PONE-D-23-14176Long Covid symptoms and diagnosis in primary care: a cohort study using structured and unstructured data in The Health Improvement Network primary care databasePLOS ONE

Dear Dr. Shah,

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"This work was supported by Health Data Research UK, which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and the Wellcome Trust. This study was supported by the National Institute for Health Research (NIHR) CONVALESCENCE grant (COV-LT-0009). ADS is funded by a postdoctoral fellowship from THIS Institute, NIHR (AI_AWARD01864 and COV-LT-0009), UKRI (Horizon Europe Guarantee for DataTools4Heart) and British Heart Foundation Accelerator Award (AA/18/6/24223). VK is supported by the UKRI/NIHR Strategic Priorities Award in Multimorbidity Research (MR/V033867/1) for the Multimorbidity Mechanism and Therapeutics Research Collaborative. EF is supported by the NIHR Applied Research Collaboration Kent Surrey and Sussex (grant number NIHR200179). KN has been awarded research grants from NIHR, UKRI/MRC, Kennedy Trust for Rheumatology Research, Health Data Research UK, Wellcome Trust, European Regional Development Fund, Institute for Global Innovation, Boehringer Ingelheim, Action Against Macular Degeneration Charity, Midlands Neuroscience Teaching and Development Funds, South Asian Health Foundation, Vifor Pharma, College of Police, and CSL Behring, all payments were made to his academic institution; Krishnarajah Nirantharakumar received consulting fees from BI, Sanofi, CEGEDIM, MSD and holds a leadership/fiduciary role with NICST, a charity and OpenClinical, a Social Enterprise."

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A preprint has also been published on medRxiv:

Long Covid symptoms and diagnosis in primary care: a cohort study using structured and unstructured data in The Health Improvement Network primary care database. medRxiv, version 1, PPR: PPR594314, DOI: 10.1101/2023.01.06.23284202"

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Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The value of this paper is the agnostic approach using open text analysis, since long COVID is still a condition which is not well understood. I have some questions regarding the design and the available data:

- The authors talk about cohorts and have 4 exposure groups. It is confusing when these groups are named cases and controls which suggest that they are describing the outcomes (the symptoms).

- The cohorts should be more clearly defined, are they fixed or dynamic cohorts?

- Do all cohort members have prepandemic data on symptoms?

- Are the patients their own controls when comparing symptoms before and after infections?

- It seems that there was 1-to-1 matching between exposed (confusingly called cases) and unexposed patients for practice, age and sex. How is it then possible to estimate effects of sex and age, as is being done.

- Are cohort participants clustered within practices? If so, is there any intra-practice correlation of symptoms?

- How is index date defined?

- In other studies of long COVID, symptoms that has to do with cognitive function ("brain fog" etc) are often described. Are these type of symptoms not picked up by the techniques used here?

- It would be interesting if more data on the functional level of patients had been available, like ability to work or perform daily activities of living.

Reviewer #2: Shah et al describe an analysis of primary care EHR data to investigate symptoms associated with a diagnosis of long COVID. A particular strength of the study is the use of natural language processing to extract information from free text records that would otherwise be missed, and the work seems to provide a useful contribution to the literature (from my perspective as a statistician who has worked on COVID-19 studies). I have only relatively minor comments regarding the description of the study, along with some technical queries regarding the analysis.

As noted by previous reviewers, the dataset for analysis in this study is truncated at the end of December 2020. I do not think that this means that the analysis presented is no longer useful. However, given that the situation with long-COVID could now be quite different (given widespread vaccination and repeat infections with different variants etc.), I do feel that the timespan for the analysis needs to be specified in the Abstract and that the Introduction also needs to frame the analysis as effectively only considering the pre-vaccine period. These issues could also be explored in a little more depth in the Discussion.

I have some queries regarding the matching process:

-Firstly, were controls required to be ‘unexposed’ for the entire analysis period? I think that in an ideal nested case-control study design, control status would only be defined at the index date of the case (with follow-up censored in the event of subsequent infection). I don’t think that this should have had too large an impact for an analysis within this time period, but it would be a more important consideration for any analyses stretching into 2021 or 2022 given the very high cumulative incidence of SARS-CoV-2 at later time points.

-How precisely was age matched?

-I found it quite confusing to work out how the authors ended up with their final cohort of cases and controls. This is not clear at all in the text, but Figure 1 is helpful. I am not sure why exclusions based on index date, registration date or available follow-up would be conducted after the initial matching process – were these initial oversights? Or did this relate to data access constraints? I can see why reclassification of cases based on free text analysis would happen after the initial matching given the analysis required to process this information, but this is not explained clearly in the text.

-If the analysis had retained 1:1 matching, then I would have suggested consideration of a frailty term in the analysis models for matched pairs, but I don’t think that this is possible given the subsequent deviation from 1:1 matching. There is a related issue that the baseline hazard functions from index dates of controls don’t really have a clear interpretation given the deviation from 1:1 matching to cases, but the overall balance in calendar period of follow-up for cases and controls should be largely retained despite the reclassification of some cases and controls.

Further minor comments:

-It is stated that “We stratified hazard by general practice…”, I think that it would be correct and slightly clear to state “We stratified baseline hazard functions by general practice…”.

-“No patients had a coded diagnosis of post COVID condition (Long Covid)…”, I understand that the relevant ICD-10 code was not created until after the analysis period considered. This should perhaps be mentioned.

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Reviewer #1: No

Reviewer #2: No

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Long Covid symptoms and diagnosis in primary care: a cohort study using structured and unstructured data in The Health Improvement Network primary care database

PONE-D-23-14176R1

Dear Dr. Shah,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

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