risk factors for long covid analyses of 10 longitudinal studies and electronic health CORD-Papers-2022-06-02 (Version 1)

Title: Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK
Abstract: The impact of long COVID is increasingly recognised but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID as part of the UK National Core Study for Longitudinal Health and Wellbeing. Methods Analysis was conducted on 6899 adults self-reporting COVID-19 from 45096 participants of the UK LS and on 3327 cases assigned a long COVID code in primary care EHR out of 1199812 adults diagnosed with acute COVID-19. In LS we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age sex ethnicity socioeconomic factors smoking general and mental health overweight/obesity diabetes hypertension hypercholesterolaemia and asthma) with these two outcomes were assessed using logistic regression with meta-analyses of findings presented alongside equivalent results from EHR analyses. Results Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20) and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]) poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]) as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]) a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Conclusions Long COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.
Published: 2021-06-25
DOI: 10.1101/2021.06.24.21259277
DOI_URL: http://doi.org/10.1101/2021.06.24.21259277
Author Name: Thompson E J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/thompson_e_j
Author Name: Williams D M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/williams_d_m
Author Name: Walker A J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/walker_a_j
Author Name: Mitchell R E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mitchell_r_e
Author Name: Niedzwiedz C L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/niedzwiedz_c_l
Author Name: Yang T C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_t_c
Author Name: Huggins C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/huggins_c
Author Name: Kwong A S F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kwong_a_s_f
Author Name: Silverwood R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/silverwood_r
Author Name: Di Gessa G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/di_gessa_g
Author Name: Bowyer R C E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bowyer_r_c_e
Author Name: Northstone K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/northstone_k
Author Name: Hou B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hou_b
Author Name: Green M J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/green_m_j
Author Name: Dodgeon B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dodgeon_b
Author Name: Doores K J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/doores_k_j
Author Name: Duncan E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/duncan_e
Author Name: Williams F M K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/williams_f_m_k
Author Name: OpenSAFELY Collaborative
Author link: https://covid19-data.nist.gov/pid/rest/local/author/opensafely_collaborative
Author Name: Steptoe A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/steptoe_a
Author Name: Porteous D J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/porteous_d_j
Author Name: McEachan R R C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mceachan_r_r_c
Author Name: Tomlinson L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tomlinson_l
Author Name: Goldacre B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/goldacre_b
Author Name: Patalay P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/patalay_p
Author Name: Ploubidis G B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ploubidis_g_b
Author Name: Katikireddi S V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/katikireddi_s_v
Author Name: Tilling K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tilling_k
Author Name: Rentsch C T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rentsch_c_t
Author Name: Timpson N J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/timpson_n_j
Author Name: Chaturvedi N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chaturvedi_n
Author Name: Steves C J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/steves_c_j
sha: a30212dccfc44e85e0bf1a318fd892308e78578d
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2021.06.24.21259277v1?rss=1 https://doi.org/10.1101/2021.06.24.21259277
has_full_text: TRUE
Keywords Extracted from Text Content: UK 1.21-1.42] Non-white 95%CI:1.24 UK National Core Studies -Longitudinal Health OR=0.32 OR=1.56 OR=1.25 OR=1.31 between-LS OR=1.26 OR=1.57 samples participants people COVID-19 UK's National Institute for Health Care left panel Supplementary Table 3 Supplementary Table 1 EHRs multi-organ pre-morbid OR=1.26 PCS people IMD medRxiv preprint cancer survivors Figures Table 1 OR=1.66 OR=1.62 [kg]/(height [m] 2 BiB OR=1.58 hospitalised/Emergency patients [1] [2] [3] Extended GitHub https://github.com/opensafely/long-covid-historical-health liver COVID-19 ALSPAC-G0 OR=1.31 ONS cholesterol Supplementary figures 1 4-12 Figure 2 lateral BCS70 Supplementary Table 6 OR=1.13;95%CI People 95%CI:1.21-1.42 OR=1.57 CIs OSC; participants underweight/normal http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf nose Supplementary Figures 3 1.01-1.53 cardiac Women volunteers Unmeasured/residual SARS-CoV-2 antibody LS, 6866 PCS 20 women studies.(34,35 long COVID 1.15-2.17 Weights organ OR=1.45 Supplementary Information 1 confi medRxiv https://www.opensafely.org/ Nigeria. NHS individuals UK SARS-CoV-2 https://doi.org/10.1101/2021.06.24.21259277 doi COVID 26,29,35 post-COVID-19 excellent-good non-COVID-19 https://github.com/opensafely/long-covid-historical-health/tree/main/codelists Supplementary figures 4 OpenSAFELY UK SNOMED OSC IPWs men BIB 95%CI:1.47-1.68 http://www.bristol.ac.uk/alspac/researchers/our-data/. People acknowledgments
Extracted Text Content in Record: First 5000 Characters:The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing. Analysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses. Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Long COVID is clearly distributed differentially according to several sociodemographic and preexisting health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID. Methods The UK National Core Studies -Longitudinal Health and Wellbeing programme draws together data from multiple UK population-based LS and electronic health records (EHR) to answer questions relevant to the pandemic. We coordinated analyses within each LS, then pooled results statistically to provide more robust estimates and to identify explanations for between-LS heterogeneity. Parallel coordinated investigation in EHR enabled comparison of population-based findings with those in individuals who sought healthcare. Data were drawn from 10 UK LS that had conducted surveys before and during the COVID-19 pandemic (ten samples were yielded in total as one parent-offspring cohort was split into two samples by generation). These included five age-homogenous samples: the Millennium Cohort Study (MCS); 9 SARS-CoV-2 infection can lead to sustained or recurrent multi-organ symptoms in some individuals. [1] [2] [3] Extended COVID-19 symptomatology over weeks to months has been defined by individuals as 'long COVID'. 4 More formally, the UK's National Institute for Health Care and Excellence defined acute COVID-19 (AC; lasting <4 weeks), ongoing symptomatic COVID-19 (OSC; 4-12 weeks), and post-COVID-19 syndrome (PCS; >12 weeks), with the latter two categories combined as 'long COVID'. 1 Estimates of long COVID prevalence range from 13.3% in highly selected, community-based survey respondents with test-confirmed COVID-19, to at least 71% among those hospitalised by the infection. [5] [6] [7] Given the scale of the pandemic, even a low proportion of individuals with long COVID will generate a major burden of lingering illness. 8 In order to target appropriate support and focus research on possible causal mechanisms, we first need to understand risk factors for the disease. Current understanding of frequency of, and risk factors for, long COVID remains poor, impeding mechanistic investigation for intervention development and constraining service planning. Obtaining accurate estimates of association and risk requires large generalisable samples with comprehensive measures of pre-morbid health. UK national primary care records, which cover >95% of the population, afford one such data source, but are limited to those who consult with symptoms and depend on diagnosis and recording of long COVID. Furthermore, risk factor and co-morbidity data are limited to those who consult and are tested. Population-based longitudinal studies (LS), established decades before the pandemic, overco
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