scholarly journals Consent to Data Linkage for Different Data Domains – The Role of Question Order, Question Wording, and Incentives

2022 ◽  
Author(s):  
Christoph Beuthner ◽  
Florian Keusch ◽  
Henning Silber ◽  
Bernd Weiß ◽  
Jette Schröder
Keyword(s):  
Survey Data ◽  
Data Linkage ◽  
Data Bank ◽  
Sensor Data ◽  
Modern World ◽  
Health Insurance Data ◽  
Question Wording ◽  
Question Order ◽  
Insurance Data ◽  
Usage Data

As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimentally explore three factors of interest to survey designers seeking to maximize consent rates: consent question order, consent question wording, and incentives. The results of the study using a German online sample (n = 3,374) show that survey respondents have a relatively high probability of consent to share smartphone usage data, Facebook data, and biomarkers, while they are least likely to share their bank data in a survey. Of the three experimental factors, only the consent question order affected consent rates significantly. Additionally, the study investigated the interactions between the three experimental manipulations and the seven data domains, of which only the interaction between the data domains and the consent question order showed a consistent significant effect.

scholarly journals Correction to: Comparing health insurance data and health interview survey data for ascertaining chronic disease prevalence in Belgium

2020 ◽  
Vol 78 (1) ◽  
Author(s):  
Finaba Berete ◽  
Stefaan Demarest ◽  
Rana Charafeddine ◽  
Olivier Bruyère ◽  
Johan Van der Heyden
Keyword(s):  
Health Insurance ◽  
Chronic Disease ◽  
Survey Data ◽  
Disease Prevalence ◽  
Health Insurance Data ◽  
Health Interview Survey ◽  
Health Interview ◽  
Interview Survey ◽  
Insurance Data ◽  
Chronic Disease Prevalence

An amendment to this paper has been published and can be accessed via the original article.

A study on the health effects of residents in Namhae and Hadong - Centered on health insurance data

2020 ◽  
Vol 19 (3) ◽  
pp. 268-277
Author(s):  
YoonDeok Han ◽  
◽  
Sunghyeon Jung ◽  
Kwang-tae Ha ◽  
Seung-Mi Kwon ◽  
...  
Keyword(s):  
Health Insurance ◽  
Health Effects ◽  
Health Insurance Data ◽  
Insurance Data

A Novel Approach for Labelling Health Insurance Data for Cardiovascular Disease Risk Classification

2016 ◽  
Author(s):  
César L. C. Mattos ◽  
Amauri H. Souza Júnior ◽  
Ajalmar R. Rocha Neto ◽  
Guilherme A. Barreto ◽  
Ronaldo F. Ramos ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Insurance ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Classification ◽  
Health Insurance Data ◽  
Novel Approach ◽  
Insurance Data

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

Seasonal variation of patulous Eustachian tube diagnoses using climatic and national health insurance data

2021 ◽  
pp. 1-7
Author(s):  
S Lee ◽  
S-W Choi ◽  
J Kim ◽  
H M Lee ◽  
S-J Oh ◽  
...  
Keyword(s):  
Relative Risk ◽  
Eustachian Tube ◽  
Climatic Factors ◽  
Data Sets ◽  
National Data ◽  
Health Insurance Data ◽  
Number Of Patients ◽  
Autumn And Winter ◽  
Insurance Data ◽  
Patulous Eustachian Tube

Abstract Objectives This study aimed to analyse if there were any associations between patulous Eustachian tube occurrence and climatic factors and seasonality. Methods The correlation between the monthly average number of patients diagnosed with patulous Eustachian tube and climatic factors in Seoul, Korea, from January 2010 to December 2016, was statistically analysed using national data sets. Results The relative risk for patulous Eustachian tube occurrence according to season was significantly higher in summer and autumn, and lower in winter than in spring (relative risk (95 per cent confidence interval): 1.334 (1.267–1.404), 1.219 (1.157–1.285) and 0.889 (0.840–0.941) for summer, autumn and winter, respectively). Temperature, atmospheric pressure and relative humidity had a moderate positive (r = 0.648), negative (r = –0.601) and positive (r = 0.492) correlation with the number of patulous Eustachian tube cases, respectively. Conclusion The number of patulous Eustachian tube cases was highest in summer and increased in proportion to changes in temperature and humidity, which could be due to physiological changes caused by climatic factors or diet trends.

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