Abstract
Background:
Care of COVID-19 patients has been shown to affect the mental health of healthcare personnel (HCP), however, there is little data reflecting psychological health of HCP in India.
Aims:
The present study was undertaken to assess the prevalence of psychological outcomes and its association with various sociodemographic and occupational factors among the HCP in India.
Methodology:
A cross-sectional, online survey, using snowball sampling method was conducted between June 1, 2020, and June 22, 2020. The HCP working in COVID-19 designated hospitals across India were invited to participate. Patient Health Questionnaire-4 and 19-item stress-related questionnaire were used to evaluate symptoms of overall anxiety, depression, COVID-19 infection specific anxiety, exhaustion, and workload.
Results:
In this cross-sectional study with 2334 HCP from 27 states and 7 union territories of India; 17.9% of participants had depression, 18.7% had overall anxiety, 26.5% had exhaustion, 30.3% reported heavy workload, and 25.4% had COVID-19 infection-specific anxiety, respectively. The HCP working in states with higher caseload was a common risk factor for overall anxiety (odds ratio [OR], 1.7; P < 0.001), depression (OR, 1.6; P < 0.001), COVID-19 infection-specific anxiety (OR, 2.5; P < 0.001), exhaustion (OR, 3.1; P < 0.001), and heavy workload (OR, 2.6; P < 0.001). Nurses were more at risk for depression (OR, 2.2; P < 0.001), anxiety specific to COVID-19 infection (OR, 1.3; P = 0.034), and heavy workload (OR, 2.9; P < 0.001); while doctors were more at risk for overall anxiety (OR, 2.0; P = 0.001) and exhaustion (OR, 3.1; P < 0.001).
Conclusions:
Frontline workers, specifically nurses and doctors, and those working in states with high COVID-19 caseload are more at risk for adverse psychological outcomes. The relatively less prevalence compared with other countries, is perhaps a reflection of measures undertaken, including early lockdown, ensuring better all-round preparedness and social norms.
Key words: Anxiety, COVID-19, depression, healthcare personnel, stress
INTRODUCTION
SARS-CoV-2 has accounted for more than 9.67 million confirmed cases and 502,000 deaths in over 215 countries worldwide as on June 25, 2020.[1] The unprecedented increase in COVID-19 cases and the resultant increased workload have led to a shortage of medical staff,[2,3] deficiency of Personal Protective Equipment kits,[4] and scarcity of resources and equipment[5] even in the developed world. Despite the early measures taken by the Government of India, including complete lockdown, social distancing, quarantine of infected and suspected cases, there is a rapid surge in COVID-19 cases across India, making it the third-highest country in terms of the number of patients with COVID-19 infection. The increasing caseload and the skewed nurse/doctor and patient ratio with inevitable long duty hours amidst exposure to a high-risk environment, all constitute distinctive challenges.
These factors have been demonstrated to contribute to the culmination of stress and anxiety among healthcare personnel (HCP).[2,6,7,8] There are limited studies from India or the developing world, that have attempted to understand the enduring psychological impact of the COVID-19 outbreak. Moreover, there are few studies that have looked at the role of sociodemographic and occupational factors on psychological outcomes in health care workers. The present study was undertaken to assess the prevalence of psychological outcomes in HCP. The study further sought to ascertain the impact of various sociodemographic and occupational factors on the psychological outcomes.
METHODOLOGY
Study design
The current cross-sectional study was conducted between June 1, 2020, and June 22, 2020, in COVID-19 designated hospitals and COVID-19 care centers across India. The data were collected using a web-based questionnaire spread through social media using snowball sampling and a print version where required. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant National and Institutional Committees on Human Experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by Ethical Committee of the All India Institute of Medical Science, New Delhi; and online consent was obtained from the participants. The participants were assured about the anonymity and confidentiality of their participation; and they could terminate the survey whenever they wished.
Study population
HCP working in COVID-19 designated hospitals and care centers in different states of India during COVID-19 pandemic were eligible to participate. HCP infected with COVID-19 were excluded from the study. To study the effect of spatio-temporal burden of COVID-19 confirmed cases on psychological outcomes; states were classified in two categories viz-a-viz, <50,000 confirmed cases and more than or equal to 50,000 confirmed cases.
Instruments
The survey questionnaire consisted of three sections - (1) A set of 10 questions to evaluate the socio-demographic characteristics of HCP including age, gender, marital status (married, unmarried, and divorced/widowed), educational qualification (up to graduate, and postgraduate or higher), occupational role (doctor, nursing professional, and nonmedical staff). Hospital characteristics includes type of health care setting (primary and secondary, tertiary and special COVID-19 centers), posting (COVID-19 outpatient department [OPD], COVID-19 emergency/wards, and others including screening areas, containment zones, quarantine centers, infection control works, and laboratories), location (based on number of confirmed COVID-19 cases - <50,000 and more than or equal to 50,000 confirmed cases). To ascertain the level of participation in the treatment (frontline, nonfrontline, and anticipating), participants were asked whether they are treating/taking care of patients suffering from COVID-19. They were also asked whether they have any comorbidity (any preexisting medical condition for which they are seeking treatment currently). (2) Prevalidated Patient Health Questionnaire-4 (PHQ-4), an ultra-brief self-reported questionnaire was used to assess symptoms of anxiety and depression.[9] It is a 4-point scale and response ranges from, “0 = Not at all,” to “3 = Nearly every day.” The scale has adequate internal consistency ranging from 0.78 to 0.85 and construct validity.[9] In screening of depression and anxiety, a cut-off ≥3 is recommended. (3) We also used the 19-item Stress-related questionnaire,[10] to evaluate the experience of HCP while managing COVID-19 patients. It is a 19-item scale and response lies on a 4-point Likert scale ranging from “0 = Never,” to “3 = Always.” This questionnaire has four domains: Anxiety about infection, exhaustion, workload, and feeling of being protected, with the Cronbach alpha of 0.81, 0.80, 0.89, and 0.69 respectively and one item for work motivation. It has been used for assessing psychological stress in HCPs during the previous H1N1 influenza outbreak. Third quartile (Q3) was used as a cut-off to dichotomize the study participants into two mutually exclusive groups for domains of 19 stress-related questionnaires. Participants scoring more than or equal to Q3 were considered high on the respective domains.
All questions were set as mandatory fields to avoid missing data.
Statistical analysis
The prevalence of psychological outcomes was reported as number (%) with 95% confidence interval (CI). Chi-square test was applied to determine the association between various sociodemographic and occupational factors with psychological outcomes. Further, to assess the independent association of sociodemographic and occupational factors on psychological outcomes, a multivariable logistic regression model was used in two steps. (1) Bivariate analysis was done and variables showing P value up to 0.10, were considered as potential factors to be considered simultaneously in multivariable logistic regression analysis. (2) A multivariable logistic regression model with candidate variables identified in step 1 was performed. In this study, P < 0.05 was considered statistically significant. Stata 14.2 software (StataCorp., CollegeStation, Texas, USA) statistical software was used for statistical analysis.
RESULTS
Participant characteristics
A total of 2,334 HCPs from 27 states and 7 union territories of India participated in the study. The mean (standard deviation) age of the participants was 33.6 (9.1) years. In our study, 1523 (65.2%) of the participants were below the age of 35 years (classified as younger age), 1387 (59.4%) were female, and 1455 (62.3%) were married. Majority of them were frontline staff-HCP directly involved in treating/taking care of patients suffering from COVID-19-1221 (52.3%), from the tertiary healthcare sector 1360 (58.3%), nursing professionals 1403 (60.1%). Around 1670 (71.6%) of the study participants were working in states with confirmed COVID-19 cases <50,000.
Prevalence of psychological outcomes associated with COVID-19
The prevalence of depression and anxiety was found to be 17.9% (418 [95% CI, 16.4–19.5]), and 18.7% (438 [95% CI, 17.2–20.4]) respectively as shown in Table 1; while, 9.5% (224 [95% CI, 8.4–10.9]) of the participants had symptoms of both depression and anxiety. Along with this, we also found the prevalence of COVID-19 infection specific anxiety, exhaustion, and heavy workload to be 25.4% (593 [95% CI, 23.7–27.2]), 26.5% (619 [95% CI, 24.5–28.4]) and 30.3% (708 [95% CI, 28.5–32.2]), respectively. Around one in ten, i.e., 11.4% (267 [95% CI, 10.2–12.8]) of the study participants experienced all three COVID-19 stress-related psychological outcomes, i.e., anxiety specific to COVID-19 infection, exhaustion, and heavy workload. Of note, we also found that 3.9% (91 [95% CI, 3.2–4.8]) of our study participants reported to have all five psychological outcomes namely depression, overall anxiety, exhaustion, heavy workload, and anxiety specific to COVID-19 infection.
Table 1.
Prevalence of psychological outcomes in healthcare personnel (n=2334) during COVID-19 pandemic
| Psychological outcome(s) | n/total (%) | 95% CI |
|---|---|---|
| Depression | 418/2334 (17.9) | 16.4-19.5 |
| Anxiety | 438/2334 (18.7) | 17.2-20.4 |
| Anxiety about infection | 593/2334 (25.4) | 23.7-27.2 |
| Exhaustion | 619/2334 (26.5) | 24.5-28.4 |
| Workload | 708/2334 (30.3) | 28.5-32.2 |
| Feeling of being protected | 692/2334 (29.7) | 27.8-31.5 |
CI – Confidence interval
The prevalence and association of psychological outcomes according to participant characteristics are shown in Table 2. Symptoms of overall anxiety, exhaustion, heavy workload, and anxiety specific to COVID-19 infection were associated with gender and marital status of the participants. Similar trend, except for overall anxiety, was found for the educational level. Occupational role demanding direct care and treatment of patients was associated with all five psychological outcomes, i.e., depression, overall anxiety, exhaustion, heavy workload, and anxiety specific to COVID-19 infection. Along with this, the level of healthcare setting, and states with more than 50,000 confirmed COVID-19 cases were associated with all outcomes of interest. We also found that having any preexisting comorbidities were associated with all these aforementioned outcomes. Symptoms of overall anxiety, exhaustion, heavy workload, and anxiety specific to COVID-19 infection were associated with level of participation in terms of frontline, nonfrontline, and anticipating involvement in COVID-19 treatment.
Table 2.
The prevalence and association of psychological outcomes according to participant characteristics
| Characteristic | Groups | Total (n=2334), n/total (%) | Anxiety about infection (n=593) | Exhaustion (n=619) | Workload (n=708) | Anxiety (n=438) | Depression (n=418) |
|---|---|---|---|---|---|---|---|
| Age (years) | Above 50 | 116/2334 (5.0) | 24/116 (20.7) | 29/116 (25.0) | 42/116 (36.2) | 24/116 (20.7) | 20/116 (17.2) |
| 36-50 | 695/2334 (29.8) | 193/695 (27.8) | 191/695 (27.5) | 222/695 (31.9) | 150/695 (21.6) | 123/695 (17.7) | |
| Up to 35 | 1523/2334 (65.2) | 376/1523 (24.7) | 399/1523 (26.2) | 444/1523 (29.2) | 264/1523 (17.3) | 275/1523 (18.1) | |
| P | 0.148 | 0.760 | 0.153 | 0.051 | 0.961 | ||
| Gender | Male | 947/2334 (40.6) | 273/947 (28.8) | 222/947 (23.4) | 256/947 (27.0) | 159/947 (16.8) | 162/947 (17.1) |
| Female | 1387/2334 (59.4) | 320/1387 (23.1) | 397/1387 (28.6) | 452/1387 (32.6) | 279/1387 (20.1) | 256/1387 (18.5) | |
| P | 0.002 | 0.005 | 0.004 | 0.043 | 0.403 | ||
| Education level | Up to UG | 1562/2334 (66.9) | 366/1562 (23.4) | 369/1562 (23.6) | 440/1562 (28.2) | 286/1562 (18.3) | 265/1562 (17.0) |
| PG or higher | 772/2334 (33.1) | 227/772 (29.4) | 250/772 (32.4) | 268/772 (34.7) | 152/772 (19.7) | 153/772 (19.8) | |
| P | 0.002 | <0.001 | 0.001 | 0.422 | 0.091 | ||
| Marital status | Unmarried | 839/2334 (35.9) | 174/839 (20.7) | 182/839 (21.7) | 215/839 (25.6) | 114/839 (13.6) | 142/839 (16.9) |
| Married | 1455/2334 (62.3) | 406/1455 (27.9) | 418/1455 (28.7) | 478/1455 (32.9) | 313/1455 (21.5) | 268/1455 (18.4) | |
| Divorced/widowed | 40/2334 (1.8) | 13/40 (32.5) | 19/40 (47.5) | 15/40 (37.5) | 11/40 (27.5) | 8/40 (20.0) | |
| P | <0.001 | P<0.001 | P<0.001 | P<0.001 | P=0.628 | ||
| Level of participation In treatment | Nonfrontline | 762/2334 (32.6) | 201/762 (26.4) | 166/762 (21.8) | 208/762 (27.3) | 121/762 (15.9) | 140/762 (18.4) |
| Anticipating | 351/2334 (15.1) | 105/351 (29.9) | 103/351 (29.3) | 121/351 (34.5) | 68/351 (19.4) | 65/351 (18.5) | |
| Frontline | 1221/2334 (52.3) | 287/1221 (23.5) | 350/1221 (28.7) | 379/1221 (31.0) | 249/1221 (20.4) | 213/1221 (17.4) | |
| P | 0.039 | 0.001 | 0.040 | 0.041 | 0.827 | ||
| Posting | Others | 1348/2334 (57.8) | 315/1348 (23.4) | 289/1348 (21.4) | 339/1348 (25.2) | 196/1348 (14.5) | 222/1348 (16.5) |
| COVID-19 OPD | 167/2334 (7.2) | 46/167 (27.5) | 56/167 (33.5) | 62/167 (37.1) | 40/167 (24.0) | 31/167 (18.6) | |
| COVID-19 emergency/wards | 819/2334 (35.0) | 232/819 (28.3) | 274/819 (33.5) | 307/819 (37.5) | 202/819 (24.7) | 165/819 (20.2) | |
| P | 0.030 | <0.001 | <0.001 | <0.001 | <0.001 | ||
| Occupational Role | Nonclinical staff | 692/2334 (29.6) | 138/692 (19.9) | 73/692 (10.6) | 92/692 (13.3) | 76/692 (11.0) | 69/692 (10.0) |
| Doctor | 239/2334 (10.2) | 59/239 (24.7) | 76/239 (31.8) | 79/239 (33.1) | 52/239 (21.8) | 47/239 (19.7) | |
| Nurse | 1403/2334 (60.2) | 396/1403 (28.2) | 470/1403 (33.5) | 537/1403 (38.3) | 310/1403 (22.1) | 302/1403 (21.5) | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||
| Level of healthcare setting | Primary and secondary | 598/2334 (25.6) | 156/598 (26.1) | 160/598 (26.8) | 172/598 (28.8) | 103/598 (17.2) | 118/598 (19.7) |
| Tertiary | 1360/2334 (58.3) | 307/1360 (22.6) | 296/1360 (21.8) | 367/1360 (27.0) | 237/1360 (17.4) | 212/1360 (15.6) | |
| Special COVID-19 center | 376/2334 (16.1) | 130/376 (34.6) | 163/376 (43.4) | 169/376 (45.0) | 98/376 (26.1) | 88/376 (23.4) | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||
| States with confirmed COVID-19 case-load | <50,000 | 1670/2334 (71.6) | 322/1670 (19.3) | 308/1670 (18.4) | 377/1670 (22.6) | 255/1670 (15.3) | 250/1670 (15.0) |
| ≥50,000 | 664/2334 (28.4) | 271/664 (40.8) | 311/664 (46.8) | 331/664 (49.9) | 183/664 (27.6) | 168/664 (25.3) | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||
| Co morbidity | No | 2090/2334 (89.5) | 505/2090 (24.2) | 519/2090 (24.8) | 610/2090 (29.2) | 369/2090 (17.7) | 359/2090 (17.2) |
| Yes | 244/2334 (10.5) | 88/244 (36.1) | 100/244 (41.0) | 98/244 (40.2) | 69/244 (28.3) | 59/244 (24.2) | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | 0.007 |
Values are indicated as n/total (%). PG –Postgraduate; UG – Under graduate; Others – Screening areas, containment zones, quarantine centers, infection control works and laboratories; OPD – Outpatient department
Factors associated with psychological outcomes in healthcare personnel
Results of multivariate linear regression including all factors associated with symptoms of depression and overall anxiety HCP are presented in Table 3. Being a nurse (odds ratio [OR], 2.2; 95% CI, 1.6–3.1; P < 0.001) or working in states with more than 50,000 COVID-19 confirmed cases (OR, 1.6; 95% CI, 1.3–2.0; P < 0.001) had higher risk for depression; but being divorced or widowed (OR, 2.2; 95% CI, 1.1–4.8; P = 0.037), having any preexisting comorbidities (OR, 1.4; 95% CI, 1.0–2.0; P = 0.037), being a doctor (OR, 2.0; 95% CI, 1.3–3.1; P = 0.001), posted in COVID-19 OPD/emergency or wards (OR, 1.7; 95% CI, 1.1–2.6; P = 0.011/OR, 1.7; 95% CI, 1.3–2.2; P < 0.001), or working in states with more than 50,000 COVID-19 confirmed cases (OR, 1.7; 95% CI, 1.3–2.2; P < 0.001) were risk factors for overall anxiety. However, if we consider anxiety and depression together then doctors (OR, 3.3; 95% CI, 1.9–5.7; P < 0.001), having any preexisting comorbidities (OR, 1.6; 95% CI, 1.1–2.4; P = 0.022), or working in states with more than 50,000 COVID-19 confirmed cases (OR, 2.2; 95% CI, 1.6–2.9; P < 0.001) were potential risk factors. CTRI Number: CTRI/2020/05/025290.
Table 3.
Factors associated with anxious and depressive symptoms: Results of multivariable logistic regression analysis
| Characteristics | Groups | OR (95% CI) | ||
|---|---|---|---|---|
|
| ||||
| Depression | Anxiety | Both depression and anxiety | ||
| Age (years) | Above 50 | 1.0 | 1.0 | 1.0 |
| 36-50 | 1.1 (0.6-1.3) | 1.0 (0.6-1.7) | 1.2 (0.6-2.5) | |
| Up to 35 | 1.3 (0.7-2.2) | 1.0 (0.6-1.7) | 1.3 (0.6-2.6) | |
| Gender | Male | 1.0 | 1.0 | 1.0 |
| Female | 0.9 (0.7-1.2) | 1.2 (0.9-1.6) | 1.1 (0.8-1.5) | |
| Education level | Up to UG | 1.0 | 1.0 | 1.0 |
| PG or higher | 1.1 (0.9-1.4) | 0.9 (0.7-1.2) | 1.1 (0.8-1.5) | |
| Marital status | Unmarried | 1.0 | 1.0 | 1.0 |
| Married | 1.0 (0.8.-1.4) | 1.7 (1.3-2.2) | 1.3 (0.9-1.9) | |
| Divorced/widowed | 1.0 (0.4-2.3) | 2.2 (1.1-4.8) | 1.4 (0.5-4.0) | |
| Level of participation in treatment | Nonfrontline | 1.0 | 1.0 | 1.0 |
| Anticipating | 0.9 (0.6-1.3) | 1.1 (0.8-1.5) | 0.9 (0.5-1.4) | |
| Frontline | 0.8 (0.7-1.1) | 1.1 (0.8-1.4) | 1.2 (0.8-1.7) | |
| Posting | Others | 1.0 | 1.0 | 1.0 |
| COVID-19 OPD | 1.1 (0.7-1.7) | 1.7 (1.1-2.6) | 1.1 (0.6-2.0) | |
| COVID-19 emergency/wards | 1.1 (0.9-1.4) | 1.7 (1.3-2.2) | 1.2 (0.9-1.7) | |
| Occupational role | Nonclinical staff | 1.0 | 1.0 | 1.0 |
| Doctor | 2.0 (1.3-3.1) | 2.0 (1.3-3.1) | 3.3 (1.9-5.7) | |
| Nurse | 2.2 (1.6-3.1) | 1.6 (1.2-2.2) | 2.0 (1.3-3.1) | |
| Level of healthcare setting | Primary and secondary | 1.0 | 1.0 | 1.0 |
| Tertiary | 0.7 (0.5-0.9) | 1.1 (0.9-1.5) | 1.1 (0.8-1.6) | |
| Special COVID-19 Centre | 0.9 (0.7-1.3) | 1.4 (1.0-2.0) | 1.3 (0.8-2.0) | |
| States with confirmed COVID-19 case load | <50,000 | 1.0 | 1.0 | 1.0 |
| ≥50,000 | 1.6 (1.3-2.0) | 1.7 (1.3-2.2) | 2.2 (1.6-2.9) | |
| Comorbidity | No | 1.0 | 1.0 | 1.0 |
| Yes | 1.3 (0.9-1.8) | 1.4 (1.0-2.0) | 1.6 (1.1-2.4) | |
Adjusted for age, gender, education level, marital status, level of participations, posting, occupational role, level of health care setting, state with confirmed COVID-19 cases and comorbidity. PG – Postgraduate; UG – Undergraduate; Others – Screening areas, containment zones, quarantine centers, infection control works and laboratories; OR – Odds ratio; CI – Confidence interval; OPD – Outpatient department
Results of multivariate linear regression including all factors associated with COVID-19 outbreak-related psychological outcomes in HCP are presented in Table 4. Younger age (OR, 1.8; 95% CI, 1.1–3.1; P = 0.017), nurses (OR, 1.3; 95% CI, 1.0–1.7; P = 0.034), having any preexisting comorbidities (OR, 1.5; 95% CI, 1.1–2.0; P = 0.013), higher educational level (OR, 1.3; 95% CI, 1.0–1.5; P = 0.035), or working in states with more than 50,000 cases (OR, 2.5; 95% CI, 2.0–3.0; P < 0.001) were at higher risk for COVID-19 infection specific anxiety, while HCP working in frontline (OR, 0.8; 95% CI, 0.6–0.9; P = 0.048) were at lower risk. In case of exhaustion, doctors (OR, 3.1; 95% CI, 2.1–4.6; P < 0.001), staff with higher education level (OR, 1.4; 95% CI, 1.1–1.7; P = 0.005), divorced or widowed participants (OR, 2.5; 95% CI, 1.2–5.2; P = 0.011), presence of preexisting comorbidities (OR, 1.6; 95% CI, 1.2–2.2; P = 0.004), posted in COVID-19 OPD (OR, 1.6; 95% CI, 1.1–2.4; P = 0.015) working in states with more than 50,000 cases (OR, 3.1; 95% CI, 2.5–3.9; P < 0.001) were potential risk factors. Whereas, nurses (OR, 2.9; 95% CI, 2.2–3.8; P < 0.001), posting in COVID-19 OPD (OR, 1.9; 95% CI, 1.3–2.7; P = 0.001), working in states with more than 50,000 cases (OR, 2.6; 95% CI, 2.1–3.2; P < 0.001) were risk factors for heavy workload.
Table 4.
Multivariable logistic regression analysis for COVID-19 outbreak-related psychological outcomes
| Characteristic | Groups | OR (95% CI) | |||
|---|---|---|---|---|---|
|
| |||||
| Anxiety about infection | Exhaustion | Workload | All three problems | ||
| Age (years) | Above 50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 36-50 | 1.6 (0.9-2.7) | 1.1 (0.7-1.9) | 0.7 (0.5-1.1) | 1.3 (0.6-2.5) | |
| Up to 35 | 1.8 (1.1-3.1) | 1.5 (0.9-2.4) | 0.8 (0.5-1.2) | 1.5 (0.8-3.1) | |
| Gender | Male | 1.0 | 1.0 | 1.0 | 1.0 |
| Female | 0.7 (0.5-0.8) | 1.1 (0.9-1.4) | 1.1 (0.9-1.3) | 0.8 (0.6-1.1) | |
| Education level | Up to UG | 1.0 | 1.0 | 1.0 | 1.0 |
| PG or higher | 1.3 (1.0-1.5) | 1.4 (1.1-1.7) | 1.2 (0.9-1.4) | 1.5 (1.1-2.0) | |
| Marital status | Unmarried | 1.0 | 1.0 | 1.0 | 1.0 |
| Married | 1.2 (0.9-1.6) | 1.3 (1.0-1.7) | 1.2 (0.9-1.6) | 1.4 (0.9-2.0) | |
| Divorced/widowed | 1.4 (0.7-2.8) | 2.5 (1.2-5.2) | 1.3 (0.6-2.7) | 2.3 (1.0-5.5) | |
| Level of participation | Nonfrontline | 1.0 | 1.0 | 1.0 | 1.0 |
| Anticipating | 1.1 (0.8-1.5) | 1.2 (0.9-1.7) | 1.1 (0.8-1.5) | 1.4 (0.9-2.1) | |
| Frontline | 0.8 (0.6-0.9) | 1.2 (0.9-1.6) | 0.9 (0.8-1.2) | 1.0 (0.7-1.5) | |
| Posting | Others | 1.0 | 1.0 | 1.0 | 1.0. |
| COVID-19 OPD | 1.2 (0.8-1.8) | 1.6 (1.1-2.4) | 1.9 (1.3-2.7) | 1.6 (0.9-2.7) | |
| COVID-19 emergency/wards | 1.2 (0.9-1.6) | 1.4 (1.1-1.8) | 1.5 (1.2-1.9) | 1.5 (1.1-2.0) | |
| Occupational role | Nonclinical staff | 1.0 | 1.0 | 1.0 | 1.0 |
| Doctor | 1.1 (0.8 -1.6) | 3.1 (2.1-4.6) | 2.7 (1.9-3.9) | 3.2 (1.8-5.5) | |
| Nurse | 1.3 (1.0-1.7) | 2.8 (2.1-3.8) | 2.9 (2.2-3.8) | 3.1 (2.0-4.9) | |
| Level of healthcare setting | Primary and secondary | 1.0 | 1.0 | 1.0 | 1.0 |
| Tertiary | 0.8 (0.6-1.0) | 0.7 (0.5-0.9) | 0.9 (0.7-1.1) | 0.7 (0.5-1.1) | |
| Special COVID-19 Centre | 1.2 (0.9-1.6) | 1.4 (1.0-1.9) | 1.4 (1.0-1.9) | 1.4 (0.9-2.1) | |
| States with confirmed COVID-19 case load | <50,000 | 1.0 | 1.0 | 1.0 | 1.0 |
| ≥50,000 | 2.5 (2.0-3.0) | 3.1 (2.5-3.9) | 2.6 (2.1-3.2) | 2.6 (1.9-3.3) | |
| Co morbidity | No | 1.0 | 1.0 | 1.0 | 1.0 |
| Yes | 1.5 (1.1-2.0) | 1.6 (1.2-2.2) | 1.1 (0.8-1.5) | 1.4 (0.9-2.1) | |
We also found that doctors (OR, 3.2; 95% CI, 1.8–5.5; P < 0.001), divorced or widowed participants (OR, 2.3; 95% CI, 1.0–5.5; P = 0.05), those with higher education (OR, 1.5; 95% CI, 1.1–2.0; P = 0.004), posted in COVID-19 emergency/ward (OR, 1.5; 95% CI, 1.1–2.0; P = 0.017), or working in states with more than 50,000 cases (OR, 2.6; 95% CI, 1.9–3.3; P < 0.001) were more prone to all three COVID-19 stress related outcomes.
DISCUSSION
This survey was conducted to assess the prevalence of psychological outcomes and their associated factors among the HCP in India during the current COVID-19 pandemic. The prevalence of overall anxiety in the study population was 18.7%, which is consistent with the research carried out by Liu et al. in China,[7] and Rossi et al. in Italy,[6] that reported an anxiety level of 16.8% and 19.8%, respectively. In these studies, the prevalence of depression was 34.6% and 24.7%, respectively, while we reported a relatively lower prevalence of 17.9%. The lower prevalence of depression in our studied population could be temporally correlated with the timing of the study. Their studies were conducted in the initial phase of the outbreak, whereas our study was carried out months later. Moreover, in India, the Government opted for a strict nationwide lockdown at an early stage leading to a relatively less steep growth curve. The lockdown gave adequate time for health infrastructure readiness, while giving ample time to HCP for psychological preparedness. The sudden explosion of an exponential growth was softened. Recent studies from India have reported prevalence of anxiety and depression ranging from 17.7% to 32.2% and 11.4% to 31.4%, respectively.[11,12,13] We speculate that this variation in prevalence could be due to various factors, including timeline of data collection, characteristics of the population under study, and the degree and duration of exposure to the COVID-19 patients.
In the current survey, we endeavored to assess the psychological impact on the health workers specifically due to the COVID-19 infection using the specially designed 19-item stress questionnaire,[10] which was used in the previous H1N1 pandemic. We found that 25.4% of participants reported COVID-19 infection-specific anxiety. A study from Portugal, conducted during the initial phase of the current pandemic, reported 54.9% of health workers are anxious about contracting COVID-19 infection.[14] Our study revealed that 26.5% of participants experienced exhaustion, and 30.3% reported a heavy workload. The prevalence of workload is lower compared to a study done in Italy, in which 44% of the participants experienced an increased workload.[15] The reason for the lower prevalence of COVID-19 infection-specific anxiety and workload could be because of a late and gradual outbreak of the disease in India. As mentioned earlier, in India, the pandemic trajectory was trailing by months after it broke out in China and Europe. This gave an opportunity for Indian authorities to learn from the administrative and clinical measures taken in those countries. Medical equipment including personal protective gear production was ramped up to make sure that there was no scarcity. The level of preparedness and availability of equipment possibly contributed to less COVID-19 specific anxiety. Compared to the West, India has experienced a pattern of less sick patients with a relatively low intensive care unit requirement and also a lower mortality rate per million population.[16] Taking care of a less critical patient population has led to a lesser workload for the HCP.
The current study also highlighted potential risk factors that predispose HCP to develop the aforementioned psychological outcomes. The HCP working in states with a higher caseload (more than 50,000 COVID-19 confirmed cases) was a common risk factor for overall anxiety and depression, along with COVID-19 infection-specific anxiety, exhaustion, and heavy workload. This may imply that mental health outcomes in HCP during the COVID-19 outbreak are strongly associated with pandemic severity. Working in overburdened emergencies or stressful situations are often associated with disruption to work-life balance, insufficient rest and sleep,[17] which may further increase the risks of developing mental health issues. A previous study similarly demonstrated more distress in health care staff posted in Wuhan’s city, with the highest number of COVID-19 cases.[2,18] Comparing occupational roles, we found that nurses were more at risk for depression, workload burden, and COVID-19 infection-specific anxiety, while doctors were more at risk for exhaustion and overall anxiety symptoms. Both nurses and doctors were vulnerable to mental health issues in comparison to nonmedical staff, possibly because of their inevitable constant involvement in patient care, challenging work schedule, a high risk of exposure,[19] and continuous use of protective gear resulting in physical and mental exhaustion.[5,20,21] These results are comparable to previous studies conducted during the SARS outbreak.[22,23,24,25] The assessment of additional factors specified that frontline staff had less COVID-19 infection-specific anxiety, which appears counterintuitive and is contrary to previous researches.[2,7,10] This can be explained by the fact that in the 6 months of the pandemic, the health workers have acquired substantially more knowledge about the disease.[26] As emphasized earlier, the time frame also helped them to adapt to the pandemic psychologically.[27] A recent study also attributed resilience of HCP to a larger clinical experience, social support, and more optimal level of personal protection training.[28]
Regarding the sociodemographic factors, our findings showed younger age as a potential risk factor for COVID-19 infection-specific anxiety. This perhaps could be a result of the lack of assurance that comes with less experience at the beginning of a career. However, other parameters did not vary across different age groups. A similar result had previously been noted in a study by Matsuishi et al. in Japan.[10] The HCP with higher levels of education had COVID-19 infection-specific anxiety and exhaustion. This has been previously reported and is attributed to the fact that the majority of highly educated staff comprise nurses and doctors who are more likely to be aware of the virulence of the disease and are consequently more likely to worry about it.[25] Divorced or widowed participants had overall higher anxiety and exhaustion, which can be explained by their increased responsibilities at home and possible inadequate family support. HCP with preexisting comorbidities had COVID-19 infection-specific anxiety, exhaustion, and an overall anxiety. Harboring an underlying ailment is an independent risk factor for mental health problems in HCP as has been reported in a previous study by Zhang et al.[5] It could be that preexisting altered metabolic states affect mental health and responses to difficult situations. Moreover, being more vulnerable to infection, leads to significant fear and distress.[29]
Our study identified numerous sociodemographic and occupational factors that may lead to psychological problems in hospital staff. Interestingly, both doctors and nurses were susceptible to all five psychological outcomes to varying degrees. Along with this, we also found that HCP working in states with high caseload reported more mental health issues. Findings from this study would be helpful to the policymakers and healthcare organizations for implementing early intervention among the vulnerable population in the midst of a pandemic. Hospital administration and stakeholders should execute campaigns to raise awareness on mental health issues and their effective management. At the same time, efforts should be geared toward establishing appropriate colleague support structures to share duties and workload during the outbreak, thus alleviating their distress.
Till date, to the best of our knowledge, this is the first large-sample study on the psychological impact of COVID-19 pandemic among HCP in India. The large sample size enables the possibility of generalization.
The present study has some limitations. First, although we have a reasonable sample size, it is not equally distributed across participant’s characteristics. Second, PHQ4 is a screening tool and not diagnostic tool to evaluate clinical diagnosis unlike PHQ9 and GAD7; however, to maintain the moderate length of the questionnaire PHQ4 was used. Thirdly, there might be a possibility of selection bias, since our study was a web-based survey; limiting the participation of healthcare workers to those with internet access and thereby preventing a truly pan India survey. Besides this, other psychological outcomes such as posttraumatic stress, insomnia, and burnout were not explored in the present study. It will be worth analyzing them in-depth to have a precise understanding of their psychological well-being in future research.
CONCLUSIONS
We identified major mental health issues and their associated factors in the Indian health care personnel amidst the COVID-19 outbreak. The presence of such problems can jeopardize health and safety of HCP and influence their decision-making ability. Nevertheless, the timely measures including an early national lockdown have ensured better preparedness and all-round awareness, leading to a relatively less psychological impact on the health care workers. Awareness and understanding of the psychological issues may help health policymakers and administrators devise mechanisms for their effective management.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
The authors would like to acknowledge the Ministry of AYUSH, Government of India, for their support through the Center of Excellence grant for “Yoga and Ayurveda” to Centre for Integrative Medicine and Research (CIMR), AIIMS, New Delhi. We would also like to thank the staff of CIMR for their support in the research.
We thank Mr. Arinjai Rampal, and Mr. Vishal Saroj for grammatical correction and data management.
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