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Muslim-majority Countries Have Fewer COVID-19 Cases and Deaths: A Cross-country Analysis of 165 Countries During the 3 Global Peak Dates in 2020-2021

Ponn P. Mahayosnand MPH, Gloria Gheno PhD, ZM Sabra, DM Sabra

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Citation

Mahayosnand P. Gheno G. Sabra Z. Sabra D. Muslim-majority countries have fewer COVID-19 cases and deaths: a cross-country analysis of 165 countries during the 3 global peak dates in 2020-2021. HPHR. 2021;48. 

 

DOI: 10.54111/0001/vv2

Muslim-majority Countries Have Fewer COVID-19 Cases and Deaths: A Cross-country Analysis of 165 Countries During the 3 Global Peak Dates in 2020 ​

Abstract

Objective

To determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for anydisparities.

Methods

A cross-country panel analysis of the total number of new COVID-19 cases per million for 165 countries was conducted from May 1, 2020 to July 1, 2021. Regression models of  the total number of COVID-19 cases and deaths per million were created for the 3 global peak dates of July 31, 2020 and January 7 and April 29, 2021. 

Results

The number of daily new COVID-19 cases per million was signficantly less in Muslim-majority countries (N = 49) than non-Muslim countries (N=116), SD 1.57E-1, p-value <0.001 from May 1, 2020 to July 1, 2021. Total number of cases per million of Muslim-majority countries was significantly less on  July 31st: 0.089, p-value <0.001; January 7th: SD 0.012, p-value 0.04; April 29th: SD 0.009, p-value <0.01. Total number of deaths per million of Muslim-majority countries was also significantly less on July 31st: 0.510, p-value 0.009; January 7th: SD 0.090, p-value <0.001; April 29th: SD 0.065, p-value 0.03.

Discussion

The data suggests a relationship between Islamic practices and the incidence of COVID-19 and COVID-19 related deaths. This study explored how that Muslims’ practice of tahara (purity or cleanliness) is similar to many COVID-19 containment measures and tawakkul (trust in Allah) helps them remain resilient and hopeful during difficult unpredictable times, such as living through a pandemic.

Conclusion

It is hoped that this paper brings awareness to the positive practices of the Islamic faith as it relates to COVID-19, and to population and individual health. Research should be conducted with Muslims in Muslim-majority and non-Muslim countries to further study the relationship between Islam and health.

Introduction

The objective of this research was to determine if there was a difference in the total number of COVID-19 cases and deaths per million between Muslim-majority (N=49)and non-Muslim countries (M=116), and to investigate possible explanations for any disparities.

COVID-19 in LMIC

Healthcare systems are reported to be corrupt, limited, or under-resourced in LMIC  (low- and middle-income countries).1 With limited healthcare resources and poor living conditions, it was believed that LMICwere more vulnerable to COVID-19. In regards to combating COVID-19, LMICs average 1-10 SAO (surgeons, anesthesiologists, and obstetricians) per 100,000 compared to the estimated need of 20 SAO per 100,000.2 It is estimated that LMICs have 0.1-2.5 ICU beds per 100,000 while higher-income countries have 5-30 in.

 

In Bangladesh, a Muslim-majority country, full lockdown was nearly impossible as there was a strong association between loss of livelihood and an increased unemployment rate due to full business shutdown.3 Partial lockdown with social distancing and multi-sectoral (health, economy, agriculture, food, etc.) collaboration was recommended. Identifying and isolating active COVID-19 cases, rapid testing, and contact tracing were found to be extremely difficult for under-resourced LMICs. In LMIC, a percentage of the population is dependent on daily wages (meaning funds are sufficient for only a day’s worth of food) both in the rural and urban settings.4 In the slums of India, a non-Muslim country, if people did not go to work, they had a high likelihood of losing their jobs. For individuals living in these situations, following social distancing or lockdown directives meant weighing the potential risks of COVID-19 versus the immediate risk of hunger.5 If governments want this population to stay home in hopes of reducing the spread of COVID-19, they must provide them daily income and necessary resources in order to survive.

 

LMIC currently in war and crisis face more imminent death and destruction as shown in the following examples of Muslim-majority countries: Afghanistan had trouble managing its wounded citizens, and Yemen faced daily airstrikes and the reemergence of diseases such as cholera, diarrhea, dengue, and measles.6,7 Both reports stressed that the United Nations should pressure for ceasefires to combat the expansion of COVID-19, while also lifting blockades in Gaza toallow the transit of much-needed healthcare aid and assistance.8Certain measures were conducive to possibly containing the spread of COVID-19. For example, due to Gaza’s land, air and water blockade, its borders were mostly closed during the early months of the outbreak which prevented travelers and foreigners from entering.9 Border quarantine and isolation of positive COVID-19 cases was said to inhibit the proliferation of the pandemic.

 

Two methods reported to help contain COVID-19 in LMIC were found to be: (1) public education and community outreach, and (2) pragmatic multi-sectoral (health, business, schools, agricultural, etc.) collaboration in adhering to amended WHO COVID-19 guidelines after individual countries weighed the ethical and economical risks against their health and social benefits.10 Beneficial counseling included canceling elective medical procedures, seeking only emergency medical care, self-isolating if sick, and allocating limited PPE (personal protective equipment) usage for healthcare professionals. Appropriating resources for telepsychiatry services for the growing need during this pandemic was continuously recommended.2

Religion and COVID-19

Growing objective scientific research suggests religious faith is an important resource for health and well-being and benefits the “immune functioning and vulnerability to infection.”11 Quoting various religions including Islam, Koenig stressed the importance of maintaining spiritual, mental, and physical resilience during the COVID-19 pandemic. Religious beliefs and practices helped individuals in their abilities to cope with disease, recover from hospitalization, and have positive attitudes.11 An Italian study showed that more severe COVID-19 affectees reported higher religious behavior and that Google searches across 95 countries for topics related to prayer increased during the pandemic.12

Religion and Cleanliness

Hand hygiene among health care workers was analyzed across eight religions.13 Islam was one of three religions that had precise rules for handwashing specified in sacred texts. Islam and two other religions emphasized the importance of cleanliness and personal hygiene. Their followers were encouraged to adhere to daily hygienic practices for individual, communal, and environmental benefits.

 

Litman et al. suggested that individuals with both intrinsic and extrinsic religious motivation to maintain high levels of cleanliness were more interested in staying clean to remain physically and religiously cleansed.14 Litman recommended that further research be conducted to examine if enhanced religious cleanliness would translate into actual health benefits, such as reduced incidence of infectious diseases or food-borne illnesses.

Methods

Data

This study focused on the confirmed COVID-19 numbers of cases and deaths per million population in 165 countries. Data was obtained from publicly compiled resources that are updated daily throughout the world.15 To address possible contributing factors, the following variables were also compiled: stringency, population density, GDP, and vaccinated per hundred.Muslim-majority countries (N=49) had more than 50.0% Muslims (50.7 – 100%) with an average of 87.5% Muslim population.16 Non-Muslim countries (N=116) consisted of countries with less than 49.6% Muslims (49.6 – 0%) with an average of 6.6% Muslim population. [See Table 1]  .

 

 

Country

Percentage of Muslim population

Muslim_binary:

0 = Non-Muslim

1 = Muslim-majority

LMIC classification

Freedom Category

1

Bolivia

0

0

Lower middle income

Partly Free

2

Chile

0

0

High income

Free

3

Costa Rica

0

0

Upper middle income

Free

4

Dominican Republic

0

0

Upper middle income

Partly Free

5

Ecuador

0

0

Upper middle income

Partly Free

6

El Salvador

0

0

Lower middle income

Partly Free

7

Estonia

0

0

High income

Free

8

Guatemala

0

0

Upper middle income

Partly Free

9

Haiti

0

0

Lower middle income

Partly Free

10

Laos

0

0

Lower middle income

Not Free

11

Nicaragua

0

0

Lower middle income

Not Free

12

Papua New Guinea

0

0

Lower middle income

Partly Free

13

Paraguay

0

0

Upper middle income

Partly Free

14

Peru

0

0

Upper middle income

Partly Free

15

Uruguay

0

0

High income

Free

16

Mexico

0.01

0

Upper middle income

Partly Free

17

Poland

0.02

0

High income

Free

18

Bahamas

0.1

0

High income

Free

19

Cuba

0.1

0

Upper middle income

Not Free

20

Japan

0.1

0

High income

Free

21

Lesotho

0.1

0

Lower middle income

Partly Free

22

Lithuania

0.1

0

High income

Free

23

South Korea

0.1

0

High income

Free

24

Timor

0.1

0

Lower middle income

Free

25

Vietnam

0.1

0

Lower middle income

Not Free

26

Czechia

0.15

0

High income

Free

27

Latvia

0.15

0

High income

Free

28

Slovakia

0.15

0

High income

Free

29

Belize

0.2

0

Lower middle income

Free

30

Bhutan

0.2

0

Lower middle income

Partly Free

31

Colombia

0.2

0

Upper middle income

Partly Free

32

Dominica

0.2

0

Upper middle income

Free

33

Iceland

0.2

0

High income

Free

34

Jamaica

0.2

0

Upper middle income

Free

35

Angola

0.3

0

Lower middle income

Not Free

36

Honduras

0.3

0

Lower middle income

Partly Free

37

Brazil

0.36

0

Upper middle income

Free

38

Botswana

0.4

0

Upper middle income

Free

39

Moldova

0.4

0

Upper middle income

Partly Free

40

Namibia

0.4

0

Upper middle income

Free

41

Portugal

0.4

0

High income

Free

42

Venezuela

0.4

0

Lower middle income

Not Free

43

Hungary

0.5

0

High income

Partly Free

44

Romania

0.65

0

Upper middle income

Free

45

Panama

0.7

0

Upper middle income

Free

46

Zimbabwe

0.7

0

Lower middle income

Not Free

47

Belarus

0.75

0

Upper middle income

Not Free

48

Argentina

0.9

0

Upper middle income

Free

49

New Zealand

0.9

0

High income

Free

50

Zambia

1

0

Lower middle income

Partly Free

51

Seychelles

1.1

0

High income

Free

52

United States

1.1

0

High income

Free

53

Ireland

1.4

0

High income

Free

54

Barbados

1.5

0

High income

Free

55

Croatia

1.5

0

High income

Free

56

Ukraine

1.7

0

Lower middle income

Partly Free

57

China

1.725

0

Upper middle income

Not Free

58

Cambodia

1.9

0

Lower middle income

Not Free

59

South Africa

1.9

0

Upper middle income

Free

60

Cape Verde

2

0

Lower middle income

Free

61

Congo

2

0

Lower middle income

Not Free

62

Andorra

2.6

0

High income

Free

63

Australia

2.6

0

High income

Free

64

Malta

2.6

0

High income

Free

65

Spain

2.6

0

High income

Free

66

Finland

2.7

0

High income

Free

67

Luxembourg

3

0

High income

Free

68

Serbia

3.1

0

Upper middle income

Partly Free

69

Canada

3.2

0

High income

Free

70

Slovenia

3.6

0

High income

Free

71

Nepal

4.2

0

Lower middle income

Partly Free

72

Myanmar

4.3

0

Lower middle income

Not Free

73

Thailand

4.3

0

Upper middle income

Not Free

74

Italy

4.8

0

High income

Free

75

Rwanda

4.8

0

Low income

Not Free

76

Mongolia

5

0

Lower middle income

Free

77

Netherlands

5.1

0

High income

Free

78

Switzerland

5.2

0

High income

Free

79

Denmark

5.4

0

High income

Free

80

Germany

5.7

0

High income

Free

81

Greece

5.7

0

High income

Free

82

Norway

5.7

0

High income

Free

83

Trinidad and Tobago

5.8

0

High income

Free

84

Fiji

6.3

0

Upper middle income

Partly Free

85

United Kingdom

6.3

0

High income

Free

86

Guyana

7.3

0

Upper middle income

Free

87

Belgium

7.6

0

High income

Free

88

Austria

8

0

High income

Free

89

Philippines

8

0

Lower middle income

Partly Free

90

Sweden

8.1

0

High income

Free

91

France

8.8

0

High income

Free

92

Sri Lanka

9.7

0

Lower middle income

Partly Free

93

Burundi

10

0

Low income

Not Free

94

Democratic Republic of Congo

10

0

Low income

Not Free

95

Eswatini

10

0

Lower middle income

Not Free

96

Gabon

10

0

Upper middle income

Not Free

97

Madagascar

10

0

Low income

Partly Free

98

Georgia

10.7

0

Upper middle income

Partly Free

99

Kenya

11.2

0

Lower middle income

Partly Free

100

Bulgaria

13.4

0

Upper middle income

Free

101

Russia

13.5

0

Upper middle income

Not Free

102

Suriname

13.9

0

Upper middle income

Free

103

Uganda

14

0

Low income

Not Free

104

India

14.2

0

Lower middle income

Partly Free

105

Singapore

14.7

0

High income

Partly Free

106

Central African Republic

15

0

Low income

Not Free

107

Mauritius

17.3

0

Upper middle income

Free

108

Mozambique

17.9

0

Low income

Partly Free

109

Ghana

18

0

Lower middle income

Free

110

Israel

18

0

High income

Free

111

Liberia

20

0

Low income

Partly Free

112

Malawi

20

0

Low income

Partly Free

113

South Sudan

20

0

Low income

Not Free

114

Togo

20

0

Low income

Partly Free

115

Benin

27.7

0

Lower middle income

Partly Free

116

Cyprus

28.2

0

High income

Free

117

Cameroon

30

0

Lower middle income

Not Free

118

Ethiopia

33.9

0

Low income

Not Free

119

Tanzania

35.2

0

Lower middle income

Partly Free

120

Cote d’Ivoire

42.9

0

Lower middle income

Partly Free

121

Eritrea

43.8

0

Low income

Not Free

122

Nigeria

49.6

0

Lower middle income

Partly Free

123

Bosnia and Herzegovina

50.7

1

Upper middle income

Partly Free

124

Lebanon

57.7

1

Upper middle income

Partly Free

125

Chad

58

1

Low income

Not Free

126

Albania

58.8

1

Upper middle income

Partly Free

127

Malaysia

61.3

1

Upper middle income

Partly Free

128

Burkina Faso

61.5

1

Low income

Partly Free

129

Kazakhstan

70.2

1

Upper middle income

Not Free

130

Bahrain

73.7

1

High income

Not Free

131

Kuwait

74.6

1

High income

Partly Free

132

United Arab Emirates

76

1

High income

Not Free

133

Qatar

77.5

1

High income

Not Free

134

Sierra Leone

78.6

1

Low income

Partly Free

135

Brunei

78.8

1

High income

Not Free

136

Kyrgyzstan

80

1

Lower middle income

Not Free

137

Oman

85.9

1

High income

Not Free

138

Indonesia

87.2

1

Lower middle income

Partly Free

139

Guinea

89.1

1

Low income

Partly Free

140

Bangladesh

90.4

1

Lower middle income

Partly Free

141

Egypt

92.35

1

Lower middle income

Not Free

142

Mali

95

1

Low income

Not Free

143

Gambia

95.7

1

Low income

Partly Free

144

Iraq

95.7

1

Upper middle income

Not Free

145

Senegal

96.1

1

Lower middle income

Partly Free

146

Pakistan

96.5

1

Lower middle income

Partly Free

147

Uzbekistan

96.5

1

Lower middle income

Not Free

148

Tajikistan

96.7

1

Lower middle income

Not Free

149

Azerbaijan

96.9

1

Upper middle income

Not Free

150

Djibouti

97

1

Lower middle income

Not Free

151

Libya

97

1

Upper middle income

Not Free

152

Sudan

97

1

Low income

Not Free

153

Saudi Arabia

97.1

1

High income

Not Free

155

Jordan

97.2

1

Upper middle income

Not Free

155

Palestine

97.5

1

Lower middle income

Not Free

156

Niger

98.3

1

Low income

Partly Free

157

Algeria

99

1

Lower middle income

Not Free

158

Morocco

99

1

Lower middle income

Partly Free

159

Yemen

99.1

1

Low income

Not Free

160

Turkey

99.2

1

Upper middle income

Not Free

161

Iran

99.4

1

Lower middle income

Not Free

162

Afghanistan

99.6

1

Low income

Not Free

163

Somalia

99.8

1

Low income

Not Free

164

Tunisia

99.8

1

Lower middle income

Free

165

Mauritania

100

1

Lower middle income

Partly Free

 

Freedom scores and categories (free, partly free, and not free) were obtained by Freedom House.17 A country’s freedom score is based on the combination of the overall score of its political rights and civil liberties after being equally weighted.18 The freedom score is then used to determine its freedom category. All 49 Muslim-majoritycountries were considered “partly” or “not free”.17  Countries were further categorized as low- lower-middle- upper-middle- and high-income countries.19

Data Analysis

A cross-country panel analysis of the total number of new COVID-19 cases for 165 countries was conducted from May 1, 2020 to July 1, 2021. Control variables included stringency index at t-15 and t-5, and low- lower-middle- and upper-middle-income. Regression models of the 165 countries were created for the 3 global COVID-19 peak dates of January 7 and July 31, 2020, and April 29, 2021 to analyze the total number of COVID-19 cases and deaths per million. Control variables included vaccinated per hundred, population density, low- low-middle- and upper-middle-income, whether a country is free or partly free, and GDP per capita.

Results

Cross-country panel analysis

The number of daily new COVID-19 cases per million was signficantly less in Muslim-majority countries (N = 49) compared to non-Muslim countries (N=116), SD 1.57E-1, p-value <0.001, controling for stringency index at t-15 and t-5, low- lower-middle- and upper-middle-income of a country. (See Table 2).

Table 2

Dependent variable: new daily cases

 

estimate

std.error

p.value

 

Control variables

 

 

 

 

Stringency index at t-15

-0.028

0.003

0

***

Stringency index at t-5

0.050

0.003

0

***

Low income

-3.805

1.93E-19

0

***

Lower middle income

-1.697

1.77E-17

0

***

Upper middle income

-0.543

1.1E-17

0

***

Variable of interest

 

 

 

 

Muslim

-0.048

1.57E-17

0

***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

covid timeline

Regression Models for total number of COVID-19 cases

Total number of cases per million of Muslim-majority countries was significantly less than non-Muslim countries in the 3 peak dates controling for vaccinated per hundred, population density, low- lower-middle- and upper-middle-income, being not or partly free, and GDP per capita.  July 31, 2020: 0.089, p-value <0.001 (See Table 3), January 7, 2021: SD 0.012, p-value 0.04 (See Table 4), April 29, 2021: SD 0.009, p-value <0.01 (see Table 5),

Table 3

Dependent variable: total cases, day:   July 31, 2020

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

7.796

0.004

0

***

7.614

0.005

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

 

 

 

 

 

 

 

 

Population density

-7.73E-05

7.85E-07

0

***

-7.4E-05

8.69E-07

0

***

Low income

-3.328

0.014

0

***

-2.196

0.021

0

***

Lower middle income

-1.568

0.006

0

***

-0.858

0.008

0

***

Upper middle income

-0.429

0.005

0

***

0.468

0.006

0

***

Not Free

0.979

0.004

0

***

-0.370

0.008

0

***

Partly Free

0.766

0.005

0

***

0.225

0.005

0

***

GDP per capita

1.38E-05

7.62E-08

0

***

1.43E-05

8.95E-08

0

***

Interaction:

 Muslim, Vaccinated per hundred

 

 

 

 

 

 

 

 

Interaction:

Muslim, Population density

 

 

 

 

0.001

4.44E-06

0

***

Interaction:

Muslim, Low income

 

 

 

 

-0.845

0.029

0

***

Interaction:

Muslim, Lower middle income

 

 

 

 

-0.246

0.013

0

***

Interaction:

Muslim, Upper middle income

 

 

 

 

-1.392

0.011

0

***

Interaction:

Muslim, Not Free

 

 

 

 

2.986

0.087

0

***

Interaction:

Muslim, Partly Free

 

 

 

 

2.239

0.088

0

***

Interaction:

Muslim, GDP per capita

 

 

 

 

1.08E-05

1.91E-07

0

***

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-1.761

0.089

0

***

Statistics

 

 

 

 

 

 

 

 

AIC

1,889,724

1,758,959

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 4

Dependent variable: total cases, day:   January 07, 2021

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

10.153

0.002

0

***

10.132

0.002

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

0.035

2.00E-04

0

***

0.031

2.00E-04

0

***

Population density

-5.80E-05

3.72E-07

0

***

-6.8E-05

3.98E-07

0

***

Low income

-3.675

0.009

0

***

-3.336

0.012

0

***

Lower middle income

-1.455

0.003

0

***

-1.430

0.003

0

***

Upper middle income

-0.203

0.002

0

***

-0.140

0.002

0

***

Not Free

-0.305

0.002

0

***

-1.038

0.004

0

***

Partly Free

0.025

0.002

0

***

0.005

0.002

0.03

*

GDP per capita

6.04E-06

3.26E-08

0

***

6.4E-06

3.37E-08

0

***

Interaction:

Muslim, Vaccinated per hundred

 

 

 

 

-0.123

0.003

0

***

Interaction:

Muslim, Population density

 

 

 

 

0.001

6.56E-06

0

***

Interaction:

Muslim, Low income

 

 

 

 

0.320

0.018

0

***

Interaction:

Muslim, Lower middle income

 

 

 

 

0.666

0.008

0

***

Interaction:

Muslim, Upper middle income

 

 

 

 

0.581

0.006

0

***

Interaction:

Muslim, Not Free

 

 

 

 

0.248

0.010

0

***

Interaction:

Muslim, Partly Free

 

 

 

 

-0.840

0.010

0

***

Interaction:

Muslim, GDP per capita

 

 

 

 

1.03E-05

1.46E-07

0

***

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-0.024

0.012

0.04

*

Statistics

 

 

 

 

 

 

 

 

AIC

2,963,844

2,704,589

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 5

Dependent variable: total cases ; day:  April 29, 2021

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

10.788

0.001

0

***

10.809

0.001

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

0.013

3.41E-05

0

***

0.012

3.65E-05

0

***

Population density

-3.09E-05

2.69E-07

0

***

-3.8E-05

2.86E-07

0

***

Low income

-3.628

0.006

0

***

-3.199

0.008

0

***

Lower middle income

-1.564

0.002

0

***

-1.586

0.002

0

***

Upper middle income

-0.282

0.001

0

***

-0.324

0.002

0

***

Not Free

-0.330

0.001

0

***

-0.981

0.003

0

***

Partly Free

-0.003

0.001

0.01

*

-0.025

0.002

0

***

GDP per capita

1.14E-06

2.55E-08

0

***

1.43E-06

2.63E-08

0

***

Interaction:

 Muslim, Vaccinated per hundred

 

 

 

 

0.011

0.001

0

***

Interaction:

Muslim, Population density

 

 

 

 

0.001

2.48E-06

0

***

Interaction:

Muslim, Low income

 

 

 

 

0.390

0.013

0

***

Interaction:

Muslim, Lower middle income

 

 

 

 

1.097

0.006

0

***

Interaction:

Muslim, Upper middle income

 

 

 

 

1.189

0.005

0

***

Interaction:

Muslim, Not Free

 

 

 

 

-0.003

0.008

0.68

 

Interaction:

Muslim, Partly Free

 

 

 

 

-0.874

0.007

0

***

Interaction:

Muslim, GDP per capita

 

 

 

 

1.17E-05

1.18E-07

0

***

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-0.301

0.009

0

***

Statistics

 

 

 

 

 

 

 

 

AIC

525,969

433,350

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Regression Models for total number of COVID-19 deaths

Total number of deaths per million of Muslim-majority countries was significantly less than non-Muslim countries in the 3 peak dates controling for the same variables as above. July 31, 2020: 0.510, p-value 0.009 (See Table 6); January 7, 2021: SD 0.090, p-value <0.001 (See Table 7), April 29, 2021: SD 0.065, p-value 0.03 (See Table 8).

Table 6

Dependent variable: total deaths; day:  July 31, 2020

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

3.025

0.098

0

***

1.375

0.109

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

 

 

 

 

 

 

 

 

Total cases per million

1.32E-04

 

1.65E-06

0

***

1.78E-04

2.04E-06

0

***

Population density

-4.93E-04

3.43E-05

0

***

-4.8E-04

2.72E-05

0

***

Low income

-0.641

0.107

0

***

-0.474

0.165

0.004

**

Lower middle income

0.319

0.058

0

***

0.259

0.066

0

***

Upper middle income

0.688

0.035

0

***

0.631

0.039

0

***

Not Free

-0.266

0.035

0

***

-0.714

0.066

0

***

Partly Free

0.692

0.027

0

***

1.159

0.031

0

***

GDP per capita

-6.02E-06

4.90E-07

0

***

-9.8E-07

4.58E-07

0.032

*

Median age

0.092

0.002

0

***

0.112

0.002

0

***

Cardiovasc death rate

-0.006

1.16E-04

0

***

-0.005

1.18E-04

0

***

Diabetes prevalence

-0.170

0.004

0

***

-0.130

0.005

0

***

Interaction:

Muslim, Vaccinated per hundred

 

 

 

 

 

 

 

 

Interaction:

Muslim, Total cases per million

 

 

 

 

-1.5E-04

4.07E-06

0

***

Interaction:

Muslim, Population density

 

 

 

 

2.56E-04

6.97E-05

0

***

Interaction:

Muslim, Low income

 

 

 

 

0.121

0.204

0.553

 

Interaction:

Muslim, Lower middle income

 

 

 

 

0.292

0.107

0.006

**

Interaction:

Muslim, Upper middle income

 

 

 

 

-0.446

0.098

0

***

Interaction:

Muslim, Not Free

 

 

 

 

4.128

0.506

0

***

Interaction:

Muslim, Partly Free

 

 

 

 

1.265

0.504

0.012

*

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-1.322

0.510

0.009

**

Statistics

 

 

 

 

 

 

 

 

AIC

12,146

8,632

 

 

 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 7

Dependent variable: total deaths; day:  January 7, 2021

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

4.587

0.045

0

***

4.315

0.050

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

0.007

0.002

0.001

***

0.015

0.002

0

***

Total cases per million

2.3E-05

1.97E-07

0

***

2.11E-05

2.05E-07

0

***

Population density

-5.66E-04

2.53E-05

0

***

-7.1E-04

3.51E-05

0

***

Low income

-1.814

0.064

0

***

-1.884

0.103

0

***

Lower middle income

-0.063

0.027

0.02

*

-0.062

0.032

0.06

°

Upper middle income

0.560

0.017

0

***

0.671

0.020

0

***

Not Free

-0.316

0.018

0

***

-1.225

0.033

0

***

Partly Free

0.351

0.014

0

***

0.477

0.015

0

***

GDP per capita

-6.57E-06

2.9E-07

0

***

-6.8E-06

2.97E-07

0

***

Median age

0.045

0.001

0

***

0.058

0.001

0

***

Cardiovasc death rate

-0.002

5.16E-05

0

***

-0.003

5.36E-05

0

***

Diabetes prevalence

-0.042

0.002

0

***

-0.056

0.002

0

***

Interaction:

Muslim, Vaccinated per hundred

 

 

 

 

0.118

0.036

0.001

**

Interaction:

Muslim, Total cases per million

 

 

 

 

3.37E-05

1.55E-06

0

***

Interaction:

Muslim, Population density

 

 

 

 

-2.6E-04

8.33E-05

0.002

**

Interaction:

Muslim, Low income

 

 

 

 

1.849

0.137

0

***

Interaction:

Muslim, Lower middle income

 

 

 

 

1.337

0.067

0

***

Interaction:

Muslim, Upper middle income

 

 

 

 

0.418

0.047

0

***

Interaction:

Muslim, Not Free

 

 

 

 

0.913

0.066

0

***

Interaction:

Muslim, Partly Free

 

 

 

 

-1.003

0.059

0

***

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-0.624

0.090

0

***

Statistics

 

 

 

 

 

 

 

 

AIC

26,527

22,898

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 8

Dependent variable: total deaths; day:  April 29, 2021

 

Model 1

Model 2

term

estimate

std.error

p.value

 

estimate

std.error

p.value

 

(Intercept)

5.324

0.033

0

***

5.128

0.035

0

***

Control variables

 

 

 

 

 

 

 

 

Vaccinated per hundred

-0.014

4.09E-04

0

***

-0.011

4.28E-04

0

***

Total cases per million

1.52E-05

1.06E-07

0

***

1.33E-05

1.13E-07

0

***

Population density

-4.45E-04

1.73E-05

0

***

-0.001

2.54E-05

0

***

Low income

-2.052

0.048

0

***

-2.093

0.070

0

***

Lower middle income

-0.333

0.020

0

***

-0.427

0.024

0

***

Upper middle income

0.397

0.013

0

***

0.431

0.015

0

***

Not Free

-0.409

0.014

0

***

-1.068

0.024

0

***

Partly Free

0.333

0.010

0

***

0.479

0.011

0

***

GDP per capita

-6.42E-06

2.31E-07

0

***

-6.7E-06

2.35E-07

0

***

Median age

0.037

0.001

0

***

0.048

0.001

0

***

Cardiovasc death rate

-0.002

3.88E-05

0

***

-0.002

4.03E-05

0

***

Diabetes prevalence

-0.034

0.001

0

***

-0.048

0.002

0

***

Interaction:

Muslim, Vaccinated per hundred

 

 

 

 

-0.027

0.002

0

***

Interaction:

Muslim, Total cases per million

 

 

 

 

2.15E-05

6.45E-07

0

***

Interaction:

Muslim, Population density

 

 

 

 

1.3E-05

4.5E-05

0.77

 

Interaction:

Muslim, Low income

 

 

 

 

1.326

0.098

0

***

Interaction:

Muslim, Lower middle income

 

 

 

 

0.940

0.050

0

***

Interaction:

Muslim, Upper middle income

 

 

 

 

-0.011

0.043

0.80

 

Interaction:

Muslim, Not Free

 

 

 

 

0.369

0.047

0

***

Interaction:

Muslim, Partly Free

 

 

 

 

-1.234

0.043

0

***

Variable of interest

 

 

 

 

 

 

 

 

Muslim

 

 

 

 

-0.142

0.065

0.03

*

Statistics

 

 

 

 

 

 

 

 

AIC

40,780

35,608

 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Discussion

Panel data analysis was used to analyze the total number of COVID-19 cases per million for each country from May 1, 2020 to July 1, 2021. Conducting a panel data analysis helps to eliminate possible multicollinearity problems and is a good test for a time series analysis. Table 2 depicts a strong statistical difference between the 49 Muslim-majority countries (muslim_binary = 1) versus the 116 non-Muslim countries (muslim_binary = 2) during this 13-month. The one outlying date in December 2020 was due to the Muslim-majority country of Turkey having reported an extraordinary number of cases assumed to be reporting error. Regression models were made for each of the individual 3 peak dates; with one model for total number of cases per million and the other for total number of deaths per million. Choosing to study 3 distinct peak dates during the pandemic’s 3 COVID-19 waves was chosen to strengthen the argument that Muslim-majority countries have fewer cases and deaths over various moments in time.

 

 

The incidence of COVID-19 cases or COVID-19 related deaths in Muslim-majority countries can be a potential result of other contributing factors. To account for country population variation, the total number of cases and deaths per million were studied. To address other possible confounding factors of COVID-19, the following variables were added: population density, stringency, vaccinated per hundred, population density, and GDP per capita. The stringency index takes into account a country’s ability to enforce 9 possible preventive measures ranging from school or work closures, cancellation or restrictions of public gatherings; to restrictions on domestic and international travel.20 Including freedom categories was important because all 49  Muslim-majority countries are classified to be partly or not free. Therefore, determining the effects of similar countries was of interest. Last, the status of LMICwas of interest because 40/49 (82%) of the Muslim-majority countries are low- low-middle- or upper-middle-income countries, yet the remaining 9 countries are quite wealthy.

Implications

The results of this data poses an interesting global public health issue. It suggests the possibility that Muslims’ religious practices may have an impact on COVID-19 incidence. It is not to say that all citizens (Muslim or non-Muslims) in Muslim-majority countries follow prescribed practices of the Islamic faith. However, given the strong association of a possible religion and health connection is reason to explore the possibilities of this implication. This section is meant to share insights to some Islamic practices that may influence the numbers of COVID-19 cases and deaths, and a population’s health in general.

 

In Islam, social iolation, quarantine, and sanitation are in alignment with the WHO pandemic guidelines.21 Bentley at el showed that the Islamic faith and social connection helped Somalis cope with the COVID-19 pandemic, as well as other collective traumas.22 Islam also fosters tawakkul (trust in Allah) as a possible means for Muslims to rationalize that the COVID-19 pandemic was a divine decree, and may be a means to preventing mental distress or  depression..21

Islam and Health

The aim of medicine in Islam is to “preserve health, ward off disease, and restore health when it is lost.”23 There are 28 Quranic verses that focus on the importance of maintaining a healthy lifestyle, and promoting personal hygiene, good diet, nutrition, and alcohol abstinence.24

It is incumbent that Muslim physicians dissuade or prevent their patients from participating in hazardous behaviors that undermine individual and collective well-being.25 While Western cultures emphasize individual choice, individual autonomy is more limited in Islam, as beneficence to others is an act of worship emphasized in the Quran (9:7-8)26 and encouraged by the Prophet (PBUH*) (Muslim 16:1508).27 For example, if a Muslim physician advises an Muslim patient to partake in a healthy behavior that will benefit both the individual and community-at-large, a practicing Muslim would feel obligated and willing to commit such an act for the greater good, rather than possibly disregard the medical advise. According to Amin “worldwide public health organizations are almost in line with the teachings of Islam.”328 Muslims perform daily ablution, wash hands after sleeping, cover one’s face when sneezing, and avoid hand shaking with a leper or infected person.

Tahara (purity or cleanliness)

Tahara (purity or cleanliness) is an essential tenet of the Islamic faith analogous to common practices that prevent, treat, and reduce the chances of contracting or dying from COVID-19. While today’s experts highly recommend social distancing or quarantine to stop and reduce the spread of COVID-19, the Prophet (PBUH) told Muslims to avoid plagued lands 1400 years ago.29 Cleanliness is paramount in Islam. Muslims believe that “cleanliness is half our [Muslims’] faith” (Muslim 223) and “Allah loves cleanliness” (Muslim 2230).27 The Quran also states that Allah loves those who cleanse and purify themselves (2:222).26 Therefore, the acts of cleanliness must precede all Muslims’ behaviors and activities.30

 

When the Ebola virus reached Nigeria, a Muslim-majority country, the federal government advised citizens to follow the words of the Prophet (PBUH) who urged Muslims to be clean and wash their hands frequently. Rassool30 stressed that cleanliness has significant spiritual (intrinsic) and physical (extrinsic) importance in Islam, similar to Litman et al.’s14 reasonings explained in the Introduction.

Tawakkul (trust in Allah)

The belief and practice of tawakkul helps Muslims to be more resilient during difficult and unpredictable times, such as a pandemic.31 The Muslim worldview on health and illness is unique, with Muslims “receiving illness and death with patience, meditation and prayers.”30 In a Belgian study, it was found that religion played a crucial role in how Muslim women percieved and dealt with illness.32 Health was interpreted to be a trust and blessing from Allah. Participants underlined the importance of accepting illness with gratitude as it is part of Allah’s divine decree. Muslims do so because they consider them natural parts of life and tests from Allah. They see illness as atonement for sins, and death as part of their journey to meet Allah.

 

According to Hammoudeh et al., most elderly Palestinian women who participated in their study recognized faith and tawakkul as ways of coping, alongside physical activity and healthy eating.33 Muslims are required to work hard towards achieving a well-balanced life (religiously, academically or vocationaly, physically, nutritionally, emotionally, socially, etc.) and to have tawakkul.34

 

While Muslims rely upon Allah, they must also do their part. When the Prophet (PBUH) was asked by a man whether he should tie his camel and rely upon Allah or leave it loose and rely upon Allah, the answer was, “Tie it and rely (upon Allah)” (at-Tirmidhi 4,11:2517).27 The Quran instructs Muslims “to obey Allah, and obey the Messenger (PBUH), and those in authority among you,” stressing the seeking of credible advice (4:59).26 When a man was injured and two doctors were called to examine him, the Prophet (PBUH) asked who was the better doctor, further indicating the need for superior consultation.35

 

Various religious practices, such as voluntary prayers, supplications, and Quranic recitations, serve as additional healing aids.32 The Quran mentions deeds that purify Muslims, including generosity (16:90), charity (3:42), compassion (17:23), obligatory prayers (9:103), and almsgiving. Muslims perform these deeds as testaments to their trust in Allah.26 In terms of health and disease, Muslims believe that there is a remedy for every illness or disease on earth, except old age (Sahih al-Bukhari 5678).27 As long as Muslims trust in Allah, their belief of acceptance leads to greater happiness as it includes contentment and peacefulness.

Conclusions

Despite most (40/49, 82%) Muslim-majority countries being LMIC and 100% considered unfree, they had significantly less number of daily new cases than the 116 non-Muslim countries from May 1, 2020 to July 1, 2021. While many Muslim-majority countries were not able to strictly follow social distancing, lockdown, testing, contact tracing, and PPE guidelines, when compared to non-Muslim countries during the 3 global COVID-19 peak dates, they had lower number of COVID-19 cases and deaths per million  with statistical significance.

This study shows that Muslims’ practice of tahara is similar to many COVID-19 containment measures, while tawakkul helps Muslims remain resilient and hopeful during difficult unpredictable times, such as living through a pandemic. Strong educational campaigns centered around religious faith that emphasized the practice of strict personal hygiene have proven beneficial for Muslims during this COVID-19 pandemic. It can be beneficial for other  countries to stress religious faith and cleanliness practices as a means of attaining greater overall health. It is hoped that this paper brings awareness to the positive practices of the Islamic faith as it relates to COVID-19, and to population and individual health in general.

 

Research should be conducted in Muslim-majority countries and Muslims living in non-Muslim countries to further study the association of health and  adhering to Islamic practices, principles, and beliefs. For example, a number of Muslim countries are currently studying the medicinal benefits of black cumin seed in relation to COVID-19, because the Prophet said that it “can heal all diseases except death” (Sahih al-Bukhari 5687).27 It is hoped that more studies are undertaken to study Islam and Health in general.

*PBUH = Peace Be Upon Him (Prophet Muhammad)

Acknowledgments

The authors would like to thank Sheilamae Ablay, PhD for her data consultations, Winnie Lu for her data analysis assistance, Samiha Ahmed for her technical editing assistance and insightful critiques, Dr. Tamseela M. Hussain for her medical consultation, Lisa Kahler for her thoughtful feedback, and Maryam O. Funmilayo, MA for her edits. The lead author would like to acknowledge SS for her astuteness; their discussions led to the hypotheses of this research project.

Author Contributions

PPM contributed to the concept, design, data acquisition, analysis and results of the research; conducted the literature review, drafted the manuscript and approved final revisions. GG contributed to the design of the research, devised the methodology of and conducted the analysis of the data, created the tables, helped draft the methods and results, and approved final revisions. ZMS contributed to the literature review and offered critical analysis; helped  draft the manuscript, provided technical editing, fact-checking and proofreading assistance; and approved final revisions. DMS contributed to the literature review and analysis, helped draft the manuscript, and approved final revisions.

Disclosure Statement

The views expressed in the submitted article are those of the authors and not an official position of our institutions.

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About the Authors

Ponn P. Mahayosnand MPH

Ponn P. Mahayosnand, MPH is a Research Scholar at Ronin Institute. Her research focuses on (1) public health and preventive lifestyle medicine + primary care reform in LMIC, (2) Islam and Health, Prophetic medicine, and health in Gaza, Palestine, and (3) e-mentoring and remote research for women. Ponn earned her BS in Biology, minor in Environmental Health, and concentration in Health Policy and Management from Providence College, and MPH from the University of Connecticut.  

Gloria Gheno, MA, MS, PhD

Gloria Gheno, MA, MS, PhD is a Research Scholar at Ronin Institute. A data analyst and statistical researcher. Gloria earned her masters in Economics and her professional masters in Economics and Finance from Ca’ Foscari University of Venice, her masters and PhD in Statistics from University of Padova.

ZM Sabra

ZM Sabra is a medical student at the Islamic University of Gaza. Her research interests are in public health preventive medicine, nutrition & lifestyle medicine and digital health.

DM Sabra

DM Sabra is a medical student at the Islamic University of Gaza. In preparation for a speciality in pediatrics, her research interests are in childhood preventive care and nutrition

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