Harmful Alcohol Use Among Healthcare Workers at the Beginning of the COVID-19 Pandemic in Kenya

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Alcohol use is a common maladaptive response to stress that is associated with adverse health consequences and that could impair productivity in the workplace for the health workforce. The aim of this study is to document the burden and factors associated with harmful alcohol use among health care workers at the beginning of the COVID-19 pandemic in Kenya. Methods: This study was a cross-sectional analysis of data obtained from a parent online survey that investigated the prevalence and factors associated with mental disorders among healthcare workers during the COVID-19 pandemic in Kenya. Analyses for this study were conducted to examine the burden and factors associated with harmful alcohol use among a sub-group of 887 participants who completed the Alcohol Use Disorder Identification Test (AUDIT) questionnaire. Results: Three hundred and eighty nine (43.9%) participants reported harmful alcohol use. The factors significantly associated with increased odds of endorsing harmful alcohol use were: being male (AOR= 1.56; 95% CI=1.14, 2.14; p=0.006), being not married (AOR= 2.06; 95% CI=1.48, 2.89; p<0.001), having 11-20 years of experience as compared to having 20+ years of experience (AOR= 1.91; 95% CI=1.18, 3.12; p=0.009), and being a specialist (AOR=2.78; CI=1.64, 4.78; P=<0.001) or doctor (AOR= 2.82; 95% CI=1.74, 4.63; p<0.001) as compared to being a nurse. Conclusions: A high proportion of health care workers reported harmful alcohol use at the beginning of the COVID-19 pandemic in Kenya. Males, the unmarried, those with 11-20 years of experience in the health field, doctors and specialists were more likely to report harmful alcohol use. These findings highlight the need to institute interventions for harmful alcohol use targeting these groups of health care workers in Kenya during the COVID-19 pandemic. Health Economics & Outcomes Research harmful alcohol healthcare workers COVID-19 Kenya Background The coronavirus disease of 2019 (COVID-19) pandemic which has caused close to 2.5 million deaths to date (1), has resulted in adverse consequences on the mental health of people around the globe (2). This has been occasioned by the pandemic’s impact on health and the economy(3), and disruptions to daily routine as a result of disease containment measures (4). In a meta-analysis of studies conducted among the general population during the COVID-19 pandemic, Salari et al (5) reported that: the prevalence of stress in 5 studies with a total sample size of 9074 was 29.6% (95% confidence limit: 24.3–35.4); that of anxiety in 17 studies with a sample size of 63,439 was 31.9% (95% confidence interval: 27.5–36.7), and the prevalence of depression in 14 studies with a sample size of 44,531 people was 33.7% (95% confidence interval: 27.5–40.6). These figures are significantly higher than the global rates for depression and anxiety(6). Harmful alcohol use, a pattern of alcohol consumption that results in consequences to physical and mental health (7), is a common maladaptive method of coping with stress and has been shown to increase during and following major disasters. High rates of harmful alcohol use have been reported in the aftermath of the Oklahoma bombing (8); the World Trade Centre bombing (9,10); hurricane Katrina (11) and the Severe Acute Respiratory Syndrome (SARS) pandemic of 2002 (12). The COVID-19 pandemic is no exception and has been characterized by an increase in alcohol sales and consumption. For example in the Unites States (US), a study conducted over a seven week COVID-19 impacted period between March and April 2020, reported a 234% increase in online sales of alcohol compared to a similar time in the previous year (13). In addition, the study found that there was a preference for purchase of larger pack sizes for wines, spirits and beer (13). Indeed in the US, in a study conducted by Grossman et al (14), 60% of participants reported an increase in alcohol consumption during the COVID-19 pandemic. In that study, participants who reported being stressed by the pandemic reported greater and longer alcohol consumption (14). Increases in levels of alcohol use have also been reported among general population adults in Australia (15) and the UK (16). Health care workers are highly vulnerable to psychological distress and therefore to increased alcohol use during pandemics. Health care workers often have direct contact with infected persons, face increased workload, and are constantly exposed to potentially traumatic events in the course of disease outbreaks. In the aftermath of the SARS 2003 pandemic, significant levels of post-traumatic stress symptoms were reported among health care workers (17,18). Similarly, studies conducted during the COVID-19 pandemic indicate a high psychological impact on health care workers including depression, anxiety and post- traumatic stress (19). Surprisingly, little has been done to explore the burden of alcohol use among health care workers during earlier viral epidemics and the current COVID-19 pandemic. One study we found reported that 42.6% of health care workers in the US had probable alcohol use disorder during the COVID-19 pandemic (20). The negative impact of harmful alcohol use among healthcare workers cannot be overemphasized. In addition to the well documented negative health consequences (21), harmful alcohol use among health care workers could result in inefficiencies in health service delivery emanating from impaired work performance (22). This is particularly concerning at a time when the world is facing a health crisis and the health workforce is already constrained. In Kenya, data on harmful alcohol use among healthcare workers is limited. The aim of the present study was therefore to document the prevalence and factors associated with harmful alcohol use among health care workers in Kenya at the beginning of the COVID-19 pandemic. Such information could be useful in guiding interventions in Kenya and in other Low and Middle Income Countries (LMICs). Materials And Methods Data used for these analyses were derived from a parent online survey investigating the prevalence and factors associated with mental disorders among healthcare workers during the COVID-19 pandemic in Kenya. Eligible healthcare workers for the online survey were trained health professionals working in healthcare during the COVID-19 pandemic. Health professionals working outside hospital settings, e.g. insurance companies were excluded. A virtual snowball sampling was used to recruit participants. In total, 1190 health care workers consented to participate in the survey. Of these, 957 completed at least one or more of the questionnaires. The detailed methods for the parent study have been submitted for publication elsewhere. The analyses for this study are based on a sub-population of 887 participants who completed the Alcohol Use Disorder Identification Test (AUDIT) questionnaire(7). All participants provided informed consent. The invitation to the online survey included study information and the option to select “I agree” or “I disagree” to participate in the study. Participants were informed that selecting the “agree” option meant that they had read and understood the invitation, had confirmed that they were the age of 18 and above, and had voluntarily agreed to participate in the study. Ethical approval to conduct the study was obtained from the Institutional Research Ethics Committee (IREC) of Moi University/Moi Teaching & Referral Hospital. The survey instrument was programmed into Redcap, (Research Electronic Data Capture) (23) a secure, web-based software platform designed to support data capture for research studies. The online survey was sent to healthcare workers in various networks on Facebook, WhatsApp and E-mail between April 1 and April 30, 2020. The healthcare workers were requested to respond to the survey while a track of responses was kept using the Redcap software. A weekly reminder was sent for a duration of one month. Measures Socio-demographic data: A researcher designed questionnaire was used for collecting socio-demographic data including age, sex (male/female), marital status (married/not married), cadre (doctor/nurse/specialist/other), type of facility (public/private), contact with COVID-19 patients (yes/no), years of experience in health care (0-10, 11-20, 20+), and history of a chronic medical condition (yes/no). Harmful alcohol use: The primary outcome for this study was harmful alcohol use. This was measured using the AUDIT (7) which examines for past year alcohol use. The AUDIT consists of 10 questions and total scores range from 0 to 40. A score of 8 and above was considered harmful alcohol use for our study (7). The AUDIT has been used among adults in Kenya (24). Depression: Depression was measured using the Patient Health Questionnaire-9 (PHQ-9) (25). It is a 9-item self-report instrument and examines for symptoms over the past two week period. Total scores range from 0-27. For our study, a score of 0-4 was considered none/minimal depression, 5-9 mild depression, 10-14 moderate depression, 15-19 moderately severe depression, and 20-27 severe depression (25). The PHQ-9 has excellent reliability and validity. The PHQ-9 has been validated among adults in Kenya (26). Generalized Anxiety Disorder (GAD) : GAD was assessed using the GAD-7 scale (27). It is a seven item self-report instrument that examines for symptoms over the past two week period. Total scores range from 0 to 21. A score of 0-4 was considered minimal GAD, 5-9 mild GAD, 10-14 moderate GAD, and 15-21 severe GAD for our study (27). The GAD-7 has been validated in Kenya (28). Post-Traumatic Stress Disorder (PTSD): We used the Primary Care- Post Traumatic Stress disorder (PC- PTSD) for Diagnostic statistical manual- 5 to measure PTSD(29). The tool is a 5-item screen used for past month PTSD symptoms. A score of 3 and above was considered probable PTSD in our study (29). Sleep quality: This study used the Pittsburgh Sleep Quality Index (PSQI) (30) to assess for sleep quality. It is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. A score of 5 and above indicated poor quality sleep for our study. Such a score has a sensitivity of 89.6% and specificity of 86.5% in distinguishing good and poor quality of sleep (30). The tool has been used among adults in Kenya (31). Statistical analysis: Descriptive statistics were used to summarize the socio-demographic characteristics of the participants. Chi square test was used in the bivariate analysis, to assess for the association between harmful alcohol use and socio-demographic and mental health factors. Significant variables were subjected to the multivariable logistic regression analysis and presented as adjusted odds ratios (AORs) and 95% CIs. Data analysis was performed using R Core Team (2013). In all analysis a p-value less than 0.05 was considered significant. Results Socio-demographic characteristics of participants: A total of 887 participants completed the AUDIT questionnaire. Most respondents were aged 35 years and above (51.4%), were female (54.6%), worked in public health facilities (70.0%) and had 10 years or less of experience in health care (57.7%) . Less than one third of the respondents (24.0%) had come into contact with a patient diagnosed with COVID-19. Of the 887 respondents, 39.9% were doctors, 18.8% were nurses, 16.8% were specialists and 24.4% belonged to other cadres (Table 1). Table 1: Socio-demographic characteristics of participants Variable N (%) Age in years =35 456 (51.4) Gender Male 403 (45.4) Female 484 (54.6) Marital status Married 579 (65.3) Not married 308 (34.7) Years of experience in health care 0-10 512 (57.7) 11–20 219 (24.7) 20+ 156 (17.6) Cadre Doctor 354 (39.9) Nurse 167 (18.8) Other 216 (24.4) Specialist 149 (16.8) Type of facility Public 621 (70.0) Private 266 (30.0) Have a chronic medical condition Yes 202 (22.8) No 685 (77.2) Contact with COVID-19 patients Yes 212 (23.9) No 675 (76.1) Mental health characteristics of the participants: Out of the 887 participants who responded to the AUDIT questionnaire, 858 (96.7%) completed the PHQ-9; 807 (91.0%) completed the GAD-7, 348 (39.2%) completed the PC-PTSD and 772 (87.0%) completed the PSQI. Of the respondents who completed the PHQ-9, all respondents (100%) endorsed some level of depression. Thirty six percent of those who completed the GAD-7 reported some level of GAD, while poor sleep quality was endorsed by 24.5% of those who completed the PSQ-I. The PC-PTSD was completed by the least number of respondents. Sixty-five percent of participants responding to that questionnaire reported symptoms of probable PTSD (Table 2). Table 2: Mental health characteristics of the participants Depression (N= 858) N (%) Mild 581 (67.7) Moderate 144 (16.8) Severe 133 (15.5) GAD (N= 807) Mild/Moderate 232 (28.7) None/minimal 516 (64.0) Severe 59 (7.3) PTSD (N=348) None 123 (35.3) Probable PTSD 225 (64.7) Sleep quality (N=772) Poor quality of sleep 189 (24.5) Good quality of sleep 583 (75.5) Prevalence of harmful alcohol use: Three hundred and eighty nine (43.9%) participants reported harmful alcohol use based on an AUDIT score of 8 and above (95%CI: [40.6%,47.2%]). Factors associated with harmful alcohol use In bivariate analysis, gender, marital status, cadre and years of experience in the health field were significantly associated with harmful alcohol use (Tables 3 and 4). In multivariate analysis, the factors significantly associated with increased odds of endorsing harmful alcohol use were: being male (AOR= 1.56; 95% CI=1.14, 2.14; p=0.006), being not married (AOR= 2.06; 95% CI=1.48, 2.89; p<0.001), having 11-20 years of experience in health care as compared to having 20+ years of experience (AOR= 1.91; 95% CI=1.18, 3.12; p=0.009), and being a specialist (AOR=2.78; CI=1.64, 4.78; P=<0.001) or doctor (AOR= 2.82; 95% CI=1.74, 4.63; p<0.001) as compared to being a nurse. Age, and endorsing depression or generalized anxiety were not associated with harmful alcohol use (Table 5). Table 3: Bivariate analysis of socio demographic factors and harmful alcohol use Variable Alcohol use (N=887) p-value Harmful a N (%) Not Harmful N (%) Age in years =35 191 (41.9) 265 (58.1) Gender Male 198 (49.1) 205 (50.9) 0.005 Female 191 (39.5) 293 (60.5) Marital status Married 224 (38.7) 355 (61.3) <0.001 Not married 165 (53.6) 143 (46.4) Years of experience in health care 0-10 238 (46.5) 274 (53.5) 0.001 11–20 104 (47.5) 115 (52.5) 20+ 47 (30.1) 109 (69.9) Cadre Doctor 178 (50.3) 176 (49.7) <0.001 Nurse 38 (22.8) 129 (77.2) Other 104 (48.1) 112 (51.9) Specialist 68 (45.6) 81 (54.4) Type of facility Public 265 (42.7) 356 (57.3) 0.312 Private 124 (46.6) 142 (53.4) Have a known medical condition Yes 97 (48.0) 105 (52.0) 0.202 No 292 (42.6) 393 (57.4) Contact COVID-19 patients Yes 92 (43.4) 120 (56.6) 0.940 No 297 (44.0) 378 (56.0) a harmful alcohol use was defined by a score of 8 and above on the AUDIT Table 4: Bivariate analysis of mental disorder and harmful alcohol use Variable Alcohol use (N=887) p-value Harmful b N (%) Not Harmful N (%) Depression Mild 241 (41.5) 340 (58.5) 0.065 Moderate 63 (43.8) 81 (56.2) Severe 70 (52.6) 63 (47.4) GAD Mild/Moderate 115 (49.6) 117 (50.4) 0.061 None/minimal 211 (40.9) 305 (59.1) Severe 29 (49.2) 30 (50.8) PTSD None 57 (46.3) 66 (53.7) 0.430 Probable PTSD 93 (41.3) 132 (58.7) PSQI Poor quality sleep 86 (45.5) 103 (54.5) 0.672 Good quality sleep 253 (43.4) 330 (56.6) b harmful alcohol use was defined by a score of 8 and above on the AUDIT Table 5: Multivariate analysis of association between harmful alcohol use and socio-demographic and mental health factors Characteristic AOR C 95% CI d p-value Age in years =35 1.10 0.70, 1.72 0.700 Gender Female 1 Male 1.56 1.14, 2.14 0.006 Marital status Married 1 Not married 2.06 1.48, 2.89 <0.001 Years of experience in health care 20+ 1 11–20 1.91 1.18, 3.12 0.009 0-10 1.53 0.88, 2.69 0.140 Cadre Nurse 1 Specialist 2.78 1.64, 4.78 <0.001 Doctor 2.82 1.74, 4.63 <0.001 Other 2.59 1.57, 4.34 <0.001 PHQ Mild 1 Moderate 1.15 0.73, 1.81 0.500 Severe 1.50 0.90, 2.52 0.120 GAD None/minimal 1 Mild/Moderate 1.07 0.72, 1.57 0.700 Severe 1.13 0.52, 2.44 0.800 c Adjusted Odds Ratio d Confidence Interval Discussion To the best of our knowledge, this is the first study to examine harmful alcohol use among health care workers during the COVID-19 pandemic in a LMIC. Our findings indicate that 43.9% of the participants endorsed harmful patterns of alcohol use based on the AUDIT. Using a similar tool Hennein et al (20) reported comparable findings. The authors found that 42.6% of health care workers in the US had probable alcohol use disorder during the COVID-19 pandemic. Our findings are considerably higher than those reported by Mokaya et al. (32) among health care workers in the pre-pandemic period in Kenya. The study found that only 2.9% of health care workers endorsed moderate risk alcohol use and that none endorsed high risk use (32). This suggests an increase in rates of alcohol consumption during the COVID-19 pandemic among health care workers in Kenya. Such a high burden of harmful alcohol use is likely to further constrain the already limited workforce (33) and contribute to inefficiencies and disruptions to health service delivery at this crucial time. In our study, being male was associated with increased odds of harmful alcohol use. This finding is consistent with prior studies conducted among health care workers (32) and the general population (34) in Kenya, and might be explained by the fact that in many cultures, traditional gender roles may prevent the development of problematic substance use for women (35). Unmarried health care workers were more likely to report harmful alcohol use compared to the married. This is comparable to other studies that have shown a higher prevalence of alcohol use among single or divorced persons (36). Being unmarried may be associated with social isolation, a well documented risk factor for harmful substance use (37,38). Specialists, doctors and other cadres were significantly more likely to endorse harmful alcohol use as compared to nurses. Nurses in Kenya have strong social welfare systems that could potentially prevent the use of alcohol as a way of coping with stress during the pandemic. Having 11-20 years of experience in the health profession was associated with increased odds of harmful alcohol use as compared to having 20+ years or having 0-10 years of experience. Findings concerning the association between years of experience and harmful alcohol use have been inconsistent. Obadeji et al., in a study conducted among doctors in Nigeria reported no association between years of experience and hazardous alcohol use (39). Kenna and Lewis found alcohol use disorder among health care providers to be associated with having younger licenses (40). A possible reason for significant harmful alcohol use among healthcare workers with 11-20 years of experience could be that that phase represents a period of heightened psychological stress linked to residency, and increasing family and work place responsibilities. Our study reported no significant differences in the rates of harmful alcohol use among health care workers with and without mental health disorders. This was an unexpected finding since prior studies conducted during the COVID-19 pandemic have overwhelmingly reported a positive association between harmful alcohol use and mental health symptoms including depression (15,16,41); PTSD (42); anxiety (15,42,43), and Stress (43). It is not clear why the present study did not find an association between harmful alcohol use and mental health symptoms. Future longitudinal research in our setting could shed more light on this. Implications for practice: The high prevalence of harmful alcohol use among health care workers in Kenya during the COVID-19 pandemic specifically among doctors and specialists, males, the unmarried, and those with 11-20 years of experience highlights the urgent need to put in place appropriate prevention and treatment interventions targeting these groups. Several interventions may be delivered including (i) health education on the harmful impact of alcohol use and debunking of myths that encourage alcohol use during the pandemic (ii) education on strategies for health promotion such as a healthy diet, adequate sleep, physical activity and stress management (iii) screening and brief interventions for alcohol use and (iv) interventions to promote social connectedness (44,45). Virtual platforms and mobile health strategies represent a potential platform for delivering the above interventions given the current COVID-19 restrictions. Currently the Ministry of Health has established a call centre whose aim is to offer both knowledge and psychosocial support to frontline health workers (46). This presents an avenue through which alcohol related health education and screening and brief interventions may be conducted. If the patterns observed post other disasters are anything go by (12,47), the rise in alcohol use observed during the COVID-19 outbreak is likely to persist for several years beyond the pandemic period. It is therefore important that long term strategies are put in place to manage alcohol use within health care settings. We recommend that health care settings in Kenya establish employee assistance programs and develop policies that address substance use in the workplace. Fortunately, the National Authority for the Campaign Against Alcohol & Drug Abuse (NACADA), has published guidelines for the development of workplace substance use programs and policies that institutions can use for reference (48). At policy level, delisting alcohol use as an essential commodity during the pandemic could reduce its availability and thus limit its use as a way of coping during the pandemic. We acknowledge some limitations. Firstly, this being an online survey, it may have been less accessible to people who lacked smartphones, had no internet access, or were not on the social media platforms we utilized. Our findings may therefore not include their experiences. Secondly, our sample was not representative of the composition of healthcare workers in Kenya. Our sample was comprised of mostly doctors while nurses comprise more than a half of health care workers in Kenya. Thirdly, this was a cross-sectional study and therefore no causal relationships may be determined. Nonetheless this study provides for the first time important information on harmful alcohol use among health care workers during the COVID-19 pandemic in a LMIC. Conclusion In conclusion, a high proportion of health care workers in Kenya reported harmful alcohol use. Males, the unmarried, those with 11-20 years of experience in health care, doctors and specialists were more likely to report harmful alcohol use. Given the potential negative impact of harmful alcohol use not only on the mental and physical health of the HCWs but also on health service delivery, it is critical that the government puts in place interventions to address this problem. In the short term, virtual platforms and mobile health strategies could be utilized to deliver health education, as well as screening and brief interventions for harmful alcohol use. In the long term, health care settings ought to establish substance use workplace programs and policies. Abbreviations AOR - Adjusted Odds Ratio AUDIT - Alcohol Use Disorder Identification Test COVID-19 - Corona Virus Disease of 2019 DSM-5 - Diagnostic Statistical Manual 5 th Edition GAD - Generalized Anxiety Disorder IREC - Institutional Research Ethics Committee LMIC - Low and Middle Income Country NACADA - National Authority for the Campaign Against Alcohol & Drug Abuse PHQ-9 - Patient Health Questionnaire-9 PC- PTSD - Primary Care- Post Traumatic Stress disorder PSQI - Pittsburgh Sleep Quality Index PTSD - Post Traumatic Stress Disorder SARS - Severe Acute Respiratory Syndrome US - United States Declarations Ethics approval and consent to participate The authors confirm 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. The study was approved by the Moi Teaching and Referral Hospital/Moi University School of Medicine Institutional Research and Ethics Committee (IREC/2020/59: FAN 003589) and the National Council for Science and Technology (Nacosti/P/20/4835). Informed consent was obtained from the participant by ensuring that only those who clicked on agree to participate were able to access the online survey. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was completed with support by Kenya Medical Association, Equity project. Authors’ contributions All authors participated in designing the study. A.M. conducted the analyses. F.J. drafted the manuscript. All authors contributed to and reviewed all versions of the manuscript. 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The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. doi: 10.1046/j.1525-1497.2001.016009606.x . Monahan PO, Shacham E, Reece M, et al. Validity/reliability of PHQ-9 and PHQ-2 depression scales among adults living with HIV/AIDS in western Kenya. J Gen Intern Med. 2009;24(2):189–97. doi: 10.1007/s11606-008-0846-z . Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. doi: 10.1001/archinte.166.10.1092 . Nyongesa MK, Mwangi P, Koot HM, Cuijpers P, Newton CRJC, Abubakar A. The reliability, validity and factorial structure of the Swahili version of the 7-item generalized anxiety disorder scale (GAD-7) among adults living with HIV from Kilifi, Kenya. Ann Gen Psychiatry. 2020;19:62. doi: 10.1186/s12991-020-00312-4 . Prins A, Bovin MJ, Smolenski DJ, et al. The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5): Development and Evaluation Within a Veteran Primary Care Sample. J Gen Intern Med. 2016;31(10):1206–11. doi: 10.1007/s11606-016-3703-5 . Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. doi: 10.1016/0165-1781(89)90047-4 . Sokwalla SM, Joshi MD, Amayo EO, Acharya K, Mecha JO, Mutai KK. Quality of sleep and risk for obstructive sleep apnoea in ambulant individuals with type 2 diabetes mellitus at a tertiary referral hospital in Kenya: a cross-sectional, comparative study. BMC Endocr Disord. 2017;17(1):7. doi: 10.1186/s12902-017-0158-6 . Mokaya AG, Mutiso V, Musau A, et al. Substance Use among a Sample of Healthcare Workers in Kenya: A Cross-Sectional Study. J Psychoactive Drugs. 2016;48(4):310–9. doi: 10.1080/02791072.2016.1211352 . Mulaki A, Muchiri S. Kenya Health System Assessment. Washington, DC: Palladium, Health Policy Plus. http://www.healthpolicyplus.com/ns/pubs/11328-11600_KenyaHSAReport.pdf . Accessed 8 April 2021. Kendagor A, Gathecha G, Ntakuka MW, et al. Prevalence and determinants of heavy episodic drinking among adults in Kenya: analysis of the STEPwise survey, 2015. BMC Public Health. 2018;18(Suppl 3):1216. doi: 10.1186/s12889-018-6057-6 . Kubicka L, Csémy L. Women's gender role orientation predicts their drinking patterns: a follow-up study of Czech women. Addiction. 2008;103(6):929–37. doi: 10.1111/j.1360-0443.2008.02186.x . discussion 938-9. Power C, Rodgers B, Hope S. Heavy alcohol consumption and marital status: disentangling the relationship in a national study of young adults. Addiction. 1999;94(10):1477–87. doi: 10.1046/j.1360-0443.1999.941014774.x . Day BF, Rosenthal GL. Social isolation proxy variables and prescription opioid and benzodiazepine misuse among older adults in the U.S.: A cross-sectional analysis of data from the National Survey on Drug Use and Health, 2015–2017. Drug Alcohol Depend. 2019;204:107518. doi: 10.1016/j.drugalcdep.2019.06.020 . McKay MT, Konowalczyk S, Andretta JR, Cole JC. The direct and indirect effect of loneliness on the development of adolescent alcohol use in the United Kingdom. Addict Behav Rep. 2017;6:65–70. doi: 10.1016/j.abrep.2017.07.003 . Obadeji A, Oluwole LO, Dada MU, Adegoke BO. Hazardous alcohol use among doctors in a Tertiary Health Center. Ind Psychiatry J. 2015;24(1):59–63. doi: 10.4103/0972-6748.160935 . Kenna GA, Lewis DC. Risk factors for alcohol and other drug use by healthcare professionals. Subst Abuse Treat Prev Policy . 2008;3:3. Published 2008 Jan 29. doi: 10.1186/1747-597X-3-3 . Stanton R, To QG, Khalesi S, et al. Depression, Anxiety and Stress during COVID-19: Associations with Changes in Physical Activity, Sleep, Tobacco and Alcohol Use in Australian Adults. Int J Environ Res Public Health. 2020;17(11):4065. doi: 10.3390/ijerph17114065 . Young KP, Kolcz DL, O’Sullivan DM, Ferrand J, Fried J, Robinson K. Health Care Workers’ Mental Health and Quality of Life During COVID-19: Results From a Mid-Pandemic, National Survey. Psychiatr Serv. 2020. https://doi.org/10.1176/appi.ps.202000424 . Avery AR, Tsang S, Seto EYW, Duncan GE. Stress, Anxiety, and Change in Alcohol Use During the COVID-19 Pandemic: Findings Among Adult Twin Pairs. Front Psychiatry. 2020;11:571084. doi: 10.3389/fpsyt.2020.571084 . Muller AE, Hafstad EV, Himmels JPW, et al. The mental health impact of the covid-19 pandemic on healthcare workers, and interventions to help them: A rapid systematic review. Psychiatry Res. 2020;293:113441. doi: 10.1016/j.psychres.2020.113441 . Ames GM, Bennett JB. Prevention interventions of alcohol problems in the workplace. Alcohol Res Health. 2011;34(2):175–87. Republic of Kenya. Ministry of Health. CS ICT launched Covid-19 call centre for health care workers [Internet]. 2020. Available from: https://www.health.go.ke/cs-ict-launches-covid-19-call-centre-for-health-care-workers/ . Accessed 31 January 2021. Welch AE, Caramanica K, Maslow CB, et al. Frequent binge drinking five to six years after exposure to 9/11: findings from the World Trade Center Health Registry. Drug Alcohol Depend. 2014;140:1–7. doi: 10.1016/j.drugalcdep.2014.04.013 . National Authority for the Campaign Against alcohol and Drug Abuse. Guidelines for Developing a Workplace Substance Abuse Prevention Policy. 2018. https://nacada.go.ke/sites/default/files/2019-10/ADA%20Policy%20Guidelines%202018.pdf . Accessed 31 January 2021. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-403929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":20587195,"identity":"09858b75-0f89-4e46-936e-83614e51f987","order_by":0,"name":"FLORENCE JAGUGA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBAC9nYGZiB1gIGNvQFIG1gQ1sJzGKaF5wBIiwQJWhgkEkB8YrQwMx82+JhzR45P8vnVDT8KJBj427sTCGhhS06cue2ZMZt0TtnNHqDDJM6c3YBXiz0zj/Fh3m2HE9ukc9Ju8AC1GEjk4tfCA9LyF6RF8kzazT/EaklmBGmRYD92m0hb2JINe0F+4clhuy1jIMFD0C887M2HJX5uuyMn33782c03f2zk+Nt78WtB1m0AJolVDgLsD0hRPQpGwSgYBSMIAADg8UMLYGSlkQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8381-6309","institution":"Moi Teaching and Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"FLORENCE","middleName":"","lastName":"JAGUGA","suffix":""},{"id":20587196,"identity":"4919a47e-c5d0-499f-be34-7512d07453e1","order_by":1,"name":"EDITH KWOBAH","email":"","orcid":"","institution":"Moi Teaching and Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"EDITH","middleName":"","lastName":"KWOBAH","suffix":""},{"id":20587197,"identity":"60df992c-d171-491f-bbb5-d166ba2bb7d6","order_by":2,"name":"ANN MWANGI","email":"","orcid":"","institution":"Moi University","correspondingAuthor":false,"prefix":"","firstName":"ANN","middleName":"","lastName":"MWANGI","suffix":""},{"id":20587198,"identity":"6b0a3062-d69b-4cab-ade5-497497b235a7","order_by":3,"name":"KIRTIKA PATEL","email":"","orcid":"","institution":"Moi University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"KIRTIKA","middleName":"","lastName":"PATEL","suffix":""},{"id":20587199,"identity":"cc713133-6284-40a9-beb7-d6fea592f0d3","order_by":4,"name":"THOMAS MWOGI","email":"","orcid":"","institution":"Moi Teaching and Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"THOMAS","middleName":"","lastName":"MWOGI","suffix":""},{"id":20587200,"identity":"c4564914-03fa-48db-a136-5fa496c948cf","order_by":5,"name":"ROBERT KIPTOO","email":"","orcid":"","institution":"Moi Teaching and Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"ROBERT","middleName":"","lastName":"KIPTOO","suffix":""},{"id":20587201,"identity":"a28bf11c-9c32-4399-88a1-dd65e8cb7433","order_by":6,"name":"LUKOYE ATWOLI","email":"","orcid":"","institution":"Moi University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"LUKOYE","middleName":"","lastName":"ATWOLI","suffix":""}],"badges":[],"createdAt":"2021-04-09 09:17:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-403929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-403929/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/fpsyt.2022.821610","type":"published","date":"2022-02-28T06:22:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":18671545,"identity":"a9dd05b0-7231-4116-91f5-7e99da9f125b","added_by":"auto","created_at":"2022-02-28 06:22:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":475776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-403929/v1/0ae9100c-fb37-4070-8771-cd09b9dcf5cf.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHarmful Alcohol Use Among Healthcare Workers at the Beginning of the COVID-19 Pandemic in Kenya\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe coronavirus disease of 2019 (COVID-19) pandemic which has caused close to 2.5 million deaths to date (1), has resulted in adverse consequences on the mental health of people around the globe (2). This has been occasioned by the pandemic\u0026rsquo;s impact on health and the economy(3), and disruptions to daily routine as a result of disease containment measures (4). In a meta-analysis of studies conducted among the general population during the COVID-19 pandemic, Salari et al (5) reported that: the prevalence of stress in 5 studies with a total sample size of 9074 was 29.6% (95% confidence limit: 24.3\u0026ndash;35.4); that of anxiety in 17 studies with a sample size of 63,439 was 31.9% (95% confidence interval: 27.5\u0026ndash;36.7), and the prevalence of depression in 14 studies with a sample size of 44,531 people was 33.7% (95% confidence interval: 27.5\u0026ndash;40.6). These figures are significantly higher than the global rates for depression and anxiety(6).\u003c/p\u003e\n\u003cp\u003eHarmful alcohol use, a pattern of alcohol consumption that results in consequences to physical and mental health (7), is a common maladaptive method of coping with stress and has been shown to increase during and following major disasters. High rates of harmful alcohol use have been reported in the aftermath of the Oklahoma bombing (8); the World Trade Centre bombing (9,10); hurricane Katrina (11) and the Severe Acute Respiratory Syndrome (SARS) pandemic of 2002 (12). The COVID-19 pandemic is no exception and has been characterized by an increase in alcohol sales and consumption. For example in the Unites States (US), a study conducted over a seven week COVID-19 impacted period between March and April 2020, reported a 234% increase in online sales of alcohol compared to a similar time in the previous year (13). In addition, the study found that there was a preference for purchase of larger pack sizes for wines, spirits and beer (13). Indeed in the US, in a study conducted by Grossman et al (14), 60% of participants reported an increase in alcohol consumption during the COVID-19 pandemic. In that study, participants who reported being stressed by the pandemic reported greater and longer alcohol consumption (14). Increases in levels of alcohol use have also been reported among general population adults in Australia (15) and the UK (16).\u003c/p\u003e\n\u003cp\u003eHealth care workers are highly vulnerable to psychological distress and therefore to increased alcohol use during pandemics. Health care workers often have direct contact with infected persons, face increased workload, and are constantly exposed to potentially traumatic events in the course of disease outbreaks. In the aftermath of the SARS 2003 pandemic, significant levels of post-traumatic stress symptoms were reported among health care workers (17,18). Similarly, studies conducted during the COVID-19 pandemic indicate a high psychological impact on health care workers including depression, anxiety and post- traumatic stress (19). Surprisingly, little has been done to explore the burden of alcohol use among health care workers during earlier viral epidemics and the current COVID-19 pandemic. One study we found reported that 42.6% of health care workers in the US had probable alcohol use disorder during the COVID-19 pandemic (20).\u003c/p\u003e\n\u003cp\u003eThe negative impact of harmful alcohol use among healthcare workers cannot be overemphasized. In addition to the well documented negative health consequences (21), harmful alcohol use among health care workers could result in inefficiencies in health service delivery emanating from impaired work performance (22). This is particularly concerning at a time when the world is facing a health crisis and the health workforce is already constrained. In Kenya, data on harmful alcohol use among healthcare workers is limited. The aim of the present study was therefore to document the prevalence and factors associated with harmful alcohol use among health care workers in Kenya at the beginning of the COVID-19 pandemic. Such information could be useful in guiding interventions in Kenya and in other Low and Middle Income Countries (LMICs).\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eData used for these analyses were derived from a parent online survey investigating the prevalence and factors associated with mental disorders among healthcare workers during the COVID-19 pandemic in Kenya. Eligible healthcare workers for the online survey were trained health professionals working in healthcare during the COVID-19 pandemic. Health professionals working outside hospital settings, e.g. insurance companies were excluded. A virtual snowball sampling was used to recruit participants. In total, 1190 health care workers consented to participate in the survey. Of these, 957 completed at least one or more of the questionnaires. The detailed methods for the parent study have been submitted for publication elsewhere.\u003c/p\u003e\n\u003cp\u003eThe analyses for this study are based on a sub-population of 887 participants who completed the Alcohol Use Disorder Identification Test (AUDIT) questionnaire(7). All participants provided informed consent. The invitation to the online survey included study information and the option to select \u0026ldquo;I agree\u0026rdquo; or \u0026ldquo;I disagree\u0026rdquo; to participate in the study. Participants were informed that selecting the \u0026ldquo;agree\u0026rdquo; option meant that they had read and understood the invitation, had confirmed that they were the age of 18 and above, and had voluntarily agreed to participate in the study. Ethical approval to conduct the study was obtained from the Institutional Research Ethics Committee (IREC) of Moi University/Moi Teaching \u0026amp; Referral Hospital.\u003c/p\u003e\n\u003cp\u003eThe survey instrument was programmed into Redcap, (Research Electronic Data Capture) (23) a secure, web-based software platform designed to support data capture for research studies. The online survey was sent to healthcare workers in various networks on Facebook, WhatsApp and E-mail between April 1 and April 30, 2020. The healthcare workers were requested to respond to the survey while a track of responses was kept using the Redcap software. A weekly reminder was sent for a duration of one month.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocio-demographic data:\u003c/strong\u003e A researcher designed questionnaire was used for collecting socio-demographic data including age, sex (male/female), marital status (married/not married), cadre (doctor/nurse/specialist/other), type of facility (public/private), contact with COVID-19 patients (yes/no), years of experience in health care (0-10, 11-20, 20+), and history of a chronic medical condition (yes/no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHarmful alcohol use:\u003c/strong\u003e The primary outcome for this study was harmful alcohol use. This was measured using the AUDIT (7) which examines for past year alcohol use. The AUDIT consists of 10 questions and total scores range from 0 to 40. A score of 8 and above was considered harmful alcohol use for our study (7). The AUDIT has been used among adults in Kenya (24).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepression:\u003c/strong\u003e Depression was measured using the Patient Health Questionnaire-9 (PHQ-9) (25). It is a 9-item self-report instrument and examines for symptoms over the past two week period. Total scores range from 0-27. For our study, a score of 0-4 was considered none/minimal depression, 5-9 mild depression, 10-14 moderate depression, 15-19 moderately severe depression, and 20-27 severe depression (25). The PHQ-9 has excellent reliability and validity. The PHQ-9 has been validated among adults in Kenya (26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralized Anxiety Disorder (GAD)\u003c/strong\u003e: GAD was assessed using the GAD-7 scale (27). It is a seven item self-report instrument that examines for symptoms over the past two week period. Total scores range from 0 to 21. A score of 0-4 was considered minimal GAD, 5-9 mild GAD, 10-14 moderate GAD, and 15-21 severe GAD for our study (27). The GAD-7 has been validated in Kenya (28).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-Traumatic Stress Disorder (PTSD):\u003c/strong\u003e We used the Primary Care- Post Traumatic Stress disorder (PC- PTSD) for Diagnostic statistical manual- 5 to measure PTSD(29). The tool is a 5-item screen used for past month PTSD symptoms. A score of 3 and above was considered probable PTSD in our study (29).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSleep quality: \u003c/strong\u003eThis study used the Pittsburgh Sleep Quality Index (PSQI) (30) to assess for sleep quality. It is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven \"component\" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. A score of 5 and above indicated poor quality sleep for our study. Such a score has a sensitivity of 89.6% and specificity of 86.5% in distinguishing good and poor quality of sleep (30). The tool has been used among adults in Kenya (31).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize the socio-demographic characteristics of the participants. Chi square test was used in the bivariate analysis, to assess for the association between harmful alcohol use and socio-demographic and mental health factors. Significant variables were subjected to the multivariable logistic regression analysis and presented as adjusted odds ratios (AORs) and 95% CIs. Data analysis was performed using R Core Team (2013). In all analysis a p-value less than 0.05 was considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic characteristics of participants:\u003c/strong\u003e A total of 887 participants completed the AUDIT questionnaire. Most respondents were aged 35 years and above (51.4%), were female (54.6%), worked in public health facilities (70.0%) and had 10 years or less of experience in health care (57.7%) . Less than one third of the respondents (24.0%) had come into contact with a patient diagnosed with COVID-19. Of the 887 respondents, 39.9% were doctors, 18.8% were nurses, 16.8% were specialists and 24.4% belonged to other cadres (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Socio-demographic characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; N (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eAge in years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026lt;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e431 (48.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026gt;=35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e456 (51.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e403 (45.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e484 (54.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e579 (65.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNot married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e308 (34.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eYears of experience in health care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e0-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e512 (57.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e11\u0026ndash;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e219 (24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e20+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e156 (17.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eCadre\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eDoctor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e354 (39.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNurse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e167 (18.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e216 (24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eSpecialist\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e149 (16.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eType of facility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePublic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e621 (70.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e266 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eHave a chronic medical condition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e202 (22.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e685 (77.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003eContact with COVID-19 patients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e212 (23.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29%\"\u003e\n\u003cp\u003e\u0026nbsp;675 (76.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eMental health characteristics of the participants: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the 887 participants who responded to the AUDIT questionnaire, 858 (96.7%) completed the PHQ-9; 807 (91.0%) completed the GAD-7, 348 (39.2%) completed the PC-PTSD and 772 (87.0%) completed the PSQI.\u0026nbsp; Of the respondents who completed the PHQ-9, all respondents (100%) endorsed some level of depression. Thirty six percent of those who completed the GAD-7 reported some level of GAD, while poor sleep quality was endorsed by 24.5% of those who completed the PSQ-I. The PC-PTSD was completed by the least number of respondents. Sixty-five percent of participants responding to that questionnaire reported symptoms of probable PTSD (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Mental health characteristics of the participants \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"4\" width=\"100%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDepression (N= 858)\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; N (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"62%\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003e581 (67.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"62%\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003e144 (16.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"62%\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003e133 (15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"4\" width=\"100%\"\u003e\n\u003cp\u003e\u003cstrong\u003eGAD (N= 807)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"63%\"\u003e\n\u003cp\u003eMild/Moderate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"36%\"\u003e\n\u003cp\u003e232 (28.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"63%\"\u003e\n\u003cp\u003eNone/minimal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"36%\"\u003e\n\u003cp\u003e516 (64.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"63%\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"36%\"\u003e\n\u003cp\u003e59 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"4\" width=\"100%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePTSD (N=348)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"62%\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003e\u0026nbsp;123 (35.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"62%\"\u003e\n\u003cp\u003eProbable PTSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"37%\"\u003e\n\u003cp\u003e\u0026nbsp;225 (64.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"4\" width=\"100%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSleep quality (N=772)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"64%\"\u003e\n\u003cp\u003ePoor quality of sleep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"35%\"\u003e\n\u003cp\u003e189 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35.8664px;\"\u003e\n\u003ctd style=\"height: 35.8664px;\" colspan=\"3\" width=\"64%\"\u003e\n\u003cp\u003eGood quality of sleep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.8664px;\" width=\"35%\"\u003e\n\u003cp\u003e583 (75.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of harmful alcohol use: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree hundred and eighty nine (43.9%) participants reported harmful alcohol use based on an AUDIT score of 8 and above (95%CI: [40.6%,47.2%]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with harmful alcohol use \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn bivariate analysis, gender, marital status, cadre and years of experience in the health field were significantly associated with harmful alcohol use (Tables 3 and 4). In multivariate analysis, the factors significantly associated with increased odds of endorsing harmful alcohol use were: being male (AOR= 1.56; 95% CI=1.14, 2.14; p=0.006), being not married (AOR= 2.06; 95% CI=1.48, 2.89; p\u0026lt;0.001),\u0026nbsp; \u0026nbsp;having 11-20 years of experience in health care as compared to having 20+ years of experience (AOR= 1.91; 95% CI=1.18, 3.12; p=0.009), and being a specialist (AOR=2.78; CI=1.64, 4.78; P=\u0026lt;0.001) or doctor (AOR= 2.82; 95% CI=1.74, 4.63; p\u0026lt;0.001) as compared to being a nurse. Age, and endorsing depression or generalized anxiety were not associated with harmful alcohol use (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Bivariate analysis of socio demographic factors and harmful alcohol use\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"28%\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"36%\"\u003e\n\u003cp\u003eAlcohol use (N=887)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eHarmful\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eN (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eNot Harmful\u003c/p\u003e\n\u003cp\u003eN (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eAge in years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026lt;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e198 (45.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e233 (54.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.251\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e\u0026gt;=35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e191 (41.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e265 (58.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e198 (49.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e205 (50.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e191 (39.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e293 (60.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e224 (38.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e355 (61.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNot married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e165 (53.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e143 (46.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eYears of experience in health care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e0-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e238 (46.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e274 (53.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e11\u0026ndash;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e104 (47.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e115 (52.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e20+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e47 (30.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e109 (69.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eCadre\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eDoctor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e178 (50.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e176 (49.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNurse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e38 (22.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e129 (77.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e104 (48.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e112 (51.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eSpecialist\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e68 (45.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e81 (54.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eType of facility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePublic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e265 (42.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e356 (57.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.312\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePrivate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e124 (46.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e142 (53.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eHave a known medical condition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e97 (48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e105 (52.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e292 (42.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e393 (57.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eContact COVID-19 patients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e92 (43.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e120 (56.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e297 (44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e378 (56.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e harmful alcohol use was defined by a score of 8 and above on the AUDIT\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Bivariate analysis of mental disorder and harmful alcohol use\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35.0881px;\"\u003e\n\u003ctd style=\"height: 97.0881px;\" rowspan=\"2\" width=\"26%\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.0881px;\" width=\"23%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.0881px;\" colspan=\"2\" width=\"33%\"\u003e\n\u003cp\u003eAlcohol use (N=887)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 97.0881px;\" rowspan=\"2\" width=\"15%\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 62px;\"\u003e\n\u003ctd style=\"height: 62px;\" width=\"23%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 62px;\" width=\"16%\"\u003e\n\u003cp\u003eHarmful\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eN (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 62px;\" width=\"16%\"\u003e\n\u003cp\u003eNot Harmful\u003c/p\u003e\n\u003cp\u003eN (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e241 (41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e340 (58.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e63 (43.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e81 (56.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e70 (52.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e63 (47.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003eGAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eMild/Moderate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e115 (49.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e117 (50.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eNone/minimal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e211 (40.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e305 (59.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e29 (49.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e30 (50.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003ePTSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e57 (46.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e66 (53.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e0.430\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eProbable PTSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e93 (41.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e132 (58.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003ePSQI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003ePoor quality sleep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e86 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e103 (54.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"23%\"\u003e\n\u003cp\u003eGood quality sleep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e253 (43.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e330 (56.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e harmful alcohol use was defined by a score of 8 and above on the AUDIT\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Multivariate analysis of association between harmful alcohol use and socio-demographic and mental health factors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; AOR\u003csup\u003eC\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; p-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026lt;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026gt;=35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.70, 1.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.700\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.14, 2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eNot married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.48, 2.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eYears of experience in health care\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e20+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e11\u0026ndash;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.18, 3.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e0-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.88, 2.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.140\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eCadre\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eNurse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eSpecialist\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.64, 4.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eDoctor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.74, 4.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"119\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.57, 4.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003ePHQ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.73, 1.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.500\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.90, 2.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u003cstrong\u003eGAD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eNone/minimal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eMild/Moderate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.72, 1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.700\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.52, 2.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"336\"\u003e\n\u003cp\u003e0.800\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Confidence Interval\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to examine harmful alcohol use among health care workers during the COVID-19 pandemic in a LMIC. Our findings indicate that 43.9% of the participants endorsed harmful patterns of alcohol use based on the AUDIT. Using a similar tool Hennein et al (20) reported comparable findings. The authors found that 42.6% of health care workers in the US had probable alcohol use disorder during the COVID-19 pandemic. Our findings are considerably higher than those reported by Mokaya et al. (32) among health care workers in the pre-pandemic period in Kenya. The study found that only 2.9% of health care workers endorsed moderate risk alcohol use and that none endorsed high risk use (32). This suggests an increase in rates of alcohol consumption during the COVID-19 pandemic among health care workers in Kenya. Such a high burden of harmful alcohol use is likely to further constrain the already limited workforce (33) and contribute to inefficiencies and disruptions to health service delivery at this crucial time.\u003c/p\u003e\n\u003cp\u003eIn our study, being male was associated with increased odds of harmful alcohol use. This finding is consistent with prior studies conducted among health care workers (32) and the general population (34) in Kenya, and might be explained by the fact that in many cultures, traditional gender roles may prevent the development of problematic substance use for women (35). Unmarried health care workers were more likely to report harmful alcohol use compared to the married. This is comparable to other studies that have shown a higher prevalence of alcohol use among single or divorced persons (36). Being unmarried may be associated with social isolation, a well documented risk factor for harmful substance use (37,38). Specialists, doctors and other cadres were significantly more likely to endorse harmful alcohol use as compared to nurses. Nurses in Kenya have strong social welfare systems that could potentially prevent the use of alcohol as a way of coping with stress during the pandemic. Having 11-20 years of experience in the health profession was associated with increased odds of harmful alcohol use as compared to having 20+ years or having 0-10 years of experience. Findings concerning the association between years of experience and harmful alcohol use have been inconsistent. Obadeji et al., in a study conducted among doctors in Nigeria reported no association between years of experience and hazardous alcohol use (39). Kenna and Lewis found alcohol use disorder among health care providers to be associated with having younger licenses (40). A possible reason for significant harmful alcohol use among healthcare workers with 11-20 years of experience could be that that phase represents a period of heightened psychological stress linked to residency, and increasing family and work place responsibilities.\u003c/p\u003e\n\u003cp\u003eOur study reported no significant differences in the rates of harmful alcohol use among health care workers with and without mental health disorders. This was an unexpected finding since prior studies conducted during the COVID-19 pandemic have overwhelmingly reported a positive association between harmful alcohol use and mental health symptoms including depression (15,16,41); PTSD (42); anxiety (15,42,43), and Stress (43). It is not clear why the present study did not find an association between harmful alcohol use and mental health symptoms. Future longitudinal research in our setting could shed more light on this.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for practice: \u003c/strong\u003eThe high prevalence of harmful alcohol use among health care workers in Kenya during the COVID-19 pandemic specifically among doctors and specialists, males, the unmarried, and those with 11-20 years of experience highlights the urgent need to put in place appropriate prevention and treatment interventions targeting these groups. Several interventions may be delivered including (i) health education on the harmful impact of alcohol use and debunking of myths that encourage alcohol use during the pandemic (ii) education on strategies for health promotion such as a healthy diet, adequate sleep, physical activity and stress management (iii) screening and brief interventions for alcohol use and (iv) interventions to promote social connectedness (44,45). Virtual platforms and mobile health strategies represent a potential platform for delivering the above interventions given the current COVID-19 restrictions. Currently the Ministry of Health has established a call centre whose aim is to offer both knowledge and psychosocial support to frontline health workers (46). This presents an avenue through which alcohol related health education and screening and brief interventions may be conducted.\u003c/p\u003e\n\u003cp\u003eIf the patterns observed post other disasters are anything go by (12,47), the rise in alcohol use observed during the COVID-19 outbreak is likely to persist for several years beyond the pandemic period. It is therefore important that long term strategies are put in place to manage alcohol use within health care settings. We recommend that health care settings in Kenya establish employee assistance programs and develop policies that address substance use in the workplace. Fortunately, the National Authority for the Campaign Against Alcohol \u0026amp; Drug Abuse (NACADA), has published guidelines for the development of workplace substance use programs and policies that institutions can use for reference (48).\u003c/p\u003e\n\u003cp\u003eAt policy level, delisting alcohol use as an essential commodity during the pandemic could reduce its availability and thus limit its use as a way of coping during the pandemic.\u003c/p\u003e\n\u003cp\u003eWe acknowledge some limitations. Firstly, this being an online survey, it may have been less accessible to people who lacked smartphones, had no internet access, or were not on the social media platforms we utilized. Our findings may therefore not include their experiences. Secondly, our sample was not representative of the composition of healthcare workers in Kenya. Our sample was comprised of mostly doctors while nurses comprise more than a half of health care workers in Kenya. Thirdly, this was a cross-sectional study and therefore no causal relationships may be determined. Nonetheless this study provides for the first time important information on harmful alcohol use among health care workers during the COVID-19 pandemic in a LMIC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, a high proportion of health care workers in Kenya reported harmful alcohol use. Males, the unmarried, those with 11-20 years of experience in health care, doctors and specialists were more likely to report harmful alcohol use. Given the potential negative impact of harmful alcohol use not only on the mental and physical health of the HCWs but also on health service delivery, it is critical that the government puts in place interventions to address this problem. In the short term, virtual platforms and mobile health strategies could be utilized to deliver health education, as well as screening and brief interventions for harmful alcohol use. In the long term, health care settings ought to establish substance use workplace programs and policies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col\u003e\n\u003cli\u003eAOR - Adjusted Odds Ratio\u003c/li\u003e\n\u003cli\u003eAUDIT - Alcohol Use Disorder Identification Test\u003c/li\u003e\n\u003cli\u003eCOVID-19 - Corona Virus Disease of 2019\u003c/li\u003e\n\u003cli\u003eDSM-5 - Diagnostic Statistical Manual 5\u003csup\u003eth\u003c/sup\u003e Edition\u003c/li\u003e\n\u003cli\u003eGAD - Generalized Anxiety Disorder\u003c/li\u003e\n\u003cli\u003eIREC - Institutional Research Ethics Committee\u003c/li\u003e\n\u003cli\u003eLMIC - Low and Middle Income Country\u003c/li\u003e\n\u003cli\u003eNACADA - National Authority for the Campaign Against Alcohol \u0026amp; Drug Abuse\u003c/li\u003e\n\u003cli\u003ePHQ-9 - Patient Health Questionnaire-9\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003ePC- PTSD - Primary Care- Post Traumatic Stress disorder\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003ePSQI - Pittsburgh Sleep Quality Index\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003ePTSD - Post Traumatic Stress Disorder\u003c/li\u003e\n\u003cli\u003eSARS - Severe Acute Respiratory Syndrome\u003c/li\u003e\n\u003cli\u003eUS - United States\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm 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. The study was approved by the Moi Teaching and Referral Hospital/Moi University School of Medicine Institutional Research and Ethics Committee (IREC/2020/59: FAN 003589) and the National Council for Science and Technology (Nacosti/P/20/4835).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from the participant by ensuring that only those who clicked on agree to participate were able to access the online survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was completed with support by Kenya Medical Association, Equity project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in designing the study. A.M. conducted the analyses. F.J. drafted the manuscript. All authors contributed to and reviewed all versions of the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organisation. WHO Coronavirus Disease (COVID-19) Dashboard. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19.who.int/\u003c/span\u003e\u003c/span\u003e. Accessed 31 Jan 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M, et al. Impact of COVID-19 pandemic on mental health: An international study. 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Frequent binge drinking five to six years after exposure to 9/11: findings from the World Trade Center Health Registry. Drug Alcohol Depend. 2014;140:1\u0026ndash;7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.drugalcdep.2014.04.013\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Authority for the Campaign Against alcohol and Drug Abuse. Guidelines for Developing a Workplace Substance Abuse Prevention Policy. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nacada.go.ke/sites/default/files/2019-10/ADA%20Policy%20Guidelines%202018.pdf\u003c/span\u003e\u003c/span\u003e. Accessed 31 January 2021.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"harmful alcohol, healthcare, workers, COVID-19, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-403929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-403929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The on-going COVID-19 pandemic has resulted in a major negative impact on public mental health particularly among health care workers. Alcohol use is a common maladaptive response to stress that is associated with adverse health consequences and that could impair productivity in the workplace for the health workforce. The aim of this study is to document the burden and factors associated with harmful alcohol use among health care workers at the beginning of the COVID-19 pandemic in Kenya.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study was a cross-sectional analysis of data obtained from a parent online survey that investigated the prevalence and factors associated with mental disorders among healthcare workers during the COVID-19 pandemic in Kenya. Analyses for this study were conducted to examine the burden and factors associated with harmful alcohol use among a sub-group of 887 participants who completed the Alcohol Use Disorder Identification Test (AUDIT) questionnaire.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Three hundred and eighty nine (43.9%) participants reported harmful alcohol use. The factors significantly associated with increased odds of endorsing harmful alcohol use were: being male (AOR= 1.56; 95% CI=1.14, 2.14; p=0.006), being not married (AOR= 2.06; 95% CI=1.48, 2.89; p\u0026lt;0.001),\u0026nbsp;having 11-20 years of experience as compared to having 20+ years of experience (AOR= 1.91; 95% CI=1.18, 3.12; p=0.009), and being a specialist (AOR=2.78; CI=1.64, 4.78; P=\u0026lt;0.001) or doctor (AOR= 2.82; 95% CI=1.74, 4.63; p\u0026lt;0.001) as compared to being a nurse. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e A high proportion of health care workers reported harmful alcohol use at the beginning of the COVID-19 pandemic in Kenya. Males, the unmarried, those with 11-20 years of experience in the health field, doctors and specialists were more likely to report harmful alcohol use. These findings highlight the need to institute interventions for harmful alcohol use targeting these groups of health care workers in Kenya during the COVID-19 pandemic.\u003c/p\u003e","manuscriptTitle":"Harmful Alcohol Use Among Healthcare Workers at the Beginning of the COVID-19 Pandemic in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-13 19:27:28","doi":"10.21203/rs.3.rs-403929/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8526dc1-f162-437b-9bb3-936881d50027","owner":[],"postedDate":"April 13th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3615639,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2022-02-28T06:22:05+00:00","versionOfRecord":{"articleIdentity":"rs-403929","link":"https://doi.org/10.3389/fpsyt.2022.821610","journal":{"identity":"frontiers-in-psychiatry","isVorOnly":true,"title":"Frontiers in Psychiatry"},"publishedOn":"2022-02-28 06:22:05","publishedOnDateReadable":"February 28th, 2022"},"versionCreatedAt":"2021-04-13 19:27:28","video":"","vorDoi":"10.3389/fpsyt.2022.821610","vorDoiUrl":"https://doi.org/10.3389/fpsyt.2022.821610","workflowStages":[]},"version":"v1","identity":"rs-403929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-403929","identity":"rs-403929","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}