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. 2022 Jan 21;23(1):8. doi: 10.1186/s43042-022-00218-8

Morbidity and mortality of COVID-19 negatively associated with the frequency of consanguineous marriages, an ecologic study

Mostafa Saadat 1,✉
PMCID: PMC8776372  PMID: 37521847

Abstract

Background

Union between second cousins and closer relatives is called consanguineous marriage. Consanguineous marriage is associated with increased risk of autosomal recessive diseases and several multifactorial traits. In order to evaluate the association between prevalence/mortality of COVID-19 and the frequency of consanguineous marriage, the present ecologic study was carried out. For the present study, data of prevalence (per 106 people) and mortality (per 106 people) and number of performed laboratory diagnostic test (per 106 people) of COVID-19 disease at four time points (December 2020; March, August and October 2021) of 65 countries were used.

Results

Univariable correlation and generalized estimating equation analysis were used. In analysis, prevalence and mortality of COVID-19 were used as dependent variables and human development index, number of performed diagnosis test and the mean of inbreeding coefficient (α-value) were introduced into model as covariates, and time point was used as a factor in analysis. The square root (SR) of prevalence (P = 0.008) and SR-mortality (P < 0.001) of COVID-19 negatively associated with the log-transformed of α-value.

Conclusions

The present finding means that in countries with high levels of consanguineous marriages, the prevalence of COVID-19 and mortality due to COVID-19 were lower than countries having low level of marriage with relatives.

Keywords: Consanguineous marriage, COVID-19, Ecologic study

Background

In genetics, union between second cousins and closer relatives is called consanguineous marriage [1]. Previously it has been shown that consanguineous marriage is a long-standing social custom which depends with numerous factors, such as religious, socio-economic and demographic variables [2–4]. The frequency of consanguineous marriage has geographical distribution; it has high prevalence in several Asia and Africa countries. Iranian populations showed high levels of consanguineous marriages [5–7].

Numerous studies have shown that this type of marriage is associated with increased risk of autosomal recessive diseases and several multifactorial traits [1, 8–15]. It is well established that primary immunodeficiency diseases are genetically heterogeneous group and are associated with parental consanguinity [16]. There is significant association between parental consanguineous marriage and the risk of some infections [17, 18]. There is about one-fifth of the world’s population living in countries where marriage with biological relatives is prevalent. Therefore, for countries with high prevalence of consanguineous marriage, the association between frequency of consanguineous marriage and the Coronavirus Disease 2019 (COVID-19) is highly important. To the best of our knowledge, there is no study regarding the above-mentioned association; therefore, the present ecologic study was carried out.

Methods

Prevalence (per 106 people) and mortality (per 106 people) and number of performed laboratory diagnostic test (per 106 people) of COVID-19 disease at four time points (December 31, 2020; March 19, 2021; August 31, 2021 and October 25, 2021) were obtained from the Web site www.worldometers.info/coronavirus (Table 1).

Table 1.

Prevalence and mortality of COVID-19 (at four time points), α-value and human development index (HDI) in 65 countries used in the study

Country α (× 10–4) HDI Prevalence (per 106 people) Mortality (per 106 people) Number of diagnostic tests performed (per 106 people)
I II III IV I II III IV I II III IV
Afghanistan 277 0.511 1334 1418 3837 3893 56 62 178 181 5090 8335 18,818 19,232
Argentina 3 0.845 35,801 49,128 113,525 115,468 952 1197 2448 2533 105,970 181,382 484,192 543,219
Australia 1 0.944 1107 1135 2084 6189 35 35 39 64 438,894 587,753 1,220,092 1,635,789
Bahrain 165 0.852 53,547 77,161 153,973 155,486 203 283 784 783 1,367,218 1,941,619 3,352,349 3,837,157
Bangladesh 45 0.632 3103 3418 9007 9398 46 52 157 167 19,500 26,336 53,592 61,392
Belgium 3 0.931 55,466 70,774 101,543 113,449 1674 1946 2178 2220 594,130 888,169 1,600,817 1,814,678
Bolivia 3 0.718 13,475 22,309 41,361 42,915 778 1021 1554 1591 35,029 70,567 192,444 211,825
Brazil 21 0.765 35,983 55,594 96,949 101,310 914 1360 2709 2824 134,070 133,871 265,480 297,262
Canada 4 0.929 15,337 24,410 39,320 44,597 412 596 706 755 363,376 694,395 1,063,272 1,198,495
Chile 6 0.851 31,719 47,733 84,874 87,082 865 1148 1913 1949 336,033 541,582 1,050,216 1,200,325
China 27 0.761 60 63 66 67 3 3 3 3 111,163 111,163 111,163 111,163
Colombia 35 0.767 32,113 45,337 95,296 96,764 845 1205 2425 2463 159,013 237,702 469,190 514,898
Costa Rica 11 0.810 33,087 41,328 90,075 108,078 427 565 1069 1355 96,770 147,838 389,729 489,646
Croatia 1 0.851 51,519 62,543 91,762 109,531 958 1405 2045 2225 249,025 357,598 624,180 745,949
Cuba 5 0.783 1048 5754 57,680 83,672 13 34 469 724 130,412 242,869 701,100 917,622
Czech Republic 1 0.900 67,054 135,193 156,464 161,273 1087 2269 2833 2853 352,869 980,182 3,346,877 3,720,067
Ecuador 13 0.759 11,954 17,363 27,915 28,656 789 920 1796 1831 42,113 61,332 98,334 106,401
Egypt 123 0.707 1337 1872 2758 3104 74 111 160 175 9681 9643 29,344 35,220
El Salvador 14 0.673 7068 9606 14,681 17,198 205 303 447 548 95,594 123,973 184,668 206,332
France 2 0.901 40,100 63,962 103,385 108,872 989 1402 1749 1795 535,994 894,795 1,906,567 2,309,763
Guinea 131 0.477 1031 1387 2178 2253 6 8 25 28 7995 12,430 39,642 41,545
Honduras 11 0.634 12,203 18,050 33,581 37,026 314 439 882 1009 30,696 48,518 96,817 106,821
Hungary 1 0.854 33,428 57,021 84,339 88,337 989 1850 3121 3175 275,409 435,797 682,588 757,183
India 238 0.645 7417 8315 23,507 24,468 107 115 315 326 124,060 166,492 373,653 429,788
Indonesia 95 0.718 2703 5262 14,771 15,291 81 143 480 516 26,748 43,419 116,350 163,285
Iran 185 0.783 14,493 21,075 58,563 68,712 653 727 1265 1468 89,515 142,043 330,974 381,937
Iraq 225 0.674 14,636 19,204 45,751 49,457 315 341 505 555 111,811 185,560 347,760 379,122
Ireland 1 0.955 18,483 46,072 70,462 86,168 451 919 1018 1072 478,239 763,477 1,282,683 1,576,220
Italy 4 0.892 34,877 55,174 75,217 78,610 1227 1726 2141 2185 440,252 765,907 1,392,283 1,678,211
Japan 13 0.919 1824 3587 11,652 13,630 27 69 127 144 38,422 72,856 172,729 207,359
Jordan 200 0.729 28,720 50,749 77,234 82,527 374 555 1009 1061 309,687 525,879 896,743 1,042,639
Kuwait 205 0.806 35,002 50,188 94,351 94,765 217 280 557 565 291,671 452,251 871,679 1,085,320
Lebanon 91 0.744 26,653 63,838 88,701 94,036 216 833 1186 1247 292,040 495,158 700,912 704,511
Libya 209 0.724 14,495 21,670 44,266 50,646 214 358 608 719 78,997 117,011 218,213 252,516
Malaysia 47 0.810 3469 10,105 53,161 74,034 14 38 507 866 102,668 216,935 691,764 1,019,218
Mexico 1 0.779 10,909 16,799 25,602 28,944 964 1514 1981 2191 27,751 44,955 74,504 85,806
Mongolia 63 0.737 368 1405 64,025 104,733 0 2 281 498 181,857 634,290 1,092,266 1,203,868
Morocco 89 0.686 11,828 13,192 23,008 25,193 199 235 338 390 120,046 157,123 239,820 267,224
Nepal 203 0.602 8864 9345 25,639 27,150 63 102 361 381 65,733 75,594 131,621 146,752
Netherlands 1 0.944 46,460 69,164 112,990 121,495 666 947 1048 1066 336,388 406,153 965,933 1,046,785
Nigeria 242 0.539 420 770 907 992 6 10 12 14 4543 8030 13,108 15,500
Norway 2 0.957 9107 15,691 29,279 36,774 80 119 149 163 512,352 783,127 1,312,037 1,475,055
Oman 169 0.813 24,920 28,688 57,501 57,645 290 312 773 779 110,512 298,161 294,829 4,737,668
Pakistan 282 0.557 2151 2765 5136 5603 45 61 114 125 30,023 43,276 78,612 90,633
Panama 6 0.815 56,749 80,269 104,123 107,102 925 1383 1607 1660 300,323 471,752 834,635 915,784
Peru 16 0.777 30,574 43,595 64,168 65,414 1135 1498 5918 5959 166,330 258,036 499,973 560,933
Philippines 3 0.718 4297 5859 17,881 24,766 84 117 301 376 61,243 86,269 168,302 203,555
Portugal 9 0.864 40,630 80,255 102,138 106,861 678 1647 1746 1786 548,976 857,034 1,672,408 1,934,376
Qatar 275 0.848 51,226 61,506 82,892 84,992 87 97 214 217 442,127 589,354 892,029 996,997
Saudi Arabia 223 0.854 10,339 10,917 15,361 15,434 177 187 241 247 313,818 413,298 774,341 847,162
Singapore 20 0.938 9977 10,227 11,453 29,745 5 5 9 56 923,868 1,369,279 3,010,388 3,496,485
Slovakia 1 0.860 32,877 63,379 72,294 83,731 392 1628 2297 2364 261,676 410,607 607,648 706,312
Slovenia 3 0.917 58,775 98,349 128,506 154,195 1297 1905 2140 2257 324,385 480,333 695,973 813,725
South Africa 16 0.709 17,713 25,658 46,157 48,428 477 870 1367 1475 110,737 160,173 273,773 304,837
Spain 6 0.904 41,415 68,687 103,794 106,934 1087 1559 1803 1864 577,712 879,117 1,295,942 1,415,474
Sri Lanka 92 0.782 2018 4167 20,462 24,924 10 25 427 633 57,607 105,902 225,629 249,927
Sweden 3 0.945 43,172 73,369 110,770 114,515 861 1307 1440 1469 421,728 653,331 1,155,756 1,278,224
Tunisia 213 0.740 11,711 20,558 55,510 59,412 394 714 1960 2100 51,829 88,741 216,642 254,282
Turkey 74 0.820 26,047 34,966 74,809 92,124 246 351 664 811 288,986 423,309 895,106 1,109,007
United Arab Emirates 223 0.890 20,886 43,770 71,627 73,590 67 143 204 212 2,099,449 3,499,933 7,455,727 9,112,446
UK 2 0.932 36,564 62,895 99,406 128,883 1080 1849 1940 2042 806,468 1,650,892 3,950,983 4,762,083
Uruguay 9 0.817 5494 22,514 110,367 112,427 52 218 1729 1740 183,955 344,737 958,295 1,081,873
USA 1 0.926 61,604 91,537 120,368 139,161 1067 1667 1974 2272 763,725 1,165,212 1,753,221 2,069,420
Venezuela 7 0.711 3999 5256 11,828 14,164 36 52 142 170 85,033 107,288 118,517 118,568
Yemen 215 0.470 70 106 257 316 20 24 48 60 577 575 8272 8644
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I, II, III and IV means four time points of data collections December 2020; March, August and October 2021, respectively

Very recently importance of socioeconomic position and Human Development Index (HDI) in study of COVID-19 has been reported [19–21]. The HDI reflects three major dimensions of human development, life expectancy at birth, education and the gross national income (PPP) per capita. Countries with higher life expectancy, income and educational levels have higher HDI values. The HDI values are calculated annually and reported by the United Nations Development Program’s Human Development Report Office. The latest report (2019) was used in the present analysis as a potential confounder.

The inbreeding coefficient means the probability that an individual has received both alleles of a pair from an identical ancestral allele and shown by F-value. The F-value, for double first cousins, first cousins, first cousin once removed, second cousins, and unrelated marriages, was 1/8, 1/16, 1/32, 1/64 and 0, respectively. The mean of F-values which is shown by α-value, and it is calculated as F = ∑PiFi, where Pi and Fi are frequency and the F-value of each marriage type. The α-values were estimated from data presented in the Web site www.consang.net.

Data from 65 countries were included in the study. The countries were Afghanistan, Argentina, Australia, Bahrain, Bangladesh, Belgium, Bolivia, Brazil, Canada, Chile, China, Colombia, Costa Rica, Croatia, Cuba, Czech Republic, Ecuador, Egypt, El Salvador, France, Guinea, Honduras, Hungary, India, Indonesia, Iran, Iraq, Ireland, Italy, Japan, Jordan, Kuwait, Lebanon, Libya, Malaysia, Mexico, Mongolia, Morocco, Nepal, Netherlands, Nigeria, Norway, Oman, Pakistan, Panama, Peru, Philippines, Portugal, Qatar, Saudi Arabia, Singapore, Slovakia, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Tunisia, Turkey, United Arab Emirates, UK, Uruguay, USA, Venezuela, and Yemen.

Normality of variables was investigated using Kolmogrov–Smirnov test. If a variable was not distributed normally, its log-transformed or square root-transformed (SR-transformed) was used in statistical analysis. Univariable correlation analysis was used. To overcome the effects of confounders on prevalence/mortality, generalized estimating equation analysis was used. Prevalence/mortality of COVID-19 was considered as outcome variables, and the α-values as well as the potential confounders (number of performed test and HDI) were introduced into model as covariates. It should be noted that time point was used as a factor in analysis. Data were analyzed using SPSS software (version 25; SPSS Inc., Chicago, IL). Statistical analysis was performed using P < 0.05 as the cutoff point for significant association.

Results

Normality test showed that HDI has normal distribution. Other variables were not distributed normally. For statistical analysis, the log-transformed of α-value, and the SR-prevalence and SR-mortality of COVID-19 and SR-number of performed test were used.

Table 2 summarizes the correlations between the study variables. It should be noted that almost all of the studies variables had significant correlation with each other. The SR-prevalence (P < 0.001) and SR-mortality of COVID-19 (P  < 0.001) were negatively associated with the log-transformed of α-value.

Table 2.

Correlation analysis between the studied variables

Date/variables SR-prevalence SR-mortality HDI
r P r P r P
End December 2020
 Log-α value −0.395 0.001 −0.556  < 0.001 –0.618  < 0.001
 SR-performed tests 0.598  < 0.001 0.298 0.016 0.733  < 0.001
 HDI 0.593  < 0.001 0.468  < 0.001 – –
March 19, 2021
 Log-α value −0.439  < 0.001 −0.603  < 0.001 –0.618  < 0.001
 SR-performed tests 0.625  < 0.001 0.338 0.006 0.731  < 0.001
 HDI 0.624  < 0.001 0.493  < 0.001 – –
August 31, 2021
 Log-α value −0.372 0.002 −0.498  < 0.001 –0.618  < 0.001
 SR-performed tests 0.602  < 0.001 0.233 0.062 0.711  < 0.001
 HDI 0.609  < 0.001 0.399 0.001 – –
October 25, 2021
 Log-α value −0.404 0.001 −0.506  < 0.001 –0.618  < 0.001
 SR-performed tests 0.600  < 0.001 0.208 0.096 0.733  < 0.001
 HDI 0.642  < 0.001 0.407 0.001 – –
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df is 63 for all comparisons. SR and Log mean square root- and logarithmic-transformed variables

On the other hand, there was significant negative relationship between log-transformed of α-value and HDI (r = −0.618, df = 63, P < 0.001). In all of the study time points, the SR-prevalence (P < 0.001) and SR-mortality of COVID-19 (P < 0.001) were significantly associated with HDI.

In order to neutralized the potential confounding effects of the HDI and number of performed tests on the correlation between α-values and epidemiologic parameters, generalized estimating equations were used (Table 3). The construction final models showed that SR-prevalence (P = 0.008) and SR-mortality (P < 0.001) parameters of COVID-19 negatively associated with the log-transformed of α-value.

Table 3.

Results of generalized estimation equations for investigation of associations of prevalence and mortality of COVID-19 with the α-values as frequency of consanguineous marriages in the 65 countries around the world

Variables Wald Chi-square df P-value
SR-prevalence as dependent variable
 Time 37.494 3  < 0.001
 Log-α value 7.055 1 0.008
 SR-performed tests 19.586 1  < 0.001
SR-mortality as dependent variable
 Time 186.856 3  < 0.001
 Log-α value 17.147 1  < 0.001
 Human development index (HDI) 2.466 1 0.116
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Discussion

The current study revealed that in countries with high levels of consanguineous marriages, the prevalence of COVID-19 and mortality due to COVID-19 were lower than countries having low frequency of marriage with relatives. The α-value can explain 36% of the differences observed in the mortality due to COVID-19 between different countries. Although the main finding of the current study is not consistent with previous reports which reported that primary immunodeficiency diseases [16] and risk of infection of tuberculosis and hepatitis [17] positively correlated with consanguineous marriages, it is consistent with the negative association between parental consanguinity and risk of HIV-1 infection [18].

The present finding that countries with high frequency of consanguineous marriages have low prevalence/mortality of COVID-19 might be interpreted by increased number of resistant individuals against infection SARS-CoV-2 or outcome of COVID-19 due to parental consanguinity. Consanguineous marriage results in elevation of homozygosity of mutant alleles with low frequency. Let’s assume the frequency of a given mutant allele which is resistance to COVID-19 be equal to q in a given population. The probability of homozygosity for this allele when marriage with biologic relatives is present and absent at population level becomes equal to q2 + apq and q2, respectively (α is the mean of inbreeding coefficient). Therefore, parental consanguinity increases the frequency of mutant homozygotes which they are resistance to COVID-19. Taken together, it is concluded that the present negative association between α-value and prevalence/mortality of COVID-19 might be a reflection of elevation of the homozygosity of several mutant alleles involved in resistance against infection of SARS-CoV-2 and/or severe form of COVID-19.

The present study is an ecologic study, and other ecologic studies have some considerations and limitations. It should be noted that frequency of consanguineous marriages and the epidemiologic parameters COVID-19 are not uniformly distributed on different parts of countries. Here average levels of consanguinity and COVID-19 epidemiologic parameters for countries were used for analysis. Hence, the present finding does not mean a causal relationship between parental consanguinity and susceptibility/mortality due to COVID-19. Several case–control and/or cohort studies are needed to confirm the present findings. Finding mutations that induced the resistance to the COVID-19 is also needed.

Conclusions

The findings of present ecologic study revealed that countries with high frequency of consanguineous marriages, the prevalence of COVID-19 and mortality due to COVID-19 were lower than countries having low level of marriage with relatives. Considering that here the average levels of consanguinity and COVID-19 epidemiologic parameters for countries were used for analysis, the present finding does not mean a causal relationship between parental consanguinity and susceptibility/mortality due to COVID-19.

Acknowledgements

None.

Abbreviations

COVID-19

Coronavirus disease-2019

HDI

Human development index

SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2

SR-transformed

Square root-transformed

Authors' contributions

Conceptualization, data collection, methodology, data analysis, and writing of the manuscript were done by MS. The author read and approved the final manuscript.

Funding

The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Availability of data and materials

All data generated for analysis are presented in Table 1 of the manuscript.

Declarations

Ethics approval and consent to participate

This is a ecologic study and does not need ethical approval ad consent to participants.

Consent for publication

None.

Competing interests

The author declares that he has no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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Data Availability Statement

All data generated for analysis are presented in Table 1 of the manuscript.

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