Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification

Introduction: Spontaneous reports of suspected adverse drug reactions (ADRs) can be analyzed to yield additional drug safety evidence for the pediatric population. Signal detection algorithms (SDAs) are required for these analyses; however, the performance of SDAs in the pediatric population specifically is unknown. We tested the performance of two SDAs on pediatric data from the US FDA Adverse Event Reporting System (FAERS) and investigated the impact of age stratification and age adjustment on the performance of SDAs. Methods: We tested the performance of two established SDAs: the proportional reporting ratio (PRR) and the empirical Bayes geometric mean (EBGM) on a pediatric dataset from FAERS (2004–2012). We compared the performance of the SDAs with a published pediatric-specific reference set by calculating diagnostic test-related statistics, including the area under the curve (AUC) of receiver operating characteristics. Impact of age stratification and age-adjustment on the performance of the SDAs was assessed. Age adjustment was performed by pooling (Mantel-Hanszel) stratum-specific estimates. Results: A total of 115,674 pediatric reports (patients aged 0–18 years) comprising 893,587 drug–event combinations (DECs) were analysed. Crude values of the AUC were similar for both SDAs: 0.731 (PRR) and 0.745 (EBGM). Stratification unmasked four DECs, e.g., ‘ibuprofen and thrombocytopenia’. Age adjustment did not improve performance. Conclusion: The performance of the two tested SDAs was similar in the pediatric population. Age adjustment does not improve performance and is therefore not recommended to be performed routinely. Stratification can reveal new associations, and therefore is recommended when either drug use is age-specific or when an age-specific risk is suspected

Systematic Review and Meta-analysis of Postlicensure Observational Studies on Human Papillomavirus Vaccination and Autoimmune and Other Rare Adverse Events

BACKGROUND: Because of the limited number of subjects in prelicensure studies, autoimmune diseases and other rare adverse effects of vaccines may go undetected. Since 2006, millions of human papillomavirus (HPV) vaccine doses have been distributed and a considerable amount of postlicensure safety data has been generated. The objective of this study was to review available HPV postlicensure safety studies and to summarize risk estimates of autoimmune and other rare diseases. METHODS: For this systematic review and meta-analysis, we searched literature databases to identify any postlicensure safety studies related to HPV vaccination and autoimmune adverse events from inception to April 16, 2019. Pooled risk estimates were computed using fixed- or random-effects models if at least 2 estimates per disease and per HPV vaccine were available. RESULTS: Twenty-two studies met our inclusion criteria. The studies applied various methodologies and used different types of data sources and outcome definitions. Quadrivalent HPV vaccine (4vHPV) was most commonly assessed. Type 1 diabetes mellitus, immune thrombocytopenia purpura and thyroiditis diseases were most frequently reported. The meta-analysis was conducted on 35 diseases corresponding to 48 pooled risk estimates. Majority of the pooled estimates showed no significant effect (n = 43). Three negative (paralysis, immune thrombocytopenia purpura and chronic fatigue syndrome) and 2 positive (Hashimoto and Raynaud diseases) associations were detected. CONCLUSION: Our study demonstrated an absence of clear association between HPV vaccines and autoimmune and other rare diseases. The review also highlights the need for more systematic collaborations to monitor rare safety adverse events

No Evidence for Disease History as a Risk Factor for Narcolepsy after A(H1N1)pdm09 Vaccination

OBJECTIVES: To investigate disease history before A(H1N1)pdm09 vaccination as a risk factor for narcolepsy.METHODS: Case-control study in Sweden. Cases included persons referred for a Multiple Sleep Latency Test between 2009 and 2010, identified through diagnostic sleep centres and confirmed through independent review of medical charts. Controls, selected from the total population register, were matched to cases on age, gender, MSLT-referral date and county of residence. Disease history (prescriptions and diagnoses) and vaccination history was collected through telephone interviews and population-based healthcare registers. Conditional logistic regression was used to investigate disease history before A(H1N1)pdm09 vaccination as a risk-factor for narcolepsy.RESULTS: In total, 72 narcolepsy cases and 251 controls were included (range 3-69 years mean19-years). Risk of narcolepsy was increased in individuals with a disease history of nervous system disorders (OR range = 3.6-8.8) and mental and behavioural disorders (OR = 3.8, 95% CI 1.6-8.8) before referral. In a second analysis of vaccinated individuals only, nearly all initial associations were no longer statistically significant and effect sizes were smaller (OR range = 1.3-2.6). A significant effect for antibiotics (OR = 0.4, 95% CI 0.2-0.8) and a marginally significant effect for nervous system disorders was observed. In a third case-only analysis, comparing cases referred before vaccination to those referred after; prescriptions for nervous system disorders (OR = 26.0 95% CI 4.0-170.2) and ADHD (OR = 35.3 95% CI 3.4-369.9) were statistically significant during the vaccination period, suggesting initial associations were due to confounding by indication.CONCLUSION: The findings of this study do not support disease history before A(H1N1)pdm09 vaccination as a risk factor for narcolepsy

Pandemic influenza vaccine & narcolepsy: Simulations on the potential impact of bias

Several studies have identified an association between PandemrixTM, an AS03 adjuvanted pandemic influenza A(H1N1) vaccine, and narcolepsy, a rare and under-diagnosed sleep disorder with a median onset-to-diagnosis interval of ten years. This paper reviews potential sources of bias in published studies and aims to provide, through simulation, methodological recommendations for assessment of vaccine safety signals. Our simulation study showed that in the absence of an association between the vaccine and the outcome, presence of detection bias and differential exposure misclassification could account for elevated risk estimates. These may play a major role, particularly in alert situations when observation times are limited and the disease has a long latency period. Estimates from the case-control design were less inflated than those from the cohort design when these biases were present. Overall, these simulations provide useful insights for the design and interpretation of future studies

Impact of different assumptions on estimates of childhood diseases obtained from health care data: A retrospective cohort study

Purpose: Accurate estimates of disease incidence in children are required to support pediatric drug development. Analysis of electronic health care records (EHR) may yield such estimates but pediatric-specific methods are lacking. We aimed to understand the impact of assumptions regarding duration of disease episode and length of run-in period on incidence estimates from EHRs. Methods: Children aged 0 to 17 years (5-17 years for asthma) registered in the Integrated Primary Care Information database between 2002 and 2014 were studied. We tested the impact of the following: max

Age-specific vaccination coverage estimates for influenza, human papillomavirus and measles containing vaccines from seven population-based healthcare databases from four EU countries – The ADVANCE project

Background: The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public–private collaboration aiming to develop and test a system for rapid benefit-risk monitoring of vaccines using existing healthcare databases in Europe. We estimated vaccine coverage from electronic healthcare databases as part of a fit-for-purpose assessment for vaccine benefit-risk studies. Methods: A retrospective dynamic cohort study was conducted through a distributed network approach. Coverage with measles-vaccine for birth year 2006, human papillomavirus (HPV)-vaccine for birth years 1990–2000 and influenza-vaccine for birth years 1920–1950 was estimated using period-prevalence and inverse probability weighting methods. Seven databases from four countries participated: Italy (Pedianet, Val Padana), Spain (BIFAP, SIDIAP), UK (RCGP-RSC, THIN), Denmark (SSI/AUH). Database access providers extracted the data, transformed it into a common structure and ran an R-script locally. The created output tables were shared and pooled at a central server. Results: The total study population comprised 274,616 persons for measles-vaccine, 2,011,666 persons for HPV-vaccine and 14,904,033 persons for influenza-vaccine. Measles-vaccine coverage varied from 84.3% (Denmark) to 96.5% (Italy, Val Padana) for the first dose and from 82.8% (Italy, Val Padana) to 90.9% (UK) for the second dose at the age of 7 years. The HPV-vaccine coverage, aggregated over birth years 1997–2000, ranged from 60% (UK) to 88.3% (Denmark) at the age of 15 years. The influenza-vaccine coverage for the influenza seasons from 2009 to 2015 for persons aged 65 years and more was roughly stable around 43% in Denmark and around 68% in the UK while a decrease from 58 to 50% was observed in Catalonia (Spain). Conclusions: We obtained detailed, age-specific coverage estimates though a common procedure. We discussed between database comparability and comparability to published national estimates

ADVANCE system testing: Can coverage of pertussis vaccination be estimated in European countries using electronic healthcare databases: An example

Introduction: The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines, using existing healthcare databases in Europe. The objective of this paper was to assess the feasibility of using electronic healthcare databases to estimate dose-specific acellular pertussis (aP) and whole cell pertussis (wP) vaccine coverage. Methods: Seven electronic healthcare databases in four European countries (Denmark (n = 2), UK (n = 2), Spain (n = 2) and Italy (n = 1)) participated in this study. Children were included from birth and followed up to age six years. Vaccination exposure was obtained from the databases and classified by type (aP or wP), and dose 1, 2 or 3. Coverage was estimated using period prevalence. For the 2006 birth cohort, two estimation methods for pertussis vaccine coverage, period prevalence and cumulative incidence were compared for each database. Results: The majority of the 2,575,576 children included had been vaccinated at the country-specific recommended ages. Overall, the estimated dose 3 coverage was 88–97% in Denmark (birth cohorts from 2003 to 2014), 96–100% in the UK (2003–2014), 95–98% in Spain (2004–2014) and 94% in Italy (2006–2007). The estimated dose 3 coverage per birth cohort in Denmark and the UK differed by 1–6% compared with national estimates, with our estimates mostly higher. The estimated dose 3 coverage in Spain differed by 0–2% with no consistent over- or underestimation. In Italy, the estimates were 3% lower compared with the national estimates. Except for Italy, for which the two coverage estimation methods generated the same results, the estimated cumulative incidence coverages were consistently 1–10% lower than period prevalence estimates. Conclusion: Thi

ADVANCE system testing: Can safety studies be conducted using electronic healthcare data? An example using pertussis vaccination

Introduction: The Accelerated Development of Vaccine benefit-risk Collaboration in Europe (ADVANCE) public-private collaboration, aimed to develop and test a system for rapid benefit-risk monitoring of vaccines using healthcare databases in Europe. The objective of this proof-of-concept (POC) study was to test the feasibility of the ADVANCE system to generate incidence rates (IRs) per 1000 person-years and incidence rate ratios (IRRs) for risks associated with whole cell- (wP) and acellular- (aP) pertussis vaccines, occurring in event-specific risk windows in children prior to their pre-school-entry booster. Methods: The study population comprised almost 5.1 million children aged 1 month to <6 years vaccinated with wP or aP vaccines during the study period from 1 January 1990 to 31 December 2015. Data from two Danish hospital (H) databases (AUH and SSI) and five primary care (PC) databases from, UK (THIN and RCGP RSC), Spain (SIDIAP and BIFAP) and Italy (Pedianet) were analysed. Database-specific IRRs between risk vs. non-risk periods were estimated in a self-controlled case series study and pooled using random-effects meta-analyses. Results: The overall IRs were: fever, 58.2 (95% CI: 58.1; 58.3), 96.9 (96.7; 97.1) for PC DBs and 8.56 (8.5; 8.6) for H DBs; convulsions, 7.6 (95% CI: 7.6; 7.7), 3.55 (3.5; 3.6) for PC and 12.87 (12.8; 13) for H; persistent crying, 3.9 (95% CI: 3.8; 3.9) for PC, injection-site reactions, 2.2 (95% CI 2.1; 2.2) for PC, hypotonic hypo-responsive episode (HHE), 0.4 (95% CI: 0.4; 0.4), 0.6 (0.6; 0.6) for PC and 0.2 (0.2; 0.3) for H; and somnolence: 0.3 (95% CI: 0.3; 0.3) for PC. The pooled IRRs for persistent crying, fever, and ISR, adjusted for age and healthy vaccinee period were higher after wP vs. aP vaccination, and lower for convulsions, for all doses. The IRR for HHE was slightly lower for wP than aP, while wP was associated with somnolence only for dose 1 and dose 3 compared with aP. Conclusions: The estimated IRs and IRRs were comparable with published data, therefore demonstrating that the ADVANCE system was able to combine several European healthcare databases to assess vaccine safety data for wP and aP vaccination

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

Introduction: The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of vaccines. Objective: To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an example. Methods: Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept studies. Results: The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of ev

Quantifying outcome misclassification in multi-database studies: The case study of pertussis in the ADVANCE project

Background: The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using European healthcare databases. Event misclassification can result in biased estimates. Using different algorithms for identifying cases of Bordetella pertussis (BorPer) infection as a test case, we aimed to describe a strategy to quantify event misclassification, when manual chart review is not feasible. Methods: Four participating databases retrieved data from primary care (PC) setting: BIFAP: (Spain), THIN and RCGP RSC (UK) and PEDIANET (Italy); SIDIAP (Spain) retrieved data from both PC and hospital settings. BorPer algorithms were defined by healthcare setting, data domain (diagnoses, drugs, or laboratory tests) and concept sets (specific or unspecified pertussis). Algorithm- and database-specific BorPer incidence rates (IRs) were estimated in children aged 0–14 years enrolled in 2012 and 2014 and followed up until the end of each calendar year and compared with IRs of confirmed pertussis from the ECDC surveillance system (TESSy). Novel formulas were used to approximate validity indices, based on a small set of assumptions. They were applied to approximately estimate positive predictive value (PPV) and sensitivity in SIDIAP. Results: The number of cases and the estimated BorPer IRs per 100,000 person-years in PC, using data representing 3,173,268 person-years, were 0 (IR = 0.0), 21 (IR = 4.3), 21 (IR = 5.1), 79 (IR = 5.7), and 2 (IR = 2.3) in BIFAP, SIDIAP, THIN, RCGP RSC and PEDIANET respectively. The IRs for combined specific/unspecified pertussis were higher than TESSy, suggesting that some false positives had been included. In SIDIAP the estimated IR was 45.0 when discharge diagnoses were included. The sensitivity and PPV of combined PC specific and unspecific diagnoses for BorPer cases in SIDIAP were approximately 85% and 72%, respectively. Conclusion: Retrieving BorPer cases using only specific concepts has low sensitivity in PC databases, while including cases retrieved by unspecified concepts introduces false positives, which were approximately estimated to be 28% in one database. The share of cases that cannot be retrieved from a PC database because they are only seen in hospital was approximately estimated to be 15% in one database. This study demonstrated that quantifying the impact of different event-finding algorithms across databases and benchmarking with disease surveillance data can provide approximate estimates of algorithm validity