Hypertension, Gender, Older Age, and Their Relationships with COVID-19 Mortality: Meta-Analysis
DOI:
https://doi.org/10.26911/jepublichealth.2021.06.01.10Abstract
Background: Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Corona virus 2 (SARSCoV-2). SARS-CoV-2 is a new type of coronavirus that has never been pre vi ously identified in humans. Globally, 213 countries (as of August 11, 2020) are facing serious consequences from the ongoing COVID-19 pandemic. This study aimed to ana lyze the magnitude of the relationship of hyper tension, gender, and older age to COVID-19 mortality with a meta-analysis study.
Subjects and Method: This was a systematic review and meta-analysis conducted by follow ing the PRISMA flow diagram. The article search process is carried out through a journal database which includes: PubMed, Science Direct, Springer Link, and PMC Europe by selecting articles published in 2020-2021. The keywords used included: “sex” OR “gender” AND “older age” AND “hypertension” AND “mortality” OR “death” OR “fatal outcome” OR “Predictors outcome” OR “Impact” AND “coro navirus” OR “Covid-19” OR “SARS-COV-2” OR “2019 n-Cov” OR “severe acute respira tory syn drome related coronavirus”. The inclu sion cri teria were full text articles with a retro spec tive cohort study design. The article is in English, and the analysis used is multivariate with adjusted Odds Ratio. Eligible articles were analyzed using the Revman 5.3 app.
Results: A total of 20 articles were reviewed in this study. A meta-analysis of 10 retrospective cohort studies showed that hypertension increased COVID-19 mortality by 1.40 times compared with no hypertension (aOR= 1.40; 95% CI= 1.04 to 1.89; p = 0.030). A meta-ana lysis of 10 retrospective cohort studies showed that males had a 1.42 times increased risk of COVID-19 mortality compared to females (aOR= 1.42; 95% CI= 1.20 to 1.67; p<0.001). Meta-analysis of 7 retrospective cohort studies showed that old age has a 3.42 times increased risk of COVID-19 death compared to younger age (aOR=4.15; 95% CI= 2.35 to 7.32; p <0.001).
Conclusion: Hypertension, gender, and old age increase the risk of dying from COVID-19.
Keywords:
Hypertension, Gender, Old Age, COVID-19 Death, meta-analysisCorrespondence
Annissa Devi Permata. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami, 36A, Surakarta 57126, Central Java, Indonesia. Email: 96annissadevipermata@ gmail.com
BACKGROUND
Coronavirus Disease (COVID-19) was first documented in Wuhan City, Hubei Pro vince, China in December 2019 as an out break of pneumonia. Based on established phylogeny, taxonomy and practice, on February 11, 2020, the World Health Orga nization (WHO) named the disease COVID-19, WHO declared COVID-19 a global emergency on January 30, 2020 and as a pandemic on March 11, 2020. Globally, 213 countries (as of 11 August 2020) are facing serious consequences from the ongoing COVID-19 pandemic (Eslam et al., 2020).
Based on the World Health Organiza tion (WHO), as of February 6, there were 104,370,550 confirmed cases and 2,271,180 deaths due to COVID-19 worldwide (CDC, 2020). The current standard of care test for diagnosing COVID-19 is to collect a naso pharyngeal swab (NP) and use a reverse transcription polymerase chain reaction (RT-PCR) assay to detect SARS-CoV-2 RNA (Severe Acute Respiratory Syndrome Coro navirus-2 Ribonucleic Acid). Investigation of risk factors for mortality with COVID-19 in patients has mostly focused on patient characteristics such as older age, gender, and comorbidities (Magleby, 2020).
Comorbid patients with COVID-19 have a high risk of disease severity, ICU admission, including death. Hypertension is the most common comorbidity among COVID-19 patients and the prevalence of death among hypertensive patients due to COVID-19 is 58.3% (Musharrat, 2020). Global data also shows a higher COVID-19 case fatality rate among men than women. Most of the few countries for which data are available show a male to female case fatality ratio higher than 1.0, ranging up to 3.5 in some cases (Dehingia et al., 2020). Old age also has a higher mortality rate. About 80% of deaths have occurred in patients aged > 60 years in South Korea and Italy (Kang et al., 2020).
Primary Study Criteria Total
1 2 3 4 5 6 7 8 9 10 11 12
Hoobs et al. (2021) 1 1 1 0 1 1 1 0 1 1 1 1 10
Imam et al. (2020) 1 1 1 1 1 1 1 1 1 1 1 1 12
Islam et al. (2020) 1 1 1 1 1 1 1 0 1 1 1 1 11
Leulseged et al. (2021) 1 1 1 1 1 1 1 0 1 1 1 1 11
Yahui et al. (2021) 1 1 1 1 1 1 1 0 1 1 1 1 11
Cedano et al. (2020) 1 1 1 1 1 1 1 0 1 1 1 1 11
Gerwen et al. (2020) 1 1 1 0 1 1 1 0 1 1 1 1 10Research in the Federal Capital Terri tory (FCT) state showed that significant risk factors were associated with COVID-19 mortality in older age and male gender, progressively the older age group experi enced an increase of about 51 times and in men 78% higher than women in COVID-19 mortality (Elimian et al., 2020).
COVID-19 mortality and outcomes differ significantly around the world. Pakistan has passed the peak of the pande mic. Pakistan's death rate of 2.13% is significantly lower than that reported in the Americas and Europe and lower than that of its neighbors Iran, Afghanistan and China, but comparable to India. This study reported deaths by gender, old age and hypertension (Nasir et al., 2020).
During the COVID-19 pandemic, vari ous studies have been carried out to see the relationship between hypertension, gender and old age with COVID-19 mortality, but the results of the research still do not show consistent results. Further analysis is needed to arrive at a convincing conclusion. Therefore, this study may help further in understanding the results of the collection of COVID-19 deaths, particularly the asso ciation with hypertension, gender, and old age with COVID-19 mortality. Researchers use a systematic review approach to rele vant studies by conducting meta-analysis.
SUBJECTS AND METHOD
1.Study Design
This study is a systematic review and meta-analysis. The articles used in this study were obtained from several databases, including PubMed, Science Direct, Springer Link, and PMC Europe. The keywords to search for articles were as follows: “sex” OR “gender” AND “older age” AND “hyperten sion” AND “mortality” OR “death” OR “fatal outcome” OR “Predictors outcome” OR “Impact” AND “coronavirus” OR “Covid-19” OR “SARS-COV-2” OR “2019 n-Cov” OR “severe acute respiratory syn drome related coronavirus”.
2. Inclusion Criteria
Articles included in this study must be full papers. Titles that match hypertension, gender, and old age are associated with death in COVID-19 patients, Articles selected for old age are with age charac teristics >50 years, articles that use the design retrospective cohort, article publish ed in English, the analysis used was multi variate by ascertaining the adjusted odds ratio (aOR).
3.Exclusion Criteria
The articles excluded in this study were the sample in articles n < 100, and not multivariate analysis studies, mixed studies such as stroke, cerebrospinal hemorrhage, chronic liver disease and others.
4.Operational Definition of Variables
The population in the study included COVID-19 patients with intervention in the form of hypertension, gender and old age.
The comparison was not hypertension, female gender and not old age and out comes in the form of COVID-19 death.
COVID-19 is a disease that has shown seri ous infection or severe symptoms causing death caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2).
Hypertension is a major cardio vascular disease and premature death, due to increased intravascular volume as one of the risks of COVID-19 death.
Gender is the biological difference between women and men as one of the risks of death from COVID-19
Old age is a phase of decreasing physical ability and susceptibility to infection as a person ages as one of the risks of COVID-19 death.
5.Data Analysis
Data processing was carried out by Review Manager (RevMan 5.3) by calculating effect size and heterogeneity to determine the combined research model and form the final result of the meta-analysis.
RESULTS
The article review process can be seen in the PRISMA flow diagram in Figure 1. Figure 2 shows the areas where articles were taken according to the inclusion crite ria. This meta-analysis analyzed 3 articles from Africa, 8 from North America, 4 from Asia, and 5 from Europe.
Figure 1. PRISMA Flow Diagram
Figure 2. Distribution of Primary Studies by Continent (Buzan Tony, 2021)
The assessment of the quality of the primary studies was carried out quanti tatively and qualitatively which can be seen in Table 1, Table 2 and Table 3 below. This research was conducted using the Critical Appraisal Checklist for Cohort Study sourced from the Center for Evidence Based Management (CEBMa, 2014).
Table 1. Quality Assessment of Hypertension Articles: Retrospective Cohort Study
Primary Study | Criteria | Total | |||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
Bourgne et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Hashemi et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Hoobs et al. (2021) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Leulseged et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Magleby et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Osibagun et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Rosental et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Surendra et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Walker et al. (2021) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Yip et al. (2021) | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Answer: 1 = Yes and 0 = No
Table 2. Assessment of Article Quality Gender: Retrospective Cohort Study
Primary Study | Criteria | Total | |||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
Elimian et al. (2021) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Guzman et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Harisson et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Hashemi et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Hoobs et al. (2021) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Leulseged et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Manuel et al. (2020) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
Rossa et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Rosental et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Surendra et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Answer: 1 = Yes and 0 = No
Table 3. Quality Assessment of Old Age Articles: Retrospective Cohort Study
Primary Study | Criteria | Total | |||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
Hoobs et al. (2021) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Imam et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
Islam et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Leulseged et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Yahui et al. (2021) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Cedano et al. (2020) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 11 |
Gerwen et al. (2020) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 10 |
Answer: 1 = Yes and 0 = No
Based on the assessment of the quality of the primary articles above, the article quality scores are 10 to 11. This indicates that the articles have good quality for meta-analysis (CEBMa, 2014).
Table 4. Description of the primary studies of hypertension included in the meta-analysis
No | Author (Year) | Country (Conti nent) | Study Design | Sample | P (Popula tion) | I (Inter ven tion) | C (Com pa rison) | O (Out come) |
1 | Bourgne et al (2021) | France (Europe) | Retrospective Cohort Study | 287 | COVID-19 patient | Hyper tension | Not Hypertension | COVID-19 death |
2 | Hashemi et al. (2020) | USA (North America) | Retrospective Cohort Study | 363 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
3 | Hoobs et al (2021) | USA (North America) | Retrospective Cohort Study | 502 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
4 | Leulseged et al. (2021) | Ethiopia (Africa) | Retrospective Cohort Study | 429 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
5 | Magleby et al. (2020) | USA (North America) | Retrospective Cohort Study | 678 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
6 | Osibagun et al. (2021) | Nigeria (Africa) | Retrospective Cohort Study | 2184 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
7 | Rosental et al (2021) | USA (North America) | Retrospective Cohort Study | 64781 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
8 | Surendra et al. (2021) | Indonesia (Asia) | Retrospective Cohort Study | 4265 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
9 | Walker et al. (2021) | UK (Eropa) | Retrospective Cohort Study | 347 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
10 | Yip et al. (2021) | Hongkong (Asia) | Retrospective Cohort Study | 1040 | COVID-19 patient | Hypertension | Not Hypertension | COVID-19 death |
1. Results of the Relationship between Hypertension and Covid-19 Death
Figure 3. Forest plot of the relationship between hypertension and Covid-19 mortality
Based on the results of the forest plot (Figure 3). The results of a meta-analysis of a retrospective cohort study showed that hypertension increased the incidence of death in COVID-19 patients by 1.40 times compared to non-hypertensive (p = 0.030). The heterogeneity of the research data shows I²= 77%, so that the distribution of the data is declared heterogeneous (random effect model).
Figure 4. Funnel plot of the relationship between hypertension and Covid-19 mortality
The funnel plot (Figure 4) shows no publica tion bias as indicated by the plots on the right and left sides being symmetrical to each other and not forming an inverted funnel, where 5 plots are on the right and 5 plots are on the left. The plot on the left of the graph appears to have a standard error between 0 and 0.5 and the plot on the right has a standard error between 0.1 and 0.5.
Table 5. Description of the primary studies by gender included in the meta-analysis
No | Author (Year) | Country
| Study Design | Sam ple | P (Popu lation) | I (Inter ven tion) | C (Compa rison) | O (Out come) |
1 | Elimian et al (2021) | Nigerian | Retrospec tive Cohort Study | 36,496 | COVID-19 patient | Male | Female | COVID-19 mortality |
2 | Guzman et al. (2020) | England | Retrospective Cohort Study | 347 | COVID-19 patient | Male | Female | COVID-19 mortality |
3 | Harisson et al. (2020) | United States | Retrospective Cohort Study | 31461 | COVID-19 patient | Male | Female | COVID-19 mortality |
4 | Hashemi et al. (2020) | United States | Retrospective Cohort Study | 363 | COVID-19 patient | Male | Female | COVID-19 mortality |
5 | Hoobs et al (2021) | United States) | Retrospective Cohort Study | 502 | COVID-19 patient | Male | Female | COVID-19 mortality |
6 | Leulseged et al. (2021) | Ethiopia | Retrospective Cohort Study | 429 | COVID-19 patient | Male | Female | COVID-19 mortality |
7 | Manuel et al (2020) | Spain | Retrospective Cohort Study | 272 | COVID-19 patient | Male | Female | COVID-19 mortality |
8 | Rossa et al. (2021) | Italy | Retrospective Cohort Study | 1538 | COVID-19 patient | Male | Female | COVID-19 mortality |
9 | Rosental et al. (2021) | United States | Retrospective Cohort Study | 64781 | COVID-19 patient | Male | Female | COVID-19 mortality |
10 | Surendra et al. (2021) | Indonesia | Retrospective Cohort Study | 4265 | COVID-19 patient | Male | Female | COVID-19 mortality |
2.Results of the Relationship of Gender with Covid-19 Deaths
Figure 5. Forest plot of the relationship between gender and Covid-19 deaths
Based on the results of the forest plot (Figure 5) The results of the meta-analysis of retro spective cohort studies showed that gender increased the incidence of death in COVID-19 patients by 1.42 times compared to female sex (p<0.001). The heterogeneity of the research data shows I²= 77% so that the distribution of the data is declared heteroge neous (random effect model).
Figure 6. Funnel plot of the relationship between sex and Covid-19 deaths
The funnel plot (Figure 6) shows a publica tion bias which is indicated by the plots on the right and left sides being symmetrical to each other and not forming an inverted funnel, where 5 plots are on the right and 5 plots are on the left. The plot on the left of the graph appears to have a standard error between 0 and 0.3 and the plot on the right has a standard error between 0 and 0.5.
Table 5. Description of the primary studies of old age included in the meta-analysis
Author (Year) | Country | Study Design | Sample | P (Popu lation) | I (Inter vention) | C (Compa rison) | O (Out come) |
Hoobs et al. (2021) | United States | Retro spec tive Cohort Study | 502 | COVID-19 pati ents aged 65-79 years | Young age | Old Age | COVID-19 deaths |
Imam et al. (2020) | United States | Retro spec tive Cohort Study | 1305 | COVID-19 pati ents aged >60 years | Young age | Old Age | COVID-19 deaths |
Islam et al. (2020) | Bangla desh | Retro spec tive Cohort Study | 1016 | COVID-19 pati ents aged >60 years | Young age | Old Age | COVID-19 deaths |
Leulseged et al. (2021) | Ethiopia | Retro spec tive Cohort Study | 429 | COVID-19 pati ents >50 years old | Young age | Old Age | COVID-19 deaths |
Yuhui et al. (2021) | China (Asia) | Retro spec tive Cohort Study | 396 | COVID-19 pati ents >65 years old | Young age | Old Age | COVID-19 deaths |
Cedano et al. (2020) | United States of America | Retro spec tive Cohort Study | 132 | COVID-19 pati ents aged 51-72 years | Young age | Old Age | COVID-19 deaths |
Gerwen et al. (2020) | United States of America | Retro spec tive Cohort Study | 4,343 | COVID-19 pati ents >60 years old | Young age | Old Age | COVID-19 deaths |
3.Results of the relationship between old age and covid-19 deaths
Figure 4. Forest plots the relationship between old age and Covid-19 mortality
Based on the results of the forest plot (Figure 4), the results of a meta-analysis of retrospective cohort studies show that old age increases mortality in COVID-19 pati ents. The results of a meta-analysis of retro spective cohort studies showed that old age increased the incidence of death in COVID-19 patients by 4.15 times compared to young age (p < 0.001). The heterogeneity of the research data shows I²= 82% so that the distribution of the data is declared heterogeneous (random effect model).
Figure 5. Funnel plot of the relationship between old age and Covid-19 deaths
The funnel plot (Figure 5) shows a publica tion bias which is indicated by the plots on the right and left sides being symmetrical to each other and not forming an inverted funnel, where 4 plots are on the right and 4 plots are on the left. The plot on the left of the graph appears to have a standard error between 0 and 0.5 and the plot on the right has a standard error between 0 and 1.5.
DISCUSSION
This systematic review and meta-analysis research raised the theme of the relation ship between hypertension, gender and old age with COVID-19 mortality. This meta-analysis study uses research sources that control confounding factors or confounding factors that can be seen from the inclusion requirements of the study, namely multi variate analysis and the statistical results reported are adjusted odds ratio (aOR). The combined results of the relationship between hypertension, sex and old age with COVID-19 mortality were processed using the RevMan 5.3 application, while the results of the systematic study and meta-analysis were presented in the form of forest plots and funnel plots.
The relationship between hyper ten sion and Covid-19 deaths
The forest plot results in this meta-analysis using a retrospective cohort showed that hypertension was 1.40 times the risk of dying from COVID-19 compared to without hypertension and the results were statisti cally significant (aOR=1.40; 95% CI= 1.04 to 1.89; p=0.030).
A similar study was conducted by Khan et al. (2020), who stated that hyper tension had a 3.6 times risk of dying from COVID-19 (OR=3.6; 95% CI=1.6-7.8; p= 0.001). This is supported by the theory of angiotensin converting enzyme 2 (ACE-2) in the body, thereby increasing its express ion and causing damage to alveolar cells. These damaged alveolar cells in turn cause a series of inflammatory responses of the body that lead to a pattern of acute respire tory distress syndrome and even death. In line with this research, Darquenes et al. (2021) also stated that hypertension was 2.93 times the risk for COVID-19 death (aOR= 2.93; 95% CI=1.59-5.43; p=0.0006).
Yuhui et al. (2020) mentioned that in a recent review, hypertension was consider ed as the main independent factor for poorer clinical outcome. hypertension alone contributes only to a relatively small degree to the development of severe infection, but not to death or the development of ARDS/ respiratory failure. Also, after ARDS/ respi ratory failure developed hypertension in all investigated circumstances, severe respire tory failure may increase mortality in COVID-19 patients.
Relationship between gender and Covid-19 deaths kematian
The forest plot results in this meta-analysis using a retrospective cohort showed that male sex was at 1.42 times the risk of dying from COVID-19 compared to male female and the results were statistically significant (aOR=1.42; 95% CI= 1.20 to 1.67; p<0.001).
The results of this study are supported by Minhas et al. (2021), that the male sex is at risk for COVID-19 death, the female sex is at 2.04 times the risk of dying from COVID-19 compared to the male sex and the results are statistically significant (aOR= 2.04; 95% CI= 1.44 to 2.90; p < 0.001).
Gupta et al. (2020) in their study stated that the male sex was at risk of 1.96 times for COVID-19 death compared to the female sex and the results were statistically significant (OR= 1.96; 95% CI= 1.34 to 2.90; p= 0.001). These results are consist ent with the number of X chromosomes containing many genes that code for the modulation of immune function. Fe males have a higher proportion of this immune-boosting Mgen which is usually protective. Other things like increasing the expression and activation of angiotensin type 2 (AT2) receptors in women lead to a stronger anti-inflammatory immune response.
Relationship between old age and Covid-19 death
The forest plot results in this meta-analysis using a retrospective cohort showed that older people were 4.15 times more likely to die from COVID-19 compared to young people and the results were statistically significant (aOR=4.15; 95% CI= 2.35 to 7.32; p<0.001).
Chung et al. (2020) in their study stated that old age has a 7.10 times risk of dying from COVID-19 compared to young people and the results are statistically sig nificant (OR= 7.10; 95% CI= 1.82 to 27.6; p= 0.005). This study is in line with Gupta et al. (2020) in their study stated that old age is at risk of 1.02 times for COVID-19 death compared to young age and the results are statistically significant (OR= 1.02; 95% CI= 1.01 to 1.04; p= <0.001).
A similar study on the association of old age with COVID-19 mortality was conducted by Khan et al. (2020), who stated that age was 8.3 times more likely to die from COVID-19 compared to younger age and the results were statistically signi ficant (OR=8.3; 95% CI=1.1 to 63.1; p= 0.040). Additional findings in our study suggest that the risk of death is higher among older patients with a higher risk among older adults similar to global trends. Other reasons suggested for higher morta lity include behavioral, social and biological differences. .
Klang et al. (2020) stated that old age is 1.7 times the risk 7.10 times for COVID-19 death. In immunopathology, susceptibi lity to infection in the elderly is usually explained by immunosenescence. Innate immune cells are impaired as a result, cells involved in innate immunity are not acti vated efficiently during infection, and pro gression to an adaptive immune response does not occur in a coordinated manner. These changes reduce the effectiveness of viral clearance and increase the likelihood of triggering an aberrant immune response in which cytokines are extensively released by activated immune cells, generating a cytokine storm. Another well-known feature of aging immunity is chronic sub clinical systemic inflammation, also known as inflammation. Inflammation is the main pathogenic mechanism in COVID-19. Therefore, inflammation is thought to contribute to poorer outcomes in elderly patients with COVID-19.
The limitation of this study is that there is a publication bias shown in the funnel plot of the case control study in the variables of gender and old age. There is a language bias, because in this study the selected articles were only published in English, thus ignoring articles in other languages. Search bias because in this study, researchers only used 4 databases (PubMed, Science Direct, Springer Link, and PMC Europe) thus ignoring other search sources.
AUTHOR CONTRIBUTION
Annissa is the main researcher who selects the topic, searches and collects research data. Bhisma Murti and Didik G Tamtomo played a role in analyzing data and review ing research documents.
FUNDING and sponsorship
This study is self-funded.
CONFLICT OF INTEREST
There is no conflict of interest in this study.
ACKNOWLEDGEMENT
We are very grateful to database providers PubMed, Science Direct, Springer Link and Europe PMC.
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Published
2021-07-11
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Vol. 6 No. 1 (2021)
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Articles
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