The Healthcare Integrated Research Database (HIRD) is a real-world data source for health-related research. Data elements of the HIRD, including sourcing, timeliness, and validity, demographic and healthcare-related characteristics of individuals in the HIRD, and aspects of the HIRD relevant to real-world evidence generation, are described. The HIRD includes health insurance claims and other health-related information for individuals enrolled in health insurance plans offered or managed by Elevance Health and has been utilized for research for almost two decades. Individuals in the HIRD reside throughout the United States. Data in the HIRD are available since January 1, 2006, and are updated monthly. As of July 31, 2024, the researchable population of the HIRD included over 91 million individuals with medical benefits, and over 24 million individuals were actively enrolled. The median age of individuals in the HIRD is 36 years (interquartile range, IQR: 22, 54) and 50% of individuals in the HIRD are female. The median duration of continuous enrollment in the HIRD is 2.0 years (IQR: 0.8, 4.7). For those actively enrolled, the median duration of continuous enrollment is 3.8 years (IQR: 1.7, 8.3). Other important characteristics of the HIRD include the ability to trace data back to their source, to support both deterministic and probabilistic linkage with external data sources, and to link family members within health plans. The HIRD has been a trusted resource to generate real-world evidence for a variety of health-related research, including regulatory required safety studies, comparative effectiveness studies, and health outcomes and economics research.

Masking is a reporting bias where drug safety signals are muffled by elevated reporting of other medications in spontaneous reporting databases. While impacts of masking are often limited, its effect on restricted designs, such as active comparators, can be consequential. We used data from the United States Food and Drugs Administration Adverse Event Reporting System (1999Q3-2013Q3) to study masking in a real-world example. Rosiglitazone, a thiazolidinedione with elevated reporting after safety concerns over cardiovascular risks, was the masking candidate. We hypothesized stimulated reporting masked signals for another thiazolidinedione, pioglitazone. We computed estimates of proportional reporting ratios and information components, using the Bayesian confidence propagation neural network, for pioglitazone-myocardial infarction and pioglitazone-cardiac failure under unrestricted and active comparator designs, both with and without the mask, and before (1999Q3-2007Q1) and after (2007Q1-2013Q3) safety concerns. Relative change-in-estimates were computed to compare results with and without rosiglitazone. From 1999Q3-2007Q1, relative change-in-estimates of proportional reporting ratio for pioglitazone-myocardial infarction was 0.00 in unrestricted design and 0.10 in active comparator; For pioglitazone-cardiac failure, the change was 0.01 and 0.62, respectively. From 2007Q2-2013Q3, relative change in estimate for pioglitazone-myocardial infarction was 0.41 in unrestricted design and 18.00 in active comparator; the change for pioglitazone-cardiac failure was 0.04 and 1.03, respectively. Relative changes in estimates of information component mirrored these trends. In conclusion, masking can influence signal detection in active comparator designs where external events impact reporting rates in reference sets. Evaluating masking in related contexts is essential for drug safety monitoring and resource allocation for follow-up studies.

Purpose: To present the prevalence of medicine use for headache in a nationally representative sample of adolescents in Denmark, and to examine the association between poor mental health, frequent headache, and medicine use for headache. Our hypothesis was that poor mental health increases the risk of headache which triggers medicine use for headache. Methods: The Danish arm of the 2022 Health Behaviour in School-aged Children (HBSC) study included 5,767 11-, 13- and 15-year-old students with self-reported data about headache frequency, medicine use for headache, and five indicators of mental health: Life satisfaction, emotional symptoms, loneliness, self-efficacy, and self-esteem. We used multivariate logistic regression analyses to examine whether the indicators of mental health were associated with medicine use for headache and whether inclusion of headache frequency in the statistical models changed the associations. Results: The prevalence of headache at least weekly was 32.7%. In the last month, 42.7% had used medicine for headache. Students with poor mental health had significantly higher rates of headache and significantly higher rates of medicine use for headache. For example, the OR (95% CI) for medicine use for headache was 2.27 (1.90-2.72) among students with low life satisfaction. When headache frequency was included in the statistical models, the associations between poor mental health and medicine use for headache attenuated and became insignificant. For example, the OR for medicine use for headache attenuated to 1.18 (0.96-1.41) among students with low life satisfaction. Conclusions: Poor mental health was associated with medicine use for headache. Poor mental health was also associated with frequent headache. The analyses suggest that frequent headache may be the explanatory factor behind the association between poor mental health and medicine use for headache. There is a need for promotion of rational medicine use among adolescents and mental health promotion may contribute to this effort.

Background and purpose: The aim of the study was to provide valuable real-world long-term safety data of the fixed pravastatin 40 mg/fenofibrate 160 mg combination in comparison of monotherapy with statins of moderate intensity. Materials and methods: POSE study was an observational, comparative study conducted in three European countries. Patients treated or planned to be treated with pravastatin 40 mg/fenofibrate 160 mg or with a moderate-intensity statin in monotherapy were assessed over 3 years. The main safety endpoints included the incidence of renal or urinary disorder, musculoskeletal or connective tissue disorder, hepatobiliary disorder and cardiovascular events. Results: The study included 3075 patients treated for dyslipidemia, with diabetes mellitus (47%), hypertension (56%) and/or established cardiovascular disease (61%). Over the three years of follow-up, the incidence of each safety event was low (≤3%) and similar in both treatment groups. The most frequently occurring events were; musculoskeletal and connective tissue disorders (44 events in the pravastatin 40 mg/fenofibrate 160 mg group and 36 in the statin group), renal and urinary disorders (28 versus 24), and aggravated diabetes mellitus (31 versus 21). Most events occurred during the first year, then incidence decreased over the three-year period. The incidence of cardiovascular events was low in both groups (≤1.6%), with no new signal emerged from the long-term follow-up. Conclusions: This study demonstrates a reassuring long-term safety profile of the fixed pravastatin 40 mg/fenofibrate 160 mg combination in routine clinical practice, with low and similar incidence of events over the three years follow-up compared to a monotherapy with statins of moderate intensity.

Background. Drug shortages are an increasing and worldwide problem. Oral antibiotics are one of the most used medicines worldwide and have recently been affected by drug shortages. Despite this, little is known about the impact of antibiotic shortages on clinical and prescribing practices. Aim. To explore the impact of oral antibiotic shortages on national antibiotic utilisation. Methods. A longitudinal study of oral antibiotic shortages and antibiotic utilisation was conducted using Australian reimbursement and regulatory data from January 2022 to December 2023. All nationally reimbursed oral antibiotics were included in the study. The number and duration of reported antibiotic shortages per product were determined for each active ingredient. The clinical impact was assessed using national utilisation in Defined Daily Doses per 100,000 inhabitants. Changes in trends were analysed using Joinpoint regression. Results. Shortages were reported for eighteen of the twenty-one (86%) oral antibiotics reimbursed in Australia. No clear relation between the number and duration of shortages was observed for most antibiotics. Changes in utilisation coinciding with shortages were observed for eight active ingredients. For cefaclor (-20% decrease in utilisation) and roxithromycin (-26% decrease), the impact of shortages is most clearly reflected by decreases in utilisation. For the other six, either minor or mixed changes in utilisation were observed. Conclusions. Antibiotic shortages were common in Australia during 2022 and 2023. The impact of shortages appears complex with various factors most likely influencing impact. Australia’s healthcare professionals seem successful in mitigating the effects of shortages on drug utilisation.

Purpose Prior approval for reimbursement is a cost containment policy, aimed at reducing demand. It has been employed in Ireland to address financial challenges due to the shift from warfarin to Direct Oral Anticoagulant (DOACs). Studies assessing the effectiveness of this policy are limited. Thus, we aimed to examine the effectiveness of prior approval for reimbursement of DOACs (apixaban, rivaroxaban) as a cost containment policy in Ireland. Methods The Irish Health Service Executive-Primary Care Reimbursement Service database was used in this cross-sectional study. We examined the prescribing frequencies and associated costs of the oral anticoagulants; ((OACs) apixaban, rivaroxaban, and warfarin) listed in the top 100 most frequently prescribed drugs, between 2018 and 2021. Interrupted time series Poisson regression was used to assess the impact of removing the approval requirement of apixaban in September 2019 followed by the other DOACs in November 2020. Results The prescribing frequency of OACs increased by almost 20% from 2018-2021. This study showed there were significant differences in the proportion of OACs prescribed among the Community Drug Schemes. A statistically significant decreased use of apixaban (<1%, p<0.05) occurred when prior approval was removed for all DOACs. Conclusions The removal of prior approval for reimbursement of DOACs in Ireland had a minimal impact on the prescribing frequency trends of the OACs. Future use of these potentially useful policies by healthcare systems requires careful consideration of drug type, approval criteria and length of time the policy remains in place to minimise any negative effects associated with their use.

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimised through appropriate study design and statistical analysis methods. However, residual biases can remain, for example due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies.

Purpose: Record linkage based on quasi-identifiers remains an important approach as not every data source provides a comprehensive unique identifier. In this study, reasons for the failure of a linkage based on quasi-identifiers were examined. Furthermore, informed algorithms using information on gold-standard links were developed to investigate the potentially achievable linkage quality based on quasi-identifiers. Methods: Linkage algorithms were applied on German claims and cancer registry data using information on gold-standard links. Informed linkage algorithms based on deterministic linkage, logistic regression, random forests, gradient boosting and neural networks were derived and compared. Descriptive analyses were performed to identify reasons for failure of linkage such as discrepancies between data sources. Results: A linkage approach based on gradient boosting performed best and reached a precision of 77%, a recall of 81% and an F*-measure of 64%. Of 641 patients in GePaRD, 8% were not uniquely identifiable using birth year, sex, area of residence, year and quarter of diagnosis, whereas 33% of 42,817 cancer registries patients of Bremen and Lower Saxony were not uniquely identifiable with these quasi-identifiers. Conclusions: Linkage of German claims and cancer registry data based on quasi-identifiers does result in insufficient linkage quality since subjects cannot be uniquely identified. It is advisable to use unique identifiers from a subsample, if available, to derive informed linkage algorithms for the entire sample. In this case the machine learning technique gradient boosting has been found to outperform other methods.

Purpose: This study aims to investigate the potential causal relationship between the use of indapamide and rhabdomyolysis. Previous research and pharmacovigilance monitoring activities have suggested a potential association between indapamide use and rhabdomyolysis. However, limited information is available on this association. Methods: A case-control study conducted using EHR data, between July 1,2016, and December 31,2022. Patients who have rhabdomyolysis event (cases) were matched to four controls bases on age, gender and date. We examined the odds for indapamide exposure through three risk periods: current use, recent use, and former. The study outcome was ascertained through the presence of CK level over 1,000 U/L (i.e. rhabdomyolysis event). Subsequently, a multivariable conditional logistic regression analysis was utilized to assess the causal impact of indapamide exposure on the likelihood of developing rhabdomyolysis, while accounting for potential confounding variables. Results: The study population consisted of 2,965 cases and 11,860 controls. The results of the conditional logistic regression analysis indicated a lack of association between exposure to indapamide for the current users the OR was 0.7 (95% CI: 0.49 - 0.92) and the AOR was 0.6 (95% CI: 0.39 - 1.05). The recent users showed OR of 0.9 (95% CI: 0.56 - 1.50) and AOR of 0.2 (95% CI: 0.14 - 0.47). Lastly, the former users demonstrated an OR of 0.8 (95% CI: 0.47 - 1.26), and AOR of 0.1 (95% CI: 0.07 - 0.23). Conclusions: In this study, we did not find association between indapamide use and rhabdomyolysis regardless timing of exposure.

Background: Adverse drug events (ADEs) are a frequent cause of injury in patients. Our aim was to assess pharmacist interventions and their association with ADEs and potential adverse drug events (PADE). Methods: The search criteria: a published RCT, evidence of a pharmacist intervention, a comparison control group, and measurement of ADEs or PADEs. The information sources included MEDLINE, Embase, and two other databases through September 19, 2022. The risk of bias was assessed using the Cochrane tool for RCTs. A random-effects model for pooled studies was employed Results: Fifteen references meeting inclusion criteria were discovered. For ADEs, the pooled results showed a statistically significant benefit of pharmacist intervention in comparison to the control group (RR = 0.86; [95% CI 0.80-0.94); P = 0.0005. The heterogeneity was insignificant (P = 0.72; I 2 = 0%). Patients receiving a pharmacist intervention were 14% less likely for ADE than those who did not receive a pharmacist intervention. The estimated number of patients needed to prevent one ADE across all patient locations was 33. For PADEs, the pooled results did not show a statistically significant benefit for pharmacist intervention in comparison to the control group (RR = 0.79; [95% CI 0.47 – 1.32]; P =0.37.There was substantial heterogeneity in the pooled studies (P = 0.01; I 2 = 77%). However, there was a statistically significant subgroup difference (P = 0.005) for the intervention type. Conclusions: To our knowledge, this is the first systematic review and meta-analysis of RCTs seeking to understand the association of pharmacist interventions with ADEs and PADEs. The risk of having an ADE is reduced by a seventh for patients receiving a pharmacist care intervention versus no such intervention. This fraction could be higher for certain high-risk patients. The estimated number of patients needed to be followed across all patient locations to prevent one preventable ADE across all patient locations is 33. Also, a subgroup analysis of pharmacist intervention focus suggests that further research is necessary to fully understand the impact of TOC pharmacist intervention on PADEs. If validated, these findings have potential to significantly reduce drug-related morbidity and related healthcare costs.

Purpose: Long-term opioid therapy (LTOT) has been shown to be associated with opioid overdose, but the definition of LTOT varies widely across studies. We use a rigorous LTOT definition to examine risk of opioid overdose by duration of treatment. Methods: Data were from a large private health insurance provider in North Carolina linked to mortality records from 2006-2018. Eligible patients were adults (18-64) newly initiating opioid therapy after a pain diagnosis or surgery. We defined LTOT as ≥1 opioid prescription per month totaling ≥60 days’ supply within 90 days. We used inverse probability- (IP) weighted cumulative incidence functions to estimate three-year risk of opioid overdose and IP-weighted Fine-Gray models to estimate subdistribution hazard ratios, comparing LTOT to short- to medium-term opioid therapy (SMTOT). We also examined modification by derived indication of acute pain or surgery versus chronic pain. Results: We identified 491,369 patients, and 1.7% were exposed to LTOT. The three-year risk of opioid overdose was 0.3 percentage points (RD w= 0.003, 95% CI: 0.001, 0.005) higher in LTOT patients compared to patients with SMTOT. The weighted hazard of opioid overdose was 4.4 times as high (HR w 4.42, 95% CI 2.41, 8.11) among patients exposed to LTOT versus SMTOT. We did not find meaningful modification by clinical indication for opioid therapy. Conclusions: Exposure to LTOT was associated with increased risk of opioid overdose in this population of privately insured patients using a rigorous definition of LTOT. These findings confirm the importance of guidelines to minimize duration of opioid therapy whenever possible.

Background: Pregnant individuals are at high risk for developing severe illness related to COVID-19. We adapted the “COVID-19 infectiOn aNd medicineS In pregnancy” (CONSIGN) CONSIGN study protocol as part of an international collaboration to understand the natural history of COVID-19 disease among pregnant individuals in the U.S. Methods: We identified individuals aged 12 to 55 years with documented live-birth deliveries in the Sentinel Distributed Database who had at least one qualifying diagnosis for COVID-19 or positive-confirmed nucleic acid amplification test for SARS-CoV-2. We conducted separate 1:1 propensity score matched analyses comparing pregnant individuals with COVID-19 to: 1) pregnant individuals without COVID-19 during six months prior to or during pregnancy; and 2) non-pregnant individuals with COVID-19. Results: From January 2020 to December 2022 , we identified 52,355 pregnant individuals with COVID-19 matched to 52,355 pregnant individuals without COVID-19. Outpatient medication use in the 30 days surrounding COVID-19 date (or matched date) was generally low but similar between pregnant individuals with and without COVID-19. We identified 40,518 pregnant individuals with COVID-19 matched to 40,518 non-pregnant individuals with COVID-19. Medication use in 30 days prior to COVID-19 was higher among non-pregnant than pregnant individuals with COVID-19. More pregnant individuals than matched non-pregnant individuals were classified as non-severe (87.2% vs. 79.9%); however, more non-pregnant individuals could not have their COVID-19 severity determined (19.0% vs. 10.0%). Conclusions: Medication use is generally low in pregnancies with COVID-19, compared to pregnancies without COVID-19 and non-pregnant individuals with COVID-19. Interpretation of medication utilization patterns is challenging due to changing treatment and prevention recommendations during the pandemic.

Comorbidity Influence in Observational Studies: Why Ignore the Real World ?Comorbidity is any coexisting disease or medical condition with another medical condition, at a particular time point or during a particular period, in a patient. As such they can be acute or of chronic nature. Comorbidities may or may not interact with each other. Indeed, comorbid conditions may elevate the risk of the development of other conditions, increase the risk of death, reduce the quality of life, worsen the mental and physical health and impair the general functional ability of the patient. However, the literature on empirical evidence of treatment effect modification by comorbidity from randomized clinical trials (RCTs) is very limited, although this has been attributed to the underrepresentation of people with comorbidities in trials.1 Despite this, the standard assumption that efficacy is constant across subgroups of patients by levels of comorbidity in trials, is often criticised despite the power granted by treatment randomization to such studies.2 In contrast, the influence of comorbidity on health outcomes and treatment effectiveness is well established from observational studies.3-4In most observational studies, the influence of comorbidity on treatment effect is modelled as constant regardless of how long the patient has had the condition up to the start of follow-up, with the inherent assumption that the duration does not influence the outcome of interest. The question we inadvertently fail to address is whether it is reasonable to assume a constant influence for each of the comorbidities- namely, a level of influence that is independent of how long the patient has had each comorbid condition since diagnosis? We readily ignore the reality that there may be no evidence on the assumption of a constant, unchanging effect over time by the comorbidity of interest. For example, in assessing the treatment effect of a particular drug in a diabetic cohort, we generally consider the risk associated with each of the comorbidities of interest, such as hypertension as the same between patients diagnosed five years ago and those diagnosed recently. By so doing, we run the risk of not properly controlling for the influence of some of these comorbidities on the outcome of interest. Indeed, this assumption was challenged in a study that demonstrated the duration-dependent nature of the influence of certain comorbidities on survival among chronic obstructive pulmonary disease (COPD) patients in the UK’s Clinical Practice Research Datalink (CPRD) and since then, using similar approaches that are easy to apply, two other studies have also demonstrated the duration-dependent nature of certain comorbidities within the real-world evidence generation space.5-7Another common practice we find in observational studies is the modelling of and adjusting for comorbidity influence additively, either as the number of comorbidities or as an index (such as the Charlson Comorbidity Index), and we do so often without regard to the specific outcome of interest, which may be different from the outcome used in the derivation of the index.8-10 In so doing, we are inadvertently assuming that (1) the outcome is associated linearly with the comorbidity count or index and (2) the impact of every comorbidity is one directional- namely, to elevate the risk of the outcome and hence, the weights we derive from combining the set of comorbidities must be positive (i.e. the null hypothesis that outcome worsens with increasing comorbidity is inadvertently assumed as already proven). Although these common practices are generally suitable in many observational studies, there is evidence some of these assumptions may not necessary hold true in some disease populations, such as in COPD patients.11-13 One such evidence is that as a result of adequate control of COPD by medications and lifestyle changes, among others, its influence on the risk of an outcome of interest, may not necessarily be as a risk elevator - suggesting, risk reduction by an adequately managed comorbid condition is possible in real-life studies. An additional, possible consequence of these approaches is our likely loss of ability to adequately identify specific interactions between those comorbidities that influence prognosis.Some of the problems associated with our current simplistic approach to handling comorbidity influence were first highlighted in a 2012 paper on the conduct of prospective observational post-authorization safety studies.14 The paper recommends assessing the suitability of the assumptions that underpin the methodologies we intend to apply to the data within the context of the specific outcome(s) of interest as the least we could do with real-life data. In other words, we need to demonstrate that such assumptions are appropriate and reasonable on the basis of the results from exploratory assessments of the data. We need not ignore what the specific study data may reveal about the nature of the true association between the outcome and each specific comorbidity and/or combination of comorbidities. We particularly need not ignore evidence from routine clinical practice. The current common assumption in observation studies, that the influence of every comorbid condition on any health outcome of interest is independent of both its duration and management in routine clinical practice, is unlikely to be tenable in the real world.Acknowledgement :The authors are grateful to Professor Gilbert MacKenzie for his valuable contributions to initial discussions on the subject.Prof Victor A Kiri MPH, PhDEpidemiology DirectorMarket Access Consulting, RWE & AnalyticsFortreaMaidenhead, Berkshire SL6 3QHUnited Kingdomvictor.kiri@fortrea.comORCID iD: https://orcid.org/0000-0001-7171-2011Dr Maurille Feudjo-Tepie PhDVice PresidentHead of GlobalRWE & Digital sciences Head of Global RWE & Digital sciencesUCBSlough, Berkshire SL1 3WEUnited Kingdommaurille.feudjotepie@ucb.comReferencesHanlon P, Hannigan L, Rodriguez-Perez J, et al. Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis. BMC Med. 2019; 17(1):201Hanlon P, Butterly EW, Shah ASV, et.al. Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. PLoS Med 2023; 20(6): e1004176Iversen LH, Nørgaard M, Jacobsen J, et al. The impact of comorbidity on survival of Danish colorectal cancer patients from 1995 to 2006 – a population-based cohort study. Dis Colon Rectum 2009;52(1):71–78Lund L, Jacobsen J, Nørgaard M, et al. The prognostic impact of comorbidities on renal cancer, 1995 to 2006: a Danish population based study. J Urol. 2009;182(1):35–40; discussion 40.Kiri VA, Oyee J. Assessing the time-dependency nature of comorbidity influence in COPD, Pharmacoepidemiology and Drug Safety (2006); 15 (Suppl 2): S8Plana-Ripoll O, Pedersen CB, Holtz Y, et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 2019b; 76: 259–270McGrath JJ, Lim CCW, Plana-Ripoll O, et al. Comorbidity within mental disorders: A comprehensive analysis based on 145,990 survey respondents from 27 countries. Epidemiology and Psychiatric Sciences 2020; 29: [e153]Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (5):373-383Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.Yngvar Nilssen, Trond-Eirik Strand, Robert Wiik, et al. Utilizing national patient-register data to control for comorbidity in prognostic studies, Clinical Epidemiology 2014;395-404Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27Kiri VA, Visick G, Muellerova H, MacKenzie G. A Novel Methodology for Measuring the Influence of Comorbidity in Disease Outcome Studies, Pharmacoepidemiology and Drug Safety (2005); 14 (Suppl 2): S135Kiri VA, Muellerova H, Visick G, MacKenzie G. Assessing the multivariate structure and influence of comorbidity in COPD, Eur Respir J (2005); 26 (Suppl 49):A470sKiri VA. A Pathway to Improved Prospective Observational Post-Authorization Safety Studies. Drug Saf 2012; 35 (9): 711-724

Purpose. We aimed to investigate the awareness of oral retinoid teratogenicity and the adherence to the pregnancy prevention measures related to oral retinoid use by physicians, pharmacists and patients in Denmark. Methods. As part of the multi-country survey, web-based questionnaires were distributed among Danish dermatologists, general practitioners, community pharmacists, and women of childbearing age, who were using or had used oral retinoids within the past five years. Results. A total of 62 physicians, 96 pharmacists, and 50 oral retinoid using women responded; 95%, 100%, and 98%, respectively, were aware of the teratogenic risks of oral retinoids. For physicians, the most applied PPP measures were the usage of the patient (44%) and the healthcare professional (19%) guides, while the least applied measure was signing medication risk awareness form (3%). Among the pharmacists, the warning sign on the outer medication package was the most used measure (45%). Among the women, a majority (90%) had read the patient information leaflet included in the medication package and 72% discussed the use of contraception with their healthcare provider, while risk awareness forms and patient cards were seen by only few. Conclusions. In Denmark, physicians, pharmacists and medicine users are aware about the teratogenic effects of oral retinoids. Adherence to pregnancy prevention measures varied, suggesting unwillingness to use the measures that require patients’ signatures among physicians and a lack of awareness of pharmacy targeting measures. Accessibility of the latter measures need to be optimized to improve the safety of oral retinoid use.

Purpose Pregnancies ending before gestational week 12 are common but not notified to the Medical Birth Registry of Norway. Our goal was to develop an algorithm that more completely detects and dates pregnancy outcomes by using diagnostic codes from primary and secondary care registries to complement information from the birth registry. Methods We used nationwide linked registry data between 2008 and 2018 in a hierarchical manner: We developed an algorithm to arrive at unique pregnancy outcomes, considering codes within 56 days as the same event. To infer gestational age of pregnancy outcomes before gestational week 12, we used the median gestational week of pregnancy markers (45 ICD-10 codes and 9 ICPC-2 codes). When no pregnancy markers were available, we assigned outcome-specific gestational age estimates. The performance of the algorithm was assessed by blinded clinicians. Results Using only the medical birth registry, we identified 649,703 pregnancies, including 1,369 (0.2%) miscarriages and 3,058 (0.5%) elective terminations. With the new algorithm, we detected 859,449 pregnancies, including 642,712 live-births (74.8%), 112,257 miscarriages (13.1%), 94,664 elective terminations (11.0%), 6,429 ectopic pregnancies (0.7%), 2,564 stillbirths (0.3%), and 823 molar pregnancies (0.1%). The median gestational age was 10 +0 weeks (IQR 10 +0-11 +3) for miscarriages and 8 +0 weeks (IQR 8 +0-9 +6) for elective terminations. Gestational age could be inferred using pregnancy markers for 66.3% of miscarriages and 47.2% of elective terminations. Conclusion The pregnancy algorithm improved the detection and dating of early non-live pregnancy outcomes that would have gone unnoticed if relying solely on the medical birth registry information.

Background: Several cases of renal complications including acute kidney injury (AKI) after influenza vaccination have been reported, but the association remains unproven. We evaluated the association between influenza vaccination and AKI occurrence among the Korean elderly in the 2018–2019 and 2019–2020 seasons. Methods: We used a large database that combined vaccination registration data from the Korea Disease Control and Prevention Agency and the claims data from the National Health Insurance Service. The study subjects were patients hospitalized with AKI for the first time following vaccination among those who received one influenza vaccine in the 2018–2019 or 2019–2020 season. Only those who were 65 or older at the date of vaccination were included in our study. We performed a self-controlled case series study in which the risk period was designated as 1 to 28 days after vaccination, and the observation period as each influenza season. We conducted sensitivity analyses with varied risk periods and observation periods. Results: A total of 16,713 and 16,272 AKI events were identified during each influenza season. The adjusted incidence rate ratio (aIRR) for AKI was 0.83 (95% confidence interval [CI]= 0.79–0.87) in the 2018–2019 season. The IRR of the 2019–2020 influenza vaccination was similar to the 2018–2019 season (aIRR=0.86; 95% CI=0.82–0.90). Sensitivity analyses showed similar results. Conclusions: We suggest that influenza vaccination is associated with a lower risk of AKI in the elderly over 65.

Purpose The generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU®). We illustrate the use of this framework by generating phenotypes for two complex diseases: pancreatic cancer and systemic lupus erythematosus (SLE). Methods The phenotyping process involves a 14-step process based on a standard operating procedure co-created by the DARWIN EU® Coordination Centre in collaboration with the European Medicines Agency. A number of bespoke R packages were utilised to generate and review codelists for two phenotypes based on real world data mapped to the OMOP Common Data Model. Results Phenotypes were generated for both pancreatic cancer and SLE, and cohorts were generated using the Clinical Practice Research Datalink (UK primary care records) and Pharmetrics (US health claims data). Diagnostic checks were performed, which showed these cohorts had broadly similar incidence and prevalence figures to previously published literature. Additionally, co-occurrent symptoms, conditions, and medication use were in keeping with pre-specified clinical descriptions based on previous knowledge. Conclusions Our detailed phenotyping process makes use of bespoke tools and allows for comprehensive codelist generation and review, as well as large-scale exploration of the characteristics of the generated cohorts. Wider use of structured phenotyping methods will be important in ensuring the reliability of observational studies for regulatory purposes.

Background: Bleeding is an important health outcome of interest in epidemiological studies. We aimed to develop and validate rule-based algorithms to identify major bleeding and all bleeding within real-world electronic healthcare data. Methods: We took a random sample (n=1630) of patient admissions to Singapore public hospitals in 2019 and 2020, stratifying by hospital and year of admission. We adopted the International Society on Thrombosis and Haemostasis definition for major bleeding. Presence of major bleeding and all bleeding was ascertained by two annotators through chart review. A total of 630 and 1,000 records were used for algorithm development and validation, respectively. We formulated two algorithms: sensitivity- and positive predictive value (PPV)-optimized algorithms. A combination of hemoglobin test patterns and diagnosis codes were used in the final algorithms. Results: During validation, diagnosis codes alone yielded low sensitivities for major bleeding (0.14) and all bleeding (0.24), although specificities and PPV were high (>0.97). For major bleeding, the sensitivity-optimized algorithm had much higher sensitivity and negative predictive values (NPV) (sensitivity=0.94, NPV=1.00), however false positive rates were also relatively high (specificity=0.90, PPV=0.34). PPV-optimized algorithm had improved specificity and PPV (specificity=0.96, PPV=0.52), with little reduction in sensitivity and NPV (sensitivity=0.88, NPV=0.99). For all bleeding events, our algorithms had less optimal performances, with lower sensitivities (0.53 to 0.61). Conclusions: The use of diagnosis codes alone misses many genuine major bleeding events. We have developed major bleeding algorithms with high sensitivities which can be used in conjunction with chart reviews to ascertain events within populations of interest.