No Cover Image

Journal article 518 views 108 downloads

How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?

Tony Whiffen, Ashley Akbari Orcid Logo, Tony Paget, Sarah Lowe, Ronan Lyons Orcid Logo

International Journal of Population Data Science, Volume: 5, Issue: 1

Swansea University Authors: Ashley Akbari Orcid Logo, Ronan Lyons Orcid Logo

  • 54467.pdf

    PDF | Version of Record

    This work is licensed under a Creative Commons Attribution 4.0 International License.

    Download (412.64KB)

Check full text

DOI (Published version): 10.23889/ijpds.v5i1.1151

Published in: International Journal of Population Data Science
ISSN: 2399-4908
Published: Swansea University 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54467
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2020-07-22T12:51:56Z
last_indexed 2020-07-22T19:17:36Z
id cronfa54467
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>54467</id><entry>2020-06-12</entry><title>How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?</title><swanseaauthors><author><sid>aa1b025ec0243f708bb5eb0a93d6fb52</sid><ORCID>0000-0003-0814-0801</ORCID><firstname>Ashley</firstname><surname>Akbari</surname><name>Ashley Akbari</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>83efcf2a9dfcf8b55586999d3d152ac6</sid><ORCID>0000-0001-5225-000X</ORCID><firstname>Ronan</firstname><surname>Lyons</surname><name>Ronan Lyons</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2020-06-12</date><deptcode>HDAT</deptcode><abstract/><type>Journal Article</type><journal>International Journal of Population Data Science</journal><volume>5</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Swansea University</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2399-4908</issnElectronic><keywords/><publishedDay>12</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-06-12</publishedDate><doi>10.23889/ijpds.v5i1.1151</doi><url/><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2024-01-08T11:50:58.3885786</lastEdited><Created>2020-06-12T00:00:00.0000000</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Tony</firstname><surname>Whiffen</surname><order>1</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>2</order></author><author><firstname>Tony</firstname><surname>Paget</surname><order>3</order></author><author><firstname>Sarah</firstname><surname>Lowe</surname><order>4</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>5</order></author></authors><documents><document><filename>54467__17750__ea6815eb98534fab91f0f9913bc3c7df.pdf</filename><originalFilename>54467.pdf</originalFilename><uploaded>2020-07-22T13:50:23.0294933</uploaded><type>Output</type><contentLength>422548</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This work is licensed under a Creative Commons Attribution 4.0 International License.</documentNotes><copyrightCorrect>true</copyrightCorrect><licence>https://creativecommons.org/licenses/by/4.0/deed.en</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 54467 2020-06-12 How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data? aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2020-06-12 HDAT Journal Article International Journal of Population Data Science 5 1 Swansea University 2399-4908 12 6 2020 2020-06-12 10.23889/ijpds.v5i1.1151 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University 2024-01-08T11:50:58.3885786 2020-06-12T00:00:00.0000000 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Tony Whiffen 1 Ashley Akbari 0000-0003-0814-0801 2 Tony Paget 3 Sarah Lowe 4 Ronan Lyons 0000-0001-5225-000X 5 54467__17750__ea6815eb98534fab91f0f9913bc3c7df.pdf 54467.pdf 2020-07-22T13:50:23.0294933 Output 422548 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 International License. true https://creativecommons.org/licenses/by/4.0/deed.en
title How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
spellingShingle How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
Ashley Akbari
Ronan Lyons
title_short How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
title_full How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
title_fullStr How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
title_full_unstemmed How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
title_sort How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
author_id_str_mv aa1b025ec0243f708bb5eb0a93d6fb52
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Ashley Akbari
Ronan Lyons
author2 Tony Whiffen
Ashley Akbari
Tony Paget
Sarah Lowe
Ronan Lyons
format Journal article
container_title International Journal of Population Data Science
container_volume 5
container_issue 1
publishDate 2020
institution Swansea University
issn 2399-4908
doi_str_mv 10.23889/ijpds.v5i1.1151
publisher Swansea University
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
published_date 2020-06-12T11:51:00Z
_version_ 1787522879679627264
score 11.037603

Similar Items