scholarly journals Influenza-attributable burden in United Kingdom primary care

2015 ◽  
Vol 144 (3) ◽  
pp. 537-547 ◽  
Author(s):  
D. M. FLEMING ◽  
R. J. TAYLOR ◽  
F. HAGUINET ◽  
C. SCHUCK-PAIM ◽  
J. LOGIE ◽  
...  

SUMMARYInfluenza is rarely laboratory-confirmed and the outpatient influenza burden is rarely studied due to a lack of suitable data. We used the Clinical Practice Research Datalink (CPRD) and surveillance data from Public Health England in a linear regression model to assess the number of persons consulting UK general practitioners (GP episodes) for respiratory illness, otitis media and antibiotic prescriptions attributable to influenza during 14 seasons, 1995–2009. In CPRD we ascertained influenza vaccination status in each season and risk status (conditions associated with severe influenza outcomes). Seasonal mean estimates of influenza-attributable GP episodes in the UK were 857 996 for respiratory disease including 68 777 for otitis media, with wide inter-seasonal variability. In an average season, 2·4%/0·5% of children aged <5 years and 1·3%/0·1% of seniors aged ⩾75 years had a GP episode for respiratory illness attributed to influenza A/B. Two-thirds of influenza-attributable GP episodes were estimated to result in prescription of antibiotics. These estimates are substantially greater than those derived from clinically reported influenza-like illness in surveillance programmes. Because health service costs of influenza are largely borne in general practice, these are important findings for cost-benefit assessment of influenza vaccination programmes.

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 49.1-50
Author(s):  
S. Swain ◽  
C. Coupland ◽  
V. Strauss ◽  
C. Mallen ◽  
C. F. Kuo ◽  
...  

Background:Multimorbidity (≥2 chronic conditions) escalates the risk of adverse health outcomes. However, its burden in people with osteoarthritis (OA) remains largely unknown.Objectives:To identify the clusters of patients with multimorbidity and associated factors in OA and non-OA populations and to estimate the risk of developing multimorbidity clusters after the index date (after diagnosis).Methods:The study used the Clinical Practice Research Datalink – a primary care database from the UK. Firstly, age, sex and practice matched OA and non-OA people aged 20+ were identified to explore patterns and associations of clusters of multimorbidity within each group. Non-OA controls were assigned with same index date as that of matched OA cases. Secondly, multimorbidity trajectories for 20 years after the index date were examined in people without any comorbidities at baseline in both OA and non-OA groups. Latent class analysis was used to identify clusters and latent class growth modelling was used for cluster trajectories. The associations between clusters and age, sex, body mass index (BMI), alcohol use, smoking habits at baseline were quantified through multinomial logistic regression.Results:In total, 47 long-term conditions were studied in 443,822 people (OA- 221922; non-OA- 221900), with a mean age of 62 years (standard deviation ± 13 years), and 58% being women. The prevalence of multimorbidity was 76.6% and 68.9% in the OA and non-OA groups, respectively. In the OA group five clusters were identified including relatively healthy (18%), ‘cardiovascular (CVD) and musculoskeletal (MSK)’ (12.3%), metabolic syndrome (28.2%), ‘pain and psychological (9.1%), and ‘musculoskeletal’ (32.4%). The non-OA group had similar patterns except that the ‘pain+ psychological’ cluster was replaced by ‘thyroid and psychological’. (Figure 1) Among people with OA, ‘CVD+MSK’ and metabolic syndrome clusters were strongly associated with obesity with a relative risk ratio (RRR) of 2.04 (95% CI 1.95-2.13) and 2.10 (95% CI 2.03-2.17), respectively. Women had four times higher risk of being in the ‘pain+ psychological’ cluster than men when compared to the gender ratio in the healthy cluster, (RRR 4.28; 95% CI 4.09-4.48). In the non-OA group, obesity was significantly associated with all the clusters.Figure 1: Posterior probability distribution of chronic conditions across the clusters in Osteoarthritis (OA, n=221922) and Non-Osteoarthritis (Non-OA, n=221900) group. COPD- Chronic Obstructive Pulmonary Disease; CVD- Cardiovascular; MSK- MusculoskeletalOA (n=24139) and non-OA (n=24144) groups had five and four multimorbidity trajectory clusters, respectively. Among the OA population, 2.7% had rapid onset of multimorbidity, 9.5% had gradual onset and 11.6% had slow onset, whereas among the non-OA population, there was no rapid onset cluster, 4.6% had gradual onset and 14.3% had slow onset of multimorbidity. (Figure 2)Figure 2: Clusters of multimorbidity trajectories after index date in OA (n=24139) and Non-OA (n=24144)Conclusion:Distinct identified groups in OA and non-OA suggests further research for possible biological linkage within each cluster. The rapid onset of multimorbidity in OA should be considered for chronic disease management.Supported by:Acknowledgments:We would like to thank the University of Nottingham, UK, Beijing Joint Care Foundation, China and Foundation for Research in Rheumatology (FOREUM) for supporting the study.Disclosure of Interests:Subhashisa Swain: None declared, Carol Coupland: None declared, Victoria Strauss: None declared, Christian Mallen Grant/research support from: My department has received financial grants from BMS for a cardiology trial., Chang-Fu Kuo: None declared, Aliya Sarmanova: None declared, Michael Doherty Grant/research support from: AstraZeneca funded the Nottingham Sons of Gout study, Consultant of: Advisory borads on gout for Grunenthal and Mallinckrodt, Weiya Zhang Consultant of: Grunenthal for advice on gout management, Speakers bureau: Bioiberica as an invited speaker for EULAR 2016 satellite symposium


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Thomas Inns ◽  
Kate M. Fleming ◽  
Miren Iturriza-Gomara ◽  
Daniel Hungerford

Abstract Background Rotavirus infection has been proposed as a risk factor for coeliac disease (CD) and type 1 diabetes (T1D). The UK introduced infant rotavirus vaccination in 2013. We have previously shown that rotavirus vaccination can have beneficial off-target effects on syndromes, such as hospitalised seizures. We therefore investigated whether rotavirus vaccination prevents CD and T1D in the UK. Methods A cohort study of children born between 2010 and 2015 was conducted using primary care records from the Clinical Practice Research Datalink. Children were followed up from 6 months to 7 years old, with censoring for outcome, death or leaving the practice. CD was defined as diagnosis of CD or the prescription of gluten-free goods. T1D was defined as a T1D diagnosis. The exposure was rotavirus vaccination, defined as one or more doses. Mixed-effects Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CIs). Models were adjusted for potential confounders and included random intercepts for general practices. Results There were 880,629 children in the cohort (48.8% female). A total of 343,113 (39.0%) participants received rotavirus vaccine; among those born after the introduction of rotavirus vaccination, 93.4% were vaccinated. Study participants contributed 4,388,355 person-years, with median follow-up 5.66 person-years. There were 1657 CD cases, an incidence of 38.0 cases per 100,000 person-years. Compared with unvaccinated children, the adjusted HR for a CD was 1.05 (95% CI 0.86–1.28) for vaccinated children. Females had a 40% higher hazard than males. T1D was recorded for 733 participants, an incidence of 17.1 cases per 100,000 person-years. In adjusted analysis, rotavirus vaccination was not associated with risk of T1D (HR = 0.89, 95% CI 0.68–1.19). Conclusions Rotavirus vaccination has reduced diarrhoeal disease morbidity and mortality substantial since licencing in 2006. Our finding from this large cohort study did not provide evidence that rotavirus vaccination prevents CD or T1D, nor is it associated with increased risk, delivering further evidence of rotavirus vaccine safety.


BMJ Open ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. e009147 ◽  
Author(s):  
Lamiae Grimaldi-Bensouda ◽  
Olaf Klungel ◽  
Xavier Kurz ◽  
Mark C H de Groot ◽  
Ana S Maciel Afonso ◽  
...  

2018 ◽  
Vol 78 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Dahai Yu ◽  
Kelvin P Jordan ◽  
Kym I E Snell ◽  
Richard D Riley ◽  
John Bedson ◽  
...  

ObjectivesThe ability to efficiently and accurately predict future risk of primary total hip and knee replacement (THR/TKR) in earlier stages of osteoarthritis (OA) has potentially important applications. We aimed to develop and validate two models to estimate an individual’s risk of primary THR and TKR in patients newly presenting to primary care.MethodsWe identified two cohorts of patients aged ≥40 years newly consulting hip pain/OA and knee pain/OA in the Clinical Practice Research Datalink. Candidate predictors were identified by systematic review, novel hypothesis-free ‘Record-Wide Association Study’ with replication, and panel consensus. Cox proportional hazards models accounting for competing risk of death were applied to derive risk algorithms for THR and TKR. Internal–external cross-validation (IECV) was then applied over geographical regions to validate two models.Results45 predictors for THR and 53 for TKR were identified, reviewed and selected by the panel. 301 052 and 416 030 patients newly consulting between 1992 and 2015 were identified in the hip and knee cohorts, respectively (median follow-up 6 years). The resultant model C-statistics is 0.73 (0.72, 0.73) and 0.79 (0.78, 0.79) for THR (with 20 predictors) and TKR model (with 24 predictors), respectively. The IECV C-statistics ranged between 0.70–0.74 (THR model) and 0.76–0.82 (TKR model); the IECV calibration slope ranged between 0.93–1.07 (THR model) and 0.92–1.12 (TKR model).ConclusionsTwo prediction models with good discrimination and calibration that estimate individuals’ risk of THR and TKR have been developed and validated in large-scale, nationally representative data, and are readily automated in electronic patient records.


Gut ◽  
2018 ◽  
Vol 68 (8) ◽  
pp. 1458-1464 ◽  
Author(s):  
Zhiwei Liu ◽  
Rotana Alsaggaf ◽  
Katherine A McGlynn ◽  
Lesley A Anderson ◽  
Huei-Ting Tsai ◽  
...  

ObjectiveTo evaluate the association between statin use and risk of biliary tract cancers (BTC).DesignThis is a nested case–control study conducted in the UK Clinical Practice Research Datalink. We included cases diagnosed with incident primary BTCs, including cancers of the gall bladder, bile duct (ie, both intrahepatic and extrahepatic cholangiocarcinoma), ampulla of Vater and mixed type, between 1990 and 2017. For each case, we selected five controls who did not develop BTCs at the time of case diagnosis, matched by sex, year of birth, calendar time and years of enrolment in the general practice using incidence density sampling. Exposures were defined as two or more prescription records of statins 1 year prior to BTC diagnosis or control selection. ORs and 95% CIs for associations between statins and BTC overall and by subtypes were estimated using conditional logistic regression, adjusted for relevant confounders.ResultsWe included 3118 BTC cases and 15 519 cancer-free controls. Current statin use versus non-use was associated with a reduced risk of all BTCs combined (adjusted OR=0.88, 95% CI 0.79 to 0.98). The reduced risks were most pronounced among long-term users, as indicated by increasing number of prescriptions (ptrend=0.016) and cumulative dose of statins (ptrend=0.008). The magnitude of association was similar for statin use and risk of individual types of BTCs. The reduced risk of BTCs associated with a record of current statin use versus non-use was more pronounced among persons with diabetes (adjusted OR=0.72, 95% CI 0.57 to 0.91). Among non-diabetics, the adjusted OR for current statin use versus non-use was 0.91 (95% CI 0.81 to 1.03, pheterogeneity=0.007).ConclusionCompared with non-use of statins, current statin use is associated with 12% lower risk of BTCs; no association found with former statin use. If replicated, particularly in countries with a high incidence of BTCs, our findings could pave the way for evaluating the value of statins for BTC chemoprevention.


2018 ◽  
Vol 28 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Rory J. Ferguson ◽  
Daniel Prieto‐Alhambra ◽  
Christine Walker ◽  
Dahai Yu ◽  
Jose M. Valderas ◽  
...  

Author(s):  
Mihail Samnaliev ◽  
Volkan Barut ◽  
Sharada Weir ◽  
Julia Langham ◽  
Sue Langham ◽  
...  

Abstract Objectives To describe direct healthcare costs for adults with systemic lupus erythematosus (SLE) in the UK over time and by disease severity and encounter type. Methods Patients aged ≥18 years with SLE were identified using the linked Clinical Practice Research Datalink—Hospital Episode Statistics database from January 2005 to December 2017. Patients were classified as having mild, moderate, or severe disease using an adapted claims-based algorithm based on prescriptions and comorbid conditions. We estimated all-cause healthcare costs and incremental costs associated with each year of follow-up compared with a baseline year adjusting for age, sex, disease severity, and comorbid conditions (2017 UK pounds). Results We identified 802 patients; 369 (46.0%) with mild, 345 (43.0%) moderate, and 88 (11.0%) severe disease. The mean all-cause cost increased in the 3 years before diagnosis, peaked in the first year after diagnosis and remained high. Adjusted total mean annual increase in costs per patient was £4476 (95% confidence interval £3809–5143) greater in the year of diagnosis compared with the baseline year (p &lt; 0.0001). The increase in costs per year were 4.7-fold and 1.6-fold higher among patients with severe SLE compared with those with mild and moderate SLE respectively. Primary care utilisation was the leading component of costs during the first year of diagnosis. Conclusion The healthcare costs for patients with SLE in the UK are substantial, remain high after diagnosis and increase with increasing severity. Future research should assess whether earlier diagnosis and treatment may reduce disease severity and associated high healthcare costs.


2018 ◽  
Vol 37 (8) ◽  
pp. 2103-2111 ◽  
Author(s):  
Jeremy G. Royle ◽  
Peter C. Lanyon ◽  
Matthew J. Grainge ◽  
Abhishek Abhishek ◽  
Fiona A. Pearce

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