Demographic, clinical, Q-PCR and viral sequencing profiles of COVID-19 patients

INTRODUCTION

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a major public health risk in 2020. Coronavirus is a single-stranded positive-sense RNA virus belonging to the Coronaviridae family, which rapidly replicates in a host. The resultant disease inflicted huge distress upon the healthcare facilities worldwide.^[1] The COVID-19 outbreak originated in Wuhan, China and was reported to the World Health Organisation (WHO) on 31 December 2019. COVID-19 was declared a global pandemic by the WHO on 11 March 2020. In India, the first COVID-19 case appeared on January 30, 2020, in Kerala. Furthermore, in early March 2020, positive cases surged in India. Himachal Pradesh reported its first case on March 20, 2020.^[2] Around 45 million confirmed cases of COVID-19 infection have been confirmed by India till November 2025, and more than 3 lakh samples sequenced.^[3,4]

There was no laboratory in the Una district of Himachal Pradesh, India, for conducting quantitative real-time polymerase chain reaction (Q-PCR) of the SARS-CoV-2 virus. The samples were sent to a laboratory situated in another district of HP. This resulted in delayed testing and reporting as the pandemic was at its peak. Thus, a bio-safety level 2 laboratory (BSL-2) was constructed in Una district to circumvent these problems. The laboratory became functional on February 14, 2022. The laboratory has performed 14,767 Q-PCR tests.

COVID-19 infection shows global heterogeneity, so it is important to characterise the clinical and epidemiological features of the patients. In this case-control retrospective study, we are presenting the secondary data of COVID-19 patients for identifying their demographic, clinical, Q-PCR and viral sequencing characteristics from a secondary care hospital of Una district in Himachal Pradesh.

MATERIAL AND METHODS

This study was a secondary retrospective, case-control record-based evaluation of patients tested for COVID-19 by Q-PCR from February 2022 to April 2023 at the reverse transcription– polymerase chain reaction laboratory under the Regional Hospital Una, Himachal Pradesh, India. Information on patient demographics (including age, gender, residing in rural or urban area), Q-PCR test results, clinical categorisation (symptomatic, asymptomatic, vaccination) from the specimen referral form was gathered and analysed. The study included only confirmed positive and negative patients for COVID-19. Patients whose samples were rejected or whose Q-PCR results were found to be inconclusive were excluded from the study. The cause of rejection of samples was the unavailability of sufficient sample quantity for processing due to spillage, etc.

RESULTS

In this retrospective case-control study, we evaluated 1324 laboratory records of patients tested by Q-PCR for SARSCoV-2 causing COVID-19 at our laboratory. Positive (n=662) and negative (n=662) patients were matched for age, gender and type of residential areas (rural/urban). Hence, the distribution of the patients within these parameters was similar. Table 1 provides the details of all the patients.

The majority of our subjects (62.6%) got tested as per their own wishes or for undertaking international or domestic travelling, or for joining jobs or educational institutes as required by the concerned employer or institute. Others (37.4%) were tested as per the recommendation of the treating physicians. According to the guidelines of the Department for Biotechnology, Ministry of Science and Technology, Government of India, 288 positive samples were eligible for whole genome sequencing (WGS).^[6] As expected, there was a significant difference (P<0.0001) between average Ct values for the RdRp gene in positive (24.7; range = 14 to35) versus negative patients (37.7; range = 35.6 to 40).

The average and median ages of patients were 39.9 years and 37 years (range 2 to 100 years), respectively. Age of positive patients showed a significant negative correlation with Ct values of the RdRp gene (Pearson r = -0.1341, R-squared = 0.01798, P= 0.0005), implying that Ct values decrease (higher viral load) as the age of individuals increases.

Males preponderated the study population at 55.6% (n=736) and females at 44.4% (n=588) with a male-to-female ratio of 1.2:1. Almost three-fourths of the patients (n=1000) resided in rural areas, and approximately one-fourth of the patients (n=324) were residents of urban areas.

A total of 66.5% (n=881) of patients recorded symptoms, and rest 33.5% (n=443) had no symptoms. The OR and CI were found to be significant for symptomatic cases as compared to asymptomatic cases for COVID-19 infection (1.9; 95% CI 1.5 - 2.4). Within the positive group, 13.1% (n=174) were asymptomatic and 36.9% (n=488) were symptomatic, which was almost 2.8 times more than the asymptomatic positive patients. Even though the proportion of symptomatic positive patients was higher than that of asymptomatic positive patients, there was no significant difference between their mean Ct values of the RdRp gene (24.6 vs 24.8, P = 0.2), respectively. In the negative group, the percentage of symptomatic and asymptomatic patients was 29.7% (n=393) and 20.3% (n=269), respectively.

Regarding the vaccination status, around 79.4% (n=1051) of individuals were vaccinated, 74.4% (n=985) had received both doses, and only 20.6% (n=273) were either unvaccinated or no information was available for the same. Patients who were not vaccinated or had no information on vaccination were clubbed together for analysis. On comparison, the overall odds of COVID-19 positivity were significantly lower in the vaccinated group than the unvaccinated group (OR 0.3; 95% CI 0.2 - 0.4). Vaccinated constituted of 70.1% (n=464) and unvaccinated constituted of 29.9% (n=198) of positive patients. Further, 65.7% (n=435) of positive patients had received a second dose of vaccination as well. Nevertheless, no significant association was noted between receiving the first or both doses of vaccine and Ct values (OR 0.9; 95% CI 0.7 - 1.3). There was no difference in the vaccination efficacy in positive male and female patients (OR 0.8; 95%CI 0.6–1.1). Meanwhile, in case of negative patients, only 11.3% (n=75) were unvaccinated, and 88.7% (587) were vaccinated; out of these, 83.1% (n=550) patients had been administered both doses. Similarly, there was no gender wise difference in the efficacy of vaccination among negative patients (OR 0.9; 95%CI 0.6–1.5).

As per the guidelines, 288 samples (53.5% males, 46.5% females) were sent to the designated laboratories for whole genome sequencing (WGS) of SARS-CoV-2. Results were available for 259 samples, and in the remaining 29 samples, sequencing had failed. “Omicron other sub-lineage” was the most common lineage (n=192), “Omicron BA.2” variant was found in 20 samples, and no lineage could be detected in 47 samples [Figure 1].

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Figure 1:

Results of sequenced samples

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Out of the 20 individuals infected with the omicron BA.2 variant, 4 were asymptomatic, 6 were symptomatic, 18 were vaccinated, and 2 were unvaccinated. Amid “omicron other sub-lineage”, a greater number of patients (n=150) were asymptomatic, 42 were symptomatic, 162 were vaccinated, and 30 were unvaccinated. The percentages of various sublineages identified were 0.5% each (BA.2.75.6, BA.2.75.7, BA.2.75.9, BA.5, BE.1, BL.1, CK.1, XBB.1.28, 1.0% each (BA.1.1, BY.1, XBB.1, XBB.1.5.16), 1.6% (XBB.1.5), 2.1% each (BA.2.76, XBB.1.16.1), 3.6% (BA.2.75.2), 5.2% (XBB.2.3), 5.7% (BA.2.75.1), 15.6% (BA.2.75), 55.7% (XBB.1.16). Nevertheless, there was no relationship amongst sequencing results, gender (OR 1.4; 95% CI 0.8 - 2.4), residential area (OR 1.2; 95% CI 0.6 - 2.1), vaccination (OR 0.6; 95% CI 0.4 - 1.7), symptomatic status (OR 1.3; 95% CI 0.7 - 2.4) and Ct values (OR1.6; 95% CI 0.9 - 2.8).

DISCUSSION

Our retrospective assessment reported the demographic profiles, vaccination status, symptomatic status, and SARSCoV-2 sequence characteristics in 1324 patients tested for COVID-19 through Q-PCR at a BSL-2 laboratory of a secondary care hospital from Una district of Himachal Pradesh, India. The study results offer insights and understanding of the dynamic profiles of COVID-19 patients in this region.

At the time of testing, the average age of our study subjects was 39.9 years, and the median age was 37 years, although the age range was quite wide, ranging from 2 years to 100 years. Studies have reported varied mean and median ages of patients.^[7] Some have reported a younger population (average/median age being 33.5 years/29 years), while some an older population (average/median age of 63.7 years/54.5 years).^[8,9] Our study found the mean/median age belonging to the tricenarian group as reported earlier.^[10,11] In addition, a significant negative correlation between RdRp gene Ct value and age of positive patients was noted, implying a decrease in Ct values (higher viral load) with increased age. The immunity of a person decreases with age, leading to a slower infection clearance. Studies have also reported an association of older age with higher COVID-19 contagiousness.^[12,13] The individuals in this study mainly got tested themselves either on their own or for joining duties in government, private institutes, joining an educational institute as a new student, or after holidays, travelling within India or abroad, where a COVID-19 Q-PCR report was required. Hence, this could be the reason for a younger population in our study.

The number of males was higher in this study than the number of females, which is consistent with other reports.^[11,14] We found both genders prone to COVID-19 infection. The larger male population in our study might be due to the workplace or travel-related exposure. As the workforce is mostly male-dominant, they might be exposed to COVID-19 infection at the workplace. A systematic review and meta-analysis conducted on gender specific COVID-19 clinical outcomes established male gender as an important risk factor for COVID-19 illness.^[15] In contrast, a study by Kushwaha et al. (2021) described higher odds for COVID-19 infection in females across different age categories, for example, in females of 18-35 years (OR1.03, 95%CI 0.9–1.0), but not in females of 36–55 years (OR 0.8,95%CI 0.8-0.9). The authors concluded that the odds for COVID-19 infection in females increase with lower age and vice versa, as compared to males.^[16] However, several factors like lifestyle choices, COVID-19 appropriate behaviour, sex hormones (androgen response elements/oestrogen response elements), immunity, expression of angiotensin-converting enzyme-2 (ACE-2) receptors expressed by various human organs through which SARS-CoV-2 enters host cells, affect the vulnerability for infection in males and females.^[17]

The share of patients from rural areas (75.5%) was more than that from urban areas (24.5%) in the present study. Una district mainly consists of rural population (91.3%) and a marginal urban population (8.62%), which might be the cause for a greater number of rural patients. ^[18,19] Thus, COVID-19 affected both rural and urban populations. In some studies, a greater positivity rate of COVID-19 infection was found in the urban population. The factors responsible for this might be high population densities and lower immunity in the urban population.^[20] Whereas others found a greater positivity in COVID-19 infection in the rural population, resulting from inaccessible healthcare and a lack of public health outreach programs.^[21,22]

In the present work, the percentage of symptomatic subjects was greater than that of asymptomatic subjects. The global percentage of infected asymptomatic subjects in the COVID-19 positive population was observed to be 40.5% in a meta-analysis study, emphasising the possible spread of asymptomatic infection.^[23] In our study, the symptomatic patients showed a significant association with COVID-19 infectivity compared to asymptomatic patients. These findings are in accordance with multiple reports depicting the connection between symptoms and disease positivity.^[24,25] Symptomatic profiles of the patients can help in the early detection of the disease. In addition, the proportion of symptomatic patients was more than that of asymptomatic patients in the positive group. Despite the higher number of symptomatic positive patients than asymptomatic positive patients, the difference between their average Ct values of the RdRp gene was insignificant. Likewise, others have found a similar distribution of virus-specific genes among symptomatic and asymptomatic infected patients.^[26,27] The asymptomatic cases could be drivers of the infection during the pandemic. Therefore, following strict public health interventions and clinical management for both symptomatic and asymptomatic cases has been recommended.^[26] The symptomatic cases negative for SARS-CoV-2 might be harbouring some other infections, although they were not tested for these other infections.

Approximately four-fifths of the patients were vaccinated, three-fourths had received both doses, and one-fifth were unvaccinated, or no information was available on vaccination. There were higher chances of contracting COVID-19 infection by unvaccinated individuals in comparison to vaccinated individuals. This result is in line with the findings of other studies stating the reduction in the risk of infection in vaccinated people.^[28,29] No gender wise relationship between infected, non-infected patients and vaccination status was revealed. Thus, there was no difference in the efficacy of vaccination in males and females, as supported by the prevailing literature, which found no gender difference in COVID-19 vaccination efficiency.^[30] Analysis between Ct values, symptomatic and vaccination condition in positive subjects, including whether the patient received only one or both doses, did not reveal any association, as reported previously.^[29] Vaccination doses are not the standalone factor impacting the Ct values (viral load) in affected patients, and other factors, such as host genetics, might influence the viral load.

The available sequencing results of our study identified the predominance of omicron sub-lineages (66.7%), and the omicron variant (BA.2) accounted only for 6.9% of the sequenced samples. The Omicron variant initially emerged in Botswana, South Africa, in November 2021, and the WHO later declared it a variant of concern . The high transmissibility of omicron, coupled with the potential to counteract host antibodies obtained via vaccination or previous infectious contact, resulted in community transmission.^[31] XBB.1.16 (55.7%) was the most prominently circulating omicron sub-lineage in the Una district. In India, XBB.1.16 omicron sub-lineage was first reported in Chennai, Tamil Nadu, in December 2022 and was designated as a variant under monitoring in March 2023 and as a variant of interest in April 2023 by the WHO.^[32] The same lineage was the most prevalent one in other Indian states as well.^[32,33] Among the patients (154 males, 134 females) whose samples were sent for sequencing, 84.4% were vaccinated, 15.6% were either not vaccinated or no information could be obtained on vaccination, 77.1% showed symptoms, and 22.9% showed no symptoms. Upon analysis, no association could be deduced between virus lineages, gender, type of residence (rural/urban), vaccination done or not, symptomatic status and RdRp gene Ct values. It can be stated that any variant of SARS-CoV-2 can infect individuals irrespective of the examined parameters. Hence, following appropriate COVID-19 precautionary measures is necessary. Apart from this, research has delineated that different lineages (resulting from mutations in the viral genome) have enhanced the capability of evading the host immune system. The mutations also confer improved infection transmission ability of the virus and need to be very closely supervised for the evolving landscape of the circulating variants.^[34] Therefore, research focusing on the interaction between viral mutations and their influence on host immunity should be carried out.

CONCLUSION

We have shown that patients of all age groups (2 years to 100 years) can be affected by COVID-19. While a major proportion of the patients in our study were symptomatic, many were positive and asymptomatic and posed a threat of spreading infection to others. Such patients should be properly quarantined and monitored. With age, the viral load increases owing to decreased immunity and aged patients need to be prioritised. Omicron sublineages were the prevalent lineages in the sequenced samples and were even found in vaccinated patients. It is important to identify various viral mutations over the course of the pandemic for devising better clinical and public health strategies. More research is still required for understanding the population-level spread and control of the SARS-CoV-2 virus.