Primary Care Database Research Articles

The integrated GOR-COVID-19 health monitor: quarterly reporting (“short-cycle monitoring”)

Abstract Introduction/objective Given the capricious nature of COVID-19, infections and accompanying restrictive measures to reduce spread of the virus are known to strongly vary over time. The impact of the corona crisis on the mental and physical health of the general population may fluctuate accordingly. The quarterly monitoring research line is aimed to monitor the health impact of the corona crisis over time on a high frequency basis. Methods This is achieved through a multi-method approach, combining: a) survey data on perceived impact of corona measures and several health outcomes from two representative panels (∼ 5.000 adolescents/young adults aged 12 and 25 years; ∼ 5.000 adults aged 26+ years), and b) surveillance data of acute health problems from the Nivel Primary Care Database (∼1,6 million patients from 380 general practitioners). Data collection started in September 2021 and continues during the five-year monitoring program. Panel data is collected every quarter; Nivel surveillance data is collected weekly. Our contribution to the EUPHA conference will include data up and until September 2022 (i.e. five rounds of survey and surveillance data). Results Preliminary results from September 2021 through March 2022 indicate that adolescents/young adults reported worse mental and physical health outcomes compared to adults. Moreover, they experienced a more negative impact of corona measures. Accordingly, their mental health related problems, including suicide ideation, spiked between December 2021 and February 2022, a period characterized by highly restrictive COVID-19-measures. Discussion The quarterly monitoring research line showcase the relevance and feasibility of integrating multiple data sources in understanding the short- and long-term effect of the corona crisis. The results increase our understanding in the potential adverse effects of corona-related restrictive measures on population health, potentially aiding policy making and health promotion.

BMI and HbA1c are metabolic markers for pancreatic cancer: Matched case-control study using a UK primary care database.

BackgroundWeight loss, hyperglycaemia and diabetes are known features of pancreatic cancer. We quantified the timing and the amount of changes in body mass index (BMI) and glycated haemoglobin (HbA1c), and their association with pancreatic cancer from five years before diagnosis.MethodsA matched case-control study was undertaken within 590 primary care practices in England, United Kingdom. 8,777 patients diagnosed with pancreatic cancer (cases) between 1st January 2007 and 31st August 2020 were matched to 34,979 controls by age, gender and diabetes. Longitudinal trends in BMI and HbA1c were visualised. Odds ratios adjusted for demographic and lifestyle factors (aOR) and 95% confidence intervals (CI) were calculated with conditional logistic regression. Subgroup analyses were undertaken according to the diabetes status.ResultsChanges in BMI and HbA1c observed for cases on longitudinal plots started one and two years (respectively) before diagnosis. In the year before diagnosis, a 1 kg/m2 decrease in BMI between cases and controls was associated with aOR for pancreatic cancer of 1.05 (95% CI 1.05 to 1.06), and a 1 mmol/mol increase in HbA1c was associated with aOR of 1.06 (1.06 to 1.07). ORs remained statistically significant (p < 0.001) for 2 years before pancreatic cancer diagnosis for BMI and 3 years for HbA1c. Subgroup analysis revealed that the decrease in BMI was associated with a higher pancreatic cancer risk for people with diabetes than for people without (aORs 1.08, 1.06 to 1.09 versus 1.04, 1.03 to 1.05), but the increase in HbA1c was associated with a higher risk for people without diabetes than for people with diabetes (aORs 1.09, 1.07 to 1.11 versus 1.04, 1.03 to 1.04).ConclusionsThe statistically significant changes in weight and glycaemic control started three years before pancreatic cancer diagnosis but varied according to the diabetes status. The information from this study could be used to detect pancreatic cancer earlier than is currently achieved. However, regular BMI and HbA1c measurements are required to facilitate future research and implementation in clinical practice.

How many and who are patients with heart failure eligible to SGLT2 inhibitors? Responses from the combination of administrative healthcare and primary care databases

BackgroundRecent successful findings (i.e. DAPA-HF trial) in patients with heart failure (HF) with/without diabetes treated with sodium-glucose co-transporter inhibitors (SGLT2-I) have fostered real-world data analyses. Fondazione Ricerca e Salute's (ReSD) administrative and Health Search's (HSD) primary healthcare databases were combined in the ReS-HS DB Consortium, to identify and characterize HF-patients eligible to SGLT2-I, and assess their costs charged to the Italian National Health Service (INHS). Methods and resultsEligibility to SGLT2-I was HF diagnosis, age ≥ 18 years, reduced (≤40%) ejection fraction (HFrEF) and glomerular filtration rate (GFR) ≥30 ml/min. The HSD, including 13,313 HF-patients (1.5% of the total HSD population) was used to develop and test the algorithms for imputing HFrEF and GFR ≥ 30 ml/min, based on a set of covariates, to the ReSD, including 67,369 (1.5% of the total ReSD population). Subjects eligible to SGLT2-I were 2187 in HSD (61.1% of HFrEF); after the imputation, 15,145 in ReSD (58.8% of HFrEF). Prevalence of eligibility to SGLT2-I was higher in males then in females and increased with age; diabetic patients were 44.3% and 33.4% of HSD and ReSD populations eligible to SGLT2-I, respectively. Estimated from ReSD, the mean annual cost charged to the INHS per patient with HF eligible to SGLT2-I was €7122 (68% due to hospitalizations). ConclusionsApproximately 20% of patients with HF was eligible to SGLT2-I. Real-world data can identify, quantify and characterize patients eligible to SGLT2-Is and assess related costs for the health care system, thus providing useful information to Regulatory Decision makers.

Potential impact of NICE guidelines on referrals from primary care to nephrology: a primary care database and prospective research study.

National Institute for Health and Care Excellence 2021 guidelines on chronic kidney disease (CKD) recommend the use of the Kidney Failure Risk Equation (KFRE), which includes measurement of albuminuria. The equation to calculate estimated glomerular filtration rate (eGFR) has also been updated. To investigate the impact of the use of KFRE and the updated eGFR equation on CKD diagnosis (eGFR <60 mL/min/1.73 m2) in primary care and potential referrals to nephrology. Primary care database (Secure Anonymised Information Linkage Databank [SAIL]) and prospective cohort study (UK Biobank) using data available between 2013 and 2020. CKD diagnosis rates were assessed when using the updated eGFR equation. Among people with eGFR 30-59 mL/min/1.73 m2 the following groups were identified: those with annual albuminuria testing and those who met nephrology referral criteria because of: a) accelerated eGFR decline or significant albuminuria; b) eGFR decline <30 mL/ min/1.73 m2 only; and c) KFRE >5% only. Analyses were stratified by ethnicity in UK Biobank. Using the updated eGFR equation resulted in a 1.2-fold fall in new CKD diagnoses in the predominantly White population in SAIL, whereas CKD prevalence rose by 1.9-fold among Black participants in UK Biobank. Rates of albuminuria testing have been consistently below 30% since 2015. In 2019, using KFRE >5% identified 182/61 721 (0.3%) patients at high risk of CKD progression before their eGFR declined and 361/61 721 (0.6%) low-risk patients who were no longer eligible for referral. Ethnic groups 'Asian' and 'other' had disproportionately raised KFREs. Application of KFRE criteria in primary care will lead to referral of more patients at elevated risk of kidney failure (particularly among minority ethnic groups) and fewer low-risk patients. Albuminuria testing needs to be expanded to enable wider KFRE implementation.

Translating and evaluating historic phenotyping algorithms using SNOMED CT.

Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database. We developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: "primary" (primary concept and its descendants), "extended" (primary concept, descendants, and additional relations), and "value set" (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to "gold standard" manually curated Read codelists in a sample of 500000 patients from CPRD Aurum. SNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The "value set" and "extended" codelists had slightly greater recall but lower precision than "primary" codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9. SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.

Influenza Vaccination and Risk of Ischemic Stroke: A Population-Based Case-Control Study.

To assess the relationship between influenza vaccination in the general population and risk of a first ischemic stroke (IS) during pre-epidemic, epidemic, and postepidemic periods. A nested case-control study was conducted in a Spanish primary care database over 2001-2015. Individuals aged 40-99 years with at least 1 year registry and no history of stroke or cancer were selected to conform the source cohort, from which incident IS cases were identified and classified as cardioembolic or noncardioembolic. Five controls per case were randomly selected, individually matched with cases for exact age, sex, and date of stroke diagnosis (index date). A patient was considered vaccinated when he/she had a recorded influenza vaccination at least 14 days before the index date within the same season. Adjusted odds ratios (aORs) and their respective 95% CIs were computed through a conditional logistic regression. Pneumococcal vaccination was used as a negative control. From a cohort of 3,757,621 patients, we selected 14,322 incident IS cases (9,542 noncardioembolic and 4,780 cardioembolic) and 71,610 matched controls. Of them, 41.4% and 40.5%, respectively, were vaccinated yielding a crude OR of 1.05 (95% CI 1.01-1.10). Vaccinated patients presented a higher prevalence of vascular risk factors, diseases, and comedication than those nonvaccinated, and after full adjustment, the association of influenza vaccination with IS yielded an aOR of 0.88 (95% CI 0.84-0.92), appearing early (aOR15-30 days 0.79; 95% CI 0.69-0.92) and slightly declining over time (aOR>150 days 0.92; 95% CI 0.87-0.98). A reduced risk of similar magnitude was observed with both types of IS, in the 3 epidemic periods, and in all subgroups analyzed (men, women, individuals younger and older than 65 years of age, and those with intermediate and high vascular risk). By contrast, pneumococcal vaccination was not associated with a reduced risk of IS (aOR 1.08; 95% CI 1.04-1.13). Results are compatible with a moderate protective effect of influenza vaccine on IS appearing early after vaccination. The finding that a reduced risk was also observed in pre-epidemic periods suggests that either the "protection" is not totally linked to prevention of influenza infection or it may be partly explained by unmeasured confounding factors.

Development and external validation of a 1- and 5-year fracture prediction tool based on electronic medical records data: The EPIC risk algorithm.

We aimed to develop and validate a fracture risk algorithm for the automatic identification of subjects at high risk of imminent and long-term fracture risk. A cohort of subjects aged 50-85, between 2007 and 2017, was extracted from the Catalan information system for the development of research in primary care database (SIDIAP). Participants were followed until the earliest of death, transfer out, fracture, or 12/31/2017. Potential risk factors were obtained based on the existing literature. Cox regression was used to model 1 and 5-year risk of hip and major fracture. The original cohort was randomly split in 80:20 for development and internal validation purposes respectively. External validation was explored in a cohort extracted from the Spanish database for pharmaco-epidemiological research in primary care. A total of 1.76 million people were included from SIDIAP (50.7% women with mean age of 65.4years). Hip and major fracture incidence rates were 3.57 [95%CI 3.53 to 3.60] and 11.61 [95%CI 11.54 to 11.68] per 1000 person-years, respectively. The derived model included 19 risk factors. Internal validity showed good results on calibration and discrimination. The 1-year C-statistic for hip and major fracture were 0.851 (95%CI 0.853 to 0.864), and 0.717 (95%CI 0.742 to 0.749) respectively. The 5-year C-statistic for hip and major fracture were 0.849 (95%CI 0.847 to 0.852) and 0.724 (95%CI 0.721 to 0.727) respectively. External validation showed good performance for hip and major fracture risk prediction. We have developed and validated a clinical prediction tool for 1- and 5-year hip and major osteoporotic fracture risks using electronic primary care data. The proposed algorithm can be automatically estimated at the population level using the available primary care records. Future work is needed on the cost-effectiveness of its use for population-based screening and targeted prevention of osteoporotic fractures.

Liver blood marker testing in UK primary care: a UK wide cohort study, 2004–2016

ObjectiveWe aimed to determine (1) the temporal trends of liver enzyme testing in UK general practice and (2) how these vary among different subgroups at risk of chronic liver disease...

Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada

BackgroundThere is limited population-based data on Neurofibromatosis type 1 (NF1) in North America. We aimed to develop and validate algorithms using administrative health data and electronic medical records (EMRs) to identify individuals with NF1 in Ontario, Canada.MethodsWe conducted an electronic free-text search of 15 commonly-used terms related to NF1 in the Electronic Medical Records Primary Care Database. Records were reviewed by two trained abstractors who classified them as confirmed, possible, and not NF1. An investigator with clinical expertise performed final NF1 classification. Patients were classified as confirmed if there was a documented diagnosis, meeting NIH criteria. Patients were classified as possible if (1) NF1 was recorded in the cumulative patient profile, but no clinical information to support the diagnosis; (2) only one criterion for diagnosis (e.g. child of confirmed case) but no further data to confirm or rule out. We tested different combinations of outpatient and inpatient billing codes, and applied a free-text search algorithm to identify NF1 cases in administrative data and EMRs, respectively.ResultsOf 273,440 eligible patients, 2,058 had one or more NF1 terms in their medical records. The terms “NF”, “café-au-lait”, or “sheath tumour” were constrained to appear in combination with another NF1 term. This resulted in 837 patients: 37 with possible and 71 with confirmed NF1. The population prevalence ranged from 1 in 3851 (confirmed NF1) to 1 in 2532 (possible and confirmed NF1). Billing code algorithms had poor performance, with overall low PPV (highest being 71%). The accuracy of the free-text EMR algorithm in identifying patients with NF1 was: sensitivity 85% (95% CI 74–92%), specificity 100% (95% CI 100–100%), positive predictive value 80% (95% CI 69–88%), negative predictive value 100% (95% CI 100–100%), and false positive rate 20% (95% CI 11–33%). Of false positives, 53% were possible NF1.ConclusionsA free-text search algorithm within the EMR had high sensitivity, specificity and predictive values. Algorithms using billing codes had poor performance, likely due to the lack of NF-specific codes for outpatient visits. While NF1 ICD-9 and 10 codes are used for hospital admissions, only ~ 30% of confirmed NF1 cases had a hospitalization associated with an NF1 code.

Mortality from angiotensin-converting enzyme-inhibitors and angiotensin receptor blockers in people infected with COVID-19: a cohort study of 3.7 million people.

BackgroundConcerns have been raised that angiotensin-converting enzyme-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) might facilitate transmission of severe acute respiratory syndrome coronavirus 2 leading to more severe coronavirus disease (COVID-19) disease and an increased risk of mortality. We aimed to investigate the association between ACE-I/ARB treatment and risk of death amongst people with COVID-19 in the first 6 months of the pandemic.MethodsWe identified a cohort of adults diagnosed with either confirmed or probable COVID-19 (from 1 January to 21 June 2020) using computerized medical records from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care database. This comprised 465 general practices in England, United Kingdom with a nationally representative population of 3.7 million people. We constructed mixed-effects logistic regression models to quantify the association between ACE-I/ARBs and all-cause mortality among people with COVID-19, adjusted for sociodemographic factors, comorbidities, concurrent medication, smoking status, practice clustering, and household number.ResultsThere were 9,586 COVID-19 cases in the sample and 1,463 (15.3%) died during the study period between 1 January 2020 and 21 June 2020. In adjusted analysis ACE-I and ARBs were not associated with all-cause mortality (adjusted odds ratio [OR] 1.02, 95% confidence interval [CI] 0.85–1.21 and OR 0.84, 95% CI 0.67–1.07, respectively).ConclusionUse of ACE-I/ARB, which are commonly used drugs, did not alter the odds of all-cause mortality amongst people diagnosed with COVID-19. Our findings should inform patient and prescriber decisions concerning continued use of these medications during the pandemic.

Primary nonadherence to drugs prescribed by general practitioners: A Dutch database study.

Primary nonadherence (PNA) is defined as not filling the first prescription for a drug treatment. PNA can lead not only to poor patient outcomes but also to exposure misclassification in written prescription databases. This study aims to estimate PNA in primary care in the Netherlands and to investigate associated factors. Patients from the Nivel Primary Care Database (Nivel-PCD) who received a new prescription (>1year not prescribed) from a general practitioner in 2012 were linked to pharmacy dispensing information of consenting pharmacies based on sex, year of birth, four-digit postal code and at least 50% matching Anatomical Therapeutic Classification codes. PNA was defined as not having a prescription dispensed within 30 days from the prescribing date. PNA was assessed overall and per drug class. The associations between PNA and several patient- and prescription-related characteristics were assessed using mixed-effects logistic regression models. After matching 86 361 of 396 251 subjects (21.8%) in the Nivel-PCD records to the pharmacy records, this study included 65 877 subjects who received 181 939 new drug prescriptions. Overall, PNA was 11.5%. PNA was lowest for thyroid hormones (5.5%) and highest for proton pump inhibitors (12.8%). Several factors were associated with PNA, such as having comorbidities (odds ratio [OR] 1.46, 95% confidence interval [CI] 1.37-1.56 for >3 active diagnoses, compared to no active diagnoses) or reimbursement status (OR 2.78, 95% CI 2.65-2.92 for not reimbursed drugs compared to fully reimbursed drugs). A total of 11.5% of newly prescribed drugs were not dispensed. This can lead to overestimation of the actual drug exposure status when using written prescription databases.

Opportunities for an early recognition of spinal muscular atrophy in primary care: a nationwide, population-based, study in Italy.

Spinal muscular atrophy (SMA) is a rare genetic disease with a broad spectrum of severity. Although an early diagnosis of SMA is crucial to allow proper management of patients, the diagnostic delay is still an issue. Therefore, this study aimed to investigate the clinical correlates of SMA among primary care patients. The Health Search Database (HSD) was adopted. To estimate the prevalence and incidence rate of SMA, a cohort study was conducted on the population (aged ≥6 years) being registered in HSD from 1 January 2000 up to 31 December 2019. To investigate the clinical correlates of SMA, a nested case-control study was performed. SMA cases have been classified according to a clinically based iterative process as "certain", "probable" or "possible". To test the association between clinical correlates and SMA cases a multivariate conditional logistic regression model was estimated. The SMA prevalence combining "certain", "probable" and "possible" cases was 5.1 per 100,000 in 2019 (i.e. 1.12 per 100,000 when limited to "certain" cases), while the yearly incidence rate ranged from 0.12 to 0.56 cases per 100,000. Comparing "certain" cases with matched controls, the presence of neurology visits (OR = 6.5; 95% CI: 1.6-25.6) and prescription of electromyography (OR = 4.6; 95% CI: 1.1-18.7) were associated with higher odds of SMA diagnosis. Our findings suggest that primary care databases may be used to enhance the early identification of SMA. Additional efforts are needed to exploit the electronic health records of general practitioners to allow early recognition of SMA.

Variations in comorbidity burden in people with type 2 diabetes over disease duration: A population-based analysis of real world evidence.

SummaryBackgroundThe prevalence of type 2 diabetes (T2DM) is increasing, but increasing longevity among persons with diagnosed diabetes may be is associated with more extensive and diverse types of morbidity. The extent and breadth of morbidity and how this varies across sub-groups is unclear and could have important clinical and public health implications. We aimed to estimate comorbidity profiles in people with T2DM and variations across sub-groups and over time.MethodsWe identified approximately 224,000 people with T2DM in the Discover-NOW dataset, a real-world primary care database from 2000 to 2020 covering 2.5 million people across North-West London, England, linked to hospital records. We generated a mixed prevalence and incidence study population through repeated annual cross sections, and included a broad set of 35 comorbidities covering traditional T2DM conditions, emerging T2DM conditions and other common conditions.We estimated annual age-standardised prevalence of comorbidities, over the course of the disease in people with T2DM and several sub-groups.FindingsMultimorbidity (two or more chronic conditions) is common in people with T2DM and increasing, but the comorbidity profiles of people with T2DM vary substantially. Nearly 30% of T2DM patients had three or more comorbidities at diagnosis, increasing to 60% of patients ten years later. Two of the five commonest comorbidities at diagnosis were traditional T2DM conditions (hypertension (37%) and ischaemic heart disease (10%)) the other three were not (depression (15%), back pain (25%) and osteoarthritis (11%)). The prevalence of each increased during the course of the disease, with more than one in three patients having back pain and one in four having depression ten years post diagnosis.People with five or more comorbidities at diagnosis had higher prevalence of each of the 35 comorbidities. Hypertension (73%) was the commonest comorbidity at diagnosis in this group; followed by back pain (69%), depression (67%), asthma (45%) and osteoarthritis (36%). People with obesity at diagnosis had substantially different comorbidity profiles to those without, and the five commonest comorbidities were 50% more common in this group.InterpretationPreventative and clinical interventions alongside care pathways for people with T2DM should transition to reflect the diverse set of causes driving persistent morbidity. This would benefit both patients and healthcare systems alike.FundingThe study was funded by the National Institute for Health and Care Excellence (NICE).

Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case–control study

ObjectivesHeart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may...

Prevalence of atherosclerotic cardiovascular disease stratified by low-density-lipoprotein cholesterol and associated treatment patterns within the four nations of the United Kingdom: A routine database study

Background and Aims : To estimate the prevalence of atherosclerotic cardiovascular disease (ASCVD) and associated lipid-lowering therapy (LLT) and low-density-lipoprotein cholesterol (LDL-C) levels in the United Kingdom (UK).Methods: The study was conducted in the Clinical Practice Research Datalink (CPRD) GOLD database; a longitudinal UK primary-care database. Patients ≥18 years registered on the midpoint date (30-06-2018) formed the denominator. ASCVD patients were selected by a diagnosis recorded in the database prior to the midpoint date. LDL-C values recorded three years prior to the midpoint date and LLT prescribed six months prior were detailed. The prevalence of ASCVD, overall and stratified by LDL-C thresholds, was calculated within each nation, and applied to UK population estimates.Conclusions: Estimated ASCVD prevalence in the UK was 6.82%. LDL-C for a significant proportion of ASCVD patients was above guideline recommendations, yet, despite the association of elevated LDL-C with cardiovascular events, many were untreated with LLT. Background and Aims : To estimate the prevalence of atherosclerotic cardiovascular disease (ASCVD) and associated lipid-lowering therapy (LLT) and low-density-lipoprotein cholesterol (LDL-C) levels in the United Kingdom (UK). Methods: The study was conducted in the Clinical Practice Research Datalink (CPRD) GOLD database; a longitudinal UK primary-care database. Patients ≥18 years registered on the midpoint date (30-06-2018) formed the denominator. ASCVD patients were selected by a diagnosis recorded in the database prior to the midpoint date. LDL-C values recorded three years prior to the midpoint date and LLT prescribed six months prior were detailed. The prevalence of ASCVD, overall and stratified by LDL-C thresholds, was calculated within each nation, and applied to UK population estimates. Conclusions: Estimated ASCVD prevalence in the UK was 6.82%. LDL-C for a significant proportion of ASCVD patients was above guideline recommendations, yet, despite the association of elevated LDL-C with cardiovascular events, many were untreated with LLT.

Symptoms and risk factors for long COVID in non-hospitalized adults

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02–8.39), hair loss (3.99, 3.63–4.39), sneezing (2.77, 1.40–5.50), ejaculation difficulty (2.63, 1.61–4.28) and reduced libido (2.36, 1.61–3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors.

Epidemiology and management of hepatitis B and C in primary care in the Netherlands: data from the Rijnmond Primary Care database.

The Dutch guideline for general practitioners (GPs) advises biannual surveillance of hepatitis B (HBV) patients and referral of every hepatitis C (HCV) patient. We aimed to study the prevalence, incidence, and the management of hepatitis B and C in primary care. This is a retrospective cohort study using the Rijnmond Primary Care database (RPCD), including health care data of medical records of GPs of approximately 200,000 patients in the area of Rotterdam, the Netherlands. Patient records were selected based on laboratory results, International Classification of Primary Care (ICPC) codes, and free-text words. In total, 977 patients were included: 717 HBV, 252 HCV, and 8 HBV/HCV coinfected patients. Between 2013 and 2019, the prevalence of HBV and HCV declined from 5.21 to 2.99/1,000 person-years (PYs) and 1.50 to 0.70/1,000 PYs, respectively. We observed that the majority of the patients had been referred to a medical specialist at least once (71% HBV and 89% HCV patients). However, among chronic patients, we observed that 36.2% of the HBV patients did not receive adequate surveillance by their GP (≥2 alanine aminotransferase checks within 3 years) or a medical specialist. In addition, 44.4% of the HCV patients had no record about successful antiviral treatment. This study demonstrated a declining prevalence in viral hepatitis B and C in primary care in the Netherlands. However, a substantial part of the patients did not receive adequate surveillance or antiviral therapy. It is therefore crucial to involve GPs in case finding and in follow-up after treatment.

A Cohort Study on Influenza Vaccine and All-Cause Mortality in Older Adults: Methodological Concerns and Public Health Implications.

IntroductionIn 2020, the restrictions adopted to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic led to an unprecedented reduction in influenza-related burden. As such, the reduced chance to characterize the circulating virus strains might have increased the risk of vaccine mismatch for the forthcoming winter seasons. The role of an effective influenza vaccination campaign might therefore assume even more value, especially for frail and multimorbid older individuals. Methodological concerns on confounding by indication are always debated in vaccine effectiveness studies and it might be instrumental to give a pragmatic message on an individual’s responsibility to receive the influenza vaccine. We therefore investigated the role of specific confounders to explain the association between influenza vaccine and mortality among older adults.MethodsUsing a primary care database, we formed a cohort of patients aged 65 years or older who were actively registered with their general practitioner (GP) at the beginning of each of nine influenza seasons through to the 2018/2019 season. The study index date was the related seasons’ starting date. Exposure to the influenza vaccine was operationally defined in the 2 months preceding the index date up to 2 weeks before the exit date. Cox regression models were estimated to calculate hazard ratios (HRs) and their 95% confidence intervals (CI) of death between vaccinated and unvaccinated patients in a time-dependent fashion. The potential confounders sequentially entered the model based on their increasing effect size observed in univariate analyses.ResultsOver the 10 years under study, the influenza vaccine showed a significant protective effect in terms of mortality, reaching 13% reduction (HR 0.87, 95% CI 0.80–0.95) in the 2018/2019 influenza season. When we estimated the multivariate model by sequentially adding the potential confounders, there was an inversion of HR (below the unit) that was significantly explained by the covariates coding for a prior history of lower respiratory tract infections and the presence of the pneumococcal vaccine.ConclusionIn the current pandemic scenario, we cannot divert attention to proper use of face masks, social distancing, and hand hygiene, which are important measures to prevent influenza and other respiratory viral infections. Nonetheless, their effectiveness might be negligible without acceptable coverage for influenza vaccine, especially in older patients with a history of lower respiratory tract infections, which appears to be the main source of confounding by indication.

Antidepressant prescriptions and therapy intensification in men and women newly diagnosed with depression in the UK

BackgroundEvidence on therapeutic interventions and factors driving treatment intensification (TI) in people with incident depression in UK are scarce. AimsTo explore antidepressant prescribing patterns and factors influencing TI. Designand setting: Retrospective cohort study of adults with incident depression diagnosed between 2006 and 2017 using UK primary care database. MethodsPatterns of antidepressant prescriptions, and factors influencing TI were evaluated by sex. ResultsIn 931,302 people with depression (90% initiating antidepressants), mean age was 39 years, 41% were male, 14% had cardiometabolic multimorbidity (CMM), and 54% were diagnosed at < 40 years. Being the most prescribed first-line antidepressant (62%), SSRI prescribing rate increased from 66 per 1000 person-years to 170 per 1000 person-years; 24% (2% dose escalation, 4% adding, 18% switching) of first-line antidepressant initiators intensified with 13 months median time to TI. Compared to 60–70 years, younger adults had significantly higher TI risk (range of hazards ratio, HR: 1.08–1.42). CMM and anxiety were associated with 15–24% and 39–49% significantly higher TI risks respectively. First-line antidepressant and deprivation status influenced TI differently by gender. ConclusionsMen and women with depression in UK have different antidepressant prescription patterns in real-world. Age at diagnosis, deprivation status and cardiometabolic multimorbidity are the major sociodemographic and non-psychiatric risk factors for therapeutic changes.

Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries.

BackgroundAtrial fibrillation (AF) burden on patients and healthcare systems warrants innovative strategies for screening asymptomatic individuals.ObjectiveWe sought to externally validate a predictive model originally developed in a German population to detect unidentified incident AF utilising real-world primary healthcare databases from countries in Europe and Australia.MethodsThis retrospective cohort study used anonymized, longitudinal patient data from 5 country-level primary care databases, including Australia, Belgium, France, Germany, and the UK. The study eligibility included adult patients (≥45 years) with either an AF diagnosis (cases) or no diagnosis (controls) who had continuous enrolment in the respective database prior to the study period. Logistic regression was fitted to a binary response (yes/no) for AF diagnosis using pre-determined risk factors.ResultsAF patients were from Germany (n = 63,562), the UK (n = 42,652), France (n = 7,213), Australia (n = 2,753), and Belgium (n = 1,371). Cases were more likely to have hypertension or other cardiac conditions than controls in all validation datasets compared to the model development data. The area under the receiver operating characteristic (ROC) curve in the validation datasets ranged from 0.79 (Belgium) to 0.84 (Germany), comparable to the German study model, which had an area under the curve of 0.83. Most validation sets reported similar specificity at approximately 80% sensitivity, ranging from 67% (France) to 71% (United Kingdom). The positive predictive value (PPV) ranged from 2% (Belgium) to 16% (Germany), and the number needed to be screened was 50 in Belgium and 6 in Germany. The prevalence of AF varied widely between these datasets, which may be related to different coding practices. Low prevalence affected PPV, but not sensitivity, specificity, and ROC curves.ConclusionsAF risk prediction algorithms offer targeted ways to identify patients using electronic health records, which could improve screening number and the cost-effectiveness of AF screening if implemented in clinical practice.