Research studies

Impact of Coronavirus on Society Health in Gaza Strip: Scientific Paper from the Perspective of the Safety and Infection Control Unit

 

Prepared by the researcher   :  Alabadla Rami & Al_Shareef Ahmed & Alodany Suliman

Democratic Arab Center

Journal of Strategic Studies for disasters and Opportunity Management : Tenth Issue – June 2021

A Periodical International Journal published by the “Democratic Arab Center” Germany – Berlin.

Nationales ISSN-Zentrum für Deutschland
ISSN 2629-2572
Journal of Strategic Studies for disasters and Opportunity Management

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Abstract

The emergence of the Coronavirus pandemic has led to the implementation of many precautionary measures around the world. This may have been challenging in the densely populated regions in the world, especially in the Gaza Strip. This research sought to deals many important issues related to the lives of infected individuals within the community in the Gaza Strip, where the Corona virus has spread.  This study aims to investigate the impact of the Coronavirus on community health in terms of symptoms and the health status of those infected with the virus. Epidemiological data was collected by The Epidemiological Investigation Committee affiliated to the Safety and Infection Control Unit and was compared with the data presented on coronavirus map to ensure consistency. Descriptive statistics were used to interpret the data collected over the period of the study. The interesting results of this study is that the majority of people infected with the virus are young people and suffer from diseases such as hypertension 56.7%, diabetes 54.1% and they suffered from health symptoms such as heat 76.6%, loss of the sense of smell 79.3% and loss of the sense of taste 78.7%. Most of the infections of coronavirus occurred as a result of contact with infected people 46.7% and human gatherings 43%. The study had recommended to strengthening the measures previously taken by the government in Gaza to prevent the spread of the Corona epidemic, by adhering to wearing a mask and adhering to the rules of social distancing, so that the virus does not activate again in our society

1.1 Introduction

Covid-19 is a novel Coronavirus that is causing the recent outbreak of pneumonia that is spreading in a terrible rapid way around the world. It was declared by the WHO as public health international concern because it has a profound impact on public health. Despite all precautions, the virus has spread rapidly around the globe and brought significant challenges to all aspects of life in response to the high incidence rates of infection, and resulting a dramatic loss of human life worldwide. The economic and social disruption caused by the pandemic is devastating on health and wellbeing:  decimated jobs and placed millions of livelihoods at risk, tens of millions of people are at risk of falling into extreme poverty, while the number of undernourished people, currently estimated at nearly 690 million, could increase by up to 132 million by the end of the year. Symptoms of Covid-19 vary and can range from asymptomatic to severe pneumonia and death. WHO- China 2020 reported that fever, dry cough, fatigue, sputum production and shortness of breath were the most common symptoms. The major contributor of ICU admission and mortality of Covid-19 is the severe damage of the lung tissue which mostly occur in those older than 60 years, with history of smoking, and underlying disorders [1] .

Increasing data indicates that people with comorbidities such as diabetes, cardiovascular disease, hypertension and chronic respiratory disease are more susceptible to COVID-19 poor outcomes, severe complications and higher mortality rates compared to patients with no comorbid condition. It is important to evaluate the impact of the pandemic on the society, especially, understanding the interlinked associations between pre-existing diagnoses and COVID-19 status in order to mitigate Covid-19 complications and mortalities and to help in optimizing the management of susceptible patients.

1.2 General Objectives

This study aims to investigate the impact of the Coronavirus on community health in terms of symptoms and the health status of those infected with the virus.

1.3 Specific Objectives

  1. To assess the prevalence of comorbidities among Covid-19 confirmed cases.
  2. To explore the association between preexisting diseases, severity of symptoms and infection consequences.
  3. To give results and recommendations that may aid the public health sector while developing policies for surveillance, preparedness, and response to COVID-19 and its severe outcome.

1.4 Research problem

The pandemic of corona virus disease has caused large number of deaths and millions of patients suffering from its complications. Evidence to date   suggests that the disease presents in a severe form in patients with pre-existing chronic conditions which means that patients with underlying health conditions are more susceptible to Covid-19 and have higher fatality rates compared to patients with no preexisting conditions [2] .

As from the period from August 2020 to December 31, a total of 40,738 cases of Covid-19 have been diagnosed in Gaza Strip. Public health measures were taken, contributed to a very low infection rate during the first 3 months of the crisis; Gaza has recommended that individuals returning from outside Gaza through the Rafah or Erez crossing remain in quarantine for 21 days, instead of 14 days [3] .

Thus, it is essential to assess the impact among Covid-19 patients and to identify the features, clinical characteristics and outcomes of COVID-19 patients with chronic conditions in order to mitigate the Covid-19 complications and to support policy makers, clinicians, and researchers in making informed decisions as new strategies to overcome this pandemic.

1.5 Study Variable

1.5.1 Independent Variables

  1. Age (continuous variable).
  2. Sex (nominal variable either male or female).
  3. Personal status (Her husband or wife is died, Single, Married, married more than wives, Divorced).
  4. Governorates (Rafah governorate, khan Younis governorate, central governorate, Gaza governorate, North governorate).
  5. Source of infection (Markets, Wedding and Events, Schools, Mourning homes, Human gathering. Universities, Infected person, Government institutions, Non-governmental institutions, Hospitals and clinics).
  6. Occupations (Administrative, House wife, Driver, University, student, Doctor, Employee, Lawyer, Teacher, Nurse, Health professions, Engineer, Private sector, employee, doesn’t work, Retired from work, Other).
  7. Place of work (Free business, Universities, Private sector, doesn’t work, International organization, Non-governmental organization, Other ministries, Ministry of communications, Ministry of education, Ministry of Local Government, Ministry of Interior Ministry of Health).

1.5.2 Dependent Variables

It will be classified to:

1.Variable related to health status of individuals infected:

  • Health status measure (nominal variable with two choice yes; no)
  1. variable related to sign and symptom of individuals infected:
  • sign and symptom measure (nominal variable with two choice yes; no)

1.6 Study Question

  1. Question about (age, gender, governorate, personal status, source of infection, occupations, place of work, position).
  2. Question about health status of individuals infected.
  3. Question about to sign and symptom of individuals infected.

2.Literature review

  • Prevalence of comorbidities among covid-19 patients:

A cross sectional study was carried out in Mexico to identify the association of comorbidities with pneumonia and death among Covid-19 patients. The prevalence of comorbidities among the 51053 Covid-19 patients enrolled in the study was 13 652 (26.7%), 6518 (12.8%) and 3216 (6.3%) were reported to have 1, 2, and 3 or more simultaneous conditions, respectively. Overall, a significant incremental gradient was observed for the association between multimorbidity and pneumonia (p<0.001); for 2 chronic conditions, the adjusted odds ratio (a OR) was 2.07 (95% confidence interval [CI], 1.95 to 2.20), and for ≥3 conditions, a OR was 2.40 (95% CI, 2.22 to 2.60). A significant incremental gradient was also found for the relationship between multimorbidity and death (p<0.001); OR of 2.51 (95% CI, 2.30 to 2.73) was found for 2 chronic conditions and an OR of 3.49 (95% CI, 3.15 to 3.86) for ≥3 conditions [4] .

A single centered retrospective study was carried in Saudi Arabia among 439 Covid-19 hospitalized patients showed that the most prevalent comorbidities were vitamin D deficiency (74.7%), DM (68.3%), hypertension (42.6%) and obesity (42.2%). DM patients have a higher mortality rate than their non-DM counterparts, other factors such as old age, congestive heart failure, smoking, β-blocker use, presence of bilateral lung infiltrates, elevated creatinine and severe vitamin D deficiency, appear to be more significant predictors of fatal outcome [5] .

A meta-analysis and systematic review  of seven studies assessed the prevalence of comorbidities in Covid-19 infected patients and the risk of underlying diseases in severe patients compared to non-severe patients showed that he most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.  .88-6.22) respectively [6] .

A systematic review and meta-analysis were carried out [7] to study the prevalence of underlying diseases in died cases of Covid-19 including 32 relevant articles that reported underlying disease in died cases of COVID-19 from June to October 2020, showed that the most prevalent comorbidities were hypertension, diabetes, cardiovascular disease, liver disease, lung disease, malignancy, cerebrovascular disease, COPD and asthma. Among all reported underlying disease, highest and lowest prevalence was related to hypertension and asthma which were estimated 46% (37% – 55%) and 3% (2%- 6%), respectively.

A systematic review and meta-analysis of twenty-three studies on COVID-19 patients published [8] before 10th April 2020 was carried. These studies containing data for 202,005 COVID-19 patients  showed that the most prevalent chronic comorbid conditions were: any type of chronic comorbidity (37%; 95% CI 32–41%), hypertension (22%; 95% CI 17–27%), diabetes (14%; 95% CI 12–17%), respiratory diseases (5%; 95% CI 3–6%), cardiovascular diseases (13%; 95% CI 10–16%) and other chronic diseases (e.g., cancer) (8%; 95% CI 6–10%). The overall pooled CFR was 7% (95% CI 6–7%).  The overall pooled CFR was 7% and the crude CFRs increased significantly with increasing number of chronic comorbid conditions, ranging from 6% for at least one chronic comorbid condition to 21% for 6 or more chronic comorbid conditions.

  • The association between preexisting diseases and severity of symptoms and consequences.

A retrospective case control study of 219,961 individuals who were 18 years old and older studied the effect of underlying comorbidities on the infection and severity of COVID-19 in Korea, found that severe COVID-19 group had older patients and a proportion of male ratio than did the non-severe group. Diabetes (odds ratio range [ORR], 1.206–1.254), osteoporosis (ORR, 1.128–1.157), rheumatoid arthritis (ORR, 1.207–1.244), substance use (ORR, 1.321–1.381), and schizophrenia (ORR, 1.614–1.721) showed significant association with COVID-19. In terms of severity, diabetes (OR, 1.247; 95% confidential interval, 1.009–1.543), hypertension (ORR, 1.245–1.317), chronic lower respiratory disease (ORR, 1.216–1.233), chronic renal failure, and end-stage renal disease (ORR, 2.052–2.178) were associated with severe COVID-19 [9] .

A retrospective case control study was held in United Arab Emirates.[10] To study the epidemiological characterization of symptomatic and asymptomatic COVID-19-positive cases found that DM and hypertension were the most frequent chronic comorbidities reported in the study COVID-19 sample and patients with at least one chronic comorbidity were also positively associated with the symptomatic state.  The study found also that individuals with DM are more likely to be infected and are at a higher risk for complications and death from COVID-19.

A rapid review of 27 articles consisting of 22,753 patient cases from major epicenters worldwide showed that that hypertension with prevalence of 27.4% followed by diabetes (17.4%) and cardiovascular diseases (8.9%) were the most common comorbidity seen in COVID-19 positive patients across those major epicenters world-wide. Although having one or more comorbidity is linked to increased disease severity and no clear association was found between having these risk factors and increased risk of mortality [11] .

A meta-analysis of 24 studies among them, 20 studies were from China, 2 from United States, 1 from Italy, and 1 from France with a total of 1049 Covid-19 patients showed that both preexisting diabetes and hypertension are highly correlated with increased risk of disease severity (OR 2.61, 95% CI 1.93 to 3.52, I2=26.7%; and OR 2.84, 95% CI 2.22 to 3.63, I2=36.8%, respectively); and that preexisting CAD/CVD and chronic pulmonary disease are also correlated with disease severity (OR 4.18, 95% CI 2.87 to 6.09, I2=31.6%; and OR 3.83, 95% CI 2.15 to 6.80, I2=0.0%, respectively). This study conducted by Liu et al., (2020).

A systematic review  and  a meta-analysis of articles in the period January 1, 2020, to April 2, 2020 estimated  the prevalence of clinical symptoms of COVID-19 and found that the most common symptoms in COVID-19 patients include: Fever 81.2% (95% CI: 77.9-84.4); Cough: 58.5% (95% CI: 54.2-62.8); Fatigue 38.5% (95% CI: 30.6-45.3); Dyspnea: 26.1% (95% CI: 20.4-31.8); and the Sputum: 25.8% (95% CI: 21.1-30.4). This study carried out [12] .

2.3 To determine the impact of pandemic on socioeconomic life.

A study done [13] in Gaza Strip during the school closures showed that profound economic and social consequences were profound. Most of the interviewed households (88.1%) were supportive of the school closure, whereas only 11.9% did not support it. Despite the restriction on attending gatherings or visiting public places, 30.5% of the school student visited relatives, 8.5% went to public places, and 3.4% went to parents’ workplaces. Overall, 25.4% of the interviewed households reported workplace absenteeism, whereas the highest percentage (74.6%) were not absenteeism from their work. The economic harms of school closures are high, where 77.9% of households reported their wage loss during the closure. The daily wage lost per household ranged from 3 to 265 ILS.

As the Covid-19 pandemic upended the 2019–2020 school year, much debate about the magnitude of stopped learning process; though education systems rushed to meet the needs of students and families, Lectures have rapidly been developed to be delivered online as webinars using various platforms such as Zoom, with such technologically enhanced approaches already being proven to have high levels of engagement with medical students [14].

  1. Material and Methods

3.1 Design of the Study

In order to complete and achieve the objectives of the present study, a descriptive analytical approach was used the views expressed by respondents who infected by coronavirus were taken throughout a modified questionnaire. where the injured were contacted and obtain views through the Epidemiological Investigation Committee of the Palestinian Ministry of Health in the Gaza Strip.

3.2 Study Place

A study was accomplished in the all governorates of Gaza Strip (Rafah Governorate, Khan Younis Governorate, Middle Governorate, Gaza Governorate, North Governorate). Where the Coronavirus spreading in these five governorates and caused a large number of injuries within the community

3.3 Study Population and study sample

A study population composed of 40,738 persons who infected by corona virus in all Gaza governorate as shown in the Table (1) (where the coronavirus spreading)

The calculated sample size was 381 person infected by virus corona (400 using the sample size calculator by RAOSOFT (http://www.raosoft.com/samplesize.html) with confidence level 95%, error 5%,  and response distribution 50%  + 10% drop out) The number of infected person from each governorate  was chosen in proportion method which means ; ( the numbers of infected person participants from each governorate was calculated by the ratio of the infected persons in that governorate to the total number of infected person in all these governorate)

Table (1): Number of infected persons who participating in a study.

Governorate N. of infected person N. of infected person in the test sample Male Female
Rafah Governorate 3644 34 13 21
Khan Younis Governorate 6089 57 20 37
Middle Governorate 4485 42 27 15
Gaza Governorate 17.890 167 90 77
North Governorate 8630 81 45 36
Total Number 40.738 381 195 186

381 people who have been infected with Virus Corona have at least four of the following signs and symptoms (fever, headache, cough, diarrhea, distress, loss of sense of smell and taste, fatigue, pain in the throat, pain in the chest, muscle pain, nausea, nasal mucous, skin rash). And include this signs and symptom in the study. These cases were observed through 4 months.

3.4 Polymerase Chain Reaction collection

A study was ready in the interval from October to December 2020. The polymerase chain reaction samples were collected from random human gatherings, suspected infection, government institutions, contact circles for the injured and targeted surveying and transferred to a special sterile bottle. The virus was identified in the central laboratory of the Ministry of Health in Gaza using the polymerase chain reaction method.

3.5 Ethical Considerations

Infected persons were persuaded to participate in the study and signed consent form. The researcher took the agreement of the General Directorate of Human Resources Development of Ministry of Health to allow data and information to be taken from those infected with the virus to start study, then the researcher collected the data then analyzed it and wrote the results.

3.6 Statistical Methods Used

The questionnaire was unloaded and analyzed through the Statistical Package for the Social Sciences (SPSS) version 23; the following statistical tools were used:

  • Percentages and frequencies
  • Mean and standard deviation.
  • T-test.
  • One way- ANOVA.
  • Post hoc Scheffe test.

4.Results and Discussion

4.1 Statistical description of the study population according to personal data.

This chapter includes a presentation to analyze the data of the study, by answering the study questions and reviewing the most prominent results of the questionnaire, which were reached by analyzing its paragraphs, and identifying the study variables that included: (gender, age, social status, occupation, workplace, governorate, source of the injury). So statistical treatments were performed for the data collected from the study questionnaire, as the Statistical Packages for Social Studies (SPSS) program was used to obtain the results of the study that will be presented and analyzed in this chapter.

Three hundred and eighty-one questionnaires were distributed. Figure (1) is a presentation of the characteristics of the study sample (n=381) according to personal data.

Figure (1): Distribution of study sample individuals according to gender

Figure (1) shows the distribution of male and female participants in the study by gender. It is clear that the percentage of infected male in all governorates was195(51.2%), while the percentage of females was 186 (48.8%).

Table (2): Distribution of the study sample according to age group.

Percent (100%) Frequency Age group
11.3 43 10-20 Years
26.2 100 21-30 Years
33.3 127 31-40 Years
15.2 58 41-50 Years
13.9 53 Over 51 Years
100.0 381 Total

From Table (2) it is found that 127(33.3%) of the study sample infected with Coronavirus are between 31 years old to 40 years old, and that 100(26.2%) of the study sample are between 21 years to 30 years old, and 58(15.2%) are aged between 41 years and 50 years, and 53(13.9%) are the average age of 51 years and over, while 43(11.3%) are aged between 10-20 years. Figure (2) shows the distribution of the study sample infected with the Coronavirus according to the age group.

Figure (2): Distribution of study sample infected with the Coronavirus according to the age group.

Table (3): Distribution of the study sample according to personal status.

Percent (100%) Frequency Married Status
1.8 7 Her husband or wife is dead
22.8 87 Single
73.5 280 Married
0.5 2 Married more than wives
1.3 5 Divorced
100.0 381 Total

The Table (3) shows the number of the sample individuals infected with Coronavirus according to personal status, where the percentage of married people reached 280(73.5%), while the percentage of unmarried people reached 87(22.8%). The percentage of husband or wife is dead 7(1.8%), divorced 5(1.3%) and the percentage of married more than wives 2(0.5%). Figure (3) shows the distribution of the study sample infected with the Coronavirus according to the married status.

Figure (3) shows the distribution of the study sample infected with the Coronavirus according to the personal status.

Table (4): Distribution of the study sample according to Governorate.

Percent (%) Frequency Governorate
8.9 34 Rafah Governorate
15.0 57 Khan Younis Governorate
11.0 42 Middle Governorate
43.8 167 Gaza Governorate
21.3 81 North Governorate
100.0 381 Total

Table (4) shows the distribution of the numbers of people infected with Coronavirus by governorate, where the percentage of infected person of virus corona at Gaza governorate 167(43.8%), followed by North Governorate 81(21.3%), While Rafah governorate the least number of infected persons 34(8.9%). Figure (4) shows the distribution of the study sample infected with the Coronavirus according to the governorate.

Figure (4) shows the distribution of the study sample infected with the Coronavirus according to the Governorate.

Table (5): Distribution of the study sample according to source of infection.

Percent (100%) Frequency Source of infected of coronavirus
2.6 10 Markets
0.3 1 Wedding and Events
0.3 1 Schools
0.3 1 Mourning homes
43.0 164 Human gathering
0.3 1 Universities
46.7 178 Infected person
1.6 6 Government institutions
1.3 5 Non-governmental institutions
3.7 14 Hospitals and clinics
100.0 381 Total

The above Table (5) shows the source of Coronavirus infection that the study sample infected, as 178(46.7%) of the study sample said that the source of infection was due to their contact with infected people and that 164(43%) said that the source of infection was due to human gatherings, while 14(3.7%) said that the cause of infection was from hospitals and clinics. Figure (5) shows the distribution of the study sample infected with the Coronavirus according to source of infection.

Figure (5) shows the distribution of the study sample infected with the Coronavirus according to source of infection.

Table (6): Distribution of the study sample according to occupation.

Percent (100%) Frequency Occupation
0.3 1 Administrative
11.0 42 House wife
0.3 1 Driver
4.7 18 University student
0.8 3 Doctor
3.1 12 Employee
0.5 2 Lawyer
1.3 5 Teacher
1.8 7 Nurse
0.5 2 Health professions
0.3 1 Engineer
19.9 76 Private sector employee
6.6 25 Doesn’t work
0.8 3 Retired from work
48.0 183 Other
100.0 381 Total

Table (6) showed that distribution of infected persons with coronavirus according to occupation showed that 259(67.9%) from infected persons working in private sector and other, 42(11%) house wife, while the sample of respondent of infected virus have occupation administrative, driver, engineer 3 (0.3%, 0.3%,0.3%) respectively. Figure (6) shows the distribution of the study sample infected with the Coronavirus according to occupation.

Figure (6) shows the distribution of the study sample infected with the Coronavirus according to occupation.

Table (7): Distribution of the study sample according to place of work.

Percent(100%) Frequency Place of Work
15.7 60 Free business
0.8 3 Universities
5.2 20 Private sector
13.9 53 Doesn’t work
0.5 2 International organization
0.5 2 Non-governmental organization
0.5 2 Other ministries
0.3 1 Ministry of communications
9.4 36 Ministry of education
1.0 4 Ministry of Local Government
24.7 94 Ministry of Interior
27.3 104 Ministry of Health
100.0 381 Total

From Table (7) it is found that 104(27.3%) of the study sample individuals work in the ministry of health, following 94(24.7%) working at ministry of interior, while 5(1.3%) working in International organization, Non-governmental organization, Other ministries and Ministry of communications (0.5%,0.5%,0.5% and 0.1%) respectively. It is noted that the majority of the injured work in the Ministry of Health, which negatively affected the functioning of health services provided to patients. Figure (7) shows the distribution of the study sample infected with the Coronavirus according to the place of work.

Figure (7) shows the distribution of the study sample infected with the Coronavirus according to the place of work.

4.2 Descriptive statistic of health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip.

The Tables 8 and Table 9 below show the opinion of those infected with Coronavirus who participated in the study about their health status and the signs and symptoms they experienced during the period of infection.

4.2.1 Analysis of parameters of the first field: Health status of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip.

Rank % Frequency Valid Parameters
3 13.1 50 Yes Excessive obesity 1
86.9 331 No
2 54.1 206 Yes Diabetic 2
45.9 175 No
1 56.7 216 Yes Hypertension 3
43.3 165 No
5 7.6 29 Yes Heart problem 4
92.4 352 No
6 5.8 22 Yes Cancer 5
94.2 359 No
4 8.9 34 Yes Kidney failure 6
91.1 347 No
7 4.7 18 Yes Chest illness 7
95.3 363 No
11 0.8 3 Yes Organ transplantation 8
99.2 378 No
7 4.7 18 Yes Take cortisone 9
95.3 363 No
10 1.6 6 Yes Pregnant 10
98.4 375 No
9 2.6 10 Yes Others 11
97.4 371 No

Table (8): Distribution of the study sample according health status.

Table (8) shows the items related to the health status of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip. The results showed that the highest score was in item “3” were 56.7% of the study population complain of hypertension disease followed by item “2” were 54.1% of study population complain of diabetic disease, while the lowest score was in item “8” were 0.8% from the infected study community complain of undergone an organs transplantation. This result differed with the study of Alguwaaihes et al., (2020) which revealed Diabetic (68.3%) and hypertension (42.6%). But these consequences higher than the results of the study by Yin et al., (2020). which revealed hypertension 19%, diabetic 9%. Also, the result of present study differed with the study of Mahumud et al., (2020) which found hypertension (22%; 95% CI 17–27%), diabetes (14%; 95% CI 12–17%). This sudy also not consistent with the by Bajgain et al., (2020) which found hypertension with prevalence of 27.4% followed by diabetes (17.4%).

4.2.2 Analysis of parameters of the second field: Sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip.

Table (9): Distribution of the study sample according sign and symptom.

Rank % Frequency Valid Parameters
3 76.6 292 Yes Temperature 1
23.4 89 No
4 58.5 223 Yes Headache 2
41.5 158 No
5 54.9 209 Yes Cough 3
45.1 172 No
10 19.2 73 Yes Diarrhea 4
80.8 308 No
9 23.9 91 Yes Dyspnea 5
76.1 290 No
2 78.7 300 Yes Loss of sense of taste 6
21.3 81 No
1 79.3 302 Yes Loss of sense of smell 7
20.7 79 No
6 41.2 157 Yes Fatigue and tiredness 8
58.8 224 No
12 17.3 66 Yes Pains in the throat 9
82.7 315 No
11 18.6 71 Yes Chest pain 10
81.4 310 No
7 39.4 150 Yes Muscle pains 11
60.6 231 No
14 7.3 28 Yes Nausea 12
92.7 353 No
8 26.2 100 Yes Nasal mucous 13
73.8 281 No
15 7.1 27 Yes Skin rash 14
92.9 354 No
13 10.2 39 Yes Without symptoms 15
89.8 342 No

Table (9) shows the items related to the health status of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip. The results showed that the highest score was in item “7” were 79.3% of the study population suffer from Loss of sense of smell followed by item “6” were 78.7% of study population suffer from Loss of sense of taste, while the lowest score was in item “14”  were 7.1%  from the infected study community said that they suffer from skin rash. The result of this study did not agree with the results of any previous study regarding to sign and symptom of individuals infected.

4.2.3 Health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip in regard to sex.

Table (10): T- test – for two independent samples – Gender (Male = 195, Female = 186).

Variable Sex N Mean S.D T P value
Health Status of individuals infected Male 195 1.15 0.10 0.070 0.944
Female 186 1.15 0.10
Sign and Symptom of individuals infected Male 195 1.37 0.18 -0.336 0.737
Female 186 1.38 0.19

Table (10) showed that there were no statistically significant differences in Health Status of individuals infected (t= 0.070, P= 0.944), and Sign and Symptom of individuals infected (t= -0.336, P= 0.737) related to gender of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip.

4.2.4 Health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip in regard to age group.

Table (11): Results of the test One-Way ANOVA – Age group.

Variable Age group N Mean S.D F P value
Health Status of individuals infected 10-20 Years 43 1.18 0.04 32.86 0.000*
21-30 Years 100 1.22 0.11
31-40 Years 127 1.12 0.09
41-50 Years 58 1.11 0.08
More than 50 years 53 1.07 0.09
Sign and Symptom of individuals infected 10-20 Years 43 1.35 0.17 1.551 0.187
21-30 Years 100 1.37 0.16
31-40 Years 127 1.46 0.20
41-50 Years 58 1.38 0.29
More than 50 years 53 1.33 0.18

*Significant

Table (11) showed that there were statistically significant differences in Health Status of individuals infected (F= 32.86, P value= 0.000), and Post hoc Scheffe test as shown in Table (12) indicated statistically significant differences between health status of individuals infected of coronavirus in favor of infected individual aged 10-20 years compared to infected individuals aged between 31-40 years, 41-50 years and more than 50 Years (P-value = 0.009, 0.004 and 0.000) respectively, and indicated statistically significant differences between health status of individuals infected of coronavirus in favor of infected individuals aged 21-30 years compared to infected individuals aged  between 31-40 years, 41-50 years and more than 50 years (P-value =0.000, 0.000. and 0.000) respectively. Also, there were indicated statistically significant differences between health status of individuals infected of coronavirus in favor of infected individual aged between 31-40 years compared to infected individuals aged group more than 50 Years (P- value=0.009). As illustrated in Table (11) findings showed that there were no statistically significant differences (P-value >0.05) in the study sample responses regarding sign and symptom of individuals infected.

Table (12): Post hoc Scheffe test (for age group).

            Variable Age group (I) Age group (J) Mean Difference (I-J) Sig.
 

 

 

 

 

Health Status of individuals infected

 

 

 

 

 

 

10-20 Years 21-30 Years -0.03727 0.255
31-40 Years 0.05798 0.009*
41-50 Years 0.07053 0.004*
More than 50 Years 0.11149 0.000*
21-30 Years 10-20 Years 0.03727 0.255
31-40 Years 0.09525 0.000*
41-50 Years 0.10781 0.000*
More than 50 Years 0.14877 0.000*
31-40 Years 10-20 Years -0.05798- 0.009*
21-30 Years -0.09525- 0.000*
41-50 Years 0.01255 0.938
More than 50 Years 0.05351 0.009*
41-50 Years 10-20 Years -0.07053- 0.004*
21-30 Years -0.10781- 0.000*
31-40 Years -0.01255 0.938
More than 50 Years 0.04096 0.205
More than 50 Years 10-20 Years -0.11149- 0.000*
21-30 Years -0.14877- 0.000*
31-40 Years -0.05351- 0.009*
41-50 Years -0.04096 0.205

*Significant

4.2.5 Health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip in regard to Governorate.

Table (13): Results of the test One-Way ANOVA – Governorate.

Variable Governorate N Mean S.D F Sig.  
Health Status of individuals infected Rafah 34 1.160 0.138 0.247 0.912
Khan Younis 57 1.150 0.102
Central 42 1.147 0.102
Gaza 167 1.143 0.093
North 81 1.144 0.104
Sign and Symptom of individuals infected Rafah 34 1.388 0.189 0.129 0.972
Khan Younis 57 1.368 0.170
Central 42 1.375 0.192
Gaza 167 1.367 0.186
North 81 1.379 0.192

As illustrated in Table (13) Findings showed that there were no statistically significant differences (P-value >0.05) in the study sample responses  of infected individuals of coronavirus on all variable.

4.2.6 Health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip in regard to Occupation.

Table (14): Results of the test One-Way ANOVA – Occupation.

Variable Occupation N Mean S.D F Sig.  
Health Status of individuals infected Private sector employee 76 1.158 0.159 0.885 0.575
Engineer 1 1.182
Health professions 2 1.000 0.000
Nurse 7 1.169 0.097
Teacher 5 1.146 0.081
Lawyer 2 1.182 0.000
Retired from work 3 1.061 0.052
Doesn’t work 25 1.142 0.091
Employee 12 1.106 0.065
Doctor 3 1.152 0.189
University student 18 1.177 0.058
Driver 1 1.182
House wife 42 1.134 0.090
Administrative 1 1.091
Other 183 1.146 0.079
Sign and Symptom of individuals infected Private sector employee 76 1.394 0.185  
Engineer 1 1.400  
Health professions 2 1.367 0.047
Nurse 7 1.314 0.137
Teacher 5 1.333 0.205
Lawyer 2 1.300 0.047 0.647 0.825
Retired from work 3 1.333 0.133
Doesn’t work 25 1.384 0.208
Employee 12 1.272 0.183
Doctor 3 1.378 0.168
University student 18 1.370 0.198
Driver 1 1.667
House wife 42 1.387 0.192
Administrative 1 1.467
Other 183 1.368 0.185

As illustrated in Table (14) Findings showed that there were no statistically significant differences (P-value >0.05) in the study sample responses of infected individuals of coronavirus on all variable.

4.2.7 Health status, sign and symptom of the sample individuals infected with Coronavirus in all governorates of the Gaza Strip in regard place of work.

Table (15): Results of the test One-Way ANOVA – Place of work.

Variable Place of work N Mean S.D F Sig.
Health Status of individuals infected Ministry of Health 104 1.170 0.125 1.329 0.206
Ministry of Interior 94 1.146 0.095
Ministry of Local Government 4 1.205 0.136
Ministry of education 36 1.144 0.091
Ministry of communications 1 1.091
Other ministries 2 1.091 0.000
Non-governmental organization 2 1.091 0.000
International organization 2 1.091 0.000
Doesn’t work 53 1.142 0.098
Private sector 20 1.105 0.090
Universities 3 1.121 0.052
Free business 60 1.127 0.077
Sign and Symptom of individuals infected Ministry of Health 104 1.376 0.172 1.395 0.173
Ministry of Interior 94 1.385 0.182
Ministry of Local Government 4 1.500 0.275
Ministry of education 36 1.367 0.206
Ministry of communications 1 1.067
Other ministries 2 1.233 0.141
Non-governmental organization 2 1.200 0.000
International organization 2 1.333 0.000
Doesn’t work 53 1.413 0.209
Private sector 20 1.313 0.184
Universities 3 1.444 0.038
Free business 60 1.338 0.173

As illustrated in Table (15) Findings showed that there were no statistically significant differences (P-value >0.05) in the study sample responses of infected individuals of coronavirus on all variable related to place of work.

5.Conclusion and Recommendation

5.1 Conclusion

  1. The rationale for this work is that it deals with important issues related to the lives of infected individuals within the community in the Gaza Strip, where the Coronavirus has spread, as the study revealed the health status of those infected with the virus and the symptoms they faced during the period of isolation.
  2. The interesting results of this study is that the majority of people infected with the virus are young people and suffer from diseases such as hypertension 56.7%, diabetes 54.1% and they suffered from health symptoms such as heat 76.6%, loss of the sense of smell 3% and loss of the sense of taste 78.7%. The most of the infections of coronavirus occurred as a result of contact with infected people 46.7% and human gatherings 43%.
  3. The study fills a knowledge gap that could serve a life of guidance for those who care about the lives of society affected by the Coronavirus.

5.2 Recommendation

  1. The need to go to the nearest hospital when feeling sick, as symptoms of infection with the Coronavirus come in the form of headache, sore throat, high temperature, shortness of breath, cough, colds, and diarrhea in some cases.

2.The necessity for health workers to wear medical masks while dealing with patients suffering from acute respiratory symptoms, and to inform the competent authorities if health workers begin to cough or sneeze or feel a fever after providing care to a patient suspected of having the disease.

  1. 3. The necessity to wear medical or household masks made of cloth in case ready-made masks are not available.
  2. The necessity to refrain from receiving services from any commercial store, employee or worker who does not adhere to public health and safety standards, such as wearing masks and gloves, and adhering to the rules of social distancing.
  3. 5. Strengthening the measures previously taken by the government in Gaza to prevent the spread of the Corona epidemic, by adhering to wearing a mask and adhering to the rules of social distancing, so that the virus does not activate again in our society.

6.References

  1. Alguwaihes, A., Al-Sofiani, M., Megdad, M., Albader, S. (2020).Diabetes  and Covid-19 among hospitalized patients in Saudi Arabia: a single-centre retrospective study. Cardiovascular Diabetology,19(1).
  2. Al-Rifai, R.H., Acuna, J., Al Hossany, F.I., Aden, B., Al Memari, A., AlMazrouei, S.K., and Ahmed, L.A. (2021). Epidemiological characterization of symptomatic and asymptomatic COVID-19 cases and positivity in subsequent RT-PCR tests in the United Arab Emirates.19:205.
  3. Alimohamadi, Y., Sepandi, M., Taghdir, M. & Hosamirudsari, H.(2020). Factors Associated with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis. National Library of Medicine: 49(7):1211-1221.
  4. Alyammahi, Sh., Abdin, Sh., Alhamad, D., Elgendy, S., Altell, A., and  Omar, H. (2021). The dynamic association between COVID-19 and chronic disorders: An updated insight into prevalence, mechanisms and therapeutic modalities. Infect Genet Evol, 87:104647.
  5. Bajgain, K.T., Badel, S., Bajgain, B.B., & Santana, M.J. (2020). Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature. National Library of Medicine, 49(2):238-246.
  6. Javanmardi, F., Keshavarzi, A., Akbari, A., Emami, A., & pirbonyeh, N. (2020). Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis. PLoS ONE, 15(10).
  7. Ji et. al, (2020). Clinical characteristics of 2019 novel coronavirus infection in China. New England Journal of Medicine,382:1708-1720.
  8. Kakodkar, P., Kaka, N., Baig, M.N. (2020). A Comprehensive Literature Review on the Clinical Presentation, and Management of the Pandemic Coronavirus Disease 2019 (COVID-19). 12(4).
  9. Kay, D., Pasarica, M. (2019). Using technology to increase student (and faculty satisfaction with) engagement in medical education. Adv Physiol Educ. 1;43(3):408-413.
  10. Mahumud, R.A., Kamara, J.k., & Renzaho, A.M.N. (2020). The epidemiological burden and overall distribution of chronic comorbidities in coronavirus disease-2019 among 202,005 infected patients: evidence from a systematic review and meta-analysis. National Library of Medicine, 48, 813-833.
  11. (2020).https://www.moh.gov.ps/portal/wpcontent/uploads/2020/03/Corona-Eng.-1-4.pdf,(2020).
  12. Radwan, A., Radwan, E. (2020). Social and Economic Impact of School Closure during the Outbreak of the COVID-19 Pandemic: A Quick Online Survey in the Gaza Strip. Pedagogical Research, 5(4), ISSN:2468-4929.
  13. Hernandez-Vásquez, A., Azañedo, D., Vargas-Fernández, R., Bendezu-Quispe, G. (2020). Association of Comorbidities with Pneumonia and Death Among COVID-19 Patients in Mexico: A Nationwide Cross-sectional Study. J Prev Med Public Health, 53(4):211-219.
  14. Yang, P., Wang, X. (2020). COVID-19: a new challenge for human beings. Cellular & Molecular Immunology, Vol. 17, P:555–557.

1 Kakodkar, P., Kaka, N., Baig, M.N. (2020). A Comprehensive Literature Review on the Clinical Presentation, and Management of the Pandemic Coronavirus Disease 2019 (COVID-19). Cureus. 12(4).

1 Alyammahi, Sh., Abdin, Sh., Alhamad, D.,  Elgendy, S., Altell, A., and  Omar, H. (2021). The dynamic association between COVID-19 and chronic disorders: An updated insight into prevalence, mechanisms and therapeutic modalities. Infect Genet Evol, 87:104647.

2PNA.(2020).https://www.moh.gov.ps/portal/wpcontent/uploads/2020/03/Corona-Eng.-1-4.pdf,(2020).

[4] Hernandez-Vásquez, A., Azañedo, D., Vargas-Fernández, R., Bendezu-Quispe, G. (2020). Association of Comorbidities with Pneumonia and Death Among COVID-19 Patients in Mexico: A Nationwide Cross-sectional Study. J Prev Med Public Health, 53(4):211-219.

[5]  Alguwaihes, A., Al-Sofiani, M., Megdad, M., Albader, S. (2020).Diabetes  and Covid-19 among hospitalized patients in Saudi Arabia: a single-centre retrospective study. Cardiovascular Diabetology,19(1).

[6] Yang, P., Wang, X. (2020). COVID-19: a new challenge for human beings. Cellular & Molecular Immunology, Vol. 17, P:555–557.

[7] Javanmardi, F., Keshavarzi, A., Akbari, A., Emami, A., & pirbonyeh, N. (2020). Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis. PLoS ONE, 15(10).

2Mahumud, R.A., Kamara, J.k., & Renzaho, A.M.N. (2020).The epidemiological burden and overall distribution of chronic comorbidities in coronavirus disease-2019 among 202,005 infected patients: evidence from a systematic review and meta-analysis. National Library of Medicine, 48, 813-833.

[9] Ji et. al, (2020). Clinical characteristics of 2019 novel coronavirus infection in China. New England Journal of Medicine, 382:1708-1720.

[10]Al-Rifai, R.H., Acuna, J., Al Hossany, F.I., Aden, B., Al Memari, A., AlMazrouei, S.K., and Ahmed, L.A. (2021). Epidemiological characterization of symptomatic and asymptomatic COVID-19 cases and positivity in subsequent RT-PCR tests in the United Arab Emirates.19:205.

[11] Bajgain, K.T., Badel, S., Bajgain, B.B., & Santana, M.J. (2020). Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature. National Library of Medicine, 49(2):238-246.

[12] Alimohamadi, Y., Sepandi, M., Taghdir, M. & Hosamirudsari, H. (2020). Factors Associated with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis. National Library of Medicine: 49(7):1211-1221.

[13] Radwan, A., Radwan, E. (2020). Social and Economic Impact of School Closure during the Outbreak of the COVID-19 Pandemic: A Quick Online Survey in the Gaza Strip. Pedagogical Research, 5(4), ISSN:2468-4929.

[14] Kay, D., Pasarica, M. (2019). Using technology to increase student (and faculty satisfaction with) engagement in medical education. Adv Physiol Educ. 1;43(3):408-413.

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المركز الديمقراطي العربي مؤسسة مستقلة تعمل فى اطار البحث العلمى والتحليلى فى القضايا الاستراتيجية والسياسية والاقتصادية، ويهدف بشكل اساسى الى دراسة القضايا العربية وانماط التفاعل بين الدول العربية حكومات وشعوبا ومنظمات غير حكومية.

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