Evidence map›Paper›PMID 36539760›Full record

Observational studyBMC public health2022

Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors.

Sumit Mukherjee, Meghana Kshirsagar, Nicholas Becker, Yixi Xu, William B Weeks, Shwetak Patel, Juan Lavista Ferres, Michael L Jackson

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Understanding how social determinants of health shape Long COVID outcomes: a rapid review of evidence.Archives of public health = Archives belges de sante publique · 2025
    Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Sumit Mukherjee *Insitro Labs, work done while at Microsoft, South San Francisco, USA.
Meghana Kshirsagar *AI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA, 98052, Redmond, USA. Meghana.Kshirsagar@microsoft.com.
Nicholas BeckerAI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA, 98052, Redmond, USA.
Yixi XuAI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA, 98052, Redmond, USA.
William B WeeksAI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA, 98052, Redmond, USA.
Shwetak PatelUniversity of Washington, Seattle, USA.
Juan Lavista FerresAI for Good Research Lab, Microsoft Corporation, 1 Microsoft Way, WA, 98052, Redmond, USA.
Michael L JacksonKaiser Permanente Washington, Seattle, USA.
Microsoft (United States) · USKaiser Permanente · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors.

methodsThe medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health.

resultsAmong the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to 'other anemias' and 'hypertension', that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with 'other sepsis', income was only associated with 'Alopecia areata' (autoimmune disease causing hair loss), while education level was only associated with 'Maternal infectious and parasitic diseases' (p-value < 0.05).

conclusionWe identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.

Indexed as

COVID-19ComorbidityHumansRisk FactorsSARS-CoV-2Social Determinants of HealthInfectious diseasesMedical claimsObservational studySARS-CoV-2COVID-19 Long-term effectsSocial determinants of health

Identifiers

PMID36539760
PMCPMC9765366
OpenAlexW4312115064

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.