scholarly journals Agreement between state registry, health record, and self-report of influenza vaccination

2021 ◽  
Author(s):  
Joshua G. Petrie ◽  
Helene Fligiel ◽  
Lois Lamerato ◽  
Emily T. Martin ◽  
Arnold S. Monto
Keyword(s):  
Positive Predictive Value ◽  
Influenza Vaccination ◽  
Predictive Value ◽  
Vaccine Effectiveness ◽  
Vaccination Status ◽  
Self Report ◽  
Health Record ◽  
Specific Product ◽  
Care Improvement

ABSTRACTBackgroundDocumentation of influenza vaccination, including the specific product received, is critical to estimate annual vaccine effectiveness (VE).MethodsWe assessed performance of the Michigan Care Improvement Registry (MCIR) in defining influenza vaccination status relative to documentation by provider records or self-report among subjects enrolled in a study of influenza VE from 2011 through 2019.ResultsThe specificity and positive predictive value of MCIR were high; however, >10% of vaccinations were identified only by other sources each season. The proportion of records captured by MCIR increased from a low of 67% in 2013-2014 to a high of 89% in 2018-2019, largely driven by increased capture of vaccination among adults.ConclusionsState vaccine registries, such as MCIR, are important tools for documenting influenza vaccination, including the specific product received. However, incomplete capture suggests that documentation from other sources and self-report should be used in combination with registries to reduce misclassification.

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

2021 ◽  
pp. 088307382110195
Author(s):  
Sabrina Pan ◽  
Alan Wu ◽  
Mark Weiner ◽  
Zachary M Grinspan
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Hypoxic Ischemic Encephalopathy ◽  
Pediatric Epilepsy ◽  
Health Record ◽  
Treatment Resistant ◽  
Ischemic Encephalopathy ◽  
F Measure

Introduction: Computable phenotypes allow identification of well-defined patient cohorts from electronic health record data. Little is known about the accuracy of diagnostic codes for important clinical concepts in pediatric epilepsy, such as (1) risk factors like neonatal hypoxic-ischemic encephalopathy; (2) clinical concepts like treatment resistance; (3) and syndromes like juvenile myoclonic epilepsy. We developed and evaluated the performance of computable phenotypes for these examples using electronic health record data at one center. Methods: We identified gold standard cohorts for neonatal hypoxic-ischemic encephalopathy, pediatric treatment-resistant epilepsy, and juvenile myoclonic epilepsy via existing registries and review of clinical notes. From the electronic health record, we extracted diagnostic and procedure codes for all children with a diagnosis of epilepsy and seizures. We used these codes to develop computable phenotypes and evaluated by sensitivity, positive predictive value, and the F-measure. Results: For neonatal hypoxic-ischemic encephalopathy, the best-performing computable phenotype (HIE ICD-9 /10 and [brain magnetic resonance imaging (MRI) or electroencephalography (EEG) within 120 days of life] and absence of commonly miscoded conditions) had high sensitivity (95.7%, 95% confidence interval [CI] 85-99), positive predictive value (100%, 95% CI 95-100), and F measure (0.98). For treatment-resistant epilepsy, the best-performing computable phenotype (3 or more antiseizure medicines in the last 2 years or treatment-resistant ICD-10) had a sensitivity of 86.9% (95% CI 79-93), positive predictive value of 69.6% (95% CI 60-79), and F-measure of 0.77. For juvenile myoclonic epilepsy, the best performing computable phenotype (JME ICD-10) had poor sensitivity (52%, 95% CI 43-60) but high positive predictive value (90.4%, 95% CI 81-96); the F measure was 0.66. Conclusion: The variable accuracy of our computable phenotypes (hypoxic-ischemic encephalopathy high, treatment resistance medium, and juvenile myoclonic epilepsy low) demonstrates the heterogeneity of success using administrative data to identify cohorts important for pediatric epilepsy research.

scholarly journals Accuracy of Self-reported Human Papillomavirus Vaccination Status Among Gay and Bisexual Adolescent Males: Cross-sectional Study

10.2196/32407 ◽  
2021 ◽  
Vol 7 (12) ◽  
pp. e32407
Author(s):  
Eric PF Chow ◽  
Christopher K Fairley ◽  
Rebecca Wigan ◽  
Jane S Hocking ◽  
Suzanne M Garland ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Hpv Vaccination ◽  
Vaccination Status ◽  
Adolescent Males ◽  
Vaccination Program ◽  
School Based ◽  
And Bisexual

Background Men who have sex with men are a risk group for anal human papillomavirus (HPV) and anal cancer. Australia introduced a universal school-based HPV vaccination program in 2013. Self-reported HPV vaccination status has been widely used in clinical and research settings, but its accuracy is understudied. Objective We aimed to examine the accuracy of self-reported HPV vaccination status among gay and bisexual adolescent males. Methods We included 192 gay and bisexual males aged 16-20 years from the Human Papillomavirus in Young People Epidemiological Research 2 (HYPER2) study in Melbourne, Australia. All participants had been eligible for the universal school-based HPV vaccination program implemented in 2013 and were asked to self-report their HPV vaccination status. Written informed consent was obtained to verify their HPV vaccination status using records at the National HPV Vaccination Program Register and the Australian Immunisation Register. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value of self-reported HPV vaccination status. Results The median age of the 192 males was 19 (IQR 18-20) years. There were 128 males (67%) who had HPV vaccination records documented on either registry. Self-reported HPV vaccination had a sensitivity of 47.7% (95% CI 38.8%-56.7%; 61/128), a specificity of 85.9% (95% CI 75.0%-93.4%; 55/64), a positive predictive value of 87.1% (95% CI 77.0%-93.9%; 61/70), and a negative predictive value of 45.1% (95% CI 36.1%-54.3%; 55/122). Conclusions Self-reported HPV vaccination status among Australian gay and bisexual adolescent males underestimates actual vaccination and may be inaccurate for clinical and research purposes.

scholarly journals Investigating the impact of disease and health record duration on the eMERGE algorithm for rheumatoid arthritis

2020 ◽  
Vol 27 (4) ◽  
pp. 601-605
Author(s):  
Vanessa L Kronzer ◽  
Liwei Wang ◽  
Hongfang Liu ◽  
John M Davis ◽  
Jeffrey A Sparks ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Index Date ◽  
Model Performance ◽  
Area Under The Curve ◽  
Population Based ◽  
Health Record ◽  
The Impact

Abstract Objective The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration. Materials and Methods Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC). Results The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration <2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration >10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases. Conclusions eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.

scholarly journals Estimating Influenza Vaccination Status Among Medicare Beneficiaries: Comparison of Billing Claims and Vaccination Data Obtained from Medical Records and Self-report

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S575-S575
Author(s):  
Elif Alyanak ◽  
Alicia M Fry ◽  
Courtney Strickland ◽  
Jeffrey Kelman ◽  
Yoganand Chillarige ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Medical Records ◽  
Vaccination Status ◽  
Self Report ◽  
Medicare Beneficiaries

scholarly journals Influence of Prior Influenza Vaccination on Current Influenza Vaccine Effectiveness in Children Aged 1 to 5 Years

Vaccines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1447
Author(s):  
Kazuhiro Matsumoto ◽  
Wakaba Fukushima ◽  
Saeko Morikawa ◽  
Masashi Fujioka ◽  
Tohru Matsushita ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Reference Group ◽  
Vaccination Strategy ◽  
Negative Influence ◽  
Vaccine Effectiveness ◽  
Vaccination Status ◽  
Current Season ◽  
Vaccination Rates ◽  
Vaccination History ◽  
Group 2

Background: Although annual influenza vaccination is an important strategy used to prevent influenza-related morbidity and mortality, some studies have reported the negative influence of prior vaccination on vaccine effectiveness (VE) for current seasons. Currently, the influence of prior vaccination is not conclusive, especially in children. Methods: We evaluated the association between current-season VE and prior season vaccination using a test-negative design in children aged 1–5 years presenting at nine outpatient clinics in Japan during the 2016/17 and 2017/18 influenza seasons. Children with influenza-like illness were enrolled prospectively and tested for influenza using real-time RT-PCR. Their recent vaccination history was categorized into six groups according to current vaccination doses (0/1/2) and prior vaccination status (unvaccinated = 0 doses/vaccinated = 1 dose or 2 doses): (1) 0 doses in the current season and unvaccinated in prior seasons (reference group); (2) 0 doses in the current season and vaccinated in a prior season; (3) 1 dose in the current season and unvaccinated in a prior season; (4) 1 dose in the current season and vaccinated in a prior season; (5) 2 doses in the current season and unvaccinated in a prior season, and (6) 2 doses in the current season and vaccinated in a prior season. Results: A total of 799 cases and 1196 controls were analyzed. The median age of the subjects was 3 years, and the proportion of males was 54%. Overall, the vaccination rates (any vaccination in the current season) in the cases and controls were 36% and 53%, respectively. The VEs of the groups were: (2) 29% (95% confidence interval: −25% to 59%); (3) 53% (6% to 76%); (4) 70% (45% to 83%); (5) 56% (32% to 72%), and (6) 61% (42% to 73%). The one- and two-dose VEs of the current season were significant regardless of prior vaccination status. The results did not differ when stratified by influenza subtype/lineage. Conclusion: Prior vaccination did not attenuate the current-season VE in children aged 1 to 5 years, supporting the annual vaccination strategy.

scholarly journals Accuracy of Self-reported Human Papillomavirus Vaccination Status Among Gay and Bisexual Adolescent Males: Cross-sectional Study (Preprint)

2021 ◽  
Author(s):  
Eric PF Chow ◽  
Christopher K Fairley ◽  
Rebecca Wigan ◽  
Jane S Hocking ◽  
Suzanne M Garland ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Hpv Vaccination ◽  
Vaccination Status ◽  
Adolescent Males ◽  
Vaccination Program ◽  
School Based ◽  
And Bisexual

BACKGROUND Men who have sex with men are a risk group for anal human papillomavirus (HPV) and anal cancer. Australia introduced a universal school-based HPV vaccination program in 2013. Self-reported HPV vaccination status has been widely used in clinical and research settings, but its accuracy is understudied. OBJECTIVE We aimed to examine the accuracy of self-reported HPV vaccination status among gay and bisexual adolescent males. METHODS We included 192 gay and bisexual males aged 16-20 years from the Human Papillomavirus in Young People Epidemiological Research 2 (HYPER2) study in Melbourne, Australia. All participants had been eligible for the universal school-based HPV vaccination program implemented in 2013 and were asked to self-report their HPV vaccination status. Written informed consent was obtained to verify their HPV vaccination status using records at the National HPV Vaccination Program Register and the Australian Immunisation Register. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value of self-reported HPV vaccination status. RESULTS The median age of the 192 males was 19 (IQR 18-20) years. There were 128 males (67%) who had HPV vaccination records documented on either registry. Self-reported HPV vaccination had a sensitivity of 47.7% (95% CI 38.8%-56.7%; 61/128), a specificity of 85.9% (95% CI 75.0%-93.4%; 55/64), a positive predictive value of 87.1% (95% CI 77.0%-93.9%; 61/70), and a negative predictive value of 45.1% (95% CI 36.1%-54.3%; 55/122). CONCLUSIONS Self-reported HPV vaccination status among Australian gay and bisexual adolescent males underestimates actual vaccination and may be inaccurate for clinical and research purposes.

Clinical validation of the self-reported Glasgow Antipsychotic Side-effect Scale using the clinician-rated UKU side-effect scale as gold standard reference

2020 ◽  
Vol 34 (8) ◽  
pp. 820-828
Author(s):  
Marlene Schouby Bock ◽  
Oona Nørgaard Van Achter ◽  
David Dines ◽  
Maria Simonsen Speed ◽  
Christoph U Correll ◽  
...  
Keyword(s):  
Side Effects ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Gold Standard ◽  
Side Effect ◽  
Self Report ◽  
Measurement Based Care ◽  
Gold Standard Reference ◽  
Antipsychotic Side Effects

Background: Antipsychotics are key for the treatment of psychotic and several non-psychotic disorders. Unfortunately, antipsychotic medications are associated with side effects, which may reduce quality of life and treatment adherence. Therefore, regular screening of antipsychotic side effects is essential. The Glasgow Antipsychotic Side-effect Scale is a patient self-report scale developed for this purpose. However, the Glasgow Antipsychotic Side-effect Scale has only been validated against another self-report side effect measure, which is suboptimal. Objective: We aimed to validate the Glasgow Antipsychotic Side-effect Scale using the clinician-rated Udvalg for Kliniske Undersøgelser side-effect rating scale as the gold standard reference. Results: 81 antipsychotic-treated outpatients with schizophrenia-spectrum disorders (age = 42±13 years; males = 43%, schizophrenia = 77%, illness duration: median = 11 years) completed the Glasgow Antipsychotic Side-effect Scale and were subsequently scored on the Udvalg for Kliniske Undersøgelser by trained raters. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for paired Glasgow Antipsychotic Side-effect Scale and Udvalg for Kliniske Undersøgelser items. Sensitivity of Glasgow Antipsychotic Side-effect Scale items ranged from 33–96%, with 19 (86%) having >75% sensitivity. Lowest sensitivity emerged for “nocturnal enuresis” (33%), “galactorrhea” (50%) and “hyperkinesia” 14–99%, with 14 items (64%) having >75% specificity, being lowest for “asthenia” (14%), “polyuria/polydipsia” (35%), “sedation” (41%), “akathisia” (53%), “dystonia” (65%), “hyperkinesia” (68%), “hypokinesia” (70%) and “accommodation” (70%). Positive predictive value ranged from 7–85%, with six items (27%) having a positive predictive value >75%. Negative predictive value ranged from 40–98%, with 21 items (95%) having a negative predictive value >75%. The mean time to complete the Glasgow Antipsychotic Side-effect Scale was 4±2 minutes. Conclusion: The Glasgow Antipsychotic Side-effect Scale demonstrated satisfactory validity as a self-rated tool for antipsychotic side effects and may aid measurement-based care and decision-making.

scholarly journals Vaccination Behaviour Influences Self-Report of Influenza Vaccination Status: A Cross-Sectional Study among Health Care Workers

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e39496 ◽  
Author(s):  
Anna Llupià ◽  
Alberto L. García-Basteiro ◽  
Guillermo Mena ◽  
José Ríos ◽  
Joaquim Puig ◽  
...  
Keyword(s):  
Health Care ◽  
Influenza Vaccination ◽  
Health Care Workers ◽  
Cross Sectional Study ◽  
Vaccination Status ◽  
Self Report ◽  
Sectional Study ◽  
Cross Sectional ◽  
Care Workers

scholarly journals Diagnostic value of self-report compared with Beck Depression Inventory (BDI-II) in screening of depression

2020 ◽  
Author(s):  
Mehran zarghami ◽  
Fatemeh Taghizadeh ◽  
Mahmood mousazadeh
Keyword(s):  
Positive Predictive Value ◽  
Beck Depression Inventory ◽  
Predictive Value ◽  
Total Population ◽  
False Negative ◽  
Diagnostic Value ◽  
Self Report ◽  
Case Group ◽  
Predictive Values ◽  
Sensitivity Specificity

AbstractBackgroundDepression is a common cause of mortality and morbidity worldwide. To detect depression, we compared Beck Depression Inventory scoring as a valid tool with participants self-reporting depression.MethodologyThis cross-sectional study aimed to explore the diagnostic values of self-reporting in patients’ with depression comparing to Beck Depression Inventory scoring in Mazandaran Persian cohort study, with a total of 1300 samples. The sample size was determined to include 155 participants through the census method. In order to increase the test power, 310 healthy participants were included in the study through random selection. In order to evaluate the diagnostic value of self-reporting, BDI-II was completed by blind interviewing to the case group as well as to another group who reported that they were not depressed, as control.ResultsSensitivity, specificity, accuracy, false positive, false negative, positive and negative predictive values of self-reporting was calculated 58.4%, 79.1%,73.4%, 20.8%, 41.6%, 51.8%, and 83.2% for the total population respectively, as well as, sensitivity, specificity, accuracy, positive and negative predictive values of self-report in males were 83.3%, 77.2%, 77.1%, 43.8% and 95.6% and 53.7%, 78.1%, 71.2%, 49.2%, and 81.1% for females, respectively.ConclusionThe positive predictive value and sensitivity of self-reporting are insufficient in total population and females, and therefore self-reporting cannot detect depressed patients, but regarding to its average positive predictive value, perhaps, it can be used to identify non-depressant individuals.

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