Skip to main content
NCBI home page
Gerontology & Geriatric Medicine logoLink to Gerontology & Geriatric Medicine
. 2021 Dec 1;7:23337214211057392. doi: 10.1177/23337214211057392

Factors Associated for Mortality of Older People With COVID 19: A Systematic Review and Meta-analysis

H D W T Damayanthi 1,✉, K I P Prabani 1, Ishanka Weerasekara 2,3
PMCID: PMC8649451  PMID: 34888405

Abstract

Background

Whilst people of all ages are affected in some way by COVID-19 virus, older people are at a high mortality risk. This study aimed to systematically review the numerous factors associated with mortality among COVID-19 infected older people.

Method

PubMed and Science Direct were searched from inception to the April 15, 2021. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and Joanna Briggs Institute critical appraisal tool to assess the methodological quality of the included studies.

Results

Of the 4957 studies identified, 20 were included in the qualitative analysis, while 10 were included in the quantitative analysis. Male sex (OR = 2.22, 95% CI = 1.23–3.99), age (over 75 years old) (OR = 3.36, 95% CI = 2.30–4.90), Dementia (OR = 3.69, 95% CI = 1.99–6.83) and Dyspnoea (OR = 3.16, 95% CI = 2.61–3.82), were found to be significantly associated with mortality. There is no significant association between Diabetes, or Hypertension.

Conclusion

Older age, male gender, dyspnoea and dementia were associated with a greater risk of death of older people from COVID-19 infection. These findings may help health care professionals to identify high-risk groups, facilitate appropriate remedial measures, and control mortality among older people.

Keywords: aging, community, gerontology, long-term care, nursing

Introduction

Globally, people of all ages are affected in some way by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) (also known as COVID-19) pandemic since it was reported in Wuhan, China, in December 2019 (Chen et al., 2021). Among all age groups, older people who were defined as those of age 60 or above are at a risk of getting the infection and are at a higher risk of dying from the illness (Leung, 2020; Lithander et al., 2020). In May 2021, Centres for Disease Control and Prevention highlighted that older people are at a greater risk of hospitalization or dying with COVID-19 infection due to age-related decline in immune function (Centers for Disease Control and Prevention, 2021; Kang & Jung, 2020; Yang & Li, 2020). The majority of deaths associated with COVID-19 were due to a respiratory failure, sepsis, cardiac failure, kidney injury, or coagulopathy (Yang & Li, 2020).

Globally, older people are at a higher risk of getting COVID-19. Eighty percent of deaths reported in the U.S. have been in adults 65 and older (Centres for Disease Control and Prevention, 2021). By May 14, 2021, in Australia, most deaths have been reported in people aged 70 years and over (Health, 2021). Further, the likelihood of mortality in this age group was comparatively higher in the Asian region (Susilowati et al., 2020).

Numerous published studies focused on factors affecting the mortality of older people diagnosed with COVID-19. Identified factors related to the increased risk of mortality are demographic characteristics, pre-existing diseases or clinical aspects, and social factors (Tarteret et al., 2021; Williamson et al., 2020). Yanez et al. (2020) found that persons aged 65 or older had strikingly higher COVID-19 mortality rates than younger individuals. Severe cognitive decline, respiratory symptoms, low Barthel index, and lymphocytopenia were identified as factors associated with mortality (Heras et al., 2020). Living with someone of working age, living in a care home, living in neighbourhoods with the highest population density were found to be social factors associated with the mortality of older people diagnosed with COVID-19 (Brandén et al., 2020).

It is essential to summarize the significant factors associated with mortality to provide reliable evidence for clinical decision-makers, public health officials, researchers, and the general public to better control the pandemic and reduce the mortality of older people who diagnosed with COVID-19 (Sepandi et al., 2020; Tian et al., 2021). Further, identifying factors related to mortality is very important when the demand for health care is surging and resources are limited (Rosenbaum, 2020). Hence, systematic reviews can be used to summarize and synthesize evidence accurately and reliably (Ejiri et al., 2021). Up to now, no studies have systematically reviewed the factors associated with the mortality of older people with COVID-19. Therefore, this systematic review aimed to identify various factors associated with mortality among older people infected with COVID-19.

Methods

Design

The authors performed a systematic searching following the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement 2020 (Moher et al., 2009; Page et al., 2021) for evidence synthesis and Joanna Briggs Institute’s manual in methodological quality assessment (Aromataris & Riitano, 2014).

Search Strategy

The authors searched two online databases of PubMed, and Science Direct from inception to April 15, 2021. These two databases were chosen for the purpose of data search, because authors have free access to these two databases, while other databases are only accessible via paid subscription. Keywords used in this search were ‘factors’, ‘predictors’, ‘mortality’, ‘fatal’ ‘death’, ‘elderly’, ‘older’, ‘geriatric’, ‘COVID-19’, ‘coronavirus disease’. Boolean operators were used for the advanced search strategy with the following combined text in Title/Abstract in PubMed: (‘factors’ OR ‘predictors’) AND (‘mortality’ OR ‘fatal’ OR ‘death’) AND (‘elderly’ OR ‘older’ OR ‘geriatric’) AND (‘COVID-19’ OR ‘coronavirus disease’). These search terms were adapted to search the other database (Table 1). In addition, we included some more studies from citation tracking related to the aim of the review, and Google Scholar searching was done as a secondary source. The protocol for this systematic review was registered in PROSPERO (CRD42021252946) on May 5, 2021.

Table 1.

Example of search strategy in PubMed.

(((((((((Factors [Title/Abstract]) OR (Predictors [Title/Abstract])) AND (mortality [Title/Abstract])) OR (fatal [Title/Abstract])) OR (death [Title/Abstract])) AND (elderly [Title/Abstract])) OR (older [Title/Abstract])) OR (geriatric [Title/Abstract])) AND (COVID-19 [Title/Abstract])) OR (Corona virus disease [Title/Abstract])
 Factors‘ [Title/Abstract] OR ’‘Predictors’‘[Title/Abstract]) AND ’‘mortality’‘[Title/Abstract]) OR ’‘fatal’‘[Title/Abstract] OR ’‘death’‘[Title/Abstract]) AND ’‘elderly’‘[Title/Abstract]) OR ’‘older’‘[Title/Abstract] OR ’‘geriatric’‘[Title/Abstract]) AND ’‘COVID-19’‘[Title/Abstract]) OR ’‘corona virus disease’‘[Title/Abstract]’
Narrow by publication year: January 1, 2020, to March 31, 2021
Narrow by language: English
Search modes: Boolean
Open in a new tab

Eligibility criteria

All clinical studies evaluating factors associated with mortality of older people aged 60 years or over and diagnosed with COVID-19 were included (World Health Organization, 2001). Given that there were no much studies published on COVID-19 at the time of the search, we included the studies if authors identified their study sample as COVID-19 infected. All included articles were published in English. Both peer and non-peer reviewed papers were included to ensure the inclusion of all or majority of the available published data assessing the interested outcomes. We excluded article types such as comments, reviews and editorials.

Study selection

Authors exported the search identified through databases into the EndNote reference management software. All duplicates were removed. Two reviewers then independently screened the studies based on their titles and abstracts for eligibility, against a pre-defined inclusion and exclusion criteria explained above (HDWT and KIPP). Similarly, the full-text screening was carried out (HDWT and KIPP). Any discrepancies between the authors were resolved by consensus.

Methodological quality assessment

The authors used Joanna Briggs institute’s (JBI) critical appraisal tool for descriptive/case series studies to assess the quality of the selected studies (Joanna Briggs Institute, 2017; Kiljunen et al., 2017). Two authors (HDWT and KIPP) independently assessed the included studies and resolved discrepancies by consensus.

Data extraction

The authors used a pre-defined data extraction table for the data extraction. The extracted data included details of authors’ names, publication year, mean age/age range, gender of participants, country, setting, sample size, the method for diagnosing COVID-19 and factors associated for mortality such as pre-existing diseases.

Analysis of results

The extracted data were summarized and tabulated to address the research objectives. The authors used hazard ratios (HRs), odds ratios (ORs), relative risks (RRs) (and their 95% confidence intervals) reported for the association between risk factors and mortality from COVID-19 infection.

When the data is available to pool together as heterogeneity between studies allowed, they were quantitatively analysed with meta-analyses. All the meta-analyses were conducted in the Review Manager Software 5.4 (Review Manager [RevMan] [Computer program]). Version 5.4. The Cochrane Collaboration, 2020). Odd Ratios (OR) were used to describe risks between people with the identified risk factor and people without those risk factors. Random-effects model was used when there was a considerable heterogeneity (I2 > 30%) among studies. Otherwise, fixed-effects model was used. The overall effects were presented by a combined Z value, with indicating statistical significance (p < 0.05).

Results

Studies included in the review

The initial database search identified 4947 articles. A total of 4934 potentially relevant articles were screened after the removal of 13 duplicates. Based on title screening, 4899 articles were excluded. Ten articles were identified via other sources and added for screening (Google scholar, and citation tracking). Thirty-seven articles were selected for evaluation at the full-text screening process. Based on JBI criteria, two studies were excluded because of not defining inclusion criteria, objectives of the study and not identifying confounding factors properly in those studies.

Against the eligibility criteria, 20 articles met the inclusion criteria for systematic review (qualitative analysis). During qualitative synthesis, we were able to pool 10 studies together for meta-analyses (Figure 1). The included studies were published during the time between January 1, 2020 and March 31, 2021.

Fig. 1.

Fig. 1

Open in a new tab

Flowchart – study selection process.*Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers).**If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools.

Among the included studies, 15 were retrospective (Bavaro et al., 2021; Bianchetti et al., 2020; Covino et al., 2021; De Smet et al., 2020; Fagard et al., 2021; Heras et al., 2020; Lee et al., 2020; Mendes et al., 2020; Owen et al., 2021; Sun et al., 2020; Trecarichi et al., 2020; Yu et al., 2020; Zhang et al., 2020; Zhou et al., 2020), while two studies were prospective designs (Baktash et al., 2020; Covino et al., 2020). The remaining four studies used a post-hoc analysis (Becerra-Muñoz et al., 2021), cross-sectional observational study (de Souza et al., 2020), and population-based observational study (Brandén et al., 2020) and clinical data review of geriatric patients infected with COVID-19 (Leung, 2020).

Five studies were conducted in China (Leung, 2020; Sun et al., 2020; Yu et al., 2020; Zhang et al., 2020; Zhou et al., 2020), five in Italy (Bavaro et al., 2021; Bianchetti et al., 2020; Covino et al., 2020, 2021; Trecarichi et al., 2020), 2 in the United Kingdom (Baktash et al., 2020; Owen et al., 2021), 2 in Spain (Becerra-Muñoz et al., 2021; Heras et al., 2020), 2 in Belgium (De Smet et al., 2020; Fagard et al., 2021) and each one in Sweden (Brandén et al., 2020), Korea (Lee et al., 2020), Switzerland (Mendes et al., 2020) and Brazil (de Souza et al., 2020).

Thirteen studies (Baktash et al., 2020; Bavaro et al., 2021; Becerra-Muñoz et al., 2021; Covino et al., 2020, 2021; Fagard et al., 2021; Lee et al., 2020; Mendes et al., 2020; Owen et al., 2021; Sun et al., 2020; Yu et al., 2020; Zhang et al., 2020; Zhou et al., 2020) were conducted in hospital settings. Three of the included studies were from long-term care centres (De Smet et al., 2020; Heras et al., 2020; Trecarichi et al., 2020), while two other studies were particular states in Sweden (Brandén et al., 2020) and Brazil (de Souza et al., 2020).

The sample size of the studies in this review ranged from 48 to 274,712. Seventeen studies used real-time reverse transcriptase-polymerase chain reaction (RT-PCR) to confirm COVID-19 infection (Baktash et al., 2020; Bavaro et al., 2021; Becerra-Muñoz et al., 2021; Bianchetti et al., 2020; Covino et al., 2020; De Smet et al., 2020; de Souza et al., 2020; Fagard et al., 2021; Heras et al., 2020; Lee et al., 2020; Mendes et al., 2020; Owen et al., 2021; Trecarichi et al., 2020; Yu et al., 2020; Zhang et al., 2020; Zhou et al., 2020). One study used World Health Organization interim guidance to diagnose the infection (Covino et al., 2021), whereas another two studies did not clearly mention the diagnostic method of the participants in their studies (Brandén et al., 2020; Leung, 2020). The abstracted data are presented in Table 2.

Table 2.

Key characteristics of studies included in the systematic review.

No Authors and Year Study
Design		 Mean age/Age range (years) Country Setting Sample (n) Diagnosis method- COVID-19 *Overall Quality Appraisal
1 Baktash et al. (2020) Prospective cohort study 81 years, range 65–102; male (n = 57)
Female (n = 48)		 UK Hospital 105 Viral reverse transcriptase-polymerase chain reaction (PCR) swab or supporting radiological evidence Fair
2 Bavaro et al. (2021) Multicentre-retrospective-case-series Mean age 80 (IQR 72–86) Italy Hospitals 206 Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) Good
3 Becerra-Muñoz et al. (2021) Post-hoc analysis Median age of 76 (IQR 71–83)
Male = 60.3%		 Spain Hospitals 1520 RT-PCR Good
4 Bianchetti et al. (2020) Retrospective study Dementia
Mean age 82.6 (SD 5.3)
Female = 57.3%
No dementia
Mean age 68.9 (SD 12.7)
Female = 52.8%		 Italy Hospitals 627 RT-PCR Good
5 Brandén et al. (2020) Population-based, observational study Female = 55·8% Sweden Residing in Stockholm
Cause-of-death register held by the Swedish national board of health and welfare		 274 712 Not clearly mentioned about the diagnosis method Fair
6 Covino et al. (2020) Single-centre, retrospective, observational study 84 years (IQR 82–89)
Male = 53.6%		 Italy Hospital 69 COVID-19 was diagnosed based on the World Health Organization interim guidance Good
7 Covino et al. (2021) Single-centre prospective study Median age 85 (IQR 82–89
Males = 46.9%)		 Italy Hospital 239 RT-PCR Good
8 De Smet et al. (2020) Retrospective single-centre observational study Median age 85 (Range 65–97)
Female = 59%		 Belgium Long-term care residence 48 RT-PCR Fair
9 de Souza et al. (2020) Cross-sectional observational study Average age 70.21 Brazil State of Alagoas, Brazil 5145 RT-PCR Good
10 Fagard et al. (2021) Retrospective observational cohort study Median age 82 Belgium Hospital 105 A PCR test or based on the clinical picture and a chest C.T. scan Good
11 Heras et al. (2020) Retrospective analysis Median age 85
Female:62%		 Spain Long-term care centre 100 RT-PCR Good
12 Lee et al. (2020) Retrospective study Median age 72
Female = 55.1%		 Korea Hospitals 98 RT-PCR assay of a nasopharyngeal swab or sputum referring to the national guidelines Fair
13 Leung (2020) Clinical review Deceased group
Median age 75
Surviving group
Median age 68		 China Hospitals 154 Not clearly mentioned about the diagnosis method Fair
14 Mendes et al. (2020) Retrospective study 86.5(SD:6.5)
Male = 43%		 Switzerland Hospital 235 RT-PCR Fair
15 Owen et al. (2021) Retrospective observational 79.7
Female = 49.4%		 United Kingdom Hospital 1,071 RT-PCR Fair
16 Sun et al. (2020) Retrospective case-control Discharged patient group median age 67
Female = 58.5%
Diseased group
Median age 72
Female = 32.2%		 China Hospital 244 Diagnosis and treatment guideline for COVID-19 published by the national health commission of People’s republic of China Good
17 Trecarichi et al. (2020) Retrospective, observational cohort study 80 (SD 12)
Male = 57.1%		 Italy long-term care facilities 50 SARS-CoV-2 molecular test Fair
18 Yu et al. (2020) Retrospective Median age 81
Female = 59.6%		 China Hospital 141 Nucleic acid of the SARS-CoV-2 virus Fair
19 Zhang et al. (2020) Retrospective cohort study Median 55.6 (IQR 44–69)
Male: 48.4%		 China Hospital 663 RT-PCR for the presence of SARS-CoV-2 in both nasal and pharyngeal swab specimens Fair
20 Zhou et al. (2020) Retrospective Survivors
70.6 (SD 6.9)
Female = 67.2%
No-survivors
73.1 (SD ± 7.3)
Female = 39.2%		 China Hospital 118 RT-PCR Fair
Open in a new tab

IQR, interquartile range; RT-PCR, real-time reverse transcriptase-polymerase chain reaction; SD, standard deviation.

*Joanna Briggs institute critical appraisal tool.

Related to JBI critical appraisal tool, nine studies (Bavaro et al., 2021; Becerra-Muñoz et al., 2021; Bianchetti et al., 2020; Covino et al., 2020, 2021; de Souza et al., 2020; Fagard et al., 2021; Heras et al., 2020; Sun et al., 2020) can be categorized as ‘good’ while 11 studies (Baktash et al., 2020; Brandén et al., 2020; De Smet et al., 2020; Lee et al., 2020; Leung, 2020; Mendes et al., 2020; Owen et al., 2021; Trecarichi et al., 2020; Yu et al., 2020; Zhang et al., 2020; Zhou et al., 2020) can be categorized as ‘fair’.

Results of the reviewed studies

The qualitative synthesis was presented as a tabulated summary. The main findings on the factors associated with mortality among older people with COVID-19 were presented under three main categories, after careful evaluation of the factors associated with mortality; sociodemographic characteristics, comorbidity factors and haematologic and biochemical indicators associated with mortality. The summary of the qualitative analysis is depicted in Table 3.

Table 3.

Factors associated with mortality.

No Authors and Year Main Findings
1 Baktash et al. (2020) Comparing to the replete group, there was a higher peak D-dimer level among patients with vitamin D deficiency (1914.00 µgFEU/L vs. 1268.00 µgFEU/L) (p = 0.034) and higher incidence of non-invasive ventilation support and high dependency unit admission (30.77% vs. 9.68%) (p = 0.042). No increased mortality was observed between groups
2 Bavaro et al. (2021) Male sex (aOR = 2.87, 95% CI: 1.15–7.18), CFS 7–9 (aOR = 9.97, 95% CI: 1.82–52.99), dehydration at admission (aOR = 4.27, 95% CI: 1.72–10.57) and non-invasive/invasive ventilation (aOR = 4.88, 95% CI: 1.94–12.26) were independent predictors of mortality
3 Becerra-Muñoz et al. (2021) Age ≥75 (OR = 3.54, 95% CI: 1.76–8.38), (OR = 3.36, 95% CI:11.00–11.33), dementia (OR = 8.06, 95% CI: 1.45–44.85), peripheral oxygen saturation <92% (OR = 5.85, 95% CI: 2.89–11.84), (Becerra-Muñoz et al., 2021) (OR = 3.36, 95% CI: 1.53–7.38) and qSOFA >1 (OR = 8.31, 95% CI: 2.29–30.16) were independent predictors of mortality
4 Bianchetti et al. (2020) Dementia independently associated with a higher mortality (OR = 1.84, 95% CI: 1.09–3.13, p < 0.05)
5 Brandén et al. (2020) Household and neighbourhood characteristics were associated with COVID-19 mortality among older people. Living with someone of working age (<66 years) (HR = 1.6, 95% CI: 1.3–2.0) and living in a care home were associated with an increased risk of COVID-19 mortality (HR = 4.1, 95% CI: 3.5–4.9) compared with living in independent housing. Living in neighbourhoods with the highest population density (≥5000 individuals per km2) was associated with higher COVID-19 mortality (HR = 1.7, 95% CI: 1.7–2.7) compared with living in the least densely populated neighbourhoods (0 to <150 individuals per km2)
6 Covino et al. (2020) Severe dementia (HR = 3.87, 95% CI: 1.23–12.17), pO2 ≤90 at admission (HR = 2.98, 95% CI: 0.68–13.11) and lactate dehydrogenase >464 U/L (HR = 4.11, 95% CI: 1.34–12.63) were independent risk factors for mortality
7 Covino et al. (2021) Age ≥85 years (HR = 2.40, 95% CI: 1.32–4.35), dependency in activities of daily living (ADL) (HR = 2.57, 95% CI: 1.14–5.82), and dementia (HR = 2.34, 95% CI: 1.33–4.12), congestive heart failure (HR = 1.94, 95% CI: 1.06–3.53), LDH >489 (UI/L) (HR = 2.08,95% CI: 1.19–3.64), CRP >121 (mg/L) (HR = 2.40, 95% CI: 1.39–4.15), serum lactate >1.5 (mmol/L) (HR = 1.66, 95% CI: 1.02–2.72), PaO2/FiO2 < 261 (HR = 1.67, 95% CI: 1.03–2.72) were risk factors for death
8 De Smet et al. (2020) Mortality was associated with age (Spearman r = 0.241, p = 0.03), CFS score (r = 0.282, p = 0.011), baseline lactate dehydrogenase (r = 0.301, p = 0.009), lymphocyte count (r = 0.262, p = 0.02) and RT-PCR cycle threshold (r = 0.285, p = 0.015)
9 de Souza et al. (2020) Male gender (OR = 1.54, 95% CI:, 1.35–1.76), age ≥75 years (OR 2.40, 95% CI:, 2.10–2.74), dyspnoea (OR 2.92, 95% CI: 2.34–3.64), chronic diseases such as diabetes (OR 2.33, 95% CI: 1.99–2.74), hypertension (OR 1.53, 95% CI: 1.20–1.94) and kidney disease (OR 2.02, 95% CI: 1.27–3.20) were associated with mortality
10 Fagard et al. (2021) Clinical Frailty Scale/CFS (OR = 2.325, 95% CI: 1.10–4.94) and cognitive decline (OR = 11.50, 95% CI: 1.32–100.35) were independently associated with in-hospital mortality
11 Heras et al. (2020) Male gender (OR = 38.1, p = 0.001), low Barthel index (OR = 0.92, p = 0.006), pharmacological treatment/hydroxychloroquine plus azithromycin (OR = 0.045, p = 0.004) and lymphocytopenia (OR = 6.55, p = 0.039) were identified as independent factors for mortality
12 Lee et al. (2020) Nosocomial acquisition (OR = 7.86, 95% CI: 2.16–28.57), diabetes (OR = 4.74, 95% CI: 1.68–13.38), chronic lung diseases (OR = 8.33, 95% CI: 1.80–38.68), chronic neurologic diseases (OR = 8.00, 95% CI: 2.36–27.16), a higher white blood cell count: Neutrophil count >4,500,/mm3 (OR = 14.48, 95% CI: 3.83–54.78), blood urea nitrogen level >20 mg/dL (OR =6.77, 95% CI: 2.29–19.99), serum creatinine level >1.0 mg/dL (OR = 14.42, 95% CI: 3.84–54.14), lymphocyte count <900/mm3 (OR = 10.88, 95% CI: 3.23–36.63) and C-reactive protein >8.0 mg/dL (OR = 27.95, 95% CI: 5.93–131.63) were associated with mortality
13 Leung (2020) Age (OR = 1.04, 95% CI: 1.00,1.10), fever (OR = 0.23, 95% CI: 0.08,0.64), diarrhoea (OR = 8.62, 95% CI: 0.97,76.78) were associated with mortality
14 Mendes et al. (2020) Male gender (HR = 4.00, 95% CI: 2.08–7.71), increased fraction of inspired oxygen (HR = 1.06, 95% CI: 1.03–1.09), and crackles (HR = 2.42, 95% CI: 1.15–6.06) were the best predictors of mortality, while better functional status was protective (HR = 0.98, 95% CI: 0.97–0.99)
15 Owen et al. (2021) Age (HR = 1.03, 95% CI: 1.01, 1.04), male sex (HR = 1.40, 95% CI: 1.09, 1.79), Early Warning Score EWS (HR = 1.16, 95% CI: 1.12, 1.20) and Charlson scores (HR = 1.09, 95% CI: 1.03, 1.15) were all associated with relatively minor increases in mortality hazard in the adjusted model
16 Sun et al. (2020) Lymphocyte count (OR = 0.01,95% CI: 0.001–0.138), older age (OR = 1.122; 95% CI: 1.007–1.249) and white blood cell count (OR = 1.28, 95% CI: 1.00–1.64) were risk factors
17 Trecarichi et al. (2020) Hypernatraemia (HR = 9.12, 95% CI: 2.15–38.52), lymphocyte count <1000 cells/μL (HR = 7.45, 95% CI: 1.81–30.68), cardiovascular diseases other than hypertension (HR = 6.41, 95% CI: 1.51–27.22) and higher levels of serum interleukin-6 (IL-6, pg/mL) (HR = 1.005, 95% CI: 1.001–1.009) were significant predictors of mortality
18 Yu et al. (2020) Male sex (OR = 13.1, 95% CI: 1.1–160.1), body temperature >37.3°C (OR = 80.5, 95%, 95% CI: 4.6–1407.6), SpO2 ≤ 90% (OR = 70.1, 95% CI: 4.6–1060.4), and NT-proBNP> 1800 ng/L (OR =273.595% CI: 14.7–5104.8) were independent risk factors of in-hospital death
19 Zhang et al. (2020) Older people (>60 years) were more likely to die in hospital than those 60 years old (p 0.004). Dyspnoea (Respiratory symptoms) and unconsciousness (neurological symptoms) were the only two symptoms that were associated with mortality
20 Zhou et al. (2020) Neutrophil to lymphocyte ratio (OR = 31.2, 95% CI: 6.7–144.5), lactate dehydrogenase (OR = 73.4, 95% CI: 11.8–456.8), albumin (OR < 0.1, 95% CI: <0.1–0.2, p < 0.0001), D-dimer (OR = 13.6, 95% CI: 3.4–54.9) and urea nitrogen (OR = 12.0, 95% CI: 3.0–48.4)
Open in a new tab

ADL, activities of daily living; aOR, adjusted odds ratio; CI, confidence interval; CRP, C – reactive protein; HR, hazard ratio; ICU, intensive care units; NT-proBNP, N-terminal pro hormone BNP; OR, odds ratio; PaO2/FiO2, ratio of arterial oxygen partial pressure to fractional inspired oxygen; qSOFA, quick sepsis related organ failure assessment; RT-PCR, real-time reverse transcriptase-polymerase chain reaction; p, significance level was set at 0.05.

Meta-analysis of risk factors of mortality

Several factors were able to pool together for meta-analyses, including age, sex, Dementia, Dyspnoea, Diabetes and Hypertension. The other identified risk factors were not able to pool together for meta-analyses mainly due to lack of studies for those outcomes (less than two studies reported) (Baktash et al., 2020; Fagard et al., 2021; Trecarichi et al., 2020) or due to the inability to locate the raw data (indicated odds ratios and hazard ratios were reported instead) (Bavaro et al., 2021; Brandén et al., 2020; De Smet et al., 2020; Heras et al., 2020; Owen et al., 2021; Sun et al., 2020; Zhou et al., 2020).

Three articles (de Souza et al., 2020; Mendes et al., 2020; Yu et al., 2020) that included older people with COVID-19 were pooled together. We analysed 10,683 (males = 4831, females = 5362) people to find any significant difference between the two sexes. Male sex was found to be significantly higher in reported deaths (p < 0.001, OR = 2.22 [95% CI = 1.23–3.99], and I2 = 73%) (Figure 2 (a)). Two articles (Becerra-Muñoz et al., 2021; de Souza et al., 2020) were pooled together with 11,327 (over 75 years = 3294, less than 75 years = 8033) older people with COVID-19 to find any significant difference in advanced age. Deaths in advanced age (over 75 years old) were significantly high (p < 0.001, OR = 3.36 [95% CI = 2.30–4.90], and I2 = 87%) (Figure 2(b)). To find any significant difference of deaths in older people with and without Dementia, three studies (Bianchetti et al., 2020; Covino et al., 2020, 2021) with 935 (people with Dementia = 130, people with no Dementia = 805) were pooled together. Deaths were significantly higher in people with Dementia (p < 0.001, OR = 3.69 [95% CI = 1.99–6.83], and I2 = 46%) (Figure 2(c)). There was a significantly higher incidence of deaths in people with Dyspnoea when two available studies (de Souza et al., 2020; Zhang et al., 2020) pooled together, with 10,470 (people with Dyspnoea = 720, people without Dyspnoea = 9750) older people with COVID-19 (p < 0.001, OR = 3.16 [95% CI = 2.61–3.82], and I2 = 0%) (Figure 2(d)). Three studies (de Souza et al., 2020; Lee et al., 2020; Leung, 2020) were pooled together with 10,059 older people with COVID-19 (with Diabetes = 1629, without Diabetes = 8430), and found to be there is no significant difference in deaths between people with and without Diabetes (p = 0.77, OR = 1.31 [95% CI = 0.22–7.83], and I2 = 96%) (Figure 2(e)). Two articles (de Souza et al., 2020; Leung, 2020) were pooled together with 9961 (with Hypertension = 676, with no Hypertension = 9285) older people with COVID-19 to find any significant difference in advanced age. There were no significant differences in deaths in people with Hypertension and without (p = 0.55, OR = 1.73 [95% CI = 0.28–10.79], and I2 = 97%) (Figure 2 (f)).

Figure 2.

Figure 2.

Open in a new tab

Meta-analysis findings of the association between risk factors and deaths in older people with COVID-19 (a) sex, (b) advanced age, (c) Dementia, (d) Dyspnoea, (e) Diabetes and (f) Hypertension.

Discussion

This systematic review provides current evidence of associations between various factors and mortality based on 20 studies assessing older people infected with COVID-19. The quality of the searched studies is in the acceptable range (fair or good) according to Joanna Briggs institute’s critical appraisal tool (Joanna Briggs Institute, 2017) for descriptive/case series studies. The majority of the articles in this review contained a clear description about the diagnosis method of COVID-19 that is ‘real-time reverse transcriptase-polymerase chain reaction' (RT-PCR) (Borges do Nascimento et al., 2020) or any other acceptable method which may follow the triple algorithm (epidemiological history, clinical symptoms and laboratory or radiological findings), a standard procedure proposed by the World Health Organization (Borges do Nascimento et al., 2020).

Different types of study designs identified in this review may be one of the possible reasons for different findings for a similar research question (Peinemann et al., 2013). Although the different methodologies caused methodological heterogeneity, several factors were able to pool to identify the association with mortality.

Further, this review consists of studies from nine countries; China, Italy, United Kingdom, Spain, Belgium, Sweden, Korea, Switzerland and Brazil. Navar et al. (2021) and Deeb et al. (2021) found significant difference in mortality between race and ethnicity. However, no study included in our review elaborated such an association.

The result of the current meta-analyses showed that male sex, advanced age, Dementia and Dyspnoea significantly increase the risk of deaths in older people with COVID-19, while there were no significant risks of deaths by having Diabetes or Hypertension.

Older age has been recognized as an important risk factor of mortality in COVID-19 among the studied population (Lithander et al., 2020). This may be because ageing leads impaired functioning of various systems, including the immune system. Two reasons need to be considered related to the immune system; 1) the gradual decline in the immune function called immunosenescence and, 2) chronic increase in systemic inflammation called inflammaging (Mueller et al., 2020). Both immunosenescence and inflammaging are considered vital features of the ageing immune system that cause immune dysfunction in older people (Bajaj et al., 2021). Further, the increased prevalence of comorbidities in older people may be a another reason for ageing as a high mortality risk (Cho et al., 2021).

Male gender has been found as a risk factor of mortality among older people diagnosed with COVID-19. This might be related to the action of angiotensin-converting enzyme 2 (ACE-2). This acts as a functional receptor for COVID-19 (Moradi et al., 2020). It has been reported that males may have a higher expression of angiotensin-converting enzyme 2 (ACE-2) levels rendering them at more risk for COVID-19 infection as well as mortality (La Vignera et al., 2020). In addition, the androgen-mediated expression of ACE-2 among males could be another possibility (Moradi et al., 2020). However, Naaraayan et al. (2020) found no difference in the odds of mortality between males and females. This disparity of findings warrants future clinical studies assessing gender differences in the mortality of COVID-19.

The findings of this study were in line with the conclusion made by Legris et al. (2021) and showed Dementia as a risk factor for mortality. The nature of this association has not yet been clearly evaluated from a global perspective (Azarpazhooh et al., 2020). However, patients with Dementia are immunocompromised and may present with non-respiratory symptoms such as delirium or isolated functional decline. These situations may reduce early disease recognition and increase patients’ mortality (Azarpazhooh et al., 2020; Saragih et al., 2021). In addition, the current review found that dyspnoea was associated with the mortality of older people with COVID-19. Dyspnoea is a subjective symptom, and it suggests poor lung function and lacking oxygen which deteriorates patients’ conditions (Pesola & Ahsan, 2016; Zheng et al., 2020). In this review, comorbidity factors such as hypertension and diabetes were not associated with the risk of mortality. This might be due to the possible confounding effect of other comorbidities of this vulnerable population. The effect of unreported comorbidities such as neurodegenerative diseases is common among institutionalized older people (Legris et al., 2021; Martín-Jiménez et al., 2020; Woolcott & Castilla-Bancayán, 2021).

This review has several limitations. The main limitation is using a few online databases for literature searching. Moreover, there are a limited number of studies for certain outcomes, and it would be difficult to provide meta-analysis. Another limitation is the restriction of the language to English, which limits potential inclusion of related studies in other languages. Next, we did not consider the mechanisms to confirm COVID-19 in inclusion of articles which directly affect the credibility of the included studies. There were three different types of study designs were included in this review. While this broadly collect all the available evidence, it is encouraged to implement more quality observational studies. Finally, it was unable to perform funnel plots given that there were two to three studies included in the meta-analysis. One of the strengths of this review is that this review gives a broad overview of factors associated with mortality among older people. Another strength of the study is that JBI evaluation indicated that all the included studies were in the acceptable level and included into the analysis. In terms of future research, it is interesting to explore the country-wise differences in reported mortalities. We found that there is a lack of studies for some outcomes to pool together for a meta-analysis, and quality studies are encouraged on those outcomes with future research.

Conclusion

Older age, male gender, dyspnoea and dementia were associated with a greater risk of death of older people from COVID-19 infection. The results of the present review could help health care professionals to identify high-risk groups, facilitate appropriate remedial measures and help control mortality among this vulnerable population. In addition, using prognostic information identified in this search could be helpful to selecting old patients in most need of medical attention and resources.

Footnotes

Author Contributions: H.D.W.T.D. conceived the study idea, performed searches, primary data extraction, and quality assessment aided by K.I.P.P., H.D.W.T.D., and I.W. were involved in data analysis and manuscript preparation. All the authors read the final version of the manuscript and approved it.

Conflict of interest: The authors of the study declare that there is no conflict of interest

Funding sources: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

  1. Aromataris E., Riitano D. (2014). Systematic reviews. American Journal of Nursing, 114(5), 49–56. 10.1097/01.NAJ.0000446779.99522.f6. [DOI] [PubMed] [Google Scholar]
  2. Azarpazhooh M. R., Amiri A., Morovatdar N., Steinwender S., Rezaei Ardani A., Yassi N., Biller J., Stranges S., Tokazebani Belasi M., Neya S. K., Khorram B., Sheikh Andalibi M. S., Arsang-Jang S., Mokhber N., Di Napoli M. (2020). Correlations between COVID-19 and burden of dementia: An ecological study and review of literature. Journal of the Neurological Sciences, 416, 117013. 10.1016/j.jns.2020.117013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bajaj V, Gadi N, Spihlman AP, Wu SC, Choi CH, Moulton VR. (2021). Aging, immunity, and COVID-19: How age influences the host immune response to coronavirus infections? Frontiers in Physiology, 11, 571416. 10.3389/fphys.2020.571416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baktash V., Hosack T., Patel N., Shah S., Kandiah P., Van den Abbeele K., Mandal A. K. J., Missouris C. G. (2020). Vitamin D status and outcomes for hospitalised older patients with COVID-19. Postgraduate Medical Journal, 97(1149), 442–447. 10.1136/postgradmedj-2020-138712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bavaro D. F., Diella L., Fabrizio C., Sulpasso R., Bottalico I. F., Calamo A., Santoro C. R., Brindicci G., Bruno G., Mastroianni A., Buccoliero G. B., Carbonara S., Lo Caputo S., Santantonio T., Monno L., Angarano G., Saracino A. (2021). Peculiar clinical presentation of COVID- 19 and predictors of mortality in the elderly: a multicentre retrospective cohort Study. International Journal of Infectious Diseases, 105, 709–715. 10.1016/j.ijid.2021.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Becerra-Muñoz V. M., Núñez-Gil I. J., Eid C. M., García Aguado M., Romero R., Huang J., Mulet A., Ugo F., Rametta F., Liebetrau C., Aparisi A., Fernández-Rozas I., Viana-Llamas M. C., Feltes G., Pepe M., Moreno-Rondón L. A., Cerrato E., Raposeiras-Roubín S., Alfonso E., Carrero-Fernández A. (2021). Clinical profile and predictors of in-hospital mortality among older patients hospitalised for COVID-19. Age and Ageing, 50(2), 326–334. 10.1093/ageing/afaa258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bianchetti A., Rozzini R., Guerini F., Boffelli S., Ranieri P., Minelli G., Bianchetti L., Trabucchi M. (2020). Clinical Presentation of COVID19 in Dementia Patients. The Journal of Nutrition, Health & Aging, 24(6), 560–562. 10.1007/s12603-020-1389-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Borges do Nascimento I. J., Cacic N., Abdulazeem H. M., von Groote T. C., Jayarajah U., Weerasekara I., Esfahani M. A., Civile V. T., Marusic A., Jeroncic A., Carvas Junior N., Pericic T. P., Zakarija-Grkovic I., Meirelles Guimarães S. M., Luigi Bragazzi N., Bjorklund M., Sofi-Mahmudi A., Altujjar M., Tian M., Arcani D. M. C. (2020). Novel coronavirus infection (COVID-19) in humans: A scoping review and meta-analysis. Journal of Clinical Medicine, 9(4), 941. 10.3390/jcm9040941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brandén M., Aradhya S., Kolk M., Härkönen J., Drefahl S., Malmberg B., Rostila M., Cederström A., Andersson G., Mussino E. (2020). Residential context and COVID-19 mortality among adults aged 70 years and older in Stockholm: a population-based, observational study using individual-level data. The Lancet Healthy Longevity, 1(2), e80–e88. 10.1016/s2666-7568(20)30016-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Centers for Disease Control and Prevention (2021). Older adults and COVID - 19. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html. [Google Scholar]
  11. Chen Y., Klein S. L., Garibaldi B. T., Li H., Wu C., Osevala N. M., Li T., Margolick J. B., Pawelec G., Leng S. X. (2021). Aging in COVID-19: Vulnerability, immunity and intervention. Ageing Research Reviews, 65, 101205. 10.1016/j.arr.2020.101205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cho S. I., Yoon S., Lee H.-J. (2021). Impact of comorbidity burden on mortality in patients with COVID-19 using the Korean health insurance database. Scientific Reports, 11(1), 6375. 10.1038/s41598-021-85813-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Covino M., De Matteis G., Polla D. A. D., Santoro M., Burzo M. L., Torelli E., Simeoni B., Russo A., Sandroni C., Gasbarrini A., Franceschi F. (2021). Predictors of in-hospital mortality AND death RISK STRATIFICATION among COVID-19 PATIENTS aged ≥ 80 YEARs OLD. Archives of Gerontology and Geriatrics, 95, 104383. 10.1016/j.archger.2021.104383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Covino M., De Matteis G., Santoro M., Sabia L., Simeoni B., Candelli M., Ojetti V., Franceschi F. (2020). Clinical characteristics and prognostic factors in COVID‐19 patients aged ≥80 years. Geriatrics & Gerontology International, 20(7), 704–708. 10.1111/ggi.13960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. De Smet R., Mellaerts B., Vandewinckele H., Lybeert P., Frans E., Ombelet S., Lemahieu W., Symons R., Ho E., Frans J., Smismans A., Laurent M. R. (2020). Frailty and mortality in hospitalized older adults with COVID-19: Retrospective observational study. Journal of the American Medical Directors Association, 21(7), 928–932.e921. 10.1016/j.jamda.2020.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. de Souza C. D., de Arruda Magalhães A. J., Lima A. J., Nunes D. N., de Fátima Machado Soares É., de Castro Silva L., Santos L. G., Dos Santos Cardoso V. I., Nobre Y. V., do Carmo R. F. (2020). Clinical manifestations and factors associated with mortality from COVID ‐19 in older adults: Retrospective population‐based study with 9807 older Brazilian COVID‐19 patients. Geriatrics & Gerontology International, 20(12), 1177–1181. 10.1111/ggi.14061. [DOI] [PubMed] [Google Scholar]
  17. Deeb A, Khawaja K, Sakrani N, AlAkhras A, Al Mesabi A, Trehan R, Kumar PC, Babiker Z, Nagelkerke N, Fru-Nsutebu E. (2021). Impact of ethnicity and underlying comorbidity on COVID-19 inhospital mortality: An observational study in Abu Dhabi, UAE. Biomed Research International, 2021, 6695707. 10.1155/2021/6695707. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ejiri M., Kawai H., Ishii K., Oka K., Obuchi S. (2021). Predictors of older adults’ objectively measured social isolation: A systematic review of observational studies. Archives of Gerontology and Geriatrics, 94, 104357. 10.1016/j.archger.2021.104357. [DOI] [PubMed] [Google Scholar]
  19. Fagard K., Gielen E., Deschodt M., Devriendt E., Flamaing J. (2021). Risk factors for severe COVID-19 disease and death in patients aged 70 and over: a retrospective observational cohort study. Acta Clinica Belgica, 1-8. 10.1080/17843286.2021.1890452. [DOI] [PubMed] [Google Scholar]
  20. Health, A G D O (2021). COVID-19 deaths by age group and sex. Health A G D O. [Google Scholar]
  21. Heras E., Garibaldi P., Boix M., Valero O., Castillo J., Curbelo Y., Gonzalez E., Mendoza O., Anglada M., Miralles J. C., Llull P., Llovera R., Piqué J. M. (2020). COVID-19 mortality risk factors in older people in a long-term care center. European Geriatric Medicine, 12(3), 601–607. 10.1007/s41999-020-00432-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Joanna Briggs Institute (2017). Critical appraisal checklist for systematic reviews and research syntheses. Joanna Briggs Institute. https://doi.org/https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Systematic_Reviews2017_0.pdf. [Google Scholar]
  23. Kang S.-J., Jung S. I. (2020). Age-related morbidity and mortality among patients with COVID-19. Infection & Chemotherapy, 52(2), 154–164. 10.3947/ic.2020.52.2.154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kiljunen O, Välimäki T, Kankkunen P, Partanen P. (2017). Competence for older people nursing in care and nursing homes: An integrative review. International Journal of Older People Nursing, 12(3). 10.1111/opn.12146. [DOI] [PubMed] [Google Scholar]
  25. La Vignera S, Cannarella R, Condorelli RA, Torre F, Aversa A, Calogero AE. (2020). Sex-specific SARS-CoV-2 mortality: Among hormone-modulated ACE2 expression, risk of venous thromboembolism and hypovitaminosis D. International Journal of Molecular Sciences, 21(8), 2948. 10.3390/ijms21082948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lee JY, Kim HA, Huh K, Hyun M, Rhee JY, Jang S, Kim JY, Peck KR, Chang HH. (2020). Risk factors for mortality and respiratory support in elderly patients hospitalized with COVID-19 in Korea. Journal of Korean Medical Science, 35(23), e223. 10.3346/jkms.2020.35.e223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Legris P., Vaillard L., Nonciaux C., Hourdain P., Taroux M., Petit J.-M., Vergès B., Bouillet B. (2021). Diabetes is not associated with COVID-19-related mortality in older institutionalized people. Diabetes & Metabolism, 47(3), 101235. 10.1016/j.diabet.2021.101235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Leung C. (2020). Risk factors for predicting mortality in elderly patients with COVID-19: A review of clinical data in China. Mechanisms of Ageing and Development, 188, 111255. 10.1016/j.mad.2020.111255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lithander F. E., Neumann S., Tenison E., Lloyd K., Welsh T. J., Rodrigues J. C. L., Higgins J. P. T., Scourfield L., Christensen H., Haunton V. J., Henderson E. J. (2020). COVID-19 in older people: a rapid clinical review. Age and Ageing, 49(4), 501–515. 10.1093/ageing/afaa093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Martín-Jiménez P., Muñoz-García M. I., Seoane D., Roca-Rodríguez L., García-Reyne A., Lalueza A., Maestro G., Folgueira D., Blanco-Palmero V. A., Herrero-San Martín A., Llamas-Velasco S., Pérez-Martínez D. A., González-Sánchez M., Villarejo-Galende A. (2020). Cognitive impairment is a common comorbidity in deceased COVID-19 patients: A hospital-based retrospective cohort study. Journal of Alzheimer’s Disease, 78(4), 1367–1372. 10.3233/jad-200937. [DOI] [PubMed] [Google Scholar]
  31. Mendes A., Serratrice C., Herrmann F. R., Genton L., Périvier S., Scheffler M., Fassier T., Huber P., Jacques M.-C., Prendki V., Roux X., Di Silvestro K., Trombert V., Harbarth S., Gold G., Graf C. E., Zekry D. (2020). Predictors of In-Hospital Mortality in Older Patients With COVID-19: The COVIDAge Study. Journal of the American Medical Directors Association, 21(11), 1546–1554. 10.1016/j.jamda.2020.09.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Moher D., Liberati A., Tetzlaff J., Altman D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Journal of Clinical Epidemiology, 62(10), 1006–1012. 10.1016/j.jclinepi.2009.06.005. [DOI] [PubMed] [Google Scholar]
  33. Moradi F., Enjezab B., Ghadiri-Anari A. (2020). The role of androgens in COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(6), 2003–2006. 10.1016/j.dsx.2020.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mueller A. L., McNamara M. S., Sinclair D. A. (2020). Why does COVID-19 disproportionately affect older people? Aging, 12(10), 9959–9981. 10.18632/aging.103344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Naaraayan A, Nimkar A, Hasan A, Pant S, Durdevic M, Elenius H, Nava Suarez C, Jesmajian S. (2020). Analysis of male sex as a risk factor in older adults with Coronavirus disease 2019: A retrospective cohort study from the New York City Metropolitan region. Cureus, 12(8), e9912. 10.7759/cureus.9912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Navar A. M., Purinton S. N., Hou Q., Taylor R. J., Peterson E. D. (2021). The impact of race and ethnicity on outcomes in 19,584 adults hospitalized with COVID-19. PLoS One, 16(7), e0254809. 10.1371/journal.pone.0254809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Owen R. K., Conroy S. P., Taub N., Jones W., Bryden D., Pareek M., Faull C., Abrams K. R., Davis D., Banerjee J. (2021). Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: a retrospective observational study using electronic health records. Age and Ageing, 50(2), 307–316. 10.1093/ageing/afaa167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Page M. J., McKenzie J. E., Bossuyt P. M., Boutron I., Hoffmann T. C., Mulrow C. D., Shamseer L., Tetzlaff J. M., Moher D. (2021). Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. Journal of Clinical Epidemiology, 134, 103–112. 10.1016/j.jclinepi.2021.02.003. [DOI] [PubMed] [Google Scholar]
  39. Peinemann F., Tushabe D. A., Kleijnen J. (2013). Using multiple types of studies in systematic reviews of health care interventions – a systematic review. PLoS One, 8(12), e85035. 10.1371/journal.pone.0085035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Pesola G. R., Ahsan H. (2016). Dyspnea as an independent predictor of mortality. The Clinical Respiratory Journal, 10(2), 142–152. 10.1111/crj.12191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rosenbaum L. (2020). Facing covid-19 in Italy – ethics, logistics, and therapeutics on the Epidemic’s Front Line. The New England Journal of Medicine, 382(20), 1873–1875. 10.1056/NEJMp2005492. [DOI] [PubMed] [Google Scholar]
  42. Saragih I. D., Saragih I. S., Batubara S. O., Lin C.-J. (2021). Dementia as a mortality predictor among older adults with COVID-19: A systematic review and meta-analysis of observational study. Geriatric Nursing, 42(5), 1230–1239. 10.1016/j.gerinurse.2021.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sepandi M, Taghdir M, Alimohamadi Y, Afrashteh S, Hosamirudsari H. (2020). Factors associated with mortality in COVID-19 patients: A systematic review and meta-analysis. Iranian Journal of Public Health, 49(7), 1211–1221. 10.18502/ijph.v49i7.3574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sun H, Ning R, Tao Y, Yu C, Deng X, Zhao C, Meng S, Tang F, Xu D. (2020). Risk factors for mortality in 244 older adults with COVID-19 in Wuhan, China: A retrospective study. Journal of the American Geriatrics Society, 68(6), E19–e23. 10.1111/jgs.16533. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Susilowati I. H., Nakatani H., Nugraha S., Pengpid S., Keawpan W., Hasiholan B. P., Toai N. P., Abdeali A., Isahak M., Kamso S. (2020). COVID-19 handling report for pre-case, case (pre-hospital and hospital), and post-case phases in the elderly as vulnerable populations in 6 Asia Pacific countries. Global Health & Medicine, 2(6), 350–359. 10.35772/ghm.2020.01061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Tarteret P., Strazzulla A., Rouyer M., Gore C., Bardin G., Noel C., Benguerdi Z.-E., Berthaud J., Hommel M., Aufaure S., Jochmans S., Diamantis S. (2021). Clinical features and medical care factors associated with mortality in French nursing homes during the COVID-19 outbreak. International Journal of Infectious Diseases, 104, 125–131. 10.1016/j.ijid.2020.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Tian T., Zhang J., Hu L., Jiang Y., Duan C., Li Z., Wang X., Zhang H. (2021). Risk factors associated with mortality of COVID-19 in 3125 counties of the United States. Infectious Diseases of Poverty, 10(1), 3. 10.1186/s40249-020-00786-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Trecarichi E. M., Mazzitelli M., Mazzitelli M., Serapide F., Pelle M. C., Tassone B., Arrighi E., Perri G., Fusco P., Scaglione V., Davoli C., Lionello R., La Gamba V., Marrazzo G., Busceti M. T., Giudice A., Ricchio M., Cancelliere A., Lio E., Procopio G. (2020). Clinical characteristics and predictors of mortality associated with COVID-19 in elderly patients from a long-term care facility. Scientific Reports, 10(1), 20834. 10.1038/s41598-020-77641-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Williamson E. J., Walker A. J., Bhaskaran K., Bacon S., Bates C., Morton C. E., Curtis H. J., Mehrkar A., Evans D., Inglesby P., Cockburn J., McDonald H. I., MacKenna B., Tomlinson L., Douglas I. J., Rentsch C. T., Mathur R., Wong A. Y. S., Grieve R., Harrison D. (2020). Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584(7821), 430–436. 10.1038/s41586-020-2521-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Woolcott O. O., Castilla-Bancayán J. P. (2021). The effect of age on the association between diabetes and mortality in adult patients with COVID-19 in Mexico. Scientific Reports, 11(1), 8386. 10.1038/s41598-021-88014-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. World Health Organization (2001). Definition of an older or elderly person. World Health Organization. http://www.who.int/healthinfo/survey/ageingdefnolder/en/index.html. [Google Scholar]
  52. Yanez N. D., Weiss N. S., Romand J.-A., Treggiari M. M. (2020). COVID-19 mortality risk for older men and women. BMC Public Health, 20(1), 1742. 10.1186/s12889-020-09826-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yang J., Li H. (2020). The impact of aging and COVID-19 on our immune system: a high-resolution map from single cell analysis. Protein & Cell, 11(10), 703–706. 10.1007/s13238-020-00782-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Yu Z., Ke Y., Xie J., Yu H., Zhu W., He L., Zheng Q., Li C., Lu J., Li S., Wen S., Wei S., Liu N., Wei L., Bai R. (2020). Clinical characteristics on admission predict in-hospital fatal outcome in patients aged ≥75 years with novel coronavirus disease (COVID-19): a retrospective cohort study. BMC Geriatrics, 20(1), 514. 10.1186/s12877-020-01921-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Zhang J., Wang X., Jia X., Li J., Hu K., Chen G., Wei J., Gong Z., Zhou C., Yu H., Yu M., Lei H., Cheng F., Zhang B., Xu Y., Wang G., Dong W. (2020). Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clinical Microbiology and Infection, 26(6), 767–772. 10.1016/j.cmi.2020.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zheng Z., Peng F., Xu B., Zhao J., Liu H., Peng J., Li Q., Jiang C., Zhou Y., Liu S., Ye C., Zhang P., Xing Y., Guo H., Tang W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. Journal of Infection, 81(2), e16–e25. 10.1016/j.jinf.2020.04.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhou J., Huang L., Chen J., Yuan X., Shen Q., Dong S., Cheng B., Guo T.-M. (2020). Clinical features predicting mortality risk in older patients with COVID-19. Current Medical Research and Opinion, 36(11), 1753–1759. 10.1080/03007995.2020.1825365. [DOI] [PubMed] [Google Scholar]

Articles from Gerontology and Geriatric Medicine are provided here courtesy of SAGE Publications

RESOURCES