Abstract
Objective
The objective of this study was to determine the impact of COVID-19 in patients suffering from NCDs in terms of their knowledge, awareness, perception about COVID-19, use of AYUSH immune boosters (AIB), and management of chronic condition during the pandemic.
Method
During the unlock down period (October 2020), a cross-sectional study was conducted in the Krishna and Darjeeling district of Andhra Pradesh and West Bengal, India. 499 individuals suffering from at least one chronic disease were interviewed using a structured questionnaire. Logistic regression was applied to investigate the relationship of socio-demographic characteristics, AIB, and morbidity with pandemic-related care challenges. Principal component analysis was applied to minimize the dimensionality of factors related to COVID care challenges.
Results
499 individuals were surveyed. 91% identified at least three correct COVID appropriate behaviours. 92.2% considered the coronavirus to be a potential threat (mean ± SD: 5.8 ± 2.6). 44.7% and 55.3% lived with one and 2 or more chronic conditions, respectively. Hypertension alone (27.4%) and diabetes with hypertension (33%) were leading presentations. Out of 499, participants, 88.8% had at least one form of AIB. 52% took Ars. alb. with other AIB and 40% took Ars. alb. alone. Only 9 participants were infected with COVID-19.
Conclusion
In the interest of a densely populated country like India, the inclusion of simple and safe AYUSH measures is realistic, ethical, and cost-effective. AYUSH interventions as COVID-19 prophylactic and treatment as well as integrative care of chronic illnesses such as NCDs are suggested.
Keywords: AYUSH, COVID-19, AYUSH immune boosters, India, Non-communicable diseases
Zusammenfassung
Ziel
Mit dieser Studie sollten die Auswirkungen von COVID-19 bei Patienten mit nicht übertragbaren Krankheiten (NÜK) in Bezug auf ihr Wissen und Bewusstsein sowie die Wahrnehmung von COVID-19, die Anwendung von AYUSH-Immunboostern (AIB) und die Behandlung von chronischen Erkrankungen während der Pandemie ermittelt werden.
Methode
Während der Öffnungsphase nach dem Lockdown (Oktober 2020) wurde in den Distrikten Krishna und Darjeeling in Andhra Pradesh und Westbengalen, Indien, eine Querschnittsstudie durchgeführt. Dafür wurden 499 Personen, die an mindestens einer chronischen Krankheit litten, mithilfe eines strukturierten Fragebogens befragt. Der Zusammenhang von soziodemografischen Merkmalen, AIB und chronischen Erkrankungen mit pandemiebedingten Behandlungspro-blemen wurde mittels logistischer Regression untersucht. Es erfolgte eine Hauptkomponentenanalyse, um die Dimensionalität der Faktoren im Zusammenhang mit den pandemiebedingten Behandlungsproblemen zu minimieren.
Ergebnisse
Es wurden 499 Personen befragt. 91% gaben mindestens drei korrekte COVID-entsprechende Verhaltensweisen an. 92,2% hielten das Coronavirus für eine potenzielle Bedrohung (Mittelwert ± SD: 5,8 ± 2,6). 44,7% und 55,3% hatten eine bzw. zwei oder mehr chronische Erkrankungen. Die häufigsten Krankheiten waren Hypertonie (27,4%) und Diabetes mit Hypertonie (33%). Von den 499 Teilnehmern verwendeten 88,8% mindestens eine Form von AIB. 52% nahmen Ars. alb. in Kombination mit anderen AIB und 40% nahmen nur Ars. alb. Nur 9 Teilnehmer zogen sich eine COVID-19-Infektion zu.
Schlussfolgerung
Die Einbeziehung einfacher und sicherer AYUSH-Maßnahmen ist im Interesse eines dicht besiedelten Landes wie Indien realistisch, ethisch vertretbar und wirtschaftlich. AYUSH-Maßnahmen als COVID-19-Prophylaxe und -Behandlung sowie als integrative Versorgung chronischer Erkrankungen wie NÜKs werden empfohlen.
Schlüsselwörter: AYUSH, COVID-19, AYUSH-Immunbooster, Indien, Nicht übertragbare Krankheiten
Introduction
The World Health Organization designated the new coronavirus strain (SARS-COV-2) as COVID-19 and announced it to be a pandemic on March 11, 2020. It has affected 223 countries globally (until 28 February 2021), COVID-19 with cumulative cases per 100,000 people to be 1,427.30 and 2,470,772 deaths [1]. The risk of infection is higher in the elderly and those with pre-existing non-communicable diseases (NCDs). Cardiovascular diseases, chronic respiratory disease (chronic obstructive pulmonary disease), diabetes, cancer, chronic kidney diseases are among the most suffered NCDs [2]. Hypertension (16.9%) was the most prevalent comorbidity in a Chinese study on 1,590 patients, followed by diabetes (8.2%) [3].
With 1,380 million people, India is the second-highest populated country [4]. Yet new cases and total mortality of COVID-19 are relatively low when compared to figures from many other countries [5]. India also has a high rate of patients with diabetes (10%) and hypertension (25%), both of which might exacerbate the COVID-19 outcomes [6]. India has high rates of tuberculosis (193 per 100,000) and pneumonia and houses one-third of the global slum population, making it impossible for them to maintain a safe social distancing due to their compromised economic and social circumstances. With this in context, the hospitalized cases in India are numerically low when compared with countries with better population indices [6, 7].
Wolf et al. [8] in their study on adults with chronic conditions observed 24.6% of participants were “very worried” about getting the coronavirus, 28.3% correctly identify symptoms or 30.2% could not tell the ways to prevent infection. While Pati et al. [9] in their community based found that individuals with multimorbidity experienced significantly higher care challenges than those with a single condition with notable disruption in treatment and routine check-ups. The most frequently cited concerns were physician consultation (43%), diagnostic services (26%), transport (33%), and mobility restrictions (21%).
India is a country with medical pluralism [10, 11]. Apart from modern medicine, the National Health Policy 2017 advocates mainstreaming the potential of AYUSH (Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homoeopathy) within a pluralistic system of Integrative healthcare [12]. With government funding and organized infrastructure through the Ministry of AYUSH, research has been given ample thrust [13]. Soon after the reported outbreak of COVID-19 in China [14], on the recommendation of the different focus groups, the Ministry of AYUSH [15, 16] issued an advisory on the use of AYUSH immune boosters (AIB) such as Kadha, consumption of golden milk (Ayurveda), Arsenicum album 30C (Homoeopathy) (Ars. alb.), herbal decoction (Unani), and practising Yoga to prevent COVID-19. India has launched its National Program for prevention and control of cardiovascular diseases, diabetes, cancer, and stroke (NPCDCS) in the year 2008, and later in the year 2015, the Ministry of AYUSH with its research councils collaborated and integrated with the Ministry of Health and Family Welfare, Govt. of India for screening, behavioural modification, treatment of people suffering from the identified NCDs covering 6 six districts across the country: three for Ayurveda, two for Homoeopathy, and one for Unani system of Medicine [17].
Homeopathy along with Yoga was implemented at two districts: Krishna District, Andhra Pradesh and Darjeeling District, West Bengal. In these districts, 16 AYUSH Lifestyle disorder clinics (ALSDC) were established at different healthcare levels such as district hospital/area hospital/rural hospital/community health centres. This present survey was undertaken on people suffering from NCDs and visiting these ALSDC to determine the impact of COVID-19 in managing their conditions during the pandemics, their current awareness, assessment of the seriousness of its threat, level of worry and concern related to contracting the virus, effect on a daily routine or existing plans, management of chronic conditions, usage of AIB and if affected with COVID-19 and their recovery.
Material and Methods
Study Design, Setting, and Participants
This cross-sectional study was conducted on the patients visiting ALSDC at Krishna and Darjeeling district for management of NCDs with Homeopathy, Lifestyle modification, and Yoga. As of September 30, 2020, these states had reported 35,312 cases, including 3,234 active, and 550 fatalities [18]. Patients aged 18 years and above participated in the study. It was conducted between 5 and 15 October 2020 (during the first wave), the time corresponding to the unlock down phase enquiring the knowledge, concern, bout COVID-19, about AIB and COVID care challenges during the past 12 weeks. The reporting in the paper follows STROBE guidelines for observational studies [19].
Sample Size and Sampling
A convenient sample of at least 30 patients per ALSDC totalling 499 patients for 16 ALSDC was assumed. The primary reason for such convenient sampling was the low-resource cost and feasibility, easing out potential data collection challenges at the pandemic time. The patients who were already enrolled in the integrated National NPCDCS project were contacted either over telephone or face-to-face interviews.
Data Collection Tool and Technique
A hybrid strategy that included both face-to-face (through written informed consent) and telephonic interview (verbal consent) was done as per the feasibility. All of the interviews were conducted under the guidance of the research team. Prior training was given to the data collection team and any queries regarding the questionnaire were resolved and clarified. A structured questionnaire was developed following the studies published by Wolf et al. [8] and Pati et al. [9]. Perceived concern for COVID-19 was evaluated by asking participants to rate, on a scale of 1–10 (1 being no threat at all and 10 being very serious), how worried are they about getting COVID-19 on a Likert scale (very worried to not worried at all) and how likely they or their relatives are going to get sick from COVID-19 on a Likert scale of very likely to not at all likely. Demonstrated knowledge of COVID-19 was assessed through open-ended questions asking participants to name 3 symptoms of the coronavirus and 3 actions that the government has recommended that they could take to avoid becoming infected. Knowledge, intake, and perceived attitude towards protection through AIB on Likert scale of very likely to not at all likely were also recorded. Self-rated physical, mental, and overall health was also assessed on a Likert scale of very good to very bad. The questionnaire of data collection is provided in online supplementary Appendix I (see www.karger.com/doi/10.1159/000521904 for all online suppl. material).
Statistical Analysis
The data were entered into a pre-designed spreadsheet and then transferred to SPSS ver. 20 (IBM SPSS, USA) for statistical analysis. Frequency (n) and percentage (%) were used for categorical variables and mean for quantitative variables. Associations between patient characteristics and responses to COVID-19 awareness, perceived concern, knowledge, and related behaviour items were then examined in bivariate analyses using χ2 tests, t tests, or analysis of variance, as appropriate. Principal component analysis was applied to transform seven co-related variables, which assessed routine care challenges during the pandemic into a smaller number of uncorrelated variables through the projection of eigenvectors of co-variance followed the method by Pati et al. [9]. AIB intake pattern was also estimated and graphically depicted. Logistic regression was undertaken to analyse the association of socio-demographic characteristics and multimorbidity status with care challenges; odds ratio, adjusted odds, 95% confidence interval for the association, and p value for the significance were obtained. Self-rated physical, mental, and overall health was assessed for multimorbidity and AIB using binary logistic regression. A p value of <0.05 was considered statistically significant.
Results
Table 1 shows the sample characteristics of the respondents. A total of 499 patients participated in the survey. Among the sample collected: 314 (62.9), 185 (47.1), belonged to rural and urban areas, respectively, with a 100% consent rate. 225 (45%) and 274 (55%) had face-to-face interviews/telephonic interviews, respectively. The mean age of participants was 51 years with male:female is 48%:52%. 44.7% and 55.3% lived with one and 2 or more chronic conditions, respectively. Hypertension alone (27.5%) and diabetes with hypertension (33%) were leading presentations.
Table 1.
Sample characteristics (n = 499)
| Variable | Summary value |
|---|---|
| Mean age | 51.8±10.1 |
| Age group | |
| 30−45 years | 149 (29.9) |
| 46−60 years | 244 (48.9) |
| 61 years and above | 106 (21.2) |
| Male:Female | 238 (48):261 (52) |
| Rural: Urban | 314 (63):185 (47) |
| Education | |
| Illiterate | 73 (14.6) |
| Junior high school | 133 (26.6) |
| High school | 152 (30.4) |
| Graduate and above | 141 (28.2) |
| Family environment | |
| Living along | 6 (1.2) |
| Living spouse/family | 493 (99.8) |
| Profession | |
| Farmers | 85 (17.0) |
| Office job | 57 (11.4) |
| Business/self-employed | 99 (19.8) |
| Homemaker/retired | 291 (51.6) |
| Morbidity | |
| Single morbidity | 223 (44.7) |
| Multimorbidity | 276 (55.3) |
| Number of chronic conditions | |
| 1 | 223 (44.7) |
| 2 | 227 (45.5) |
| >3 | 49 (9.8) |
| Diseases suffered/pattern | |
| Hypertension alone | 137 (27.5) |
| Diabetes alone | 75 (15.0) |
| Chronic respiratory disease alone | 16 (2.6) |
| Hypertension + diabetes | 165 (33.1) |
| Hypertension + others | 37 (7.4) |
| Hypertension + diabetes + others | 37 (7.4) |
| Other combinations | 35 (7.0) |
Knowledge
The knowledge, attitude and awareness, and concern are reflected in Table 2. Participants received information about COVID-19 from television (462, 92.6%), friends (249, 49.9%), and newspaper (240, 48.1%). Only 46% received information from at least three sources. Ninety-one per cent identified at least three correct COVID-19 appropriate behaviour such as wearing a mask (498, 99.8%), social distancing (457, 91.6%), and hand washing for at least 20 s (421, 84.4%). Eighty-six per cent of the participants identified at least three symptoms of COVID-19. The three most identified symptoms were fever (461; 92.4%), dry cough (427; 85.6%), and shortness of breath (327; 66.9%). Regarding the government's suggestion about AIB, 443 (88.8%) had knowledge about AIB (shown in Table 2).
Table 2.
Knowledge, attitudes, and self-reported behaviours towards COVID-19 and AIB
| Item | Summary value |
|---|---|
| COVID-19 awareness and concern | |
| Mean response about seriousness of COVID-19 threat | 5.8±2.6 |
| How worried are you about getting the COVID-19 | |
| Very worried | 137 (27.5) |
| Somewhat worried | 167 (33.5) |
| A little worried | 93 (18.6) |
| Not worried at all | 102 (20.4) |
| Do you think that you will get sick from the coronavirus | |
| I definitely will | 30 (6) |
| I probably will | 149 (29.9) |
| It's possible | 173 (34.7) |
| Not at all | 147 (29.5) |
| How likely do you think it is that you or someone you know may get sick from the coronavirus this year? | |
| Very likely | 51 (10.21) |
| Somewhat likely | 222 (44.5) |
| Not that likely | 109 (21.8) |
| Not at all likely | 117 (23.4) |
|
| |
| COVID-19 Knowledge | |
| At least 3 sources of information related to COVID-19 | 234 (46):265 (53) |
| Yes:No | |
| Correctly identified 3 symptoms of COVID-19 | |
| Yes:No | 429 (86):70 (14) |
| Correctly identified 3 prevention methods for COVID-19 | |
| Yes:No | 455 (91.2):44 (8.8) |
| Do you know about AIB suggested by Govt. of India | |
| Yes:No | 443 (88.8%):56 (11.2%) |
| Do you think AIB will benefit/protect you from COVID-19 | |
| Very likely | 226 (51.0) |
| Somewhat likely | 195 (44.0) |
| Not at all likely /not sure | 22 (5.0) |
|
| |
| Related behaviours | |
| Number of AIB taken | 443 (88.8) |
| 1 | 204 (46.0) |
| 2 | 154 (34.8) |
| >3 | 85 (19.2) |
| In general, how would you rate your overall health today | |
| Very good | 60 (12.0) |
| Good | 245 (49.1) |
| Moderate | 184 (36.9) |
| Bad | 6(1.2) |
| Very bad | 4 (0.8) |
Awareness and Concern
Table 2 mentions the information about the awareness and concern of the participants. All the participants had heard of the coronavirus, and most (92.2%) considered it as a potential threat (mean ± SD: 5.8 ± 2.6). 397 (79.6%) said that they were “worried” about getting the coronavirus, and 20.4% were not worried at all. Few participants believed that they would definitely (6%) or probably (29.9%) get the coronavirus, whereas 34.7% and 29.5% selected possible or not getting at all, respectively. The percentage of likely getting sick with COVID-19 by the participant or one of the relatives was also similar: very likely (10.2%), somewhat likely (44.5%), not that likely, or not at all likely 45.2%. Univariate analysis of variance (ANOVA) reflected a similar rating for the seriousness of the COVID-19 threat among the age groups (p = 0.60) and gender (p = 0.06). Respondents' ratings of the seriousness significantly differed with more concern among urban residents (p = 0.01), with higher education (graduation and above), doing a job (p = 0.01), and with more than one chronic condition (multimorbidity) (p = 0.0001). However, rural participants, education of high school and above and with multimorbidity were worried, and thought they would likely to get sick (p = 0.0001) (shown in Table 3).
Table 3.
Awareness and concern and self-reported behaviours towards COVID-19
| Variable | Awareness and concern |
Reported behaviour |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| mean seriousness of threat | worried | likely to get sick | dally routine |
physical activity |
diet |
|||||
| no change | change | no change | change | no change | change | |||||
| Age group | ||||||||||
| 30−45 years | 5.66 (±2.75) | 114(28.7) | 94 (26.7) | 19(12.8) | 130(87.2) | 18(12.1) | 131 (87.9) | 19(12.8) | 130(87.2) | |
| 46−60 year | 5.81 (±2.64) | 194 (48.9) | 178(50.6) | 33(13.5) | 211 (86.5) | 35(14.3) | 209 (85.7) | 33(13.5) | 211 (86.5) | |
| 61 years and above | 5.99 (±2.38) | 89 (22.4) | 80 (22.7) | 13(12.3) | 93 (87.7) | 14(13.2) | 92 (86.8) | 17(16.0) | 89 (84.0) | |
| Gender | ||||||||||
| Male | 5.58 (±2.56) | 181 (45.6) | 161 (45.7) | 33(13.9) | 205 (86.1) | 32(13.4) | 206 (86.6) | 25(10.5)* | 213(89.5) | |
| Female | 6.01 (±2.66) | 216(54.4) | 191 (54.3) | 32(12.3) | 229 (87.7) | 35(13.4) | 226 (86.6) | 44(16.9) | 217(83.1) | |
| Place | ||||||||||
| Rural | 5.58 (±2.57)* | 240 (60.5)* | 205 (58.2)** | 27 (8.6)** | 287 (91.4) | 33(10.5)* | 281 (89.5) | 28 (8.9)** | 286 (91.1) | |
| Urban | 6.18 (±2.66)* | 157(39.5) | 157(41.8) | 38 (20.5) | 147 (79.5) | 34(18.4) | 151 (81.6) | 41 (22.2) | 144(77.8) | |
| Education | ||||||||||
| Illiterate | 4.96 (±2.57)** | 53 (13.4)* | 35 (9.9)** | 7 (9.6) | 66 (90.4) | 10(13.7) | 63 (86.3) | 7 (9.6) | 66 (90.4) | |
| Junior high school | 5.35 (±2.69)** | 97 (24.4) | 89 (25.3) | 19(14.3) | 114(85.7) | 16(12) | 117 (88) | 15(11.3) | 116(88.7) | |
| High school | 6.15 (±2.53)** | 202(15.9) | 187(53.1) | 30(12.6) | 208 (87.4) | 34(14.3) | 204 (85.7) | 35(14.7) | 203 (85.3) | |
| Graduate and above | 6.53 (±2.44)** | 45(11.3) | 41 (11.6) | 9 (16.4) | 46 (83.6) | 7 (12.7) | 48 (87.3) | 12(21.8) | 43 (78.2) | |
| Family environment | ||||||||||
| Living along | 5.33 (±3.50) | 4(1.0) | 3 (0.9) | 1 (16.7) | 5 (83.3) | 2 (33.3) | 4 (66.7) | 1 (16.7) | 5 (83.3) | |
| Living spouse/famlly | 5.81 (±2.61) | 393 (99) | 349 (99.1) | 64(13) | 429 (87) | 65(13.2)5 | 428 (86.8) | 68(13.8) | 425 (86.2) | |
| Profession | ||||||||||
| Farmers | 5.02 (±2.25)* | 68 (17.1) | 56(15.9) | 7(8.2)** | 78 (91.8) | 10(11.8) | 75 (88.2) | 6(7.1) | 79 (92.9) | |
| Office job | 6.25 (±3.10) | 42 (10.6) | 39(11.1) | 8 (14.0) | 49 (86.0) | 9 (15.8) | 48 (88.2) | 9 (15.8) | 48 (84.2) | |
| Business/self-employed | 5.79 (±2.41) | 79 (19.9) | 74 (21.0) | 26 (26.3) | 73 (73.7) | 17(17.2) | 82 (82.8) | 15(15.2) | 84 (84.4) | |
| Homemaker and retired | 5.97 (±2.65) | 208 (52.4) | 183 (52.0) | 249.3) | 234 (90.7) | 31 (12.0) | 227 (88.0) | 39(15.1) | 219 (84.9) | |
| Number of chronic conditions | ||||||||||
| Single morbidity | 5.39 (±2.6)** | 166 (41.8)* | 144(40.9)* | 16(7.2)** | 207 (92.8) | 18(8.1)** | 205 (91.9) | 19(8.5)** | 204(91.5) | |
| Multimultimorbidity | 6.13 (±2.5) | 231 (58.2) | 208(59.1) | 49(17.8) | 227 (82.2) | 49(17.8) | 127 (82.2) | 50(18.1) | 226 (81.9) | |
Values are presented in mean ± SD, n (%), one-way ANOVA was applied for parametric and x2 was applied for non-parametric data. Statistically significant values are shown in bold.
p≤ 0.01.
p = 0.0001.
Behaviour towards AIB
Fifty-six participants did not take any AIB. Table 4 shows association of socio-demographics and disease pattern with knowledge and attitude towards AIB. Participants residing in urban areas (93%, p = 0.023), having higher education (96.4%, p = 0.0001), office job (96.5%, p = 0.01), and with multimorbidity (92.4%, p = 0.004) had knowledge about AIB. Out of 443 participants, 95% (n = 421) believed in getting benefits from AIB. Participants who were educated had association for their belief (p = 0.032), while no association was found among different age groups (p = 0.97), profession (p = 0.83), gender (p = 0.83), place of residence (p = 0.80), suffering from single/multimorbidity (p = 0.88), living alone/spouse (p = 0.12), and different professions (p = 0.83) (Table 4). Out of 499 participants, 88.8% of participants had at least one form of AIB. Among them, Ars. alb. with other AIB was consumed by (52%) and Ars. alb. alone by 40%. 13.9% consumed Ayurveda kadha alone or in combination with other AIB. Similarly, Yoga practice with other AIB, consumption of Golden milk with other AIB, Chyawanprash with other AIB, Unani decoction was taken by 47.5%, 19.8%, 6.3%, 0.9%, respectively. The pattern of AIB taken is given in Figure 1.
Table 4.
Response to AIB
| Variable | Knowledge on AIB |
X2/p value | Attitude towards AIB |
X2/p value | ||
|---|---|---|---|---|---|---|
| yes | no | likely to be benefitted | not benefitted | |||
| Age group | ||||||
| 30−45 years | 132 (88.6) | 17 (11.4) | 3.1; 0.207 | 125 (94.7) | 7 (5.3) | 0.06; 0.970 |
| 46−60 years | 212 (86.9) | 32 (13.1) | 202 (95.3) | 10 (4.7) | ||
| 61 years and above | 99 (93.4) | 7 (6.6) | 94 (94.9) | 5 (5.1) | ||
| Gender | ||||||
| Male | 212 (89.1) | 26 (10.9) | 0.04; 0.840 | 201 (94.8) | 11 (5.2) | 0.04; 0.836 |
| Female | 231 (88.5) | 30 (11.5) | 220 (95.2) | 11 (4.8) | ||
| Place | ||||||
| Rural | 271 (86.3) | 43 (13.7) | 5.19; 0.023 | 257 (94.8) | 14 (5.2) | 0.05; 0.808 |
| Urban | 172 (93.0) | 13 (7) | 164 (95.3) | 8 (4.7) | ||
| Education | ||||||
| Illiterate | 54 (74.0) | 19 (26.0) | 23.9; 0.0001 | 48 (88.9) | 6 (11.1) | 8.7; 0.032 |
| Junior high school | 115 (86.5) | 18 (13.5) | 108 (93.9) | 7 (6.1) | ||
| High school | 221 (92.9) | 17 (7.1) | 216 (97.7) | 5 (2.3) | ||
| Graduate and above | 53 (96.4) | 2 (3.6) | 49 (92.5) | 4 (7.5) | ||
| Family environment | ||||||
| Living along | 5 (83.3) | 1 (16.7) | 0.18; 0.671 | 4 (80) | 1 (20) | 2.42; 0.120 |
| Living spouse /family | 438 (88.8) | 55 (11.2) | 417 (95.2) | 21 (4.8) | ||
| Profession | ||||||
| Farmers | 68 (80) | 17 (20) | 10.25; 0.017 | 65 (95.6) | 3 (4.4) | 0.86; 0.834 |
| Office job | 55 (96.5) | 2 (3.5) | 52 (94.5) | 3 (5.5) | ||
| Business/self-employed | 89 (89.9) | 10 (10.1) | 83 (93.3) | 6 (6.7) | ||
| Homemaker and retired | 231 (89.5) | 27 (10.5) | 221 (95.7) | 10 (4.3) | ||
| Number of chronic conditions | ||||||
| Single morbidity | 188 (84.3) | 35 (15.7) | 8.09; 0.004 | 179 (95.2) | 9 (4.8) | 0.02; 0.882 |
| Multimorbidity | 255 (92.4) | 21 (7.6) | 242 (94.9) | 13 (5.1) | ||
Data presented in n (%). AIB, AYUSH immune boosters.
Fig. 1.
Pattern of intake of AYUSH Immune boosters. AK, Ayurveda kadha; GM, Golden milk; Ch., Chwanprash; UK, Unani kadha.
Challenges in Daily Routine and Chronic Care during the Pandemic
The participants reported challenges across all dimensions in the disease management behaviours in the past 12 weeks. 33% (n = 166) informed that the pandemic has affected their long-term disease care very much and 29% (n = 146) reported their daily routine being affected very much. 30% reported very much changes in physical activity, 37% in the diet, 40.5% in the continuity of treatment, 45.9% reported deviations in the physician consultation, and 32.3% reported changes in their routine investigations and health check-ups (e.g., blood pressure). There was no differences among age groups (p > 0.05), family environment (p > 0.05), and education (p > 0.05) concerning changes in daily routine, physical activity, and diet (shown in Table 3). However, the rural participants were affected in all the dimensions compared to the urban residents (p < 0.05) (shown in Table 3). Further with regards to their experiences during the past 12 weeks, the participants reported as follows: very much need to consult a doctor (29.3%), very much need to visit a hospital (31.3%), very much difficulty in getting the medicine (45.1%), very much difficulty in getting investigations done (30.7%), very much difficulty in day care procedures like physiotherapy, dialysis, etc. (37.1%), very much difficulty in getting emergency care (38.5%), difficulty in reaching a hospital or day care (34.5%).
After principal component analysis, out of 499 participants, 250 (50.2%) participants were found to have care challenges having above the median value, and 249 (49.8%) had below or equal to median scores. An eigenvalue for each component was presented in the screen plot (Fig. 2). Components with eigenvalues more than 1 were extracted. The first component explains 51.6% of total variance; when considering the first two components, the cumulative variance was 64% (online suppl. Appendix II).
Fig. 2.
Scree plot of eigenvalues after principal component analysis.
The association of socio-demographic characteristics, AIB and morbidity conditions with care challenges during the pandemic, is presented in Table 5. The male respondents reported greater care challenges than females (51.3% vs. 49.0%). The perceived care challenges were significantly higher among age group 46–60 years (52.5%), rural dwellers (54.1%), no formal schooling (64.4%), office going people (59.6%), and residing alone or separately from family members (50%). On univariate logistic regression, rural residents (OR = 1.55), homemakers/retired employee/students (OR: 2.35), farmers (OR: 3.24), office jobs (OR: 2.64), and taking Multiple AIB (OR = 1.47) were found to have significant care challenges. Similar findings were seen in multivariable regression with the probability of reporting care challenges being higher in males than their female counterparts (AOR = 3.80, 95% CI = 2.04, 7.10, p = 0.0001) and with different professions. Individuals taking multiple AIB were more likely to have significant care challenges than those who had single AIB (AOR = 1.62, 95% CI = 1.04, 2.52, p = 0.032).
Table 5.
Association of socio-demographic characteristics, AIB, and morbidity with care challenges
| Characteristics | No significant challenge in care (N = 249), n (%) |
Significant challenge in care (N = 250), n (%) |
OR [95% CI] | p value | AOR [95% CI] | p value |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Female (Ref.) | 133 (51.0) | 128 (49.0) | ||||
| Male | 116(48.7) | 122 (51.3) | 1.09 [0.77, 1.55] | 0.621 | 3.81 [2.04, 7.11] | 0.0001 |
| Age group in years | ||||||
| <45 years (Ref.) | 72 (48.3) | 77(51.7) | ||||
| 46−60 years | 116(47.5) | 128 (52.5) | 1.03 [0.69, 1.55] | 0.880 | 1.02 [0.63, 1.64] | 0.943 |
| >60 years | 61 (57.5) | 45 (42.5) | 0.69 [0.42, 1.14] | 0.147 | 0.38 [0.20,0.72] | 0.003 |
| Residence | ||||||
| Rural | 144 (45.9) | 170(54.1) | 1.55 [1.08, 2.23] | 0.019 | 1.89 [1.21, 2.96] | 0.006 |
| Urban (Ref.) | 105 (56.8) | 80 (43.2) | ||||
| Education | ||||||
| No formal schooling | 26 (35.6) | 47 (64.4) | 1.21 [0.59, 2.48] | 0.612 | 1.36 [0.51, 3.60] | 0.537 |
| Junior High School | 73 (54.9) | 60(45.1) | 0.55 [0.29, 1.04] | 0.065 | 0.44 [0.20, 0.99] | 0.048 |
| High school | 128 (53.8) | 110 (46.2) | 0.57 [0.32, 1.04] | 0.067 | 0.49 [0.23, 1.04] | 0.062 |
| Graduation and above (Ref.) | 22 (40.0) | 33 (60.0) | ||||
| Occupation | ||||||
| Homemaker/student/retired employee | 117 (45.3) | 141 (54.7) | 2.35 [1.29, 4.29] | 0.005 | 2.57 [1.25,5.21] | 0.010 |
| Farmers | 41 (48.2) | 44 (51.8) | 3.24 [1.65, 6.39] | 0.001 | 3.62 [1.58, 8.29] | 0.002 |
| Office job | 23 (40.4) | 24 (59.6) | 2.64 [1.62, 4.32] | 0.0001 | 7.54 [3.57, 15.93] | 0.0001 |
| Business/self-employed (Ref.) | 68 (68.7) | 31 (31.3) | ||||
| Family environment | ||||||
| Living alone (Ref.) | 3 (50.0) | 3 (50.0) | ||||
| Living with spouse/family | 246 (49.9) | 247 (50.1) | 1.00 [0.20, 5.02] | 0.996 | 1.09 [0.15, 7.81] | 0.933 |
| Morbidity | ||||||
| Single (Ref.) | 104 (46.6) | 119 (53.4) | ||||
| Multimorbidity | 145 (52.5) | 131 (47.5) | 0.79 [0.55, 1.12] | 0.190 | 0.74 [0.49, 1.12] | 0.149 |
| AIB | ||||||
| Single (Ref.) | 112 (54.9) | 92(45.1) | ||||
| Multiple | 108 (45.2) | 131 (54.8) | 1.47 [1.01, 2.15] | 0.042 | 1.62 [1.04, 2.52] | 0.032 |
Bold indicates statistically significant.
Overall Health
The self-rated overall health was very good (12%, n = 60), good (49.1%, n = 245), moderate (36.9%, n = 184), bad (1.2%, n = 6), and very bad 3 (0.6%). Further, the participants who took either single AIB or multiple AIB showed no significant association in either overall (OR = 1.26, p = 0.23), physical (OR = 1.05, p = 0.79), or mental health (OR = 0.98, p = 0.92) (shown in Table 6). There was no difference in their self-rated health depending on their morbidity status (OR = 1.05 [1.3, 3.0]) for overall health and is similar for physical and mental health (shown in Table 6). Out of 499 participants, only 9 got infected with COVID-19 among these 8 were symptomatic and 1 was asymptomatic. All the positive cases with each 3 of them were treated in-home, quarantine centre, and hospital, respectively. Four participants recovered fully healthy and 4 recovered with some sort of sequelae. Three participants had taken single AIB (01 Ayurvedic Kadha, 02 Ars. alb. 30) and three had Multiple AIB (01 Ars. alb. + Yoga + Golden Milk, 01 Ars. alb. + Golden Milk, 01 Ars. alb. + Chwanprash). All the participants recovered, and no death was reported.
Table 6.
Self-reported health
| Pattern | Self-rated overall health |
Self-rated physical health |
Self-rated mental health |
|||
|---|---|---|---|---|---|---|
| good | bad | good | bad | good | bad | |
| AIB taken (n = 443) | ||||||
| Single | 135 | 69 | 122 | 82 | 116 | 88 |
| Multiple | 145 | 94 | 140 | 99 | 137 | 102 |
| OR = 1.26 (0.8, 1.8) | OR = 1.05 (0.7, 1.5) | OR = 0.98 (0.6, 1.4) | ||||
| p = 0.23 | p = 0.79 | p = 0.92 | ||||
| Multimorbidity (n = 499) | ||||||
| Present | 167 | 109 | 163 | 113 | 156 | 120 |
| Absent | 138 | 85 | 122 | 101 | 123 | 100 |
| OR = 1.05 (1.32, 3.0) | OR = 0.83 (0.58, 1.19) | OR = 0.94 (0.66, 1.35) | ||||
| p = 0.78 | p = 0.32 | p = 0.76 | ||||
Discussion
COVID-19 pandemic has created new obstacles for healthcare care providers with implications for patient care particularly for people living with NCDs. This cross-sectional survey conducted during the un-lockdown period on 499 adults with chronic health conditions determined their knowledge, concern, behaviour about COVID-19 and intake of AIB propagated by Ministry of AYUSH, Govt. of India. The respondents viewed the COVID-19 outbreak to be of serious concern. Wolf et al. [8] in their study found the rating of threat to be 9 ± 1.7 whereas in this study the rating was 5.8 ± 2.6. The study shows that 79.6% are worried about the COVID-19 disease which is similar to the findings of Wolf et al. [8] at 87%. This may be due to the timing of the study in India, i.e., during the unlock down period wherein more awareness was created due to the Indian government's wide circulation and awareness drive about the pandemic.
According to Gummidi et al. [20] study on the continuum of care for NCDs during the COVID-19 pandemic in rural India, 68% of the participants had adequate knowledge of symptoms of COVID-19, while 43% were not aware of the mode of transmission of the virus. In this study, 86% correctly identified at least 3 symptoms and 91% identified 3 methods of prevention. This may be due to the period of surveying with temporal effect. Multimorbidity was prevalent in 55.3 per cent of the respondents who suffered from two or more diseases. The prevalence is similar to the findings of Pati et al. [9].
Accessing essential healthcare has been challenging in many places in India as the public transport system has not been available for patients as well as for healthcare workers to access health facilities [21]. Pati et al. [9] in their study reported different challenges of people suffering from NCDs such as physician consultation (43%) followed by diagnostic investigations (26%), transport logistics (33%), financial arrangements (26%), mobility restrictions (21%), and fear of going to hospital owing to the risk of contagion (18%). In our study, though conducted during unlock down period had similar findings such as physician consultation (29.3%), visiting a hospital (31.3%), getting the medicine (45.1%), getting investigations done (30.7%), difficulty in daycare procedures like physiotherapy, dialysis, etc. (37.1%), difficulty in getting emergency care (38.5%), difficulty in reaching a hospital or daycare (34.5%). These findings reflect the persistence of fear towards the COVID-19 disease.
The burden of NCDs in general and multimorbidity in particular can be leveraged through AYUSH systems, and a strategy to tackle this global burden is urgently needed. Building human resources for health has been an important domain in resource-poor settings [22]. With the public health advisory by Ministry to promote AYUSH medicines as immune boosters [23] health personnel at the state level campaigned and distributed AIB. Wide advisory through television, radio, and social media created awareness among the masses for the consumption of AIB. From the sample of 499, 88.8% of the participants knew AIB. Among them, 95% of the participants informed likely to benefit from taking it to protect themselves from COVID-19. 443 participants had taken AIB and the most frequently used AIB was Ars. alb. either in single (40%) or in combination (52%) with other AIBs. This survey also evaluated the overall health of the participants who were already under AYUSH treatment. 98.2% of participants rated their health as good, very good, or moderate whereas only 2% rated as bad or very bad. The COVID-19 pandemic has prompted the involvement of different health systems in fighting the disease. These systems were also effective in other infectious diseases such as encephalitis, thrombocytopenia due to dengue, chikungunya, influenza-like illness [24, 25, 26, 27, 28, 29]. Thus, the AYUSH systems require government focus in the battles to prevent and treat different diseases [30].
Future Implication and Conclusion
AYUSH system focuses on a holistic approach in the management of disease [31]. AYUSH care utilization is higher among patients with chronic diseases [32]; however, much less is studied on multimorbidity and what constitutes “best care” for these patients. Areas for potential investigation of multimorbidity fall primarily into three categories defining and categorizing the population; developing the tools needed to explore multimorbidity and its consequences; and using these tools to investigate promising processes of care [33]. The National Health Policy 2017 advocates “medical pluralism” and re-emphasizes the need for integrating AYUSH in the National Health Mission, research and education [34]. Multimorbidity will be a major problem bringing a gross health burden to a nation. Holistic treatment approaches here AYUSH systems if added can bring down the cost of care, reduce polypharmacy, and improve quality of life [32, 35]. Pati et al. [9] stated, “The observed higher presence of multimorbidity in younger population requires tailored health advisories harnessing on digital and traditional communication with stringent compliance to COVID-19 protection measures.” AYUSH systems are suggested in the integrative approach and larger public benefit [36].
Conclusion
In the interest of a densely populated country like India, the inclusion of simple and safe AYUSH measures is realistic, ethical, and cost-effective. AYUSH interventions as COVID-19 prophylactic and treatment as well as integrative care of chronic illnesses such as NCDs is suggested.
Statement of Ethics
Ethical clearance was obtained from the 24th Central Ethics Committee of Central Council for Research in Homoeopathy (Ref. No.1-3/2019-20/CCRH/Tech./24EC/2493 dated 24 September 2020). Necessary measures were taken to ensure confidentiality and anonymity of patients. The study registered at CTRI-India-CTRI/2020/09/028132.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
There is no funding granted for this study.
Author Contributions
R.V. and D.N. conceptualized the study and final version was approved by all authors. G.C. and R.C. responsible for data collection, A.K. and R.V. conducted the statistical analysis. R.V. drafted the first version of the manuscript with relevant suggestions from D.N., G.C., R.C., P.O., A.K., and A.K. All the authors approved the version submitted for publication.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Supplementary Material
Supplementary data
Supplementary data
Acknowledgments
We are grateful to all study participants for their invaluable responses and cooperation. The authors are grateful to Dr. Sanghamitra Pati, Director, RMRC-ICMR for her valuable guidance in the conceptualization of the study, permission to adapt the MAQ-COVID-19 questionnaire. Dr. Srikanta Kanungo, Scientist C, RMRC, ICMR, Bhuvaneswar for his expertise and help in CPA analysis. We thank our doctors, Yoga instructors at AYUSH LSD clinics at Krishna district (Dr. K.Rajesh, Dr. P. Bhaskar, Dr. S. Lokesh kumar, Dr. I. Lakshmi Deepak, Dr. G. Swathi, Dr.Ch.Aruna Sri and Dr. Asha Dasari, Dr. M. Jaya Lakshmi, Dr. S. Siva Kumar, Dr. M. Jaya Subhashini, Dr. V. Pragathi Pawan, Dr. N. Sree Hanumantha Rao, Dr. R. Jyothsna, Dr. K. Kishore, Dr. Masarath Sulthana, Dr. B. Poorna Chandra Rao, Dr. V. Dhanasree, Dr. K. Srinivasulu, and Dr. Y. Surya Narayana R.A) and Darjeeling district (Dr. Abhiram Banerjee, Dr. Indra Dev Pratap, Dr. Santosh Kumar Suman, Dr. Sabiha Rafique, Dr. Md Mostafijur Rahaman J, Dr. Sabiha Perween, Yoga Instructor Mukto Rajowar and Dhananjoy Chutia for facilitating data collection. Dr. Shashi Giri, RO(H), DRDPRCRI, Noida, Mr. Khushal Negi, Office assistant, CCRH Hqrs, for merit special appreciation and extending technical support in data curating and developing tables.
Funding Statement
There is no funding granted for this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary data
Supplementary data
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.