Comparison of COVID19(SARS-COV-2) infection, based on gender, age group and sequential waves by RT-PCR analysis among the population of western districts of Odisha

Bhuban Mohan  Panda; Lopamudra; Satish  Dalai; Sanghamitra  Padhi

doi:10.18231/j.joapr.2023.11.1.26.32

Authors

Bhuban Mohan Panda Department of Microbiology, Bhima Bhoi Medical College and Hospital, Balangir, Odisha https://orcid.org/0000-0003-3601-3826
Lopamudra Department of Microbiology, Bhima Bhoi Medical College and Hospital, Balangir, Odisha
Satish Dalai Department of Microbiology, Bhima Bhoi Medical College and Hospital, Balangir, Odisha, India https://orcid.org/0009-0007-8822-9588
Sanghamitra Padhi Department of Microbiology, Bhima Bhoi Medical College and Hospital, Balangir, Odisha, India

DOI:

https://doi.org/10.18231/j.joapr.2023.11.1.26.32

Keywords:

COVID19, RT-PCR, Age group, Gender, Wave of infection

Abstract

As SARS-CoV-2 has rapidly spread all over the world, it is evident that patient’s gender, age, and comorbidity might render these patients more vulnerable to either increased mortality or increased risk of infection. In the first wave of pandemic, percentage of infection was 5% in May 2020, which rose to 23% in August. Towards the end of the year, it was around 1.8%. In second wave, infection gradually increases to 3.4% in the later part of March 2021, reaching maximum of 31% in April. of the recorded positive cases, infection rate in male population was comparatively higher them the female. In September 2020 where the rate of infection was highest in the first wave the male infectivity rate is 16% compared to 7.13% in females. In April 2021, male infection rate was at 21% compared to 9.7% in the female population. The positivity rate in male population was at 3.7% in third wave of infection. During this period the positivity rate in female population was at 1.87%. Our study shows the positivity rare was maximum in the age group of 18-29(25.5%) followed by 30-39(23.5%) and 40-49(17.2%) in 2020. Similar trend was observed in the year 2021 where the infection rate was 24.2%,23.6%,18.46% in the age group of 18-29,30-39 and 40-49 respectively. However, in 2022 maximum infection was recorded in the age group of 18-29 at around 29%, followed by age group of 30-39(20.9%) and 6-17(15%)

Downloads

Download data is not yet available.

References

COVID-19 coronavirus Pandemic. Available from, https//:www.worldometers.info/coronavirus

Chen Y, Liu Q, and Guo D. Emerging coronaviruses, Genome structure, replication, and pathogenesis. J Med Virol. 92, 418 423 (2020).

Zhu N, Zhang D, Wang W et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 382, 727-733(2020).

Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. Sensitivity of Chest CT for COVID-19, Comparison to RTPCR. Radiology 296(2), E115-E117 (2020).

Ye G, Li Y, Lu M, Chen S, Luo Y, Wang S, Wang Y , Wang X. Experience of different upper respiratory tract sampling strategies for detection of COVID-19. J Hosp Infect. 105(1), 1-2 (2020).

Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, Yu J, Kang M, Song Y, Xia J, Guo Q, Song T, He J, Yen HL, Peiris M, Wu J. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med. 382,1177-1179(2020).

Harapan H, Itoh N, Yufika A, Winardi W, Keam S, Te H, Megawati D, Hayati Z, Wagner AL, Mudatsir M. Coronavirus disease 2019 (COVID-19): A literature review. J Infect and Public Health. 8,667-673(2020).

Wu A, Peng Y, Huang B, Ding X, Wang X, Niu P, Meng J, Zhu Z, Zhang Z, Wang J, Sheng J, Quan L, Xia Z, Tan W, Cheng G, Jiang T..Genome composition and divergence of the novel coronavirus (2019 nCoV) originating in China. Cell Host Microbe. 27, 325 328(2020).

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 395(10223), 497-506(2020).

Franco N, Coletti P, Willem L, Angeli L, Lajot A, Abrams S. Inferring agespecific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data. PLoS Comput Biol. 18(3), e1009965(2022).

Fischer J, Jung N, Robinson N, Lehmann C. Sex differences in immune responses to infectious diseases. Infection. 43, 399-403(2015).

Furman D, Hejblum BP, Simon N, Jojic V, Dekker CL, Thiébaut R, Tibshirani RJ, Davis MM.Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc Natl Acad Sci U S A. 111(2),869-874(2014).

WelteT.Risk factors and severity scores in hospitalized patients with communityacquired pneumonia, prediction of severity and mortality. Eur J Clin Microbiol Infect Dis. 31(1),33-47(2012).

Druce J, Garcia K, Tran T, Papadakis G, Birch C. Evaluation of swabs, transport media, and specimen transport conditions for optimal detection of viruses by PCR. J Clin Microbiol. 50(3),1064-5(2012).

www.cdc.gov/coronavirus/2019-ncov/lab/guidelines-clinical-specimens.html

Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, Bleicker T, Brünink S, Schneider J, Schmidt ML, Mulders DG, Haagmans BL, van der Veer B, van den Brink S, Wijsman L, Goderski G, Romette JL, Ellis J, Zambon M, Peiris M, Goossens H, Reusken C, Koopmans MP, Drosten C. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 25(3): 2000045 (2020).

Souyris S, Hao S, Bose S,England AC, Mukherjee U, Seshadri S.Detecting and mitigating simultaneous waves of COVID 19 infections. Sci Rep. 12,16727(2022).

ZhangYP. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID19) in China.Chinese J Epidemiol. 41(2), 145-151(2020).

Kushwaha S, Khanna P, Rajagopal V, Kiran T. Biological attributes of age and gender variations in Indian COVID-19 cases, A retrospective data analysis. Clin Epidemiology Glob Health. 11,100788 (2021)

Chakraborty S. Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm. Annals of Data Sci. (2022)

Nasker SS, Nanda A, Ramadass S. Epidemiological Analysis of SARS-CoV-2 Transmission Dynamics in the State of Odisha, India, A Yearlong Exploratory Data Analysis. Int J Environ Res Public Health. 18,11203 (2021).

JayadevanR, Shenoy R, Anithadevi TS. COVID-19 third wave experience in India, a survey of 5971 adults. Med Rxiv preprint. (2022)

Wenham C, Smith J, Morgan R. COVID-19: the gendered impacts of the outbreak. Lancet 395, 846-8 (2020).

Gupta N. Research to support evidence-informed decisions on optimizing gender equity in health workforce policy and planning. Hum Res Health. 17,46 (2019).

Biswas M, Rahaman S, Biswas TK Haque Z, Ibrahim B. Association of Sex, Age, and Comorbidities with Mortality in COVID-19 Patients, A Systematic Review and Meta-Analysis. Intervirology. 64,36-47(2021).

https,//www.censusindia2011.com/odisha-population.html

Betron M, Gottert A, Pulerwitz J, Shattuck D, Stevanovic-Fenn N. Men and COVID- 19, adding a gender lens. Global Publ Health. 15,1090-1092(2020).

La Vignera S, Cannarella R, Condorelli RA, Torre F, Aversa A, Calogero AE. Sex-specific SARS-CoV2 mortality, among hormone modulated ace2 expression, risk of venous thromboembolism and hypovitaminosis D. Int J Mol Sci. 21(8)2948 (2020).

Davies NG, Klepac P, Liu Y, Prem K, Jit M, CMMID COVID-19 working group; Eggo RM. CMMID COVID-19 working group and Eggo, R.M. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 26,1205-1211 (2020).

Jakhmola S, Baral B, Jha HC. A comparative analysis of COVID-19 outbreaks on age groups and both the sexes of population from India and other countries. J Infect Dev Ctries. 15(3), 333-341(2021).