Suchergebnisse

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.

In the context of an European Centre for Disease Prevention and Control (ECDC) research project, our objective was to describe current recommendations regarding HIV testing and counselling targeting migrants and ethnic minorities in the European Union/European Economic Area/European Free Trade Association (EU/EEA/EFTA) Member States. An on-line survey was conducted among 31 EU/EEA/EFTA Member States. The survey inquired on the existence of specific HIV testing and counselling recommendations or policies for migrants and/or ethnic minorities and the year of their publication. Additionally, we performed a review of national recommendations, guidelines or any other policy documents retrieved from an Internet search through the different countries' competent bodies. Twenty-nine (94%) country representatives responded the survey, and 28 documents from 27 countries were identified. National guidelines on HIV testing are heterogeneous and tailored, according to the epidemiological situation. Twenty-two countries identify migrants and four countries identify ethnic minorities as particularly vulnerable to HIV. Sixteen countries explicitly recommend offering an HIV test to migrants/ethnic minorities. Guidelines especially target people originating from HIV endemic countries, and benefits of HIV early detection are highlighted. HIV testing is not mandatory in any country, but some countries overtly facilitate this practice. Benefits of HIV testing in migrants and ethnic minorities, at both individual and community levels are recognized by many countries. In spite of this, not all countries identify the need to test these groups. ; European Centre for Disease Prevention and Control, Spanish Network of HIV/AIDS Research [RIS- RD06/0006] and the Biomedical Research Centre Network for Epidemiology and Public Health [CIBER de Epidemiología y Salud Pública]. ; Sí

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (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 and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (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 and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

Funding Information: AS, JM, and CR are members of the Scottish Government Chief Medical Officer's COVID-19 Advisory Group. JM is a member of the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and AS is a member of the NERVTAG Risk Stratification Subgroup and an unfunded member of Astra-Zeneca's COVID-19 Strategic Consultancy Group: Thrombocytopenia Taskforce. JM is a member of the Scientific Advisory Group on Emergencies (SAGE) and chairs the COVID Scottish National Incident Management Team and the Scientific Committee of the European Centre for Disease Prevention and Control/WHO-funded IMOVE-COVID-19 group. CM reports research funding from Medical Research Council (MRC), Health Data Research UK, National Institute for Health Research (NIHR), and Scottish Chief Scientist Office (CSO). SJS reports research funding from Wellcome Trust, MRC, NIHR, and Scottish CSO. CRS declares funding from the MRC, NIHR, Scottish CSO, and the New Zealand Ministry for Business, Innovation and Employment and Health Research Council during the conduct of this study. SVK is co-chair of the Scottish Government's Expert Reference Group on COVID-19 and ethnicity, is a member of the SAGE subgroup on ethnicity, and acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship, MRC, and Scottish CSO. CR is a member of the Scientific Pandemic Influenza Group on Modelling and the Medicines and Healthcare Products Regulatory Agency Vaccine Benefit and Risk Working Group. JLKM is a member of the COVID Scottish National Incident Management Team. SdL has received funding through his University from AstraZeneca. FDRH acknowledges part support from the NIHR Applied Research Collaboration Oxford Thames Valley and the NIHR Oxford University Hospital Biomedical Research Centre. All other authors declare no competing interests. Funding Information: EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE?The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director-General Health and Social Care. We thank Dave Kelly from Albasoft for his support with making primary care data available and James Pickett, Wendy Inglis-Humphrey, Vicky Hammersley, Maria Georgiou, Laura Gonzalez Rienda, Pam McVeigh, Amanda Burridge, Sumedha Asnani-Chetal, and Afshin Dastafshan for their support with project management and administration. We acknowledge the support of the EAVE II Patient Advisory Group. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK?HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). Funding Information: EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director-General Health and Social Care. We thank Dave Kelly from Albasoft for his support with making primary care data available and James Pickett, Wendy Inglis-Humphrey, Vicky Hammersley, Maria Georgiou, Laura Gonzalez Rienda, Pam McVeigh, Amanda Burridge, Sumedha Asnani-Chetal, and Afshin Dastafshan for their support with project management and administration. We acknowledge the support of the EAVE II Patient Advisory Group. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK—HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). ; Peer reviewed ; Publisher PDF

Background: The UK COVID-19 vaccination programme has prioritised vaccination of those at the highest risk of COVID-19 mortality and hospitalisation. The programme was rolled out in Scotland during winter 2020–21, when SARS-CoV-2 infection rates were at their highest since the pandemic started, despite social distancing measures being in place. We aimed to estimate the frequency of COVID-19 hospitalisation or death in people who received at least one vaccine dose and characterise these individuals. Methods: We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) national surveillance platform, which contained linked vaccination, primary care, RT-PCR testing, hospitalisation, and mortality records for 5·4 million people (around 99% of the population) in Scotland. Individuals were followed up from receiving their first dose of the BNT162b2 (Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines until admission to hospital for COVID-19, death, or the end of the study period on April 18, 2021. We used a time-dependent Poisson regression model to estimate rate ratios (RRs) for demographic and clinical factors associated with COVID-19 hospitalisation or death 14 days or more after the first vaccine dose, stratified by vaccine type. Findings: Between Dec 8, 2020, and April 18, 2021, 2 572 008 individuals received their first dose of vaccine—841 090 (32·7%) received BNT162b2 and 1 730 918 (67·3%) received ChAdOx1. 1196 (<0·1%) individuals were admitted to hospital or died due to COVID-19 illness (883 hospitalised, of whom 228 died, and 313 who died due to COVID-19 without hospitalisation) 14 days or more after their first vaccine dose. These severe COVID-19 outcomes were associated with older age (≥80 years vs 18–64 years adjusted RR 4·75, 95% CI 3·85–5·87), comorbidities (five or more risk groups vs less than five risk groups 4·24, 3·34–5·39), hospitalisation in the previous 4 weeks (3·00, 2·47–3·65), high-risk occupations (ten or more previous COVID-19 tests vs less than ten previous COVID-19 tests 2·14, 1·62–2·81), care home residence (1·63, 1·32–2·02), socioeconomic deprivation (most deprived quintile vs least deprived quintile 1·57, 1·30–1·90), being male (1·27, 1·13–1·43), and being an ex-smoker (ex-smoker vs non-smoker 1·18, 1·01–1·38). A history of COVID-19 before vaccination was protective (0·40, 0·29–0·54). Interpretation: COVID-19 hospitalisations and deaths were uncommon 14 days or more after the first vaccine dose in this national analysis in the context of a high background incidence of SARS-CoV-2 infection and with extensive social distancing measures in place. Sociodemographic and clinical features known to increase the risk of severe disease in unvaccinated populations were also associated with severe outcomes in people receiving their first dose of vaccine and could help inform case management and future vaccine policy formulation. Funding: UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Scottish Government, and Health Data Research UK.

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK—HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). ; Background The UK COVID-19 vaccination programme has prioritised vaccination of those at the highest risk of COVID-19 mortality and hospitalisation. The programme was rolled out in Scotland during winter 2020–21, when SARS-CoV-2 infection rates were at their highest since the pandemic started, despite social distancing measures being in place. We aimed to estimate the frequency of COVID-19 hospitalisation or death in people who received at least one vaccine dose and characterise these individuals. Methods We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) national surveillance platform, which contained linked vaccination, primary care, RT-PCR testing, hospitalisation, and mortality records for 5·4 million people (around 99% of the population) in Scotland. Individuals were followed up from receiving their first dose of the BNT162b2 (Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines until admission to hospital for COVID-19, death, or the end of the study period on April 18, 2021. We used a time-dependent Poisson regression model to estimate rate ratios (RRs) for demographic and clinical factors ...

The trial was financed by Ministerio de Sanidad y Consumo Español through their call for independent clinical research, Orden Ministerial SAS/2377, 2010 (EC10-115, EC10-116, EC10-117, EC10-119, EC10-122); CAIBER—Spanish Clinical Research Network, Instituto de Salud Carlos III (ISCIII) (CAI08/010044); and Gerencia Asistencial de Atención Primaria de Madrid. This study is also supported by the Spanish Clinical Research Network (SCReN), funded by ISCIII-Subdirección General de Evaluación y Fomento de la Investigación, project number PT13/0002/0007, within the National Research Program I+D+I 2013-2016 and co-funded with European Union ERDF funds (European Regional Development Fund). This project received a grant for the translation and publication of this article from the Foundation for Biomedical Research and Innovation in Primary Care (FIIBAP) Call 2017 for grants to promote research programs. ; Objectives To compare the effectiveness of oral versus intramuscular (IM) vitamin B12 (VB12) in patients aged ≥65 years with VB12 deficiency. Design Pragmatic, randomised, non-inferiority, multicentre trial in 22 primary healthcare centres in Madrid (Spain). Participants 283 patients ≥65 years with VB12 deficiency were randomly assigned to oral (n=140) or IM (n=143) treatment arm. Interventions The IM arm received 1 mg VB12 on alternate days in weeks 1–2, 1 mg/week in weeks 3–8 and 1 mg/month in weeks 9–52. The oral arm received 1 mg/day in weeks 1–8 and 1 mg/week in weeks 9–52. Main outcomes Serum VB12 concentration normalisation (≥211 pg/mL) at 8, 26 and 52 weeks. Non-inferiority would be declared if the difference between arms is 10% or less. Secondary outcomes included symptoms, adverse events, adherence to treatment, quality of life, patient preferences and satisfaction. Results The follow-up period (52 weeks) was completed by 229 patients (80.9%). At week 8, the percentage of patients in each arm who achieved normal B12 levels was well above 90%; the differences in this percentage between the oral and IM arm were −0.7% (133 out of 135 vs 129 out of 130; 95% CI: −3.2 to 1.8; p>0.999) by per-protocol (PPT) analysis and 4.8% (133 out of 140 vs 129 out of 143; 95% CI: −1.3 to 10.9; p=0.124) by intention-to-treat (ITT) analysis. At week 52, the percentage of patients who achieved normal B12 levels was 73.6% in the oral arm and 80.4% in the IM arm; these differences were −6.3% (103 out of 112 vs 115 out of 117; 95% CI: −11.9 to −0.1; p=0.025) and −6.8% (103 out of 140 vs 115 out of 143; 95% CI: −16.6 to 2.9; p=0.171), respectively. Factors affecting the success rate at week 52 were age, OR=0.95 (95% CI: 0.91 to 0.99) and having reached VB12 levels ≥281 pg/mL at week 8, OR=8.1 (95% CI: 2.4 to 27.3). Under a Bayesian framework, non-inferiority probabilities (Δ>−10%) at week 52 were 0.036 (PPT) and 0.060 (ITT). Quality of life and adverse effects were comparable across groups. 83.4% of patients preferred the oral route. Conclusions Oral administration was no less effective than IM administration at 8 weeks. Although differences were found between administration routes at week 52, the probability that the differences were below the non-inferiority threshold was very low. ; Publisher PDF ; Peer reviewed