The burden & contributing factors of psychological distress across India during the COVID pandemic.

The unprecedented COVID-19 pandemic spread rapidly and engulfing the entire world, forcing people to stay home, muting the hustle and bustle of modern world with tide of fear for contracting disease and death. This brutal disease has infected millions of people worldwide, many lost their job, world economies have ravaged and many more uncountable consequences.
OBJECTIVE: To assess the psychological distress due to COVID-19 outbreak and to determine contributing factors towards psychological distress.
METHOD: A cross-sectional survey was conducted between 12th May to 20th June 2020 & 1537 valid responses were received. Modified K10 scale was used to assess psychological distress. Binary logistic regression analysis was used to determine extent of relationship between the contributing factors and psychological distress scale by estimating the odds of having significant stress with P ≤ 0.05. RESULT: A total of 1537 valid responses were obtained. The overall psychological distress score was 19.79 ± .75 which implies mild psychological distress. Analysis of degree of psychological distress revealed 815 (53.0%) with no psychological distress, 385 (25.0%) mild, 194 (12.6%) moderate and 143 respondents (9.3%) had severe degree of psychological distress. Females psychological distress was 1.448 times as compared to male (CI 0.191-10.986). The odds of having significant psychological distress for above 60 years as compared to 16-30 years. Shop owner & business man had more stress in compared to professionals (OR 1.176, CI 0.058-2.362). As compared to married, the psychological distress was 13.203 times higher among divorcee/separated (0.786-221.787) and 3.629 times higher among unmarried (0.376-35.054).
CONCLUSION: This study showed 39.2% of the subject had psychological distress which is quite high. So, government and other policy makers have to develop strategy to relieve psychological distress among Indian population.

Introduction

In last two decades several infectious diseases have shaken the human civilisation by various viral outbreaks like Avian influenza in the year 2000s, the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002, the H1N1 in 2009 to 2010, the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, Zika virus epidemics in 2015 and now COVID-19 in 2019 (Cascella et al., 2019). It is spreading like a forest fire in the forest of mankind. This deadly outbreak was emerged from Wuhan, China in December 2019 (C. Wang et al., 2020; Y. Wang et al., 2020) and disseminated its seed to each & every corner of the world. As of 2nd Aug 2020, 17,628,109 number of confirmed cases has been identified globally with 680,354 death whereas 1,757,393 confirmed cases and 1,149,460 death in India (www.who.int, 2020).

Initially, the new virus was termed 2019-nCoV, later it was renamed as SARS-CoV-2 virus due to its similarity with the virus of SARS outbreak (SARS-CoVs). SARS-CoV-2 is a beta coronavirus and belongs to the Coronaviridae family (Cascella et al., 2019; Lee et al., 2007; Ko et al., 2006). The most common symptoms of COVID-19 are fever (>90%), malaise & dry cough (80%), shortness of breath (20%), and 15% of people had respiratory distress (Abdulmohsen et al., 2020). Most of the affected peoples are asymptomatic to mild symptom who recover without special treatment but the outcome is worse among elderly people and having comorbid medical conditions like diabetes, hypertension, kidney disease and cancer (Lu et al., 2020; C. Wang et al., 2020; Y. Wang et al., 2020). The virus is transmitted from human to human through droplet infection and further, 80% of transmission occurs from pre or asymptomatic individual. Thus, home confinement is considered as the powerful weapon to contain the spread of this deadly disease (Cascella et al., 2019). In India, the first COVID-19 positive case was identified on 30th January 2020 from Kerala. To contain the spread of COVID-19 pandemic, Govt of India has implemented many preventive strategies like universal screening of international passenger, mandatory quarantine for passengers arriving from COVID-19 affected country, suspension of international travel, Janata curfew, nation-wide lockdown, temporary closure of educational Institution and public awareness through mass media (Hui et al., 2020). But due to the exponential spread of COVID-19, both live and livelihood has devastated. Many people had either lost their job or got a fraction of their salaries specifically in airlines, hotels, restaurant, retailer, malls etc.

Although there are numerous advancements of medical science & technology, still emerging & re-emerging infectious diseases are the major cause of morbidity & death across the globe (Asmundson & Taylor, 2020). Outbreak of infectious diseases not only cause physical problems but also poses superfluous psychosocial problems in comparison to other existing diseases (Qiu et al., 2020). Pandemic causes many psychological problems like panic disorder, depression & anxiety disorder (Kumar et al., 2020).

Psychological distress is an unpleasant objective state of depression & anxiety that have physical & psychological manifestations. The psychological problems are not only due to mortality & morbidities associated with the disease but it has many social & economical consequences. It is obvious that individuals psychological state will be altered due to unpredictability of COVID-19 outbreak, fast-spreading of disease, continuous flashing of COVID updates in mass media and social networking sites. However, the extent to which these factors are associated with psychological wellbeing during a pandemic is not clear. The previous study also explained that psychological wellbeing of individual users to be affected tremendously during this kind of pandemic outbreak. Therefore, in this current study investigators tries to understand the psychological impact of COVID 19 outbreak and identify potential factors that contribute to psychological distress among people during this devastating outbreak. Further, the study findings will help to alleviate mental health morbidities and help stakeholders to formulate guidelines to improve psychological wellbeing. We took this study intending to assess the psychological distress due to COVID-19 outbreak and determine contributing factors towards the psychological distress.

Study design & procedure

A cross-sectional online survey was conducted to evaluate the psychological state of the public during this COVID-19 pandemic from 12th May to 20th June 2020. People above the age of 16 years and who could answer digitally were included to participate in this study. Since, the Govt of India declared countrywide lockdown from 25th March to 31st May 2020, so it was not possible to collect the data through direct interview. Therefore, subjects were invited to participate through online mode. The questionnaire was developed in the Google form and the link was shared among various groups in social media through WhatsApp and Facebook. The sample size was calculated to be 1536 by keeping 95% as confidence level, confidence limit of 5%, design effect of 4 and anticipated frequency of 50% in Openepi.com software. Quality control of data was achieved by daily checking the responses. By 20th June 2020, total 1540 responses were received, and thus further responses were deactivated in the Google form link.

Study tool

The tool used for the study consisted of 3 sections. Section A was about personal information about participants, consisting of 12 questions like age, gender, education, occupation, monthly income, marital status, type of residence, travel history, Health care worker, quarantine, contact with COVID-19 positive patient and history of the co-morbid disease condition. Section B was a modified Kesseler-10 scale (K-10) consisted of a 5-point rating scale that helps to quantify the psychological state of people during COVID-19 outbreak. Copyright permission was obtained from the primary author of Kesseler-10 scale. The developed scale had 10 question statements with a total score of 50. Higher score indicates more degree of psychological distress. The respondents could be able to express their psychological state depending on how they feel about their psychological state ranging from 1 (None of the time), 2 (A little of the time), 3 (some of the time), 4 (Most of the time) and 5 (almost all of the time). The scoring system was also adopted from K-10 scale. The degree of psychological distress was labelled into 4 categories; no psychological distress (score 1–19), mild degree of psychological distress (score between 20 and 24), moderate degree of psychological distress (score between 25 and 29) and severe degree of psychological distress (score 30–50). The content of the developed tool was validated from experts of the concerned field and the reliability of the scale was established by using Cronbach alpha which was found to be 0.84. Section C consisted of 18 questions like fear of getting COVID-19 disease, lockdown, stigma, home quarantine, an insufficient supply of PPE & hand sanitizer, online education of children, child behaviour, compromise in meeting food need, continuous flashing of COVID-19 related news.

Ethical consideration

Ethical permission was obtained from the Institute ethics committee of AIIMS Bhubaneswar to conduct the study vide letter no: T/IM-NF/Nursing/20/03. Since there was countrywide lockdown in India and a large geographical area to be covered, so questionnaires were developed in the Google form and administered to target population in social media. In the Google form, it was mentioned about the study and its purpose. It was also mentioned that participation in the study is purely voluntary and they are free to withdraw themselves from the study without giving any reason, confidentiality and anonymity of their response will be maintained.

Data analysis

The data were organized and analysed by using IBM SPSS 20.0 software. Categorical data were analysed by frequency and percentage, degree of psychological distress was by the mean & SD. Binomial logistic regression analysis was used to evaluate predictors of psychological distress. The psychological distress scale was dichotomised into 2 categories; a score below 19 was designated as no distress and score above 19 was taken as psychological distress.

Result

By 20th June, total 1540 responses were received and out of which 1537 participants had provided complete data regarding study variables. Responses were received from maximum states of India except for Andaman & Nicobar and Jammu & Kashmir. Maximum responses were from Odisha (39.8%), West Bengal (14.5%) and Kerala (7.7%) (Fig. 1 ).

Fig. 1

State/UT wise participation.

General characteristics of study population

The basic characteristics of study participants are depicted in Table 1 . Among 1537 respondents, 923 responses were obtained in May and 614 were in June 2020. About 57.8% were female, a majority (66%) were belonging to the age group of 16–30 years, about 41.6% were graduate & post-graduate, 42.3% were unemployed, 61.5% were unmarried, 57.2% of the participants were from an urban area, 72.7% had no history of quarantine, only 21.3% were health care provider and only 3.5% of respondents had contact history with COVID-19 positive patient. Approximately 17% of respondents had comorbid disease conditions like hypertension, diabetes, high cholesterol, thyroid diseases, renal diseases, asthma, arthritis etc. (Table 1). (See Table 2.)

Table 1

Binary logistic regression model determining predictors of psychological distress.

Characteristics	Frequency (%)	No psychological distress (N%)	Psychological distress	OR	95% CI		P-value
					Lower	Upper
Gender
Male	644 (41.9%)	344(22.38%)	300 (19.51%)	Ref
Female	889 (57.8%)	469 (30.51%)	420 (27.32%)	1.448	0.191	10.986	0.720
Other	4 (0.3%)	2 (0.13%)	2 (0.13%)	1.636	0.216	12.386	0.633
Age
16-30 years	1015 (66%)	537 (34.94%)	478 (31.09%)	Ref
31-45 years	318 (20.7%)	173 (11.26%)	145 (9.44%)	0.292	0.107	0.795	0.016
46-60 years	178 (11.6%)	90 (5.85%)	88 (5.73%)	0.937	0.570	1.540	0.797
Above 60 years	26 (1.7%)	15 (0.97%)	11(0.72%)	1.175	0.780	1.770	0.441
Education
Professional degree	604 (39.3%)	346 (22.51%)	258 (16.79%)	1.137	0.885	1.462	0.314
Graduate or postgraduate	640 (41.6%)	354 (23.03%)	286 (18.61%)	1.802	1.263	2.570	0.001
Intermediate, post high school diploma	247 (16.1%)	103 ()6.7%	144 (9.37%)	3.368	1.466	7.738	0.004
Up to secondary education	46 (3.0%)	12 (0.78%)	34 (2.21%)	Ref
Occupation
Unemployed	650 (42.3%)	305 (19.84%)	345 (22.45%)	0.668	0.400	1.117	0.124
Unskilled worker	52 (3.4%)	18 (1.17%)	34 (2.21%)	0.999	0.477	2.092	0.999
Semiskilled worker	34 (2.2%)	17 (1.11%)	1717 (1.11%)	0.531	0.296	0.950	0.033
Skilled worker	69 (4.5%)	45 (2.93%)	24 (1.56%)	0.822	0.621	1.086	0.168
Professional	626 (40.7%)	369 (24.01%)	257 (16.72%)	Ref
Clerical/shop owner/farm/business	106 (6.9%)	61(3.96%)	45 (2.93%)	1.176	0.585	2.362	0.650
Marital status
Married	574 (37.3%)	319 (20.75%)	255 (16.58%)	Ref
Unmarried	945 (61.5%)	489 (31.85%)	456 (29.67%)	3.629	0.376	35.054	0.265
Widowed	12 (0.8%)	2 (0.12%)	10 (0.65%)	5.743	0.578	57.097	0.136
Divorcee/separated	6 (0.4%)	5 (0.32%)	1(0.06%)	13.203	0.786	221.787	0.073
Monthly family income
≤2640	173 (11.3%)	92 (5.98%)	81(5.27%)	0.579	0.358	0.935	0.026
2641-7886	146 (9.5%)	95 (6.18%)	51(3.32%)	1.098	0.706	1.707	0.678
7887-13,160	184 (12.0%)	95 (6.18%)	89 (5.79%)	1.123	0.720	1.752	0.610
13,161-19,758	185 (12.0%)	94 (6.11%)	91 (5.92%)	1.776	1.129	2.795	0.013
19,759-26,354	166 (10.8%)	74 (4.81%)	92 (5.98%)	1.326	0.879	2.000	0.178
26,355-52,733	268 (17.4%)	145 (9.43%)	123 (8.01%)	1.530	1.039	2.252	0.031
≥52,734	415 (27%)	220 (14.31%)	195 (12.69%)	Ref
Habitation
Rural	658 (42.8%)	328 (21.34%)	330 (21.47%)	0.845	0.677	1.057	0.140
Urban	879 (57.2%)	487 (31.68%)	392 (25.51%)	Ref
Travel history
Yes	359 (23.4%)	186 (12.10%)	173 (11.25%)	1.002	0.763	1.316	0.987
No	1178 (76.6%)	629 (40.92%)	549 (35.72%)	Ref
Quarantine status
Yes	420 (27.3%)	217 (14.12%)	203 (13.20%)	0.984	0.757	1.280	0.906
No	1117 (72.7%)	598 (38.91%)	519 (33.77%)	Ref
Contact history
Yes	54 (3.5%)	33 (2.15%)	21 (1.37%)	1.337	0.710	2.519	0.368
No	1483 (96.5%)	782 (50.88%)	701 (45.60%)	Ref
HCW
Yes	327 (21.3%)	205 (13.34%)	122 (7.94%)	1.508	1.124	2.023	0.006
No	1210 (78.7%)	610 (39.69%)	600 (39.03%)	Ref
Co-morbidity
Yes	262 (17%)	94 (6.11%)	168 (10.93%)	0.371	0.269	0.510	0.000
No	1275 (83%)	721 (46.91%)	554 (36.04%)	Ref

Table 2

Binary logistic regression model of psychological distress related factors of general public during COVID 19 outbreak.

Sl no	Contributing factor	f (%)	No psychological distress	Psychological distress	OR	95% CI		P-value
						Lower	Upper
1	Fear of getting Covid-19 disease
	Yes	679 (44.2%)	306 (19.9%)	373 (24.3%)	1.507	1.197	1.897	0.000
	No	858 (55.8%)	509 (33.1%)	349 (22.7%)	Ref
2	Feeling stressed due to lockdown
	Yes	759 (49.4%)	293 (19.1%)	466 (30.3%)	2.627	2.096	3.292	0.000
	No	778 (50.6%)	522 (34%)	256 (16.6%)	Ref
3	Worried for wearing mask & frequent hand washing
	Yes	450 (29.3%)	190 (12.4%)	260 (17%)	1.301	1.012	1.673	0.087
	No	1087 (70.7%)	625 (40.6)	462 (30%)	Ref
4	Worried as organization is not allowing work from home
	Yes	228 (14.8%)	113 (7.3%)	115 (7.5%)	1.194	0.866	1.647	0.280
	No	1309 (85.1%)	702 (45.7%)	607 (39.5%)	Ref
5	Worried due to home quarantine
	Yes	246 (16.0%)	99 (6.4%)	147 (9.6%)	0.779	0.571	1.064	0.117
	No/NA	1291(84%)	716 (46.6%)	575 (37.4%)	Ref
6	Compromise of financial security
	Yes	686 (44.6%)	343 (22.3%)	343 (22.3%)	0.896	0.711	1.129	0.353
	No	851 (55.4%)	472(30.7%)	379 (24.7%)	Ref
7	Feel compromised in meeting food need
	Yes	599 (39%)	320 (20.8%)	279 (18.1%)	1.455	1.147	1.845	0.002
	No	938 (61%)	495 (32.3%)	443 (28.8%)	Ref
8	Awareness through mass media reduces stress
	Yes	959 (62.4%)	531 (34.5%)	428 (27.9%)	1.307	1.038	1.645	0.023
	No	578(37.6)	284 (18.5%)	294 (19.1%)	Ref
9	Stressed due to continuous COVID 19 related news
	Yes	962 (62.6%)	445 (29%)	517 (33.6%)	0.815	0.636	1.045	0.106
	No	575 (37.4%)	370 (24.1%)	205 (13.3%)	Ref
10	Think less risk in comparison to other country
	Yes	744 (48.4%)	368 (23.9%)	376 (24.5%)	0.839	0.670	1.051	0.126
	No	793 (51.6%)	447 (29.1%)	346 (22.5%)	Ref
11	Worried due to non availability of treatment
	Yes	1055 (68.6%)	505 (32.8%)	550 (35.8%)	0.769	0.593	0.997	0.048
	No	482 (31.4%)	310 (20.2%)	172 (11.2%)	Ref
12	People will stigmatise if develop COVID 19
	Yes	1039 (67.6%)	502 (32.7%)	537 (34.9%)	1.469	1.154	1.871	0.002
	No	498 (32.4%)	313 (20.4%)	185 (12%)	Ref
13	Feeling nervous as vulnerable for getting infection
	Yes	569 (37%)	274 (17.8%)	295 (19.2%)	1.184	0.923	1.519	0.184
	No	968 (63%)	541 (35.2%)	427 (27.8%)	Ref
14	Inadequate supply of mask, hand sanitizer, hand wash solution
	Yes	500 (32.5%)	230 (15%)	270 (17.6)	1.442	1.123	1.850	0.004
	No	1037 (53.3%)	585 (38%)	452 (29.4)	Ref
15	Worried as steps of govt is inadequate towards control & prevention of COVID 19
	Yes	541 (35.2%)	246 (16%)	295 (19.2%)	0.947	0.743	1.207	0.659
	No	996 (64.8%)	569 (37%)	427 (27.8%)	Ref
16	Online education of child
	Yes	267 (17.4%)	143 (9.3%)	123 (8%)	0.634	0.411	0.979	0.040
	No	297 (19.3%)	190 (12.4%)	107 (7%)	Ref
	Not applicable	973 (63.3%)	482 (31.4%)	491 (31.9%)	0.556	0.359	0.860	0.008
17	Abnormal reaction of child
	Yes	251 (16.3%)	109 (7.1%)	142 (9.2%)	1.083	0.778	1.509	0.000
	No	324 (21.1%)	220 (14.3%)	104 (6.8%)	Ref
	Not applicable	962 (62.6)	486 (31.6%)	476 (31%)	1.876	1.190	2.956	0.000
18	Domestic violence
	Yes	72 (4.7%)	33 (2.1%)	39 (2.6%)	0.862	0.504	1.475	0.588
	No	1465(95.3%)	782 (50.9%)	683 (44.4%)	Ref

Investigation of psychological distress during COVID 19 outbreak

The psychological distress of the general population during COVID-19 outbreak was determined by modified K10 scale. The overall score was calculated as 19.79 ± 6.75 which shows mild psychological distress; among them, 815(53.0%) were found to be no psychological distress, 385 (25.0%) had mild degree of psychological distress, 194 (12.6%) participants had a moderate degree of psychological distress and 143 respondents (9.3%) had a severe degree of psychological distress (Fig. 2 ).

Fig. 2

Distribution of psychological distress according to time.

It was also determined that there was a difference in psychological distress from May than June 2020. The difference of mean psychological distress score from 1st week of data collection till the last week was 20.35, 19.66, 18.48, 17.73, 21.33, 20.14 respectively. It shows psychological distress is reducing gradually till the end of May and again it increased due to release of lockdown (Fig. 3 ).

Fig. 3

Degrees of psychological distress.

Analysis of related factors of psychological distress during the COVID-19 outbreak

Questions were asked to general population to identify the reason of their worries or psychological distress during COVID 19 outbreak, it reveals that only 44.2% of the people were having fear of getting disease, 49.4% were stressed due to lockdown, 71% of people were not having any problem with frequent hand washing & wearing of mask, 44.6% had compromise in financial, 39% had faced problem to meet their food needs, 62.6% expressed that continuous COVID related news in mass media increases their stress level, nearly 69% of people worried due to non-availability of confirmed treatment & vaccine against COVID-19, 48.4% of partakers expressed that they were feeling safe in comparison to other country, 67.6% of participants revealed that, they will be stigmatised if they found COVID 19 positive, 32.5% disclosed that they were worried due to inadequate supply of mask, hand sanitizer, 64.8% replied that steps of government were inadequate for prevention and control of COVID-19 and only 4.7% of them were faced domestic violence during lock down period.

In binary logistic regression model, it was observed that, compared to male, females had 1.448 times psychological distress (CI 0.191–10.986). The odds of psychological distress increased with age, for 46-60 years OR 0.937 (CI 0.57–1.54) & >60 years OR 1.175 (CI 0.78–1.77). As compared with professionals, the psychological distress was 1.176 times (CI 0.058–2.362) higher among shop owner & business man. The psychological distress was 13.203 times higher among divorcee/separated (0.786–221.787) and 3.629 times higher among unmarried (CI 0.37–35.05). Lesser income group have comparatively less stress (OR 0.57, CI 0.35–0.93) than per capita income of Rs. >52,734. Compared to general people, health care providers had 1.508 times psychological distress (CI 1.124–2.023). Lockdown, wearing mask & frequent hand washing, compromise in meeting food need, mass media & stigma correlated with psychological distress (OR 2.627, CI 2.096–3.292, OR 1.301, CI 1.012–1.673, OR 1.455, CI 1.147–1.845, OR 1.307, CI 1.038–1.645, OR 1.469, CI 1.154–1.871) respectively.

Discussion

Covid-19 has produced extensive distress and shaken the whole world. In comparison to physical symptoms of COVID-19, the mental health of public has affected to a great extent. It caused panic, fear, and stress among all strata of society (Ramasubramanian et al., 2020). Viral epidemics in the past like SARS also imposed great psychological impact on public and suggested need of social support and mental health support programme (Lee et al., 2007). Most of the countries have reported psychological distress amid COVID-19 pandemic. By late April 2020, mental health in the UK had deteriorated compared with pre-COVID-19 trends (Pierce et al., 2020).

In the present study, the mean psychological distress score was 19.79 ± 6.75, among them 815(53.0%) were found to be no psychological distress, 385(25.0%) had mild degree of psychological distress, 194 (12.6%) participants had moderate degree of psychological distress and 143 respondents (9.3%) had severe degree of psychological distress which is notable as compared with previous mental health of general public. This is supported by other study that has revealed an overall increase of mental distress; mean GHQ-12 score increased from 11·5 (95% CI 11·3–11·6) in 2018–19 to 12·6 (12·5–12·8) during COVID pandemic & lockdown (Pierce et al., 2020).

Around 47% of study population reported psychological distress which is similar to other studies which shows elevated levels of depression (43.3%), anxiety (45.4%), and PTSD symptoms (31.8%) (Liu et al., 2020). Unlikely, around 18.3% of Chinese population developed psychological health problems (Zhu et al., 2020). The higher rate of psychological distress among study participants may be partially attributable to the specific characteristics of our sample and pattern of increasing COVID vary from country to country.

Across gender, men show a decreasing trend from mild (348), moderate (196), severe (45) and very severe (5), as same follows in case of women; mild (535), moderate (305), severe (44) and very severe (7). On contrary to this gender-based findings, female shows increasing degree of psychological distress from normal (46.8%), through mild (55%) to severe (57.8%) distress; whereas male shows decreasing trend normal (53.2%), through mild (45.0%) to severe (42.2%) distress levels of psychological distress as revealed by a cross-sectional study conducted in Saudi Arabia (Al-Hanawi et al., 2020).

The sociodemographic factors that contributing to psychological stress differed widely between studies. In this present study, younger adults were less likely to develop psychological distress (OR < 1) as compared to age >60 years (OR 1.175, CI 0.780–1.770). It may be due to younger people are more exposed to smart phone & social medias. In contrary, studies reveal that people 40 years old or below had an increased risk of anxiety than those above 40 years old (Rehman et al., 2020). In respect to gender, females had a higher risk of anxiety than males which supported by a Chinese study which reveals females had 3.01 times anxiety than males (Y. Wang et al., 2020; Rehman et al., 2020). Our study determined the psychological distress is high among intermediate group in comparison to secondary education (OR = 3.368, 95% CI 1.466–7.738), and decreases with increase of education which is supported by a study conducted by Wang Y et al. which reveals master degrees had a less depression risk compared to bachelor's degree. Our study also identified factors like marital status, contact with COVID patient & travel history to/from COVID affected area were correlated with psychological distress. This may be due to direct contact with COVID patient difficult to balance professional, personal & family life (Aksoy & Kocak, 2020; Zhu et al., 2020; Al-Hanawi et al., 2020).

Stigma causes more stress (OR = 1.469, 95% CI 1.154–1.871) as previous studies reported stigma and the uncertainty about the consequence of the infection are the two main causes of the negative feelings and thoughts among COVID-19 patients (Guo et al., 2020). Similar findings reported in Indonesia which mentioned the negative social stigma that arises in society is largely due to rejection due to fear of contracting COVID-19. Many people refuse to recover patients who return to their territory, reject the patient's family, reject health workers and all individuals who fall into the positive, suspicious and surveillance categories (Nursalam et al., 2020).

Lock down more likely to create significant psychological distress (OR = 2.627 95% CI 2.096–3.292). A similar phenomenon was observed where significantly higher levels of depression were observed (F 1,1378 = 5.51, P = 0.019, η p 2 = 0.004) among those who were locked down. Due to lock down freedom was limited, people lost their job, educational institution closed, basic requirement was compromised & people did not know how long the epidemic and lockdown would last. Such uncertainty may have influenced the increase in the levels of depression and perceived stress during lockdown (Zhu et al., 2020; Gan et al., 2020).

In addition to these above probable factors, fear of getting COVID-19 disease (OR = 1.507, 95% CI 1.197‐1.897), unavailability of hand sanitizer and PPE including mask (OR = 1.442, 95% CI 1.123–1.850) and continuous wearing of mask (OR = 1.301, 95% CI 1.012–1.673) are more like to create psychological distress. Other possible factors include media coverage and misinformation about the COVID-19 outbreak in social media (Al-Hanawi et al., 2020). Other commonly reported contributing factors of psychological distress during COVID pandemic are compromised financial security, unverified voluminous information in social media & mass media (Ramasubramanian et al., 2020) which is analogous to our study findings.

The results of this study generate comprehensive insight to contributing factors of psychological distress. Arguably psychological distress among public is not only pertaining to India, but certain factors lead to deteriorate psychological state in whole world. This study findings underlines the need of mental health assistance to vulnerable section of society like female, elderly, unemployed, businessman & shop owner, health care professionals etc.

Limitation

Although this study finding has made significant contributions that can be used by the government and other agencies to confront the adverse psychological impacts of Indian population during Covid-19 pandemic. Still here are some limitations of this study. Firstly, the researchers utilised online Google forms for collection of data that hindered the participation of a larger section of the population specifically those who do not have internet access and smart phone usage. Secondly, self-reported psychological state bias may substantially affect interpretation of our result. Thirdly, an equal number of responses were not obtained from each state and union territory and finally this is a cross-sectional study & follow-up were not done.

Conclusion

COVID-19 pandemic has created stress across all spheres of human life. This study shows 47% of the public had psychological distress which is quite high. This would help the mental health professionals and the policymakers to institute appropriate mental health efforts and solutions during this crisis period in order to bring normalcy within different strata of population.

Recommendation

Health authorities should develop mental health services and systems for public.

Funding

The authors have not received any financial grant for research, authorship and publication of this research article.

Declaration of competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.