Association of COVID-19 vaccine intake with diagnosis, hospitalization, and oxygenation/ventilation: A longitudinal analysis, 2021-2022, Japan

Association of COVID-19 vaccine intake with diagnosis, hospitalization, and oxygenation/ventilation: A longitudinal analysis, 2021-2022, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of COVID-19 vaccine intake with diagnosis, hospitalization, and oxygenation/ventilation: A longitudinal analysis, 2021-2022, Japan Satomi Odani, Hitoshi Honda, Takahiro Tabuchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3925778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Limited national-level data exist on COVID-19 vaccine effectiveness against infection outcomes based on individual characteristics. We analyzed 19,482 individuals aged 16-81 who responded to baseline (2021) and follow-up (2022) Internet-based surveys. COVID-19 vaccine intake (0/1/2+ doses) during the follow-up period was examined, and outcomes included COVID-19 diagnosis, hospitalization, and oxygenation/ventilation. Adjusted prevalence ratios (APRs) were computed using Poisson regression models, controlling for baseline characteristics including precautionary measures practiced. 81.6% of respondents received ≥1 dose of COVID-19 vaccine during the follow-up period. Among those without COVID-19 history at baseline (N=19,182), 10.9% and 6.6% reported COVID-19 diagnosis within the past year and past 2 months at follow-up, respectively. Respondents who received 1 or 2+ doses were less likely to be diagnosed in the past year (APR=0.76 and 0.43) and past 2 months (APR=0.87 [not statistically significant] and 0.51) compared to those who did not. Among 1,999 respondents diagnosed with COVID-19 during the follow-up, those with 1 or 2+ vaccine doses showed lower likelihoods of hospitalization (APR=0.78 and 0.86) and receipt of oxygenation/ventilation (APR=0.87 and 0.61), although not statistically significant. Considering the interaction of socioeconomic and behavioral characteristics, the results supported the protective effect of the COVID-19 vaccine against infection. Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors Figures Figure 1 INTRODUCTION The COVID-19 pandemic has posed significant challenges to public health systems worldwide, necessitating the development and implementation of effective vaccination strategies. In Japan, the government launched a national COVID-19 vaccination campaign in February 2021, initially targeting high-priority groups such as healthcare professionals, rescue workers, and public health center employees [ 1 ]. Subsequently, the campaign expanded to include individuals aged 65 or older in April 2021 and the general population with specific priority given to those with underlying health conditions [ 1 ]. This vaccination campaign progressed rapidly, with over 70% of Japanese residents having completed the recommended two-dose immunization course by the end of October 2021 [ 2 ]. To further enhance protection against COVID-19, booster doses were introduced in December 2021 [ 1 ]. The booster rollout aimed to provide an additional layer of immunity, particularly for individuals who had completed the primary vaccination series. By the end of May 2022, approximately 60% of the entire population had received a third vaccine dose [ 2 ]. These vaccination efforts have played a crucial role in mitigating the impact of the pandemic and reducing the incidence of COVID-19 in Japan. Assessing the effectiveness of the COVID-19 vaccines and understanding the factors associated with vaccine uptake and their impact on disease outcomes require comprehensive data sources. In Japan, however, medical care information is maintained within the health insurance system, while the vaccination campaign is administered through a separate system governed by the Immunization Act. Moreover, the use of governmental vaccination records for research purposes is not permitted. The absence of data linkage platform at the national level has posed challenges for evaluating the vaccine effectiveness and its interplay with individual characteristics, including demographic, socioeconomic, clinical, and behavioral characteristics. A few case-control studies and subnational-level population-based studies have been conducted to investigate the efficacy of COVID-19 vaccines in Japanese population [ 3 – 8 ]. Findings from these previous studies consistently showed high effectiveness in reduction in overall incidence of COVID-19 and the severity of illness due to COVID-19, over 80% for individuals fully vaccinated with two doses of the vaccine during the time when the delta variant was dominant [ 5 – 7 ]. During the omicron-dominant period, although the vaccine efficacy was reported to be lower than that during the Delta-variant period, the estimates of vaccine effectiveness against infection ranged from 57–74% for those who received the booster [ 3 , 6 ]. Although these studies provided consistent results regarding vaccine efficacy, it is important to conduct a comprehensive assessment that takes into account the unique population characteristics in Japan on a large scale. Given that preventive behaviors such as wearing masks and avoiding risky situations were commonly practiced in Japan, understanding the interplay of these factors and their impact on promoting vaccination and preventing infections is crucial. Therefore, this study aims to contribute to the existing knowledge by providing evidence on the effectiveness of COVID-19 vaccines and investigating the associations between individual characteristics, vaccination, and infection outcomes in Japan. We considered vaccine intake as an individual's practice of a preventive measure and took into account the practice of other preventive behaviors, as well as a variety of individual characteristics, allowing us to explore a broader perspective on the impact of preventive measures during the observed period. MATERIALS AND METHODS Data This study involved a longitudinal analysis of the 2021 and 2022 waves from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, internet-based, self-reported survey targeting individuals aged 15 years or older. The initial JACSIS survey was conducted during August and September 2020, using a private vendor, Rakuten Insight Inc., which has 2.3 million panelists [ 9 ]. Participants were randomly selected from various demographic (including age, sex, and place of residence) and socioeconomic subgroups (including education, housing, and marital status), as defined by the Japan census. All participants were required to provide online informed consent [ 10 ]. Annual follow-up surveys were administered during August-September in 2021 and 2022, with sample replenishment. The survey period coincided with the end of the fifths wave of COVID-19 epidemic (July 1-September 30, 2021), driven by the Delta variant, and the sixth (January 1 – March 31, 2022) and seventh waves (July 1 – September 30, 2022) in which the Omicron variant was dominant. A total of 32,000 and 31,000 individuals responded to the 2021 and 2022 waves, respectively. From this pool, we excluded 3,370 and 2,825 individuals who provided irregular answers (from the 2021 and 2022 respondents, respectively) using a set of predefined questions incorporated into the questionnaire [ 10 ]. For example, individuals who responded all multiple-choice items for illegal substance use (7 items) or presence of chronic conditions (15 items), those who answered with the same number over an entire set of questions, or those who chose a wrong answer for the question “ Choose the second item from the bottom ” were excluded. Ultimately, the analysis included 19,482 individuals who responded to both waves. The selection process of the analytical sample is depicted in Fig. 1 . The Research Ethics Committee of the Osaka International Cancer Institute approved this study (no. 20084-9) in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects. Exposure The exposure variable in this analysis was the COVID-19 vaccine intake during the observation window. We calculated the difference in the number of completed doses between baseline (2021) and the 1-year follow-up (2022) for each participant. The doses were categorized as 0, 1, or 2 + based on this calculation. At baseline, respondents were asked to indicate their COVID-19 vaccine status using the following response choices: "Received 2 doses," "Received 1 dose (plan to receive the second dose)/(do not plan to receive the second dose)/(received a 1-dose type vaccine)," "Never received vaccine (cannot get a vaccine due to allergies or other health conditions)/(want to get a vaccine/already have an appointment for a vaccine)/(prefer to wait)/(do not want to get a vaccine)." At the 1-year follow-up, respondents were asked the same question with the following response categories: "Received 4 doses/3 doses/2 doses/1 dose" and "Never received vaccine (cannot get a vaccine due to allergies or other health conditions)/(prefer to wait)/(do not need to get a vaccine)/(do not want to get a vaccine)." Outcome We assessed three outcomes: COVID-19 diagnosis, COVID-19-induced hospitalization, and receipt of oxygen supplementation (referred to as “oxygenation” hereinafter) and/or mechanical ventilation (referred to as “ventilation” hereinafter) during hospitalization. COVID-19 infection was assessed by asking participants whether and when (in the past 2 months, 2 months to 1 year ago, or more than 1 year ago) they were diagnosed with COVID-19. We created dichotomous variables (yes/no) for the past-year infection and past-2-month infection separately. Participants were also asked with separate questions whether they were admitted to a hospital and received oxygenation and/or ventilation due to COVID-19 infection. We created dichotomous variables (yes/no) for past-year hospitalization and receipt of oxygenation and/or ventilation among hospitalized individuals. Independent variables The independent variables assessed in this study included sex, age, education, employment, presence of underlying conditions (chronic respiratory illness, cardiac disease, kidney disease, cancer, diabetes, hypertension, and body mass index ≥ 30), smoking status, current use of heated tobacco products (HTPs), alcohol drinking, fear of COVID-19-induced death (yes/no) [ 11 ], and COVID-19 preventive behaviors. Participants were asked whether they wore a mask when other people were present, with response options of "always" or "sometimes/rarely/never." Preventive behavior was further assessed regarding the avoidance of the "three Cs" (closed spaces, crowded places, and close-contact settings) which was recommended by the Japanese government [ 12 ]. Separate questions were asked for each "C," and the number of times participants answered "always" (vs. "sometimes/rarely/never") was summed and categorized into 0, 1, 2, or 3. Vaccination status at baseline was assessed as the number of doses that had been received before/at baseline (0/1/2). Statistical analysis To account for potential selection bias of the internet-based sample and nonresponse bias, we applied inverse probability weighting (IPW) to weight the data. Logistic regression models were fitted to compute propensity scores for "being an Internet survey respondent" using a nationally representative sample from the Comprehensive Survey of Living Conditions [ 13 ] as the reference. We controlled for demographic, socioeconomic, and behavioral characteristics (e.g., sex, age, residing region, marital status, education, employment, health status, tobacco product use) in the propensity score calculation. All analyses were weighted. Additional details regarding the IPW method are reported elsewhere [ 10 , 14 ]. We employed multivariable Poisson regression to investigate factors associated with COVID-19 vaccine intake during the observation window. Furthermore, we analyzed the associations between vaccine intake and COVID-19 infection among participants with no history of COVID-19 at baseline (N = 19,182), as well as the associations with hospitalization and receipt of oxygenation and/or ventilation among those diagnosed with COVID-19 during the observation window (N = 1,999). Adjusted prevalence ratios (APRs) and 95% confidence intervals (CIs) were estimated, controlling for the aforementioned independent variables. These variables were either identified in the univariate analysis with a significance level of p < 0.1 or were deemed to have clinical and behavioral relevance in the context of vaccine intake. We assessed multicollinearity among independent variables using variance inflation factors, which were confirmed to be below 10. All analyses were performed using R version 4.2.2. RESULTS Table 1 presents the baseline characteristics of the respondents and factors associated with COVID-19 vaccine intake during the observation window (N = 19,482). Overall, the majority of respondents (72.9%) had received two doses of the COVID-19 vaccine at baseline. 90.9% reported always wearing a mask when in the presence of other people. 27.4% reported always avoiding all of the "three Cs" (closed spaces, crowded places, and close-contact settings), and 76.5% partially practiced the avoidance of the “three Cs”. 40.1% reported a fear of death from COVID-19. 81.6% reported receiving at least one dose of the COVID-19 vaccine between baseline and the 1-year follow-up, with 38.4%, 40.2%, 2.9%, and 0.1% having received one, two, three, and four doses, respectively. At follow-up, while a majority of respondents (76.1%) had completed booster vaccination (received 3 + doses), 11.6% had no vaccination history, and 12.2% had received one or two doses (data not shown). The most significant association with COVID-19 vaccine intake during the observation window was seen with the baseline vaccine status. Those who had received one or two doses before/at baseline were more likely to receive additional doses (APR = 2.64, 95% CI = 2.45–2.84; APR = 2.38, 95%CI = 2.21–2.56) than those without vaccination history. Other groups that had a higher likelihood of vaccine intake during the observation window included those with a fear of COVID-19-induced death (APR = 1.05, 95% CI = 1.03–1.07) compared to those without the fear, those with underlying health conditions (APR = 1.03, 95% CI = 1.01–1.05) compared to those without them, current (past 30-day) alcohol drinkers (APR = 1.03, 95% CI = 1.01–1.05) compared to non-current/never drinkers, the elderly aged 65 + years (APR = 1.07, 95% CI = 1.05–1.10) compared to younger individuals, and self-employed (APR = 1.03, 95% CI = 1.002–1.06) and unemployed (APR = 1.03, 95% CI = 1.004–1.06) individuals compared to full-time workers. Groups with a lower likelihood of vaccine intake included those who did not avoid any of the "three Cs" (APR = 0.97, 95% CI = 0.94–0.99) and those who partially practiced the measure (avoided two of the "three Cs") (APR = 0.97, 95%CI = 0.95–0.99) compared to those who avoided all of the "three Cs", current smokers (APR = 0.96, 95%CI = 0.93–0.99) compared to never smokers, and part-time workers (APR = 0.95, 95%CI = 0.92–0.99) compared to full-time workers. Table 1 Baseline characteristics and one-year vaccine intake, 2021–2022, Japan Distribution Received 1 + dose of the COVID-19 vaccine during the 1-year observation window Baseline characteristics N (%) % (SE) APR (95% CI) Total 19482 (100.0%) 81.6 (0.5) - Vaccination status (number of COVID-19 vaccine doses completed before/at baseline) 0 3155 (18.1%) 36.5 (1.4) Ref. 1 1579 (9.0%) 97.4 (0.8) 2.64 (2.45–2.84) 2 14748 (72.9%) 91.0 (0.4) 2.38 (2.21–2.56) Mask-wearing No 1558 (9.1%) 67.5 (2.0) 0.95 (0.90-1.001) Yes 17924 (90.9%) 83.0 (0.5) Ref. Avoidance of risky situations (number of the "three Cs" avoided) 0 4162 (23.5%) 76.1 (1.1) 0.97 (0.94–0.99) 1 4399 (22.8%) 81.0 (0.9) 0.98 (0.95–1.002) 2 5221 (26.2%) 82.9 (0.9) 0.97 (0.95–0.99) 3 5700 (27.4%) 85.6 (0.7) Ref. Fear of COVID-19-induced death No 11780 (59.9%) 78.8 (0.6) Ref. Yes 7702 (40.1%) 85.8 (0.6) 1.05 (1.03–1.07) Underlying medical conditions Not present 13166 (67.1%) 79.1 (0.6) Ref. Present 6316 (32.9%) 86.8 (0.8) 1.03 (1.01–1.05) Ever diagnosed with COVID-19 No 19182 (98.3%) 81.7 (0.5) Ref. Yes 300 (1.7%) 76.3 (4.4) 1.01 (0.90–1.14) Smoking status Never 10667 (55.8%) 80.9 (0.6) Ref. Former 6023 (28.7%) 85.1 (0.8) 0.99 (0.97–1.01) Current 2792 (15.4%) 77.6 (1.2) 0.96 (0.93–0.99) Current use of heated tobacco products No 17817 (90.6%) 81.6 (0.5) Ref. Yes 1665 (9.4%) 81.7 (1.5) 1.02 (0.98–1.06) Current alcohol drinking No 9173 (62.0%) 80.0 (0.7) Ref. Yes 10309 (38.0%) 84.2 (0.6) 1.03 (1.01–1.05) Sex Female 9751 (50.5%) 81.7 (0.7) Ref. Male 9731 (49.5%) 81.5 (0.6) 1.01 (0.99–1.03) Age, years old 16–64 13870 (72.6%) 78.0 (0.6) Ref. 65+ 5612 (27.4%) 91.1 (0.7) 1.07 (1.05–1.10) Education Some college/college or higher 13875 (47.8%) 81.9 (0.5) Ref. High school or less 5506 (52.2%) 81.6 (0.7) 0.99 (0.97–1.01) Employment status Full time 7199 (35.8%) 82.2 (0.7) Ref. Self-employed 1384 (6.7%) 72.5 (2.0) 1.03 (1.002–1.06) Part time 3798 (21.1%) 80.3 (1.0) 0.95 (0.92–0.99) Unemployed 7101 (36.4%) 83.5 (0.8) 1.03 (1.004–1.06) Abbreviations: APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, three Cs=closed spaces, crowded places, and close-contact settings, SE=standard error. Note: Data were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis Table 2 presents the association between COVID-19 vaccine intake during the observation window and COVID-19 diagnosis among those with no COVID-19 history at baseline (N = 19,182). At the 1-year follow-up, 10.9% (n = 1,999) and 6.6% (1,192) reported having been diagnosed with COVID-19 in the past year and in the past 2 months. Past-year infection was significantly less likely among those who received one or 2 + doses of the COVID-19 vaccine during follow-up (APR = 0.76, 95% CI = 0.60–0.97; APR = 0.43, 95% CI = 0.34–0.55, respectively) compared to those who did not receive a vaccine. In particular, receipt of 2 + vaccine doses was significantly associated with reduced likelihood of COVID-19 diagnosis in the past 2 months (APR = 0.51, 95%CI = 0.36–0.71). Self-employed and unemployed individuals were also less likely to report past-year COVID-19 diagnosis (APR = 0.52, 95% CI = 0.39–0.70; APR = 0.52, 95% CI = 0.42–0.64, respectively) and the past-2-month diagnosis of COVID-19 (APR = 0.54, 95% CI = 0.38–0.78; APR = 0.58, 95% CI = 0.45–0.74, respectively) than full-time workers. Groups with a higher likelihood of COVID-19 infection were those who had received one dose of the COVID-19 vaccine at baseline (APR = 1.67, 95% CI = 1.21–2.28 for past-year infection) compared to those without vaccination, those with underlying health conditions (APR = 1.23, 95% CI = 1.05–1.43 for past-year infection) compared to those without them, and former smokers (APR = 1.26, 95% CI = 1.02–1.56 for past-2-month infection) compared to never smokers. Table 2 Percentage and adjusted ratio of COVID-19 diagnosis during the 1-year observation window among infection-naïve individuals, 2021–2022, Japan Distribution COVID-19 diagnosis during the 1-year observation window COVID-19 diagnosis in the past 2 months Characteristics N (%) % (SE) APR (95%CI) % (SE) APR (95%CI) Total 19182 (100.0%) 10.9 (0.4) - 6.6 (0.3) - 1-year COVID-19 vaccine intake (number of doses received during follow-up) 0 3130 (18.3%) 14.7 (1.0) Ref. 8.2 (0.8) Ref. 1 7475 (39.3%) 13.5 (0.7) 0.76 (0.60–0.97) 8.4 (0.5) 0.87 (0.62–1.23) 2+ 8419 (42.4%) 6.5 (0.5) 0.43 (0.34–0.55) 4.3 (0.4) 0.51 (0.36–0.71) Vaccination status at baseline (number of completed doses of the COVID-19 vaccine) 0 3065 (18.0%) 11.2 (0.9) Ref. 6.5 (0.7) Ref. 1 1497 (8.5%) 15.6 (1.5) 1.67 (1.21–2.28) 9.3 (1.3) 1.53 (0.99–2.35) 2 14620 (73.4%) 10.2 (0.4) 1.05 (0.81–1.36) 6.3 (0.3) 1.04 (0.72–1.50) Mask-wearing No 1440 (8.5%) 14.9 (1.6) 0.91 (0.72–1.15) 7.8 (1.3) 1.07 (0.77–1.50) Yes 17742 (91.5%) 10.5 (0.4) Ref. 6.5 (0.3) Ref. Avoidance of risky situations (number of the "three Cs" avoided) 0 4024 (23.1%) 13.0 (0.9) 1.04 (0.86–1.27) 7.7 (0.7) 1.11 (0.86–1.43) 1 4331 (22.8%) 12.0 (0.8) 1.13 (0.93–1.37) 7.5 (0.7) 1.23 (0.96–1.58) 2 5172 (26.5%) 9.0 (0.6) 0.93 (0.77–1.12) 6.0 (0.5) 1.05 (0.83–1.33) 3 5655 (27.7%) 9.8 (0.7) Ref. 5.6 (0.5) Ref. Fear of COVID-19-induced death No 11575 (59.8%) 11.1 (0.5) Ref. 7.0 (0.4) Ref. Yes 7607 (40.2%) 10.5 (0.6) 1.00 (0.87–1.15) 6.1 (0.4) 0.89 (0.75–1.07) Underlying medical conditions Not present 13024 (67.5%) 11.1 (0.4) Ref. 6.6 (0.3) Ref. Present 6158 (32.5%) 10.3 (0.7) 1.23 (1.05–1.43) 6.6 (0.6) 1.23 (0.99–1.52) Smoking status Never 10555 (56.2%) 10.2 (0.5) Ref. 5.9 (0.4) Ref. Former 5932 (28.6%) 12.1 (0.7) 1.16 (0.98–1.36) 8.0 (0.6) 1.26 (1.02–1.56) Current 2695 (15.2%) 10.9 (1.0) 0.83 (0.65–1.05) 6.9 (0.8) 0.86 (0.64–1.16) Current HTP use No 17618 (91.0%) 10.3 (0.4) Ref. 6.3 (0.3) Ref. Yes 1564 (9.0%) 16.1 (1.8) 1.25 (0.97–1.63) 10.1 (1.3) 1.26 (0.95–1.67) Current alcohol drinking No 9009 (62.0%) 10.0 (0.5) Ref. 5.9 (0.4) Ref. Yes 10173 (38.0%) 12.3 (0.5) 1.07 (0.94–1.22) 7.7 (0.5) 1.11 (0.93–1.32) Sex Female 9651 (50.9%) 9.7 (0.5) Ref. 5.6 (0.4) Ref. Male 9531 (49.1%) 12.1 (0.5) 1.04 (0.89–1.22) 7.7 (0.5) 1.16 (0.95–1.41) Age, years 16–64 13590 (72.2%) 13.1 (0.5) Ref. 7.8 (0.4) Ref. 65+ 5592 (27.8%) 5.2 (0.6) 0.80 (0.60–1.07) 3.5 (0.6) 0.80 (0.54–1.17) Education Some college/college or higher 13651 (47.7%) 12.1 (0.5) Ref. 6.9 (0.3) Ref. High school or less 5434 (52.3%) 9.7 (0.6) 0.89 (0.78–1.02) 6.4 (0.5) 1.01 (0.85–1.21) Employment status Full time 7030 (35.5%) 15.3 (0.7) Ref. 9.1 (0.5) Ref. Self-employed 1356 (6.7%) 7.1 (1.0) 0.52 (0.39–0.70) 4.5 (0.8) 0.54 (0.38–0.78) Part time 3746 (21.1%) 13.4 (0.9) 0.97 (0.82–1.16) 8.1 (0.8) 1.02 (0.81–1.29) Unemployed 7050 (36.7%) 5.8 (0.5) 0.52 (0.42–0.64) 3.7 (0.4) 0.58 (0.45–0.74) Abbreviations: APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, three Cs=closed spaces, crowded places, and close-contact settings, SE=standard error. Note: Data were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis Table 3 presents the association of COVID-19 vaccine intake with hospitalization and the receipt of oxygenation and/or ventilation among those who were diagnosed with COVID-19 during the observation window (N = 1,999). Of 1,999 respondents, 10.1% (n = 195) and 7.0% (n = 131) of them reported hospitalization and receipt of oxygenation and/or ventilation during the 1-year observation window, respectively. Although it did not reach statistical significance, decreased likelihoods of hospitalization and oxygenation and/or ventilation were observed among those who received one or 2 + doses of the COVID-19 vaccine during follow-up; the APRs of hospitalization were 0.78 (95%CI = 0.42–1.44) and 0.87 (95% CI = 0.47–1.61), respectively, and those of oxygenation and/or ventilation were 0.86 (95% CI = 0.39–1.90) and 0.61 (95% CI = 0.27–1.36), respectively. For hospitalization, the only significant association was observed among males, with a 2.69 (95% CI = 1.59–4.55) times higher likelihood than females. Similarly, male sex was the strongest factor for oxygenation and/or ventilation (APR = 3.16, 95% CI = 0.62–6.17 vs. females), followed by age 65 + years (APR = 2.25, 95% CI = 1.01–4.99 vs. younger age) and the presence of underlying health conditions (APR = 1.70, 95% CI = 1.03–2.80 vs. non-presence). Individuals who reported current alcohol drinking were less likely to receive oxygenation and/or ventilation (APR = 0.56, 95% CI = 0.31–0.99 vs. those who did not). Table 3 Percentage and adjusted ratio of hospitalization and receipt of oxygenation due to COVID-19 during the 1-year observation window among infected individuals, 2021–2022, Japan Distribution Hospital admission Hospital admission + oxygenation and/or ventilation Baseline characteristics N (%) % (SE) APR (95%CI) % (SE) APR (95%CI) Total 1999 (100.0%) 10.1 (1.2) - 7.0 (1.0) - 1-year COVID-19 vaccine intake (number of doses received during follow-up) 0 473 (25.1%) 10.4 (2.1) Ref. 8.0 (1.7) Ref. 1 998 (49.3%) 7.9 (1.4) 0.78 (0.42–1.44) 5.5 (1.3) 0.86 (0.39–1.90) 2+ 489 (25.6%) 10.6 (2.5) 0.87 (0.47–1.61) 5.5 (1.8) 0.61 (0.27–1.36) Vaccination status at baseline (number of completed doses of the COVID-19 vaccine) 0 365 (18.6%) 9.7 (2.2) Ref. 8.2 (2.1) Ref. 1 211 (12.3%) 12.2 (4.4) 1.12 (0.47–2.70) 10.0 (4.3) 1.04 (0.35–3.10) 2 1423 (69.1%) 9.8 (1.4) 0.99 (0.51–1.92) 6.2 (1.1) 0.69 (0.31–1.54) Underlying medical conditions Not present 1434 (69.2%) 8.2 (1.1) Ref. 5.1 (0.8) Ref. Present 565 (30.8%) 14.4 (2.7) 1.33 (0.83–2.13) 11.3 (2.6) 1.70 (1.03–2.80) Smoking status Never 1103 (52.9%) 8.1 (1.3) Ref. 5.8 (1.2) Ref. Former 648 (31.9%) 11.8 (2.3) 1.06 (0.64–1.77) 6.7 (1.8) 0.86 (0.46–1.62) Current 248 (15.2%) 13.5 (3.6) 0.98 (0.57–1.71) 12.1 (3.5) 1.20 (0.65–2.21) Current HTP use No 1783 (86.7%) 10.0 (1.3) Ref. 7.0 (1.1) Ref. Yes 216 (13.3%) 10.8 (2.7) 1.11 (0.64–1.93) 7.5 (2.0) 1.10 (0.61–1.99) Current alcohol drinking No 897 (57.0%) 9.7 (1.5) Ref. 7.5 (1.3) Ref. Yes 1102 (43.0%) 10.6 (1.8) 0.75 (0.48–1.17) 6.4 (1.5) 0.56 (0.31–0.99) Sex Female 962 (45.5%) 5.1 (1.0) Ref. 3.2 (0.9) Ref. Male 1037 (54.5%) 14.3 (1.9) 2.69 (1.59–4.55) 10.2 (1.6) 3.16 (1.62–6.17) Age, years 16–64 1771 (86.8%) 9.3 (1.1) Ref. 6.5 (1.0) Ref. 65+ 228 (13.2%) 15.5 (4.9) 1.85 (0.94–3.62) 10.4 (4.3) 2.25 (1.01–4.99) Education Some college/college or higher 1483 (53.1%) 9.9 (1.3) Ref. 7.0 (1.1) Ref. High school or less 503 (46.9%) 10.4 (2.0) 0.83 (0.52–1.32) 7.2 (1.8) 0.70 (0.38–1.30) Abbreviations: APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, SE=standard error. Note: Data were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis. DISCUSSION The intake of the COVID-19 vaccine was found to be significantly associated with a lower likelihood of COVID-19 diagnosis during the 1-year observation window. This period coincided with the seventh wave of the epidemic in Japan (July 1 – September 30, 2022), which resulted in the highest number of cases and deaths up to the time of data collection. This study represents the first longitudinal investigation to explore the interplay between population characteristics, vaccination, and infection outcomes on a large scale in the Japanese population, contributing to the existing knowledge about the effectiveness of the COVID-19 vaccine. It is worth noting that the majority of respondents in our study reported practicing preventive behaviors such as mask-wearing and avoidance of risky situations (the "three Cs"). This high compliance with government recommendations is a unique behavioral characteristic of the Japanese population during the COVID-19 epidemic. Studies have reported the effect of mask-wearing in preventing self-infection and reducing community transmission, even from asymptomatic individuals when it was implemented with other non-pharmaceutical control measures and that recommendations for universal mask-wearing have led to decreases in new infections, hospitalizations, and deaths [ 15 – 19 ]. Avoidance of the “three Cs” evaluated in this study can be considered a form of social distancing, which has also been shown to delay or flatten the epidemic curve and consequently avert new COVID-19 and critical illness, even with modest reductions in contact among adults [ 17 – 20 ]. While these preventive behaviors contribute to preventing the spread of infection between individuals, thereby serving as an effective measure for population-level infection control, the findings of this study did not allow for verification of their specific effectiveness in protecting the individuals who practiced these preventive behaviors. Several unassessed factors, such as the quality of masks worn, the consistency of mask usage and social distancing practices, circumstances where such preventive measures were impractical, and differences in occupational practices related to these preventive behaviors, could potentially confound the results. Our findings also revealed disparities in COVID-19 diagnosis by employment status, showing that self-employed and unemployed individuals had a lower likelihood of acquiring COVID-19 during the observation window. This may reflect fewer opportunities for close contact with others in these groups, as they have less necessity for commuting and working in shared office spaces compared to full-time or part-time workers. It may also indicate an increased possibility of COVID-19 for essential workers who could not comply with remote-work recommendations. According to a survey conducted by the Ministry of Internal Affairs and Communications, there were variations in the implementation of remote-work by industry during the COVID-19 pandemic. The industries with higher implementation rates of remote-work were information and communication (55.7%), academic research and professional/technical services (43.2%), and finance and insurance (30.2%) [ 21 ]. On the other hand, the remote-work implementation rates were lower in the healthcare sector (4.3%), accommodation and food services (11.1%), and transportation and postal services (11.3%) [ 21 ]. It can be inferred that the risk of infection increased for people working in these industries. Furthermore, our study found that individuals with underlying medical conditions had a higher likelihood of being infected in the past year. This could be due to their increased opportunities for clinical visits and COVID-19 testing, as many clinics and hospitals performed COVID-19 tests for all patients with fever and those admitted to the hospital. In this study, we evaluated hospitalization and receipt of oxygenation and/or ventilation as measures of severe illness due to COVID-19. However, in Japan, hospitalization may not be an accurate proxy for illness because the decision of hospitalization depended on the capacity of healthcare facilities, which widely varied across regions, especially during the seventh wave of the epidemic when cases and deaths reached their highest levels [ 22 ]. Among the study respondents, the presence of underlying medical conditions was the only associated factor for hospitalization. Receipt of oxygenation and/or ventilation was significantly associated with the presence of underlying medical conditions, male sex, and age 65 + years, consistent with the known risk factors for progression of COVID-19 into a critical stage [ 23 ]. We observed a lower likelihood of individuals who currently consume alcohol receiving oxygenation and/or ventilation. We hypothesized that this difference could be attributed to variations in the distribution of known risk factors such as male sex, the presence of underlying medical conditions, and older age between drinkers and nondrinkers. However, we did not find notable biases in these factors between the two groups. Therefore, we suspect that the observed negative association may be influenced by unadjusted biases within our study respondents or the relatively rare nature of the outcome event. The emergence of the Omicron variant has shifted the role of vaccines. Despite the established effectiveness of COVID-19 vaccines in reducing the incidence of COVID-19 and severity of illness due to COVID-19, the ability to prevent infection diminishes more rapidly than preventing severity of illness [ 24 – 26 ]. Therefore, additional doses have been recommended alongside the initial two-dose regimen. Omicron's high mutation rate has led to its rapid global spread since November 2021, causing breakthrough infections even among the vaccinated individuals [ 27 ]. However, the Omicron variant is considered less pathogenic, and individuals with prior natural SARS-CoV2 infection may be less likely to be reinfected with SARS-CoV2 [ 28 , 29 ]. Consequently, the significance of vaccination for preventing infections in low-risk populations may have diminished compared to that in the initial phase. While mRNA vaccines swiftly respond to variants, their effectiveness against Omicron falls short, potentially due to immune imprinting [ 30 – 32 ]. Nonetheless, vaccines remain effective in preventing severe illness due to COVID-19, emphasizing the need to vaccinate high-risk individuals. The Japanese government plans a biannual vaccination schedule for high-risk individuals and an annual schedule for others starting in the fiscal year 2023 [ 33 ]. With nearly 80% of the population having received the initial dose [ 2 ] and Omicron's prevalence, the focus shifts to preventing severe illness in high-risk individuals. However, COVID-19 vaccine still offers significant benefits, particularly for those uninfected and incomplete with their initial series of vaccination. This study has several limitations. First, we were unable to establish a causal relationship due to the lack of information on the chronological order of vaccine intake and the outcomes. However, it is likely that a significant portion of the survey participants who reported vaccine intake were actually vaccinated within the earlier phase of the study's observation window. According to data obtained from the Digital Agency's Vaccination Record System, by the end of June 2022, more than 70% of the Japanese population had completed the two-dose regimen, and approximately 60% had received the third dose [ 2 ]. These vaccination completion rates have remained relatively stable since then. Based on this information, it can be inferred that most participants in this study had already completed the initial vaccine series or received the booster dose before the onset of the largest and deadliest seventh wave of the epidemic, which began in late June and reached its peak in late August 2022 in Japan. Despite this limitation, we considered vaccine intake as an individual's practice of a preventive measure and took into account the practice of other preventive behaviors, as well as a variety of individual characteristics, distinguishing our study from existing research. Second, we were unable to assess the possible behavioral changes before and after vaccine intake or outcome events. For example, individuals who received a vaccine might have engaged in risky behaviors with the expectation that the vaccination would protect them from contracting COVID-19, or those who contracted COVID-19 might have stopped receiving the vaccine, speculating that vaccination would not be effective in protecting against recontraction of the disease. Regarding the latter example, however, our results showed that a history of COVID-19 at baseline did not affect vaccine intake during the 1-year observation window. Third, respondents of the baseline survey dropped out of the follow-up survey if they were dead or severely ill from COVID-19 at the 1-year follow-up, although the effect of this is not likely to be large given the low COVID-19 mortality rate in Japan compared to other countries. Fourth, the self-reported nature of the survey might have led to recall bias and misunderstanding of the questions. Fifth, because the sample was collected through Internet-based recruitment, our findings may not be generalizable to populations with limited Internet access or literacy. However, over 90% of the Japanese population had access to the Internet as of 2021 [ 34 ], and this study used weighted data to address differences in key socioeconomic and demographic characteristics and tobacco use behavior between the respondents of this Internet survey and a nationally representative population. Lastly, there may be other factors not assessed in this study that contributed to the outcomes. Specifically, this study did not consider the type of COVID-19 vaccine or detailed patterns of preventive and risky behaviors of the respondents. Further research is needed to elucidate the interaction of these factors and their effect on the outcomes in real-world settings. Considering demographic, socioeconomic, medical, and behavioral characteristics, the intake of the COVID-19 vaccine was significantly associated with a reduced likelihood of COVID-19 diagnosis during the period when there were highest numbers of infections and fatalities in Japan. These findings contribute to the existing knowledge that the COVID-19 vaccine is effective in protecting individuals from infection and severe outcomes from COVID-19. Continued assessment of vaccine efficacy and effectiveness is essential to inform future strategies that benefit public health and society. Declarations ACKNOWLEDGEMENTS This study was supported by grants from the Japan Society for the Promotion of Science (JSPS) KAKENHI program (grant numbers 16KK0059, 18H03107, 19K10446, and 21H04856) and Health Labour Sciences Research Grants (grant numbers 19FA1012 and 21HA2016). AUTHOR CONTRIBUTIONS SO designed the work, performed data analysis and interpretation, and drafted the manuscript. HH contributed to data interpretation and visualization, and critically reviewed and revised the manuscript. TT conceptualized the study, administered data collection and verification, and critically reviewed and revised the manuscript. DATA AVAILABILITY STATEMENT Data available on request from the author TT at [email protected] . COMPETING INTERESTS STATEMENT The authors have no potential conflict of interest. References Prime Minister's Office of Japan. COVID-19 vaccination schedule (in Japanese) . Prime Minister's Office of Japan https://www.kantei.go.jp/jp/headline/kansensho/vaccine_supply.html (2023). Digital Agency. Vaccination status for the novel coronavirus (in Japanese) . Digital Agency https://info.vrs.digital.go.jp/dashboard (2023). Arashiro, T. et al. Coronavirus disease 19 (COVID-19) vaccine effectiveness against symptomatic severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) infection during delta-dominant and omicron-dominant periods in Japan: a multicenter prospective case-control study (Factors associated with SARS-CoV-2 infection and the effectiveness of COVID-19 vaccines study). Clinical Infectious Diseases 76 , e108-e115 (2023). Ono, S. et al. Comparative effectiveness of BNT162b2 and mRNA-1273 booster dose after BNT162b2 primary vaccination against the Omicron variants: A retrospective cohort study using large-scale population-based registries in Japan. Clinical Infectious Diseases 76 , 18-24 (2023). Maeda, H. et al. Effectiveness of Messenger RNA Coronavirus Disease 2019 Vaccines Against Symptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections During the Delta Variant Epidemic in Japan: Vaccine Effectiveness Real-time Surveillance for SARS-CoV-2 (VERSUS). Clinical Infectious Diseases 75 , 1971-1979 (2022). Mimura, W., Ishiguro, C., Maeda, M., Murata, F. & Fukuda, H. Effectiveness of messenger RNA vaccines against infection with SARS-CoV-2 during the periods of Delta and Omicron variant predominance in Japan: the Vaccine Effectiveness, Networking, and Universal Safety (VENUS) study. International Journal of Infectious Diseases 125 , 58-60 (2022). Hara, M. et al. Real-World effectiveness of the mRNA COVID-19 vaccines in Japan: a case–control study. Vaccines 10 , 779 (2022). Fukuda, H., Maeda, M. & Murata, F. Development of a COVID-19 vaccine effectiveness and safety assessment system in Japan: The VENUS study. Vaccine 41 , 3556-3563 (2023). Rakuten Insight Inc. Monitor profiles. Rakuten Insight https://insight.rakuten.com/ (2020). Tabuchi, T., Shinozaki, T., Kunugita, N., Nakamura, M. & Tsuji, I. Study profile: The Japan “Society and New Tobacco” Internet Survey (JASTIS): A longitudinal internet cohort study of heat-not-burn tobacco products, electronic cigarettes, and conventional tobacco products in Japan. Journal of epidemiology 29 , 444-450 (2019). Okubo, R., Yoshioka, T., Ohfuji, S., Matsuo, T. & Tabuchi, T. COVID-19 Vaccine Hesitancy and Its Associated Factors in Japan. Vaccines 9 , 662 (2021). Japanese Ministry of Health, Labour and Welfare. Response to COVID 19 (Novel Coronavirus) after the classification change. Japanese Ministry of Health, Labour and Welfare https://www.mhlw.go.jp/stf/covid-19/kenkou-iryousoudan_00006.html (2023). Japan Ministry of Health LaW. https://www.mhlw.go.jp/english/database/db-hss/cslc-index.html. Japan Ministry of Health, Labour and Welfare 2017. Tabuchi, T. et al. Awareness and use of electronic cigarettes and heat‐not‐burn tobacco products in Japan. Addiction 111 , 706-713 (2016). Li, H. et al. Efficacy and practice of facemask use in general population: a systematic review and meta-analysis. Translational Psychiatry 12 , 49 (2022). Ford, N. et al. Mask use in community settings in the context of COVID-19: a systematic review of ecological data. EClinicalMedicine 38 , 101024 (2021). Ayouni, I. et al. Effective public health measures to mitigate the spread of COVID-19: a systematic review. BMC public health 21 , 1-14 (2021). Talic, S. et al. Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis. bmj 375 (2021). Chu, D. K. et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. The lancet 395 , 1973-1987 (2020). Matrajt, L. & Leung, T. Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease. Emerging infectious diseases 26 , 1740 (2020). Information and communications Bureau. Information and Communications in Japan 2021 . Japanese Ministry or Internal Affairs and Communications https://www.soumu.go.jp/johotsusintokei/whitepaper/eng/WP2021/2021-index.html (2021). Coronavirus Resource Center, Johns Hopkins Univeristy & Medicine. Japan. Johns Hopkins University & Medicine https://coronavirus.jhu.edu/region/japan (2023). Grasselli, G. et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med 180 , 1345-1355, doi:10.1001/jamainternmed.2020.3539 (2020). Thomas, S. J. et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine through 6 months. New England Journal of Medicine 385 , 1761-1773 (2021). Tartof, S. Y. et al. Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 months in a large integrated health system in the USA: a retrospective cohort study. The Lancet 398 , 1407-1416 (2021). Chemaitelly, H. et al. Waning of BNT162b2 vaccine protection against SARS-CoV-2 infection in Qatar. New England Journal of Medicine 385 , e83 (2021). Wang, Q. et al. Antibody evasion by SARS-CoV-2 Omicron subvariants BA. 2.12. 1, BA. 4 and BA. 5. Nature 608 , 603-608 (2022). Fantini, J. et al. The puzzling mutational landscape of the SARS‐2‐variant Omicron. Journal of medical virology 94 , 2019-2025 (2022). Khandia, R. et al. Emergence of SARS-CoV-2 Omicron (B. 1.1. 529) variant, salient features, high global health concerns and strategies to counter it amid ongoing COVID-19 pandemic. Environmental research 209 , 112816 (2022). Wang, Q. et al. Antibody Response to Omicron BA. 4–BA. 5 Bivalent Booster. New England Journal of Medicine (2023). Qu, P. et al. Enhanced evasion of neutralizing antibody response by Omicron XBB. 1.5, CH. 1.1, and CA. 3.1 variants. Cell reports 42 (2023). Davis-Gardner, M. E. et al. Neutralization against BA. 2.75. 2, BQ. 1.1, and XBB from mRNA Bivalent Booster. New England Journal of Medicine 388 , 183-185 (2023). Japanese Ministry of Health, Labour and Welfare. Novel Coronavirus (COVID-19). Japanese Ministry of Health, Labour and Welfare https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html (2023). Statistica. Internet usage in Japan - statistics & facts. Statistica https://www.statista.com/topics/2361/internet-usage-in-japan/#dossierKeyfigures (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3925778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274069363,"identity":"c94b50eb-aede-4527-9211-c4514d38a8e6","order_by":0,"name":"Satomi Odani","email":"data:image/png;base64,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","orcid":"","institution":"The Tokyo Foundation for Policy Research","correspondingAuthor":true,"prefix":"","firstName":"Satomi","middleName":"","lastName":"Odani","suffix":""},{"id":274069364,"identity":"9db1e4e4-3766-4678-be71-853308361a65","order_by":1,"name":"Hitoshi Honda","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Honda","suffix":""},{"id":274069365,"identity":"1bb75891-d784-48b5-b615-851f2007d6ff","order_by":2,"name":"Takahiro Tabuchi","email":"","orcid":"","institution":"Osaka International Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Tabuchi","suffix":""}],"badges":[],"createdAt":"2024-02-04 01:29:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3925778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3925778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51513792,"identity":"ab0c7144-6f71-4068-9823-fbed302bb0c2","added_by":"auto","created_at":"2024-02-22 21:34:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":460609,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram for respondent inclusion in the analysis, the Japan COVID-19 and Society Internet Survey (JACSIS), 2021-2022\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3925778/v1/1a033db1925f5268da94f172.jpeg"},{"id":59255359,"identity":"36f40601-286b-4853-9f95-5e9e9af40358","added_by":"auto","created_at":"2024-06-28 08:39:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1641294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3925778/v1/df46664b-7723-4051-9482-49625b0da15c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of COVID-19 vaccine intake with diagnosis, hospitalization, and oxygenation/ventilation: A longitudinal analysis, 2021-2022, Japan","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe COVID-19 pandemic has posed significant challenges to public health systems worldwide, necessitating the development and implementation of effective vaccination strategies. In Japan, the government launched a national COVID-19 vaccination campaign in February 2021, initially targeting high-priority groups such as healthcare professionals, rescue workers, and public health center employees [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Subsequently, the campaign expanded to include individuals aged 65 or older in April 2021 and the general population with specific priority given to those with underlying health conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This vaccination campaign progressed rapidly, with over 70% of Japanese residents having completed the recommended two-dose immunization course by the end of October 2021 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To further enhance protection against COVID-19, booster doses were introduced in December 2021 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The booster rollout aimed to provide an additional layer of immunity, particularly for individuals who had completed the primary vaccination series. By the end of May 2022, approximately 60% of the entire population had received a third vaccine dose [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These vaccination efforts have played a crucial role in mitigating the impact of the pandemic and reducing the incidence of COVID-19 in Japan.\u003c/p\u003e \u003cp\u003eAssessing the effectiveness of the COVID-19 vaccines and understanding the factors associated with vaccine uptake and their impact on disease outcomes require comprehensive data sources. In Japan, however, medical care information is maintained within the health insurance system, while the vaccination campaign is administered through a separate system governed by the Immunization Act. Moreover, the use of governmental vaccination records for research purposes is not permitted. The absence of data linkage platform at the national level has posed challenges for evaluating the vaccine effectiveness and its interplay with individual characteristics, including demographic, socioeconomic, clinical, and behavioral characteristics.\u003c/p\u003e \u003cp\u003eA few case-control studies and subnational-level population-based studies have been conducted to investigate the efficacy of COVID-19 vaccines in Japanese population [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Findings from these previous studies consistently showed high effectiveness in reduction in overall incidence of COVID-19 and the severity of illness due to COVID-19, over 80% for individuals fully vaccinated with two doses of the vaccine during the time when the delta variant was dominant [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the omicron-dominant period, although the vaccine efficacy was reported to be lower than that during the Delta-variant period, the estimates of vaccine effectiveness against infection ranged from 57\u0026ndash;74% for those who received the booster [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough these studies provided consistent results regarding vaccine efficacy, it is important to conduct a comprehensive assessment that takes into account the unique population characteristics in Japan on a large scale. Given that preventive behaviors such as wearing masks and avoiding risky situations were commonly practiced in Japan, understanding the interplay of these factors and their impact on promoting vaccination and preventing infections is crucial. Therefore, this study aims to contribute to the existing knowledge by providing evidence on the effectiveness of COVID-19 vaccines and investigating the associations between individual characteristics, vaccination, and infection outcomes in Japan. We considered vaccine intake as an individual's practice of a preventive measure and took into account the practice of other preventive behaviors, as well as a variety of individual characteristics, allowing us to explore a broader perspective on the impact of preventive measures during the observed period.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThis study involved a longitudinal analysis of the 2021 and 2022 waves from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, internet-based, self-reported survey targeting individuals aged 15 years or older. The initial JACSIS survey was conducted during August and September 2020, using a private vendor, Rakuten Insight Inc., which has 2.3\u0026nbsp;million panelists [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Participants were randomly selected from various demographic (including age, sex, and place of residence) and socioeconomic subgroups (including education, housing, and marital status), as defined by the Japan census. All participants were required to provide online informed consent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Annual follow-up surveys were administered during August-September in 2021 and 2022, with sample replenishment. The survey period coincided with the end of the fifths wave of COVID-19 epidemic (July 1-September 30, 2021), driven by the Delta variant, and the sixth (January 1 \u0026ndash; March 31, 2022) and seventh waves (July 1 \u0026ndash; September 30, 2022) in which the Omicron variant was dominant. A total of 32,000 and 31,000 individuals responded to the 2021 and 2022 waves, respectively. From this pool, we excluded 3,370 and 2,825 individuals who provided irregular answers (from the 2021 and 2022 respondents, respectively) using a set of predefined questions incorporated into the questionnaire [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For example, individuals who responded all multiple-choice items for illegal substance use (7 items) or presence of chronic conditions (15 items), those who answered with the same number over an entire set of questions, or those who chose a wrong answer for the question \u0026ldquo;\u003cem\u003eChoose the second item from the bottom\u003c/em\u003e\u0026rdquo; were excluded. Ultimately, the analysis included 19,482 individuals who responded to both waves. The selection process of the analytical sample is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Research Ethics Committee of the Osaka International Cancer Institute approved this study (no. 20084-9) in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExposure\u003c/h2\u003e \u003cp\u003eThe exposure variable in this analysis was the COVID-19 vaccine intake during the observation window. We calculated the difference in the number of completed doses between baseline (2021) and the 1-year follow-up (2022) for each participant. The doses were categorized as 0, 1, or 2\u0026thinsp;+\u0026thinsp;based on this calculation. At baseline, respondents were asked to indicate their COVID-19 vaccine status using the following response choices: \"Received 2 doses,\" \"Received 1 dose (plan to receive the second dose)/(do not plan to receive the second dose)/(received a 1-dose type vaccine),\" \"Never received vaccine (cannot get a vaccine due to allergies or other health conditions)/(want to get a vaccine/already have an appointment for a vaccine)/(prefer to wait)/(do not want to get a vaccine).\" At the 1-year follow-up, respondents were asked the same question with the following response categories: \"Received 4 doses/3 doses/2 doses/1 dose\" and \"Never received vaccine (cannot get a vaccine due to allergies or other health conditions)/(prefer to wait)/(do not need to get a vaccine)/(do not want to get a vaccine).\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eWe assessed three outcomes: COVID-19 diagnosis, COVID-19-induced hospitalization, and receipt of oxygen supplementation (referred to as \u0026ldquo;oxygenation\u0026rdquo; hereinafter) and/or mechanical ventilation (referred to as \u0026ldquo;ventilation\u0026rdquo; hereinafter) during hospitalization. COVID-19 infection was assessed by asking participants whether and when (in the past 2 months, 2 months to 1 year ago, or more than 1 year ago) they were diagnosed with COVID-19. We created dichotomous variables (yes/no) for the past-year infection and past-2-month infection separately. Participants were also asked with separate questions whether they were admitted to a hospital and received oxygenation and/or ventilation due to COVID-19 infection. We created dichotomous variables (yes/no) for past-year hospitalization and receipt of oxygenation and/or ventilation among hospitalized individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe independent variables assessed in this study included sex, age, education, employment, presence of underlying conditions (chronic respiratory illness, cardiac disease, kidney disease, cancer, diabetes, hypertension, and body mass index\u0026thinsp;\u0026ge;\u0026thinsp;30), smoking status, current use of heated tobacco products (HTPs), alcohol drinking, fear of COVID-19-induced death (yes/no) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and COVID-19 preventive behaviors. Participants were asked whether they wore a mask when other people were present, with response options of \"always\" or \"sometimes/rarely/never.\" Preventive behavior was further assessed regarding the avoidance of the \"three Cs\" (closed spaces, crowded places, and close-contact settings) which was recommended by the Japanese government [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Separate questions were asked for each \"C,\" and the number of times participants answered \"always\" (vs. \"sometimes/rarely/never\") was summed and categorized into 0, 1, 2, or 3. Vaccination status at baseline was assessed as the number of doses that had been received before/at baseline (0/1/2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo account for potential selection bias of the internet-based sample and nonresponse bias, we applied inverse probability weighting (IPW) to weight the data. Logistic regression models were fitted to compute propensity scores for \"being an Internet survey respondent\" using a nationally representative sample from the Comprehensive Survey of Living Conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] as the reference. We controlled for demographic, socioeconomic, and behavioral characteristics (e.g., sex, age, residing region, marital status, education, employment, health status, tobacco product use) in the propensity score calculation. All analyses were weighted. Additional details regarding the IPW method are reported elsewhere [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe employed multivariable Poisson regression to investigate factors associated with COVID-19 vaccine intake during the observation window. Furthermore, we analyzed the associations between vaccine intake and COVID-19 infection among participants with no history of COVID-19 at baseline (N\u0026thinsp;=\u0026thinsp;19,182), as well as the associations with hospitalization and receipt of oxygenation and/or ventilation among those diagnosed with COVID-19 during the observation window (N\u0026thinsp;=\u0026thinsp;1,999). Adjusted prevalence ratios (APRs) and 95% confidence intervals (CIs) were estimated, controlling for the aforementioned independent variables. These variables were either identified in the univariate analysis with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 or were deemed to have clinical and behavioral relevance in the context of vaccine intake. We assessed multicollinearity among independent variables using variance inflation factors, which were confirmed to be below 10. All analyses were performed using R version 4.2.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the respondents and factors associated with COVID-19 vaccine intake during the observation window (N\u0026thinsp;=\u0026thinsp;19,482). Overall, the majority of respondents (72.9%) had received two doses of the COVID-19 vaccine at baseline. 90.9% reported always wearing a mask when in the presence of other people. 27.4% reported always avoiding all of the \u0026quot;three Cs\u0026quot; (closed spaces, crowded places, and close-contact settings), and 76.5% partially practiced the avoidance of the \u0026ldquo;three Cs\u0026rdquo;. 40.1% reported a fear of death from COVID-19. 81.6% reported receiving at least one dose of the COVID-19 vaccine between baseline and the 1-year follow-up, with 38.4%, 40.2%, 2.9%, and 0.1% having received one, two, three, and four doses, respectively. At follow-up, while a majority of respondents (76.1%) had completed booster vaccination (received 3\u0026thinsp;+\u0026thinsp;doses), 11.6% had no vaccination history, and 12.2% had received one or two doses (data not shown). The most significant association with COVID-19 vaccine intake during the observation window was seen with the baseline vaccine status. Those who had received one or two doses before/at baseline were more likely to receive additional doses (APR\u0026thinsp;=\u0026thinsp;2.64, 95% CI\u0026thinsp;=\u0026thinsp;2.45\u0026ndash;2.84; APR\u0026thinsp;=\u0026thinsp;2.38, 95%CI\u0026thinsp;=\u0026thinsp;2.21\u0026ndash;2.56) than those without vaccination history. Other groups that had a higher likelihood of vaccine intake during the observation window included those with a fear of COVID-19-induced death (APR\u0026thinsp;=\u0026thinsp;1.05, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.07) compared to those without the fear, those with underlying health conditions (APR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05) compared to those without them, current (past 30-day) alcohol drinkers (APR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05) compared to non-current/never drinkers, the elderly aged 65\u0026thinsp;+\u0026thinsp;years (APR\u0026thinsp;=\u0026thinsp;1.07, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.10) compared to younger individuals, and self-employed (APR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;1.06) and unemployed (APR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.004\u0026ndash;1.06) individuals compared to full-time workers. Groups with a lower likelihood of vaccine intake included those who did not avoid any of the \u0026quot;three Cs\u0026quot; (APR\u0026thinsp;=\u0026thinsp;0.97, 95% CI\u0026thinsp;=\u0026thinsp;0.94\u0026ndash;0.99) and those who partially practiced the measure (avoided two of the \u0026quot;three Cs\u0026quot;) (APR\u0026thinsp;=\u0026thinsp;0.97, 95%CI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;0.99) compared to those who avoided all of the \u0026quot;three Cs\u0026quot;, current smokers (APR\u0026thinsp;=\u0026thinsp;0.96, 95%CI\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.99) compared to never smokers, and part-time workers (APR\u0026thinsp;=\u0026thinsp;0.95, 95%CI\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.99) compared to full-time workers.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics and one-year vaccine intake, 2021\u0026ndash;2022, Japan\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eReceived 1\u0026thinsp;+\u0026thinsp;dose of the COVID-19 vaccine during the 1-year observation window\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19482 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVaccination status (number of COVID-19 vaccine doses\u003c/p\u003e\n \u003cp\u003ecompleted before/at baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3155 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.5 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1579 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.64 (2.45\u0026ndash;2.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14748 (72.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.0 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.38 (2.21\u0026ndash;2.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMask-wearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1558 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.5 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.90-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17924 (90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.0 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvoidance of risky situations\u003c/p\u003e\n \u003cp\u003e(number of the \u0026quot;three Cs\u0026quot; avoided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4162 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97 (0.94\u0026ndash;0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4399 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.95\u0026ndash;1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5221 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.9 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97 (0.95\u0026ndash;0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5700 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.6 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFear of COVID-19-induced death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11780 (59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.8 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7702 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.8 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05 (1.03\u0026ndash;1.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderlying medical conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13166 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6316 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.8 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.01\u0026ndash;1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver diagnosed with COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19182 (98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.3 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.90\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10667 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.9 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6023 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2792 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.6 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96 (0.93\u0026ndash;0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent use of heated tobacco products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17817 (90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1665 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.98\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9173 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.0 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10309 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.01\u0026ndash;1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9751 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9731 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13870 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5612 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.07 (1.05\u0026ndash;1.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college/college or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13875 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.9 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5506 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.6 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7199 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1384 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.5 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.002\u0026ndash;1.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePart time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3798 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.3 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.95 (0.92\u0026ndash;0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7101 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.5 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.004\u0026ndash;1.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, three Cs=closed spaces, crowded places, and close-contact settings, SE=standard error.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eData were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association between COVID-19 vaccine intake during the observation window and COVID-19 diagnosis among those with no COVID-19 history at baseline (N\u0026thinsp;=\u0026thinsp;19,182). At the 1-year follow-up, 10.9% (n\u0026thinsp;=\u0026thinsp;1,999) and 6.6% (1,192) reported having been diagnosed with COVID-19 in the past year and in the past 2 months. Past-year infection was significantly less likely among those who received one or 2\u0026thinsp;+\u0026thinsp;doses of the COVID-19 vaccine during follow-up (APR\u0026thinsp;=\u0026thinsp;0.76, 95% CI\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.97; APR\u0026thinsp;=\u0026thinsp;0.43, 95% CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;0.55, respectively) compared to those who did not receive a vaccine. In particular, receipt of 2\u0026thinsp;+\u0026thinsp;vaccine doses was significantly associated with reduced likelihood of COVID-19 diagnosis in the past 2 months (APR\u0026thinsp;=\u0026thinsp;0.51, 95%CI\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;0.71). Self-employed and unemployed individuals were also less likely to report past-year COVID-19 diagnosis (APR\u0026thinsp;=\u0026thinsp;0.52, 95% CI\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;0.70; APR\u0026thinsp;=\u0026thinsp;0.52, 95% CI\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.64, respectively) and the past-2-month diagnosis of COVID-19 (APR\u0026thinsp;=\u0026thinsp;0.54, 95% CI\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.78; APR\u0026thinsp;=\u0026thinsp;0.58, 95% CI\u0026thinsp;=\u0026thinsp;0.45\u0026ndash;0.74, respectively) than full-time workers. Groups with a higher likelihood of COVID-19 infection were those who had received one dose of the COVID-19 vaccine at baseline (APR\u0026thinsp;=\u0026thinsp;1.67, 95% CI\u0026thinsp;=\u0026thinsp;1.21\u0026ndash;2.28 for past-year infection) compared to those without vaccination, those with underlying health conditions (APR\u0026thinsp;=\u0026thinsp;1.23, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.43 for past-year infection) compared to those without them, and former smokers (APR\u0026thinsp;=\u0026thinsp;1.26, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.56 for past-2-month infection) compared to never smokers.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePercentage and adjusted ratio of COVID-19 diagnosis during the 1-year observation window among infection-na\u0026iuml;ve individuals, 2021\u0026ndash;2022, Japan\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCOVID-19 diagnosis during the 1-year observation window\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCOVID-19 diagnosis in the past 2 months\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19182 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-year COVID-19 vaccine intake (number of doses received during follow-up)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3130 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7475 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.76 (0.60\u0026ndash;0.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.62\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8419 (42.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.43 (0.34\u0026ndash;0.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.51 (0.36\u0026ndash;0.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVaccination status at baseline (number of\u003c/p\u003e\n \u003cp\u003ecompleted doses of the COVID-19 vaccine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3065 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1497 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67 (1.21\u0026ndash;2.28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53 (0.99\u0026ndash;2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14620 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.81\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.72\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMask-wearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1440 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.72\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (0.77\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17742 (91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvoidance of risky situations (number of the \u0026quot;three Cs\u0026quot; avoided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4024 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.86\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.86\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4331 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (0.93\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.96\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5172 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.77\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.83\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5655 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFear of COVID-19-induced death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11575 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7607 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.87\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.75\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderlying medical conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13024 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6158 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23 (1.05\u0026ndash;1.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.99\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10555 (56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5932 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.98\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26 (1.02\u0026ndash;1.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2695 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.65\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.64\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent HTP use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17618 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1564 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (0.97\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (0.95\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9009 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10173 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (0.94\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.93\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9651 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9531 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.89\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.95\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13590 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5592 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.60\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.54\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college/college or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13651 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5434 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.78\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.85\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7030 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1356 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.52 (0.39\u0026ndash;0.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.54 (0.38\u0026ndash;0.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePart time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3746 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.82\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.81\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7050 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.52 (0.42\u0026ndash;0.64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58 (0.45\u0026ndash;0.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, three Cs=closed spaces, crowded places, and close-contact settings, SE=standard error.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eData were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the association of COVID-19 vaccine intake with hospitalization and the receipt of oxygenation and/or ventilation among those who were diagnosed with COVID-19 during the observation window (N\u0026thinsp;=\u0026thinsp;1,999). Of 1,999 respondents, 10.1% (n\u0026thinsp;=\u0026thinsp;195) and 7.0% (n\u0026thinsp;=\u0026thinsp;131) of them reported hospitalization and receipt of oxygenation and/or ventilation during the 1-year observation window, respectively. Although it did not reach statistical significance, decreased likelihoods of hospitalization and oxygenation and/or ventilation were observed among those who received one or 2\u0026thinsp;+\u0026thinsp;doses of the COVID-19 vaccine during follow-up; the APRs of hospitalization were 0.78 (95%CI\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;1.44) and 0.87 (95% CI\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;1.61), respectively, and those of oxygenation and/or ventilation were 0.86 (95% CI\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;1.90) and 0.61 (95% CI\u0026thinsp;=\u0026thinsp;0.27\u0026ndash;1.36), respectively. For hospitalization, the only significant association was observed among males, with a 2.69 (95% CI\u0026thinsp;=\u0026thinsp;1.59\u0026ndash;4.55) times higher likelihood than females. Similarly, male sex was the strongest factor for oxygenation and/or ventilation (APR\u0026thinsp;=\u0026thinsp;3.16, 95% CI\u0026thinsp;=\u0026thinsp;0.62\u0026ndash;6.17 vs. females), followed by age 65\u0026thinsp;+\u0026thinsp;years (APR\u0026thinsp;=\u0026thinsp;2.25, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;4.99 vs. younger age) and the presence of underlying health conditions (APR\u0026thinsp;=\u0026thinsp;1.70, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;2.80 vs. non-presence). Individuals who reported current alcohol drinking were less likely to receive oxygenation and/or ventilation (APR\u0026thinsp;=\u0026thinsp;0.56, 95% CI\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.99 vs. those who did not).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePercentage and adjusted ratio of hospitalization and receipt of oxygenation due to COVID-19 during the 1-year observation window among infected individuals, 2021\u0026ndash;2022, Japan\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHospital admission\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHospital admission\u0026thinsp;+\u0026thinsp;oxygenation and/or ventilation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1999 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-year COVID-19 vaccine intake (number of doses received during follow-up)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e473 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.42\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.39\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e489 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.47\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.27\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccination status at baseline (number of\u003c/p\u003e\n \u003cp\u003ecompleted doses of the COVID-19 vaccine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.47\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.35\u0026ndash;3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1423 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.51\u0026ndash;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 (0.31\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderlying medical conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1434 (69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e565 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (0.83\u0026ndash;2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.70 (1.03\u0026ndash;2.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1103 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e648 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.64\u0026ndash;1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.46\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.57\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (0.65\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent HTP use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1783 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.64\u0026ndash;1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.61\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e897 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1102 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.48\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56 (0.31\u0026ndash;0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e962 (45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1037 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.69 (1.59\u0026ndash;4.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.16 (1.62\u0026ndash;6.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1771 (86.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85 (0.94\u0026ndash;3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.25 (1.01\u0026ndash;4.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college/college or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1483 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.52\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.38\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e APR=adjusted prevalence ratio, CI=confidence interval, COVID-19=coronavirus disease 2019, SE=standard error.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eData were extracted from the Japan COVID-19 and Society Internet Survey (JACSIS), a nationwide, self-reported survey and were weighted to account for the selectivity bias of the internet-based sample using a nationally representative sample as the reference. APRs and CIs were computed by multivariable Poisson regression analysis.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe intake of the COVID-19 vaccine was found to be significantly associated with a lower likelihood of COVID-19 diagnosis during the 1-year observation window. This period coincided with the seventh wave of the epidemic in Japan (July 1 \u0026ndash; September 30, 2022), which resulted in the highest number of cases and deaths up to the time of data collection. This study represents the first longitudinal investigation to explore the interplay between population characteristics, vaccination, and infection outcomes on a large scale in the Japanese population, contributing to the existing knowledge about the effectiveness of the COVID-19 vaccine.\u003c/p\u003e \u003cp\u003eIt is worth noting that the majority of respondents in our study reported practicing preventive behaviors such as mask-wearing and avoidance of risky situations (the \"three Cs\"). This high compliance with government recommendations is a unique behavioral characteristic of the Japanese population during the COVID-19 epidemic. Studies have reported the effect of mask-wearing in preventing self-infection and reducing community transmission, even from asymptomatic individuals when it was implemented with other non-pharmaceutical control measures and that recommendations for universal mask-wearing have led to decreases in new infections, hospitalizations, and deaths [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Avoidance of the \u0026ldquo;three Cs\u0026rdquo; evaluated in this study can be considered a form of social distancing, which has also been shown to delay or flatten the epidemic curve and consequently avert new COVID-19 and critical illness, even with modest reductions in contact among adults [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While these preventive behaviors contribute to preventing the spread of infection between individuals, thereby serving as an effective measure for population-level infection control, the findings of this study did not allow for verification of their specific effectiveness in protecting the individuals who practiced these preventive behaviors. Several unassessed factors, such as the quality of masks worn, the consistency of mask usage and social distancing practices, circumstances where such preventive measures were impractical, and differences in occupational practices related to these preventive behaviors, could potentially confound the results.\u003c/p\u003e \u003cp\u003eOur findings also revealed disparities in COVID-19 diagnosis by employment status, showing that self-employed and unemployed individuals had a lower likelihood of acquiring COVID-19 during the observation window. This may reflect fewer opportunities for close contact with others in these groups, as they have less necessity for commuting and working in shared office spaces compared to full-time or part-time workers. It may also indicate an increased possibility of COVID-19 for essential workers who could not comply with remote-work recommendations. According to a survey conducted by the Ministry of Internal Affairs and Communications, there were variations in the implementation of remote-work by industry during the COVID-19 pandemic. The industries with higher implementation rates of remote-work were information and communication (55.7%), academic research and professional/technical services (43.2%), and finance and insurance (30.2%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. On the other hand, the remote-work implementation rates were lower in the healthcare sector (4.3%), accommodation and food services (11.1%), and transportation and postal services (11.3%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It can be inferred that the risk of infection increased for people working in these industries.\u003c/p\u003e \u003cp\u003eFurthermore, our study found that individuals with underlying medical conditions had a higher likelihood of being infected in the past year. This could be due to their increased opportunities for clinical visits and COVID-19 testing, as many clinics and hospitals performed COVID-19 tests for all patients with fever and those admitted to the hospital. In this study, we evaluated hospitalization and receipt of oxygenation and/or ventilation as measures of severe illness due to COVID-19. However, in Japan, hospitalization may not be an accurate proxy for illness because the decision of hospitalization depended on the capacity of healthcare facilities, which widely varied across regions, especially during the seventh wave of the epidemic when cases and deaths reached their highest levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Among the study respondents, the presence of underlying medical conditions was the only associated factor for hospitalization. Receipt of oxygenation and/or ventilation was significantly associated with the presence of underlying medical conditions, male sex, and age 65\u0026thinsp;+\u0026thinsp;years, consistent with the known risk factors for progression of COVID-19 into a critical stage [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We observed a lower likelihood of individuals who currently consume alcohol receiving oxygenation and/or ventilation. We hypothesized that this difference could be attributed to variations in the distribution of known risk factors such as male sex, the presence of underlying medical conditions, and older age between drinkers and nondrinkers. However, we did not find notable biases in these factors between the two groups. Therefore, we suspect that the observed negative association may be influenced by unadjusted biases within our study respondents or the relatively rare nature of the outcome event.\u003c/p\u003e \u003cp\u003eThe emergence of the Omicron variant has shifted the role of vaccines. Despite the established effectiveness of COVID-19 vaccines in reducing the incidence of COVID-19 and severity of illness due to COVID-19, the ability to prevent infection diminishes more rapidly than preventing severity of illness [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, additional doses have been recommended alongside the initial two-dose regimen. Omicron's high mutation rate has led to its rapid global spread since November 2021, causing breakthrough infections even among the vaccinated individuals [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the Omicron variant is considered less pathogenic, and individuals with prior natural SARS-CoV2 infection may be less likely to be reinfected with SARS-CoV2 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, the significance of vaccination for preventing infections in low-risk populations may have diminished compared to that in the initial phase. While mRNA vaccines swiftly respond to variants, their effectiveness against Omicron falls short, potentially due to immune imprinting [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Nonetheless, vaccines remain effective in preventing severe illness due to COVID-19, emphasizing the need to vaccinate high-risk individuals. The Japanese government plans a biannual vaccination schedule for high-risk individuals and an annual schedule for others starting in the fiscal year 2023 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. With nearly 80% of the population having received the initial dose [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and Omicron's prevalence, the focus shifts to preventing severe illness in high-risk individuals. However, COVID-19 vaccine still offers significant benefits, particularly for those uninfected and incomplete with their initial series of vaccination.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, we were unable to establish a causal relationship due to the lack of information on the chronological order of vaccine intake and the outcomes. However, it is likely that a significant portion of the survey participants who reported vaccine intake were actually vaccinated within the earlier phase of the study's observation window. According to data obtained from the Digital Agency's Vaccination Record System, by the end of June 2022, more than 70% of the Japanese population had completed the two-dose regimen, and approximately 60% had received the third dose [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These vaccination completion rates have remained relatively stable since then. Based on this information, it can be inferred that most participants in this study had already completed the initial vaccine series or received the booster dose before the onset of the largest and deadliest seventh wave of the epidemic, which began in late June and reached its peak in late August 2022 in Japan. Despite this limitation, we considered vaccine intake as an individual's practice of a preventive measure and took into account the practice of other preventive behaviors, as well as a variety of individual characteristics, distinguishing our study from existing research. Second, we were unable to assess the possible behavioral changes before and after vaccine intake or outcome events. For example, individuals who received a vaccine might have engaged in risky behaviors with the expectation that the vaccination would protect them from contracting COVID-19, or those who contracted COVID-19 might have stopped receiving the vaccine, speculating that vaccination would not be effective in protecting against recontraction of the disease. Regarding the latter example, however, our results showed that a history of COVID-19 at baseline did not affect vaccine intake during the 1-year observation window. Third, respondents of the baseline survey dropped out of the follow-up survey if they were dead or severely ill from COVID-19 at the 1-year follow-up, although the effect of this is not likely to be large given the low COVID-19 mortality rate in Japan compared to other countries. Fourth, the self-reported nature of the survey might have led to recall bias and misunderstanding of the questions. Fifth, because the sample was collected through Internet-based recruitment, our findings may not be generalizable to populations with limited Internet access or literacy. However, over 90% of the Japanese population had access to the Internet as of 2021 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and this study used weighted data to address differences in key socioeconomic and demographic characteristics and tobacco use behavior between the respondents of this Internet survey and a nationally representative population. Lastly, there may be other factors not assessed in this study that contributed to the outcomes. Specifically, this study did not consider the type of COVID-19 vaccine or detailed patterns of preventive and risky behaviors of the respondents. Further research is needed to elucidate the interaction of these factors and their effect on the outcomes in real-world settings.\u003c/p\u003e \u003cp\u003eConsidering demographic, socioeconomic, medical, and behavioral characteristics, the intake of the COVID-19 vaccine was significantly associated with a reduced likelihood of COVID-19 diagnosis during the period when there were highest numbers of infections and fatalities in Japan. These findings contribute to the existing knowledge that the COVID-19 vaccine is effective in protecting individuals from infection and severe outcomes from COVID-19. Continued assessment of vaccine efficacy and effectiveness is essential to inform future strategies that benefit public health and society.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Japan Society for the Promotion of Science (JSPS) KAKENHI program (grant numbers 16KK0059, 18H03107, 19K10446, and 21H04856) and Health Labour Sciences Research Grants (grant numbers 19FA1012 and 21HA2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSO designed the work, performed data analysis and interpretation, and drafted the manuscript. HH contributed to data interpretation and visualization, and critically reviewed and revised the manuscript. TT conceptualized the study, administered data collection and verification, and critically reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the author TT at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrime Minister\u0026apos;s Office of Japan. \u003cem\u003eCOVID-19 vaccination schedule (in Japanese)\u003c/em\u003e. Prime Minister\u0026apos;s Office of Japan https://www.kantei.go.jp/jp/headline/kansensho/vaccine_supply.html (2023).\u003c/li\u003e\n\u003cli\u003eDigital Agency. \u003cem\u003eVaccination status for the novel coronavirus (in Japanese)\u003c/em\u003e. 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E.\u003cem\u003e et al.\u003c/em\u003e Neutralization against BA. 2.75. 2, BQ. 1.1, and XBB from mRNA Bivalent Booster. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e \u003cstrong\u003e388\u003c/strong\u003e, 183-185 (2023).\u003c/li\u003e\n\u003cli\u003eJapanese Ministry of Health, Labour and Welfare. Novel Coronavirus (COVID-19). Japanese Ministry of Health, Labour and Welfare https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00079.html (2023).\u003c/li\u003e\n\u003cli\u003eStatistica. Internet usage in Japan - statistics \u0026amp; facts. Statistica https://www.statista.com/topics/2361/internet-usage-in-japan/#dossierKeyfigures (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3925778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3925778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLimited national-level data exist on COVID-19 vaccine effectiveness against infection outcomes based on individual characteristics. We analyzed 19,482 individuals aged 16-81 who responded to baseline (2021) and follow-up (2022) Internet-based surveys. COVID-19 vaccine intake (0/1/2+ doses) during the follow-up period was examined, and outcomes included COVID-19 diagnosis, hospitalization, and oxygenation/ventilation. Adjusted prevalence ratios (APRs) were computed using Poisson regression models, controlling for baseline characteristics including precautionary measures practiced.\u003c/p\u003e\n\u003cp\u003e81.6% of respondents received ≥1 dose of COVID-19 vaccine during the follow-up period. Among those without COVID-19 history at baseline (N=19,182), 10.9% and 6.6% reported COVID-19 diagnosis within the past year and past 2 months at follow-up, respectively. Respondents who received 1 or 2+ doses were less likely to be diagnosed in the past year (APR=0.76 and 0.43) and past 2 months (APR=0.87 [not statistically significant] and 0.51) compared to those who did not. Among 1,999 respondents diagnosed with COVID-19 during the follow-up, those with 1 or 2+ vaccine doses showed lower likelihoods of hospitalization (APR=0.78 and 0.86) and receipt of oxygenation/ventilation (APR=0.87 and 0.61), although not statistically significant.\u003c/p\u003e\n\u003cp\u003eConsidering the interaction of socioeconomic and behavioral characteristics, the results supported the protective effect of the COVID-19 vaccine against infection.\u003c/p\u003e","manuscriptTitle":"Association of COVID-19 vaccine intake with diagnosis, hospitalization, and oxygenation/ventilation: A longitudinal analysis, 2021-2022, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:34:07","doi":"10.21203/rs.3.rs-3925778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da543507-10e6-49ea-bfea-6cf7149c6c68","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28888423,"name":"Health sciences/Diseases"},{"id":28888424,"name":"Health sciences/Health care"},{"id":28888425,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-06-28T08:30:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 21:34:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3925778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3925778","identity":"rs-3925778","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}