FRAX™ and the assessment of fracture probability in men and women from the UK

SUMMARY: A fracture risk assessment tool (FRAX) is developed based on the use of clinical risk factors with or without bone mineral density tests applied to the UK. INTRODUCTION: The aim of this study was to apply an assessment tool for the prediction of fracture in men and women with the use of clinical risk factors (CRFs) for fracture with and without the use of femoral neck bone mineral density (BMD). The clinical risk factors, identified from previous meta-analyses, comprised body mass index (BMI, as a continuous variable), a prior history of fracture, a parental history of hip fracture, use of oral glucocorticoids, rheumatoid arthritis and other secondary causes of osteoporosis, current smoking, and alcohol intake 3 or more units daily. METHODS: Four models were constructed to compute fracture probabilities based on the epidemiology of fracture in the UK. The models comprised the ten-year probability of hip fracture, with and without femoral neck BMD, and the ten-year probability of a major osteoporotic fracture, with and without BMD. For each model fracture and death hazards were computed as continuous functions. RESULTS: Each clinical risk factor contributed to fracture probability. In the absence of BMD, hip fracture probability in women with a fixed BMI (25 kg/m(2)) ranged from 0.2% at the age of 50 years for women without CRF's to 22% at the age of 80 years with a parental history of hip fracture (approximately 100-fold range). In men, the probabilities were lower, as was the range (0.1 to 11% in the examples above). For a major osteoporotic fracture the probabilities ranged from 3.5% to 31% in women, and from 2.8% to 15% in men in the example above. The presence of one or more risk factors increased probabilities in an incremental manner. The differences in probabilities between men and women were comparable at any given T-score and age, except in the elderly where probabilities were higher in women than in men due to the higher mortality of the latter. CONCLUSION: The models provide a framework which enhances the assessment of fracture risk in both men and women by the integration of clinical risk factors alone and/or in combination with BMD

The prevalence of COPD in England: an ontological approach to case detection in primary care

Chronic obstructive pulmonary disease (COPD) is a significant cause of morbidity and mortality in England, however estimates of its prevalence vary considerably. Routinely collected and coded primary care data can be used to monitor disease prevalence, however reliance upon diagnostic codes alone is likely to miss cases.We devised an ontological approach to COPD case detection and implemented it in a large primary care database to identify definite and probable cases of COPD. We used this to estimate the prevalence of COPD in England.Use of this approach to detect definite COPD cases yielded a prevalence of 2.57% (95% CI 2.55-2.60) in the total population, 4.56% (95%CI 4.52-4.61) in those aged ≥ 35 and 5.41% (95% CI 5.36-5.47) in ex or current smokers. The ontological approach identified an additional 10,543 definite cases compared with using diagnostic codes alone. Prevalence estimates were higher than the 1.9% prevalence currently reported by the UK primary care pay for performance (P4P) disease register. COPD prevalence when definite and probable cases were combined was 3.02% (95% CI 3.0-3.05) in the total population, 5.38% (95% CI 5.33-5.42) in those aged ≥ 35 and 6.46% (95% CI 6.46-6.40-6.56) in ex or current smokers.We demonstrate a robust reproducible method for COPD case detection in routinely collected primary care data. Our calculated prevalence differed significantly from current estimates based upon P4P data, suggesting that the burden of COPD in England is greater than currently predicted

The prevalence of COPD in England: an ontological approach to case detection in primary care

Chronic obstructive pulmonary disease (COPD) is a significant cause of morbidity and mortality in England, however estimates of its prevalence vary considerably. Routinely collected and coded primary care data can be used to monitor disease prevalence, however reliance upon diagnostic codes alone is likely to miss cases.We devised an ontological approach to COPD case detection and implemented it in a large primary care database to identify definite and probable cases of COPD. We used this to estimate the prevalence of COPD in England.Use of this approach to detect definite COPD cases yielded a prevalence of 2.57% (95% CI 2.55-2.60) in the total population, 4.56% (95%CI 4.52-4.61) in those aged ≥ 35 and 5.41% (95% CI 5.36-5.47) in ex or current smokers. The ontological approach identified an additional 10,543 definite cases compared with using diagnostic codes alone. Prevalence estimates were higher than the 1.9% prevalence currently reported by the UK primary care pay for performance (P4P) disease register. COPD prevalence when definite and probable cases were combined was 3.02% (95% CI 3.0-3.05) in the total population, 5.38% (95% CI 5.33-5.42) in those aged ≥ 35 and 6.46% (95% CI 6.46-6.40-6.56) in ex or current smokers.We demonstrate a robust reproducible method for COPD case detection in routinely collected primary care data. Our calculated prevalence differed significantly from current estimates based upon P4P data, suggesting that the burden of COPD in England is greater than currently predicted

ADVANCE database characterisation and fit for purpose assessment for multi-country studies on the coverage, benefits and risks of pertussis vaccinations.

INTRODUCTION: The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of&nbsp;vaccines. OBJECTIVE: To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an&nbsp;example. METHODS: Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept&nbsp;studies. RESULTS: The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of events were comparable in magnitude and age-distribution between databases with the same characteristics. Several conditions (persistent crying and somnolence) were not captured by the databases for which outcomes were restricted to hospital discharge&nbsp;diagnoses. CONCLUSION: The database characterisation programs and workflows allowed for an efficient, transparent and standardised description and verification of electronic healthcare databases which may participate in pertussis vaccine coverage, benefit and risk studies. This approach is ready to be used for other vaccines/events to create readiness for participation in other vaccine related&nbsp;studies.</p

A case of swine influenza A(H1N2)v in England, November 2023.

Under International Health Regulations from 2005, a human infection caused by a novel influenza A virus variant is considered an event that has potential for high public health impact and is immediately notifiable to the World Health Organisation. We here describe the clinical, epidemiological and virological features of a confirmed human case of swine influenza A(H1N2)v in England detected through community respiratory virus surveillance. Swabbing and contact tracing helped refine public health risk assessment, following this unusual and unexpected finding