UNASSIGNED: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.
UNASSIGNED: This study aimed to transform primary care data into the OMOP CDM format.
UNASSIGNED: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard.
UNASSIGNED: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data.
UNASSIGNED: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

BACKGROUND: Inappropriate or overuse of antibiotic prescribing in primary care highlights an opportunity for antimicrobial stewardship (AMS) programs aimed at reducing unnecessary use of antimicrobials through education, policies and practice audits that optimize antibiotic prescribing. Evidence from the early part of the pandemic indicates a high rate of prescribing of antibiotics for patients with COVID-19. It is crucial to surveil antibiotic prescribing by primary care providers from the start of the pandemic and into its endemic stage to understand the effects of the pandemic and better target effective AMS programs.
METHODS: This was a matched pair population-based cohort study that used electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Participants included all patients that visited their primary care provider and met the inclusion criteria for COVID-19, respiratory tract infection (RTI), or non-respiratory or influenza-like-illness (negative). Four outcomes were evaluated (a) receipt of an antibiotic prescription; (b) receipt of a non-antibiotic prescription; (c) a subsequent primary care visit (for any reason); and (d) a subsequent primary care visit with a bacterial infection diagnosis. Conditional logistic regression was used to evaluate the association between COVID-19 and each of the four outcomes. Each model was adjusted for location (rural or urban), material and social deprivation, smoking status, alcohol use, obesity, pregnancy, HIV, cancer and number of chronic conditions.
RESULTS: The odds of a COVID-19 patient receiving an antibiotic within 30 days of their visit is much lower than for patients visiting for RTI or for a non-respiratory or influenza-like-illnesses (AOR = 0.08, 95% CI[0.07, 0.09] compared to RTI, and AOR = 0.43, 95% CI[0.38, 0.48] compared to negatives). It was found that a patient visit for COVID-19 was much less likely to have a subsequent visit for a bacterial infection at all time points.
CONCLUSIONS: Encouragingly, COVID-19 patients were much less likely to receive an antibiotic prescription than patients with an RTI. However, this highlights an opportunity to leverage the education and attitude change brought about by the public health messaging during the COVID-19 pandemic (that antibiotics cannot treat a viral infection), to reduce the prescribing of antibiotics for other viral RTIs and improve antibiotic stewardship.

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Menopause is a normal transition in a woman\'s life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. This study developed and validated a case definition for problematic menopause using Canadian primary care electronic medical records, which is an essential step in examining the condition and improving quality of care.
We used data from the Canadian Primary Care Sentinel Surveillance Network including billing and diagnostic codes, diagnostic free-text, problem list entries, medications, and referrals. These data formed the basis of an expert-reviewed reference standard data set and contained the features that were used to train a machine learning model based on classification and regression trees. An ad hoc feature importance measure coupled with recursive feature elimination and clustering were applied to reduce our initial 86,000 element feature set to a few tens of the most relevant features in the data, while class balancing was accomplished with random under- and over-sampling. The final case definition was generated from the tree-based machine learning model output combined with a feature importance algorithm. Two independent samples were used: one for training / testing the machine learning algorithm and the other for case definition validation.
We randomly selected 2,776 women aged 45-60 for this analysis and created a case definition, consisting of two occurrences within 24 months of International Classification of Diseases, Ninth Revision, Clinical Modification code 627 (or any sub-codes) OR one occurrence of Anatomical Therapeutic Chemical classification code G03CA (or any sub-codes) within the patient chart, that was highly effective at detecting problematic menopause cases. This definition produced a sensitivity of 81.5% (95% CI: 76.3-85.9%), specificity of 93.5% (91.9-94.8%), positive predictive value of 73.8% (68.3-78.6%), and negative predictive value of 95.7% (94.4-96.8%).
Our case definition for problematic menopause demonstrated high validity metrics and so is expected to be useful for epidemiological study and surveillance. This case definition will enable future studies exploring the management of menopause in primary care settings.

The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe\'s oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance.
The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice.
The RSC\'s pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC\'s cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort\'s sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death.
As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively.
The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.

Exacerbation-prone asthma subtype has been reported in studies using data-driven methodologies. However, patterns of severe exacerbations have not been studied.
To investigate longitudinal trajectories of severe wheeze exacerbations from infancy to school age.
We applied longitudinal k-means clustering to derive exacerbation trajectories among 887 participants from a population-based birth cohort with severe wheeze exacerbations confirmed in healthcare records. We examined early-life risk factors of the derived trajectories, and their asthma-related outcomes and lung function in adolescence.
498/887 children (56%) had physician-confirmed wheeze by age 8 years, of whom 160 had at least one severe exacerbation. A two-cluster model provided the optimal solution for severe exacerbation trajectories among these 160 children: \"Infrequent exacerbations (IE)\" (n = 150, 93.7%) and \"Early-onset frequent exacerbations (FE)\" (n = 10, 6.3%). Shorter duration of breastfeeding was the strongest early-life risk factor for FE (weeks, median [IQR]: FE, 0 [0-1.75] vs. IE, 6 [0-20], P < .001). Specific airway resistance (sRaw ) was significantly higher in FE compared with IE trajectory throughout childhood. We then compared children in the two exacerbation trajectories with those who have never wheezed (NW, n = 389) or have wheezed but had no severe exacerbations (WNE, n = 338). At age 8 years, FEV1 /FVC was significantly lower and FeNO significantly higher among FE children compared with all other groups. By adolescence (age 16), subjects in FE trajectory were significantly more likely to have current asthma (67% FE vs. 30% IE vs. 13% WNE, P < .001) and use inhaled corticosteroids (77% FE vs. 15% IE vs. 18% WNE, P < .001). Lung function was significantly diminished in the FE trajectory (FEV1 /FVC, mean [95%CI]: 89.9% [89.3-90.5] vs. 88.1% [87.3-88.8] vs. 85.1% [83.4-86.7] vs. 74.7% [61.5-87.8], NW, WNE, IE, FE respectively, P < .001).
We have identified two distinct trajectories of severe exacerbations during childhood with different early-life risk factors and asthma-related outcomes in adolescence.

Record linkage is increasingly used to expand the information available for public health research. An understanding of record linkage methods and the relevant strengths and limitations is important for robust analysis and interpretation of linked data. Here, we describe the approach used by Clinical Practice Research Datalink (CPRD) to link primary care data to other patient level datasets, and the potential implications of this approach for CPRD data analysis. General practice electronic health record software providers separately submit de-identified data to CPRD and patient identifiers to NHS Digital, excluding patients who have opted-out from contributing data. Data custodians for external datasets also send patient identifiers to NHS Digital. NHS Digital uses identifiers to link the datasets using an 8-stage deterministic methodology. CPRD subsequently receives a de-identified linked cohort file and provides researchers with anonymised linked data and metadata detailing the linkage process. This methodology has been used to generate routine primary care linked datasets, including data from Hospital Episode Statistics, Office for National Statistics and National Cancer Registration and Analysis Service. 10.6 million (M) patients from 411 English general practices were included in record linkage in June 2018. 9.1M (86%) patients were of research quality, of which 8.0M (88%) had a valid NHS number and were eligible for linkage in the CPRD standard linked dataset release. Linking CPRD data to other sources improves the range and validity of research studies. This manuscript, together with metadata generated on match strength and linkage eligibility, can be used to inform study design and explore potential linkage-related selection and misclassification biases.

Fragility fractures of the hip have a major impact on the lives of patients and their families. This study highlights significant geographical variation in secondary fracture prevention with even the highest performing regions failing the majority of patients despite robust evidence supporting the benefits of diagnosis and treatment.
The purpose of the study is to describe the geographic variation in anti-osteoporosis drug therapy prescriptions before and after a hip fracture during 1999-2013 in the UK.
We used primary care data (Clinical Practice Research Datalink) to identify patients with a hip fracture and primary care prescriptions of any anti-osteoporosis drugs prior to the index hip fracture and up to 5 years after. Geographic variations in prescribing before and after availability of generic oral bisphosphonates were analysed. Multivariable logistic regression models were adjusted for gender, age and body mass index (BMI).
Thirteen thousand sixty-nine patients (76 % female) diagnosed with a hip fracture during 1999-2013 were identified. Eleven per cent had any anti-osteoporosis drug prescription in the 6 months prior to the index hip fracture. In the 0-4 months following a hip fracture, 5 % of patients were prescribed anti-osteoporosis drugs in 1999, increasing to 51 % in 2011 and then decreasing to 39 % in 2013. The independent predictors (OR (95 % CI)) of treatment initiation included gender (male 0.42 (0.36-0.49)), BMI (0.98 per kg/m2 increase (0.97-1.00)) and geographic region (1.29 (0.89-1.87) North East vs. 0.56 (0.43-0.73) South Central region). Geographic differences in prescribing persisted over the 5-year follow-up. If all patients were treated at the rate of the highest performing region, then nationally, an additional 3214 hip fracture patients would be initiated on therapy every year.
Significant geographic differences exist in prescribing of anti-osteoporosis drugs after hip fracture despite adjustment for potential confounders. Further work examining differences in health care provision may inform strategies to improve secondary fracture prevention after hip fracture.

BACKGROUND: Technological advances in clinical data capturing and storage systems have led to recent attempts at disease surveillance and region specific population health planning through regularly collected primary care administrative clinical data. However the accuracy and comprehensiveness of primary care health records remain questionable.
METHODS: We aimed to explore the perceptions and experiences of general practice staff in maintaining accurate patient health data within clinical software used in primary care settings of regional NSW. Focus groups were conducted with general practitioners, practice nurses and practice administrative staff from 17 practices in the Illawarra-Shoalhaven region of the state of New South Wales (NSW) in Australia that had participated in the Sentinel Practices Data Sourcing (SPDS) project - a general practice based chronic disease surveillance and data quality improvement study. A total of 25 respondents that included 12 general practitioners (GPs) and 13 practice staff participated in the 6 focus groups. Focus groups were audio-recorded and transcribed verbatim. Thematic analysis of the data was undertaken.
RESULTS: Five key themes emerged from the data. Firstly, the theme of resourcing data management raised issues of time constraints, the lack of a dedicated data management role and the importance of multidisciplinary involvement, including a data champion. The need for incentives was identified as being important to motivate ongoing commitment to maintaining data quality. However, quality of software packages, including coding issues and software limitations and information technology skills were seen as key barriers. The final theme provided insight into the lessons learnt from the project and the increased awareness of the importance of data quality amongst practice staff.
CONCLUSIONS: The move towards electronic methods of maintaining general practice patient records offers significant potential benefits in terms of both patient care and monitoring of health status and health needs within the community. However, this study has reinforced the importance of human factors in the maintenance of such datasets. To achieve optimal benefits of electronic health and medical records for patient care and for population health planning purposes, it is extremely essential to address the barriers that clinicians and other staff face in maintaining complete and correct primary care patient electronic health and medical information.

BACKGROUND: General practice data provide large population-based cohorts of individuals with prospectively collected medical information with promising potential for studying the causes and consequences of congenital anomalies (CAs). We sought to validate these data through comparison with CA registries.
METHODS: Our study population was 794,209 children in The Health Improvement Network (THIN) primary care database, born between 1990 and 2009 with a median follow-up of 6.7 years. We compared the birth prevalence of any major and system-specific CAs with the European Surveillance of Congenital Anomalies (EUROCAT) United Kingdom registries.
RESULTS: The birth prevalence of any major CA for children in THIN diagnosed before 1 year of age was 198 per 10,000 (95% confidence interval, 195-201), which was slightly higher than the EUROCAT prevalence of 167 per 10,000 (relative risk, 1.18; 95% confidence interval, 1.16-1.20). Absolute differences in prevalence between THIN and EUROCAT were small across 16 system-specific anomaly groups. The majority of children in THIN with major CAs had recorded diagnoses before 1 year of age (72%), but including children diagnosed at any age increased the overall prevalence to 277 per 10,000 births.
CONCLUSIONS: The prevalence of CAs in THIN was consistent with EUROCAT for early diagnoses, demonstrating THIN to be a valuable source of data in which to investigate CAs. Age of diagnosis is an important factor in explaining a higher overall prevalence in THIN; the inclusion of diagnoses made after 1 year of age substantially improves capture of diagnoses.