Abstract
Background and aims
Proper nutrition to optimise immune function is mandatory as at present there is no specific medicine to cure COVID-19. Our study aimed to investigate the use of nutritional supplements and their key immunity enhancing components among Sri Lankans.
Methods
This current study was an online survey conducted during the period from 27th May to 2nd June 2021 disseminated via various social media platforms. Details were collected on highlighting the frequency and composition of nutritional supplements consumed.
Results
In a total of 3707 responders, a proportion of 25.5% (944) of the participants took supplements regularly, and 32.6% (1210), 14.5% (538), 27.4% (1015) intake were occasionally, rarely and never respectively. Older participants (>40 years of age) had a significantly higher intake of supplements than the younger counterpart of 31–35 years age group (OR: 0.604; 95% CI, 0.451–0.809; p < 0.001). Almost half (46.0%) took vitamin C supplements and females tend to take more (OR: 1.325; 95% CI, 1.120–1.567; p < 0.001) of vitamin C when compared to males. Comparatively to the other groups regarding monthly income, the participants with the highest income category are more likely to take vitamin D (OR:1.899; 95% CI, 1.034–3.490; p = 0.039) and multivitamin (OR: 3.222; 95% CI, 1.737–5.977; p < 0.001). The most commonly used main ingredient in home-made remedy was coriander in Sri Lanka.
Conclusion
The use of nutritional supplements and natural products to improve immunity during the pandemic were frequent among Sri Lankan adults. In general, older, female gender and high income was significantly associated with increased intake of the nutrient supplements.
Keywords: Nutritional supplements, Micronutrients, Online survey, Immunity, COVID-19, Sri Lanka
Introduction
The coronavirus disease 2019 (COVID-19) pandemic advanced relentlessly worldwide since the first cluster of cases of infection [1]. As of 31st of May 2021, it has spread over 222 countries, and caused a total of 17 million and 3.5 million active cases and deaths respectively [2]. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a substantial increase in hospitalisations for pneumonia with multi-organ disease worldwide [3]. Expression of the virus ranges from asymptomatic to life-threatening, presenting flu-like symptoms to severe lung injury in high-risk older individuals with multiple co-morbidities [4]. COVID-19 remains a global health challenge, having triggered an enormous number of human causalities and serious economic losses to countries [5]. Therefore, infection prevention, early viral detection and identification of treatment protocols provide the best approach in controlling the disease spread.
Multiple therapeutic strategies are used to treat patients with COVID-19, while no specific drugs are recommended to cure it [6]. The fundamental prevention measures such as mask-wearing, social distancing and handwashing are universally recommended [7]. In addition to the basic preventative measures, vaccination has been identified as a key strategy for preventing the infection globally [8]. A substantial population need to be vaccinated to achieve safe herd immunity to control transmission of the COVID-19 virus [9]. The demand for vaccines is expected to overcome the supply and disparities in vaccine doses across high and low-income countries can be seen [10]. In addition, it is well known that proper nutrition can support optimal immune function thus reducing the impact of infections [11].
The global burden of the COVID-19 pandemic infection is significant, requiring further safeguards. Achieving a balanced diet has become extremely challenging during this epidemic [12]. Protein-energy malnutrition and/or micronutrient deficiency are associated with an impaired immune system [13,14]. Micronutrient deficiencies are highly prevalent in the South Asian region and deficiencies of iron, zinc, vitamin D and vitamin B12 are well established [15]. A review evaluating nutrition-based interventions for respiratory viral infections, reported vitamins A and D as well as trace minerals like selenium and zinc to have immune-modulatory effects [16]. Furthermore, the same review showed vitamin C had no beneficial evidence against viral infections, and similarly vitamin E supplementation indicated no effect and may be detrimental [16]. During this epidemic, commonly recommended supplements by dieticians were fish oil, vitamin D, multivitamin, probiotics, and vitamin C [17].
Along with micronutrients, natural products with therapeutic properties such as ginger, turmeric, garlic, and others are utilised as home-based remedies which hold immune-modulating properties that may improve immunity against diseases [18]. According to a study conducted on the use of herbs and natural products in the Saudi population during the COVID-19 pandemic, 69.3% believed that these practices can improve their immunity [19]. Additionally, a survey in Bangladesh reported that 57.6% of the participants had used herbal foods/products, with tea (71%) being the most popular, followed by ginger (56.5%), black seed (32.8%), honey (30%), and clove (30%) [20].
Hence, dietary supplements defined as usable amounts of micronutrients have become a growing interest during the COVID-19 as they are widely used to potentially improve immune health [21]. Moreover, complementary and alternate medicine as Ayurveda and herbal supplements are frequently used as treatments globally [22]. Sri Lanka as a lower-middle-income country, experienced its most severe outbreak of the COVID-19 pandemic during the study period. Therefore, the main aim of this online survey was to investigate the use of nutritional supplements and their key immunity-enhancing components among Sri Lankans during the COVID-19 pandemic.
Methods
Study design and sampling
The present study is a national-level online survey conducted using Google forms, a web survey platform during the period from 27th May to 2nd June 2021. Recruitment of participants was carried out using multiple strategies. The link to the online survey was circulated across various social media platforms, and the detailed methodology is published elsewhere [23]. No incentives were given to the participants and the active promotion of the questionnaire was completely voluntary. Informed consent was taken prior to the administration of the questionnaire. The web-based survey was performed accordingly to the ethical principles laid by the World Medical Association Declaration of Helsinki [24]. The nature of anonymity and the impossibility to trace any sensitive data required no ethical approval [25].
Questionnaire
The online questionnaire was constructed using Google Forms and was made available in all three official languages; English, Sinhala, and Tamil. The participants could select the preferred language, and the questionnaire was anticipated to take 5–10 minutes to complete. The questionnaire was divided into two sections, each including personal and lifestyle details, with a focus on the use of nutritional supplements to improve immunity.
The first section investigated the participants' personal related data. Respondents' details of birth year, gender, district, area of residence, ethnicity, educational status, current employment status, and family details including the monthly income were collected by both open-ended and multiple-choice questions.
The second part of the questionnaire collected details on the usage of “immune nutrients”. The frequency and composition of nutritional supplements consumed were recorded. The frequency of the supplement intake was inquired using four options: regularly, occasionally, rarely, or never. The respondents were asked to select the nutritional supplements they consumed during this period from a list of nine options: vitamin C, vitamin D, multivitamin, kothamalli (Coriandrum sativum infused in water), Ayurvedic products, herbal products, home-made remedies, syrups, and others. To obtain more details on home-based remedies, the most common ingredient used was asked via an open-ended question.
Statistical analysis
Data were extracted from Google forms as an MS Excel spreadsheet (Version 16.50, Microsoft Corporation). The data were revised, coded, and fed into the statistical software IBM SPSS version 23 (SPSS, Inc. Chicago, IL) for statistical analysis. Descriptive analysis based on the frequency and percentage distribution was performed for all demographic variables and the results were expressed as a percentage (%) and numbers (n). The chi-square test was used to determine differences between categorical variables of interest. Multinomial and binary logistic regression analyses were conducted to investigate the association between categorical variables (dependent) and continuous or categorical ones (independent). During regression analyses, certain demographic variables representing less than 1% of the sample were removed. In addition, monthly family income groups of < 10,000 LKR and 10,000–24,999 LKR were combined together as < 25,000 LKR category. Education level groups of no schooling, primary and secondary education also were combined into a new category as ‘secondary education or below’. The results of logistic regression analyses were reported as odds ratio (OR) and 95% confidence intervals (95%; CI). The p-value was considered significant at <0.05.
Results
After removing potential duplicates and incomplete responses, a total of 3707 respondents were recorded before removing <1% categorical groups for analysis. The socio-demographic characteristics of the participants are presented in Table 1 . The majority belonged to the 26–30 years age group (24.7%; 913), were females (59.6%; 2209), and had an education level of degree or above (69.1%; 2563). The survey conducted covered all 25 districts in the country with the highest reporting from Colombo (37.8%; 1408) and most of the responders were from rural areas 40.1% (1488). Representation of all ethnic groups was recorded and the majority were Sinhalese (81.9%, 3036). In terms of employment status and monthly income, more than half of the participants were employed (63.0%; 2337), and over a quarter of participants had a monthly income of 50,000–99,000 LKR (26.6%; 986).
Table 1.
Sample characteristics
| Variables | Total (n=3707) |
|
|---|---|---|
| n | % | |
| Age | ||
| 16–25 years | 825 | 22.3 |
| 26–30 years | 913 | 24.7 |
| 31–35 years | 758 | 20.5 |
| 36–40 years | 498 | 13.5 |
| >40 years | 705 | 19.1 |
| Gender | ||
| Male | 1468 | 39.6 |
| Female | 2209 | 59.6 |
| Not specified | 30 | 0.8 |
| District | ||
| Colombo | 1403 | 37.8 |
| Gampaha | 502 | 13.5 |
| Kandy | 352 | 9.5 |
| Kalutara | 235 | 6.3 |
| Kurunegala | 183 | 4.9 |
| Batticaloa | 108 | 2.9 |
| Others | 924 | 24.9 |
| Area of residence | ||
| Municipal council area | 1208 | 32.6 |
| City council area | 1011 | 27.3 |
| Rural area | 1488 | 40.1 |
| Ethnicity | ||
| Sinhala | 3036 | 81.9 |
| Sri Lankan Tamil | 304 | 8.2 |
| Indian Tamil | 57 | 1.5 |
| Sri Lankan Moors | 259 | 7.0 |
| Others | 51 | 1.4 |
| Education level | ||
| No schooling | 2 | 0.1 |
| Primary education (up to grade 5) | 0 | 0 |
| Secondary education (up to O/L) | 160 | 4.3 |
| Tertiary education (up to A/L) | 948 | 25.6 |
| Degree or above | 2563 | 69.1 |
| Prefer not to say | 34 | 0.9 |
| Employment status | ||
| Employed | 2337 | 63.0 |
| Self-employed | 200 | 5.4 |
| Unemployed | 272 | 7.3 |
| Engaged in home duties | 114 | 3.1 |
| Retired from employment | 55 | 1.5 |
| Full time student or pupil | 618 | 16.7 |
| Other | 84 | 2.3 |
| Prefer not to say | 27 | 0.7 |
| Monthly family income (in LKR) | ||
| Less than 10,000 | 89 | 2.3 |
| 10,000–24,999 | 231 | 6.2 |
| 25,000–49,999 | 605 | 16.3 |
| 50,000–99,999 | 986 | 26.6 |
| 100,000–199,999 | 887 | 23.9 |
| >200,000 | 909 | 24.5 |
Table 2 presents the associations between demographic variables and the frequency of taking nutritional supplements. Participants who never take supplements were 27.4% (1015) whereas rarely, occasionally, and regularly reported 14.5% (538), 32.6% (1210) and 25.5% (944) respectively. Among all the demographic variables, age, gender, ethnicity, employment status and monthly family income of the respondents indicated strong significant associations with the four frequencies of supplementation intake from taking it regularly, occasionally, rarely, or never (p < 0.05). The regression analysis among regular supplement intake by demographic variables was reported in Supplementary Table 1. The odds of regular intake of supplements are lower in all ages compared to the oldest group. Moreover, participants from 31-35 years age group reported significantly lower intake of supplements compared to older > 40 years age category (OR:0.604; 95% CI, 0.451–0.809; p = 0.001). In addition, males were less likely to have regular supplement intake (OR:0.589; 95% CI, 0.485–0.716; p < 0.001) compared to the female counterpart. However, a clear significant difference of regular supplement intake was seen among participants who earned more than > 200,000 LKR when compared with < 25,000 LKR (OR: 0.506; 95% CI, 0.336–0.761; p < 0.001).
Table 2.
Association between demographic variables and frequency of taking nutritional supplements
| Variables | Regularly |
Occasionally |
Rarely |
Never |
p-value |
|---|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | ||
| Total | 944 (25.5) | 1210 (32.6) | 538 (14.5) | 1015 (27.4) | |
| Age | |||||
| 16–25 years | 176 (21.3) | 301 (36.5) | 137 (16.6) | 211 (25.6) | <0.001 |
| 26–30 years | 225 (24.6) | 307 (33.6) | 144 (15.8) | 237 (26.0) | |
| 31–35 years | 182 (24.0) | 234 (30.9) | 105 (13.9) | 237 (31.3) | |
| 36–40 years | 139 (27.9) | 170 (34.1) | 51 (10.2) | 138 (27.7) | |
| >40 years | 220 (31.2) | 195 (27.7) | 100 (14.2) | 190 (27.0) | |
| Gender | |||||
| Male | 334 (22.8) | 436 (29.7) | 213 (14.5) | 485 (33.0) | <0.001 |
| Female | 600 (27.2) | 767 (34.7) | 318 (14.4) | 524 (23.7) | |
| District | |||||
| Colombo | 377 (26.9) | 440 (31.4) | 187 (13.3) | 399 (28.4) | 0.082 |
| Gampaha | 129 (25.7) | 163 (32.5) | 83 (16.5) | 127 (25.3) | |
| Kandy | 85 (24.1) | 118 (33.5) | 46 (13.1) | 103 (29.3) | |
| Kalutara | 55 (23.4) | 84 (35.7) | 39 (16.6) | 57 (24.3) | |
| Kurunegala | 42 (23.0) | 64 (35.0) | 20 (10.9) | 57 (31.1) | |
| Batticaloa | 16 (14.8) | 35 (32.4) | 27 (25.0) | 30 (27.8) | |
| Others | 240 (26.0) | 306 (33.1) | 136 (14.7) | 242 (26.2) | |
| Area of residence | |||||
| Municipal council area | 311 (25.7) | 393 (32.5) | 171 (14.2) | 333 (27.6) | 0.939 |
| City council area | 244 (24.1) | 333 (32.9) | 155 (15.3) | 279 (27.6) | |
| Rural area | 389 (26.1) | 484 (32.5) | 212 (14.2) | 403 (27.1) | |
| Ethnicity | |||||
| Sinhala | 827 (27.2) | 1015 (33.4) | 411 (13.5) | 783 (25.8) | 0.001 |
| Sri Lankan Tamil | 61 (20.1) | 82 (27.0) | 52 (17.1) | 109 (35.9) | |
| Indian Tamil | 8 (14.0) | 23 (40.4) | 9 (15.8) | 17 (29.8) | |
| Sri Lankan Moors | 36 (13.9) | 73 (28.2) | 60 (23.2) | 90 (34.7) | |
| Others | 12 (23.5) | 17 (33.3) | 6 (11.8) | 16 (31.4) | |
| Education level | |||||
| Secondary education or below | 25 (15.6) | 68 (42.5) | 23 (14.4) | 44 (27.5) | 0.056 |
| Tertiary education | 249 (26.3) | 313 (33.0) | 134 (14.1) | 252 (26.6) | |
| Degree or above | 663 (25.9) | 815 (31.8) | 378 (14.7) | 707 (27.6) | |
| Employment status | |||||
| Employed | 610 (26.1) | 735 (31.5) | 333 (14.2) | 659 (28.2) | 0.021 |
| Self-employed | 52 (26.0) | 67 (33.5) | 24 (12.0) | 57 (28.5) | |
| Unemployed | 72 (26.5) | 88 (32.4) | 32 (11.8) | 80 (29.4) | |
| Engaged in home duties | 30 (26.3) | 35 (30.7) | 23 (20.2) | 26 (22.8) | |
| Retired from employment | 20 (36.4) | 15 (27.3) | 8 (14.5) | 12 (21.8) | |
| Full time student or pupil | 133 (21.5) | 224 (36.2) | 111 (18.0) | 150 (24.3) | |
| Other | 18 (21.4) | 36 (42.9) | 6 (7.1) | 24 (28.6) | |
| Monthly family income (in LKR) | |||||
| < 25,000 | 64 (20.0) | 88 (27.5) | 47 (14.7) | 121 (37.8) | <0.001 |
| 25,000–49,999 | 144 (23.8) | 221 (36.5) | 94 (15.5) | 146 (24.1) | |
| 50,000–99,999 | 244 (24.7) | 340 (34.5) | 153 (15.5) | 249 (25.3) | |
| 100,000–199,999 | 233 (26.3) | 288 (32.5) | 129 (14.5) | 237 (26.7) | |
| >200,000 | 259 (28.5) | 273 (30.0) | 115 (12.7) | 262 (28.8) | |
Table 3a, Table 3ba and 3b present the vitamins and multivitamin users during the COVID-19 pandemic respectively. The usage of vitamin C, vitamin D and multivitamin among the participants were 46.0% (1288), 8.6% (241), and 10.8% (302) respectively. As seen in Table 3a, Table 3ba, vitamin D, and multivitamin intake had a significant association with age (p < 0.05). Overall females were more likely to take vitamin C supplementation (OR: 1.325; 95% CI, 1.120–1.567; p < 0.001) when compared to males. A strong significant correlation resulted between all districts and areas of residence with supplementation (p < 0.001). Odds ratio presented in Table 3a, Table 3bb exhibited the district of Kandy had a less likelihood for intake of vitamin C (OR: 0.541; 95% CI, 0.0393–0.743); p < 0.001) and vitamin D (OR: 0.376; 95% CI, 0.201–0.700; p = 0.002) compared to Colombo district. Furthermore, the odds of a lower intake in the rural area were seen across all supplements especially with vitamin D (OR: 0.552; 95% CI, 0.394–0.774; p = 0.001) and multivitamin (OR: 0.570; 95% CI, 0.420–0.774); p < 0.001). Regarding the ethnicity, there was no association shown except for Sri Lankan Tamils indicating the lowest intake of vitamin C supplementation (OR: 0.558; 95% CI, 0.405–0.7688; p < 0.001) when compared to the Sinhala ethnicity group. Moreover, at all education levels supplements vitamin C, D and multivitamin demonstrated significant correlations Table 3a, Table 3b. Analysis results of participants of tertiary education presented in Table 3b, were found more likely to take vitamin C (OR: 1.585; 95% CI, 1.054–2.384; p = 0.027) supplements when compared to those lower education levels. Employment status had a strong association with vitamin D and multivitamin intake (Table 3a). Monthly family income played a significant relationship with supplement intake, with participants earning >200,000 LKR more likely to take vitamin D (OR: 1.899; 95% CI, 1.034–3.490; p = 0.039) and multivitamin supplementation (OR: 3.222; 95% CI, 1.737–5.977; p < 0.001) when compared to the least earning category.
Table 3a.
Usage of vitamins and multivitamin among the supplement users during the COVID-19 pandemic
| Variables | Vitamin C |
Vitamin D |
Multivitamin |
|||
|---|---|---|---|---|---|---|
| n (%) | p-value | n (%) | p-value | n (%) | p-value | |
| Total | 1288 (46.0) | 241 (8.6) | 302 (10.8) | |||
| Age | ||||||
| 16–25 years | 257 (43.3) | 0.277 | 27 (4.6) | <0.001 | 37 (6.2) | ˂0.001 |
| 26–30 years | 333 (47.6) | 58 (8.3) | 72 (10.3) | |||
| 31–35 years | 272 (44.7) | 46 (7.6) | 69 (11.3) | |||
| 36–40 years | 198 (49.6) | 39 (9.8) | 55 (13.8) | |||
| >40 years | 224 (45.3) | 70 (14.1) | 69 (13.9) | |||
| Gender | ||||||
| Male | 432 (40.6) | 0.001 | 86 (8.1) | 0.488 | 97 (9.1) | 0.026 |
| Female | 843 (49.1) | 152 (8.8) | 203 (11.8) | |||
| District | ||||||
| Colombo | 476 (47.8) | 0.001 | 126 (12.7) | 0.001 | 144 (14.5) | 0.001 |
| Gampaha | 204 (48.3) | 45 (10.7) | 54 (12.8) | |||
| Kandy | 114 (35.5) | 12 (3.7) | 22 (6.9) | |||
| Kalutara | 102 (52.3) | 18 (9.2) | 15 (7.7) | |||
| Kurunegala | 66 (41.0) | 8 (5.0) | 11 (6.8) | |||
| Batticaloa | 6 (60.0) | 1 (10.0) | 0 (0.0) | |||
| Others | 320 (46.0) | 31 (4.5) | 56 (8.0) | |||
| Area of residence | ||||||
| Municipal council area | 368 (49.4) | 0.013 | 91 (12.2) | 0.001 | 107 (14.4) | 0.001 |
| City council area | 349 (47.7) | 76 (10.4) | 105 (14.3) | |||
| Rural area | 571 (43.1) | 74 (5.6) | 90 (6.8) | |||
| Ethnicity | ||||||
| Sinhala | 1213 (46.3) | 0.445 | 230 (8.8) | 0.673 | 284 (10.8) | 0.668 |
| Sri Lankan Tamil | 21 (43.8) | 4 (8.3) | 7 (14.6) | |||
| Indian Tamil | 6 (27.3) | 1 (4.5) | 1 (4.5) | |||
| Sri Lankan Moors | 37 (42.5) | 4 (4.6) | 7 (8.0) | |||
| Others | 11 (45.8) | 2 (8.3) | 3 (12.5) | |||
| Education level | ||||||
| Secondary education or below | 48 (35.0) | 0.018 | 5 (3.6) | 0.014 | 9 (6.6) | 0.001 |
| Tertiary education | 383 (48.1) | 58 (7.3) | 55 (6.9) | |||
| Degree or above | 848 (46.0) | 178 (9.7) | 237 (12.9) | |||
| Employment status | ||||||
| Employed | 837 (47.2) | 0.486 | 167 (9.4) | 0.001 | 199 (11.2) | 0.006 |
| Self-employed | 61 (40.9) | 9 (6.0) | 21 (14.1) | |||
| Unemployed | 97 (44.5) | 19 (8.7) | 21 (9.6) | |||
| Engaged in home duties | 47 (50.0) | 10 (10.6) | 18 (19.1) | |||
| Retired from employment | 17 (45.9) | 8 (21.6) | 3 (8.1) | |||
| Full time student or pupil | 194 (43.6) | 17 (3.8) | 29 (6.5) | |||
| Other | 24 (39.3) | 7 (11.5) | 7 (11.5) | |||
| Monthly family income (in LKR) | ||||||
| < 25,000 | 82 (36.4) | 0.005 | 11 (4.9) | 0.001 | 7 (3.1) | 0.001 |
| 25,000–49,999 | 218 (42.9) | 30 (5.9) | 25 (4.9) | |||
| 50,000–99,999 | 384 (49.7) | 48 (6.2) | 66 (8.5) | |||
| 100,000–199,999 | 302 (45.8) | 63 (9.5) | 99 (15.0) | |||
| >200,000 | 302 (47.6) | 89 (14.0) | 105 (16.5) | |||
Table 3b.
Odds (OR) of vitamins and multivitamins usage among the supplement users by socio-demographic variables
| Variables | Vitamin C |
Vitamin D |
Multivitamin |
|||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Age | ||||||
| 16–25 years∗ | 1 | 1 | 1 | |||
| 26–30 years | 0.981 (0.718–1.342) | 0.907 | 0.994 (0.607–1.628) | 0.982 | 1.123 (0.712–1.771) | 0.619 |
| 31–35 years | 0.899 (0.642–1.258) | 0.533 | 0.802 (0.470–1.367) | 0.417 | 1.282 (0.790–2.083) | 0.315 |
| 36–40 years | 1.031 (0.717–1.482) | 0.870 | 1.046 (0.608–1.800) | 0.872 | 1.232 (0.741–2.048) | 0.421 |
| >40 years | 0.828 (0.587–1.168) | 0.282 | 1.590 (0.962–2.630) | 0.071 | 1.369 (0.843–2.224) | 0.204 |
| Gender | ||||||
| Male∗ | 1 | 1 | 1 | |||
| Female | 1.325 (1.120–1.567) | 0.001 | 1.300 (1.009–1.677) | 0.043 | 1.280 (1.015–1.615) | 0.037 |
| District | ||||||
| Colombo∗ | 1 | 1 | 1 | |||
| Gampaha | 0.950 (0.730–1.236) | 0.701 | 0.813 (0.561–1.176) | 0.271 | 0.946 (0.676–1.324) | 0.747 |
| Kandy | 0.541 (0.393–0.743) | <0.001 | 0.376 (0.201–0.700) | 0.002 | 0.725 (0.448–1.174) | 0.191 |
| Kalutara | 0.976 (0.691–1.379) | 0.891 | 0.861 (0.525–1.414) | 0.555 | 0.562 (0.330–0.958) | 0.034 |
| Kurunegala | 0.841 (0.564–1.252) | 0.393 | 0.500 (0.242–1.035) | 0.062 | 0.787 (0.429–1.446) | 0.441 |
| Batticaloa | 0.976 (0.585–1.628) | 0.927 | 0.707 (0.297–1.681) | 0.433 | 0.672 (0.302–1.494) | 0.330 |
| Others | 0.773 (0.618–0.969) | 0.025 | 0.403 (0.275–0.590) | <0.001 | 0.672 (0.490–0.992) | 0.014 |
| Area of residence | ||||||
| Municipal council area∗ | 1 | 1 | 1 | |||
| City council area | 0.956 (0.776–1.179) | 0.677 | 0.761 (0.572–1.012) | 0.060 | 0.936 (0.723–1.212) | 0.618 |
| Rural area | 0.803 (0.647–0.997) | 0.047 | 0.552 (0.394–0.774) | 0.001 | 0.570 (0.420–0.774) | <0.001 |
| Ethnicity | ||||||
| Sinhala∗ | 1 | 1 | 1 | |||
| Sri Lankan Tamil | 0.558 (0.405–0.768) | <0.001 | 0.963 (0.580–1.600) | 0.884 | 0.880 (0.556–1.393) | 0.585 |
| Indian Tamil | 0.567 (0.303–1.061) | 0.076 | 0.706 (0.263–1.893) | 0.488 | 1.364 (0.636–2.925) | 0.425 |
| Sri Lankan Moors | 0.759 (0.547–1.054) | 0.099 | 0.682 (0.394–1.181) | 0.172 | 1.007 (0.639–1.586) | 0.976 |
| Others | 1.393 (0.649–2.992) | 0.395 | 1.325 (0.563–3.120) | 0.520 | 2.098 (0.982–4.480) | 0.056 |
| Education level | ||||||
| Secondary education or below∗ | 1 | 1 | 1 | |||
| Tertiary education | 1.585 (1.054–2.384) | 0.027 | 0.769 (0.388–1.525) | 0.452 | 0.810 (0.420–1.565) | 0.531 |
| Degree or above | 1.142 (0.758–1.720) | 0.525 | 0.716 (0.363–1.413) | 0.336 | 1.167 (0.610–2.232) | 0.642 |
| Employment status | ||||||
| Employed∗ | 1 | 1 | 1 | |||
| Self-employed | 0.730 (0.514–1.038) | 0.080 | 1.126 (0.683–1.859) | 0.641 | 1.746 (1.127–2.706) | 0.013 |
| Unemployed | 0.899 (0.649–1.247) | 0.524 | 1.163 (0.704–1.921) | 0.554 | 1.312 (0.814–2.113) | 0.265 |
| Engaged in home duties | 0.676 (0.433–1.057) | 0.086 | 0.823 (0.419–1.616) | 0.571 | 1.509 (0.864–2.637) | 0.148 |
| Retired from employment | 0.637 (0.338–1.203) | 0.164 | 1.943 (0.957–3.943) | 0.066 | 1.105 90.484–2.522) | 0.812 |
| Full time student or pupil | 0.852 (0.611–1.188) | 0.345 | 0.755 (0.429–1.328) | 0.329 | 1.519 (0.935–2.468) | 0.092 |
| Other | 1.097 (0.642–1.874) | 0.735 | 1.693 (0.846–3.387) | 0.137 | 1.749 (0.879–3.481) | 0.111 |
| Monthly family income (in LKR) | ||||||
| <25,000∗ | 1 | 1 | 1 | |||
| 25,000–49,999 | 0.966 (0.688–1.355) | 0.840 | 0.844 (0.442–1.612) | 0.608 | 1.173 (0.610–2.256) | 0.632 |
| 50,000–99,999 | 1.309 (0.943–1.816) | 0.107 | 0.895 (0.486–1.646) | 0.721 | 1.729 90.940–3.181) | 0.078 |
| 100,000–199,999 | 1.008 (0.716–1.421) | 0.962 | 1.341 (0.731–2.458) | 0.343 | 2.787 (1.513–5.133) | 0.001 |
| >200,000 | 1.241 90.869–1.772) | 0.234 | 1.899 (1.034–3.490) | 0.039 | 3.222 (1.737–5.977) | <0.001 |
Reference variable.
The usage of non-vitamin supplements across the participants to enhance the immune system is shown inSupplementary Table 2a and 2b. A majority of the participants (59.5%) consumed kothamalli, while other supplements used were Ayurvedic products (26.0%; 729), herbal products (10.9%; 306) homemade remedies (16.4%; 459), and syrups (1.3%; 36). Age, area of residence, education level, and employment status showed no associations with the intake of non-vitamin supplements. In regards to gender, females tend to use kothamalli more (OR: 1.324; 95% CI, 1.096–1.599; p = 0.004) than males. Intake of kothamalli and homemade remedies demonstrated a significant association between districts (p < 0.001) (Supplementary Table 2a). In Supplementary Table 2b, the participants from the district of Kandy showed a significantly higher intake of kothamalli (OR: 1.773; 95% CI, 0.182–2.659; p = 0.006) in contrast to participants from the Colombo district. Additionally, the likelihood of making homemade remedies was significantly higher in Kurunegala (OR: 2.481; 95% CI, 1.626–3.786; p < 0.001) and Batticaloa (OR: 1.857; 95% CI, 1.101–3.132; p = 0.020). The ethnic categories had a significant association with the consumption of kothamalli and Ayurvedic products (p <0.001; p = 0.006). In comparison, all other ethnicities significantly had a lower intake of both kothamalli and Ayurvedic products than Sinhala. Nonetheless, both Sri Lankan Tamils (OR: 2.268; 95% CI, 1.629–3.158; p < 0.001) and Indian Tamils (OR: 2.243; 95% CI, 1.174–4.285; p = 0.041) and as well as Sri Lankan Moors (OR:1.886; 95% CI, 1.333–2.670; p < 0.001) were most likely to make their own remedies at home compared to Sinhalese (Supplementary Table 2b). Monthly income had a significant association with kothamalli (p < 0.001) and ayurvedic products (p = 0.002) intake, of which Ayurvedic products was more likely to be consumed by participants earning 100,000–199,999 LKR (OR: 1.794; 95% CI, 1.208–2.665; p = 0.004) and 50,000–99,000 LKR (OR: 1.623; 95% CI, 1.109–2.376; p = 0.013) than the other income groups.
The most commonly used main ingredients in homemade remedies are displayed in Figure 1 . Majority of the participants used coriander (57%; 629) and ginger (20%; 217) as main ingredients. Other commonly used ingredients were lime (4%; 49), garlic (4%; 48), lemon (3%; 37), turmeric (2%; 26) and pepper (2%; 20). The remaining 8% (87) belonged to the other group which mostly consisted of mixtures of herbs and other natural products.
Figure 1.
Common main ingredients used in home-made remedies.
Discussion
Sri Lanka was experiencing the highest number of COVID-19-infected patients and fatalities throughout the study period. Due to the limited vaccine coverage in Sri Lanka, alternative immunity-enhancing products have taken the attention of the general public. This online survey conducted explored various immunity-enhancing products during COVID-19, from nutritional supplements to many other natural products including herbs in Sri Lanka. Only a quarter of our survey respondents did not take any nutritional supplements to strengthen the immunity. Despite the fact that our study sample consisted mostly of young and educated people, we received a large number of responses from diverse ethnic groups and throughout all districts of the island, making our survey population a fair reflection of Sri Lanka. The survey showcased frequencies of supplement intake to have significant differences across the various ethnicities, this is due to the difference in beliefs and practices belonging to the specific ethnic group. A similar study conducted in Benha city, Egypt during COVID-19 reported the use of vitamin C (27%), vitamin D (17.7%), immune-boosting drinks (39.3%), honey (32.2%), and garlic (37.2%) amidst the participants [26]. In Italy, most believed that strengthening immune defences through nutrition elements enhances the function of the immune system to reduce the risk of COVID-19 infection [27].
In this study, various demographic factors were associated with the frequency of nutritional supplement intake. Regular supplement intake was seen greater among the older (>40 years) participants in contrast to the younger counterpart. A similar pattern was observed in Bangladesh, where elderly individuals were more likely to take preventive supplements (OR: 2.93; 95% CI, 1.37–6.30; p = 0.004) [20]. Older age and/or existing co-morbidities are an increased risk factor for severe COVID-19 infection compared to the younger age group which may also cause elders to take extra measures to be more careful [28]. Higher intake of supplementation was also mostly seen across females than males. Our gender analysis results reflect similar patterns as of the online study among Polish adults [25]. A study which was conducted among Google users across multiple countries to investigate their interests in nutritional supplements during the COVID-19 pandemic period revealed that, the difference of interest was either because of traditional medicine knowledge or regional beliefs in the specific country [29]. Consistently, the difference between the ethnic groups indicates they do not share the same knowledge and attitudes on supplements use [30]. The lower-earning category of participants was significantly less likely to use supplements considering the supplements tend to be more expensive [31]. These results are similar to investigations done in other countries that have indicated supplement intake is greater among the high-income populations [32,33]. Since a greater proportion of our participants were educated, there was no difference in supplement intake found. Whereas other related studies during COVID-19 discovered such preventive behaviours were higher among educated participants [20,25].
In this study, a larger proportion (46.0%; 1288) used vitamin C supplements, which have been studied to stimulate various defense mechanisms including antimicrobial and immunomodulatory properties [34]. Nonetheless, a review on therapeutic vitamin C supplementation had no consistent effect on the severity and the duration of the common cold [35]. A study in the USA with COVID-19 patients, were administered high doses of ascorbic acid and had no significant decrease of symptoms in comparison to standard care (p = 0.25) [36]. Contrarily, vitamin D and multivitamin supplementation intakes were higher among participants who were older, employed, and from affluent categories. Vitamin D and multivitamin supplementation are recommended as these can be effective, especially among individuals who are either deficient and in self-isolation or have bad dietary practices [16]. Furthermore, another study exploring dietary supplements effect on COVID-19 recorded lower risk of infection in participants taking multivitamins by 13% (OR = 0.87; p < 0.001.) and vitamin D by 9% (OR = 0.91; p < 0.001) [37]. As vitamin D deficiency has been associated with COVID-19 mortality rate in the Asian region, vitamin D supplementation could have a vital role in the prevention or treatment of the disease [38,39]. Accordingly, vitamin D supplementation was administered among elderly patients in France with COVID-19 and were less likely to exhibit severity (OR = 0.46; 95% CI, 0.01–0.81; p = 0.033) [40]. In comparison to these micronutrient supplements, other immunity enhancing products reported association with various factors; residing location and ethnicity which may have influenced by cultural practices and beliefs [41]. Besides, the use of such herbs and products was high probably due to the immediate availability in most households in Sri Lanka. Positive previous experiences with herbal therapy, as well as family traditions and practices, were among the most regularly mentioned reasons for the preferred use of herbal medicine in a focus group study in Germany [42].
Herbs and spices have been extensively studied around the world due to their greater demand for its natural food antioxidant and antimicrobial activity [43]. The majority (59.5%) of participants in our study took kothamalli to enhance their immunity. Coriander, has a long history as a traditional medicine, although a review investigating its bioactive constituents reported no effectiveness when treated for respiratory ailments such as asthma or bronchiolitis [44]. In Bangladesh, 57.6% proportion recorded the use of herbal foods to lower the risk of COVID-19 [20]. Additionally, a Saudi population study revealed approximately 44.8% of the participants used herbs and natural products daily as a protective measure with a significant increase of intake after COVID-19 [19]. Similarly, use of herbal products among patients were prevalent in the case of the previous infectious MERS outbreak in South Korea reporting 76.1% of patients used one or more type of herbal products [45]. Home-made remedies were made including various herbs and natural products, the commonly used ingredients were coriander, ginger, lime, garlic and lemon. In Morocco as well, garlic, and ginger were among the frequently consumed natural products during the pandemic [46]. Previous studies have reported that such ingredients could help in boosting an individual's immunity and may strengthen the immune system in general but just not related to the COVID-19 infection [18,47,48].
The current study has some limitations. Self-reported weight and height values in this study population were inaccurate therefore BMI values were not utilised, a previous study conducted in Sri Lankan adults showed only one-fourth can report weight and height accurately [49]. The questions asked regarding immunity enhancing supplements, only included the type and frequency of supplements but the doses are key. For instance, vitamin D supplementation at a dose of 5000IU per day is found to be more effective [16]. Moreover, not all multivitamins contain all the required vitamins and minerals as some may contain limited ingredients. A few of our questions were vague and overlapping. In the case of distinction between herbs and Ayurvedic products, as kothamalli/coriander is usually suggested under Ayurveda. In spite of the limitations, the main aim of this study was to report the types of supplements and the frequency of intake among the general public. Most countries studied similar patterns of usage frequency across various nutritional supplements [19,20,25,26]. These data are critical for making informed policy decisions at the national level. There are false claims and some products may have adverse effects on human health. Furthermore, some supplements may be overly expensive. The primary objectives of COVID-19 preventive measures are to follow public health guidelines, a balanced diet and daily physical movement. If deficiencies are present, nutritional supplementation may deem to be beneficial.
Conclusion
This study explored the proportion of Sri Lankans using nutrition supplements such as micronutrients, herbs, and other natural products to enhance their immunity during the COVID-19 pandemic. Nearly three quarter of the population take micronutrient supplements to enhance immunity. Age, gender, residing district, and income groups are strongly associated with supplement intake. A significant proportion of the participants takes non-evidence based vitamins such as vitamin C and herbs such as coriander in Sri Lanka.
Funding statement
This research study did not receive any funding.
Conflicts of interest
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Author contributions
RJ, TVF, PS conceived and designed the online survey questionnaire; disseminated the questionnaire; PS analysed and interpreted the data, TVF and RJ drafted the manuscript. PS revised the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors would express our gratitude towards every participant for their contribution in conducting this research study.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.nutos.2022.04.003.
Appendix ASupplementary data
The following are the Supplementary data to this article:
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