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Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study
Mehrdad A Mizani,
Ashkan Dashtban,
Laura Pasea,
Alvina G Lai,
Johan Thygesen,
Chris Tomlinson 
,
Alex Handy,
Jil B Mamza,
Tamsin Morris,
Sara Khalid,
Francesco Zaccardi,
Mary Joan Macleod,
Fatemeh Torabi ,
Dexter Canoy,
Ashley Akbari ,
Colin Berry,
Thomas Bolton,
John Nolan,
Kamlesh Khunti,
Spiros Denaxas,
Harry Hemingway,
Cathie Sudlow,
Amitava Banerjee ,
(on behalf of the CVD-COVID-UK Consortium)
,
Alex Handy,
Jil B Mamza,
Tamsin Morris,
Sara Khalid,
Francesco Zaccardi,
Mary Joan Macleod,
Fatemeh Torabi ,
Dexter Canoy,
Ashley Akbari ,
Colin Berry,
Thomas Bolton,
John Nolan,
Kamlesh Khunti,
Spiros Denaxas,
Harry Hemingway,
Cathie Sudlow,
Amitava Banerjee ,
(on behalf of the CVD-COVID-UK Consortium)
Journal of the Royal Society of Medicine, Volume: 116, Issue: 1, Start page: 014107682211318
Swansea University Authors:
Fatemeh Torabi , Ashley Akbari
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DOI (Published version): 10.1177/01410768221131897
Abstract
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study
| Published in: | Journal of the Royal Society of Medicine |
|---|---|
| ISSN: | 0141-0768 1758-1095 |
| Published: |
SAGE Publications
2022
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa61933 |
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2022-11-23T16:31:00Z |
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2023-01-21T04:11:51Z |
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| spelling |
v2 61933 2022-11-15 Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study f569591e1bfb0e405b8091f99fec45d3 0000-0002-5853-4625 Fatemeh Torabi Fatemeh Torabi true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 2022-11-15 HDAT Journal Article Journal of the Royal Society of Medicine 116 1 014107682211318 SAGE Publications 0141-0768 1758-1095 Clinical; epidemiology; health informatics; infectious diseases; public health 14 11 2022 2022-11-14 10.1177/01410768221131897 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University The British Heart Foundation Data Science Centre (grant no. SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS Digital) of the TRE. 2023-06-12T16:46:43.1966822 2022-11-15T22:55:15.0066118 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Mehrdad A Mizani 1 Ashkan Dashtban 2 Laura Pasea 3 Alvina G Lai 4 Johan Thygesen 5 Chris Tomlinson 0000-0002-0903-5395 6 Alex Handy 7 Jil B Mamza 8 Tamsin Morris 9 Sara Khalid 10 Francesco Zaccardi 11 Mary Joan Macleod 12 Fatemeh Torabi 0000-0002-5853-4625 13 Dexter Canoy 14 Ashley Akbari 0000-0003-0814-0801 15 Colin Berry 16 Thomas Bolton 17 John Nolan 18 Kamlesh Khunti 19 Spiros Denaxas 20 Harry Hemingway 21 Cathie Sudlow 22 Amitava Banerjee 0000-0001-8741-3411 23 (on behalf of the CVD-COVID-UK Consortium) 24 61933__25978__cbb83bdc9d7943c6afaf67eb86aabd83.pdf 61933.pdf 2022-12-01T15:56:34.3950829 Output 670059 application/pdf Accepted Manuscript true true eng 61933__25979__db35a08d725244689aa9fe686174a2c3.pdf 61933_supplementary.pdf 2022-12-01T15:56:53.5665023 Output 1402946 application/pdf Supplemental material true true eng |
| title |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
| spellingShingle |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study Fatemeh Torabi Ashley Akbari |
| title_short |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
| title_full |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
| title_fullStr |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
| title_full_unstemmed |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
| title_sort |
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study |
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f569591e1bfb0e405b8091f99fec45d3 aa1b025ec0243f708bb5eb0a93d6fb52 |
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f569591e1bfb0e405b8091f99fec45d3_***_Fatemeh Torabi aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari |
| author |
Fatemeh Torabi Ashley Akbari |
| author2 |
Mehrdad A Mizani Ashkan Dashtban Laura Pasea Alvina G Lai Johan Thygesen Chris Tomlinson Alex Handy Jil B Mamza Tamsin Morris Sara Khalid Francesco Zaccardi Mary Joan Macleod Fatemeh Torabi Dexter Canoy Ashley Akbari Colin Berry Thomas Bolton John Nolan Kamlesh Khunti Spiros Denaxas Harry Hemingway Cathie Sudlow Amitava Banerjee (on behalf of the CVD-COVID-UK Consortium) |
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Journal article |
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Journal of the Royal Society of Medicine |
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116 |
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1 |
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014107682211318 |
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2022 |
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Swansea University |
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0141-0768 1758-1095 |
| doi_str_mv |
10.1177/01410768221131897 |
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SAGE Publications |
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Faculty of Medicine, Health and Life Sciences |
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Faculty of Medicine, Health and Life Sciences |
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Faculty of Medicine, Health and Life Sciences |
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Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine |
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2022-11-14T16:46:41Z |
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