A Retrospective Comparative Study to Analyze Epidemiological Characteristics of Deaths with Covid–19 in 1st and 2nd Wave at AIIMS Raipur

 

Sharun.Nv1, Manisha Yadav2, Meenu2, Monika Rani2, Naincy Kumari2,

Neha Vashisth2, Nikita Kumari2

1Tutor/Clinical Instructor, College of Nursing, AIIMS Raipur.

2Final Year BSc. (Hons.) Nursing Student, College of Nursing, AIIMS Raipur.

*Corresponding Author Email: sharunvijayan@gmail.com

 

ABSTRACT:

Background: The COVID-19 pandemic's rapid spread and the absence of standardized treatment have exacerbated the public health challenge. This retrospective, comparative study aims to analyze the epidemiological characteristics of COVID-19 deaths during the first and second waves at AIIMS Raipur, focusing on demographic shifts, healthcare utilization, and clinical outcomes. Materials and Methods: The study population comprised all COVID-19 deaths during the first and second waves, with a sample size of 120 deaths for each wave. Data were collected from patient records, focusing on demographic, clinical, and treatment parameters. Statistical analysis utilized SPSS, employing descriptive and inferential statistics. Results: The study revealed a demographic shift towards the 40-60 age group in the second wave, with significant differences in systolic blood pressure and D-dimer levels between the waves. No significant association was found between selected demographic characteristics and survival time after intubation. Statistical analysis indicated significant epidemiological distinctions between the waves, with a notable reduction in patients seeking initial treatment outside AIIMS in the second wave. Conclusions: The research highlights significant epidemiological shifts and clinical characteristics of COVID-19 fatalities between the first and second waves at AIIMS Raipur. The findings suggest a need for adaptive treatment protocols and informed public health policies to manage the evolving pandemic. Future research should focus on broader demographic studies and longitudinal tracking to inform healthcare planning and interventions.

 

KEYWORDS: COVID-19, Epidemiological Characteristics, AIIMS Raipur, Retrospective Comparative Study.

 

 


 

INTRODUCTION:

In December 2019, a novel coronavirus strain was identified in Wuhan, China, marking the beginning of the COVID-19 pandemic1. The first case in India was confirmed on January 30, 2020, in Kerala2. By December 20, over 75 million cases and 1.6 million deaths had been reported worldwide, making COVID-19 a global health crisis3. As of September 10, 2020, India became the second most affected country, with over 4.55 million cases, 3.54 million recoveries, and 76,304 deaths. COVID-19, caused by SARS-CoV-2, ranges from mild symptoms to severe complications like respiratory failure and septic shock, posing a significant public health challenge. The rapid spread and absence of standardized treatment exacerbate the crisis. SARS-CoV-2, an RNA virus, has been a major concern since its discovery, following the MERS and SARS outbreaks in 2012 and 2002, respectively4. Research indicates that COVID-19 impacts vary regionally, with age and gender as significant risk factors. In the UK, for example, over 90% of COVID-19 deaths were among those over 60, with males constituting 60% of these fatalities5. Conditions like cardiovascular diseases, hypertension, diabetes, respiratory diseases, and cancer increase mortality risk6,7. A study in an Indian tertiary care center revealed that out of 231 patients, the majority were symptomatic, with fever, sore throat, and dry cough being the most common symptoms8. Understanding the characteristics and risk factors of COVID-19 fatalities is crucial for high-risk groups to adopt preventive measures and seek timely medical intervention, aiming to reduce mortality rates through informed awareness and action.

 

REVIEW OF LITERATURE:

The literature review encompasses a broad range of international and Indian studies on COVID-19, focusing on various aspects of the pandemic, including epidemiological characteristics, clinical presentations, and outcomes of the disease across different waves and geographic regions.

 

A study conducted by Azarudeen M et al examined COVID-19 mortality patterns standardized by age across selected states and India from January to November 2020, highlighting the critical role of considering age structure in mortality reporting to accurately assess the epidemic's impact. Utilizing the decadal growth rate from the 2011 census, the research team estimated India's 2020 population and calculated both crude and age-adjusted mortality rates per million for the nation and selected states. Through indirect standardization, age-standardized mortality rates were derived for comparison across states and age groups. Data on COVID-19 deaths up to November 16, 2020, were sourced from the Integrated Disease Surveillance Programme. Findings revealed that the crude mortality rate in India was 88.9 per million, with the highest age-adjusted mortality rate in Delhi (300.5 per million) and the lowest in Kerala (35.9 per million). The study showed significant COVID-19 mortality not only among the elderly but also within the working-age population, indicating a broader impact on society9.

 

The study titled "Clinical Characteristics of Patients with Severe and Critical COVID-19 in Wuhan: A Single-Center Retrospective Study" by Chen et al. (2021) in the journal Infectious Diseases Therapy, explores the clinical characteristics and outcomes of 753, COVID-19 patients admitted to Wuhan Union Hospital from January 22, 2020, to May 7, 2020. The research focuses on severe (493 patients) and critical (228 patients) cases, highlighting the high mortality rate (79.4%) among critical cases and identifying risk factors such as age, higher white blood cell and neutrophil counts, and lower lymphocyte counts at admission. Key findings include the efficacy of anticoagulation therapy in improving the prognosis of critical cases and the role of lactate dehydrogenase (LDH) as an independent predictor of early death. This study provides valuable insights into managing COVID-19, especially in severe and critical cases, emphasizing the importance of early recognition and treatment strategies to improve patient outcomes10.

 

Another study titled "A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden" by Drefahl et al., published in Nature Communications in 2020, investigates the socio-demographic factors influencing COVID-19 mortality rates in Sweden. Utilizing data up to May 7, 2020, the research identifies male gender, lower individual income, lower education levels, unmarried status, and being an immigrant from low- or middle-income countries as independent predictors of higher COVID-19 mortality. This comprehensive study, leveraging high-quality individual-level data from Swedish administrative registers, highlights the disproportionate impact of the pandemic on socially disadvantaged groups, emphasizing the need for targeted public health interventions and policy measures to mitigate these disparities11.

 

Aggarwal N. et al. explored the epidemiological characteristics and treatment outcomes of patients with severe acute respiratory illness (SARI) in Bihar, India. The study considered variables like age, sex, residence, comorbidities, and COVID-19 status12.

 

The study titled "Epidemiological and Clinical Characteristics and Early Outcome of COVID-19 Patients in a Tertiary Care Teaching Hospital in India: A Preliminary Analysis" by Kayina et al. (2020), published in the Indian Journal of Medical Research, presents a comprehensive analysis of 235 COVID-19 patients admitted to the All-India Institute of Medical Sciences, New Delhi, between May 11 and June 28, 2020. This study highlights the demographic, clinical features, and early outcomes, notably a 24-hour ICU mortality rate of 8.5%. The findings underscore the critical need for early intervention and specialized care for patients presenting with severe symptoms, high total leucocyte and neutrophil counts, and elevated lactate levels, which were identified as predictors of early mortality. This research adds valuable insights into the clinical management of COVID-19 in the Indian context, emphasizing the importance of understanding local epidemiological patterns for effective healthcare responses13.

 

The study by Jing et al., titled "Contagiousness and secondary attack rate of 2019 novel coronavirus based on cluster epidemics of COVID-19 in Guangzhou," published in the Chinese Medical Journal, provides a detailed analysis of COVID-19 contagion and secondary attack rates within cluster outbreaks in Guangzhou, China. It underscores the significant transmission within families, noting an average of 2.18 infections per index case in the initial 1-3 days of incubation, with family transmissions accounting for 85.32% of these cases. The secondary attack rate among close contacts was found to be between 17.12% and 18.99%, and even higher among family members, ranging from 46.11% to 49.56%. These findings highlight the critical need for robust prevention and control measures to curb community spread of COVID-19, emphasizing the virus's high contagiousness within familial settings14. Covid -19 had wide range of impacts on various aspects of health and socioeconomic parameters across the globe.15–26

 

The study titled "Secondary attack rate of COVID-19 among contacts and risk factors Tamil Nadu March–May 2020: a retrospective cohort study" by Karumanagoundar et al., published in BMJ Open in 2021, provides a comprehensive analysis of the secondary attack rate (SAR) of COVID-19 in Tamil Nadu, India. It highlights the increased risk of transmission among household contacts compared to community contacts, with a significant increase in disease transmission from primary cases with symptoms or exposure to a congregation. The findings emphasize the importance of targeted interventions such as extensive contact tracing, testing of all household contacts, and the restriction of gatherings to prevent the spread of COVID-19. This study contributes to understanding the dynamics of COVID-19 transmission and informs public health strategies to mitigate the spread of the virus27.

 

Dhont S. et al. discussed the phenomenon of "happy hypoxemia" in COVID-19 patients, focusing on pathophysiological determinants and the importance of early ICU referral28. Mohamed Hussein A. A. et al. surveyed recovered COVID-19 patients, finding a wide range of functional restrictions post-recovery, influenced by factors like age, gender, and comorbidities29. Roatami M. and Mansouritorghabeh H. analyzed D-dimer levels in COVID-19 patients, associating elevated levels with poor prognosis and emphasizing the importance of early detection of thrombosis30. Eljilany I. and Elzouki A. N. studied coagulopathy in COVID-19 patients, discussing the management and prognosis of venous thromboembolism (VTE) and highlighting the role of thromboprophylaxis31. Rohlfing A. K. et al. investigated the interaction between platelets and SARS-CoV-2, suggesting mechanisms of platelet activation and thrombus formation in COVID-19 patients, and noting altered platelet counts and chemokine profiles32.

 

These studies collectively provide a comprehensive overview of the evolving understanding of COVID-19, from clinical presentations and epidemiological trends to treatment outcomes and pathophysiological insights. The research underscores the disease's complexity, the significance of comorbidities, and the crucial role of preventive measures and early treatment interventions.

 

MATERIALS AND METHODS:

Statement of the problem:

A retrospective comparative study to analyse epidemiological characteristics of deaths with COVID-19 in 1st and 2nd wave at AIIMS Raipur.

 

Objectives of the study:

1.     To describe epidemiological characteristics of deaths with COVID-19  in 1st and wave at AIIMS Raipur.

2.     To compare the selected epidemiological characteristics of deaths with COVID-19  in 1 st and 2nd wave at AIIMS Raipur.

3.     To find the association between selected demographical characteristics and survival time after intubation among deaths due to COVID-19  in 1st and 2nd wave at AIIMS Raipur.

 

Null Hypothesis (H0)

H0(1): There is no significant difference between selected epidemiological characteristics of COVID-19 deaths in 1st and 2nd wave.

 

H0(2): There is no significant association between selected demographical characteristics and survival time after intubation among deaths due to COVID-19 in 1 st and 2nd wave at AIIMS Raipur.

 

Conceptual Frame Work:

According to triangle of epidemiology based on communicable disease model agent, host, environment and time are naturally dependent and interacting between each other. The epidemiological triad helps to understand epidemiological characteristics present in COVID-19 infection to show us how to break the chain of three vertices.

 


Figure 1: Conceptual Framework.

 


OPERATIONAL DEFITINITION:

1.       RETROSPECTIVE STUDY: An epidemiological study that looks backward in time to identify the epidemiological characteristics of deaths with COVID-19 at AIIMS Raipur C.G. using case records.

2.       ANALYZE: Analyze means critical thinking skills that involve determining the value of a study by breaking the contents of a study report into parts and examining the parts for accuracy, completeness, uniqueness of information and organization.

3.       EPIDEMIOLOGICAL CHARACTERISTICS: Any characteristics that help in the study and evaluation of COVID-19. In this study host, agent, gender, education, co -morbidity, previous history of COVID-19 etc are epidemiological characteristics

4.       COVID-19: It’s a disease condition caused by SARS-CoV-2.

5.       DEATH WITH COVID-19: In this study it refers to patients who got admitted in COVID ICU or COVID ward as laboratory-confirmed COVID patients and later on died due to COVID-19 complications.

 

Delimitation:

The study is delimited and dependent on case records of patients who died with COVID-19 in 1st and 2nd waves at AIIMS Raipur Chhattisgarh.

 

RESEARCH METHODOLOGY:

Study approach: Quantitative, non-experimental, correlational research approach. This approach is used to compare COVID-19 deaths in 1st and 2nd wave.

Study Design: Retrospective research design.

Study setting: The study was conducted at AIIMS Raipur. For data collection, we visited the Medical Record Department (MRD) of AIIMS Raipur and got access to the records after the RRC and IEC approval.

Study Population: For the study, the population was all deaths with COVID-19 in 1st and 2nd wave, which occurred at AIIMS Raipur.

Study Sample: Patient died with COVID-19 in AIIMS Raipur from June 2020 to January 2021 in 1st wave and from February 2021 to September 2021 in 2nd wave.

Sample size: From the records, a random sample of 120 deaths with COVID-19 in 1st wave and 120 deaths with COVID-19 in 2nd wave was selected for the study.

·         Inclusion criteria: Case record of deaths with COVID-19  in 1st and 2nd waves and the final cause of death is complications related to COVID-19 .

·         Exclusion criteria: Any incomplete case records of deaths with COVID-19  in 1st  and 2nd waves and any case in which the immediate cause of death is not related to COVID-19  or complication associated with it, for example road traffic accidents.

Description of tool: The patient record information tool was developed based on epidemiological characteristics used to collect the data. Its components include –

1.       Host: Age, Gender, Education, Co - morbidities, previous history of smoking, previous history of COVID.

2.       Environment:

·       Internal - CRP, CBC, Lymphocytes, Blood glucose level, platelets.

·       External - Area of residence.

Validity-Content validity of the tool obtained from 5 experts.

Data collection:

For data collection, formal permission took from respective authorities. Sample collected at AIIMS Raipur using a random sampling technique. All the data were collected from patient case record only. The data collected by the field investigators was instantly entered in the Google form which was made in concurrence with the validated tool.

 

Data analysis:

The aggregated data collected from the patient record was prepared in an Excel sheet and uploaded to SPSS for further data analysis. The data was analyzed by using both descriptive and inferential statistics with the consultation of a statistician.

 

Ethical consideration:

The study was approved by Research Review Committee (RRC) and Institutional Ethical Committee (IEC). Prior permission took from the college authorities and MRD of tertiary care center. All information was treated with strict confidentiality and used only for research purpose.

 

Analysis and Interpretation of Data:

Objective 1: To describe epidemiological characteristics of deaths with COVID-19 in 1st and wave at AIIMS Raipur.

The majority of patients in both groups were residents of the State of Chhattisgarh. The demographic data, categorized and detailed in Table 1, indicate almost a similar frequency and percentage distribution of variables across the first and second waves of the pandemic. However, there were notable differences in the age distribution of affected individuals between the two waves. Specifically, the predominant age group impacted during the first wave was individuals aged above 60 years, which accounted for 56% of cases. In contrast, the second wave saw a shift, with the 40 to 60 years age category becoming the most affected, representing 57% of the cases. This observation highlights a demographic shift in the age groups most vulnerable to the effects of the pandemic across different waves (Table.1).

 

Table 1: The frequency and percentage distribution of demographic variable

Sl.no

Parameters

First wave

Second wave

1.

Age in Years

Frequency (%)

Frequency (%)

 

<20

4 (3.3)

3 (2.5)

 

20-40

11 (9.2)

24 (20.0)

 

40-60

49 (40.8)

57 (47.5)

 

>60

56 (46.7)

36 (30.0)

2.

Gender

 

Female

40 (33.3)

39 (32.5)

 

Male

80 (66.7)

81 (67.5)

3.

Education

 

NDA

71 (59.2)

72 (60.0)

 

Graduate

3 (2.5)

1 (.8)

 

illiterate

25 (20.8)

23 (19.2)

 

Postgraduate

1 (.8)

0 (0)

 

Primary

6 (5.0)

5 (4.2)

 

Secondary

5 (4.2)

11 (9.2)

 

Senior Secondary

9 (7.5)

8 (6.7)

4.

Employment status

 

NDA

72 (60.0)

78 (65.0)

 

Employed

12 (10.0)

16 (13.3)

 

Unemployed

36 (30.0)

26 (21.7)

5.

Area of residence

 

Rural

44 (36.7)

31 (25.8)

 

Urban

76 (63.3)

89 (74.2)

6.

Travelling history

 

NDA

61 (50.8)

57 (47.5)

 

No

49 (40.8)

56 (46.7)

 

Yes

10 (8.3)

7 (5.8)

7.

Initial area Admission in the hospital for Covid 19 treatment

 

General Ward

77 (64.2)

99 (82.5)

 

ICU

43 (35.8)

21 (17.5)

8.

Treatment history outside AIIMS for Covid 19

 

No

68 (56.7)

96 (80.0)

 

Yes

52 (43.3)

24 (20.0)

NDA-No Data Available

 

Within the study's sample size of 120 patients for each wave, it was observed that 52 patients in the first wave sought initial treatment at different healthcare facilities before transferring to AIIMS Raipur for further care. However, it reduced to 24 in the 2nd wave. Notably, a significant portion of these patients did not possess a referral letter from their initial healthcare provider upon their arrival. Furthermore, it was observed that the majority of these individuals were in a critical condition at the time of their transfer. It shows the increasing trust of the public in government healthcare settings.

 

Table.2: Survival time data

STATISTIC

1st wave

2nd wave

1st wave

2nd wave

Survival time after first COVID positive result in Days

Survival time after first intubation.

(n=120)

(n=118)

(n=120)

(n=119)

Median (Q1, Q3)

7.00 (4,7)

7.00 (5,13)

1.00 (0,3)

1.00 (0,3)

 

Table.3: Survival time after first intubation in hours

Time Frame

Survival time after first intubation

First wave: F (%)

Second wave: F (%)

With in 24 hrs

42 (35)

52 (43.3)

24-48 hrs

43 (35.8)

31 (25.8)

48-72 hrs

13 (10.8)

7 (5.8)

> 72 hrs

22 (18.4)

30 (25)

Total

120 (100)

120 (100)

F=Frequency, %=Percentage

 

The analysis revealed that the median survival time post the initial COVID-19 positive result was identical in both groups, averaging 7 days. Additionally, the survival duration following the first intubation procedure was observed to be 1 day for patients in both waves (Table.2). A significant number of patients succumbed within 24 hours of intubation, with the first wave reporting 42 cases (35%) and the second wave documenting 52 cases (43.3%), as shown in Table 3. Regarding the survival time following the initial COVID-19 positive diagnosis, the majority of cases in both waves had a survival span of 7 days, with the first wave comprising 64 patients (53.3%) and the second wave consisting of 60 patients (50%), detailed in Table 4. This data underscores the critical nature of the condition and the short survival period post-intubation for many patients affected during both waves.

 

Table 4: Survival time in days after first COVID positive result in Days

Time Frame

Survival time in days after first COVID positive result in Days

First wave: F (%)

Second wave: F (%)

1-7 days

64 (53.3)

60(50)

8-14 days

42(35)

39(32.5)

>14 days

14(11.6)

21(17.5)

Total

120(100)

120(100)

F=Frequency, %=Percentage

 

Objective 2: To compare the selected epidemiological characteristics of deaths with COVID-19 in 1 st and 2nd wave at AIIMS Raipur.

Test for homogeneity performed (chi-square goodness of fit) between categorical variable of two groups and found that the majority of the variables are homogenous (p<0.05) and further proceeded for statistical analysis.

Test of normality performed among blood values and vital signs variable using SPSS and found that d-dimer, Hb, platelet, aPTT, hct, DBP, RR and PR, are normaly distributed in both the group (Shapiro-Wilk test, p>0.05) and hence selected for parametric test (Independent sample t-test) for data analysis. Other variables are proceeded with Mann-Whitney U test.

 

Respiratory Rate, Pulse Rate, Platelets, and hct (Haematocrit) show no significant difference between the two waves, indicated by their high p-values (>0.05). This suggests that, on average, these parameters were similar across the two waves. However, statistically significant differences found among variables including DBP (p = .034), D-DIMER (p = .043) and Hb (p = .005). For variables that did not conform to normal distribution, the Mann-Whitney U test was employed. The analysis revealed that, except Systolic Blood Pressure (p = .013), the differences in median values across the groups were statistically insignificant.

 

The analysis suggest that there are significant distinctions between the selected epidemiological characteristics of COVID-19 fatalities during the first and second waves, leading to the rejection of the null hypothesis H0(1).

 

With 120 subjects in 1st wave and 120 subjects in 2nd wave, with 5% level of significance, we were able to achieve a power of 97% by considering the mean difference of SBP as 5.5 mmHg with SD of 11 mmHg.


 

Table 5: Comparative table shows the difference in values of epidemiological characteristics of death with covid-19 in wave-I and wave-II with p values.

Variable and Sample size of two group

Covid19 wave-I

Covid19 wave-II

Test Statistic

(t-Value)

P value

Mean

SD

Mean

SD

RR*

25.00

6.21

24.23

5.807

.998

.319

Pulse_Rate*

99.82

23.27

99.09

21.697

.250

.803

DBP*

79.32

14.33

75.52

13.22

2.136

.034

D-DIMER (29,45)

6.09

6.84

3.52

3.83

2.064

.043

aPTT (58, 67)

37.35

13.19

47.06

53.34

1.351

.179

Hb*

11.36

2.48

12.22

2.22

2.838

.005

Platelets*

217.02

107.21

213.98

115.23

.209

.835

hct

37.57

20.97

41.39

27.01

1.223

.223

Variable

Median

(Q1, Q3)

Median

(Q1, Q3)

z- Value

P value

Spo2*

92.00

(87.25, 96.)

92

.(84.25, 95.75)

-1.030

.303

Temperature*

98.2

(98,98.60)

98.20

(97.80, 98.60)

-.690

.490

SBP*

130

(118.50, 146)

124.50

(110.5, 136.75)

-2.492

.013

IL-6 (14,12)

14.38

(10.62, 161.25)

50.40

(16.15, 115.75)

-.926

.354

PT (77,43)

11.80

(10.60, 13.25)

11.30

(8.60, 14.00)

-.837

.402

Lymphocytes (112, 120)

9.20

(4.00, 15.75)

7.25

 

(3.72, 14.00)

-1.623

.105

*n=120 in both the group.

 


Objective 3: To find the association between selected demographical characteristics and survival time after intubation among deaths due to COVID-19 in 1st and 2nd wave at AIIMS Raipur:

The Chi-square test was conducted to examine the relationship between gender, area of residence, and survival time post-intubation (less than 48 hours vs. more than 48 hours) among COVID-19 fatalities in both the first and second waves at AIIMS Raipur. The analysis revealed no significant association between these variables in either wave, indicating that gender and area of residence do not significantly affect survival time after intubation. Consequently, the null hypothesis, H0(2), stating there is no significant association between selected demographic characteristics and survival time post-intubation among COVID-19 deaths, is accepted.

DISCUSSION:

1.       Demographic Shifts and Impact: A study conducted by Kayina CA et al.at All India Institute of Medical Sciences, New Delhi highlights the demographic, clinical features, and early outcomes, notably a 24-hour ICU mortality rate of 8.5%13. In the second wave of covid19, it is noticed that there was near about 7% increase in the death among age group of 40-60 years and 20% decrease among the elderly age category (>60 years) as compare to first wave in the same age category. The death in 20-40 age category increased to 20% (24), where in first wave it was 9.2% (11). A study conducted by Azarudeen M et al showed significant COVID-19 mortality not only among the elderly but also within the working-age population, indicating a broader impact on society9. Larger level quantitative study may require to see the significance in the demographic Shifts, its validity and impact11.

2.       Treatment History and Healthcare Utilization: In the first wave of covid 19 out break 43.3% (52) of the patient died had a history of treatment taken from other health care setting for Covid 19 who were then referred to AIIMS Raipur for further treatment. However, in the second wave it is decreased to 20% (24). A qualitative study may be required to analyze the decrease in patients seeking external treatment in the second wave and what this indicates about public health messaging, healthcare accessibility, or patient trust in institutional healthcare during different phases of the pandemic. Another noticeable change is that, 35.8% (43) patient who died admitted directly to ICU in the first wave, how ever it was decreased to 17.5% (21) in the second wave.

3.       Cardiovascular Impact: The significant differences in DBP and SBP suggest variations in the cardiovascular impact or management of COVID-19 between the two waves. This might be due to differences in treatment protocols, patient demographics, or disease severity.

4.       Coagulation Status: It is proven that anticoagulation therapy in improving the prognosis of critical          cases 10. The significant difference in D-DIMER levels indicates variability in coagulation status, which could reflect differences in the severity of infection or effectiveness of anticoagulant treatments between the waves.

5.       Inflammatory Response: Despite the non-significant p-value for IL-6, the wide range in values suggests a variable inflammatory response among patients, which may require further investigation to understand its implications fully.

 

The significant differences in blood pressure, coagulation markers, and haemoglobin levels suggest areas for further research into the pathophysiology and management of COVID-19 across different phases of the pandemic.

 

LIMITATIONS:

1.       Data Scope and Accuracy: The analysis of blood parameters such as IL-6 and D-dimer is based on a constrained dataset. Consequently, the accuracy and reliability of these findings necessitate validation through more extensive, large-scale research endeavors.

2.       Geographical and Sample Size Constraints: The research was exclusively carried out at a single tertiary care center in Raipur, C.G., employing a modest sample size. This limitation confines the study's findings and diminishes their applicability to a wider population, restricting the extrapolation of results beyond the immediate study environment.

3.       Data Collection Methodology: The study utilized patient record information tools focusing on epidemiological characteristics derived from the case records of COVID-19 fatalities. This approach inherently limits the breadth and depth of data that could be collected, potentially omitting variables that could offer additional insights into the disease's impact and trajectory.

 

RECOMMENDATIONS:

1.     Expansion of Study Scope: To enhance the generalizability of the results and mitigate potential biases, it is advised to replicate this study with a larger and more diverse sample. This approach would provide a more comprehensive understanding of the subject matter, contributing to the robustness of the findings.

2.     Diverse Epidemiological Settings: Given the gaps in our understanding of the epidemiology of the condition, conducting analogous research in varied settings, encompassing different epidemiological backgrounds, is crucial. Such studies would offer valuable insights into the dynamics of the disease, informing healthcare systems and aiding in community preparedness 14 27.

3.     Longitudinal Research: Implementing longitudinal studies to track the long-term impacts and outcomes of COVID-19 among various demographic groups is essential 29. This would allow for a deeper analysis of the disease's evolution over time, aiding in the development of targeted interventions and long-term health strategies.

4.     Implications for Public Health Policy : Health awareness programmes aimed at high-risk demographics, improved healthcare preparedness, and strategies to encourage timely medical attention for COVID-19 symptoms are highly             recommended 11,28

 

CONCLUSION:

This study at AIIMS Raipur delves into the epidemiological shifts in COVID-19 fatalities between the first and second waves, highlighting a noticeable demographic shift towards the 40-60 age group in the latter wave. Key findings, such as differences in systolic blood pressure and D-dimer levels between the waves, point to the evolving clinical impact of the pandemic and the importance of adaptive treatment protocols. This research underscores the importance of flexible and informed public health policies and clinical management strategies in combating the dynamic challenges posed by COVID-19, advocating for an evidence-based approach to healthcare planning and intervention.

 

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Received on 25.05.2024         Revised on 12.09.2024

Accepted on 27.11.2024         Published on 12.12.2024

Available online on December 30, 2024

Asian J. Nursing Education and Research. 2024;14(4):273-280.

DOI: 10.52711/2349-2996.2024.00054

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