Abstract
Objective
To explore the association between human papillomavirus (HPV) vaccination and risk of coronavirus disease 2019 (COVID-19). Specifically, our study aimed to test the hypothesis that HPV vaccination may also induce trained immunity, which would potentially reduce the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and improve clinical outcomes.
Background
Several vaccines have been reported to trigger non-specific immune reactions that could offer protection from heterologous infections. A recent case report showed that verruca vulgaris regressed after COVID-19, suggesting a possible negative association between COVID‐19 and HPV infection.
Methods
We enrolled 57,584 women with HPV vaccination and compared them with propensity score-matched controls who never received HPV vaccination in relation to the risk of COVID‐19 incidence. Cox proportional hazard regression analysis was conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses stratified by age, race, comorbid asthma, and obesity were performed.
Results
The risk of COVID-19 incidence was significantly lower in those who had recently received the HPV vaccine (within 1 year after HPV vaccination, aHR: 0.818, 95% CI 0.764–0.876; within 1–2 years after HPV vaccination, aHR: 0.890, 95% CI 0.824–0.961). Several limitations were recognized in this study, including residual confounding, problems of misclassification due to the use of electronic health record data, and that we were unable to keep track of the patients' HPV infection status and the HPV antibody levels in those who had received the vaccine.
Conclusions
Recent HPV vaccination was associated with a lower risk of incident COVID-19 and hospitalization. Based on the promising protective effect of HPV vaccine shown in this study, these findings should be replicated in an independent dataset. Further studies are needed to provide a better understanding of the underlying mechanisms and the differences in risks among 2-, 4-, or 9-valent HPV vaccine recipients.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40265-023-01867-8.
Key Points for Decision Makers
| Our study demonstrated that HPV-vaccinated women within 1 year and within 1–2 years before the index date exhibited a significantly lower risk of incident COVID-19 across a 1-year follow-up period. |
| Human papillomavirus vaccination was associated with a lower risk of incident COVID-19, and the effect was particularly evident in subgroups of individuals who were aged < 15 and ≥ 21 years, Asians, with comorbid asthma, and non-obese people. |
Introduction
Over the past 3 years, a global outbreak of coronavirus disease 2019 (COVID-19), brought on by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has grown to be a significant public health concern [1]. The clinical manifestations of COVID-19 range from asymptomatic or moderate symptoms, such as mild pneumonia, to life-threatening complications, such as severe pneumonia with respiratory failure, acute respiratory distress syndrome (ARDS), multi-organ failure, and death [2–5]. While the consensus among experts is that an effective COVID-19 vaccine is critical to minimize the consequences of the COVID-19 pandemic [6], only around 60% of the global population was fully vaccinated as of June 2022 [7].
Human papillomaviruses (HPV) are small double-stranded DNA viruses and certain HPV species have been associated with a variety of diseases, including several cancers and autoimmune diseases [8–11]. Human papillomaviruses vaccination is considered a safe and effective measure to prevent the infection and further complications [12, 13]. According to a recent case report, verruca vulgaris, a common benign HPV lesion, regressed after COVID-19, suggesting a possible negative correlation between COVID‐19 and HPV infections [14]. There is growing evidence that the innate immune system may be able to acquire trained immunity, which works against a wide range of infectious diseases and possibly for extended periods of time [15, 16]. The potential nonspecific protection against heterologous infections has been demonstrated in several studies, including those on bacillus Calmette–Guérin (BCG), measles, oral polio, and influenza vaccines [17, 18]. Although the BCG vaccine has been linked to trained immunity and protection against respiratory tract infections through the general long-term stimulation of innate immune mechanisms, it is still debatable if the BCG vaccine can prevent COVID-19 [19]. According to ecological studies, COVID-19 infections and mortality are lower in nations and areas where the BCG vaccination is required for citizens [20]. Likewise, countries using oral polio vaccine were found to have a lower incidence of COVID-19 when compared to those using only inactivated polio vaccine [21]. Mysore et al. also reported that measles-mumps-rubella (MMR) and tetanus-diphtheria-pertussis (Tdap) memory T cells reactivated by SARS-CoV-2 may provide protection against severe COVID-19 [22]. A recent study by Bruxvoort et al. showed that recombinant adjuvanted zoster vaccine was associated with a lower risk of COVID-19 diagnosis and lower risk of hospitalization [23]. Furthermore, Hosseini-Moghaddam et al. revealed that among people aged ≥66 years, influenza vaccination was associated with a decreased risk of SARS-CoV-2 infection [24]. Nevertheless, it remains unclear if these vaccinations induce trained immunity, particularly for COVID-19 protection [25].
We hypothesized that HPV vaccination may induce trained immunity that would potentially reduce the risk of SARS-CoV-2 infection and improve clinical outcomes. To the best of our knowledge, little research has evaluated the risk of COVID-19 and clinical outcomes in individuals with HPV vaccination. We conducted this matched cohort study to investigate the association between HPV vaccination and COVID-19, using a large, multicenter, electronic health-record network.
Methods
Data Source
The data used in this study were extracted from the TriNetX network, a global health collaborative clinical research network that collects real-time electronic data from medical records [26]. This network provides access to electronic medical records (EMR; including diagnoses, procedures, medications, laboratory test results, and genomic information) of approximately 250 million individuals from 120 global health care organizations (HCOs) and is one of the largest global COVID-19 datasets. TriNetX has been used in numerous studies to assess the risks, patterns, clinical features, and consequences of COVID-19 [27–30]. In this study, we utilized the US Collaborative Network, a subset of the TriNetX database, which includes 49 HCOs in the USA. The most up-to-date and integral data that were accessible in TriNetX in June 2022, when we designed and conducted the study, contained the information as of March 2022. Therefore, data used in our primary analysis were collected and analyzed as of March 2022. The study period in our primary analysis was from January 1st 2020 through March 31st 2022.
Study Population
The study population included female patients aged 89 years and younger with at least two medical visits documented in their EMR in the TriNetX Network between January 1st 2020 and March 31st 2022. The first visit during the study period was referred to as the index date. We excluded patients who had received any dose of the COVID-19 vaccine, in order to mitigate any potential confounding effects related to COVID-19 vaccination. These patients were identified through their documented vaccination history in the EMR (Supplementary Table S1). We also excluded patients who had been diagnosed with COVID-19 or neoplasms prior to the index date. Participants were divided into two cohorts based on whether or not HPV vaccination was documented in their EMR in the TriNetX Network. The selection process was depicted in Supplementary Figure S1. The HPV-vaccinated cohort consisted of participants who had received a 2-valent, 4-valent, or 9-valent HPV vaccine prior to the index date. The HPV vaccination was identified based on the following current procedural terminology (CPT) codes: 2-valent (90650), 4-valent (90649), and 9-valent (90651). To further explore how the effect of vaccine may vary over time, we separately assessed the risk of outcomes in terms of four different lengths of look-back time of HPV vaccination, namely: (1) within 1 year before the index date, (2) within 1–2 years before the index date, (3) within 2–3 years before the index date, and (4) within 3–5 years before the index date (Supplementary Fig. S2). In comparison, the unvaccinated cohort had no HPV vaccination documented in their EMR in the TriNetX Network.
Outcomes
We identified the incidence of COVID-19 as the main outcome of our study by positive results in SARS-CoV-2-related viral RNA or IgM/IgG antibody tests, or the ICD-10 code U07.1 (COVID-19, virus identified) (details in Supplementary Table S2). We used hospitalization and all-cause mortality as our secondary outcomes. Hospitalization was identified by the International Classification of Diseases (ICD) procedure codes (1013661, 1013659, 1013668, or 1013729), the CPT codes (99221–99223, 99231–99233), or an inpatient encounter. The health care organizations can indicate whether a patient is known to be deceased at the organization. Mortality data were accurately recorded for patients who passed away during their stay in the hospital. Some health care organizations in the network have their data linked with the Datavant population mortality information, which are based on Social Security Administration data, obituary data, and some private claims data. We explored the risk of outcomes in both cohorts in terms of different follow-up periods.
Covariates
Demographic variables included age, race, problems related to housing and economic circumstances (Z59), problems related to education and literacy (Z55), problems related to employment and unemployment (Z56), and occupational exposure to risk factors (Z57). The comorbidities included hypertension (ICD-10 I10), ischemic heart diseases (ICD-10 code I20–I25), cerebrovascular diseases (ICD-10 code I60–I69), overweight and obesity (ICD-10 code E66), hyperlipidemia (ICD-10 code E78.5), type 2 diabetes mellitus (ICD-10 code E11), chronic lower respiratory diseases (ICD-10 code J40–J47), chronic kidney diseases (ICD-10 code N18), liver diseases (ICD-10 code K70–K77), nicotine dependence (ICD-10 code F17, used as a proxy variable for smoking status), sleep disorders (ICD-10 code G47), psychoactive substance use disorders (ICD-10 F10–F19), inflammatory polyarthropathies (ICD-10 code M05–M14), systemic connective tissue disorders (ICD-10 code M30–M36), spondylopathies (ICD-10 code M45–M49), non-infective enteritis and colitis (ICD-10 code K50–K52). For the presence of comorbidities, we looked back one year before the index date.
Statistical Analyses
We utilized the TriNetX platform and Advanced Analytics to conduct all data analysis. We used propensity score matching (PSM) to balance covariate patterns between the HPV-vaccinated and non-HPV-vaccinated cohorts in terms of age, race, socioeconomic status, and comorbidities, in order to reduce the impact of treatment selection bias [31]. Propensity score matching was performed based on greedy nearest neighbor matching with a caliper of 0.1 pooled standard deviations of the propensity scores in the aggregate. Covariate balance was assessed using standardized mean difference, and an absolute value of < 0.1 was considered a negligible difference in its distribution between the two cohorts. We used Kaplan–Meier analysis to estimate the probability of the outcomes among the two cohorts [32]. We used log-rank tests to indicate whether the survival curves were different between cohorts. We conducted Cox proportional hazard regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) [33]. Hazard ratios, 95% CIs, and the test for proportionality were calculated using R's Survival package v3.2–3 [34, 35]. The proportional hazard assumption was tested using the generalized Schoenfeld approach. All statistical tests were performed using the TriNetX platform with significance set at a 2-sided p < 0.05.
We further conducted subgroup analyses based on age (≤ 15 years, 16–20 years, and ≥ 21 years), race (White, Black/African American, Asian), comorbid asthma (with, without), and comorbid obesity (defined by body mass index [BMI] < 30 kg/m2, ≥ 30 kg/m2) to explore the differences between these subgroups in terms of the risk of incident COVID-19 over a 1-year follow-up period, comparing those who had received HPV vaccination within 1 year before the index date and those who had never received HPV vaccination. To verify the robustness and consistency of our findings, we further performed a sensitivity analysis using the same study design but with an extended study period between January 1st 2020 and December 31st 2022.
Ethical Statements
The TriNetX Analytics Network is compliant with the Health Insurance Portability and Accountability Act (HIPAA), the US federal law, which protects the privacy and security of health care data, and any additional data privacy regulations applicable to the contributing HCO. TriNetX is certified to the ISO 27001:2013 standard and maintains an Information Security Management System (ISMS) to ensure the protection of the health care data it has access to and to meet the requirements of the HIPAA Security Rule. The TriNetX Analytics Network was granted a waiver by the Western Institutional Review Board (WIRB) since it solely used aggregated counts and statistical summaries of de-identified data. The study protocol and survey instrument were approved by the Institutional Review Board of Chung Shan Medical University Hospital in Taiwan (CSMUH No: CS2-21176). The protocol for the research project conformed to the ethical norms and standards in the Declaration of Helsinki.
Results
Table 1 shows the baseline characteristics of the two cohorts. Before PSM, there was disparity between the HPV-vaccinated cohort (n = 57,584) and non-HPV-vaccinated cohort (n = 9,078,239) regarding age (mean age, 16.0 vs 39.3 years), percentage of Black/African American (22.0 vs 15.7%), and problems related to education and literacy (0.7 vs 0.1%). The HPV-vaccinated cohort had a lower percentage of all medical comorbidities listed in Table 1, except for overweight and obesity, and chronic lower respiratory diseases. After PSM, the difference between the two cohorts was within the acceptable range (standardized mean difference < 0.1).
Table 1.
Baseline characteristics of study participants before and after PSM
| Before PSM | After PSM | |||||
|---|---|---|---|---|---|---|
| HPV-vaccinated cohort (n = 57,584) | Non-HPV-vaccinated cohort (n = 9,078,239) | SMD | HPV-vaccinated cohort (n = 57,584) | Non-HPV-vaccinated cohort (n = 57,584) | SMD | |
| Age at index | ||||||
| Mean ± SD | 16.0 ± 6.1 | 39.3 ± 22.8 | 1.396 | 16.0 ± 6.1 | 16.0 ± 6.1 | 0.001 |
| Race, n (%) | ||||||
| White | 32,610 (56.6) | 5,298,677 (58.4) | 0.035 | 32610 (56.6) | 32635 (56.7) | 0.001 |
| Unknown | 9695 (16.8) | 2,047,588 (22.6) | 0.144 | 9695 (16.8) | 9768 (17.0) | 0.003 |
| Black or African American | 12,655 (22.0) | 1,425,346 (15.7) | 0.161 | 12655 (22.0) | 12648 (22.0) | < 0.001 |
| Asian | 2279 (04.0) | 254,373 (02.8) | 0.064 | 2279 (04.0) | 2276 (04.0) | < 0.001 |
| American Indian | 253 (00.4) | 41,676 (00.5) | 0.003 | 253 (00.4) | 205 (00.4) | 0.013 |
| Native Hawaiian | 92 (00.2) | 10,579 (00.1) | 0.012 | 92 (00.2) | 52 (00.1) | 0.020 |
| Socioeconomic status | ||||||
| Housing/economic circumstances problem | 515 (00.9) | 265,09 (00.3) | 0.079 | 515 (00.9) | 478 (00.8) | 0.007 |
| Problems related to education and literacy | 380 (00.7) | 5225 (00.1) | 0.101 | 380 (00.7) | 184 (00.3) | 0.049 |
| Employment and unemployment problems | 57 (00.1) | 8326 (00.1) | 0.002 | 57 (00.1) | 74 (00.1) | 0.009 |
| Occupational exposure to risk factors | 26 (00.0) | 1122 (00.0) | 0.019 | 26 (00.0) | 10 (00.0) | 0.016 |
| Comorbiditiesa | ||||||
| Hypertension | 652 (01.1) | 848,832 (09.4) | 0.375 | 652 (01.1) | 742 (01.3) | 0.014 |
| Ischemic heart diseases | 51 (00.1) | 158,347 (01.7) | 0.174 | 51 (00.1) | 77 (00.1) | 0.014 |
| Cerebrovascular diseases | 173 (00.3) | 110,442 (01.2) | 0.106 | 173 (00.3) | 186 (00.3) | 0.004 |
| Overweight and obesity | 4745 (08.2) | 429,835 (04.7) | 0.143 | 4745 (08.2) | 4763 (08.3) | 0.001 |
| Hyperlipidemia | 318 (00.6) | 365,423 (04.0) | 0.234 | 318 (00.6) | 299 (00.5) | 0.005 |
| Type 2 diabetes mellitus | 569 (01.0) | 373,216 (04.1) | 0.199 | 569 (01.0) | 803 (01.4) | 0.037 |
| Chronic lower respiratory diseases | 4903 (08.5) | 437,157 (04.8) | 0.149 | 4903 (08.5) | 4917 (08.5) | 0.001 |
| Chronic kidney diseases | 114 (00.2) | 117,384 (01.3) | 0.128 | 114 (00.2) | 178 (00.3) | 0.022 |
| Diseases of liver | 225 (00.4) | 89,426 (01.0) | 0.072 | 225 (00.4) | 300 (00.5) | 0.019 |
| Nicotine dependence (smoking) | 685 (01.2) | 249,775 (02.8) | 0.113 | 685 (01.2) | 653 (01.1) | 0.005 |
| Sleep disorders | 1946 (03.4) | 275,781 (03.0) | 0.019 | 1946 (03.4) | 1956 (03.4) | 0.001 |
| Psychoactive substance use disorders | 1179 (02.0) | 334,476 (03.7) | 0.098 | 1179 (02.0) | 1137 (02.0) | 0.005 |
| Inflammatory polyarthropathies | 105 (00.2) | 117,683 (01.3) | 0.130 | 105 (00.2) | 190 (00.3) | 0.029 |
| Systemic connective tissue disorders | 181 (00.3) | 73,473 (00.8) | 0.066 | 181 (00.3) | 323 (00.6) | 0.037 |
| Spondylopathies | 76 (00.1) | 148,797 (01.6) | 0.161 | 76 (00.1) | 135 (00.2) | 0.024 |
| Noninfective enteritis and colitis | 559 (01.0) | 100,222 (01.1) | 0.013 | 559 (01.0) | 577 (01.0) | 0.003 |
aAn absolute SMD value ≤ 0.10 indicates negligible difference between the two cohorts
Note: If the patient count was lower than 10, it was then replaced by 10. Note: HPV-vaccinated cohort was defined using the lookback time of HPV vaccination within 1 year before the index date; non-HPV-vaccinated cohort was defined as never having received an HPV vaccination
HPV human papillomavirus, PSM propensity score matching, SD standard deviation, SMD standardized mean difference
COVID-19 Incidence
Supplementary Table S3 demonstrates the risks of incident COVID-19 comparing those who received HPV vaccination and those who did not, stratified by different lengths of lookback time of HPV vaccination and follow-up period for outcomes. As presented in the forest plots (Fig. 1), we found significantly lower risks of incident COVID-19 in a 90-day follow-up period among those who received HPV vaccination within 1 year, within 1–2 years, and within 2–3 years before the index date (HR: 0.721, 0.821, and 0.821, respectively). When the length of follow-up period was extended to 180 days, significantly lower risks of incident COVID-19 were found among those who received HPV vaccination within 1 year and within 1–2 years before the index date (HR: 0.728 and 0.888, respectively). Similar patterns were found in the analysis with a 1-year follow-up period. As shown in the Kaplan–Meier curves (Fig. 2), significantly different risks of incident COVID-19 were found between those who did not receive HPV vaccination and those who received HPV vaccination within 1 year, and within 1–2 years before the index date (log-rank test, p < 0.001, and p = 0.002, respectively).
Fig. 1.
Forest plots showing HPV vaccination for the protection of COVID-19 incidence in terms of four different lengths of lookback time of HPV vaccination. HPV human papillomavirus
Fig. 2.
Kaplan–Meier survival curves for COVID-19 incidence in cohort without HPV vaccination and cohorts with HPV vaccination in terms of different lengths of lookback time. *Note: Non-HPV-vaccinated cohort was defined as never having received an HPV vaccination. HPV human papillomavirus
Clinical Outcomes
Hospitalization
We found lower risks of hospitalization throughout a 1-year follow-up period among those who received HPV vaccination within 5 years before the index date, when compared to those who did not receive HPV vaccination (Fig. 3 and Supplementary Table S4). Furthermore, the HRs appeared to increase as the lengths of look-back time of HPV vaccination and follow-up period increased.
Fig. 3.
Kaplan–Meier survival curves for hospitalization in cohort without HPV vaccination and cohort s with HPV vaccination in terms of different lengths of lookback time. *Note: Non-HPV-vaccinated cohort was defined as never having received an HPV vaccination. HPV human papillomavirus
All-cause Mortality
We found lower risks of mortality throughout a 1-year follow-up period among those who received HPV vaccination only within 1 year before the index date, when compared to those who did not receive HPV vaccination (HR: 0.260, 0.320, and 0.325, for follow-up period of 90 days, 180 days, and 1 year, respectively) (Fig. 4 and Supplementary Table S5).
Fig. 4.
Kaplan–Meier survival curves for mortality in cohort without HPV vaccination and cohorts with HPV vaccination in terms of different lengths of lookback time. *Note: Non-HPV-vaccinated cohort was defined as never having received an HPV vaccination. HPV human papillomavirus
Subgroup Analyses
Age
We further examined the risk of COVID-19 incidence in subgroups stratified by age (Supplementary Table S6 and Supplementary Fig. S3). Among subgroup who were aged ≤ 15 years and aged > 21 years, those who had received HPV vaccination within 1 year before the index date had a lower risk of incident COVID-19 across a 1-year follow-up period, when compared to the matched counterparts without HPV vaccination (HR: 0.871 and 0.752, respectively, log-rank test, p < 0.001).
Race
Among all racial subgroups, those who had received HPV vaccination within 1 year before the index date had a lower risk of incident COVID-19 across a 1-year follow-up period, when compared to the matched counterparts without HPV vaccination (log-rank test, p = 0.040, 0.024, 0.003 for White, Black/African American, and Asian respectively) (Supplementary Table S7 and Supplementary Fig. S4). Furthermore, the hazard ratios of incident COVID-19 across a 1-year follow-up period appeared to be lower among the Asian subgroup (HR: 0.617, 95% CI 0.447–0.851).
Asthma
Human papillomavirus vaccination was associated with lower risks of incident COVID-19 across a 180-day follow-up period among both subgroups with and without comorbid asthma (Supplementary Table S8 and Supplementary Fig. S5). When the follow-up period was extended to one year, the lower risk of COVID-19 remained significant among the subgroup with asthma (log-rank test, p = 0.014).
Obesity
A significant association between HPV vaccination and lower risk of incident COVID-19 across a 1-year follow-up period was only found in the subgroup without comorbid obesity (BMI < 30 kg/m2) (log-rank test, p < 0.001) (Supplementary Table S9 and Supplementary Fig. S6).
Sensitivity Analyses
In our sensitivity analysis using the same study design but with an extended study period (from January 1st 2020 through December 31st 2022), we found a similar pattern to the results from our primary analysis. We discovered significantly lower risks of incident COVID-19 in a 90-day follow-up period in those who received HPV vaccination within 1 year and within 1–2 years before the index date (HR: 0.833 and 0.851, respectively), when compared to their matched counterparts without HPV vaccination (Supplementary Table S10).
Discussion
In this population-based cohort study using the TriNetX Network and Advanced Analytics, we found lower risks of incident COVID-19 across the 1-year follow-up period in women who received an HPV vaccination within 2 years before the index date. In terms of clinical outcomes over a 1-year follow-up period, HPV vaccination within 5 years before the index date was associated with a lower risk of hospitalization, whereas HPV vaccination within 1 year before the index date was associated with a lower risk of mortality.
The definite mechanisms underlying the negative association between HPV vaccination and SARS-CoV-2 infection are unclear. A previous case report by Demirbaş et al. suggested a possible negative correlation between SARS-CoV-2 and HPV infections [12]. The immune reaction induced by SARS-CoV-2, as well as viral clearance via a delayed hypersensitive reaction, were postulated as explanations for the association. In contrast, our study found HPV vaccination to be associated with lower risks of incident COVID-19, implying a potential cross-protection effect. We hypothesized several theories to explain the findings of our study. Antibodies from memory B cells created against HPV might have the potential to modulate SARS-CoV-2 pathogenesis. Cross-reactive antibody responses were suggested in animal models that antibodies elicited by zika virus affect dengue virus pathogenesis via an antibody-dependent enhancement mechanism [36]. Although papillomaviruses and coronaviruses have little in common genetically, they may share similar traits in terms of transmission or life cycle. Studies are encouraged to explore the potential of HPV-infected epithelial cells being more susceptible to SARS-CoV-2 than HPV-negative cells [37]. On the other hand, ‘‘trained immunity” was proposed by Netea et al. to describe the features of innate immunity that provide immunological memory to innate host defense [38]. This increased non-specific response to a secondary infection mediated by the innate immune system is characterized as being independent of T and B cell responses and is largely dependent on innate immune cells such as macrophages or natural killer cells [39]. However, it is currently unknown if the HPV vaccine induces trained immunity, and further research is necessary for confirmation. Other plausible explanations include the vaccinated people's greater level of health literacy and awareness [40]. Also, HPV-vaccinated people may be more likely to follow COVID-19 prevention recommendations such as social distancing, personal hygiene practices, and use of personal protective equipment. Furthermore, according to our analysis stratified by different lengths of look-back time of HPV vaccination and follow-up period for outcomes, the risk of incident COVID-19 and adverse clinical outcomes appears to be lower among those with shorter lengths of look-back time of HPV vaccination and shorter follow-up periods, which suggested that the potential protective effect faded with time after the vaccination. Notably, such a fading effect would be consistent with that of trained immunity as reported by Kleinnijenhuis et al. [41].
In addition, stratified analysis revealed that HPV vaccination was associated with a decreased risk of incident SARS-CoV-2 infection in the subgroups of patients who were aged < 15 years and > 21 years. The finding of a significantly lower risk of COVID-19 in the two specific age groups among HPV-vaccinated people warrants more consideration and studies. The protection is less clear in those aged 16–20 years, although the prevalence of HPV infection is thought to be highest in this age range [42]. We also observed that the COVID-19 incidence of this group appeared to be lower than that of other age groups even without receiving HPV vaccination, and the potential effect of HPV vaccination on COVID-19 protection is relatively insignificant. Our findings further led us to hypothesize that HPV infection might also contribute to COVID-19 protection. Nevertheless, we are unable to keep track of HPV infection status of the patients or determine whether HPV infection induced trained immunity, which would have modified the potential effect of HPV vaccination on the incidence of COVID-19. From this point of view, we encourage more investigations to explore the association between HPV infection and incident COVID-19, along with their HPV vaccination status. In the analysis stratified by race, HPV vaccination was associated with a lower HR of SARS-CoV-2 infection among Asian participants, indicating that the effect of HPV vaccination may be more pronounced in Asians. The observed disparities across racial groups could be explained by different levels of exposure risk and availability to health care [43]. In the analysis stratified by comorbid asthma, the effect of HPV vaccination appeared to be more pronounced in those with asthma. This finding adds to the growing body of evidence suggesting potential lower risk of patients with type 2 asthma to develop COVID-19 [44]. In allergic asthma, reduced numbers of receptors in the host to which the SARS-CoV-2 binds for entry into the cell may confer protection against COVID-19 [45]. Also, the use of inhaled corticosteroids may lead to a decreased level of ACE2 and transmembrane protease serine 2 gene expression in asthmatic patients [46]. More research into type 2 immune regulation in COVID-19 pathogenesis, as well as the interaction with the HPV vaccine, is needed. In the analysis stratified by comorbid obesity, the significant association between HPV vaccination and reduced risk of SARS-CoV-2 infection was found only in those who were not obese (BMI < 30 kg/m2), suggesting that the potential effect of HPV vaccination was not consistent across different weight groups. According to prior literature, obese individuals are potentially more susceptible to contracting the SARS-CoV-2 virus, since they have impaired innate and adaptive immune responses, as well as a reduced response to immunization [47]. The observed phenomenon in obese individuals could be because the HPV vaccine is deposited in the fat rather than in the muscle, as was shown in the study by Cockshott et al. [48].
Strengths and Limitations
This study has a number of strengths. We leveraged the TriNetX Network, which is one of the largest global COVID-19 databases that covers diverse geographic locations, age groups, racial groups, income levels, and insurance types, to build a large national cohort to investigate the association between HPV vaccination and incident COVID-19. Also, the validity of HPV vaccination and diagnosis of COVID-19 are reliable in this dataset. We applied propensity score matching to balance the cohorts with and without HPV vaccination. When matching for age, socioeconomic determinants of health, and comorbid medical conditions help to eliminate differences in COVID-19 risk among the two cohorts. Furthermore, we conducted subgroup analyses to verify the potential effect measure modification of age, asthma, and obesity on such relationships.
This study has several limitations. First, the observational, retrospective design of this study may introduce biases in case selection, testing, and follow-up. Second, the use of electronic health record data may be susceptible to problems of under-/over-/mis-diagnosis and may not account for all potential confounding factors. Third, inpatient records do not contain data on time-series HPV antibody levels in vaccinated individuals, and therefore we could not correlate the antibody data. Also, we are not able to keep track of HPV infection status of the patients. Last, since the database we used restricted the source population to the patients who had medical insurance and had health care visits in the USA during the study period, the generalizability of our conclusions is limited. The generalizability of our findings needs to be validated in other populations or in other national data resources, since the TriNetX data-set contained patients from large academic medical institutions.
In conclusion, our study suggests that HPV vaccination was associated with a lower risk of incident COVID-19, and the effect was particularly evident in subgroups of individuals who were aged ≤ 15 years and ≥ 21 years, Asians with comorbid asthma, and non-obese people. More research is required to investigate vaccine-induced nonspecific immunity for possible prevention of the pandemic.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We are grateful to TriNetX for providing free consulting service and data resources for this work.
Declarations
Funding
This work was supported by funding from Chung Shan Medical University Hospital (grant number CSH-2022-A-023). The funders had no role in designing and conducting the study, the collection, analysis, interpretation of the data, and the approval of the manuscript.
Conflict of interest
The authors, Chen, Wang, Hung, Hartman, Chang and Wei, declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Data availability
As an HCO member of TriNetX, Chung Shan Medical University Hospital (CSMUH) has access to the de-identified data in TriNetX. All data related to this study have been presented in the article. The data that support the findings of this study are available from the TriNetX Analytics Network. https://live.trinetx.com/tnx/study/119672/analytics/641818164d2f485208997758/outcomes/results.
Code availability
Not applicable.
Author contributions
Study conception and design: Y-MH, RC and JC-CW. Acquisition of data: S-IW and JJH. Analysis and interpretation of data: Y-MH, TY-TC, S-IW, RC and JC-CW. Writing (original draft preparation): Y-MH, TY-TC and S-IW. Writing (review and editing): RC and JC-CW.
Footnotes
The original article has been updated: Due to co-author affiliation update.
Thomas Yen-Ting Chen, Shiow-Ing Wang and Yao-Min Hung contributed equally as first authors. Renin Chang and James Cheng-Chung Wei contributed equally as corresponding authros.
Change history
5/19/2023
A Correction to this paper has been published: 10.1007/s40265-023-01892-7
Contributor Information
Thomas Yen-Ting Chen, Email: thomas19941214@gmail.com.
Shiow-Ing Wang, Email: shiowing0107@gmail.com.
Yao-Min Hung, Email: ymhung1@gmail.com.
Joshua J. Hartman, Email: Joshua.Hartman@trinetx.com
Renin Chang, Email: rhapsody1881@gmail.com.
James Cheng-Chung Wei, Email: wei3228@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
As an HCO member of TriNetX, Chung Shan Medical University Hospital (CSMUH) has access to the de-identified data in TriNetX. All data related to this study have been presented in the article. The data that support the findings of this study are available from the TriNetX Analytics Network. https://live.trinetx.com/tnx/study/119672/analytics/641818164d2f485208997758/outcomes/results.