Characteristics of subjects

Of the 1,341 enrolled subjects, 718 (54%) were female, and 623 (46%) were male (Table 1). The mean age at enrollment was 44 years (standard deviation [SD] 21, and range 0–99 years old). Two hundred four (15%) had a history of smoking, 373 (28%) had a diagnosed underlying health condition, and the mean number of symptoms before PCR testing was 3.1 (SD 1.3).

Table 1 Characteristics of subjectsDifferences between COVID-19 positives and negatives: Lower frequency of underlying health conditions, shorter period from symptom onset to testing, and higher number of symptoms among COVID-19 positives

First, we investigated whether there were significant differences in characteristic information between COVID-19 positives (n = 477 (36%)) and negatives (n = 864 (64%)) (Table 1). Among them, COVID-19 positives exhibited a lower frequency of underlying health conditions compared to negatives (odds ratio [OR] 0.6 (95% confidence interval range [CI] 0.4–0.7), p < 0.001). Furthermore, COVID-19 positives had a shorter period from symptom onset to testing (mean ± SD: 3.0 ± 1.7 days vs. 4.8 ± 4.4 days, p < 0.001), and a higher number of symptoms (mean ± SD: 3.4 ± 1.4 vs. 2.9 ± 1.3, p < 0.001) compared to COVID-19 negatives.

COVID-19 infection and symptoms: higher frequency of upper respiratory and systemic symptoms, but lower frequency of gastrointestinal symptoms among COVID-19 positives

We next analyzed the differences in symptoms between COVID-19 positives and negatives (Table 2 and Additional file 1). A total of 32 symptoms were identified. Among these, COVID-19 positives experienced fever (OR 1.5 (95% CI 1.1-2.0), p = 0.006, q = 0.02), runny nose (OR 1.4 (95% CI 1.1–1.8), p = 0.002, q = 0.009), cough (OR 1.5 (95% CI 1.2–1.8), p < 0.001, q = 0.005), sore throat (OR 2.4 (95% CI 1.9-3.0), p < 0.001, q < 0.001), headache (OR 1.9 (95% CI 1.4–2.5), p < 0.001, q < 0.001), and joint pain (OR 2.7 (95% CI 1.8–4.1), p < 0.001, q < 0.001) significantly more frequently than negatives. Conversely, COVID-19 positives experienced diarrhea (OR 0.2 (95% CI 0.1–0.4), p < 0.001, q < 0.001) and nausea (OR 0.3 (95%CI 0.1–0.6), p < 0.001, q < 0.001) significantly less frequently than negatives. Between underlying health condition positives (n = 373) and negatives (n = 968), less frequency of sore throat (OR 0.6 (95% CI 0.4–0.7), p < 0.001, q < 0.001) and headache (OR 0.4 (95% CI 0.2–0.5), p < 0.001, q < 0.001) symptoms among underlying health condition positives compared to the negatives were identified (Additional file 1).

Table 2 Differences between COVID-19 positives and negatives in frequency of symptoms

These data suggest differences between COVID-19 positives and negatives in the development of certain COVID-19-related symptoms, particularly a higher frequency of upper respiratory and systemic symptoms. When evaluating patients with gastrointestinal symptoms, consideration of symptoms derived from diseases other than COVID-19 infection may be warranted.

Omicron vs. pre-omicron: higher testing positivity rate, shorter period from symptom onset to testing, and higher frequency of upper respiratory and systemic symptoms during omicron strain predominant period compared to pre-omicron strain predominant period

We next analyzed the differences between pre-omicron strain predominant period (from October 2020 to December 2021) and omicron strain predominant period (from January 2022 to March 2023) (Table 3). Omicron strain predominant period had a higher positive rate in testing (51% vs. 5.0%, OR 20 (95% CI 13–31), p < 0.001), younger age distribution (43 ± 20 years old vs. 46 ± 22 years old, p = 0.005), less frequency of subjects with underlying health condition (24% vs. 35%, OR 0.6 (95% CI 0.5–0.7), p < 0.001), shorter period from symptom onset to testing (2.9 ± 1.4 days vs. 5.3 ± 3.7 days, p < 0.001), and more number of symptom (3.1 ± 1.4 vs. 2.9 ± 1.3, p = 0.04). In terms of symptoms, subjects in the omicron strain predominant period experienced runny nose (OR 1.3 (95% CI 1.1–1.7), p = 0.02, q = 0.09), sore throat (OR 3.7 (95% CI 2.7–4.6), p < 0.001, q < 0.001), headache (OR 1.8 (95% CI 1.4–2.5), p < 0.001, q < 0.001), and joint pain (OR 1.8 (95% CI 1.1–2.8), p = 0.01, q = 0.05) significantly more frequently than subjects in the pre-omicron strain predominant period (Table 4 and Additional file 3). Conversely, subjects in the omicron strain predominant period experienced respiratory distress (OR 0.5 (95% CI 0.3–0.9), p = 0.01, q = 0.05), nausea (OR 0.5 (95% CI 0.3–0.8), p = 0.008, q = 0.05) and taste disorder (OR 0.1 (95% CI 0.06–0.3), p < 0.001, q < 0.001) significantly less frequently than positives in the pre-omicron strain predominant period.

Table 3 Differences between omicron strain predominant period and pre-omicron strain periodTable 4 Differences between Omicron period and Pre-omicron period in frequency of symptomsDrive-through center factors and COVID-19 testing: the importance of interviewing about estimated transmission route and nasopharyngeal swab sample collection technique in drive-through COVID-19 PCR testing

As additional factors associated with COVID-19 PCR testing, we analyzed the associations between the estimated transmission route or the experience of sample collection among collectors and the PCR testing results.

In the analysis between the estimated transmission route and PCR testing, COVID-19 positives reported the presence of an estimated transmission route (OR 2.5 (95% CI 2.0-3.2), p < 0.001, q < 0.001) significantly more often than negatives (Table 5). Specifically, contact with infected persons at home (OR 4.5 (95% CI 3.1–6.5), p < 0.001, q < 0.001) and office or school (OR 2.9 (95% CI 2.1–4.1), p < 0.001, q < 0.001) were identified as significantly associated with COVID-19 positivity. These results suggest the importance of doctors conducting interviews about estimated transmission routes prior to making decisions regarding PCR testing.

Table 5 Association between estimated transmission route and COVID-19 testing

Next, we analyzed the associations between the number of sample collection among collectors and PCR testing results. An increase in the testing positivity rate was identified based on the collectors’ experience in sample collection among 49 collectors (B 7.2 (95% CI 2.8–12, p = 0.002)) (Fig. 1). This result emphasizes the importance of acquiring the technique for nasopharyngeal swab sample collection in a drive-through testing system.

Fig. 1

Association between sample collection experience and COVID-19 PCR testing result. Nasopharyngeal swab sample was collected by 49 collectors in charge, and association between the number of sample collection among collectors and PCR testing positive rate was analyzed

Multivariate analysis: various factors contributing to drive-through nasopharyngeal COVID-19 PCR testing

Finally, to confirm whether the significant factors identified above (Tables 1, 2 and 5; Fig. 1) were independently associated with COVID-19 PCR testing, multivariate analysis was performed. The analysis revealed that estimated transmission route at home (B 3.9 (95% CI 2.5–5.8), p < 0.001) was the strongest variable independently associated with testing positivity. Estimated transmission route at office or school (B 2.8 (95% CI 1.9–4.2), p < 0.001), joint pain (B 2.7 (95% CI 1.6–4.6), p < 0.001), sore throat (B 2.4 (95% CI 1.7–3.3), p < 0.001), fever (B 2.4 (95% CI 1.6–3.6), p < 0.001), cough (B 2.1 (95% CI 1.5–3.1), p < 0.001), elderly adult (age ≥ 65 years old) patients (B 2.0 (95% CI 1.2–3.4), p = 0.01), number of sample collection by collectors (B 1.5 (95% CI 1.3–1.7), p < 0.001), time from symptom onset to testing (day) (B 0.8 (95% CI 0.7–0.9), p < 0.001), underlying health condition (B 0.6 (95% CI 0.5–0.9), p = 0.008), and diarrhea (B 0.4 (95% CI 0.2–0.8), p = 0.01), were identified as significant factors independently associated with PCR testing results (Fig. 2 and Additional file 4). These findings suggest that not only symptoms but also background information such as estimated transmission route, underlying health condition, age of patients, as well as the technique of sample collection, are important for the effectiveness of the nasopharyngeal swab drive-through COVID-19 PCR testing system.

Fig. 2

Factors associated with drive-through nasopharyngeal COVID-19 PCR testing positivity. Multivariate binary logistic regression model analyses, with B scores and their 95% confidence interval ranges are shown. Variables with significance (p < 0.05) in univariate analysis were applied to the multivariate analysis. Variables are shown from the highest B score to lower. Results for univariate analyses are shown in Additional file 4. lna; log natural