Comorbidity Patterns Among Outpatient COVID-19 Cases in Turkey

Abstract

Numerous factors contribute to COVID-19 symptoms, with individuals who have pre-existing health conditions at the highest risk for severe SARS-CoV-2 infection. This study investigated the socio-demographic and comorbidity profiles within a large Turkish population diagnosed with SARS-CoV-2, including 47,875 patients diagnosed between March 2020 and May 2022 across six hospitals in different Turkish cities. Patients with SARS-CoV-2 confirmed via laboratory tests and presenting symptoms were included. The data collected covered socio-demographic details, infection onset dates, COVID-19 symptoms, pre-existing health conditions, radiological findings, treatments, disease progression, and relevant variables. A total of 47,875 files were included in the analysis. The median age was 43.7 years, with 84.5% testing positive for PCR SARS-CoV-2, often correlating with severe symptoms. Notably, 11.8% of the participants exhibited mild symptoms, and approximately 12.8% had comorbidities, increasing to 17.6% among severe cases. Females with PCR-positive COVID-19 had a comorbidity rate of 13.8%, compared to 12.5% in males. Among the patients with comorbidities, those aged 70 and above had the highest rates, at 22.1% (n = 1103). The most prevalent comorbidity was hypertension, followed by diabetes and cardiovascular diseases. Severe cases had a significantly higher prevalence of comorbidities (58.4%) compared to non-severe cases (27.6%). We hope that the evaluation of our findings will contribute to the research and treatment processes of the COVID-19 outpatients.

1. Introduction

The outbreak caused by the coronavirus in 2019 (COVID-19) quickly became a pandemic, developing severe acute respiratory consequences later called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The pandemic led to high morbidity and mortality worldwide, affecting hundreds of millions [2].

Many factors contribute to COVID-19 symptoms and the heightened risk of transmission and infection, including age, sex, ethnicity, body mass index (BMI), smoking, deprivation, pre-existing comorbidities like diabetes, hypertension, and cardiovascular disease, cardiorespiratory fitness, 25-hydroxyvitamin D level, and inflammation [3]. However, individuals with pre-existing health conditions have the highest risk of developing severe SARS-CoV-2 infection, as shown in numerous studies, from early research in Wuhan [4] to recent studies worldwide [5,6,7,8,9,10]. While these conditions do not directly impact the cure or mortality rates among the general population, they significantly affect critical patient outcomes. However, it remains unclear which factors hold the most predictive power in forecasting adverse health outcomes in COVID-19 due to substantial overlap.

Against this backdrop, our research aims to elucidate the connections between comorbidities and clinical outcomes while estimating the varying importance of different comorbidities. We scrutinized the relationship between SARS-CoV-2 infection and socio-demographic factors, disease severity, and comorbidity in 48,875 diagnosed outpatients. This study delved into symptom frequency, pre-existing conditions, treatment modalities, radiological findings, and the impact of comorbidity and socio-economic factors on patient prognosis. Notably, it represents the first exploration of socio-demographic characteristics and comorbidity within such a vast SARS-CoV-2 diagnosed population in Turkey.

2. Methodology

2.1. Patients and Data Collection

This study included 47,875 patients diagnosed with SARS-CoV-2 infection in outpatient clinics across six Turkish hospitals from March 2020 to May 2022. Inclusion criteria comprised patients with confirmed SARS-CoV-2 through laboratory tests and associated symptoms who were symptomatically monitored daily.

Data were collected from eligible patients and encompassed socio-demographic characteristics, infection onset dates, fever status, COVID-19 symptoms, pre-existing conditions including prevalent chronic noncommunicable diseases such as cardiovascular and respiratory diseases, cancer, obesity, and diabetes, status of tobacco smoking, radiological findings, treatments, disease progression, and relevant variables. Information was retrieved from hospitals’ health information management systems, leveraging robust digital platforms. Tobacco smoking was assessed as smoking or non-smoking due to a lack of details in the files. Regarding COVID-19 symptoms, fever above 37.5, shortness of breath, diarrhea, vomiting/nausea, wheezing, and chest and abdominal pain were considered symptoms of severity. Cases with no symptoms were not included in the analysis.

2.2. Ethics

Ethical clearance was secured from the University and the Ministry of Health. This study also received ethics approval from the European Commission Ethical Board (approval number: 4265850-14/08/2020). R [11] and Python [12] libraries facilitated data privacy, quality control, and transformation. Adhering to personal data protection laws [13], patient information was anonymized and masked [14]. Data integration and distribution followed a structured approach based on dependent and independent variables. Missing data were meticulously gathered from patient files and hospital systems, ensuring data reliability and accuracy.

2.3. Statistics

Statistical analysis utilized SPSS Version 24 [15]. The data were expressed as numbers and percentages for qualitative variables and means and standard errors for quantitative variables. The normality of the analyzed variables was assessed using the Shapiro–Wilk test. The p values presented in this study were the results of 2-tailed analyses, and statistical significance was set as p < 0.05. The results are supported by a backward logistic regression model.

3. Findings

The findings of this study are presented in the following two subsections: Outpatient COVID-19 Cases and Morbidity Profiles and Comorbidities Among Outpatients with COVID-19.

3.1. Outpatient COVID-19 Cases and Morbidity Profiles

This paper analyzes outpatient data from a total of 47,875 COVID-19 patients, of whom 43.6% (7734) are males and 53.3% (8833) are females (Figure 1). Of the patients, 40.5% are in the 30–49 age group, as shown in Figure 2.

Common symptoms observed among the outpatient cases are fever (48.3%), loss of taste (36.5%), cough (31.4%), sore throat (28.4%), fatigue/malaise (36.5%), muscle aches (18.6%), headache (17.9%), runny nose (13.1%), wheezing (3.0%), etc. The various symptomatic expressions following infection are shown in

Table 1. Distribution of COVID-19 symptoms among patients in outpatient clinics in Turkey (N= 47,875).

Symptoms	Number of Patients (N)	%
Fever	23,146	48.3
Fatigue/malaise	17,490	36.5
Cough	15,024	31.4
Sore throat	13,591	28.4
Muscle aches	8907	18.6
Headache	8561	17.9
Shortness of breath	6325	13.2
Runny nose	6270	13.1
Diarrhea	5731	12.0
Vomiting/nausea	4284	8.9
Joint pain	1587	3.3
Wheezing	1458	3.0
Lower chest wall indrawing	179	0.4
Chest pain	558	1.2
Loss of smell	279	0.6
Loss of taste	778	1.6
Abdominal pain	1191	2.5

and Figure 3.

In

Table 2. Characteristics of outpatients with severe COVID-19-PCR (+) and without severe COVID-19-PCR (−).

	With Severe COVID-19-PCR (+)		Without Severe COVID-19-PCR (−)
Characteristics	N = 40,411	%	N = 7443	%
Sex
Male	17,762	85.1	3097	14.8
Female	22,649	83.9	4346	16.1
Age group
0–14	914	83.8	176	16.1
15–29	8659	81.4	1977	18.6
30–49	15,189	83.9	3019	16.0
50–69	9335	85.3	9335	14.7
70 and above	4319	86.7	4319	13.3
Presence of symptoms
Yes	6525	84.6	1266	15.4
No	33,867	83.7	6160	16.2
Comorbidity
Yes	5066	82.4	1082	17.6
No	35,346	84.7	6364	15.3
Fever
Below 37.5	23,152	83.8	4463	16.2
Above 37.5	12,707	83.4	2518	16.4
Oxygen saturation
Below 95	19,300	84.8	3449	5.2
Above 95	19,768	83.1	3994	6.8

, the characteristics of outpatients with Polymerase Chain Reaction PCR (+) and PCR (−) cases are presented. Among the 47,875 participants, the median age was 43.7 years, and 40,142 (84.5%) had PCR (+) SARS-CoV-2 infection with more severe symptoms. The rate of patients without severe COVID-19 symptoms was 11.8%. A total of 85.1% male and 83.9% female patients had a (+) PCR test (see Figure 4). As for PCR (−) patients, the rate is 16.2% for females and 14.8% for males. Of the patients examined, 40.5% were in the 30–49 age group (Figure 3). Of the total patients, 16.3% had at least one symptom and 17.6% of PCR (+) patients had at least one pre-existing disease.

The percentages of symptoms in PCR (+) and PCR (−) COVID-19 patients admitted to the outpatient clinics of six hospitals are presented in Figure 4. The percentage of having at least one symptom among the total study population is 16.3%. This rate is 15.4% in patients with PCR+.

3.2. Comorbidities Among Outpatients with COVID-19

Out of 47,875 cases, 6145 (12.8%) had comorbid conditions. This percentage rose to 17.6% among patients with PCR (+) and more severe cases as shown in Figure 5.

Hypertension was found to be the most common comorbid condition (9.9%), followed by asthma and diabetes. There were a small number of patients with chronic kidney disease (11), asplenia (28), dementia (14), and rheumatologic disorders (9) (

Table 3. Distribution of pre-existing diseases among the study population (N = 47,876).

Condition	N	%
Hypertension	4.755	9.90
Asthma	3.681	7.70
Diabetes	193	0.05
Chronic cardiac disease	140	0.03
Malignant neoplasm	113	0.02
Chronic pulmonary disease	75	0.02
Obesity	49	0.01

).

In

Table 4. Factors affecting comorbidity among COVID-19 patients (N = 47,875).

Characteristics	Yes		No		p Value
	N	%	N	%
Comorbidity by Sex
Male	2507	12.5	18,357	88.0	p < 0.05
Female	3638	13.8	23,369	86.5
Comorbidity by Age Group
0–14	92	8.4	998	91.5	p < 0.05
15–29	972	9.1	9672	90.9
30–49	2014	10.7	16,830	89.3
50–69	1964	17.9	8982	82.1
70 and above	1103	22.1	3879	77.9
Smoking
No	5535	12.6	38,402	87.4	p < 0.05
Yes	610	15.6	3325	84.5
Oxygen Saturation					p > 0.05
Below 95	3028	13.3	19,726	86.7
Above 95	3116	13.1	20,658	86.9
Fever					p > 0.05
Below 37.5	3813	13.8	23,814	86.2
Above 37.5	2033	13.3	13,197	86.7
Existing Radiologic Images Related to COVID-19
Yes	694	15.5	3777	87.4	p < 0.05
No	5451	12.5	37,949	84.5
Presence of Symptoms					p < 0.05
Yes	3655	46.9	4140	53.1
No	2490	6.2	37,550	93.8

, the factors affecting comorbidity are presented.

The data on the sex distribution of comorbidity shows that females and the 70 and above age group are slightly higher in number compared to males and other age groups.

It was found that 8.2% of patients with COVID-19 were smokers. Among patients with comorbidities, this percentage increased to 15.6%. The presence of multiple chronic diseases in both smokers and non-smokers showed statistical significance. While no correlation was found between comorbidity and fever and oxygen saturation, a statistically significant correlation was found between comorbidity and the presence of more than one COVID-19-related symptom in COVID-19 patients. At least one symptom associated with COVID-19 was observed in 47% of comorbid patients (see Table 4, Figure 6).

Tomography results showed signs of COVID-19 findings in 15.6% of patients with comorbidities, compared to 12.5% in patients without comorbidities, and this relationship was statistically significant.

Table 5. Treatment received by COVID-19 patients admitted to outpatient clinics in Turkey (March 2020–June 2022).

Medications	Yes		No
	N	%	N	%
Antiviral Therapy	22,160	46.3	25,707	53.7
Antibiotics	43,360	90.6	4505	9.4
Corticosteroid	43,532	90.0	4333	9.1

presents the treatments administered to 47.875 COVID-19 patients. It shows that 46.3% received antiviral treatment, 9.2% received antibiotics, and 9.1% received corticosteroids.

A backward logistic regression model was used to control the correlations between the variables and confounding factors affecting comorbidity and COVID-19 incidence. At the final stage, the remaining variables in the model were the presence of COVID-19 symptoms, sex, disease severity, use of antiviral treatment, and smoking status (

Table 6. Logistic regression (backward model) for factors affecting comorbidity in COVID-19 patients.

Variables	B	SE	p Value	Relative Risk
Presence of COVID-19 Symptoms	2639	0.033	0.000	14.005
Age	−0.115	0.054	0.032	0.892
Sex	0.139	0.032	0.000	1.149
Severity of Disease	0.365	0.043	0.000	1.441
Antiviral Therapy	0.530	0.034	0.000	1.699
Smoking	0.057	0.053	0.289	1.058

).

4. Discussion

The outpatient data analyzed in this paper pertain to a total of 47,875 patients with COVID-19, out of whom 43.6% (7734) were males and 53.3% (8833) were females, and 40.5% were in the 30–49 age group. The median age was 43.7 years. The average age was higher among PCR (+) severe cases versus PCR (−) cases. (44 + 18.3, 42 + 17.0). Being elderly or female was considered a risk factor for COVID-19 in our study, in which the sex ratio of females to males was 1.2, in contrast to similar studies. In a report by Wang et al. [16], which was a systematic review and meta-analysis and included studies from hospitals in mainland China, the average age was also found to be higher in severe cases as compared with non-severe cases (48.5 vs. 38.5, p = 0.010). The sex ratio (male to female) was 1.33 in severe cases and 0.95 in non-severe cases in their study. Moreover, Gade et al. [17] in their study, conducted using the same software for the analysis of the data (SPSS Version 24), reported that the overall rate of positive cases was 19.3% and was significantly higher in the elderly group (25.5%) and in symptomatic patients (22.6%) [17].

Fever and cough were the main clinical symptoms in both severe and non-severe cases, which was consistent with previous studies. Some of the common symptoms observed among the outpatient cases were fever (48.3%), loss of taste (36.5%), cough (31.4%), sore throat (28.4%), fatigue/malaise (36.5%), muscle aches (18.6%), headache (17.9%), runny nose (13.1%), and wheezing (3.0%). In their paper, Wang et al. [16] showed that, both in severe and non-severe cases, the most common clinical symptom was fever, followed by cough, myalgia or fatigue, and sputum production; similar symptoms were seen in our study.

A total of 40,142 (84.5%) patients had PCR (+) SARS-CoV-2 infection with more severe symptoms. The rate of patients without severe COVID-19 symptoms was 11.8%. The rate of (+) PCR tests was 85.1% among male patients and 83.9% among females (see Figure 2), while the rate of PCR (−) results was 16.2% for female patients and 14.8% for male patients. A total of 40.5% of the patients examined were in the 30–49 age group. Of the total patients, 16.3% had at least one symptom, and 17.6% of PCR+ patients had at least one pre-existing disease.

The percentage of the presence of at least one symptom in the total study population was 16.3. This rate was 15.4% in patients with PCR+. Similarly to our study, the overall proportion of clinical symptoms was about 10% to 15% higher in patients with severe COVID-19 in the study conducted by Wang et al. [16].

Among the 47,875 cases, 6145 (12.8%) presented with comorbid conditions. Notably, this percentage rose to 17.6% among patients with PCR (+) and more severe cases. Our findings suggest that severe COVID-19 cases commonly exhibit comorbidities upon admission, particularly diabetes, hypertension, and cardiovascular disease.

While the comorbidity rate was 13.8% in females with COVID-19 PCR (+), this rate was 12.5% in males, and this difference was statistically significant (p < 0.05). The group with the highest rate of comorbidities was the 70 years and older group, with a rate of 22.2%. The incidence of comorbidities in the group of patients with COVID-19 who smoked was found to be higher than in non-smokers (15.6–12.6%). Radiologically serious COVID-19 findings (tomography results) were observed in 15.1% of patients with comorbidities, compared to 12.5% in those without comorbidities, a statistically significant difference. Additionally, 46.9% of patients with comorbidities exhibited at least one severe symptom, whereas only 6.2% of patients without comorbidities did (p < 0.05).

Hypertension emerged as the most prevalent comorbid condition, at 9.9%, followed by asthma and diabetes. Consistent with Wang et al.’s findings [16], patients with comorbidities and complications were at a higher risk of developing severe COVID-19. This study identified hypertension, diabetes, and cardiovascular diseases as the most common comorbidities. Notably, severe cases exhibited a significantly higher prevalence of comorbidities, with 58.4% affected compared to 27.6% in non-severe cases (p < 0.05).

There are significant limitations to this study. Its retrospective design is one of the most important limitations, as is the lack of a control group, which may decrease the significance of the results. Nevertheless, the analysis included data from a considerably high number of cases, which might be considered an important strength.

5. Conclusions

To our knowledge, this is the first study to report the characteristics of COVID-19 outpatients in terms of socio-demographic characteristics, symptoms, the treatment applied, the severity of the disease, and pre-existing comorbidities among Turkish patients. Large numbers of multi-dimensional healthcare data were collected from six different hospitals in six cities from Turkey. Valuable information belonging to 47,875 outpatients with COVID-19 was integrated, anonymized, pseudonymized, and pre-processed. We hope that the evaluation of our findings will contribute to research on and treatment of COVID-19 outpatients.

Hypertension emerged as the most prevalent comorbid condition, at 9.9%, followed by asthma and diabetes. Although pre-existing health conditions or comorbidities have no direct impact on the cure rate and mortality rate of general patients, they increase the mortality rate and reduce the cure rate of critical patients. However, it is not known which of these components has the strongest prognostic power in predicting adverse health outcomes in COVID-19 because there is a significant overlap between them. Therefore, it is important to produce a symptomatic investigative checklist for any future outbreaks. Studies based on real data on COVID-19 are important to strengthen the fight against the pandemic.

Future research should focus on several key areas. First, more in-depth studies are needed to determine the specific prognostic factors among the various comorbidities identified in this study. This would help in predicting and managing severe outcomes more effectively. Second, longitudinal studies should be conducted to monitor the long-term effects of COVID-19 on patients with different socio-demographic backgrounds and pre-existing health conditions. Finally, research should also explore the impact of different treatment protocols on diverse patient groups, particularly in relation to their comorbidity profiles.

This is extremely useful for developing an integrated global response as an investment for our future.