Sex and age differences in the COVID-19 mortality in East Jakarta, Indonesia

[Full title: Sex and age differences in the COVID-19 mortality in East Jakarta, Indonesia: Analysis of COVID-19 surveillance system]

[Find the meaning and references behind the names: Nur, Tika, Malang, Heart]

Sex and age differences in the COVID-19 mortality in East Jakarta, Indonesia: Analysis of COVID-19 surveillance system Sumiati, 1 Nur Aini, 2 Tika Dwi Tama 3,4 1 Disease Prevention and Control Section, East Jakarta District Health Office, DKI Jakarta, Indonesia; 2 Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia; 3 Department of Public Health, Faculty of Sport Science, Universitas Negeri Malang, Malang, East Java, Indonesia; 4 Centre of Gender and Health, Universitas Negeri Malang, Malang, East Java, Indonesia Abstract Demographic factors have been reported to worsen COVID-19 outcomes. However, there is limited evidence about the different effects of sex and age on COVID-19 death in East Jakarta, Indonesia. This study examined the association between sex and age with COVID-19 mortality. Using COVID- 19 surveillance data of East Jakarta from March 2020 to December 2021, we calculated COVID-19 mortality and examined the risk of COVID-19 death by sex and age. The risk of COVID-19 death associated with sex and age was examined by using Multiple Logistic Regression. A total of 202.412 cases were analyzed and 1.9% of them died The elderly had a 41.88-folds increased risk of COVID-19 mortality than younger patients (<45 years) (aOR 41.88; 95% CI 37.49-46.77; p-value <0.0001). Male had a higher risk of COVID-19 death compared to female (aOR 1.27; 95% CI 1.19-1.35; pvalue <0.0001). Age and sex had a significant association with COVID-19 mortality Adequate management of COVID-19 cases, particularly in the elderly and male patients, may reduce the severity of COVID-19 or even mortality Introduction Since Coronavirus Disease 2019 (COVID-19) was declared as a Public Health Emergency of International Concern (PHEIC) by World Health Organization (WHO), Confirmed COVID-19 cases surpass 278 million with more than 5 million deaths reported worldwide 1 The number of COVID-19 cases gradually increased compared to the previous week due to the emergence of a new variant, namely Omicron 1 It is no different from the global situation, the COVID-19 situation in Indonesia also showed an increasing trend. At the end of 2021, the confirmed cases of COVID-19 reached more than 4 million cases, with active cases of nearly 5.000 2 DKI Jakarta Province was Indonesia’s epicenter of COVID-19 cases. This province contributed to almost 20% of total COVID-19 cases in Indonesia with a mortality rate of 1.57% of all confirmed cases 2 East Jakarta was the area in DKI Jakarta that has the most cumulative COVID-19 cases 3 It has been known that COVID-19 is a big challenge faced by many countries, not only Indonesia. The continued rise in COVID-19 cases and death rates is a signal to recognize what steps can be taken to avoid serious consequences and deaths Prior studies reported that age, sex, obesity, other comorbidities, such as hypertension, heart diseases, diabetes mellitus, and also laboratory parameters were identified as risk factors that increase the chance of dying from COVID-19 4–9 Among risk factors that have been investigated, demographic factors have a considerable influence in determining the severity of the risk of COVID-19 outcomes 6,10,11 However, these results have not been well established, considering that contradictory studies are still found 12 In East Jakarta, similar studies that are conducted to assess the different risks of mortality based on age and sex are limited, especially those that analyzed the big data from the surveillance system. Identifying the risk factors of COVID-19 mortality based on demographic factors is important for mapping the population at risk in the population. So, it will be useful for optimizing case management to promote better outcomes. Therefore, this study was designed to examine the relationship between age and sex with the mortality of COVID-19 Materials and Methods In this study, a secondary data analysis from COVID-19 surveillance in East Jakarta was performed. All confirmed COVID-19 cases between March 2020 and December 2021 that had a completed data on variable age, sex, and status outcome were included as study participants. Based on the national guideline, a person is confirmed as a case of COVID-19 if the result of the RT-PCR is positive 13 The total number of data that were included in the analysis was 202,412 cases. COVID-19 outcome was the dependent variable. A COVID-19 case was classified as deceased if the patient died 13 COVID-19 cases were categorized as discharged if they met the completed isolation criteria 13 The independent variables were sex (male and female) and age. Age was classified into three groups, namely < 45 years old, pre elderly (45-59 years old), and elderly (≥60 years old) 14,15 All variables, including age, sex, and outcome of COVID-19, were described by using descriptive analysis. It was presented in the distribution table that showed the number of frequencies and percentages. Bivariate analysis (the Chi- Square test) was performed to analyze the association of each independent variable (age and sex) with the outcome of COVID- 19. Multiple Logistic Regression was performed out to determine the relationship Journal of Public Health in Africa 2022; volume 13(s 2):2420 Correspondence: Sumiati, Disease Prevention and Control Section, East Jakarta District Health Office, DKI Jakarta, Indonesia, 13310, Tel: +6285692451778 E-mail: balqisiaratu@gmail.com Key words: Age, COVID-19 mortality, East Jakarta, Sex, Surveillance Contributions: Design the work: S, NA. Data analysis: NA. Interpretation of data: S, NA, TDT. Drafting: S, NA, TDT. Review & editing: S, NA, TDT Conflict of interest: The authors declare no potential conflict of interest Acknowledgments: The authors would like to express the gratitude to the East Jakarta Sub Department for Health for giving permission to access the data. Conference presentation: This article was presented at the 4 th International Scientific Meeting on Public Health and Sports (ISMOPHS 2022) Received for publication: 24 October 2022 Accepted for publication: 17 November 2022 This work is licensed under a Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0) ©Copyright: the Author(s),2022 Licensee PAGEPress, ItalyJournal of Public Health in Africa 2022; 13(s 2):2420 doi:10.4081/jphia.2022.2420 Publisher's note: All claims expressed in thisarticle are solely those of the authors and donot necessarily represent those of their affili-ated organizations, or those of the publisher,the editors and the reviewers. Any product thatmay be evaluated in this article or claim thatmay be made by its manufacturer is not guar-anteed or endorsed by the publisher. [Journal of Public Health in Africa 2022; 13(s 1):2420] [page 97]

[Find the meaning and references behind the names: Ace]

between age, sex, and the outcome of COVID-19 as the dependent variable in this study Results There were 202,412 COVID-19 patients in East Jakarta from March 2020 – December 2021 that were included in the analysis. Most COVID-19 patients in East Jakarta were <45 years old (66.4%). The number of elderlies that were confirmed as COVID-19 in East Jakarta reached 11.3% The frequency of COVID-19 cases was higher in females than males. It was 51.9% and 48.1%, respectively. Based on the outcome, 1.9% of COVID-19 patients had died. The result of the descriptive analysis was showed in Table 1 Based on bivariate analysis, it was found that age and sex had a significant association with the outcome of COVID-19 (p-value < 0.05). Those associations were also confirmed in the Multiple Logistic Regression test result. Older COVID-19 patients had a higher risk of deceased than younger patients. Pre-elderly COVID-19 patients (45 – 59 years) had a 9.02-fold risk of dying than patients < 45 years old (aOR 9.02; 95% CI 8.01 – 10.15; p-value: 0.0001). The elderly had 41.88 times higher of experience death (aOR 41.88; 95% CI 37.49 – 46.77; p-value 0.0001). In addition, the male patient was more likely to die than the female patient (Table 2) Discussion Based on the analysis of COVID-19 surveillance data, the mortality rate of COVID-19 in East Jakarta was shown to be higher than that of DKI Jakarta province and the national rate 2 The difference in deaths due to COVID-19 may be caused by several factors, including differences in demographics, presence of underreporting, differences in testing strategies, health system readiness, and presence of comorbidities 16-18 As we know, the case fatality rate (CFR) is calculated by dividing the number of COVID-19 cases that died by the number of confirmed cases of COVID-19 16,17,19 This measure is commonly used to describe the severity of disease in the short term so it is sensitive enough to changes in the denominator 16,17 The fewer confirmed cases of COVID-19, the greater the CFR number 17,19 Conversely, the more confirmed cases of COVID-19, the smaller the CFR 17,19 This study reported that age was significantly associated with COVID-19 death cases. The elderly and pre-elderly showed a higher risk of dying than younger patients This finding was consistent with other studies, both conducted with observational studies and systematic reviews 6,7,10,20,21 Based on meta-analysis study, the median age of COVID-19 patients at high risk of death was 49 years and older, with the age group 65 years and older having the highest risk 10 This age was classified as pre-elderly and elderly 15 Immunity decreases with age so elderly patients have a greater chance of getting severity or even dying. It make older people susceptible to other infections and at great risk of experiencing side effects of drugs that they are consumed 6,10 A previous study also reported that gene expression of angiotensin-converting enzyme 2 (ACE 2) was higher in older than younger ages 6 Angiotensin-converting enzyme 2 (ACE 2) binds to the spike protein and facilitates the entry of SARS-CoV-2 into host cells 22 The high level of ACE 2 plays an important role in the development of COVID-19 disease 23 The vigilance of this vulnerable elderly group has to be a concern in handling COVID-19. Ensuring that the elderly receive socialization about prevention efforts, specific protection, and health services is expected to reduce morbidity and mortality from COVID-19. There was a difference in the risk of death between male and female of COVID-19 patients. Males were reported to have a higher risk of dying than females. The risk of dying among male COVID-19 patients was about two times greater compared to females. 4,6,11,21,24 The different risk might be due to differences in lifestyle, hormone, and immune system 4,6,11,21,24 Males tend to have poorer lifestyles than females, such as smoking, and alcohol consumption. Those lifestyles had been recognized as risk factors for developing comorbid that could worsen the outcome of COVID-19 25,26 As found in the older age group, circulating ACE 2 levels were also higher in male compared to female 6 It makes males have a higher vulnerability to getting severity or even death. Conclusions Age and sex had a significant relationship with COVID-19 mortality. Older patients were at higher risk of dying from COVID-19 than younger patients. The Article [page 98] [Journal of Public Health in Africa 2022; 13(s 2):2420] Table 1. Descriptive analysis of characteristics respondents and outcome of COVID-19. Variable Frequency (n) Percentage (%) Age <45 years old 134314 66.4 Pre-elderly (45–59 years old) 45282 22.4 Elderly ( ≥ 60 years old) 22816 11.3 Sex Male 97326 48.1 Female 105086 51.9 Outcome Discharge 198561 98.1 Deceased 3851 1.9 Total 202412 100.0 Table 2. Association between age and sex with outcome of COVID-19. Variable Outcome Crude OR p-value Adjusted OR p-value Deceased Discharge (95% CI) (95% CI) n % n % Age 0,0001* 0,0001* <45 years old 371 0.3 133943 99.7 Ref Ref Pre-elderly (45-59 years old) 1104 2.4 44178 97.6 9.02 (8.02-10.15) 9.02 (8.01-10.15) Elderly ( ≥ 60 years old) 2376 10.4 20440 89.6 41.97 (37.58-46.87) 41.88 (37.49-46.77) Sex 0,0001* 0,0001* Female 1758 1.7 103328 98.3 Ref Male 2093 2.2 95233 97.8 1.29 (1.21-1.38) 1.27 (1.19-1.35)

[Find the meaning and references behind the names: Zhang, Liu, Mohamed, Khalid, Biswas, June, Ibrahim, Zhao, Gao, Malek, Han, Campbell, Haque, Bhaskar, Jeong, Bin, Allen, Senthil, Raad, Zheng, Kim, Med, Rahaman, Ortega, Guo, Chen, April, Soto, Dina]

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Article [Journal of Public Health in Africa 2022; 13(s 2):2420] [page 99]