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Global Journal of Clinical Medicine and Medical Research [GJCMMR] (ISSN: 2583-987X)

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ISSN-L 2583-987X

ISSN 2583-987X

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Global Health Research and Policy

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GJMR-B Pharma, Drug Discovery, Toxicology & Medicine: Volume 24 Issue B2 Vol. 24 No. B2 (2024)

GJMR-G Veterinary Science & Veterinary Medicine: Volume 24 Issue G1 Vol. 24 No. G1 (2024)

GJMR-A Neurology & Nervous System: Volume 24 Issue A1 Vol. 24 No. A1 (2024)

GJMR-J Dentistry & Otolaryngology: Volume 24 Issue J1 Vol. 24 No. J1 (2024)

GJMR-B Pharma, Drug Discovery, Toxicology & Medicine: Volume 24 Issue B1 Vol. 24 No. B1 (2024)

GJMR-C Microbiology & Pathology: Volume 24 Issue C1 Vol. 24 No. C1 (2024)

GJMR-F Diseases: Volume 24 Issue F1 Vol. 24 No. F1 (2024)

GJMR-F Diseases: Volume 23 Issue F10 Vol. 23 No. F10 (2023)

GJMR-G Veterinary Science & Veterinary Medicine: Volume 23 Issue G2 Vol. 23 No. G2 (2023)

GJMR-K Interdisciplinary: Volume 24 Issue K1 Vol. 24 No. K1 (2024)

GJMR-I Surgeries-and-Cardiovascular-System: Volume 23 Issue I2 Vol. 23 No. I2 (2023)

GJMR-J Dentistry & Otolaryngology: Volume 23 Issue J4 Vol. 23 No. J4 (2023)

GJMR-F Diseases: Volume 23 Issue F9 Vol. 23 No. F9 (2023)

GJMR-K Interdisciplinary: Volume 23 Issue K7 Vol. 23 No. K7 (2023)

GJMR-F Diseases: Volume 23 Issue F8 Vol. 23 No. F8 (2023)

GJMR-J Dentistry & Otolaryngology: Volume 23 Issue J3 Vol. 23 No. J3 (2023)

GJMR-H Orthopedic & Musculoskeletal System: Volume 23 Issue H2 Vol. 23 No. H2 (2023)

GJMR-K Interdisciplinary: Volume 23 Issue K5 Vol. 23 No. K5 (2023)

GJMR-K Interdisciplinary: Volume 23 Issue K6 Vol. 23 No. K6 (2023)

GJMR-F Diseases: Volume 23 Issue F7 Vol. 23 No. F7 (2023)

GJMR-B Pharma, Drug Discovery, Toxicology & Medicine: Volume 23 Issue B3 Vol. 23 No. B3 (2023)

GJMR-C Microbiology & Pathology: Volume 23 Issue C3 Vol. 23 No. C3 (2023)

GJMR-E Gynecology & Obstetrics: Volume 23 Issue E2 Vol. 23 No. E2 (2023)

GJMR-F Diseases: Volume 23 Issue F6 Vol. 23 No. F6 (2023)

GJMR-B Pharma, Drug Discovery, Toxicology & Medicine: Volume 23 Issue B2 Vol. 23 No. B2 (2023)

This paper is in the following e-collection/theme issue:

Published on 14.8.2024 in Vol 26 (2024)

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning–Based Content Analysis Study

Authors of this article:

Original Paper

• Keyang Zhao 1 * , DPhil   ; 
• Xiaojing Li 1, 2 * , Prof Dr   ; 
• Jingyang Li 3 , DPhil  

1 School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China

2 Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China

3 School of Software, Shanghai Jiao Tong University, Shanghai, China

*these authors contributed equally

Corresponding Author:

Xiaojing Li, Prof Dr

School of Media & Communication

Shanghai Jiao Tong University

800 Dongchuan Rd.

Minhang District

Shanghai, 200240

Phone: 86 13918611103

Fax:86 21 34207088

Email: [email protected]

Background: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking.

Objective: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions.

Methods: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling.

Results: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention.

Conclusions: The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study’s findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Introduction

In 2020, 4.57 million new cancer cases were reported in China, accounting for 23.7% of the world’s total [ 1 ]. Many of these cancers, however, can be prevented [ 2 , 3 ]. According to the World Health Organization (WHO), 30%-50% of cancers could be avoided through early detection and by reducing exposure to known lifestyle and environmental risks [ 4 ]. This underscores the imperative to advance education on cancer prevention and treatment.

Mass media serves not only as a primary channel for disseminating cancer information but also as a potent force in shaping the public health agenda [ 5 , 6 ]. Previous studies have underscored the necessity of understanding how specific cancer-related content is presented in the media. For example, the specific cancer types frequently mentioned in news reports have the potential to influence the public’s perception of the actual incidence of cancer [ 7 ].

Nowadays, social media plays an essential role in disseminating health information, coordinating resources, and promoting health campaigns aimed at educating individuals about prevention measures [ 8 ]. Additionally, it influences patients’ decision-making processes regarding treatment [ 9 ]. A study revealed that social media use correlates with increased awareness of cancer screening in the general population [ 10 ]. In recent years, there has been a notable surge in studies evaluating cancer-related content on social media. However, previous studies often focused on specific cancer types [ 11 ] and limited aspects of cancer-related issues [ 12 ]. The most recent comprehensive systematic content analysis of cancer coverage, conducted in 2013, indicated that cancer news coverage has heavily focused on treatment, while devoting very little attention to prevention, detection, or coping [ 13 ].

Evaluating cancer prevention information on social media is crucial for future efforts by health educators and cancer control organizations. Moreover, providing reliable medical information to individuals helps alleviate feelings of fear and uncertainty [ 14 ]. Specifically, patients often seek information online when making critical treatment decisions, such as chemotherapy [ 15 ]. Therefore, it is significant to comprehensively evaluate the types of treatment information available on social media.

Although many studies have explored cancer-related posts from the perspectives of patients with cancer [ 16 ] and caregivers [ 17 ], the analysis of posts from medical professionals has been found to be inadequate [ 18 ]. This paradox arises from the expectation that medical professionals, given their professional advantages, should take the lead in providing cancer education on social media. Nevertheless, a significant number of studies have highlighted the prevalence of unreliable medical information on social media [ 19 ]. A Japanese study highlighted a concerning phenomenon: despite efforts by medical professionals to promote cancer screening online, a significant number of antiscreening activists disseminated contradictory messages on the internet, potentially undermining the effectiveness of cancer education initiatives [ 20 ]. Hence, there is an urgent need for the accurate dissemination of health information on social media, with greater involvement from scientists or professional institutions, to combat the spread of misinformation [ 21 ]. Despite efforts to study professional medical websites [ 22 ] and apps [ 23 ], there remains a lack of comprehensive understanding of the content posted on medical social media (MSM). Further study is thus needed to compare the differences between cancer information on social media from professional medical sources and nonprofessional sources to enhance cancer education.

For this study, we defined social media as internet-based platforms characterized by social interactive functions such as reading, commenting, retweeting, and timely interaction [ 24 ]. Based on this definition, we further classified 2 types of media based on ownership, content, and contributors: common social media (CSM) and MSM. MSM refers to social media platforms owned by professional medical institutions or organizations. It primarily provides medical and health information by medical professionals, including medical-focused accounts on social media and mobile health apps. CSM refers to social media owned or managed by individuals without medical backgrounds. It mainly provides health and lifestyle content.

Similar to Facebook (Meta Platforms, Inc.), WeChat (Tencent Holdings Limited) is the most popular social media platform in China, installed on more than 90% of smartphones. Zhang et al [ 25 ] has indicated that 63.26% of people prefer to obtain health information from WeChat. Unlike other Chinese social media platforms, WeChat has a broader user base that spans various age groups [ 26 ]. WeChat Public Accounts (WPAs) operate within the WeChat platform, offering services and information to the public. Many hospitals and primary care institutions in China have increasingly registered WPAs to provide health care services, medical information, health education, and more [ 27 ]. Therefore, this study selected WPA as the focus of research.

Based on big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM, which could significantly enhance cancer coverage and assist patients in treatment decision-making. To address the aforementioned research gaps, 2 research questions were formulated.

• Research question 1: What are the characteristics of cancer prevention information discussed on social media? What are the differences between MSM and CSM?
• Research question 2: What are the characteristics of cancer treatment information discussed on social media? What are the differences between MSM and CSM?

Data Collection and Processing

We selected representative WPAs based on the reports from the “Ranking of Influential Health WeChat Public Accounts” [ 28 ] and the “2021 National Rankings of Best Hospitals by Specialty” [ 29 ]. In this study, we focused on 4 medical WPAs within MSM: Doctor Dingxiang (丁香医生), 91Huayi (华医网), The Cancer Hospital of Chinese Academy of Medical Sciences (中国医学科学院肿瘤医院), and Fudan University Shanghai Cancer Center (复旦大学附属肿瘤医院). We also selected 5 health and lifestyle WeChat Official Accounts classified as CSM for this study: Health Times (健康时报), Family Doctor (家庭医生), CCTV Lifestyle (CCTV 生活圈), Road to Health (健康之路), and Life Times (生命时报).

We implemented a Python-based (Python Foundation) crawler to retrieve posts from the aforementioned WPAs. Subsequently, we implemented a filtration process to eliminate noisy and unreliable data. Note that our focus is on WPAs that provide substantial information, defined as containing no fewer than a certain number of characters. We have deleted documents that contain less than 100 Chinese characters. Furthermore, we have removed figures and videos from the remaining documents. Eventually, we conducted an analysis at the paragraph level. According to our findings from random sampling, noise in articles from WPAs mostly originates from advertisements, which are typically found in specific paragraphs. Therefore, we retained only paragraphs that did not contain advertising keywords. In total, we collected 60,843 posts from these WPAs, comprising 20,654 articles from MSM and 40,189 articles from CSM.

The workflow chart in Figure 1 depicts all procedures following data collection and preprocessing. After obtaining meaningful raw documents, we performed word-level segmentation on the texts. We then removed insignificant stopwords and replaced specific types of cancers with a general term to facilitate coarse-grained latent Dirichlet allocation (LDA)–based filtering. Subsequently, we conducted fine-grained LDA topic modeling on the filtered documents without replacing keywords to visualize the topics extracted from the WPAs. Furthermore, we utilized a manually labeled codebook to train a long short-term memory (LSTM) network for document classification into various categories. Finally, we performed data analysis using both the topic distribution derived from fine-grained LDA and the classified documents.

Latent Dirichlet Allocation Topic Modeling

LDA is a generative statistical model that explains sets of observations by latent groups, revealing why some parts of the data are similar [ 30 ]. The LDA algorithm can speculate on the topic distribution of a document.

When comparing LDA with other natural language processing methods such as LSTM-based deep learning, it is worth noting that LDA stands out as an unsupervised learning algorithm. Unlike its counterparts, LDA has the ability to uncover hidden topics without relying on labeled training data. Its strength lies in its capability to automatically identify latent topics within documents by analyzing statistical patterns of word co-occurrences. In addition, LDA provides interpretable outcomes by assigning a probability distribution to each document, representing its association with various topics. Similarly, it assigns a probability distribution to each topic, indicating the prevalence of specific words within that topic. This feature enables researchers to understand the principal themes present in their corpus and the extent to which these themes are manifested in individual documents.

The foundational principle of LDA involves using probabilistic inference to estimate the distribution of topics and word allocations. Specifically, LDA assumes that each document is composed of a mixture of a small number of topics, and each word’s presence can be attributed to one of these topics. This approach allows for overlapping content among documents, rather than strict categorization into separate groups. For a deeper understanding of the technical and theoretical aspects of the LDA algorithm, readers are encouraged to refer to the research conducted by Blei et al [ 30 ]. In this context, our primary focus was on the application of the algorithm to our corpus, and the procedure is outlined in the following sections.

Document Selection

Initially, document selection involves using a methodological approach to sample documents from the corpus, which may include random selection or be guided by predetermined criteria such as document relevance or popularity within the social media context.

Topic Inference

Utilizing LDA or a similar topic modeling technique, we infer the underlying topical structure within each document. This involves modeling documents as mixtures of latent topics represented by a Dirichlet distribution, from which topic proportions are sampled.

Topic Assignment to Words

After determining topic proportions, we proceed to assign topics to individual words in the document. Using a multinomial distribution, each word is probabilistically associated with one of the inferred topics based on the previously derived topic proportions.

Word Distribution Estimation

Each topic is characterized by a distinct distribution over the vocabulary, representing the likelihood of observing specific words within that topic. Using a Dirichlet distribution, we estimate the word distribution for each inferred topic.

Word Generation

Finally, using the multinomial distribution again, we generate words for the document by sampling from the estimated word distribution corresponding to the topic assigned to each word. This iterative process produces synthetic text that mirrors the statistical properties of the original corpus.

To filter out noncancer-related documents in our case, we replaced cancer-related words with “癌症” (cancer or tumor in Chinese) in all documents. We then conducted an LDA analysis to compute the topic distribution of each document and retained documents related to topics where “癌症” appears among the top 10 words.

In our study, we used Python packages such as jieba and gensim for document segmentation and extracting per-topic-per-word probabilities from the model. During segmentation, we applied a stopword dictionary to filter out meaningless words and transformed each document into a cleaned version containing only meaningful words.

During the LDA analysis, to determine the optimal number of topics, our main goal was to compute the topic coherence for various numbers of topics and select the model that yielded the highest coherence score. Coherence measures the interpretability of each topic by assessing whether the words within the same topic are logically associated with each other. The higher the score for a specific number k , the more closely related the words are within that topic. In this phase, we used the Python package pyLDAvis to compare coherence scores with different numbers of topics. Subsequently, we filtered and retained only the documents related to cancer topics, resulting in 4479 articles from MSM and 3948 articles from CSM.

Among the filtered articles, we conducted another LDA analysis to extract topics from the original articles without replacing cancer-related words. Using pyLDAvis, we calculated the coherence score and identified 6 topics for both MSM and CSM articles.

To visualize the topic modeling results, we created bar graphs where the y-axis indicates the top 10 keywords associated with each topic, and the x-axis represents the weight of each keyword (indicating its contribution to the topic). At the bottom of each graph ( Figures 2 and 3 ), we generalized and presented the name of each topic based on the top 10 most relevant keywords.

Manual Content Analysis: Coding Procedure

Based on the codebook, 2 independent coders (KZ and JL) engaged in discussions regarding the coding rules to ensure a shared understanding of the conceptual and operational distinctions among the coding items. To ensure the reliability of the coding process, both coders independently coded 100 randomly selected articles. Upon completion of the pilot coding, any disagreements were resolved through discussion between the 2 coders.

For the subsequent coding phase, each coder was assigned an equitable proportion of articles, with 10% of the cancer-related articles randomly sampled from both MSM samples (450/4479) and CSM samples (394/3948). Manual coding was performed on a total of 844 articles, which served as the training data set for the machine learning model. The operational definitions of each coding variable are detailed in Multimedia Appendix 1 .

Coding Measures

Cancer prevention measures.

Coders identified whether an article mentioned any of the following cancer prevention measures [ 31 - 35 ]: (1) avoid tobacco use, (2) maintain a healthy weight, (3) healthy diet, (4) exercise regularly, (5) limit alcohol use, (6) get vaccinated, (7) reduce exposure to ultraviolet radiation and ionizing radiation, (8) avoid urban air pollution and indoor smoke from household use of solid fuels, (9) early screening and detection, (10) breastfeeding, (11) controlling chronic infections, and (12) other prevention measures.

Cancer Treatment Measures

Coders identified whether an article mentioned any of the following treatments [ 36 ]: (1) surgery (including cryotherapy, lasers, hyperthermia, photodynamic therapy, cuts with scalpels), (2) radiotherapy, (3) chemotherapy, (4) immunotherapy, (5) targeted therapy, (6) hormone therapy, (7) stem cell transplant, (8) precision medicine, (9) cancer biomarker testing, and (10) other treatment measures.

Neural-Based Machine Learning

In this part, we attempted to label each article using a neural network. As mentioned earlier, we manually labeled 450 MSM articles and 394 CSM articles. We divided the labeled data into a training set and a test set with a ratio of 4:1. We adopted the pretrained Bidirectional Encoder Representations from Transformers (BERT) model. As BERT can only accept inputs with fewer than 512 tokens [ 37 ], we segmented each document into pieces of 510 tokens (accounting for BERT’s automatic [CLS] and [SEP] tokens, where [CLS] denotes the start of a sentence or a document, and [SEP] denotes the end of a sentence or a document) with an overlap of 384 tokens between adjacent pieces. We began by utilizing a BERT-based encoder to encode each piece and predict its labels using a multioutput decoder. After predicting labels for each piece, we pooled the outputs for all pieces within the same document and used an LSTM network to predict final labels for each document.

Ethical Considerations

This study did not require institutional research board review as it did not involve interactions with humans or other living entities, private or personally identifiable information, or any pharmaceuticals or medical devices. The data set consists solely of publicly available social media posts.

Cancer Topics on Social Media

Applying LDA, we identified 6 topics each for MSM and CSM articles. The distribution of topics among MSM and CSM is presented in Table 1 , while the keyword weights for each topic are illustrated in Figures 2 and 3 .

a In each article, different topics may appear at the same time. Therefore, the total frequency of each topic did not equate to the total number of 8427 articles.

b To ensure the accuracy of the results, directly translating sampled texts from Chinese into English posed challenges due to differences in semantic elements. In English, cancer screening refers to detecting the possibility of cancer before symptoms appear, while diagnostic tests confirm the presence of cancer after symptoms are observed. However, in Chinese, the term “检查” encompasses both meanings. Therefore, we translated it as both screening and testing.

Among MSM articles, topic 5 was the most frequent (2548/8427, 30.24%), followed by topic 6 (1741/8427, 20.66%) and topic 2 (1611/8427, 19.12%). Both topics 5 and 6 focused on clinical treatments, with topic 5 specifically emphasizing cancer diagnosis. The keywords in topic 6, such as “polyp,” “tumor,” and “surgery,” emphasized the risk and diagnosis of precancerous lesions. Topic 2 primarily focused on cancer surgeries related to breast cancer, lung cancer, and ovarian cancer. The results indicate that MSM articles concentrated on specific cancers with higher incidence in China, including stomach cancer, liver cancer, lung cancer, breast cancer, and cervical cancer [ 10 ].

On CSM, topic 4 (1947/8427, 23.10%) had the highest proportion, followed by topic 5 (1790/8427, 21.24%) and topic 3 (1599/8427, 18.97%). Topic 6 had the smallest proportion. Topics 1 and 4 were related to lifestyle. Topic 1 particularly focused on cancer-causing substances, with keywords such as “food,” “nutrition,” and “carcinogen” appearing most frequently. Topic 4 was centered around exercise, diet, and their impact on cancer risk. Topics 3 and 5 were oriented toward cancer screening and diagnosis. Topic 3 specifically focused on female-related cancers, with discussions prominently featuring breast cancer screening and testing. Topic 5 emphasized early detection and diagnosis of stomach and lung cancers, highlighting keywords such as “screening” and “symptom.”

Cancer Prevention Information

Our experiment on the test set showed that the machine learning model achieved F 1 -scores above 85 for both prevention and treatment categories in both MSM and CSM. For subclasses within prevention and treatment, we achieved F 1 -scores of at least 70 for dense categories (with an occurrence rate >10%, ie, occurs in >1 of 10 entries) and at least 50 for sparse categories (with an occurrence rate <10%, ie, occurs in <1 of 10 entries). Subsequently, we removed items labeled as “other prevention measures” and “other treatment measures” due to semantic ambiguity.

Table 2 presents the distribution of cancer prevention information across MSM (n=4479) and CSM (n=3948).

a MSM: medical social media.

b CSM: common social media.

Cancer Prevention Information on MSM

The distribution of cancer prevention information on MSM (n=4479) is as follows: articles discussing prevention measures accounted for 25.39% (1137/4479) of all MSM cancer-related articles. The most frequently mentioned measure was “early screening and testing” (737/4479, 16.45%). The second and third most frequently mentioned prevention measures were “healthy diet” (278/4479, 6.21%) and “get vaccinated” (261/4479, 5.83%). The least mentioned prevention measures were “controlling chronic infections” (3/4479, 0.07%), “reduce exposure to radiation” (2/4479, 0.04%), and “breastfeeding” (1/4479, 0.02%), each appearing in only 1-3 articles.

Cancer Prevention Information on CSM

As many as 1856 out of 3948 (47.01%) articles on CSM referred to cancer prevention information. Among these, “early screening and testing” (1085/3948, 27.48%) was the most commonly mentioned prevention measure. “Exercise regularly” (661/3948, 16.74%) and “healthy diet” (598/3948, 15.15%) were the 2 most frequently mentioned lifestyle-related prevention measures. Additionally, “avoid tobacco use” accounted for 9.32% (368/3948) of mentions. Other lifestyle-related prevention measures were “limit alcohol use” (281/3948, 7.12%) and “maintain a healthy weight” (193/3948, 4.89%). The least mentioned prevention measures were “practice safe sex” (4/3948, 0.10%), “reduce exposure to radiation” (1/3948, 0.03%), and “breastfeeding” (1/3948, 0.03%), each appearing in only 1-4 articles.

Cancer Prevention Information on Social Media

Table 3 presents the overall distribution of cancer prevention information on social media (N=8427). Notably, CSM showed a stronger focus on cancer prevention (1856/3948, 47.01%) compared with MSM (1137/8427, 13.49%). Both platforms highlighted the importance of early screening and testing. However, MSM placed greater emphasis on vaccination as a prevention measure. In addition to lifestyle-related prevention measures, both CSM and MSM showed relatively less emphasis on avoiding exposure to environmental carcinogens, such as air pollution, indoor smoke, and radiation. “Breastfeeding” was the least mentioned prevention measure (2/3948, 0.05%) on both types of social media.

Cancer Treatment Information

Table 4 presents the distribution of cancer treatment information on MSM (n=4479) and CSM (n=3948).

Cancer Treatment Information on MSM

Cancer treatment information appeared in 66.22% (2966/4479) of MSM posts. “Surgery” was the most frequently mentioned treatment measure (2045/4479, 45.66%), followed by “chemotherapy” (1122/4479, 25.05%) and “radiation therapy” (1108/4479, 24.74%). The proportions of “cancer biomarker testing” (380/4479, 8.48%), “targeted therapy” (379/4479, 8.46%), and “immunotherapy” (317/4479, 7.08%) were comparable. Only a minimal percentage of articles (47/4479, 1.05%) addressed “hormone therapy.” Furthermore, “stem cell transplantation therapy” was mentioned in just 5 out of 4479 (0.11%) articles.

Cancer Treatment Information on CSM

Cancer treatment information accounted for only 19.71% (778/3948) of CSM posts. “Surgery” was the most frequently mentioned treatment measure (419/3948, 10.61%), followed by “chemotherapy” (285/3948, 7.22%) and “radiation therapy” (232/3948, 5.88%). Relatively, the frequency of “targeted therapy” (181/3948, 4.58%) was similar to that of the first 3 types. However, “cancer biomarker testing” (55/3948, 1.39%), “immunotherapy” (22/3948, 0.56%), and “hormone therapy” (14/3948, 0.35%) appeared rarely on CSM. Notably, there were no articles on CSM mentioning stem cell transplantation.

Cancer Treatment Information on Social Media

Table 5 shows the overall distribution of cancer treatment information on social media (N=8427). A total of 44.43% (3744/8427) of articles contained treatment information. MSM (2966/8427, 35.20%) discussed treatment information much more frequently than CSM (778/8427, 9.23%). Furthermore, the frequency of all types of treatment measures mentioned was higher on MSM than on CSM. The 3 most frequently mentioned types of treatment measures were surgery (2464/8427, 29.24%), chemotherapy (1407/8427, 16.70%), and radiation therapy (1340/8427, 15.90%). Relatively, MSM (380/8427, 4.51%) showed a higher focus on cancer biomarker testing compared with CSM (55/8427, 0.65%).

Cancer Topics on MSM and CSM

In MSM, treatment-related topics constituted the largest proportion, featuring keywords related to medical examinations. Conversely, in CSM, the distribution of topics appeared more balanced, with keywords frequently associated with cancer risk and screening. Overall, the distribution of topics on MSM and CSM revealed that CSM placed greater emphasis on lifestyle factors and early screening and testing. Specifically, CSM topics focused more on early cancer screening and addressed cancer types with high incidence rates. By contrast, MSM topics centered more on clinical treatment, medical testing, and the cervical cancer vaccine in cancer prevention. Additionally, MSM focused on types of cancers that are easier to screen and prevent, including liver cancer, stomach cancer, breast cancer, cervical cancer, and colon cancer.

Cancer Prevention Information on MSM and CSM

Through content analysis, it was found that 35.52% (2993/8427) of articles on social media contained prevention information, and 44.43% (3744/8427) contained treatment information. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention.

Primary prevention mainly involves adopting healthy behaviors to lower the risk of developing cancer, which has been proven to have long-term effects on cancer prevention. Secondary prevention focuses on inhibiting or reversing carcinogenesis, including early screening and detection, as well as the treatment or removal of precancerous lesions [ 38 ]. Compared with cancer screening and treatment, primary prevention is considered the most cost-effective approach to reducing the cancer burden.

From our results, “early screening and testing” (1822/8427, 21.62%) was the most frequently mentioned prevention measure on both MSM and CSM. According to a cancer study from China, behavioral risk factors were identified as the primary cause of cancer [ 10 ]. However, measures related to primary prevention were not frequently mentioned. Additionally, lifestyle-related measures such as “healthy diet,” “regular exercise,” “avoiding tobacco use,” and “limiting alcohol use” were mentioned much less frequently on MSM compared with CSM.

Furthermore, “avoiding tobacco use” (554/8427, 6.57%) and “limiting alcohol use” (409/8427, 4.85%) were rarely mentioned, despite tobacco and alcohol being the leading causes of cancer. In China, public policies on the production, sale, and consumption of alcohol are weaker compared with Western countries. Notably, traditional Chinese customs often promote the belief that moderate drinking is beneficial for health [ 39 ]. Moreover, studies indicated that the smoking rate among adult men exceeded 50% in 2015. By 2018, 25.6% of Chinese adults aged 18 and above were smokers, totaling approximately 282 million smokers in China (271 million males and 11 million females) [ 40 ]. These statistics align with the consistently high incidence of lung cancer among Chinese men [ 41 ]. Simultaneously, the incidence and mortality of lung cancer in Chinese women were more likely associated with exposure to second-hand smoke or occupation-related risk factors.

Although MSM (261/8427, 3.10%) mentioned vaccination more frequently than CSM (113/8427, 1.34%), vaccination was not widely discussed on social media overall (374/8427, 4.44%). The introduction of human papillomavirus vaccination in China has lagged for more than 10 years compared with Western countries. A bivalent vaccine was approved by the Chinese Food and Drug Administration in 2017 but has not been included in the national immunization schedules up to now [ 42 ].

According to the “European Code Against Cancer” [ 43 ], breastfeeding is recommended as a measure to prevent breast cancer. However, there were no articles mentioning the role of breastfeeding in preventing breast cancer on social media.

One of the least frequently mentioned measures was “radiation protection,” which includes sun protection. Although skin cancer is not as common in China as in Western countries, China has the largest population in the world. A study showed that only 55.2% of Chinese people knew that ultraviolet radiation causes skin cancer [ 33 ]. Additional efforts should be made to enhance public awareness of skin cancer prevention through media campaigns.

Overall, our results indicate that social media, especially MSM, focused more on secondary prevention. The outcomes of primary prevention are challenging to identify in individuals, and studies on cancer education may partly explain why primary prevention was often overlooked [ 44 ].

Cancer Treatment Information on MSM and CSM

Compared with a related content analysis study in the United States, our findings also indicate that the media placed greater emphasis on treatment [ 45 ]. Treatment information on MSM was more diverse than on CSM, with a higher proportion of the 3 most common cancer treatments—surgery, chemotherapy, and radiation therapy—mentioned on MSM compared with CSM. Notably, CSM (232/8427, 2.75%) mentioned radiation therapy less frequently compared with MSM (1108/8427, 13.15%), despite it being one of the most common cancer treatment measures in clinical practice.

In addition to common treatment methods, other approaches such as targeted therapy (560/8427, 6.65%) and immunotherapy (339/8427, 4.02%) were rarely discussed. This could be attributed to the high costs associated with these treatments. A study revealed that each newly diagnosed patient with cancer in China faced out-of-pocket expenses of US $4947, amounting to 57.5% of the family’s annual income, posing an unaffordable economic burden of 77.6% [ 46 ]. In 2017, the Chinese government released the National Health Insurance Coverage (NHIC) policy to improve the accessibility and affordability of innovative anticancer medicines, leading to reduced prices and increased availability and utilization of 15 negotiated drugs. However, a study indicated that the availability of these innovative anticancer drugs remained limited. By 2019, the NHIC policy had benefited 44,600 people, while the number of new cancer cases in China in 2020 was 4.57 million [ 47 ]. The promotion of information on innovative therapies helped patients gain a better understanding of their cancer treatment options [ 48 ].

Practical Implications

This research highlighted that MSM did not fully leverage its professional background in providing comprehensive cancer information to the public. In fact, MSM holds substantial potential for contributing to cancer education. The findings from the content analysis also have practical implications for practitioners. They provide valuable insights for experts to assess the effectiveness of social media, monitor the types of information available to the public and patients with cancer, and guide communication and medical professionals in crafting educational and persuasive messages based on widely covered or less attended content.

Limitations and Future Directions

This study had some limitations. First, we only collected 60,843 articles from 9 WPAs in China. Future research could broaden the scope by collecting data from diverse countries and social media platforms. Second, our manual labeling only extracted 10% (450/4479 for MSM and 394/3948 for CSM) of the samples; the accuracy of the machine learning model could be enhanced by training it with a larger set of labeled articles. Finally, our results only represented the media’s presentation, and the impact of this information on individuals remains unclear. Further work could examine its influence on behavioral intentions or actions related to cancer prevention among the audience.

Conclusions

The analysis of cancer-related information on social media revealed an imbalance between prevention and treatment content. Overall, there was more treatment information than prevention information. Compared with MSM, CSM mentioned more prevention information. On MSM, the proportion of treatment information was greater than prevention information, whereas on CSM, the 2 were equal. The focus on cancer prevention and treatment information was primarily limited to a few aspects, with a predominant emphasis on secondary prevention rather than primary prevention. There is a need for further improvement in the coverage of prevention measures and treatments for cancer on social media. Additionally, the findings underscored the potential of applying machine learning to content analysis as a promising research paradigm for mapping key dimensions of cancer information on social media. These findings offer methodological and practical significance for future studies and health promotion.

Acknowledgments

This study was funded by The Major Program of the Chinese National Foundation of Social Sciences under the project “The Challenge and Governance of Smart Media on News Authenticity” (grant number 23&ZD213).

Conflicts of Interest

None declared.

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Abbreviations

Edited by S Ma; submitted 02.01.24; peer-reviewed by F Yang, D Wawrzuta; comments to author 20.03.24; revised version received 19.04.24; accepted 03.06.24; published 14.08.24.

©Keyang Zhao, Xiaojing Li, Jingyang Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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We added four commercially available thickeners to white rice porridge with salty plum, which is popular in Japan, and compared the viscosity. Porridge containing water is useful even in the event of a disaster. In Japan, porridge is useful as a stockpile of food during disasters. Porridge is used as a meal of a wide range of age groups, form the e...

Line Spread Test Results for Commercially Available the White Rice Porridge with Egg - Including the Effect of Four types of Thickening Agents Added after Blending

Viscosity was evaluated using a line spread test (LST) using white rice porridge with egg, which has more lipids and proteins than white rice porridge and has a wide range of versatility. Liquid porridge is used for older people with weak chewing, and babies as baby food have low viscosity, and are highly likely to be aspirated by people with weake...

Line Spread Test Results for Commercially Available the White Rice Porridge with Salmon - Including the Effect of Four Types of Thickening Agents Added after Blending -

Commercially available porridge containing ingredients (meat, fish, vegetables, etc.) has a high nutritional value and is very useful both daily and in the event of a disaster. By making porridge into a uniform liquid porridge with a mixer, baby food and people with weak chewing ability can eat it. Uniform liquid porridge poses a risk of aspiration...

Line Spread Test Results for Commercially Available the White Rice Porridge with Sticky Barley

In the event of a disaster, we will have to take stockpiled food. Retort porridge contains water and can be eaten directly from the container. However, there is a risk of aspiration for people with impaired swallowing function. As a result of the line spread test (LST), which was performed after homogenizing white rice porridge with sticky barley u...

Line Spread Test Results for Commercially Available the White Rice Porridge - Including the Effect of Four Types of Thickening Agents Added after Blending

Lifelines are cut off during disasters. As a result, water, gas, and electricity cannot be used, making it impossible to prepare meals. If you always have ready-to-eat porridge, you do not need to cook it, and can eat it whenever you want. The porridge with a lot of water causes aspiration in older people. In welfare facilities and hospitals, a thi...

Articles under Review

Relapsing Acute Transverse Myelitis in a Young Male-A Case Report

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Oral Health in the Shadow of Cancer and Chemotherapy: Current Insights and Future Directions

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Articles under Press

Urinary Tract Infections During Pregnancy: Prevalence, Risks and Treatment

The article discusses the high incidence and risks of urinary tract infections (UTIs) in pregnant women. Due to physiological and anatomical changes during pregnancy, such as the dilation of the urinary tract and compression of the ureters, the risk of UTIs increases significantly. UTIs can be asymptomatic, present as cystitis, or progress to pyelo...

Carboprost Versus Oxytocin in Active Management of Third Stage of Labour: Comparative Study

Background: Worldwide every one minute one woman dies from pregnancy or child birth related complications. This study attempt to compare efficacy of Carboprost versus Oxytocin for active management of third stage of labour. Methodology: Three hundred obstetric cases anticipated for spontaneous vaginal delivery were randomly divided into two groups...

Self-Declared Infertility and Demand for Treatment: Findings from the National Demographic and Health Survey (PNDS) 2006

Objectives: This article aims to identify and characterize women of reproductive age who declare themselves infertile or having difficulty conceiving and wish to have children, their demand for infertility treatments in public and private services, and their outcomes. Methods: This study is based on the data on infertility available from PNDS 2006,...

Self-Reported Cardiovascular Risk Factors Among Medical Students

Cardiovascular diseases are the leading cause of death in the world, with smoking, diabetes, high blood pressure, dyslipidemia and a sedentary lifestyle being among the main risk factors. The objective of this study was to evaluate the self knowledge about cardiovascular risk factors among medical students. Methodology: Observational and cross-sect...

Influence of Psychiatric Disorders on Recovery and Prognosis from Gynecological Surgery

Introduction: The influence of psychiatric disorders on the recovery and prognosis of Gynecological surgery is a topic of clinical and scientific relevance. Patients with disorders psychiatric conditions, such as depression, anxiety, and post-traumatic stress disorder, can present additional challenges during the perioperative period. understand ho...

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Global study predicts increases in cancer cases and deaths among men, with widening disparities

In an analysis of 30 cancer types among men, investigators uncover substantial disparities in cancer cases and deaths by age and countries' economic status—disparities that are projected to widen by 2050. The study is published in the journal Cancer .

Men face higher rates of cancer and cancer-related deaths than women, likely due to various factors including lower participation in cancer prevention activities; underuse of screening and treatment options ; increased exposure to cancer risk factors such as smoking, alcohol consumption , and occupational exposure to carcinogens; and biological differences.

To assess the burden of cancer in men of different ages and living in different regions of the world, investigators analyzed 2022 information from the Global Cancer Observatory, which encompasses national-level estimates for cancer cases and deaths for 185 countries/territories worldwide.

The projected cancer cases and deaths in 2050 were derived through demographic projections: the researchers multiplied the 2022 age-specific rates with their corresponding population projections for 2050.

In 2022, poorer survival was observed among older men; for rare cancer types such as pancreatic cancer ; and in countries with a low human development index, which measures health, education, and standard of living.

Between 2022 and 2050, cancer cases are projected to increase from 10.3 million to 19 million, an 84% increase. Deaths are projected to increase from 5.4 million to 10.5 million, a 93% increase, with a greater than two-fold increase among men aged 65+ years and for countries/territories with low and medium human development index.

The research reveals an urgent need to address these trends and ensure equity in cancer prevention and care among men globally.

"A national and international collaboration , as well as a coordinated multisectoral approach, are essential to improve current cancer outcomes and to reverse the anticipated rise in cancer burden by 2050. Implementing and expanding universal health coverage and expanding health infrastructure and establishing publicly funded medical schools and scholarships for training medical and public health staff can improve cancer care and equity," said lead author Habtamu Mellie Bizuayehu, Ph.D., of the University of Queensland, in Australia.

"Emphasis should be placed on low and medium human development index countries with high unmet cancer service needs despite a significant cancer burden."

Dr. Bizuayehu added that improving access to and use of cancer prevention , screening, diagnosis, and treatment options, especially for older men, could also improve cancer outcomes and equity.

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Evaluation and treatment approaches for neurological post-acute sequelae of COVID-19: A consensus statement and scoping review from the global COVID-19 neuro research coalition

Affiliations.

• 1 Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA. Electronic address: [email protected].
• 2 Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia.
• 3 Department of Cognitive Neurology, Fleni, Buenos Aires, Argentina.
• 4 Department of Anesthesiology, Weill Cornell Medical Center, New York Presbyterian Hospital, New York, NY, USA.
• 5 Department of Neurology, Istanbul University, Istanbul Faculty of Medicine, and EMAR Medical Center, Istanbul, Turkey.
• 6 The Encephalitis Society, Malton, UK; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
• 7 Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
• 8 Department of Neurology, Neuro-Intensive Care Unit, Medical University of Innsbruck, Innsbruck, Austria; Department of Neurology, Johannes Kepler University, Linz, Austria.
• 9 Department of Neurology, Universitätsklinikum Erlangen, Erlangen, Germany.
• 10 Departmentof Neurology, Northwestern Feinberg School of Medicine, Chicago, IL, USA.
• 11 Department of Neurology, National Institute of Mental Health & Neurosciences, Bangalore, India.
• 12 Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK; National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK.
• 13 Clinical Neurology, Santa Maria della Misericordia University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASU FC), Udine, Italy.
• 14 Department of Neurology, Faculty of Medicine, Mersin University, Mersin, Turkey.
• 15 Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
• 16 Chief Executive Office, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
• 17 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; Dipartimento Universitario Di Neuroscienze, Università Cattolica del Sacro Cuore, Rome, Italy.
• 18 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Internal Medicine, University Teaching Hospital, Lusaka, Zambia.
• 19 The Walton Centre NHS Foundation Trust, Liverpool, UK.
• 20 Department of Neurology, Columbia University Irving Medical Center/New York Presbyterian Hospital, New York, NY, USA.
• 21 Clinical Neurology, Santa Maria della Misericordia University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASU FC), Udine, Italy; Department of Medicine, University of Udine Medical School, Udine, Italy.
• 22 Department of Neurology, Universitätsklinikum Erlangen, Erlangen, Germany; Department of Neurology, Center for Global Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
• 23 Department of Neurology, Center for Global Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
• 24 Moscow Research and Clinical Center for Neuropsychiatry, Moscow, Russia.
• 25 Department of Neurology, Center for Global Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Department of Community Medicine and Global Health, Institute of Health and Society, University of Oslo, Oslo, Norway; Blavatnik Institute of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
• PMID: 37856998
• DOI: 10.1016/j.jns.2023.120827

Post-acute neurological sequelae of COVID-19 affect millions of people worldwide, yet little data is available to guide treatment strategies for the most common symptoms. We conducted a scoping review of PubMed/Medline from 1/1/2020-4/1/2023 to identify studies addressing diagnosis and treatment of the most common post-acute neurological sequelae of COVID-19 including: cognitive impairment, sleep disorders, headache, dizziness/lightheadedness, fatigue, weakness, numbness/pain, anxiety, depression and post-traumatic stress disorder. Utilizing the available literature and international disease-specific society guidelines, we constructed symptom-based differential diagnoses, evaluation and management paradigms. This pragmatic, evidence-based consensus document may serve as a guide for a holistic approach to post-COVID neurological care and will complement future clinical trials by outlining best practices in the evaluation and treatment of post-acute neurological signs/symptoms.

Keywords: COVID-19; Diagnosis; Evaluation; Management; Neurologic; SARS-COV-2; Therapeutics; Therapy; Treatment.

Copyright © 2023 Elsevier B.V. All rights reserved.

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Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021

Sarah wulf hanson.

1 Institute for Health Metrics and Evaluation, University of Washington, Seattle

Cristiana Abbafati

2 Department of Juridical and Economic Studies, La Sapienza University, Rome, Italy

Joachim G. Aerts

3 Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

Ziyad Al-Aly

4 John T. Milliken Department of Internal Medicine, Washington University in St Louis, St Louis, Missouri

5 Clinical Epidemiology Center, US Department of Veterans Affairs, St Louis, Missouri

Charlie Ashbaugh

Tala ballouz.

6 Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zurich, Switzerland

Oleg Blyuss

7 Wolfson Institute of Population Health, Queen Mary University of London, London, England

8 Department of Pediatrics and Pediatric Infectious Diseases, I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Polina Bobkova

9 Clinical Medicine (Pediatric Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Gouke Bonsel

10 EuroQol Research Foundation, Rotterdam, the Netherlands

Svetlana Borzakova

11 Pirogov Russian National Research Medical University, Moscow

12 Research Institute for Healthcare Organization and Medical Management, Moscow Healthcare Department, Moscow, Russia

Danilo Buonsenso

13 Department of Woman and Child Health and Public Health, Agostino Gemelli University Polyclinic IRCCS, Rome, Italy

14 Global Health Research Institute, Catholic University of Sacred Heart, Rome, Italy

Denis Butnaru

15 I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Austin Carter

16 Department of Medicine, University of Washington, Seattle

Cristina De Rose

Mohamed mustafa diab.

17 Center for Policy Impact in Global Health, Duke University, Durham, North Carolina

18 Department of Surgery, Duke University, Durham, North Carolina

19 Uppsala University Hospital, Uppsala, Sweden

Maha El Tantawi

20 Pediatric Dentistry and Dental Public Health Department, Alexandria University, Alexandria, Egypt

Victor Fomin

21 Rector’s Office, I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Robert Frithiof

22 Department of Surgical Sciences, Anesthesiology, and Intensive Care Medicine, Uppsala University, Uppsala, Sweden

Aysylu Gamirova

23 Clinical Medicine (General Medicine Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Petr V. Glybochko

24 Administration Department, I. M. Sechenov First Moscow State Medical University, Moscow, Russia

Juanita A. Haagsma

25 Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands

Shaghayegh Haghjooy Javanmard

26 Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

Erin B. Hamilton

Gabrielle harris.

27 School of Nursing, Duke University, Durham, North Carolina

Majanka H. Heijenbrok-Kal

28 Department of Rehabilitation Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

29 Neurorehabilitation, Rijndam Rehabilitation, Rotterdam, the Netherlands

Raimund Helbok

30 Department of Neurology, Medical University Innsbruck, Innsbruck, Austria

Merel E. Hellemons

David hillus.

31 Department of Infectious Diseases and Respiratory Medicine, Charité Medical University Berlin, Berlin, Germany

Susanne M. Huijts

32 Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

Michael Hultström

33 Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden

Waasila Jassat

34 Department of Public Health Surveillance and Response, National Institute for Communicable Diseases, Johannesburg, South Africa

Florian Kurth

35 Department of Infectious Diseases and Respiratory Medicine, Charité University Medical Center Berlin, Berlin, Germany

36 Department of Clinical Research and Tropical Medicine, Bernhard-Nocht Institute of Tropical Medicine, Hamburg, Germany

Ing-Marie Larsson

Miklós lipcsey, chelsea liu.

37 Department of Epidemiology, Harvard University, Boston, Massachusetts

Callan D. Loflin

Andrei malinovschi.

38 Department of Medical Sciences, Uppsala University, Uppsala, Sweden

39 Duke Global Health Institute, Duke University, Durham, North Carolina

Lyudmila Mazankova

40 Russian Medical Academy of Continuous Professional Education, Ministry of Healthcare of the Russian Federation, Moscow

Denise McCulloch

41 Department of Medicine, University of Washington, Seattle

Dominik Menges

Noushin mohammadifard.

42 Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

Daniel Munblit

43 National Heart and Lung Institute, Imperial College London, London, England

Nikita A. Nekliudov

Osondu ogbuoji, ismail m. osmanov.

44 ZA Bashlyaeva Children’s Municipal Clinical Hospital, Moscow, Russia

José L. Peñalvo

45 Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium

46 Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts

Maria Skaalum Petersen

47 Department of Occupational Medicine and Public Health, Faroese Hospital System, Torshavn, Faroe Islands

48 Centre of Health Science, University of Faroe Islands, Torshavn

Milo A. Puhan

49 Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland

Mujibur Rahman

50 Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh

Verena Rass

Nickolas reinig, gerard m. ribbers, antonia ricchiuto.

51 Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

Sten Rubertsson

52 Department of Surgical Sciences, Hedenstierna Laboratory, Uppsala University, Uppsala, Sweden

Elmira Samitova

Nizal sarrafzadegan.

53 School of Population and Public Health, University of British Columbia, Vancouver, Canada

Anastasia Shikhaleva

Kyle e. simpson, dario sinatti, joan b. soriano.

54 Hospital Universitario de La Princesa, Madrid, Spain

55 Centro de Investigación Biomédica en Red Enfermedades Respiratorias (Center for Biomedical Research in Respiratory Diseases Network), Madrid, Spain

Ekaterina Spiridonova

Fridolin steinbeis, andrey a. svistunov, piero valentini, brittney j. van de water.

56 Department of Global Health and Social Medicine, Harvard University, Boston, Massachusetts

57 Nursing and Midwifery Department, Seed Global Health, Boston, Massachusetts

Rita van den Berg-Emons

Martin witzenrath.

58 German Center for Lung Research, Berlin

Hanzhang Xu

59 Department of Family Medicine and Community Health, Duke University, Durham, North Carolina

Thomas Zoller

Christopher adolph.

60 Department of Political Science, University of Washington, Seattle

61 Center for Statistics and the Social Sciences, University of Washington, Seattle

James Albright

Joanne o. amlag, aleksandr y. aravkin.

62 Department of Applied Mathematics, University of Washington, Seattle

63 Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle

Bree L. Bang-Jensen

Catherine bisignano, rachel castellano, emma castro, suman chakrabarti.

64 Department of Global Health, University of Washington, Seattle

James K. Collins

Xiaochen dai, farah daoud, carolyn dapper, amanda deen, bruce b. duncan.

65 Postgraduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Megan Erickson

Samuel b. ewald, alize j. ferrari.

66 School of Public Health, University of Queensland, Brisbane, Australia

Abraham D. Flaxman

Nancy fullman, amiran gamkrelidze.

67 National Center for Disease Control and Public Health, Tbilisi, Georgia

John R. Giles

Simon i. hay, monika helak, erin n. hulland, maia kereselidze, kris j. krohn, alice lazzar-atwood, akiaja lindstrom.

68 School of Public Health, Queensland Centre for Mental Health Research, Wacol, Australia

Rafael Lozano

Deborah carvalho malta.

69 Department of Maternal and Child Nursing and Public Health, Federal University of Minas Gerais, Belo Horizonte, Brazil

Johan Månsson

Ana m. mantilla herrera.

70 West Moreton Hospital Health Services, Queensland Centre for Mental Health Research, Wacol, Australia

Ali H. Mokdad

Lorenzo monasta.

71 Clinical Epidemiology and Public Health Research Unit, Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy

Shuhei Nomura

72 Department of Health Policy and Management, Keio University, Tokyo, Japan

73 Department of Global Health Policy, University of Tokyo, Tokyo, Japan

Maja Pasovic

David m. pigott, robert c. reiner, jr, grace reinke, antonio luiz p. ribeiro.

74 Department of Internal Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil

75 Centre of Telehealth, Federal University of Minas Gerais, Belo Horizonte, Brazil

Damian Francesco Santomauro

76 Policy and Epidemiology Group, Queensland Centre for Mental Health Research, Wacol, Australia

Aleksei Sholokhov

Emma elizabeth spurlock.

77 Department of Social and Behavioral Sciences, School of Public Health, Yale University, New Haven, Connecticut

Rebecca Walcott

78 Evans School of Public Policy and Governance, University of Washington, Seattle

Ally Walker

Charles shey wiysonge.

79 Cochrane South Africa, South African Medical Research Council, Cape Town

80 HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban

Janet Prvu Bettger

81 Department of Orthopedic Surgery, Duke University, Durham, North Carolina

Christopher J. L. Murray

Authors/Group Information: The authors of the Global Burden of Disease Long COVID study appear at the end of the article.

Accepted for Publication: September 25, 2022.

Published Online: October 10, 2022. doi:10.1001/jama.2022.18931

Authors/Global Burden of Disease Long COVID Collaborators: Sarah Wulf Hanson, PhD; Cristiana Abbafati, PhD; Joachim G. Aerts, MD; Ziyad Al-Aly, MD; Charlie Ashbaugh, MA; Tala Ballouz, MD; Oleg Blyuss, PhD; Polina Bobkova, MD; Gouke Bonsel, PhD; Svetlana Borzakova, MD; Danilo Buonsenso, MD; Denis Butnaru, PhD; Austin Carter, MPH; Helen Chu, MD; Cristina De Rose, MD; Mohamed Mustafa Diab, MD; Emil Ekbom, MD; Maha El Tantawi, PhD; Victor Fomin, PhD; Robert Frithiof, PhD; Aysylu Gamirova, BSc; Petr V. Glybochko, PhD; Juanita A. Haagsma, PhD; Shaghayegh Haghjooy Javanmard, PhD; Erin B. Hamilton, MPH; Gabrielle Harris, PhD; Majanka H. Heijenbrok-Kal, PhD; Raimund Helbok, MD; Merel E. Hellemons, PhD; David Hillus, MD; Susanne M. Huijts, PhD; Michael Hultström, PhD; Waasila Jassat, MMed; Florian Kurth, MD; Ing-Marie Larsson, PhD; Miklós Lipcsey, PhD; Chelsea Liu, MSc; Callan D. Loflin, BA; Andrei Malinovschi, PhD; Wenhui Mao, PhD; Lyudmila Mazankova, MD; Denise McCulloch, MD; Dominik Menges, MD; Noushin Mohammadifard, PhD; Daniel Munblit, PhD; Nikita A. Nekliudov, MD; Osondu Ogbuoji, ScD; Ismail M. Osmanov, MD; José L. Peñalvo, PhD; Maria Skaalum Petersen, PhD; Milo A. Puhan, PhD; Mujibur Rahman, MD; Verena Rass, PhD; Nickolas Reinig, BS; Gerard M. Ribbers, PhD; Antonia Ricchiuto, MD; Sten Rubertsson, PhD; Elmira Samitova, MD; Nizal Sarrafzadegan, MD; Anastasia Shikhaleva, BSc; Kyle E. Simpson, BS; Dario Sinatti, MD; Joan B. Soriano, MD; Ekaterina Spiridonova, BSc; Fridolin Steinbeis, MD; Andrey A. Svistunov, PhD; Piero Valentini, MD; Brittney J. van de Water, PhD; Rita van den Berg-Emons, PhD; Ewa Wallin, PhD; Martin Witzenrath, MD; Yifan Wu, MPH; Hanzhang Xu, PhD; Thomas Zoller, PhD; Christopher Adolph, PhD; James Albright, BS; Joanne O. Amlag, MPH; Aleksandr Y. Aravkin, PhD; Bree L. Bang-Jensen, MA; Catherine Bisignano, MPH; Rachel Castellano, MA; Emma Castro, MS; Suman Chakrabarti, MA; James K. Collins, BS; Xiaochen Dai, PhD; Farah Daoud, BS; Carolyn Dapper, MA; Amanda Deen, MPH; Bruce B. Duncan, MD; Megan Erickson, MA; Samuel B. Ewald, MS; Alize J. Ferrari, PhD; Abraham D. Flaxman, PhD; Nancy Fullman, MPH; Amiran Gamkrelidze, PhD; John R. Giles, PhD; Gaorui Guo, MPH; Simon I. Hay, DPhil; Jiawei He, MSc; Monika Helak, BA; Erin N. Hulland, MPH; Maia Kereselidze, PhD; Kris J. Krohn, MPH; Alice Lazzar-Atwood, BSc; Akiaja Lindstrom, MEpi; Rafael Lozano, MD; Deborah Carvalho Malta, PhD; Johan Månsson, MS; Ana M. Mantilla Herrera, PhD; Ali H. Mokdad, PhD; Lorenzo Monasta, DSc; Shuhei Nomura, PhD; Maja Pasovic, MEd; David M. Pigott, PhD; Robert C. Reiner Jr, PhD; Grace Reinke, MA; Antonio Luiz P. Ribeiro, MD; Damian Francesco Santomauro, PhD; Aleksei Sholokhov, MSc; Emma Elizabeth Spurlock, MPH; Rebecca Walcott, MPH; Ally Walker, MA; Charles Shey Wiysonge, MD; Peng Zheng, PhD; Janet Prvu Bettger, DSc; Christopher J. L. Murray, DPhil; Theo Vos, PhD.

Affiliations of Authors/Global Burden of Disease Long COVID Collaborators: Institute for Health Metrics and Evaluation, University of Washington, Seattle (Wulf Hanson, Ashbaugh, Carter, Hamilton, Reinig, Simpson, Wu, Albright, Amlag, Aravkin, Bang-Jensen, Bisignano, Castellano, Castro, Chakrabarti, Collins, Dai, Daoud, Dapper, Deen, Erickson, Ewald, Ferrari, Flaxman, Fullman, Giles, Guo, Hay, He, Helak, Hulland, Krohn, Lazzar-Atwood, Lozano, Månsson, Mokdad, Pasovic, Pigott, Reiner Jr, Reinke, Santomauro, Sholokhov, Spurlock, Walker, Zheng, Murray, Vos); Department of Juridical and Economic Studies, La Sapienza University, Rome, Italy (Abbafati); Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (Aerts, Hellemons); John T. Milliken Department of Internal Medicine, Washington University in St Louis, St Louis, Missouri (Al-Aly); Clinical Epidemiology Center, US Department of Veterans Affairs, St Louis, Missouri (Al-Aly); Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zurich, Switzerland (Ballouz, Menges, Puhan); Wolfson Institute of Population Health, Queen Mary University of London, London, England (Blyuss); Department of Pediatrics and Pediatric Infectious Diseases, I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Blyuss, Munblit); Clinical Medicine (Pediatric Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Bobkova, Shikhaleva); EuroQol Research Foundation, Rotterdam, the Netherlands (Bonsel); Pirogov Russian National Research Medical University, Moscow (Borzakova, Osmanov); Research Institute for Healthcare Organization and Medical Management, Moscow Healthcare Department, Moscow, Russia (Borzakova); Department of Woman and Child Health and Public Health, Agostino Gemelli University Polyclinic IRCCS, Rome, Italy (Buonsenso, De Rose, Sinatti, Valentini); Global Health Research Institute, Catholic University of Sacred Heart, Rome, Italy (Buonsenso); I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Butnaru); Department of Medicine, University of Washington, Seattle (Chu); Center for Policy Impact in Global Health, Duke University, Durham, North Carolina (Diab, Mao); Department of Surgery, Duke University, Durham, North Carolina (Diab); Uppsala University Hospital, Uppsala, Sweden (Ekbom); Pediatric Dentistry and Dental Public Health Department, Alexandria University, Alexandria, Egypt (El Tantawi); Rector’s Office, I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Fomin); Department of Surgical Sciences, Anesthesiology, and Intensive Care Medicine, Uppsala University, Uppsala, Sweden (Frithiof, Hultström, Larsson, Lipcsey, Rubertsson, Wallin); Clinical Medicine (General Medicine Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Gamirova, Nekliudov, Spiridonova); Administration Department, I. M. Sechenov First Moscow State Medical University, Moscow, Russia (Glybochko, Svistunov); Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands (Haagsma); Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran (Haghjooy Javanmard); School of Nursing, Duke University, Durham, North Carolina (Harris, Loflin); Department of Rehabilitation Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (Heijenbrok-Kal, Ribbers, van den Berg-Emons); Neurorehabilitation, Rijndam Rehabilitation, Rotterdam, the Netherlands (Heijenbrok-Kal); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (Helbok, Rass); Department of Infectious Diseases and Respiratory Medicine, Charité Medical University Berlin, Berlin, Germany (Hillus, Steinbeis, Zoller); Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (Huijts); Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden (Hultström); Department of Public Health Surveillance and Response, National Institute for Communicable Diseases, Johannesburg, South Africa (Jassat); Department of Infectious Diseases and Respiratory Medicine, Charité University Medical Center Berlin, Berlin, Germany (Kurth, Witzenrath); Department of Clinical Research and Tropical Medicine, Bernhard-Nocht Institute of Tropical Medicine, Hamburg, Germany (Kurth); Department of Epidemiology, Harvard University, Boston, Massachusetts (Liu); Department of Medical Sciences, Uppsala University, Uppsala, Sweden (Malinovschi); Duke Global Health Institute, Duke University, Durham, North Carolina (Mao, Ogbuoji); Russian Medical Academy of Continuous Professional Education, Ministry of Healthcare of the Russian Federation, Moscow (Mazankova, Samitova); Department of Medicine, University of Washington, Seattle (McCulloch); Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran (Mohammadifard, Sarrafzadegan); National Heart and Lung Institute, Imperial College London, London, England (Munblit); ZA Bashlyaeva Children’s Municipal Clinical Hospital, Moscow, Russia (Osmanov, Samitova); Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium (Peñalvo); Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts (Peñalvo); Department of Occupational Medicine and Public Health, Faroese Hospital System, Torshavn, Faroe Islands (Petersen); Centre of Health Science, University of Faroe Islands, Torshavn (Petersen); Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland (Puhan); Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh (Rahman); Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy (Ricchiuto); Department of Surgical Sciences, Hedenstierna Laboratory, Uppsala University, Uppsala, Sweden (Rubertsson); School of Population and Public Health, University of British Columbia, Vancouver, Canada (Sarrafzadegan); Hospital Universitario de La Princesa, Madrid, Spain (Soriano); Centro de Investigación Biomédica en Red Enfermedades Respiratorias (Center for Biomedical Research in Respiratory Diseases Network), Madrid, Spain (Soriano); Department of Global Health and Social Medicine, Harvard University, Boston, Massachusetts (van de Water); Nursing and Midwifery Department, Seed Global Health, Boston, Massachusetts (van de Water); German Center for Lung Research, Berlin (Witzenrath); Department of Family Medicine and Community Health, Duke University, Durham, North Carolina (Xu); Department of Political Science, University of Washington, Seattle (Adolph); Center for Statistics and the Social Sciences, University of Washington, Seattle (Adolph); Department of Applied Mathematics, University of Washington, Seattle (Aravkin); Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle (Aravkin, Dai, Flaxman, Hay, Lozano, Mokdad, Pigott, Reiner Jr, Zheng, Murray, Vos); Department of Global Health, University of Washington, Seattle (Chakrabarti, Hulland); Postgraduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil (Duncan); School of Public Health, University of Queensland, Brisbane, Australia (Ferrari, Lindstrom, Mantilla Herrera, Santomauro); National Center for Disease Control and Public Health, Tbilisi, Georgia (Gamkrelidze, Kereselidze); School of Public Health, Queensland Centre for Mental Health Research, Wacol, Australia (Lindstrom); Department of Maternal and Child Nursing and Public Health, Federal University of Minas Gerais, Belo Horizonte, Brazil (Malta); West Moreton Hospital Health Services, Queensland Centre for Mental Health Research, Wacol, Australia (Mantilla Herrera); Clinical Epidemiology and Public Health Research Unit, Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy (Monasta); Department of Health Policy and Management, Keio University, Tokyo, Japan (Nomura); Department of Global Health Policy, University of Tokyo, Tokyo, Japan (Nomura); Department of Internal Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil (Ribeiro); Centre of Telehealth, Federal University of Minas Gerais, Belo Horizonte, Brazil (Ribeiro); Policy and Epidemiology Group, Queensland Centre for Mental Health Research, Wacol, Australia (Santomauro); Department of Social and Behavioral Sciences, School of Public Health, Yale University, New Haven, Connecticut (Spurlock); Evans School of Public Policy and Governance, University of Washington, Seattle (Walcott); Cochrane South Africa, South African Medical Research Council, Cape Town (Wiysonge); HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban (Wiysonge); Department of Orthopedic Surgery, Duke University, Durham, North Carolina (Bettger).

Author Contributions: Drs Wulf Hanson and Vos had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Bettger, Murray, and Vos are co–senior authors.

Concept and design: Wulf Hanson, Butnaru, Diab, Hultström, Mao, Ogbuoji, Valentini, Hay, Kereselidze, Mokdad, Sholokhov, Spurlock, Bettger, Vos.

Acquisition, analysis, or interpretation of data: Wulf Hanson, Abbafati, Aerts, Al-Aly, Ashbaugh, Ballouz, Blyuss, Bobkova, Bonsel, Borzakova, Buonsenso, Carter, Chu, De Rose, Diab, Ekbom, El Tantawi, Fomin, Frithiof, Gamirova, Glybochko, Haagsma, Haghjooy Javanmard, Hamilton, Harris, Heijenbrok-Kal, Helbok, Hellemons, Hillus, Huijts, Hultström, Jassat, Kurth, Larsson, Lipcsey, Liu, Loflin, Malinovschi, Mao, Mazankova, McCulloch, Menges, Mohammadifard, Munblit, Nekliudov, Ogbuoji, Osmanov, Peñalvo, Petersen, Puhan, Rahman, Rass, Reinig, Ribbers, Ricchiuto, Rubertsson, Samitova, Sarrafzadegan, Shikhaleva, Simpson, Sinatti, Soriano, Spiridonova, Steinbeis, Svistunov, van de Water, van den Berg-Emons, Wallin, Witzenrath, Wu, Xu, Zoller, Adolph, Albright, Amlag, Aravkin, Bang-Jensen, Bisignano, Castellano, Castro, Chakrabarti, Collins, Dai, Daoud, Dapper, Deen, Duncan, Erickson, Ewald, Ferrari, Flaxman, Fullman, Gamkrelidze, Giles, Guo, He, Helak, Hulland, Kereselidze, Krohn, Lazzar-Atwood, Lindstrom, Lozano, Malta, Månsson, Mantilla Herrera, Mokdad, Monasta, Nomura, Pasovic, Pigott, Reiner, Reinke, Ribeiro, Santomauro, Spurlock, Walcott, Walker, Wiysonge, Zheng, Bettger, Murray, Vos.

Drafting of the manuscript: Wulf Hanson, Bobkova, Butnaru, Carter, Fomin, Gamirova, Glybochko, Helbok, Loflin, Reinig, Ricchiuto, Shikhaleva, Simpson, Sinatti, Spiridonova, Svistunov, Wu, Albright, Amlag, Castellano, He, Krohn, Lindstrom, Mokdad, Murray, Vos.

Critical revision of the manuscript for important intellectual content: Wulf Hanson, Abbafati, Aerts, Al-Aly, Ashbaugh, Ballouz, Blyuss, Bonsel, Borzakova, Buonsenso, Chu, De Rose, Diab, Ekbom, El Tantawi, Frithiof, Haagsma, Haghjooy Javanmard, Hamilton, Harris, Heijenbrok-Kal, Helbok, Hellemons, Hillus, Huijts, Hultström, Jassat, Kurth, Larsson, Lipcsey, Liu, Loflin, Malinovschi, Mao, Mazankova, McCulloch, Menges, Mohammadifard, Munblit, Nekliudov, Ogbuoji, Osmanov, Peñalvo, Petersen, Puhan, Rahman, Rass, Ribbers, Rubertsson, Samitova, Sarrafzadegan, Soriano, Steinbeis, Valentini, van de Water, van den Berg-Emons, Wallin, Witzenrath, Xu, Zoller, Adolph, Aravkin, Bang-Jensen, Bisignano, Castro, Chakrabarti, Collins, Dai, Daoud, Dapper, Deen, Duncan, Erickson, Ewald, Ferrari, Flaxman, Fullman, Gamkrelidze, Giles, Guo, Hay, Helak, Hulland, Kereselidze, Krohn, Lazzar-Atwood, Lozano, Malta, Månsson, Mantilla Herrera, Mokdad, Monasta, Nomura, Pasovic, Pigott, Reiner, Reinke, Ribeiro, Santomauro, Sholokhov, Spurlock, Walcott, Walker, Wiysonge, Zheng, Bettger, Murray, Vos.

Statistical analysis: Wulf Hanson, Blyuss, Bonsel, Buonsenso, Carter, Diab, Haagsma, Heijenbrok-Kal, Hultström, Kurth, Ogbuoji, Ricchiuto, Simpson, Soriano, Wu, Albright, Aravkin, Castellano, Castro, Collins, Dai, Flaxman, Giles, Guo, He, Kereselidze, Lazzar-Atwood, Mokdad, Pigott, Reiner, Spurlock, Zheng, Vos.

Obtained funding: Frithiof, Hultström, Kurth, Ogbuoji, Petersen, Puhan, Ribbers, van den Berg-Emons, Witzenrath, Hay, Mokdad, Pigott.

Administrative, technical, or material support: Wulf Hanson, Ashbaugh, Borzakova, Buonsenso, Butnaru, De Rose, Diab, Ekbom, Haghjooy Javanmard, Hamilton, Harris, Hillus, Kurth, Larsson, Lipcsey, Malinovschi, Mao, Mohammadifard, Munblit, Nekliudov, Ogbuoji, Osmanov, Peñalvo, Puhan, Rahman, Reinig, Samitova, Steinbeis, Valentini, Wallin, Witzenrath, Wu, Adolph, Amlag, Bang-Jensen, Chakrabarti, Collins, Daoud, Deen, Flaxman, Gamkrelidze, Guo, Hay, Helak, Krohn, Månsson, Mantilla Herrera, Mokdad, Monasta, Nomura, Pigott, Reinke, Ribeiro, Santomauro, Spurlock, Walcott, Walker, Wiysonge, Zheng.

Supervision: Wulf Hanson, Abbafati, Chu, Haghjooy Javanmard, Helbok, Hultström, Kurth, Mazankova, Munblit, Puhan, Rahman, Ribbers, Valentini, Zoller, Amlag, Daoud, Hay, Kereselidze, Mokdad, Pigott, Murray, Vos.

Conflict of Interest Disclosures: Drs Bobkova, Munblit, and Svistunov and Mss Gamirova, Shikhaleva, and Spiridonova reported receiving grants and contracts paid to Sechenov University from the British Embassy in Moscow for the StopCOVID Cohort: Clinical Characterisation of Russian Patients 2020-2021. Dr Haagsma reported receiving grants from the EuroQol Foundation. Dr Lipcsey reported receiving grants from and having contracts with Hjärt-Lungfonden (Swedish Heart Lung Foundation) and being a member of data and safety monitoring boards for the PROFLO and COVID-19 Hyperbaric Oxygen randomized clinical trials. Dr Munblit reported receiving grants paid to Sechenov University from the Russian Foundation for Basic Research and the UK Research and Innovation/National Institute for Health Research; receiving personal fees from Merck Sharp & Dohme and Bayer; and having unpaid leadership positions as co-chair of the International Severe Acute Respiratory and Emerging Infection Consortium Global Pediatric Long COVID Working Group and co-lead of the PC-COS (Post-COVID Condition Core Outcomes) Project. Dr Petersen reported being on the board of the Faroese National Data Protection Authority and receiving equipment, materials, drugs, medical writing, gifts, or other services from Beijing Wantai Biological Pharmacy Enterprise Co Ltd. Dr Puhan reported receiving support from the University of Zurich Foundation and the Department of Health, Canton of Zurich. Dr Flaxman reported having stock options in Agathos Ltd and receiving personal fees from Janssen, Swiss Re, Merck for Mothers, and Sanofi. Ms Fullman reported receiving personal fees from the World Health Organization and receiving funding from Gates Ventures. No other disclosures were reported.

Funding/Support: Erasmus University Medical Center received funding from the ZonMW COVID-19 Programme, Laurens (the Netherlands), and Rijndam Rehabilitation. The Institute for Health Metrics and Evaluation at the University of Washington received funding from the Bill & Melinda Gates Foundation and Bloomberg Philanthropies. Uppsala University received funding from the Knut and Alice Wallenburg Foundation, the Swedish Heart-Lung Foundation, the Swedish Kidney Foundation, the Swedish Society of Medicine, and the Swedish Research Council. The Queensland Centre for Mental Health Research received funding from the Queensland Department of Health. The Iran National Science Foundation, the National Institute of Health Researchers of Iran, and the World Health Organization provided funding for Drs Haghjooy Javanmard, Mohammadifard, and Sarrafzadegan. Cooperation’s p/f Krunborg and Borgartun, the Velux Foundation, the Faroese Research Council, the Faroese Parkinson’s Association, and the Faroese Health Insurance Fund provided funding for Dr Petersen. The National Institute on Aging and the National Institute on Minority Health and Health Disparities provided funding for Dr Xu. The Benificus Foundation provided funding for Dr Adolph. The National Science Foundation provided funding for Drs Aravkin and Reiner. The Ministry of Health (Rome, Italy) and the Institute for Maternal and Child Health IRCCS Burlo Garofolo (Trieste, Italy) provided funding for Dr Monasta. The Ministry of Education, Culture, Sports, Science, and Technology of Japan provided funding for Dr Nomura. The South African Medical Research Council provided funding for Dr Wiysonge.

Role of the Funder/Sponsor: The funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 5 .

Additional Contributions: We thank the researchers, health care professionals, caregivers, and people experiencing Long COVID who have shared their knowledge and experiences with us.

Associated Data

eSection 2. Estimate symptom cluster duration and proportions

eSection 3. Estimate symptom cluster overlap and severity distributions

eSection 4. Estimate symptomatic COVID cases that survive acute episode

eSection 5. Estimate symptom cluster incidence

List of abbreviations

eFigure 1. Flowchart of data, analytical processes, and long COVID symptom cluster outcomes

eFigure 2. PRISMA flow diagram of systematic literature review for long COVID

eFigure 3. Geographic distribution of published input data sources without access to individual-level data, with shading corresponding to total sample size per country

eFigure 4. Geographic distribution of cohort studies with access to individual-level data, with shading corresponding to total sample size per country

eFigure 5. Logit-linear model results of symptom cluster data with multiple follow-up points, used to calculate duration among non-hospitalized COVID cases and hospitalized COVID cases

eFigure 6. Age pattern of the proportion of surviving, symptomatic COVID-19 cases with at least one symptom cluster among the three largest cohort studies with individual record data, by sex and hospitalization status. Error bars represent 95% uncertainty intervals

eFigure 7. Model results: Overall long COVID

eFigure 8. Individual symptom clusters model results: fatigue

eFigure 9. Individual symptom clusters model results: respiratory

eFigure 10. Individual symptom clusters model results: cognitive

eFigure 11. Model results: Overlap of symptom clusters among long COVID patients

eFigure 12. Model results: Respiratory severity distributions

eFigure 13. Model results: Cognitive severity distributions

eFigure 14. Pooled estimate of proportion asymptomatic among SARS-CoV-2 infections

eFigure 15. Age distribution of asymptomatic SARS-CoV-2 infections, non-hospitalized cases, cases needing hospitalization, and cases needing ICU care, by sex

eFigure 16. Pooled estimate of proportion of COVID-19 deaths that occurred in long-term care facilities

eFigure 17. Case fatality ratios among hospitalized and ICU COVID-19 patients by age

eTable 1. Follow-up studies of long COVID, age and sex distributions, their inclusion of community and/or hospitalized cases, sample sizes, follow-up period, comparison method, and reported symptoms or symptom clusters

eTable 2. ICD-10-CM codes used to extract administrative data for cognitive symptoms, fatigue, and respiratory symptoms

eTable 3. Model coefficients for adjustment to account for underlying rates of symptom clusters

eTable 4. Model parameters for non-hospitalized long COVID duration

eTable 5. Model parameters for hospital/ICU long COVID duration

eTable 6. Model parameters for non-hospitalized overall long COVID

eTable 7. Model parameters for hospital/ICU overall long COVID

eTable 8. Model parameters for each symptom cluster model among non-hospitalized cases. Sources of the priors are the same as in the overall long COVID models

eTable 9. Model parameters for each symptom cluster model among hospital/ICU cases. Sources of the priors are the same as in the overall long COVID models

eTable 10. Model parameters for each overlap of symptom clusters model among long COVID cases

eTable 11. Model parameters for severity-specific cognitive symptom models

eTable 12. Model parameters for severity-specific respiratory symptom models

eTable 13. Input data of proportion asymptomatic among COVID infections

eTable 14. Estimated risk of long COVID among symptomatic community, hospitalized, and ICU COVID-19 cases by symptom cluster, sex and age group 3 months after symptom onset

eTable 15. Distribution of symptom clusters and their overlap among long COVID cases at 3 months after symptom onset (proportions are mutually exclusive)

eTable 16. Global new cases of long COVID symptom clusters by sex and severity of initial infection in 2020-2021, in millions

eTable 17. Symptomatic infections and new cases of long COVID by country, 2020 and 2021

eTable 18. Symptoms reported by respondents of the StopCOVID ISARIC Cohort in Russia who did not qualify for any of our long COVID symptoms clusters but reported not having recovered and worse health status than before COVID-19

eTable 19. Sensitivity analysis comparing current method with an alternative method which uses all available data to estimate the duration

eReferences

Among individuals who had symptomatic SARS-CoV-2 infection in 2020 and 2021, what proportion experienced common self-reported Long COVID symptom clusters 3 months after initial infection?

This observational analysis involved bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. The modeled estimated proportion with at least 1 of the 3 self-reported Long COVID symptom clusters 3 months after symptomatic SARS-CoV-2 infection was 6.2%, including 3.7% for ongoing respiratory problems, 3.2% for persistent fatigue with bodily pain or mood swings, and 2.2% for cognitive problems after adjusting for health status before COVID-19.

This study presents modeled estimates of the proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.

Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID).

To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration.

Design, Setting, and Participants

Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022.

Symptomatic SARS-CoV-2 infection.

Main Outcomes and Measures

Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age.

A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months.

Conclusions and Relevance

This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.

This study estimates the proportion of males and females, younger or older than 20 years of age, affected by at least 1 of the 3 Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) after SARS-CoV-2 infection in 2020 and 2021 and their symptom severity and expected duration of Long COVID.

Introduction

Much of the attention on disease surveillance during the COVID-19 pandemic has concentrated on the number of SARS-CoV-2 infections, hospital admissions, and deaths. Less attention has been given to quantifying the risk for experiencing symptoms after the acute stage of SARS-CoV-2 infection. In October 2021, the World Health Organization (WHO) released a clinical case definition for the post–COVID-19 condition as symptoms that are present 3 months after SARS-CoV-2 infection with a minimum duration of 2 months and cannot be explained by an alternative diagnosis. 1 This is often referred to as Long COVID.

Postinfection fatigue syndromes have been described for other viruses and bacteria, including Ebola virus, Epstein-Barr virus, and cytomegalovirus. 2 , 3 Ongoing low-grade inflammation has been postulated to cause these symptoms, but the pathology remains largely unknown and treatments are primarily based on symptom relief. 4 The consequences for affected individuals are substantial, and specialized clinics for individuals with Long COVID have arisen to respond to an increasing need for supportive and rehabilitative care. 5 , 6

A systematic review 7 of 45 follow-up studies of individuals with COVID-19, of which only 3 had follow-up longer than 3 months, found 84 long-term symptoms with shortness of breath, fatigue, and sleep disorders or insomnia as the most common. Studies have reported most frequently on individual symptoms or counts of symptoms and have reported less frequently on symptom severity, overlapping symptoms, and symptom duration. 8 , 9 , 10 , 11

This study collated information on 3 common clusters of Long COVID symptoms largely based on detailed data from ongoing COVID-19 follow-up studies conducted in 10 countries (Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US), supplemented by published data from 44 studies and data from 2 medical record databases. From this pooled information on the occurrence of 3 Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems), estimates were made of the proportion of individuals who had symptomatic SARS-CoV-2 infection and at least 1 of the 3 symptom clusters 3 months after infection, and the duration of these symptom clusters was derived for 2020 and 2021.

This research was undertaken as part of the Global Burden of Diseases, Injuries, and Risk Factors Study and used deidentified data. A waiver of informed consent was reviewed and approved by the University of Washington institutional review board.

Overview of the Analysis

The analysis comprised 5 components ( Figure 1 and eFigure 1 in Supplement 1 ). First, the proportion of symptomatic survivors with 1 or more of the 3 symptom clusters of Long COVID (persistent fatigue with bodily pain or mood swings, cognitive problems, or ongoing respiratory problems) and the key symptoms of fatigue, cognitive problems, and shortness of breath were extracted from 54 international cohort studies and 2 US medical record databases. Of the 10 collaborating cohort studies with individual case records available, 4 did not report on (1) excess risk of Long COVID symptom clusters compared with controls or (2) self-reported health status prior to COVID-19; therefore, these cohorts were adjusted by the ratio of excess risk of Long COVID symptoms to total symptoms from the 6 that reported both.

ICU indicates intensive care unit. The 3 Long COVID symptom clusters were persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems and were self-reported 3 months after SARS-CoV-2 infection in 2020 and 2021.

Second, the proportion of individuals with Long COVID symptom clusters after acute SARS-CoV-2 infection were estimated using a bayesian meta-regression tool separately for hospitalized and nonhospitalized individuals. Third, estimates from the studies providing distributions of symptom cluster overlap and severity gradients of cognitive and respiratory problems were pooled.

Fourth, estimates of daily SARS-CoV-2 infections, hospital admissions, intensive care unit (ICU) admissions, and deaths due to SARS-CoV-2 infection were taken from the Institute for Health Metrics and Evaluation at the University of Washington COVID-19 statistical model. 12 , 13 The number of SARS-CoV-2 infections was multiplied by the pooled estimate of the proportion of infections without symptoms, and then deaths were subtracted from the estimate of symptomatic cases to get the estimates by age, sex, and country for symptomatic survivors of SARS-CoV-2 infection. Fifth, the global estimates of symptomatic COVID-19 survivors were multiplied by the proportion of individuals experiencing at least 1 of the 3 Long COVID symptom clusters 3 months after SARS-CoV-2 infection.

Study Population

There were data from 54 studies (44 published studies and 10 collaborating cohort studies) and 2 medical record databases for individuals who had symptomatic SARS-CoV-2 infection. Data from the study populations ranged from a full account of all cases of SARS-CoV-2 infection in the Faroe Islands to cases identified at health facilities, volunteers reporting symptoms in an app, and individuals enrolled in medical insurance. Individuals with Long COVID had new-onset or persisting symptoms 3 months after onset of symptomatic SARS-CoV-2 infection and COVID-19 that were not preexisting. This description aligns with the WHO clinical case definition of the post–COVID-19 condition, which is their preferred term for Long COVID. 1

Long COVID Symptom Clusters

The symptom clusters were selected based on reporting frequency in published studies and the ability to characterize them using health state descriptions from the Global Burden of Disease Study. The 3 Long COVID symptom clusters selected were (1) persistent fatigue with bodily pain (myalgia) or mood swings; (2) cognitive problems (forgetfulness or difficulty concentrating, commonly referred to as brain fog); and (3) ongoing respiratory problems (shortness of breath and persistent cough as the main symptoms). In addition, 2 severity levels for cognitive problems were selected as well as 3 severity levels for ongoing respiratory symptoms. The health states and corresponding symptom descriptions used for the 3 Long COVID symptom clusters appear in Table 1 .

Abbreviation: UI, uncertainty interval.

Systematic Review and Data Extraction

A systematic review was conducted of the 44 published studies on the long-term symptoms after COVID-19 (eTable 1 and eFigures 2-3 in Supplement 1 and Supplement 2 ). The published studies were supplemented with more detailed individual-level data from the 10 collaborating cohort studies (eTable 1 and eFigure 4 in Supplement 1 ) and data from 2 US medical record databases (eTables 1-2 in Supplement 1 ).

For the 10 collaborating cohort studies, algorithms were developed and applied to extract the 3 Long COVID symptom clusters by symptom severity level to most closely match the symptom descriptions in Table 1 (additional information appears in eSection 1 in Supplement 1 ). Data from 4 of the collaborating cohort studies that did not report pre–COVID-19 health status were adjusted downward based on the ratio of excess risk of Long COVID symptoms to total symptoms reported from the 6 collaborating cohort studies with available individual-level data on pre–COVID-19 health status (eTable 3 in Supplement 1 ). 14 , 15 , 16 , 17 , 18 , 19 , 20 Respondents with insufficient follow-up data to apply the algorithms were excluded. All extracted data used in the analyses appear in Supplement 3 . Data also were extracted from the 44 published follow-up studies reporting on the key defining symptoms of the 3 Long COVID symptom clusters: fatigue, shortness of breath, and cognitive dysfunction.

Long COVID Outcomes

The main outcome was the proportion of individuals with at least 1 of the 3 Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection and 12 months after COVID-19 illness. Additional outcomes included the duration and relative severity of the Long COVID symptom clusters.

Statistical Analysis

Bayesian meta-regression of the data was performed using the Meta-Regression Tool (MRTool) version 0.0.1 (Institute for Health Metrics and Evaluation at the University of Washington) and R package MR-BRT 002 (R Foundation for Statistical Computing) with tabulated data from each study on the proportion of individuals who experienced at least 1 of the 3 Long COVID symptom clusters during follow-up. 21 Indicator variables for male and female sex and study-level random effects were added. Separate models were run for hospitalized and nonhospitalized individuals with an indicator variable for those who were admitted to the ICU in the hospitalized model and for individuals younger than 20 years of age in the nonhospitalized model (eSection 2, eTables 4-9, and eFigures 5-10 in Supplement 1 ). The statistical differences between the proportion of individuals by sex and age (<20 years or ≥20 years) were determined by estimating the difference at each of 1000 draws of the posterior and presented as means with 95% uncertainty intervals (UIs) and deemed statistically significant if the full range of the 95% UI was either negative or positive.

The overlap of 2 or 3 Long COVID symptom clusters and the severity gradients of the cognitive and respiratory clusters were pooled using the MRTool with indicator variables for individuals who were hospitalized and study-level random effects (eSection 3, eTables 10-12, and eFigures 11-13 in Supplement 1 ).

The Long COVID symptom cluster duration values for hospitalized and nonhospitalized individuals were derived from the final proportion models having at least 1 symptom cluster (eSection 2 in Supplement 1 ).

The estimates of SARS-CoV-2 infection were taken from the Institute for Health Metrics and Evaluation at the University of Washington COVID-19 statistical model, which is a statistical susceptible, exposed, infected, and removed compartmental model used to fit data on the daily reported deaths, hospitalizations, and SARS-CoV-2 infections; seroprevalence; and excess mortality data. 12 , 13 The Institute for Health Metrics and Evaluation at the University of Washington COVID-19 statistical model used an ensemble modeling strategy selecting predictive covariate combinations that best accounted for input data variance.

For this analysis, Long COVID was assumed to occur only in those with symptomatic SARS-CoV-2 infection (eSection 4 in Supplement 1 ). Studies were selected from a published review 22 that estimated the proportion of asymptomatic SARS-CoV-2 infections in representative samples screened with antibody testing ( Supplement 3 ). The logit-transformed proportion of individuals with asymptomatic SARS-CoV-2 infection was pooled from 6 studies in a random-effects meta-analysis (eFigure 14 and eTable 13 in Supplement 1 ), and 1 minus the pooled proportion was multiplied by the number of SARS-CoV-2 infections to estimate the incidence of symptomatic SARS-CoV-2 infections (eFigure 15 in Supplement 1 ). Deaths were then subtracted from the number of symptomatic SARS-CoV-2 infections to obtain the number of individuals who survived and had symptomatic infection separately for those needing care in a general hospital ward or in the ICU and those not needing such care (eFigures 16-17 in Supplement 1 ).

The individuals who survived were multiplied by the estimated proportion of individuals who were either hospitalized or not hospitalized with each Long COVID symptom cluster at 3 months (eSection 5 in Supplement 1 ). Uncertainty was propagated through 1000 posterior draws of every stage of the analysis. The 95% UIs are presented for all estimates based on the 25th and 975th values of the ordered 1000 draws of the final posterior distributions.

To quantify what proportion of cases with Long COVID symptoms would be missed by concentrating on the 3 large symptom clusters, the most detailed and largest cohort with individual records available from Russia was further scrutinized. 20 Reported symptoms were tabulated among cases who reported not having recovered from COVID-19 and who reported worse overall health status on the 5-dimension EuroQol 5L index measure compared with their rating using the same measure before having SARS-CoV-2 infection. A sensitivity analysis was conducted to estimate symptom duration using all available data rather than limiting the symptom duration model input data to studies with multiple follow-up points.

This analysis complies with the Guidelines for Accurate and Transparent Health Estimates Reporting 23 ( Supplement 4 ).

This observational analysis involved bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection (mean age range among the data sources, 4-66 years; range for proportion of males, 26%-88%). The participant data were derived from 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), 10 collaborating cohort studies (10 526 hospitalized individuals and 1906 nonhospitalized individuals with COVID-19), and 2 US electronic medical record databases (250 928 hospitalized individuals and 846 046 nonhospitalized individuals) ( Table 2 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 eTable 1 in Supplement 1 , and Supplement 3 ).

For data extraction in the 2 US electronic medical record databases, International Classification of Diseases, 10th Revision, codes were used for cognitive symptoms, fatigue, and respiratory symptoms (eTable 2 in Supplement 1 ). Of the 10 collaborating cohort studies, 3 included individuals who were younger than 20 years of age. Of the 12 432 participants in these collaborating cohort studies, 203 did not have responses required by the Long COVID symptom cluster algorithms and were excluded.

An estimated 6.2% (95% UI, 2.4%-13.3%) of individuals with symptomatic SARS-CoV-2 infection who survived the acute episode experienced at least 1 of the 3 Long COVID symptom clusters ( Table 3 ). The estimated proportion of individuals with at least 1 of the 3 Long COVID symptom clusters was greater in those who were admitted to ICUs (43.1% [95% UI, 22.6%-65.2%]) and in those who were admitted to general hospital wards (27.5% [95% UI, 12.1%-47.8%]) than in those who were not hospitalized (5.7% [95% UI, 1.9%-13.1%]), with higher proportions among females than males ( Table 3 and eTable 14 in Supplement 1 ).

Abbreviations: ICU, intensive care unit; UI, uncertainty interval.

Among individuals who were hospitalized, the estimated mean Long COVID symptom duration was 9.0 months (95% UI, 7.0-12.0 months) based on data from 6 studies (conducted in 5 high-income countries and in 1 upper-middle-income country) with 8660 respondents with symptomatic SARS-CoV-2 infection (eFigure 5 in Supplement 1 ). Among individuals who were not hospitalized, the estimated mean Long COVID symptom duration was 4.0 months (95% UI, 3.6-4.6 months) based data from 4 studies (conducted in 4 high-income countries) with 4918 participants with symptomatic SARS-CoV-2 infection.

Of individuals with symptomatic SARS-CoV-2 infection, an estimated 3.2% (95% UI, 0.6%-10.0%) had persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) had ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) had cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. In an estimated 38.4% (95% UI, 7.94%-96.0%) of Long COVID cases, 2 or all 3 of the symptom clusters overlapped ( Figure 2 and eTable 15 in Supplement 1 ).

The proportion estimates with the 95% uncertainty intervals appear in eTables 14-15 in Supplement 1 .

Globally, an estimated 63.2% (95% UI, 59.7%-66.3%) of individuals with Long COVID were female. The estimated risk of Long COVID at 3 months was lower in individuals with symptomatic SARS-CoV-2 infection who were not hospitalized and were younger than 20 years of age (2.7% [95% UI, 0.8%-6.7%]) than in those aged 20 years or older for both men (4.8% [95% UI, 1.5%-11.3%]) and women (9.9% [95% UI, 3.4%-21.2%]) ( Table 3 and eTable 14 in Supplement 1 ). The difference in the estimated risk of Long COVID between individuals who were younger than 20 years of age and men aged 20 years or older was 2.0% (95% UI, 0.7%-4.6%), the difference between those younger than 20 years of age and women aged 20 years or older was 7.2% (95% UI, 2.6%-15.1%), and the difference between men and women aged 20 years or older was 5.1% (95% UI, 1.8-10.9), which were statistically significant differences.

Among COVID-19 survivors who developed Long COVID in 2020 and 2021 and had symptoms 3 months after SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to have persistent symptoms at 12 months ( Table 3 ). The global new cases with Long COVID symptom clusters by sex and severity of SARS-CoV-2 infection appear in eTable 16 in Supplement 1 . The global counts of symptomatic SARS-CoV-2 infection and cases of Long COVID by country appear in eTable 17 in Supplement 1 .

The detailed analysis of the Russian cohort found that the 3 Long COVID symptom clusters were present in 136 of 198 individuals reporting not having recovered and having worse general health status than before COVID-19 (eTable 18 in Supplement 1 ). The majority (48 of 62 persons) of those in the Russian cohort not covered by the 3 Long COVID symptom clusters defined in this article reported fatigue, respiratory, and cognitive symptoms but did not get included by the study’s algorithm because they reported no worsening in their ability to carry out usual activities.

In the sensitivity analysis with all data incorporated into the duration models rather than only studies with follow-up at multiple time points, the length of Long COVID increased slightly from 9.0 months (95% UI, 7.0-12.0 months) to 9.1 months (95% UI, 6.9-12.1 months) for individuals who were hospitalized and increased slightly from 4.0 months (95% UI, 3.6-4.6 months) to 4.7 months (95% UI, 4.0-5.4 months) for individuals who were not hospitalized and the proportion of Long COVID symptom clusters remained stable (eTable 19 in Supplement 1 ).

This modeling study estimated that among patients with symptomatic SARS-CoV-2 infections who survived the acute phase in 2020 and 2021, 6.2% experienced at least 1 of the 3 Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after acute infection onset. The risk of Long COVID was greater in females and in those who needed hospitalization for the initial SARS-CoV-2 infection, particularly among those needing ICU care.

The pattern of Long COVID symptoms by sex is distinct from that of severe acute SARS-CoV-2 infection, which tends to affect more males (eFigure 15 in Supplement 1 ). 13 This difference suggests that the underlying mechanism of Long COVID may be different from that of the severity of acute SARS-CoV-2 infection. In general, women respond to viral infections with less severe disease and mount higher antibody responses but also have higher rates of adverse reactions to vaccinations and antiviral drugs; X chromosome–linked genes are thought to influence susceptibility to viral infections as well as autoimmune diseases, lending support to autoimmune processes playing a role in the development of Long COVID. 75

A prolonged state of low-grade infection with a hyperimmune response, coagulation or vasculopathy, endocrine and autonomic dysregulation, and a maladaptation of the angiotensin-converting enzyme 2 pathway have been postulated as the underlying pathophysiology of Long COVID. 76 Deconditioning due to prolonged immobilization during hospitalization may compound these problems. 77

The analyses in this study are based on the WHO case definition that stipulates a minimum period of 3 months after SARS-CoV-2 infection before referring to ongoing symptoms as Long COVID or post–COVID-19 condition. Others have suggested a threshold of 3 weeks to define a case of Long COVID, arguing that no competent virus has been replicated beyond 3 weeks of infection, but periods of up to 12 weeks have been suggested to define the start of Long COVID. 76 , 78 , 79 This analysis accounts for symptomatic SARS-CoV-2 infections through the end of 2021 and therefore does not cover the Omicron variant wave. Based on data from the UK COVID Symptom Study, 80 a reduced odds of Long COVID symptoms between 0.24 and 0.50, depending on time since the last vaccination, was found for the Omicron variants compared with the Delta variants.

The estimated decline in reporting for any of the 3 Long COVID symptom clusters during follow-up among individuals not hospitalized suggests that the majority of Long COVID cases resolve. It is not yet clear if there is a smaller proportion of individuals, especially among those hospitalized for the acute episode of SARS-CoV-2 infection, who develop a more chronic course of Long COVID. Given that the longest follow-up among the included studies was 12 months, the true long-term pattern of symptom persistence for Long COVID will only be revealed as studies conduct longer follow-up. The time-limited course of Long COVID in most people has led to some recommendations to provide rehabilitative support in the community, with specialist rehabilitation services required only for those with protracted and more severe problems, particularly when compounded by postintensive care syndrome. 78 , 81

Quantifying the number of individuals with Long COVID may help policy makers ensure adequate access to services to guide people toward recovery, return to the workplace or school, and restore their mental health and social life. The large number of individuals with Long COVID may provide insights into phenotypical and genotypical characteristics, potentially leading to treatments and predictors of postacute disease syndromes, including those known to occur after other infectious diseases and intensive care for other critical illnesses. Postinfection fatigue syndrome has been previously reported for the Influenza A (H1N1) pandemic in 1918 and SARS-CoV-1 in 2003 and after the Ebola epidemic in West Africa in 2014. Similar symptoms have been reported after other viral infections including the Epstein-Barr virus, mononucleosis, and dengue as well as after nonviral infections such as Q fever, Lyme disease, and giardiasis. 82

The collaborative structure of this study helped to provide consistent approaches in dealing with the diverse study methods and instruments used. It led to a definition of Long COVID symptom clusters and quantifying overlap among the symptom clusters. A key step was to correct for overreporting from studies that did not have a comparison with previous health status, leveraging information from the cohort studies that explicitly asked respondents to recall their pre–COVID-19 health status or existence of symptoms. In addition, the large US health insurance databases enabled identification of controls matched on demographic and disease characteristics and thus correct for the occurrence of these symptoms unrelated to SARS-CoV-2 infection. This may in part explain why these estimates of Long COVID are lower than the estimates often reported in the literature. Direct comparisons are unavailable because the Long COVID symptom clusters defined for this study have not been reported by others.

Limitations

This study has several limitations. First, the 95% UIs around the estimates are wide, reflecting limited and heterogeneous data.

Second, separate algorithms had to be formulated for each contributing study to achieve consistency in the case definitions of the 3 chosen Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings, cognitive problems, and ongoing respiratory problems). Efforts to achieve standardization of questions and instruments for studies of Long COVID are underway. 1 , 83 This would make pooling estimates among studies less prone to measurement bias.

Third, it was assumed that Long COVID follows a similar course in all countries and territories. Data were used from western European countries, Australia, China, India, Iran, Israel, Russia, Saudi Arabia, South Africa, Turkey, and the US. Additional reports from Brazil and Bangladesh suggest that Long COVID similarly affects people in other parts of the world. 84 , 85 As more information becomes available, any geographical variation in the occurrence or severity of Long COVID could be explored. The duration estimates for Long COVID relied on studies from high-income countries only. With repeated follow-up being planned in many of the studies, and with new studies being conducted, it should become clearer whether the findings related to the duration of Long COVID are generalizable.

Fourth, apart from the symptoms and Long COVID symptom clusters, new diseases and events have been reported to occur more frequently in patients after COVID-19 diagnosis, including cardiovascular complications like myocarditis, acute myocardial infarction, and thromboembolic events as well as kidney, liver, gastrointestinal, endocrine, and skin disorders. 86 , 87 , 88 The data sources to quantify these COVID-19–related changes may not yet be sufficient due to lags in the reporting of clinical informatics data, disease registries, and surveys that form the basis of estimation for such diseases.

Fifth, it was assumed that Long COVID only affects those with a symptomatic course of the initial SARS-CoV-2 infection. The participating cohorts included few people with asymptomatic SARS-CoV-2 infection. The study from the Faroe Islands observed 22 individuals with fully asymptomatic SARS-CoV-2 infection, the study from Italy included 53, the study from Switzerland included 182, and the study from the US included 9. 14 , 18 , 24 , 29 Long COVID was not identified among any individuals who were followed up in the Italy and US cohorts. In the Faroe Islands and Swiss cohorts, 3 individuals and 5 individuals, respectively, developed at least 1 of the 3 Long COVID symptom clusters during follow-up. The total number of individuals with asymptomatic SARS-CoV-2 infection followed up in these studies was low and, to be cautious, these individuals were excluded from calculations in this study. If Long COVID symptoms do occur in those who have an asymptomatic SARS-CoV-2 infection, the estimates would be higher.

Sixth, the analyses are based on 3 commonly reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings, cognitive problems, or ongoing respiratory problems) but not for other common symptoms reported as Long COVID. The main symptoms of the 3 Long COVID symptom clusters are those that reached the highest degree of consensus in the Delphi process that the WHO used to create a clinical case definition for the post–COVID-19 condition. 1 The detailed analysis of the most complete cohort from Russia suggested that two-thirds of individuals who were reported as not having recovered or being worse off than before COVID-19 were captured by the 3 Long COVID symptom clusters included in this analysis, whereas most of the remaining one-third of individuals were reported as having the same symptoms but at a less severe level by which the symptoms did not interfere with the ability to perform usual activities (eSection 4 and eTable 18 in Supplement 1 ). 20 The estimates, therefore, do not reflect the burden of the full range of Long COVID outcomes.

Conclusions

Supplement 1..

eSection 1. Extract long COVID symptom cluster input data

Supplement 2.

PRISMA compliance

Supplement 3.

Supplement 4..

GATHER checklist

Supplement 5.

Data sharing statement