A Multicenter Evaluation of the Temporal and Clinical Differences of COVID-19 in Two Different Regions in Turkey: Comparison of İstanbul and Diyarbakır
PDF
Cite
Share
Request
Original Research
P: 180-188
June 2022

A Multicenter Evaluation of the Temporal and Clinical Differences of COVID-19 in Two Different Regions in Turkey: Comparison of İstanbul and Diyarbakır

Bagcilar Med Bull 2022;7(2):180-188
1. Selahaddin Eyyubi State Hospital, Clinic of Otorhinolaryngology, Diyarbakır, Turkey
2. Gaziantep University, Şahinbey Research and Practice Hospital, Clinic of Radiology, Gaziantep, Turkey
3. Abdulkadir Yuksel State Hospital, Clinic of General Surgery, Gaziantep, Turkey
4. Biruni University Faculty of Medicine, Department of Otorhinolaryngology, İstanbul, Turkey
No information available.
No information available
Received Date: 16.02.2022
Accepted Date: 26.05.2022
Publish Date: 17.06.2022
PDF
Cite
Share
Request

ABSTRACT

Objective:

This study compared the temporal course of virulence and symptomatology of severe coronavirus-19 (COVID-19) infection in two metropolitan cities with different geographic features. The study aimed to shed light on the possible etiology of the differences observed between two regions.

Method:

We retrospectively reviewed polymerase chain reaction confirmed COVID-19 cases for the period of March-June 2020 in two different cities (İstanbul and Diyarbakır) located in northern and southern parts of Turkey, respectively. Data on demographic features, presenting symptoms, clinical history, radiological findings, laboratory parameters, and mean hospitalization duration were collected. Additionally, meteorological data including daily temperature, diurnal temperature variation, relative humidity, wind speed, mean rainfall, ultraviolet index, altitude, and latitude were retrieved for the study period.

Results:

Total case number was higher in İstanbul during March and April, whereas it was higher in Diyarbakır during May and June (p=0.001). During the study period, daily temperature, diurnal temperature variation, and ultraviolet index were higher in Diyarbakır, whereas relative humidity, wind speed, and mean rainfall was higher in İstanbul (p=0.001). In Diyarbakır, patients presented with a predominance of dyspnea, whereas there was a predominance of fever and cough in İstanbul. Patients in Diyarbakır had significantly elevated neutrophil, neutrophil/lymphocyte ratio, D-dimer, lactate dehydrogenase, ferritin and C-reactive protein values compared to patients in İstanbul (p=0.018; p=0.006; p=0.001, p=0.001; p=0.001, p=0.001 respectively). The predominant computed tomography infiltration was multicentric and bilateral crazy-paving pattern in Diyarbakır, whereas unilateral ground-glass opacity was the dominant pattern in İstanbul.

Conclusion:

The socio-cultural and genetic factors may affect the epidemiological, clinical and imaging features of COVID-19 more than meteorological variations.

Keywords:
COVID-19, etiology, geographic features

Introduction

Initial detection and peaking of Coronavirus disease-2019 (COVID-19) cases in Turkey were seen relatively later compared to the rest of the European countries. The first COVID-19 case in Turkey was confirmed on March 11, 2020 in İstanbul according to the data published by the Turkish Ministry of Health. In the upcoming weeks, İstanbul, which is a cosmopolitan city with a population of 15 million (constituting 20% of Turkey’s population) and a high density national and international city hub, became the epicenter of the disease. The peak period was seen within 2 months with a subsequent plateau phase, similar to other countries. On the other hand, Diyarbakır and other cities located in the Southeastern Turkey did have limited case numbers in the initial phase of infection with a later peak period in 3-4 months. Additionally, hospitalized cases in these southeastern cities had more severe clinical and radiological courses.

Host-related genetic factors may affect infectivity, disease course, and mortality (1). Human genetics have a high similarity ratio; however, HLA genes which constitute the pillars of human immune system and immune response to an infectious or allergic factor show considerable variation (2,3). The main entry mechanism of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is via ACE-2 receptors (4,5). Increased ACE-2 expression by host cells may increase viral pathogenicity, replication, and cellular damage (6,7). Several studies have shown a correlation between host ACE-2 receptor numbers and viral load and disease severity (8). On the other hand, mounted immune response is the most important factor in infection control and determines the disease severity, complication, and mortality.

COVID-19 may have variable clinical course ranging from asymptomatic carrier state to a fatal outcome. 18-30% of cases can be asymptomatic or mildly symptomatic, with higher complication and mortality rates seen in elderly males with comorbidities (9-13). In addition to more severe disease course, this susceptible population has higher hospitalization and intensive care unit admission rates. Younger adults usually have milder or even asymptomatic form of the disease; however, even immunocompetent healthy young adults with no risk factors or comorbidities may have severe disease course. There are numerous studies investigating the relationship between the disease course and viral (viral load, pathogenicity, viral genetic subtype/mutation analysis), host (age, sex, ACE-2 receptor status/polymorphism, immune status, comorbidities), environmental (temperature, humidity, rainfall, wind speed), and socio-economic factors (ethnicity, culture, lifestyle, abiding/adapting to isolation and disinfection measures). However, no ground truth could be established to predict transmission dynamics and disease course.

In this study, we assessed the temporal course of clinical and radiological findings of COVID-19 cases in two hospitals from different regions of Turkey. Our aim was to evaluate the relationship between clinical/radiological findings and meteorological, socio-economic, ethnic and genetic variables.

Materials and Methods

Study Population

This study was approved by the Local Ethics Committee (date: 11.06.2020 number: 2020/12). We retrospectively reviewed polymerase chain reaction confirmed COVID-19 cases from two hospitals, Acıbadem Kozyatağı Hospital and Diyarbakir Selahaddin Eyyubi State Hospital, in two different cities in Turkey for the period of March-June 2020. Demographic features, presenting symptoms, clinical history, radiological findings [typical vs atypical lung computed tomography (CT) features, infiltration pattern, and distribution], laboratory parameters [(C-reactive protein (CRP), leukocyte, neutrophil, lymphocyte counts, hemoglobin, thrombocyte, D-dimer, lactate dehydrogenase (LDH) levels], and mean hospitalization duration were collected.

Meteorological Parameters

 Monthly meteorological data including daily temperature, diurnal temperature variation, relative humidity, wind speed, mean rainfall, ultraviolet index, altitude, and latitude were retrieved for the study period. Also, air pollution quality index was recorded.

Radiological Assessment

Chest CT scans were acquired with Siemens Somatom Sensation-Syngo CT and GE Optima 660 in İstanbul and Diyarbakır, respectively. A single radiologist with 10 years of experience in thorax radiology assessed all CT images from two centers based on the Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19 (14). CT images were reviewed for typical/atypical disease patterns, dominant infiltration patterns (ground-glass, crazy-paving, consolidation), and distribution of parenchymal involvement (unilateral/bilateral involvement, lower/upper lobe predominance, diffuse/peripheral/central/mixed). Additionally, changes at follow-up chest CT scans were assessed.

Statistical Analysis

All statistical analyses were performed with NCSS (Number Cruncher Statistical System) 2007 (Kaysville, Utah, USA). Descriptive statistics were expressed as number and percentage for categorical variables and as mean, standard deviation, median, minimum, and maximum for numerical variables. The Shapiro-Wilk test was used to determine whether the data showed normal distribution. The Student’s t-test and Mann-Whitney U test were used for parametric and non-parametric data, respectively. For categorical variables, the chi-square, Fisher’s Exact and Fisher-Freeman-Halton Exact tests were used. The Spearman test was used for correlation. A p-value of <0.05 was considered statistically significant.

Results

A total of 429 cases from two cities (Acıbadem Kozyatağı Hospital in İstanbul, Gaziantep Selahaddin Eyyubi State Hospital in Diyarbakır) with a slight male dominance (209 (48.7%) female, 220 (51.3%) male patients) were included in the study. The mean age of the study population was 50.43±16.72 years (5-93). There was a female predominance with a slightly older population (52.06±17.73 years) in patients recruited from Diyarbakır (p=0.049) (Table 1) (Figure 1).

Table 1
Figure 1

The total case number was higher in İstanbul during March and April, whereas it was higher in Diyarbakır during May and June (p=0.001) (Figure 1). The interval between symptom onset and presentation to the hospital was significantly higher in Diyarbakır (5.34±3.12/3.27±1.26, p=0.001). There was a significantly higher hospitalization rate among presenting patients in Diyarbakır (99.5%/47.8%, p=0.001) with no significant difference in the duration of hospitalization (5.59±3.43/5.85±3.71, p=0.595) (Table 1).

Figure 1
Table 1

There was no significant difference in comorbidity rates between the two cities. In Diyarbakır, patients presented with a predominance of dyspnea, and myalgia whereas there was a predominance of fever, cough, headache, sore throat, and loss of appetite in İstanbul (Table 1).

Table 1

Though patients in Diyarbakır had lower white blood cell values compared to İstanbul cases, this difference did not reach a statistical significance (p=0.057). Patients in Diyarbakır had significantly elevated neutrophil, neutrophil/lymphocyte ratio, D-dimer, LDH, ferritin and CRP values compared to those in İstanbul (p=0.018; p=0.006; p=0.001, p=0.001; p=0.001, p=0.001, respectively) (Table 2).

Table 2

There were significant differences in abnormal chest CT findings according to the number of lesions between the two cities (p=0.001). There was a significant tendency for multiple and bilateral infiltration patterns in Diyarbakır compared to İstanbul (p=0.001). A total of 19% cases had normal CT findings in İstanbul whereas this rate was 4.3% in Diyarbakır. 50% of cases in İstanbul had a typical COVID-19 infiltration pattern whereas 88% of cases in Diyarbakır had the typical COVID-19 infiltration pattern. The predominant CT infiltration was a crazy-paving pattern in Diyarbakır whereas unilateral, solitary ground-glass opacity was the dominant pattern in Istanbul (Tables 3 and 4).

Table 3
Table 4

During the study period, daily temperature, diurnal temperature variation, and the ultraviolet index were significantly higher in Diyarbakır compared to İstanbul (p=0.001) (Figure 1). Similarly, the UV index was significantly higher in Diyarbakır compared to İstanbul whereas relative humidity, wind speed, and mean rainfall were higher in İstanbul (p=0.001) (Tables 5 and 6) (Figure 1). The mean air pollution index was 87 (moderate) in Diyarbakır and 101 (unhealthy for sensitive groups) in İstanbul between March and June 2020.

Figure 1
Table 5
Table 6

Discussion

Some of the most intriguing points about the COVID-19 pandemic are variability in transmission rates, clinical course and mortality rates among different countries and regions. Differences in national healthcare policies, type and amount of testing, isolation and treatment protocols might be some of the underlying factors (15). Additionally, variability in climate conditions might affect the virus pathogenicity (16,17). Ma et al. (16), in a study investigating the effects of temperature, humidity and diurnal temperature variation on COVID-19 mortality, reported a negative correlation with temperature and humidity and a positive correlation with diurnal temperature variation (18,19). Additionally, studies reported the effects of other different meteorological parameters like ultraviolet index and wind speed on virulence and transmission rates (16,20,21). However, the results of our study showed a later peak and more severe disease course in Diyarbakır despite higher temperature and ultraviolet index, suggesting that the effect of meteorological variations on disease virulence and transmission rates might be limited. Patients in İstanbul generally had a milder COVID-19 infection, similar to other viral upper respiratory tract infections. We think that these parameters may be an important factor for the virus to remain as an upper respiratory tract infection without descending to the lower respiratory tract.

Cultural differences affect the social life and behavior. Consequently, this might influence the dynamics of infectious disease like transmission rates and isolation control. Since the main transmission route is respiratory droplets, increased population density and high population mobility may account for higher transmission rates and increased case numbers in crowded cities like İstanbul. Southeastern cities like Diyarbakır have extended family structure with a much higher rate of social interaction. Traditional wedding ceremonies and funeral services with limited social isolation practice might account for high number of cases in relatively less densely populated cities like Diyarbakır. Additionally, lower educational levels in Diyarbakır may have led to lower rates of personal protective equipment utilization. We think that higher rates encountered in Diyarbakır might be related to differences in socio-cultural and educational differences. In support of this hypothesis, a study from Brazil reported varying mortality rates across different regions and ethnicities and suggested that this regional difference might be related to socio-economic factors (22). According to the results of our study, we think that sociocultural characteristics are more effective in the spread of the disease.

In addition to regional differences across countries, differences in temporal course, infectivity and pathogenicity have been reported in different districts of the same city. A study evaluating the effect of ethnicity, socio-economic and educational status on hospitalization and mortality rates in different districts of New York reported worse outcomes in Bronx, which have a higher ratio of African American young adult population (23). This supports the hypothesis that lower socio-economic and educational status and ethnicity may affect mortality rate more than patient’s age.

Viral and host-related genetic variations have been extensively studied for their effects on infectivity, disease severity and mortality. Variations in viral structure may account for the differences seen in disease severity and mortality across different nations. Karacan et al. (24) isolated 3 different viral strains in a patient population with different clinical course (mild-moderate-severe).

In the present study, hospitalization rates were significantly higher for Diyarbakır. It is possible to explain this situation with the fact that, first, the patients in Diyarbakır had a more severe disease; secondly, those who applied to the hospital in Diyarbakır were most seriously ill; and mild-moderate patients were not admitted to the hospital. On the contrary, in İstanbul, even mild to moderate patients were admitted to the hospital and were hospitalized for different indications.

Study Limitations

This study, in which we compared the temporal course of virulence and symptomatology of SARS-CoV-2 infection in two different cities, despite being a multicenter study, is still limited by lack of multiple centers from the same city. Additionally, we did not have access to regional Turkish Ministry of Health data for these two cities, which limited the assessment of actual infectivity and mortality rate for these two cities in total. We could not obtain genetic analysis due to limited funding, which renders the genetic differences between patient populations an assumption at best.

Conclusion

COVID-19 infectivity, pathogenicity, clinical course and mortality rates show variation across nations, cities and individuals. In addition to age, sex and comorbidities, environmental, meteorological, socio-economical, and racial factors might account for some of these variations.

Main points

1. The clinical course of COVID-19 varies according to host-related factors (age, sex, race, ACE-2 receptor status/polymorphism, immune status, comorbidities).

2. Environmental parameters such as temperature, humidity, rainfall, and wind speed can affect COVID-19 virulence.

3. Socio-economic status and cultural characteristics affect the spread of the disease.

Ethics

Ethics Committee Approval: This study was approved by the Local Ethics Committee (date: 11.06.2020 number: 2020/12). We retrospectively reviewed polymerase chain reaction confirmed COVID-19 cases from two hospitals, Acıbadem Kozyatağı Hospital and Diyarbakir Selahaddin Eyyubi State Hospital, in two different cities in Turkey for the period of March-June 2020.

Informed Consent: Written informed consent for publication was not necessary because no identifying patient data have been included in this manuscript.

Peer-review: Internally and externally peer-reviewed.

Authorship Contributions

Concept: Ö.V., D.E.T.Ş., A.A., Design: Ö.V., D.E.T.Ş., A.A., Data Collection or Processing: Ö.V., D.E.T.Ş., Analysis or Interpretation: D.E.T.Ş., A.N.Ş., A.A., Drafting Manuscript: Ö.V., D.E.T.Ş., A.N.Ş., Critical Revisation of Manuscript: D.E.T.Ş., A.N.Ş., A.A., Final Approval and Accountability: Ö.V., D.E.T.Ş., A.N.Ş., A.A.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study received no financial support.

References

1
Ovsyannikova IG, Haralambieva IH, Crooke SN, Poland GA, Kennedy RB. The role of host genetics in the immune response to SARS-CoV-2 and COVID-19 susceptibility and severity. Immunol Rev 2020;296(1):205-219.
2
Iturrieta-Zuazo I, Rita CG, García-Soidán A, de Malet Pintos-Fonseca A, Alonso-Alarcón N, Pariente-Rodríguez R, et al. Possible role of HLA class-I genotype in SARS-CoV-2 infection and progression: A pilot study in a cohort of Covid-19 Spanish patients. Clin Immunol 2020;219:108572.
3
Barquera R, Collen E, Di D, Buhler S, Teixeira J, Llamas B, et al. Binding affinities of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide. HLA 2020;96(3):277-298.
4
Belouzard S, Chu VC, Whittaker GR. Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. Proc Natl Acad Sci U S A 2009;106(14):5871-5876.
5
Ou X, Liu Y, Lei X, Li P, Mi D, Ren L, et al. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun 2020;11(1):1620.
6
Zhang SY, Lian JS, Hu JH, Zhang XL, Lu YF, Cai H, et al. Clinical characteristics of different subtypes and risk factors for the severity of illness in patients with COVID-19 in Zhejiang, China. Infect Dis Poverty 2020;9(1):85.
7
Strafella C, Caputo V, Termine A, Barati S, Gambardella S, Borgiani P, et al. Analysis of ACE2 Genetic Variability among Populations Highlights a Possible Link with COVID-19-Related Neurological Complications. Genes (Basel) 2020;11(7): 741.
8
Delanghe JR, Speeckaert MM, De Buyzere ML. COVID-19 infections are also affected by human ACE1 D/I polymorphism. Clin Chem Lab Med 2020;58(7):1125-1126.
9
Görmeli Kurt N, Çamcı M. COVID-19 and Other Viral Pneumonias. Dicle Med J 2021;48(1):40-46.
10
Nishiura H, Kobayashi T, Miyama T, Suzuki A, Jung SM, Hayashi K, et al. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). Int J Infect Dis 2020;94:154-155.
11
Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill 2020;25(10):2000180.
12
The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The Epidemiological Characteristics of an outbreak of 2019 Novel Coronavirus Diseases (COVID-19) - China, 2020. China CDC Wkly 2020;2(8):113-122.
13
Garg S, Kim L, Whitaker M, O’Halloran A, Cummings C, Holstein R, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019-COVID-NET, 14 States, March 1-30, 2020. MMWR Morb Mortal Wkly Rep 2020;69(15):458-464.
14
Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, et al. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiol Cardiothorac Imaging 2020;2(2):e200152.
15
Michaels JA, Stevenson MD. Explaining national differences in the mortality of Covid-19: individual patient simulation model to investigate the effects of testing policy and other factors on apparent mortality. Preprint. medRxiv 2020. https://doi.org/10.1101/2020.04.02.20050633
16
Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, et al. Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Sci Total Environ 2020;724:138226.
17
Su D, Chen Y, He K, Zhang T, Tan M, Zhang Y, et al. Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide. Preprint. medRxiv 2020. https://doi.org/10.1101/2020.04.23.20077545
18
Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ 2020;724:138201.
19
Tosepu R, Gunawan J, Effendy DS, Ahmad OAI, Lestari H, Bahar H, et al. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ 2020;725:138436.
20
Rendana M. Impact of the wind conditions on COVID-19 pandemic: A new insight for direction of the spread of the virus. Urban Clim 2020;34:100680.
21
Gunthe SS, Swain B, Patra SS, Amte A. On the global trends and spread of the COVID-19 outbreak: preliminary assessment of the potential relation between location-specific temperature and UV index. Z Gesundh Wiss 2022;30(1):219-228.
22
Baqui P, Bica I, Marra V, Ercole A, van der Schaar M. Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. Lancet Glob Health 2020;8(8):e1018-e1026.
23
Wadhera RK, Wadhera P, Gaba P, Figueroa JF, Maddox KEJ, Yeh RW, et al. Variation in COVID-19 Hospitalizations and Deaths Across New York City Boroughs. JAMA 2020;323(21):2192-2195.
24
Karacan I, Akgun TK, Agaoglu NB, Irvem A, Alkurt G, Yildiz J, et al. The origin of SARS-CoV-2 in Istanbul: Sequencing findings from the epicenter of the pandemic in Turkey. North Clin Istanb 2020;7(3):203-209