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Prevalence of and factors associated with long COVID among US adults: a nationwide survey

BMC Public Health volume 25, Article number: 1758 (2025) Cite this article

Abstract

Background

People with long COVID report prolonged, multisystem involvement and significant disability. This study aimed to determine long COVID prevalence and factors associated with it among US adults using nationally representative data.

Methods

This cross-sectional analysis utilized data from 2022 Behavioral Risk Factor Surveillance System survey, a nationally representative telephone survey conducted among noninstitutionalized adults aged ≥ 18 years residing in the United States. Age-adjusted prevalence of long COVID was calculated using weighted survey analysis. Poisson regression was employed to assess adjusted prevalence ratios (aPRs) associated with long COVID across various demographic, socioeconomic and health-related characteristics.

Results

Among 390,233 participants, 120,178 reported COVID-19, with 25,582 experiencing long COVID. Age-adjusted prevalence of self-reported COVID-19 and long COVID were estimated at 34.1% (95% CI, 33.7–34.4%) and 7.2% (95% CI, 7.0–7.4%) as of 2022, respectively. Among adults reporting COVID-19, 20.9% (95% CI, 20.5–21.4%) had ever experienced long COVID. An inverted U-shaped association was observed between long COVID risk and age, with the highest prevalence (23.5%) in the 45–54 age group. Long COVID was more prevalent among women (aPR, 1.40 [95% CI, 1.34–1.47]), individuals without a spouse (aPR, 1.06 [95% CI, 1.00–1.13]), uninsured (aPR, 1.16 [95% CI, 1.06–1.27]), and those with a high school education (aPR, 1.17 [95% CI, 1.12–1.23]), cardiovascular disease (aPR, 1.17 [95% CI, 1.09–1.25]), depressive disorder (aPR, 1.41 [95% CI, 1.34–1.48]), chronic obstructive pulmonary disease (aPR, 1.33 [95% CI, 1.24–1.43]), asthma (aPR, 1.28 [95% CI, 1.21–1.35]), and kidney disease (aPR, 1.11 [95% CI, 1.01–1.21]). Long COVID was less prevalent among non-Hispanic Black (aPR, 0.87 [95% CI, 0.81–0.95]), students (aPR, 0.87 [95% CI, 0.76–0.99]) or retired individuals (aPR, 0.89 [95% CI, 0.82–0.98]), and those with household incomes ≥$100,000 (aPR, 0.85 [95% CI, 0.79–0.92]).

Conclusions

Long COVID affects 7.2% of US adults, with higher vulnerability among women, middle-aged individuals, White individuals, socioeconomically disadvantaged groups, and those with chronic conditions. These findings underscore the need for targeted public health strategies to address disparities in long COVID burden and support high-risk populations.

Peer Review reports

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has resulted in unprecedented levels of morbidity and mortality worldwide, including within the United States [1]. Beyond the acute phase of the illness, its subsequent phases present ongoing risks to health. Long COVID, a heterogeneous condition, encompasses signs and symptoms that persist or develop four or more weeks after the onset of acute COVID-19 symptoms [2]. It is estimated that 10–30% of non-hospitalized cases and 50–70% of hospitalized cases may experience long COVID [3, 4]. The condition is characterized by various manifestations across multiple organ systems, including but not limited to fatigue, dyspnea (shortness of breath), and cognitive impairment. Despite numerous clinical trials, effective treatments for long COVID are currently lacking [5]. Recent economic assessments suggest that long COVID may account for over 15% of the labor shortage in the United States [6].

Estimates of long COVID prevalence vary considerably depending on study design, population characteristics, and case definition [7]. In the United States, prevalence assessments predominantly rely on non-representative sources, including cross-sectional convenience samples, as well as analyses of electronic health records and cohorts [2, 4, 8, 9]. Two recent national surveys—the National Health Interview Survey (NHIS) and the US Household Pulse Survey (HPS)—have assessed long COVID prevalence in the general US adult population, yet their estimates vary significantly (6.9% vs. 14.0–15.7%) [10,11,12]. These discrepancies may stem from differences in response rates, survey design, population coverage, or timing of data collection.

Limited understanding exists regarding the risk factors for long COVID. Available data from the United States and Europe consistently indicate a higher prevalence of long COVID among women and individuals with comorbidities [7, 13,14,15,16,17]. However, identifying additional risk factors is challenging due to discrepancies arising from variations in study design, population demographics—typically focusing on hospitalized or healthcare-seeking cohorts—and the operational definition of long COVID [7, 13,14,15,16,17].

This study addresses critical gaps in current evidence by: (1) leveraging the Behavioral Risk Factor Surveillance System (BRFSS), one of the largest nationally representative health surveys, to provide more precise prevalence estimates of long COVID in non-institutionalized US adults; and (2) systematically examining sociodemographic and clinical risk factors in a general population sample, overcoming limitations of previous studies that relied primarily on clinical or convenience samples. Our findings will provide more generalizable evidence to inform public health strategies targeting at-risk populations.

Methods

Study population

The BRFSS is an ongoing telephone survey system collecting data on health-related behaviors, chronic conditions, healthcare access, and preventive service usage among noninstitutionalized adults aged 18 years or older in the US and its territories [18]. The BRFSS incorporates weighting adjustments to address demographic variances, noncoverage, and nonresponse, thus ensuring the representativeness of the data [19]. This cross-sectional analysis was based on the 2022 BRFSS survey, which collected data from all 50 states, the District of Columbia, and three territories (Guam, Puerto Rico, and the US Virgin Islands) through landline and cellular telephones. Verbal consent was obtained during the telephone interview [20]. The response rate for the 2022 BRFSS survey was 45.0%. This study included 390,233 participants (87.7% of the surveyed population) who reported COVID-19 information. Approval from the institutional review board was not sought for this study, as the data are publicly accessible and de-identified. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Assessment of SARS-CoV-2 infection and long COVID

SARS-CoV-2 infection was determined using the question: “Has a doctor, nurse, or other health professional ever told you that you tested positive for COVID-19?” Participants who responded affirmatively or reported testing positive using a home test without health professional involvement were classified as having SARS-CoV-2 infection. Those reporting SARS-CoV-2 infection were subsequently asked, “Did you have any symptoms lasting 3 months or longer that you did not have prior to having coronavirus or COVID-19?” An affirmative response led to classification as having long COVID. Respondents indicating long COVID were further queried about their primary symptom experienced. Response options included: (1) tiredness or fatigue; (2) difficulty thinking or concentrating or forgetfulness/memory problems (sometimes referred to as “brain fog”); (3) difficulty breathing or shortness of breath; (4) joint or muscle pain; (5) fast-beating or pounding heart (also known as heart palpitations) or chest pain; (6) dizziness on standing; (7) depression/anxiety or mood changes; (8) symptoms that get worse after physical or mental activities; (9) you did not have any long-term symptoms that limited your activities; (10) loss of taste or smell; and (11) some other symptom. These questionnaires were similar to those employed in NHIS [11, 12].

Other study measures

Demographic and socioeconomic characteristics included age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic other, or Hispanic), marital status (married/unmarried couple, widowed/separated/divorced, or single), education level (less than high school, high school or some college, or college graduate), employment status (employed, unemployed, student, or retired), area of residence (rural or urban) [21, 22], health insurance status (having some form of insurance or being uninsured), and annual household income (<$35,000, $35,000-$99,999, or ≥$100,000).

Chronic conditions are defined as conditions lasting one year or more that require ongoing medical care, limit daily activities, or both [23]. Chronic conditions assessed included cardiovascular disease (myocardial infarction, coronary heart disease, or stroke), cancer (excluding non-melanoma skin cancer), depression, chronic obstructive pulmonary disease (COPD) (including emphysema or chronic bronchitis), asthma, and kidney disease (excluding kidney stones, bladder infection, or incontinence). Questionaries about chronic conditions are shown in Table S1. The inclusion of these chronic conditions in this study was based on prior evidence of association with long COVID [14, 16, 24,25,26], clinical plausibility, and data availability in BRFSS.

Statistical analysis

The age-adjusted prevalences of self-reported SARS-CoV-2 infection and long COVID were calculated for all adults aged 18 years and above. The Wald method was used to obtain 95% confidence intervals (CIs) for prevalence estimates. Demographic, socioeconomic, and health condition characteristics were summarized for the entire sample, as well as for individuals self-reporting SARS-CoV-2 infection and long COVID.

The age-adjusted proportion of adults self-reporting long COVID was calculated for all adults self-reporting SARS-CoV-2 infection. This proportion was determined for the overall adult population and within predefined categories, including age group (18–24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years, 65–74 years, and ≥ 75 years), sex, race/ethnicity, marital status, education, employment, household income, area of residence, health insurance coverage, and chronic conditions. Age-adjusted proportion was calculated via direct standardization using 2020 US Census data [27]. The age categories used for standardization were 18 to 24 years (12.1%), 25 to 34 years (17.6%), 35 to 44 years (16.6%), 45 to 54 years (15.9%), 55 to 64 years (16.6%), 65 to 74 years (12.5%), and 75 years or older (8.7%). Age-adjusted estimates account for the substantial variation in long COVID prevalence across age groups. It is particularly important for surveillance data as it controls for differences in population age structures when making comparisons [28,29,30,31].

Poisson regression was employed to assess prevalence ratios associated with long COVID across various demographic, socioeconomic and health-related characteristics, including age group, sex, race/ethnicity, marital status, education, employment, household income, area of residence, health insurance coverage, and chronic conditions. Poisson regression provides correct estimates and is a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio [32, 33]. Two Poisson regression models were employed: Model 1, adjusting for age group, sex, and race/ethnicity, and Model 2 adjusting for all aforementioned variables.

Missing data were multiply imputed by chained equations and five imputed datasets were generated. A complete analysis was also conducted to assess factors associated with long COVID. Survey weights were applied in all calculations to yield nationally representative estimates [19]. Statistically significance was defined as p < 0.05 (two-tailed). StataSE 15 statistical software (StataCorp) was used for all analyses.

Results

In 2022, the BRFSS surveyed 445,132 adults aged 18 or older. Participants were excluded if they either lacked COVID-19 test results or had such results but lacked information regarding long COVID, leaving 390,233 participants for analysis. Among these, 120,178 self-reported SARS-CoV-2 infection, of whom 25,582 had experienced long COVID (Fig. 1).

Fig. 1
figure 1

Determination of the study sample. BRFSS, the Behavioral Risk Factor Surveillance System; COVID, coronavirus disease

Full size image

Of the 390,233 participants included in the analysis, 183,884 (48.9%) were men and 206,349 (51.1%) were women. Participants aged over 65 years constituted 23.5% of the study population. A total of 60.3% of participants identified as non-Hispanic White adults, 11.2% as non-Hispanic Black adults, 17.9% as Hispanic adults, and 10.6% as other race or ethnicity (Table 1). No participants were missing data on age, sex, or race/ethnicity. There were missing data for marital status (0.8% of participants), education (0.4%), employment (0.9%), household income (18.0%), area of residence (2.3%), health insurance coverage (3.6%), cardiovascular disease (0.1%), cancer (0.5%), depressive disorder (0.6%), COPD (0.4%), asthma (0.4%), and kidney disease (0.4%).

Table 1 Participant characteristics overall and among those with self-reported COVID-19 and long COVID, 2022 Behavioral Risk Factor Surveillance Systema
Full size table

Prevalence of ever having long COVID

The age-adjusted prevalences of self-reported SARS-CoV-2 infection and ever having long COVID were estimated at 34.1% (95% CI, 33.7–34.4%) and 7.2% (95% CI, 7.0–7.4%), respectively, as of 2022. This corresponds to approximately 87,906,518 and 18,603,304 US adults affected by COVID-19 and long COVID, respectively. Among primary long COVID symptoms, tiredness or fatigue predominated (26.2%), followed by difficulty breathing or shortness of breath (18.9%), loss of taste or smell (17.0%), some other symptoms (9.9%), brain fog (difficulty thinking or concentrating or forgetfulness/memory problems) (9.8%), joint or muscle pain (6.2%), symptoms not limiting activities (5.2%), heart palpitations or chest pain (2.9%), dizziness on standing (1.6%), depression/anxiety or mood changes (1.4%), and symptoms worsening after physical or mental activities (0.9%).

Among adults with self-reported COVID-19, the age-adjusted estimated proportion of ever having long COVID was 20.9% (95% CI, 20.5–21.4%). The estimated proportions of adults ever having long COVID by the categories of age group, sex, race/ethnicity, marital status, education, employment, household income, area of residence, health insurance coverage, and comorbidities among those with self-reported SARS-CoV-2 infection appear in Table 2.

Table 2 Prevalence of and associated factors with long COVID among adults with self-reported COVID-19 infection, 2022 Behavioral Risk Factor Surveillance Systema
Full size table

Factors associated with self-reported long COVID

Compared with adults aged 18 to 24 years with self-reported SARS-CoV-2 infection, long COVID was more prevalent among their counterparts aged 25 to 34 years (20.6% vs. 17.8%; adjusted prevalence ratio [aPR], 1.14 [95% CI, 1.04–1.25]), 35 to 44 years (23.4% vs. 17.8%; aPR, 1.30 [95% CI, 1.18–1.43]), 45 to 54 years (23.5% vs. 17.8%; aPR, 1.31 [95% CI, 1.18–1.45]), and 55 to 64 years (22.2% vs. 17.8%; aPR, 1.19 [95% CI, 1.07–1.32]). The proportion of long COVID declined in those aged ≥ 65, with no significant difference compared with the 18 to 24 years age group (Table 2; Fig. 2). Long COVID was more prevalent among women than men (24.9% vs. 16.3%; aPR, 1.40 [95% CI, 1.34–1.47]). Long COVID was less prevalent among non-Hispanic Black (18.9%) and non-Hispanic other adults (19.3%) compared with non-Hispanic White adults (21.4%) (aPR, 0.87 [95% CI, 0.81–0.95], and 0.91 [95% CI, 0.83–1.00], respectively).

Fig. 2
figure 2

The relationship between age and long COVID reporting among US adults with self-reported SARS-CoV-2 infection. Data are presented as percentages and 95% confidence intervals

Full size image

Long COVID was more prevalent among adults who were widowed, separated, or divorced compared with those in marital or unmarried couple arrangements (26.8% vs. 20.0%; aPR, 1.06 [95% CI, 1.00–1.13]). Long COVID was more prevalent among adults with a high school education or some college education compared with those with a college graduate education (22.6% vs. 17.6%; aPR, 1.17 [95% CI, 1.12–1.23]), as well as among adults lacking any form of health insurance compared with those with coverage (22.4% vs. 20.7%; aPR, 1.16 [95% CI, 1.06–1.27]). Long COVID was less prevalent among adults who were students (18.9%) or retired (17.3%) compared with those employed (19.6%) (aPR, 0.87 [95% CI, 0.76–0.99], and 0.89 [95% CI, 0.82–0.98], respectively), as well as among adults with household income at or above $100,000 compared with those earning less than $35,000 (16.5% vs. 25.2%; aPR, 0.85 [95% CI, 0.79–0.92]). Additionally, long COVID was more prevalent among adults with cardiovascular disease (aPR, 1.17 [95% CI, 1.09–1.25]), depressive disorder (aPR, 1.41 [95% CI, 1.34–1.48]), COPD (aPR, 1.33 [95% CI, 1.24–1.43]), asthma (aPR, 1.28 [95% CI, 1.21–1.35]), and kidney disease (aPR, 1.11 [95% CI, 1.01–1.21]). The results were similar in a complete case analysis (Table S2).

Discussion

Utilizing nationally representative data from the 2022 BRFSS, this study provides robust population-level estimates of long COVID prevalence and associated risk factors among US adults. Our findings indicate that 7.2% (95% CI, 7.0–7.4%) of the adult population, equating to around 18.6 million individuals, reported experiencing long COVID as of 2022, defined as persistent symptoms lasting three months or longer following SARS-CoV-2 infection. Notably, the risk exhibited an inverted U-shaped association with age, with the highest prevalence observed among middle-aged adults (45–54 years), followed by a gradual decline in older age groups. Furthermore, significant disparities were identified across demographic and clinical subgroups, with elevated risk among women, non-Hispanic White individuals, those with lower socioeconomic status (e.g., lower education, uninsured status, and lower household income), and individuals without a spouse. Additionally, pre-existing chronic conditions such as depressive disorder, COPD, asthma, cardiovascular disease, and kidney disease, were associated with increased susceptibility to long COVID.

Our BRFSS-based estimate of long COVID prevalence (7.2%) closely aligns with the 2022 NHIS estimate (6.9%), despite the substantial difference in sample sizes (445,132 vs. 27,651 respondents) [11, 34]. This consistency between two large, nationally representative surveys employing similar methodology strengthens confidence in these prevalence estimates. However, both estimates are notably lower than the 14.0-15.7% range reported by the HPS [10]. Several key methodological differences likely explain this discrepancy. First, the HPS included both laboratory-confirmed cases and clinically diagnosed COVID-19, while BRFSS and NHIS restricted their assessment to test-confirmed cases. This distinction is particularly relevant given emerging evidence that long-term symptoms can occur at similar rates among individuals with COVID-like illness regardless of test results [35]. Second, the HPS’s substantially lower response rate (3.9-7.3% vs. 45.0% for BRFSS and 47.7% for NHIS) and reliance on digital recruitment methods may have introduced selection biases that affected prevalence estimates. These methodological considerations underscore the importance of standardized case definitions and sampling approaches in long COVID surveillance.

The age-adjusted estimated proportion of individuals with long COVID among US adults self-reporting COVID-19 in the BRFSS was 20.9% (95% CI, 20.5–21.4%). Previous studies have reported varying estimates, ranging from 9 to 52%, depending on factors such as acute phase severity, geographic region, sociodemographic characteristics, case definitions, sample sizes, COVID waves, and follow-up durations [7]. These earlier estimates were derived from non-population representative sources. In contrast, the BRFSS, as a population-representative survey, offers a valuable means of assessing the magnitude of long COVID in the United States.

The most prevalent primary symptoms of long COVID identified in the BRFSS were tiredness or fatigue, difficulty breathing or shortness of breath, loss of taste or smell, and brain fog (difficulty concentrating or memory problems). These findings are consistent with previous reports and correspond to the three symptoms explicitly included in the World Health Organization’s definition of long COVID [7, 36]. Our findings are also consistent with studies characterizing the multisystem involvement of long COVID [37,38,39,40].

Our analyses reveal an inverted U-shaped association between long COVID risk and age, with the highest risk observed among individuals aged 35–44 and 45–54 years. The risk declines among individuals aged over 65, with no increased risk compared with the reference group aged 18–24. The decline of long COVID risk among older individuals was also observed in prior studies [15, 16]. The underlying influential factors may include selective competing risk of mortality, non-response bias, reduced symptom reporting among the elderly, and misattribution of long COVID symptoms to other illnesses [16]. The finding that the prevalence of long COVID was 40% higher in women than men is consistent with the majority of previous studies [7, 41]. Additionally, our study found that Black individuals, but not Hispanic individuals, reported a lower incidence of long COVID compared to White individuals, consistent with findings from another population-based survey [42]. However, conflicting reports exist regarding the relationship between race/ethnicity and long COVID [43, 44]. Our study also found that widowed, separated, or divorced marital status was associated with long COVID in the United States, a finding not previously reported. Furthermore, low socioeconomic status, including lower income, lack of health insurance, and lower educational attainment, was associated with higher rates of reporting long COVID [14, 16, 42].

Regarding comorbidities, our analysis demonstrated that the presence of various conditions, including depressive disorder, COPD, and asthma, was associated with a higher prevalence of long COVID. Notably, these associations were robust in our study, highlighting the importance of focusing on these high-risk conditions in the context of long COVID [7, 14, 16, 24]. Conversely, cardiovascular disease and kidney disease, which have not consistently been associated with long COVID in previous studies [15, 16, 24,25,26, 43], exhibited only slight associations in our analysis, warranting further investigation. Moreover, we found that the rate of reporting long COVID was not higher among adult cancer survivors, consistent with prior studies [24, 43].

The findings from this study carry important implications for clinical practice, particularly in risk stratification and patient management. The elevated prevalence of long COVID among middle-aged adults, women, and individuals with pre-existing conditions (e.g., depression, COPD, asthma, and cardiovascular disease) suggests that clinicians should prioritize screening and longitudinal monitoring in these high-risk populations. Additionally, the socioeconomic disparities observed, including higher burden among uninsured and lower-income individuals, highlight the need for equitable access to post-COVID care, including rehabilitation and mental health support.

Several limitations should be acknowledged when interpreting our findings. First, our prevalence estimates are restricted to individuals with self-reported positive COVID-19 tests, potentially excluding undiagnosed cases and those with COVID-like illness who tested negative, which may lead to underestimation. Second, the cross-sectional design prevents causal inference regarding the relationships between socioeconomic factors, comorbidities, and long COVID development, while also introducing potential reverse causation. Third, symptom data relied on self-report without clinical validation, raising concerns about recall bias and subjective interpretation. Fourth, the BRFSS methodology limited participants to identifying only one primary long COVID symptom, precluding comprehensive analysis of symptom clusters or secondary manifestations. Finally, key clinical variables such as acute COVID-19 severity and medication use were not captured in the survey, representing important unmeasured confounders.

Conclusions

This nationally representative study estimates that 7.2% of US adults experienced long COVID in 2022, with higher prevalence among women, middle-aged individuals, White individuals, socioeconomically disadvantaged groups, and those with chronic conditions. These findings highlight the need for targeted public health strategies to address disparities in long COVID burden.

Data availability

All data are available at: https://www.cdc.gov/brfss/index.html.

Abbreviations

aPR:

Adjusted prevalence ratio

BRFSS:

Behavioral Risk Factor Surveillance System

COPD:

Chronic obstructive pulmonary disease

COVID-19:

Coronavirus disease 2019

HPS:

Household Pulse Survey

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

References

  1. Centers for Disease Control and Prevention. COVID-NET interactive dashboard [https://www.cdc.gov/covid/php/covid-net/index.html]

  2. Centers for Disease Control and Prevention. Long COVID or Post-COVID Conditions [https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html]

  3. Ceban F, Ling S, Lui LMW, Lee Y, Gill H, Teopiz KM, Rodrigues NB, Subramaniapillai M, Di Vincenzo JD, Cao B, et al. Fatigue and cognitive impairment in Post-COVID-19 syndrome: A systematic review and meta-analysis. Brain Behav Immun. 2022;101:93–135.

    Article  PubMed  CAS  Google Scholar 

  4. Kompaniyets L, Bull-Otterson L, Boehmer TK, Baca S, Alvarez P, Hong K, Hsu J, Harris AM, Gundlapalli AV, Saydah S. Post-COVID-19 symptoms and conditions among children and Adolescents - United States, March 1, 2020-January 31, 2022. MMWR Morb Mortal Wkly Rep. 2022;71(31):993–9.

    Article  PubMed  PubMed Central  Google Scholar 

  5. World Health Organization. Post COVID-19 condition (long COVID) [https://www.who.int/news-room/fact-sheets/detail/post-covid-19-condition-(long-covid)].

  6. Is. ‘long Covid’ worsening the labor shortage? [https://www.brookings.edu/research/is-long-covid-worsening-the-labor-shortage/]

  7. Chen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B. Global prevalence of Post-Coronavirus disease 2019 (COVID-19) condition or long COVID: A Meta-Analysis and systematic review. J Infect Dis. 2022;226(9):1593–607.

    Article  PubMed  CAS  Google Scholar 

  8. O’Laughlin KN, Thompson M, Hota B, Gottlieb M, Plumb ID, Chang AM, Wisk LE, Hall AJ, Wang RC, Spatz ES, et al. Study protocol for the innovative support for patients with SARS-COV-2 infections registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection. PLoS ONE. 2022;17(3):e0264260.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Saydah SH, Brooks JT, Jackson BR. Surveillance for Post-COVID conditions is necessary: addressing the challenges with multiple approaches. J Gen Intern Med. 2022;37(7):1786–8.

    Article  PubMed  PubMed Central  Google Scholar 

  10. National Center for Health Statistics. U.S, Census Bureau HP. Survey, 2022–4. Long COVID. [https://www.cdc.gov/nchs/covid19/pulse/long-covid.htm]

  11. Adjaye-Gbewonyo D, Vahratian A, Perrine CG, Bertolli J. Long COVID in adults: united States, 2022. NCHS Data Brief 2023(480):1–8.

  12. Hung CT, Hung YC, Suk CW. Prevalence and characteristics in long COVID among adults with asthma in the united States. J Asthma. 2024;61(7):736–44.

    Article  PubMed  Google Scholar 

  13. Fernández-de-Las-Peñas C, Pellicer-Valero OJ, Navarro-Pardo E, Palacios-Ceña D, Florencio LL, Guijarro C, Martín-Guerrero JD. Symptoms experienced at the acute phase of SARS-CoV-2 infection as risk factor of Long-term Post-COVID symptoms: the LONG-COVID-EXP-CM multicenter study. Int J Infect Dis. 2022;116:241–4.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Subramanian A, Nirantharakumar K, Hughes S, Myles P, Williams T, Gokhale KM, Taverner T, Chandan JS, Brown K, Simms-Williams N, et al. Symptoms and risk factors for long COVID in non-hospitalized adults. Nat Med. 2022;28(8):1706–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS, et al. Attributes and predictors of long COVID. Nat Med. 2021;27(4):626–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Thompson EJ, Williams DM, Walker AJ, Mitchell RE, Niedzwiedz CL, Yang TC, Huggins CF, Kwong ASF, Silverwood RJ, Di Gessa G, et al. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nat Commun. 2022;13(1):3528.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Yomogida K, Zhu S, Rubino F, Figueroa W, Balanji N, Holman E. Post-Acute sequelae of SARS-CoV-2 infection among adults aged ≥ 18 Years - Long beach, California, April 1-December 10, 2020. MMWR Morb Mortal Wkly Rep. 2021;70(37):1274–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Centers for Disease Control and Prevention. The Behavioral Risk Factor Surveillance System [https://www.cdc.gov/brfss/index.html]

  19. Centers for Disease Control and Prevention. Complex Sampling Weights and Preparing 2022 BRFSS Module Data for Analysis [https://www.cdc.gov/brfss/annual_data/2022/pdf/Complex-Sampling-Weights-and-Preparing-Module-Data-for-Analysis-2022-508.pdf]

  20. 2022 BRFSS Centers for Disease Control and Prevention, Overview. [https://www.cdc.gov/brfss/annual_data/2022/pdf/Overview_2022-508.pdf]

  21. Ingram DD. SJ Franco 2013 NCHS Urban-Rural classification scheme for counties. Vital Health Stat 2 2014 (166) 1–73.

    Google Scholar 

  22. Khavjou O, Tayebali Z, Cho P, Myers K, Zhang P. Rural-Urban disparities in State-Level diabetes prevalence among US adults, 2021. Prev Chronic Dis. 2025;22:E05.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Centers for Disease Control and Prevention. Chronic Diseases [https://www.cdc.gov/chronic-disease/about/index.html]

  24. Hill EL, Mehta HB, Sharma S, Mane K, Singh SK, Xie C, Cathey E, Loomba J, Russell S, Spratt H, et al. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health. 2023;23(1):2103.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Moreno-Pérez O, Merino E, Leon-Ramirez JM, Andres M, Ramos JM, Arenas-Jiménez J, Asensio S, Sanchez R, Ruiz-Torregrosa P, Galan I, et al. Post-acute COVID-19 syndrome. Incidence and risk factors: A mediterranean cohort study. J Infect. 2021;82(3):378–83.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Peghin M, Palese A, Venturini M, De Martino M, Gerussi V, Graziano E, Bontempo G, Marrella F, Tommasini A, Fabris M, et al. Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients. Clin Microbiol Infect. 2021;27(10):1507–13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. U.S. Census Bureau. Annual Estimates of the Resident Population by Single Year of Age and Sex for the United States: April 1, 2020 to July 1, 2022 [https://www.census.gov/data/datasets/time-series/demo/popest/2020s-national-detail.html#v2022]

  28. Erhabor J, Boakye E, Obisesan O, Osei AD, Tasdighi E, Mirbolouk H, DeFilippis AP, Stokes AC, Hirsch GA, Benjamin EJ, et al. E-Cigarette use among US adults in the 2021 behavioral risk factor surveillance system survey. JAMA Netw Open. 2023;6(11):e2340859.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Chinnakali P, Yadav K, Singh AK. Importance of reporting age-adjusted prevalence in epidemiological studies. N Am J Med Sci. 2012;4(2):107–8.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Muntner P, Hardy ST, Fine LJ, Jaeger BC, Wozniak G, Levitan EB, Colantonio LD. Trends in blood pressure control among US adults with hypertension, 1999–2000 to 2017–2018. JAMA. 2020;324(12):1190–200.

    Article  PubMed  Google Scholar 

  31. Black CL, O’Halloran A, Hung MC, Srivastav A, Lu PJ, Garg S, Jhung M, Fry A, Jatlaoui TC, Davenport E, et al. Vital signs: influenza hospitalizations and vaccination coverage by race and Ethnicity-United States, 2009-10 through 2021-22 influenza seasons. MMWR Morb Mortal Wkly Rep. 2022;71(43):1366–73.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Martinez BAF, Leotti VB, Silva GSE, Nunes LN, Machado G, Corbellini LG. Odds ratio or prevalence ratio? An overview of reported statistical methods and appropriateness of interpretations in Cross-sectional studies with dichotomous outcomes in veterinary medicine. Front Vet Sci. 2017;4:193.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Centers for Disease Control and Prevention. The 2022 National Health Interview Survey [https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2022/srvydesc-508.pdf]

  35. Montoy JCC, Ford J, Yu H, Gottlieb M, Morse D, Santangelo M, O’Laughlin KN, Schaeffer K, Logan P, Rising K, et al. Prevalence of symptoms ≤ 12 months after acute illness, by COVID-19 testing status among Adults - United States, December 2020-March 2023. MMWR Morb Mortal Wkly Rep. 2023;72(32):859–65.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22(4):e102–7.

    Article  PubMed  CAS  Google Scholar 

  37. Choi Y, Kim HJ, Park J, Lee M, Kim S, Koyanagi A, Smith L, Kim MS, Rahmati M, Lee H, et al. Acute and post-acute respiratory complications of SARS-CoV-2 infection: population-based cohort study in South Korea and Japan. Nat Commun. 2024;15(1):4499.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Xie Y, Xu E, Bowe B, Al-Aly Z. Long-term cardiovascular outcomes of COVID-19. Nat Med. 2022;28(3):583–90.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Xu E, Xie Y, Al-Aly Z. Long-term Gastrointestinal outcomes of COVID-19. Nat Commun. 2023;14(1):983.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Kim S, Lee H, Lee J, Lee SW, Kwon R, Kim MS, Koyanagi A, Smith L, Fond G, Boyer L, et al. Short- and long-term neuropsychiatric outcomes in long COVID in South Korea and Japan. Nat Hum Behav. 2024;8(8):1530–44.

    Article  PubMed  Google Scholar 

  41. Luo D, Mei B, Wang P, Li X, Chen X, Wei G, Kuang F, Li B, Su S. Prevalence and risk factors for persistent symptoms after COVID-19: a systematic review and meta-analysis. Clin Microbiol Infect. 2024;30(3):328–35.

    Article  PubMed  Google Scholar 

  42. Robertson MM, Qasmieh SA, Kulkarni SG, Teasdale CA, Jones HE, McNairy M, Borrell LN, Nash D. The epidemiology of long coronavirus disease in US adults. Clin Infect Dis. 2023;76(9):1636–45.

    Article  PubMed  Google Scholar 

  43. Hirschtick JL, Titus AR, Slocum E, Power LE, Hirschtick RE, Elliott MR, McKane P, Fleischer NL. Population-Based estimates of Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) prevalence and characteristics. Clin Infect Dis. 2021;73(11):2055–64.

    Article  PubMed  CAS  Google Scholar 

  44. Khullar D, Zhang Y, Zang C, Xu Z, Wang F, Weiner MG, Carton TW, Rothman RL, Block JP, Kaushal R. Racial/Ethnic disparities in Post-acute sequelae of SARS-CoV-2 infection in new York: an EHR-Based cohort study from the RECOVER program. J Gen Intern Med. 2023;38(5):1127–36.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

The study was partly supported by the Natural Science Foundation of Shaanxi Province (No.2019JQ-555; No.2020JM-408).

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Author notes

  1. Juanjuan Shi, Rui Lu, and Yan Tian contributed equally to this work.

Authors and Affiliations

  1. Department of Infectious Diseases, Second Affiliated Hospital of Xi’an Jiaotong University, 157 Xiwu Road, Xi’an, 710004, China

    Juanjuan Shi, Rui Lu, Yan Tian, Fengping Wu, Xiaozhen Geng, Song Zhai, Xiaoli Jia, Shuangsuo Dang & Wenjun Wang

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  1. Juanjuan Shi
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Contributions

Study design: Juanjuan Shi, Shuangsuo Dang, and Wenjun Wang; Data collection: Juanjuan Shi, Rui Lu, and Yan Tian; Data analysis: Juanjuan Shi, Rui Lu, Yan Tian, Fengping Wu, and Xiaozhen Geng; Writing: Juanjuan Shi and Wenjun Wang; Revision: Song Zhai, Xiaoli Jia, Shuangsuo Dang, and Wenjun Wang.

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Correspondence to Wenjun Wang.

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Shi, J., Lu, R., Tian, Y. et al. Prevalence of and factors associated with long COVID among US adults: a nationwide survey. BMC Public Health 25, 1758 (2025). https://doi.org/10.1186/s12889-025-22987-8

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BMC Public Health

ISSN: 1471-2458

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