Potential population-based electronic data sources for rapid pandemic influenza vaccine adverse event detection: a survey of health plans

2008 ◽  
Vol 17 (12) ◽  
pp. 1137-1141 ◽  
Author(s):  
Kristen M. Moore ◽  
April Duddy ◽  
M. Miles Braun ◽  
Richard Platt ◽  
Jeffrey S. Brown
2010 ◽  
Vol 63 (4) ◽  
pp. 407-411 ◽  
Author(s):  
Kristen M. Moore ◽  
April Duddy ◽  
Grace M. Lee ◽  
Priscilla Velentgas ◽  
Dale R. Burwen ◽  
...  

Vaccine ◽  
2013 ◽  
Vol 31 (10) ◽  
pp. 1438-1446 ◽  
Author(s):  
Maria Luisa Moro ◽  
Lucia Nobilio ◽  
Claudio Voci ◽  
Simona Di Mario ◽  
Silvia Candela ◽  
...  

2021 ◽  
pp. 1-11
Author(s):  
Yanan Huang ◽  
Yuji Miao ◽  
Zhenjing Da

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.


2021 ◽  
Vol 19 (S1) ◽  
Author(s):  
Vladimir Sergeevich Gordeev ◽  
◽  
Joseph Akuze ◽  
Angela Baschieri ◽  
Sanne M. Thysen ◽  
...  

Abstract Background Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide. Methods The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites (in Bangladesh, Guinea-Bissau, Ethiopia, Ghana, and Uganda) randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics (type, nature, structure) affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types. Results We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types (no correction, impulsive, flat (simple), zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent’s characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min. Conclusions The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey’s content, process, and administration, allowing for earlier real-time intervention (e.g.,, questionnaire content revision or additional staff training). Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 690-690
Author(s):  
Erin Kent

Abstract In 2020, ~1.8 million Americans are expected to be newly diagnosed with cancer, with approximately 70% of cases diagnosed over the age of 65. Cancer can have a ripple effect, impacting not just patients themselves, but their family caregivers. This presentation will provide an overview of the estimates of the number of family caregivers caring for individuals with cancer in the US, focusing on older patients, from several population-based data sources: Caregiving in the US 2020, the Health Information National Trends Survey (HINTS, 2017-2019), the Behavioral Risk Factors Surveillance System (BRFSS, 2015-2019), and the National Health and Aging Trends (NHATS) Survey. The presentation will compare features of the data sources to give a comprehensive picture of the state of cancer caregiving. In addition, the presentation will highlight what is known about the experiences of cancer caregivers, including caregiving characteristics, burden, unmet needs, and ideas for improving support for family caregivers.


Vaccine ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Brian A. Crowe ◽  
Peter Brühl ◽  
Marijan Gerencer ◽  
Michael G. Schwendinger ◽  
Andreas Pilz ◽  
...  

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