Corrigendum: Vaccine safety surveillance using routinely collected healthcare data-An empirical evaluation of epidemiological designs.
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ABSTRACT:
SUBMITTER: Schuemie MJ
PROVIDER: S-EPMC9731373 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
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Corrigendum: Vaccine safety surveillance using routinely collected healthcare data-An empirical evaluation of epidemiological designs.
Schuemie Martijn J MJ Arshad Faaizah F Pratt Nicole N Nyberg Fredrik F Alshammari Thamir M TM Hripcsak George G Ryan Patrick P Prieto-Alhambra Daniel D Lai Lana Y H LYH Li Xintong X Fortin Stephen S Minty Evan E Suchard Marc A MA
Frontiers in pharmacology 20221124
[This corrects the article DOI: 10.3389/fphar.2022.893484.]. ...[more]
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Project description:Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.
| S-EPMC9299244 | biostudies-literature
Project description:IntroductionVaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown.MethodsWe assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes.ResultsThe historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined.ConclusionWhile serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.
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Project description:Pockets of susceptibility resulting from spatial or social heterogeneity in vaccine coverage can drive measles outbreaks, as cases imported into such pockets are likely to cause further transmission and lead to large transmission clusters. Characterizing the dynamics of transmission is essential for identifying which individuals and regions might be most at risk. As data from detailed contact-tracing investigations are not available in many settings, we developed an R package called o2geosocial to reconstruct the transmission clusters and the importation status of the cases from their age, location, genotype and onset date. We compared our inferred cluster size distributions to 737 transmission clusters identified through detailed contact-tracing in the USA between 2001 and 2016. We were able to reconstruct the importation status of the cases and found good agreement between the inferred and reference clusters. The results were improved when the contact-tracing investigations were used to set the importation status before running the model. Spatial heterogeneity in vaccine coverage is difficult to measure directly. Our approach was able to highlight areas with potential for local transmission using a minimal number of variables and could be applied to assess the intensity of ongoing transmission in a region.
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Project description:ObjectiveTo review the literature on the views of healthcare professionals to the linkage of healthcare data and to identify any potential barriers and/or facilitators to participation in a data linkage system.MethodsPublished papers describing the views of healthcare professionals (HCPs) to data sharing and linkage were identified by searches of Medline, EMBASE, SCOPUS, CINAHL, and PsychINFO. The searches were limited to papers published in the English language from 2001 to 2011.ResultsA total of 2917 titles were screened. From these, 18 papers describing the views of HCPs about data linkage or data sharing of routinely collected healthcare data at an individual patient level were included. Views were generally positive, and potential benefits were reported. Facilitators included having trust in the system including data governance, reliability, and feedback. Some negative views, identified as barriers were also expressed including costs, data governance, technical issues, and privacy concerns. Effects on the physician-patient relationship, and workload were also identified as deterrent.DiscussionFrom the published literature included in this review, the views of HCPs were in general positive towards data sharing for public health purposes. The identification of barriers to contributing to a data linkage system allows these to be addressed in a planned data linkage project for pharmacovigilance. The main barriers identified were concerns about costs, governance and interference with the prescriber-patient relationship. These would have to be addressed if healthcare professionals are to support a data linkage system to improve patient safety.
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Project description:IntroductionWith increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention.ObjectiveTo develop an algorithm to predict overdose using routinely-collected healthcare databases.MethodsWithin a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance.ResultsWe identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18-25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14).ConclusionsWe demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention.
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Project description:BackgroundEfforts are needed to strengthen evidence and guidance for appropriate deprescribing for older nursing home (NH) residents, who are disproportionately affected by polypharmacy and inappropriate prescribing. Given the challenges of conducting randomized drug withdrawal studies in this population, data from observational studies of routinely collected healthcare data can be used to identify patients who are apparent candidates for deprescribing and evaluate subsequent health outcomes. To improve the design and interpretation of observational studies examining determinants, risks, and benefits of deprescribing specific medications in older NH residents, we sought to propose a conceptual framework of the determinants of deprescribing in older NH residents.MethodsWe conducted a scoping review of observational studies examining patterns and potential determinants of discontinuing or de-intensifying (i.e., reducing) medications for NH residents. We searched PubMed through September 2021 and included studies meeting the following criteria: conducted among adults aged 65 + in the NH setting; (2) observational study designs; (3) discontinuation or de-intensification as the primary outcome with key determinants as independent variables. We conceptualized deprescribing as a behavior through a social-ecological lens, potentially influenced by factors at the intrapersonal, interpersonal, organizational, community, and policy levels.ResultsOur search in PubMed identified 250 potentially relevant studies published through September 2021. A total of 14 studies were identified for inclusion and were subsequently synthesized to identify and group determinants of deprescribing into domains spanning the five core social-ecological levels. Our resulting framework acknowledges that deprescribing is strongly influenced by intrapersonal, patient-level clinical factors that modify the expected benefits and risks of deprescribing, including index condition attributes (e.g., disease severity), attributes of the medication being considered for deprescribing, co-prescribed medications, and prognostic factors. It also incorporates the hierarchical influences of interpersonal differences relating to healthcare providers and family caregivers, NH facility and health system organizational structures, community trends and norms, and finally healthcare policies.ConclusionsOur proposed framework will serve as a useful tool for future studies seeking to use routinely collected healthcare data sources and observational study designs to evaluate determinants, risks, and benefits of deprescribing for older NH residents.
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Project description:BackgroundPopulation-based, prospective studies can provide important insights into Parkinson's disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.MethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.ResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPV ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPV ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivity ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism.ConclusionsIn many settings, routinely collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.
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Project description:BackgroundStudies that use routinely collected health data (RCD studies) are advocated to complement evidence from randomized controlled trials (RCTs) for comparative effectiveness research and to inform health care decisions when RCTs would be unfeasible. We aimed to evaluate the current use of routinely collected health data to complement RCT evidence.MethodsWe searched PubMed for RCD studies published to 2010 that evaluated the comparative effectiveness of medical treatments on mortality using propensity scores. We identified RCTs of the same treatment comparisons and evaluated how frequently the RCD studies analyzed treatments that had not been compared previously in randomized trials. When RCTs did exist, we noted the claimed motivations for each RCD study. We also analyzed the citation impact of the RCD studies.ResultsOf 337 eligible RCD studies identified, 231 (68.5%) analyzed treatments that had already been compared in RCTs. The study investigators rarely claimed that it would be unethical (6/337) or difficult (18/337) to perform RCTs on the same question. Evidence from RCTs was mentioned or cited by authors of 213 RCD studies. The most common motivations for conducting the RCD studies were alleged limited generalizability of trial results to the "real world" (37.6%), evaluation of specific outcomes (31.9%) or specific populations (23.5%), and inconclusive or inconsistent evidence from randomized trials (25.8%). Studies evaluating "real world" effects had the lowest citation impact.InterpretationMost of the RCD studies we identified explored comparative treatment effects that had already been investigated in RCTs. The objective of such studies needs to shift more toward answering pivotal questions that are not supported by trial evidence or for which RCTs would be unfeasible.
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Project description:BackgroundMeasuring and reporting the different population-level effects of the acellular pertussis vaccine on pertussis disease in addition to direct effects can increase the cost-effectiveness of a vaccine.MethodsWe conducted a retrospective cohort study of children born between 1 January 2008 and 31 December 2017, in King County, Washington, who were enrolled in the Washington State Immunization Information System. Diphtheria, tetanus toxoids, and acellular pertussis (DTaP) vaccination data from in the Washington State Immunization Information System was linked with pertussis case data from Public Health Seattle and King County. Census-level vaccination coverage was estimated as proportion of age-appropriately vaccinated children residing in it. Direct vaccine effectiveness was estimated by comparing pertussis risk in fully vaccinated and undervaccinated children. Population-level vaccine effectiveness (VE) measures were estimated by comparing pertussis risk in census tracts in the highest quartile for vaccination coverage with that in the lowest quartile.ResultsFor direct protection, estimated VE was 76% (95% confidence interval, 63%-84%) in low-vaccination-coverage clusters, and it decreased to 47% (13%-68%) in high-coverage clusters, after adjustment for potential confounders. The estimated indirect VE was 45.0% (95% confidence interval, 1%-70%), the total VE 93.9% (91%-96%), and the overall VE 42.2% (19%-60%).ConclusionOur findings suggest that DTaP vaccination provided direct as well as indirect protection in the highly immunized King County, Washington. Routine DTaP vaccination programs may have the potential to provide not only protection for vaccinated individuals but also for the undervaccinated individuals living in the same area.
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Project description:Timely and precise influenza vaccine effectiveness (VE) estimates are needed to guide public health messaging and impact vaccine uptake immediately. Using routinely collected laboratory, vaccination and health administrative data from Alberta, Canada, we estimated influenza VE against infection for the 2023/24 season on a near real-time basis, to late December, at 61% (95% CI: 58-64) against influenza A(H1N1), 49% (95% CI: 28-63) against influenza A(H3N2) and 75% (95% CI: 58-85) against influenza B.
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