TY - JOUR
T1 - Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing
AU - Saunders, Gabrielle H.
AU - Christensen, Jeppe H.
AU - Gutenberg, Johanna
AU - Pontoppidan, Niels H.
AU - Smith, Andrew
AU - Spanoudakis, George
AU - Bamiou, Doris Eva
N1 - Publisher Copyright:
© 2020 Lippincott Williams and Wilkins. All rights reserved.
PY - 2020/9/23
Y1 - 2020/9/23
N2 - Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies.
AB - Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies.
KW - Big data
KW - Hearing health care
KW - Population health
KW - Public health policy
UR - http://www.scopus.com/inward/record.url?scp=85089821890&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089821890&partnerID=8YFLogxK
U2 - 10.1097/AUD.0000000000000850
DO - 10.1097/AUD.0000000000000850
M3 - Article
C2 - 31985536
AN - SCOPUS:85089821890
SN - 0196-0202
VL - 41
SP - 1057
EP - 1063
JO - Ear and hearing
JF - Ear and hearing
IS - 5
ER -