Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection first appeared in China at the end of 2019 and has rapidly turned into a global pandemic. The virus spread rapidly around the world, infecting 246 million people as of November 2, 2021, and causing the death of approximately 5 million people. 1
In the coronavirus disease 2019 (COVID-19) pandemic, which has no defined vaccine and no effective treatment, and is now considered a pandemic, health systems have been overly focused on strategies to prevent infection and prioritize, triage, and treat patients with COVID-19. By focusing almost exclusively on strategies to reduce the infection of COVID-19 pandemic, the important role of primary care in maintaining and managing the health of those with chronic diseases has had to be overlooked. However, studies have identified chronic diseases as one of the most important factors affecting the severity and consequences of COVID-19. In studies examining deaths caused by COVID-19, it was determined that chronic disease and comorbidity affect deaths. Reference Aslaner, Aslaner and Gökçek2–Reference Zhou, Yu and Du9 Despite the studies, there is no definite conclusion on the subject. The patient’s sex, age, or comorbid diseases cause adverse clinical outcomes, but their relationship to the risk of death has not been fully established. However, it is anticipated that the disease, patient gender, age, and comorbidity may make these patients more vulnerable to increased mortality or increased risk of infection. Some studies show that COVID-19 is shown higher in men than women, while others show no similar findings. Reference Yang, Yu and Xu8,Reference Chen, Zhou and Dong10–Reference Xu, Yu and Qu13 Although age over 50 y was found to be a risk factor for COVID-19 in some studies, similar findings were not obtained in other studies. Reference Yang, Zheng and Gou14–Reference Wang, Yang and Li17 The prevalence of comorbidities in COVID-19 patients is also highly variable in many studies. Reference Li, Yang and Zhao18 Although studies are still ongoing, the effect of social determinants such as chronic diseases, age, and gender on deaths caused by COVID-19 is still being investigated.
Public health professionals have faced 3 main challenges during the pandemic process. First, to determine the impact of COVID-19 on individuals with chronic diseases, to protect them, prevent the disease, and enable them to manage chronic disease processes. Second, only because the process related to COVID-19 was carried out, preventive health services for chronic diseases were not implemented and these diseases could not be diagnosed, prevented, and managed. Finally, the physical, psychological, and social problems created by the COVID-19 pandemic have not yet been identified. Reference Hacker, Briss and Richardson19 For this reason, preventive health services that have been suspended during the pandemic process should be planned and put into operation. To do this, it is necessary to know the factors such as the chronic disease, comorbidity index, age, and gender that cause death due to COVID-19 in the society and to plan the services according to these data. Thus, this study was conducted to examine the age, gender and chronic disease status of patients who died due to COVID-19 during the pandemic process and their effects on their deaths.
Research questions;
1. What is the distribution of age, gender, length of stay in the hospital, chronic disease, and comorbidity indexes of patients who died due to COVID-19?
2. Is there a difference between the age, gender, chronic disease, length of stay in the hospital, and comorbidity indexes of patients who died due to COVID-19?
3. Is there a relationship between the index and number of comorbidities and the length of stay in the hospital?
Methods
Purpose and Type of Research
It was conducted as a retrospective analysis to examine the age, gender, and chronic disease status of patients who died due to COVID-19 during the pandemic process and their effects on their deaths.
Population and Sample of the Research
The population of the study consisted of 2715 patients who lost their lives due to COVID-19 between January 10, 2020, and January 12, 2021, in a City Hospital located in Central Anatolia, which was determined as one of the pandemic hospitals by the Turkish Ministry of Health.
Inclusion Criteria for Research
Inclusion criteria were (1) death caused by a confirmed COVID-19 diagnosis between January 10, 2020, and January 12, 2021; (2) diagnosis of a case by a positive result on any of the COVID-19 tests.
Exclusion Criteria for Research
Exclusion criteria were (1) inability to diagnose a definitive case from any of the COVID-19 tests, and (2) patients with missing data.
Data Collection Tools and Data Collection
The data of the study were collected using a City Hospital database, which was determined as a pandemic hospital in the Central Anatolian region by the Turkish Ministry of Health. The statistics of patients who died due to the diagnosis of COVID-19 between January 10, 2020, and January 12, 2021, were evaluated in the database. The patients were evaluated in terms of age, gender, length of hospital stay, presence of chronic disease, and comorbidity index.
The Modified Charlson Comorbidity Index (MCCI) was created by modifying the Charlson comorbidity Index. The Charlson Comorbidity Index (CCI) is a validated, simple, and easily applicable method for estimating the risk of death from comorbid disease and is widely used as a predictor of long-term prognosis and survival. Reference Quan, Li and Couris20,Reference Imam, Odish and Gill21 The Charlson Comorbidity Index score system was developed by Mary E. Charlson in 1987. It aimed to objectively classify and score the comorbidities of patients. Charlson, who stated that risk factors such as diabetes, cerebrovascular accident (CVA), cardiovascular disease, and malignancy may also affect survival, and this cannot be ignored when calculating mortality rates during scientific research, developed this scoring system. The Index includes 19 medical conditions scored as 1, 2, 3, and 6 according to the clinical aggravation rate of comorbidity. The total score ranges from 0 to 37. Scoring is based on the 12-mo relative risk of mortality for each disease. CCI can be easily modified according to the age factor. The difference between MCCI and CCI is that 1 point is added to the index for every 10 y over the age of 40. It has been reported in reviews that it correlates well with mortality and gives reliable results. Reference Charlson, Pompei and Ales22,Reference Extermann23
Evaluation of Data
The data obtained in the research were analyzed using the SPSS (Statistical Package for Social Sciences) for Windows 25.0 program. Descriptive statistical methods (number, percentage, mean, standard deviation) were used while evaluating the data. In addition to the normality tests of the data, histogram, Q-Q graph, and box-plot graphs were evaluated with skewness and kurtosis values. In cases where the normal distribution was achieved, the independent sample t-test was used for the 2 groups to compare the quantitative data, while the F-test was used for more than 2 groups. The difference between the 2 groups, in which the normal distribution was not achieved, was analyzed with the Mann Whitney U-test. Moreover, the Kruskal Wallis H test was used for comparison between more than 2 groups. The relationship between continuous variables was examined by spearman correlation analysis.
Ethical Aspect of the Research
Academic permission from the Faculty of Health Sciences of Erciyes University, ethical permission from the Social and Human Ethics Committee (application no: 466; date: December 28, 2021), and institutional permission were obtained.
Results
When the demographic and disease characteristics of the participants were examined, it was found that 55.7% of the patients were male, 41.5% were between the ages of 66 and 79, and 12.1% had no disease. It was determined that the mean MCCI scores of the patients was 4.74 ± 2.07 and the MCCI scores of 56.9% were severe. The length of hospital stay was calculated as 78 ± 13.92 (Table 1).
Table 1. Demographic and disease characteristics of the participants
In Table 2, it was found that there was no statistically significant difference in the length of hospital stay of the patient according to the patient’s gender and disease state (P > 0.05). However, it was determined that there was a statistically significant difference in the length of hospital stay according to the number of diseases the patient had (P < 0.05). It was concluded that individuals with 3 diseases had a shorter hospital stay compared with those with 1 and 2 diseases. Moreover, individuals with 6 diseases were hospitalized in a shorter time than those with 1, 2, 4, and more than 7 diseases. It has been determined that the length of hospital stay varies according to the age of the patient (P < 0.05). It was found that people over the age of 80 had a shorter hospital stay than those aged between 18-65 and 65-79.
Table 2. Comparison of hospital stay according to demographic and disease characteristics of patients
Abberviations: KW, Kruskal Wallis H test; z, Mann Whitney U test.
According to the high MCCI value, there was a statistically significant difference between the duration of hospital stay of the patients (P < 0.05). It was determined that individuals with a moderate MCCI score had a longer hospital stay than those with a severe score (Table 2).
It was observed that there was a statistically significant difference between the duration of hospital stay of the patients according to the presence of congestive heart failure and metastatic solid tumor (P < 0.05). It was found that patients without congestive heart failure and metastatic solid tumors had longer hospital stays (Table 3).
Table 3. Comparison of the hospital stay according to the comorbidities of the patients
Abbreviation: z, Mann Whitney U test.
Table 4. Relationship between the length of hospital stay and the number of comorbidities
* Correlation is significant at the 0.05 level.
** Correlation is significant at the 0.01 level.
Table 5. Relationship between length of hospital stay and Modified Comorbidity Index (MCCI)
** Correlation is significant at the 0.01 level.
A statistically significant and negative correlation was found between the number of comorbidities and the length of hospital stay of patients without the disease (r = −0.122; P < 0.05). A statistically significant, negative correlation was found between the comorbidity index values of patients with a disease and the length of stay in the hospital (r = −0.113; P < 0.05). It was determined that there was no statistically significant relationship between hospital stay and comorbidity index values of patients with 2 or more diseases (P > 0.05) (Table 4).
A statistically significant, negative, and high-level correlation was found between the MCCI score and the length of hospital stay (r = −0.075; P = 0.001) (Table 5).
Discussion
To date, studies have been conducted to investigate the characteristics of patients and deaths caused by COVID-19. The evaluation of the hospital stay of the patients who died in our study until the death process contributes to the literature. It was found that 55.7% of the patients who died in the study were male and 44.3% were female. Similarly, in the studies conducted by Biswas et al. (2021) and Semenzato et al. (2021), men who had COVID-19 were significantly increased compared with women, and this was associated with an increased risk of mortality. Reference Biswas, Rahaman and Biswas24,Reference Semenzato, Botton and Drouin25 Although it is not known for certain, it has been argued in some sources that this situation is due to angiotensin-converting enzyme 2 (ACE2). It is thought that more males die because the expression of ACE2, which is encoded by the ACE2 gene and located on the X-chromosome. Reference Gemmati, Bramanti and Serino26 Other sources have also suggested that ACE2 may protect patients from organ injury and improve clinical outcomes in infected patients. Reference Vaduganathan, Vardeny and Michel27,Reference Gurwitz28 In addition to the higher mortality in male patients, our study found that there was no statistically significant difference in the length of hospital stay according to the gender of the patient (P > 0.05). Male individuals may carry a higher risk for morbidity and mortality compared with females, but it can be said that the gender factor does not affect the process from illness to death.
Age is a determinant that can change the course of diseases. The mean age of the patients who died in the study was 71.92 ± 13.53 y, 7.4% were 50 y old or younger, 41.7% were 66-79 y old, and 31.6% were older than 80 y old. Similarly, in the study of Biswas et al. (2021), patients aged ≥50 y were associated with a 15.4-fold higher risk of mortality compared with patients aged <50 y. Reference Biswas, Rahaman and Biswas24 In our study, it was determined that the length of hospital stay differed according to the age of the patient. The length of stay in hospital for people over 80 y of age was found to be shorter than those aged between 18-65 and 65-79 y. In other words, the deaths of individuals over the age of 80 occur in a shorter time. Natural immunity decreases with age, and the tendency to develop infections increases. Reference Leng and Goldstein29 Furthermore, they may be vulnerable to adverse drug reactions due to increased comorbidity and decreased organ functions. Reference Lavan and Gallagher30 In the study in 2021, Semenzato et al. found that the risk of in-hospital death associated with COVID-19 increased by more than 100 times in people aged 85 and over compared with people aged 40-44. Reference Semenzato, Botton and Drouin25 The results of our study were found to be compatible with the literature, but it is seen that 26.9% of the deceased individuals were 50 y old or younger. It is thought that the evaluation of young people at risk of chronic diseases, their follow-up, and protection will gain importance due to the fact that the risk of death and the length of hospitalization in young patients are longer compared with those of advanced age.
In the study, it was found that 12.1% of the individuals who died did not have a disease, and 87.9% had 1 or more diseases. In the study, the mean score of the MCCI was 4.74 ± 2.07, the index score of 56.9% was severe, 29.6% was moderate, and 10.8% was low. Similarly, in the study of Iaccarino et al. (2020) in which they compared the data of deceased and surviving patients, they found that the Charlson Comorbidity Index scores of those who died were higher than those who survived, and this difference was significant (4.3 ± 0.15 vs 2.6 ± 0.05; P < 0.001). Reference Iaccarino, Grassi and Borghi31 In addition, Djaharuddin et al. (2021) found that more than half (52.56%) of patients who died from COVID-19 had comorbidities ≥2 and the rest had only 1 comorbidity. Reference Djaharuddin, Munawwarah and Nurulita32 It is thought that the reason for this may be that more chronic diseases cause worse clinical outcomes. Guan et al. (2020) stated that more comorbidities cause worse clinical outcomes, and the hazard ratio was 1.79. Reference Guan, Liang and Zhao33 In our study, it was determined that there was a statistically significant difference in the length of hospital stay according to the number of diseases the individual had (P < 0.05). It was concluded that individuals with 3 diseases had a shorter hospital stay compared with those with 1 and 2 diseases, and patients with 6 diseases were hospitalized in a shorter time than those with 1, 2, 4, and more than 7 diseases; this means that they die faster. In the present study, it was found that there was a statistically significant difference between the length of hospital stay of the patients according to the high MCCI score. The fact that patients with moderate MCCI scores have a longer hospital stay, that is, they die later, reveals that knowing the comorbidity score is important for the protection and planning of patients.
In the study, it was observed that there was a statistically significant difference between the duration of hospital stay of the patients according to the comorbidities, whether or not they had congestive heart failure and metastatic solid tumor (P < 0.05). According to the results, the presence of congestive heart failure and solid tumor causes patients to die in a shorter time. Similarly, in other studies, it was found that the mortality rate was higher in patients with hypertension, diabetes, cardiovascular diseases, obesity diseases, malignancies and comorbidities. Reference Biswas, Rahaman and Biswas24,Reference Djaharuddin, Munawwarah and Nurulita32,Reference Karyono and Wicaksana34–Reference Özdemir36 It is thought that this finding may be due to the angiotensin-converting enzyme 2 (ACE2), but there is no conclusive evidence.
Comorbidities in the patient affect the chances of survival. In studies, it has been determined that a 1-point increase in the CCI increases the probability of mortality between 16% and 32%, and a higher CCI score is associated with increased mortality and disease severity in COVID-19 patients. Reference Sabaz and Sabaz35,Reference Tuty Kuswardhani, Henrina and Pranata37 In our study, a statistically significant and negative correlation was found between the patients who did not have the disease and those who had a disease, and the length of stay in the hospital. Furthermore, a statistically significant, negative, and high-level correlation was determined between the MCCI score and the length of hospital stay (r = −0.075; P = 0.001). This situation makes us think that, if the individual has no chronic disease, has 1 disease, or is young, which affects the comorbidity score, the time until death is prolonged, hence, the individual can be treated. However, it can be said that, as the age of the individual increases and the number or score of comorbidity increases, time until the death of the individual accelerates and the chance of intervention decreases.
Conclusions
In the study, it was found that male individuals had a higher risk of disease and mortality compared with females, but the gender factor did not affect the process from disease to death. Therefore, it is recommended to make protective plans for male individuals by considering the gender factor in the society served.
In the study, it was determined that the deaths of individuals over the age of 80 occurred in a shorter time, but 26.9% of the individuals who died were under the age of 50 and the period until death was longer. For this reason, it is important to follow up, treat, and prevent chronic diseases for all age groups. However, measures to be taken to prevent chronic diseases are gaining importance, especially for young and middle-aged individuals. In this process, it focused on the detection and follow-up of COVID-19 disease, and primary health-care and other preventive services were interrupted. For this reason, primary health-care services should be given to the whole group with care and should be maintained. On the other hand, it is thought that the chronic disease follow-up and prevention studies, which are carried out routinely, can be reduced to younger ages to reduce the risks.
In the study, the mean score of the MCCI was 4.74 ± 2.07, and the index score of 56.9% was serious. Moreover, it was found that those with a moderate MCCI score had a longer hospital stay than those with a severe score. In addition, a statistically significant, negative, and high-level correlation was found between the MCCI score and the length of hospital stay in the study. For this reason, it is predicted that creating risk maps for patients by evaluating comorbidity scores in clinics and in the field and performing preventive practices will reduce mortality rates. In the present study, it was found that the presence of congestive heart failure and solid tumor caused patients to die in a shorter time. Because congestive heart failure and metastatic solid tumor accelerate mortality rates, it can be recommended that individuals with this diagnosis be followed up regularly even if they are not infected. Furthermore, it is thought that risk factors will be reduced by performing heart failure and cancer screenings for healthy individuals.
