---
title: "Changes in recorded incidence of asthma, atopic dermatitis, and multiple sclerosis across the COVI"
id: "plos-one-6-trends-in-the-incidence-of-asthma-atopic-dermatitis-and-multiple-sclerosis"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-6-trends-in-the-incidence-of-asthma-atopic-dermatitis-and-multiple-sclerosis"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103"
published_at: "2026-07-30T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Changes in recorded incidence of asthma, atopic dermatitis, and multiple sclerosis across the COVI
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-6-trends-in-the-incidence-of-asthma-atopic-dermatitis-and-multiple-sclerosis
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103)
- **Published At:** 2026-07-30T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This claims-based cohort study used Optum’s de-identified Clinformatics Data Mart to estimate seasonal, age- and sex-standardized incidence rates for **asthma**, **atopic dermatitis (AD)**, and **multiple sclerosis (MS)** from winter 2018 through fall 2022. - Twenty season-based cohorts were created (winter 2018 to fall 2022). Each cohort required ≥12 months continuous enrollment before the season and no evidence of the outcome in the prior 12 months. - Incident asthma and AD required ≥2 ICD-10-CM codes 30–365 days apart; incident MS required ≥2 ICD-10-CM codes 30–365 days apart plus ≥1 disease-modifying therapy dispensing within one year after the first code. - Age- and sex-standardized incidence rates (IRs) and incidence rate ratios (IRRs) compared each seasonal cohort in 2019–2022 with the corresponding 2018 season; 2022 US population structure was used for standardization. - Observed IRs for all three conditions declined in spring 2020 versus spring 2018: asthma IRR 0.86 (14% decrease), AD IRR 0.72 (28% decrease), MS IRR 0.77 (23% decrease). - Reductions were most pronounced in children (6–11 years) and adolescents (12–17 years), and in older adults (≥65 years); there were no consistent sex differences. - IRs rebounded by summer 2020 for AD and MS and by spring 2021 for asthma; afterward IRs for AD and MS exceeded 2018 levels (AD up to +35%, MS up to +24%), and asthma rose up to +12% since spring 2021. - Authors interpret the early-pandemic declines primarily as reduced detection from limited healthcare access and patient avoidance, though changes in exposure (e.g., reduced respiratory virus circulation) may have contributed, especially for pediatric asthma. - Study limitations include reliance on administrative claims (licensed Optum data not publicly available), possible misclassification inherent to code-based definitions, and descriptive focus without causal inference. Funding and some authors’ employment by Sanofi were disclosed.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0355103) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#citedHeader) * 28 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103#discussedHeader) Open Access Peer-reviewed Research Article # Trends in the incidence of asthma, atopic dermatitis, and multiple sclerosis before, during, and after the COVID-19 pandemic in a US claims database * Kexin Zhu , Roles Conceptualization, Investigation, Methodology, Visualization, Writing – original draft * E-mail: Kexin.Zhu@sanofi.com Affiliation Epidemiology and Benefit Risk, Sanofi, Morristown, New Jersey, United States of America [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0001-7173-6881 ](https://orcid.org/0000-0001-7173-6881 "ORCID Registry") ⨯ * Julie Barberio, Roles Conceptualization, Investigation, Methodology, Writing – review & editing Affiliation Epidemiology and Benefit Risk, Sanofi, Cambridge, Massachusetts, United States of America ⨯ * Nicole Tsao, Roles Conceptualization, Methodology, Writing – review & editing Affiliation Global Medical Affairs, Sanofi, Cambridge, Massachusetts, United States of America ⨯ * Anil Mor, Roles Conceptualization, Methodology, Writing – review & editing Affiliation Epidemiology and Benefit Risk, Sanofi, Morristown, New Jersey, United States of America ⨯ * Venkatesh Harikrishnan, Roles Data curation, Formal analysis, Writing – review & editing Affiliation Epidemiology and Benefit Risk, Sanofi, Morristown, New Jersey, United States of America ⨯ * Xinyu Li, Roles Data curation, Formal analysis, Writing – review & editing Affiliation Epidemiology and Benefit Risk, Sanofi, Morristown, New Jersey, United States of America ⨯ * Sarah-Jo Sinnott Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing Affiliation Epidemiology and Benefit Risk, Sanofi, Cambridge, Massachusetts, United States of America ⨯ # Trends in the incidence of asthma, atopic dermatitis, and multiple sclerosis before, during, and after the COVID-19 pandemic in a US claims database * Kexin Zhu, * Julie Barberio, * Nicole Tsao, * Anil Mor, * Venkatesh Harikrishnan, * Xinyu Li, * Sarah-Jo Sinnott ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: July 30, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0355103) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355103) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0355103) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0355103) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec001) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec002) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec008) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec009) * [Conclusions](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec010) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#sec011) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0355103) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103) ## Abstract There is limited evidence on how reduced healthcare resource utilization during the COVID-19 pandemic has affected the detection of inflammatory and immunologic diseases. We aimed to describe the observed incidence rates (IRs) of asthma, atopic dermatitis (AD), and multiple sclerosis (MS) before, during, and after the pandemic. Individuals aged ≥6 years were identified in Optum’s de-identified Clinformatics® Data Mart Database from 2018 to 2022 to make 20 season-based cohorts. Age- and sex-standardized IRs of asthma, AD, and MS were estimated. Incidence rate ratios (IRR) and 95% confidence intervals (CI) were calculated comparing IRs in seasonal cohorts in 2019–2022 to the corresponding timeframe in 2018. Compared to spring 2018, IRs of asthma, AD, and MS in spring 2020 decreased by 14% (IRR: 0.86, 95% CI: 0.84–0.87), 28% (IRR: 0.72, 95% CI: 0.69–0.75), and 23% (IRR: 0.77, 95% CI: 0.68–0.87), respectively. The observed incidence reduction was most profound in children (6–11 years) and adolescents (12–17 years), followed by senior adults (≥65 years). There was no sex difference. IRs returned to or exceeded pre-pandemic levels for AD and MS in summer 2020 and for asthma in spring 2021. COVID-19 led to an apparent decline in incidence for selected inflammatory and immunologic diseases, which was more pronounced for pediatric and senior populations. The observed decrease in incidence likely reflects delayed access to healthcare, resulting in unrecorded (but still occurring) diagnoses for those time periods. Future studies using data that encompass the pandemic period should exercise caution in the design of study and interpretation of incidence or prevalence data. ## Figures ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g002) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g002) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355103.g002) **Citation:** Zhu K, Barberio J, Tsao N, Mor A, Harikrishnan V, Li X, et al. (2026) Trends in the incidence of asthma, atopic dermatitis, and multiple sclerosis before, during, and after the COVID-19 pandemic in a US claims database. PLoS One 21(7): e0355103. https://doi.org/10.1371/journal.pone.0355103 **Editor:** Omid Beiki, Karolinska Institutet, SWEDEN **Received:** February 2, 2026; **Accepted:** July 16, 2026; **Published:** July 30, 2026 **Copyright:** © 2026 Zhu et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The data used in this study were de-identified administrative health claims data obtained from Optum® under third-party licensing agreements. The authors did not have any special access privileges beyond those available to other researchers who obtain the data under license. The data are not publicly available, but application for a third-party license may be made to Optum® (contact at [https://www.optum.com/en/business/life-sciences/real-world-data/claims-data.html)](https://www.optum.com/en/business/life-sciences/real-world-data/claims-data.html). **Funding:** This work was funded by Sanofi. The data that support the findings of this study are available from a third party (Optum, Inc.) and were purchased by the funder. The funder provided support in the form of salaries for authors KZ, JB, NT, AM, VH, XL, and SJS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are stated in Author Contributions. **Competing interests:** KZ, JB, AM, VH, XL, and SJS are Sanofi employees and may hold shares and/or stock options in the company. NT was a Sanofi employee at the time of conducting this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials. ## Introduction During the early period of the COVID-19 pandemic, healthcare facilities restricted or postponed their services for elective, non-essential, and non-urgent purposes [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref001)–[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref003)]. In addition, many patients avoided seeking both urgent and routine care due to concerns about contracting COVID-19 [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref004)]. Prior research has demonstrated significant declines in access to routine medical care, decreases in referrals, and reduced inpatient admissions across the United States (US) among patients without COVID-19 in the early stage of the pandemic (e.g., winter and spring of 2020) [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref005)–[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref009)]. Reduced healthcare utilization during the pandemic led to lower detection and reporting of non-COVID-19 related diseases [[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref010),[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref011)], which likely translated to an artificially reduced incidence during the pandemic compared to pre-pandemic and post-pandemic timepoints. While the recorded diagnosis of cancer and cardiovascular outcomes from March to May 2020 compared to pre-pandemic has been well described in the literature [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref012),[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref013)], limited research has been conducted on the changes in the recorded new diagnoses of inflammatory and immunologic diseases. Furthermore, it is known that young children and older adults were disproportionately impacted by health service restrictions during the pandemic [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref004),[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref007)]. However, trends in the recorded new diagnoses of diseases by age or sex, as impacted by COVID-19 and its related restrictions, have not been well-researched. Lastly, existing literature on healthcare utilization and change in incidence have been primarily based on data in the first pandemic year [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref002),[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref007),[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref009),[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref012)], and evidence is limited on the impacts of the pandemic (e.g., recovery of healthcare utilization, other COVID-19 related changes) on estimates of disease incidence in the long term. In this study, we estimated the recorded incidence rates (IRs) of asthma, atopic dermatitis (AD), and multiple sclerosis (MS) in multiple season-based cohorts from 2018 through 2022 in a claims database in the US. We compared IRs in each cohort to those in the same seasonal cohort in 2018 (baseline reference). Furthermore, IRs were calculated by age and sex to identify whether estimates of recorded incidence were disproportionately affected in one group more than another. This descriptive study was focused on the methodological implications of the pandemic for incidence estimation using a US claims database, rather than on causal inferences about COVID-19 or specific countermeasures and disease risk. ## Materials and methods ### Data source and study population This was a cohort study using Optum’s de-identified Clinformatics® Data Mart Database (CDM) from December 2016 through November 2023. The Optum® CDM includes administrative health claims for commercial insurance and Medicare Advantage health plans from enrollees in all 50 US states. Previous studies have observed the seasonal variations in healthcare services utilization related to asthma, AD, and MS [[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref014)–[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref016)]. To account for seasonality, IRs of asthma, AD, and MS were calculated in cohorts based on seasons from winter 2018 (December 1, 2017 to February 28, 2018) to fall 2022 (September 1, 2022 to November 30, 2022); therefore, 20 seasonal cohorts for each outcome of interest were created. For each seasonal cohort, individuals eligible for inclusion met the following criteria: (1) were enrolled in the Optum® CDM for at least one day in the season, (2) had 12 months of continuous enrollment prior to the index date, (3) aged at least 6 years old, (4) had no missing or unknown sex information, and (5) had no evidence of the outcomes of interest (defined below) within 12 months before the index date. The index date was the earliest date that eligibility criteria were met during the season. ### Outcome measures For asthma and AD, incident case was identified by ≥2 inpatient or outpatient International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnostic codes for asthma (J45) or AD (L20.0, L20.81, L20.82, L20.84, L20.89, L20.9) that were 30–365 days apart. The date of the outcome occurrence was the date of the first qualifying ICD-10-CM code for asthma or AD identification. For incident MS cases, patients were required to have ≥2 inpatient or outpatient ICD-10-CM diagnostic codes for MS (G35) (30–365 days apart) and ≥1 dispensing for disease modifying therapies (DMT) ([S1 Table](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.s001)) within one year after the first qualifying ICD-10-CM code for MS identification (positive predictive value [PPV] of 95.4%–97.8% and sensitivity of 87.2%–93.4% [[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref017)]). The date of the first qualifying ICD-10-CM code for MS was defined as the date of the outcome occurrence. For all three outcomes, the date of the first qualifying code was required to fall in the cohort timeframe (within each season), while the second ICD-10-CM code and the date of dispensation for DMT could go beyond the cohort timeframe but should be within one year of the first qualifying ICD-10-CM code. ### Follow-up For each seasonal cohort, each individual was followed from the index date until the date of the outcome of interest (defined above), death, disenrollment of health insurance coverage, or the end of the cohort timeframe (last date of the season), whichever came first. ### Statistical analysis The incidence rate (IR) was calculated as the number of individuals with an incident outcome divided by the sum of each individual’s person-time at risk (from the index date to the end of follow-up in the unit of person-years) in each seasonal cohort from 2018 to 2022. The corresponding 95% confidence intervals (CI) were calculated using an Exact method [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref018)]. Incidence rate ratios (IRR) and 95% CIs comparing the IRs in each seasonal cohort in 2019–2022 to the IRs in the corresponding timeframe in 2018 (baseline reference) were calculated using Poisson regression with the Exact method [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref018)]. Age- and sex-standardized IRs and IRRs were calculated using a direct method with the 2022 US population composition by age and sex from the US Census Bureau as the reference population to reflect the most current population structure at the end of our study period [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.ref019)]. In subgroup analyses, IRs were calculated by age (6–11, 12–17, 18–64, and ≥65 years) and sex (male, female) subgroups in each seasonal cohort. IRRs were calculated to compare the IRs in each age and sex subgroup in 2019–2022 to the IRs in the corresponding age and sex subgroups in the 2018 seasonal cohort as a reference. All statistical analyses were performed in SAS version 9.4 (SAS Institute, Cary, NC, USA). ### Ethical approval This study involved only secondary database analysis of de-identified administrative health claims data and is not considered human subject research. No institutional review board approval was required. ## Results [S2 Table](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.s002) presents the characteristics of the study population included in the seasonal cohorts for each outcome in 2018. Each seasonal cohort had slightly different study sample sizes and characteristics due to different outcomes under study and eligibility criteria at cohort entry. Approximately 11 million individuals were included in each cohort with an average age of 49–51 years and equal sex distributions (51% females). [S1 Fig](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355103#pone.0355103.s003) presents IRs of asthma, AD, and MS in each seasonal cohort from 2018 to 2022. IRs for all three outcomes declined during spring 2020. Compared to spring 2018, IRs of asthma, AD, and MS decreased by 14% (IRR:
## Related Clinical Research

- [Pistachios and sleep: what evidence links pistachios to better sleep?](https://medichelpline.com/clinical-feed/medical-news-today-0-are-pistachios-a-natural-way-to-support-sleep-what-the-research-says.md)
- [Statin Use Linked to Reduced Glaucoma Risk in Recent Study](https://medichelpline.com/clinical-feed/medical-news-today-0-statins-may-help-lower-glaucoma-risk-study-finds.md)
- [Climate change worsens health for people with chronic illness](https://medichelpline.com/clinical-feed/stat-news-1-opinion-climate-change-is-making-people-with-chronic-illness-even-sicker.md)
- [Severity-stratified cis-eQTL mapping identifies candidate causal genes for critical COVID-19](https://medichelpline.com/clinical-feed/medrxiv-9-severity-stratified-longitudinal-cis-eqtl-mapping-enables-high-sensitivity.md)
- [Whale Songs: Expressions of Grief Through an OB-GYN's Perspective](https://medichelpline.com/clinical-feed/stat-news-2-opinion-whale-songs-the-sounds-of-love-and-loss-as-heard-by-an-ob-gyn.md)

## Navigation
- [← Back to General Feed](https://medichelpline.com/clinical-feed/general.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.