Case fatality and mortality rates, socio‐demographic profile, and clinical features of COVID‐19 in the elderly population: A population‐based registry study in Iran

Abstract

We aimed to investigate COVID‐19 case fatality rate (CFR), mortality, and screening in the older population of East Azerbaijan Province. We conducted a population‐based registry study from Death Registration System in the elderly population (N = 433 445) from the outbreak that emerged up to May 30, 2021 (before vaccination). We analyzed CFR and mortality rates due to COVID‐19 as well as the case findings and characteristics in the elderly population. Logistic regression analysis was carried out for the association between COVID‐19 mortality and effective factors. During the study, the province had 18 079 confirmed cases and 4390 deaths. The male to female CFR risk ratio was 3.2. The overall CFR and mortality rates were 24% and 1%, respectively. CFR and mortality ranged from 9.56% to 0.37% in the 60–64 age group to 70% and 2.6% in the age group ≥85 years, respectively. We found a significant trend in CFR and mortality of COVID‐19 with advanced age. Male sex, advanced age, marital status, and living alone were associated with an increased risk of COVID‐19 fatality. COVID‐19 mortality measures were higher in the older population of this province. Advanced treatment supports and interventions are needed to reduce mortality rates of COVID‐19 in the elderly population.

Epidemiological Context and Risk Factors Associated with the Evolution of the Coronavirus Disease (COVID-19): A Retrospective Cohort Study

“…Furthermore, a significant relationship has been established between high-risk practices (such as smoking, disregarding rules of social distancing, and refusing to wear masks) and men, which predispose them, to a relatively great extent, to the likelihood of contracting COVID-19 disease [ 16 , 26 , 27 ]. Note that the higher mortality rate found to be among the men in this study agrees with the previous literature [ 10 , 23 , 24 ]. Drawing from a number of explanations associated with biological characteristics regarding the variances in the severity of the diseases across gender [ 15 , 16 ], it is important to consider the theory of relative increase vulnerability among men due to a notably significant expression of androgen receptors, which are deemed as essential in the genetic transcription of virus proteins; for example, spike protein) [ 28 ].…”

“…Incidence, mortality, and lethality are various measures of the epidemiological context that require an assessment to efficiently manage and regulate the pandemic [ 10 ]. While intervention measures undertake a significant responsibility in this context, it is necessary for all actors to collaboratively work toward preventing the spread of the virus in a transparent and coordinated manner [ 2 ].…”

“…COVID-19 has rapidly become a disease associated with unbridled uncertainty with its aetiology and control, for the healthcare systems and health professionals who provide care, and among its ultimate victims, patients, and their families [ 11 ]. On the other hand, many different risk factors are associated with COVID-19 severity, such as older age, diabetes, hypertension, obesity, and male gender [ 10 , 12 ].…”

Seroprevalence of exposure to SARS-CoV-2 in domestic dogs and cats and its relationship with COVID-19 cases in the city of Villavicencio, Colombia

“…The older animals between 11 and 15 years exhibited this tendency predisposition, but it was not statistically different (adjusted OR = 1.61; 95% CI = 0.16-15.93) (Table 2). In this sense, a significant trend was found in the fatality and mortality rates of COVID-19 with advanced age in humans, 35 given that there is a weakened immune system, underlying chronic diseases, multiple drug therapies, lack of attention and self-care, poor environmental hygiene, loneliness, and lack of adequate support from other family members in this population. 36 These reasons could be considered with equal value in the case of animals, especially pet animals.…”

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran

“…In another study conducted in Tehran, Iran, from March 2020 up to December 2020, out of 205,654 patients admitted, 20,472 died [ 3 ]. In East Azerbaijan Province, Iran, from the outbreak up to May 2021 (before vaccination), out of 18,079 confirmed cases, 4390 died [ 4 ]. Based on the reported results of WHO, from February 2020 to, July 2021, out of 3,871,008 confirmed cases, 90,630 died in Iran [ 1 ].…”