arthritis/collagenm, hypothyroidism, deficiency anemia; (c) solid tumor and metastatic cancer; (4) peripheral vascular disorders, renal failure, fluid & electrolyte disorders, chronic pulmonary disease, uncomplicated hypertension/diabetes, pulmonary circulation disorders, valvular disease, congestive heart failure and cardiac arrhythmias. The clusters varied across sex, age and deprivation.
ConclusionClusters of disease conditions in the MM population varied mostly by age. Alcohol misuse contributed to MM among a third of under-50. Almost half of the oldest age group have hypertension and cardiac arrythmia. When considering clustering of conditions, it is important to consider age, sex and deprivation.

ABSTRACT
ObjectivesThe use of "real-time" data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland's Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI).
This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making.
ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence.
ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression.
ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.

Funding: the study was funded by the Greater Glasgow Health Board, the Scottish Government via the Scottish Learning Disabilities Observatory and the UK Medical Research Council (grant number: MC_PC_17217). ; Objective: To investigate psychotropic prescribing in the intellectual disabilities population over 10 years, and associated mental ill health diagnoses. DESIGN: Comparison of cross-sectional data in 2002-2004 (T1) and 2014 (T2). Longitudinal cohort study with detailed health assessments at T1 and record linkage to T2 prescribing data. SETTING: General community. PARTICIPANTS: 1190 adults with intellectual disabilities in T1 compared with 3906 adults with intellectual disabilities in T2. 545/1190 adults with intellectual disabilities in T1 were alive and their records linked to T2 prescribing data. Main Outcome Measures: Encashed regular and as-required psychotropic prescriptions. Results: 50.7% (603/1190) of adults in T1 and 48.2% (1881/3906) in T2 were prescribed at least one psychotropic; antipsychotics: 24.5% (292/1190) in T1 and 16.7% (653/3906) in T2; antidepressants: 11.2% (133/1190) in T1 and 19.1% (746/3906) in T2. 21.2% (62/292) prescribed antipsychotics in T1 had psychosis or bipolar disorder, 33.2% (97/292) had no mental ill health or problem behaviours, 20.6% (60/292) had problem behaviours but no psychosis or bipolar disorder. Psychotropics increased from 47.0% (256/545) in T1 to 57.8% (315/545) in T2 (p<0.001): antipsychotics did not change (OR 1.18; 95% CI 0.87 to 1.60; p=0.280), there was an increase for antidepressants (OR 2.80; 95% CI 1.96 to 4.00; p<0.001), hypnotics/anxiolytics (OR 2.19; 95% CI 1.34 to 3.61; p=0.002), and antiepileptics (OR 1.40; 95% CI 1.06 to 1.84; p=0.017). Antipsychotic prescribing increased for people with problem behaviours in T1 (OR 6.45; 95% CI 4.41 to 9.45; p<0.001), more so than for people with other mental ill health in T1 (OR 4.11; 95% CI 2.76 to 6.11; p<0.001). Conclusions: Despite concerns about antipsychotic prescribing and guidelines recommending their withdrawal, it appears that while fewer antipsychotic prescriptions were initiated by T2 than in T1, people were not withdrawn from them once commenced. People with problem behaviours had increased prescribing. There was also a striking increase in antidepressant prescriptions. Adults with intellectual disabilities need frequent and careful medication reviews. ; Publisher PDF ; Peer reviewed

Funding: UK Medical Research Council, grant number: MC_PC_17217), and the Scottish Government via the Scottish Learning Disabilities Observatory. ; Objectives To investigate mortality in adults with intellectual disabilities: rates, causes, place, demographic and clinical predictors. Design Cohort study with record linkage to death data. Setting General community. Participants 961/1023 (94%) adults (16–83 years; mean=44.1 years; 54.6% male) with intellectual disabilities, clinically examined in 2001–2004; subsequently record-linked to their National Health Service number, allowing linkage to death certificate data, 2018. Outcome measures Standardised mortality ratios (SMRs), underlying and all contributing causes of death, avoidable deaths, place, and demographic and clinical predictors of death. Results 294/961 (30.6%) had died; 64/179 (35.8%) with Down syndrome, 230/783 (29.4%) without Down syndrome. SMR overall=2.24 (1.98, 2.49); Down syndrome adults=5.28 (3.98, 6.57), adults without Down syndrome=1.93 (1.68, 2.18); male=1.69 (1.42, 1.95), female=3.48 (2.90, 4.06). SMRs decreased as age increased. More severe intellectual disabilities increased SMR, but ability was not retained in the multivariable model. SMRs were higher for most International Statistical Classification of Diseases and Related Health Problems, 10th Revision chapters. For adults without Down syndrome, aspiration/reflux/choking and respiratory infection were the the most common underlying causes of mortality; for Down syndrome adults 'Down syndrome', and dementia were most common. Amenable deaths (29.8%) were double that in the general population (14%); 60.3% died in hospital. Mortality risk related to percutaneous endoscopic gastrostomy/tube fed, Down syndrome, diabetes, lower respiratory tract infection at cohort-entry, smoking, epilepsy, hearing impairment, increasing number of prescribed drugs, increasing age. Bowel incontinence reduced mortality risk. Conclusions Adults with intellectual disabilities with and without Down syndrome have different SMRs and causes of death which should be separately reported. Both die younger, from different causes than other people. Some mortality risks are similar to other people, with earlier mortality reflecting more multimorbidity; amenable deaths are also common. This should inform actions to reduce early mortality, for example, training to avoid aspiration/choking, pain identification to address problems before they are advanced, and reasonable adjustments to improve healthcare quality. ; Publisher PDF ; Peer reviewed

AbstractBackgroundQuality of primary healthcare impacts on health outcomes. This study aimed to quantify trends in good practice and the healthcare inequalities gap.MethodIndicators of best‐practice management of long‐term conditions and health promotion were extracted from primary healthcare records on 721 adults with intellectual disabilities in 2007–2010, and 3638 in 2014. They were compared over time, and with the general population in 2014, using Fisher's Exact test and ordinal regression.ResultsManagement improved for adults with intellectual disabilities over time (OR = 5.32; CI = 2.69–10.55), but not for the general population (OR = 0.74; CI = 0.34–1.64). However, it remained poorer, but to a lesser extent, compared with the general population (OR = 0.38; CI = 0.20–0.73 in 2014, and OR = 0.05; CI = 0.02–0.12 in 2007–2010). In 2014, health care was comparable to the general population on 49/78 (62.8%) indicators.ConclusionsThe extent of the healthcare inequality gap reduced over this period, but remaining inequalities highlight that further action is still necessary.

IntroductionMulti-morbidity is a widely recognised but poorly understood global issue that appears to be increasing in prevalence, according to the UK's Academy of Medical Sciences (AMS) report in 2018. Disease clustering, their determinants and consequences are poorly researched. Better understanding would help drive prevention and improved clinical care, services and patient outcomes.
Objectives and ApproachDevelopment of two comprehensive population-wide e-cohorts, derived utilising data linkage techniques and including multi-sourced anonymised routine health and demographic data held within the SAIL Databank. The objective is to characterise multi-morbidity and its clustering, determinants and outcomes and compare methods using a) prospective cohort design using multiple data sources in Wales and b) retrospective cohort design to examine household level and environment clustering using GP data in demographically diverse populations (Wales and North East London).
The prospective e-cohort focuses on adults living in Wales on 1 st January 2000 and followed up to 2020, including data from the NHS population register, deaths, inpatients, outpatients, Emergency Department, GP, disease registries, laboratory data, and population surveys with QoL measures. This e-cohort will be harmonised with other sites across the UK. The retrospective e-cohort is designed to harmonise with a North East London e-cohort, including all individuals living in Wales on 24 th April 2018 and registered with a GP.
Results2.8 and 2.2 million individuals have been included in the prospective and retrospective cohorts respectively, with 43.6 million person years of follow up. Established comorbidity indices and published phenotypes from libraries are being applied to the data to create initial prevalence and incidence estimates for further analysis. Important clusters will be determined by associations with mortality and excess healthcare utilisation.
Conclusion/ImplicationsBuilding the e-cohorts has involved multiple disciplines across organisations. Multi-morbidity prevalence estimates and study designs will be compared prior to statistical analyses and machine learning methods to evaluate clustering and determinants.

IntroductionPopulation-based screening has been shown to reduce cancer specific mortality. Within Scotland, three national screening programmes exist: breast, cervical and bowel. Despite being a common and preventable form of cancer, the uptake for bowel cancer screening among women lags behind that for breast and cervical cancer.
Objectives and ApproachSince the benefits of screening accrue with participation, it is important to understand why differences in screening uptake exist. In this study, data on women aged 24-74 in the Greater Glasgow and Clyde Health Board, invited to take part in one or more screening programme during the period 2009-2013, were linked to demographic and medical data. Uptake was determined based on the presence of a screening attendance or result; the impact of age, deprivation and co-morbidity on uptake was determined using logistic regression for each individual programme, and for the cohort of women invited to participate in all three programmes.
ResultsOverall, 430,591 women were invited to take part in one or more screening programme during the study period. The uptake for bowel screening was, at 61.7%, lower than that seen in either the breast (72.6%) or cervical (80.7%) programme. Despite these differences, the same demographic factors were associated with uptake of each individual screening programme: older women and those living in affluent areas were most likely to attend. Medical factors did differentially influence uptake, those with multi-morbid illness being less likely to participate in breast and bowel, but not the cervical programme. For the 68,324 women invited to participate in all programmes, 52.1% took part in all three while 7.2% participated in none.
Conclusion/ImplicationsUptake of bowel screening was confirmed as lower than uptake of other programmes, although all were similarly impacted by demographic, clinical and socioeconomic factors. Individuals were more likely to complete bowel screening if they participate in another programme, suggesting these may serve as a vehicle for improving bowel screening uptake.

Funding: EAVE II is supported by the Medical Research Council (MR/R008345/1) with the support of BREATHE – The Health Data Research Hub for Respiratory Health, which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund [MC_PC_19004] and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care, 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 (grant ref MC_PC_20058;) and the Lifelong Health and Well-being study as part of the National Core Studies (MC_PC_20030). ; The emergence of the B.1.617.2 Delta variant of concern was associated with increasing numbers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and COVID-19 hospital admissions. We aim to study national population level SARS-CoV-2 infections and COVID-19 associated hospitalisations by vaccination status to provide insight into the association of vaccination on temporal trends during the time in which the SARS-CoV-2 Delta variant became dominant in Scotland. We used the Scotland-wide Early Pandemic Evaluation and Enhanced Surveillance (EAVE II) platform, covering the period when Delta was pervasive (May 01 to October 23, 2021). We performed a cohort analysis of every vaccine-eligible individual aged 20 or over from across Scotland. We determined the vaccination coverage, SARS-CoV-2 incidence rate and COVID-19 associated hospitalisations incidence rate. We then stratified those rates by age group, vaccination status (defined as "unvaccinated", "partially vaccinated" (1 dose), or "fully vaccinated" (2 doses)), vaccine type (BNT162b2 or ChAdOx1 nCoV-19), and coexisting conditions known to be associated with severe COVID-19 outcomes. During the follow-up of 4 183 022 individuals, there were 407 405 SARS-CoV-2 positive cases with 10 441 (2.6%) associated with a hospital ...