27 research outputs found

    Opportunities to reduce antibiotic prescribing for patients with COPD in primary care: a cohort study using electronic health records from the Clinical Practice Research Datalink (CPRD)

    Get PDF
    Background In primary care there is uncertainty about which patients with acute exacerbations of COPD (AECOPD) benefit from antibiotics. Objectives To identify which types of COPD patients get the most antibiotics in primary care to support targeted antibiotic stewardship. Methods Observational study of COPD patients using a large English primary care database with 12 month follow-up. We estimated the incidence of and risk factors for antibiotic prescribing relative to the number of AECOPD during follow-up, considering COPD severity, smoking, obesity and comorbidity. Results From 157 practices, 19594 patients were diagnosed with COPD, representing 2.6% of patients and 11.5% of all prescribed antibiotics. Eight hundred and thirty-three (4.5%) patients with severe COPD and frequent AECOPD were prescribed six to nine prescriptions per year and accounted for 13.0% of antibiotics. Individuals with mild to moderate COPD and zero or one AECOPD received one to three prescriptions per year but accounted for 42.5% of all prescriptions. In addition to COPD severity, asthma, chronic heart disease, diabetes, heart failure and influenza vaccination were independently associated with increased antibiotic use. Conclusions Patients with severe COPD have the highest rates of antibiotic prescribing but most antibiotics are prescribed for patients with mild to moderate COPD. Antibiotic stewardship should focus on the dual goals of safely reducing the volume of prescribing in patients with mild to moderate COPD, and optimizing prescribing in patients with severe disease who are at significant risk of drug resistance

    Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

    Get PDF
    [EN] Objectives To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. Design Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. Setting 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). Main outcomes The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). Results Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. Conclusions Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.VN is funded by a Public Health England PhD Studentship. RWA is supported by a Wellcome Trust Clinical Research Career Development Fellowship (206602/Z/17/Z). JMGG and CS contributions to this work were partially supported by the MTS4up Spanish project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R), the CrowdHealth H2020-SC1-2016-CNECT project (No. 727560) (JMGG) and the Inadvance H2020-SC1-BHC-2018-2020 project (No. 825750). PR and DA did not receive any direct funding for this project. Access to the Clinical Practice Research Datalink was supported by the UK Economic and Social Research Council (ES/P008321/1). Access to aggregated Hospital Episode Statistics was provided by Public Health England. This work was further supported by Health Data Research UK, which is funded by 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 and the Wellcome Trust.Rockenschaub, P.; Nguyen, V.; Aldridge, RW.; Acosta, D.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2020). Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015). BMJ Open. 10(2):1-9. https://doi.org/10.1136/bmjopen-2019-034396S19102Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Burton, P. R., Murtagh, M. J., Boyd, A., Williams, J. B., Dove, E. S., Wallace, S. E., … Knoppers, B. M. (2015). Data Safe Havens in health research and healthcare. Bioinformatics, 31(20), 3241-3248. doi:10.1093/bioinformatics/btv279Cruz-Correia R , Rodrigues P , Freitas A . Chapter: 4, Data quality and integration issues in electronic health records. In: Information discovery on electronic health records. CRC Press, 2009: 55–95.Massoudi, B. L., Goodman, K. W., Gotham, I. J., Holmes, J. H., Lang, L., Miner, K., … Fu, P. C. (2012). An informatics agenda for public health: summarized recommendations from the 2011 AMIA PHI Conference. Journal of the American Medical Informatics Association, 19(5), 688-695. doi:10.1136/amiajnl-2011-000507Schlegel, D. R., & Ficheur, G. (2017). Secondary Use of Patient Data: Review of the Literature Published in 2016. Yearbook of Medical Informatics, 26(01), 68-71. doi:10.15265/iy-2017-032Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Herrett, E., Thomas, S. L., Schoonen, W. M., Smeeth, L., & Hall, A. J. (2010). Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British Journal of Clinical Pharmacology, 69(1), 4-14. doi:10.1111/j.1365-2125.2009.03537.xSáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010Tate AR , Dungey S , Glew S , et al . Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? cross-sectional study using the CPRD database. BMJ Open 2017;7:e012905.doi:10.1136/bmjopen-2016-012905Calvert M , Shankar A , McManus RJ , et al . Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. BMJ 2009;338:b1870.doi:10.1136/bmj.b1870Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology, 44(3), 827-836. doi:10.1093/ije/dyv098Herbert, A., Wijlaars, L., Zylbersztejn, A., Cromwell, D., & Hardelid, P. (2017). Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). International Journal of Epidemiology, 46(4), 1093-1093i. doi:10.1093/ije/dyx015Chisholm J . The read clinical classification. BMJ 1990;300:1092.doi:10.1136/bmj.300.6732.1092Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., … Hemingway, H. (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. Journal of the American Medical Informatics Association, 26(12), 1545-1559. doi:10.1093/jamia/ocz105Department for Communities and Local Government . The English Index of Multiple Deprivation (IMD) 2015 - Guidance. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/464430/English_Index_of_Multiple_Deprivation_2015_-_Guidance.pdf [Accessed 8 Dec 2019].Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Borg, I., & Groenen, P. (2003). Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement, 40(3), 277-280. doi:10.1111/j.1745-3984.2003.tb01108.xSáez, C., & García-Gómez, J. M. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics, 119, 109-124. doi:10.1016/j.ijmedinf.2018.09.015Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Crespillo, A. P., … Rahimi, K. (2018). Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet, 391(10120), 572-580. doi:10.1016/s0140-6736(17)32520-5Herrett E , Shah AD , Boggon R , et al . Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013;346:f2350.doi:10.1136/bmj.f2350Pujades-Rodriguez M , Timmis A , Stogiannis D , et al . Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLoS One 2014;9:e104671.doi:10.1371/journal.pone.0104671Lee S , Shafe ACE , Cowie MR . Uk stroke incidence, mortality and cardiovascular risk management 1999-2008: time-trend analysis from the general practice research database. BMJ Open 2011;1:e000269.doi:10.1136/bmjopen-2011-000269Bhatnagar, P., Wickramasinghe, K., Williams, J., Rayner, M., & Townsend, N. (2015). The epidemiology of cardiovascular disease in the UK 2014. Heart, 101(15), 1182-1189. doi:10.1136/heartjnl-2015-307516Taylor, C. J., Ordóñez-Mena, J. M., Roalfe, A. K., Lay-Flurrie, S., Jones, N. R., Marshall, T., & Hobbs, F. D. R. (2019). Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ, l223. doi:10.1136/bmj.l223Gho JMIH , Schmidt AF , Pasea L , et al . An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open 2018;8:e018331.doi:10.1136/bmjopen-2017-018331Quint JK , Müllerova H , DiSantostefano RL , et al . Validation of chronic obstructive pulmonary disease recording in the clinical practice research Datalink (CPRD-GOLD). BMJ Open 2014;4:e005540.doi:10.1136/bmjopen-2014-005540Bhaskaran K , Forbes HJ , Douglas I , et al . Representativeness and optimal use of body mass index (BMI) in the UK clinical practice research Datalink (CPRD). BMJ Open 2013;3:e003389.doi:10.1136/bmjopen-2013-003389Booth, H. P., Prevost, A. T., & Gulliford, M. C. (2013). Validity of smoking prevalence estimates from primary care electronic health records compared with national population survey data for England, 2007 to 2011. Pharmacoepidemiology and Drug Safety, 22(12), 1357-1361. doi:10.1002/pds.3537Booth H , Dedman D , Wolf A . CPRD aurum frequently asked questions (FAQs). CPRD 2019.Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2011). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. doi:10.1093/pubmed/fdr054Marmot, M. G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., … Smith, G. D. (1991). Health inequalities among British civil servants: the Whitehall II study. The Lancet, 337(8754), 1387-1393. doi:10.1016/0140-6736(91)93068-kKivimäki, M., Batty, G. D., Singh-Manoux, A., Britton, A., Brunner, E. J., & Shipley, M. J. (2017). Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records. Epidemiology, 28(5), 735-739. doi:10.1097/ede.0000000000000688Crosignani, P. G. (2003). Breast cancer and hormone-replacement therapy in the Million Women Study. Maturitas, 46(2), 91-92. doi:10.1016/j.maturitas.2003.09.002Wright FL , Green J , Canoy D , et al . Vascular disease in women: comparison of diagnoses in hospital episode statistics and general practice records in England. BMC Med Res Methodol 2012;12:161.doi:10.1186/1471-2288-12-161Herrett, E., Smeeth, L., Walker, L., & Weston, C. (2010). The Myocardial Ischaemia National Audit Project (MINAP). Heart, 96(16), 1264-1267. doi:10.1136/hrt.2009.192328Silver LE , Heneghan C , Mehta Z , et al . Substantial underestimation of incidence of acute myocardial infarction by hospital discharge diagnostic coding data: a prospective population-based study. Heart 2009;95.Health and Social Care Information Centre . Coding clinic guidance. 5th edn, 2010.Health & Social Care Information Centre . National Clinical Coding Standards - ICD-10. 4th edn, 2013. https://hscic.kahootz.com/connect.ti/t_c_home/view?objectId=31445829Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. doi:10.1080/07421222.1996.1151809

    Antibiotic prescribing in UK care homes 2016-2017: retrospective cohort study of linked data.

    Get PDF
    BACKGROUND: Older people living in care homes are particularly susceptible to infections and antibiotics are therefore used frequently for this population. However, there is limited information on antibiotic prescribing in this setting. This study aimed to investigate the frequency, patterns and risk factors for antibiotic prescribing in a large chain of UK care homes. METHODS: Retrospective cohort study of administrative data from a large chain of UK care homes (resident and care home-level) linked to individual-level pharmacy data. Residents aged 65 years or older between 1 January 2016 and 31 December 2017 were included. Antibiotics were classified by type and as new or repeated prescriptions. Rates of antibiotic prescribing were calculated and modelled using multilevel negative binomial regression. RESULTS: 13,487 residents of 135 homes were included. The median age was 85; 63% residents were female. 28,689 antibiotic prescriptions were dispensed, the majority were penicillins (11,327, 39%), sulfonamides and trimethoprim (5818, 20%), or other antibacterials (4665, 16%). 8433 (30%) were repeat prescriptions. The crude rate of antibiotic prescriptions was 2.68 per resident year (95% confidence interval (CI) 2.64-2.71). Increased antibiotic prescribing was associated with residents requiring more medical assistance (adjusted incidence rate ratio for nursing opposed to residential care 1.21, 95% CI 1.13-1.30). Prescribing rates varied widely by care home but there were no significant associations with the care home-level characteristics available in routine data. CONCLUSIONS: Rates of antibiotic prescribing in care homes are high and there is substantial variation between homes. Further research is needed to understand the drivers of this variation to enable development of effective stewardship approaches that target the influences of prescribing

    Permian high-temperature metamorphism in the Western Alps (NW Italy)

    Get PDF
    During the late Palaeozoic, lithospheric thinning in part of the Alpine realm caused high-temperature low-to-medium pressure metamorphism and partial melting in the lower crust. Permian metamorphism and magmatism has extensively been recorded and dated in the Central, Eastern, and Southern Alps. However, Permian metamorphic ages in the Western Alps so far are constrained by very few and sparsely distributed data. The present study fills this gap. We present U/Pb ages of metamorphic zircon from several Adria-derived continental units now situated in the Western Alps, defining a range between 286 and 266 Ma. Trace element thermometry yields temperatures of 580-890°C from Ti-in-zircon and 630-850°C from Zr-in-rutile for Permian metamorphic rims. These temperature estimates, together with preserved mineral assemblages (garnet-prismatic sillimanite-biotite-plagioclase-quartz-K-feldspar-rutile), define pervasive upper-amphibolite to granulite facies conditions for Permian metamorphism. U/Pb ages from this study are similar to Permian ages reported for the Ivrea Zone in the Southern Alps and Austroalpine units in the Central and Eastern Alps. Regional comparison across the former Adriatic and European margin reveals a complex pattern of ages reported from late Palaeozoic magmatic and metamorphic rocks (and relics thereof): two late Variscan age groups (~330 and ~300 Ma) are followed seamlessly by a broad range of Permian ages (300-250 Ma). The former are associated with late-orogenic collapse; in samples from this study these are weakly represented. Clearly, dominant is the Permian group, which is related to crustal thinning, hinting to a possible initiation of continental rifting along a passive margin

    Incidence, healthcare-seeking behaviours, antibiotic use and natural history of common infection syndromes in England:results from the Bug Watch community cohort study

    Get PDF
    Background: Better information on the typical course and management of acute common infections in the community could inform antibiotic stewardship campaigns. We aimed to investigate the incidence, management, and natural history of a range of infection syndromes (respiratory, gastrointestinal, mouth/dental, skin/soft tissue, urinary tract, and eye). Methods: Bug Watch was an online prospective community cohort study of the general population in England (2018–2019) with weekly symptom reporting for 6 months. We combined symptom reports into infection syndromes, calculated incidence rates, described the proportion leading to healthcare-seeking behaviours and antibiotic use, and estimated duration and severity. Results: The cohort comprised 873 individuals with 23,111 person-weeks follow-up. The mean age was 54 years and 528 (60%) were female. We identified 1422 infection syndromes, comprising 40,590 symptom reports. The incidence of respiratory tract infection syndromes was two per person year; for all other categories it was less than one. 194/1422 (14%) syndromes led to GP (or dentist) consultation and 136/1422 (10%) to antibiotic use. Symptoms usually resolved within a week and the third day was the most severe. Conclusions: Most people reported managing their symptoms without medical consultation. Interventions encouraging safe self-management across a range of acute infection syndromes could decrease pressure on primary healthcare services and support targets for reducing antibiotic prescribing
    corecore