PMC:7573904 / 100-107 JSON TXT

Self-Reported Alcohol, Tobacco, and Cannabis Use during COVID-19 Lockdown Measures: Results from a Web-Based Survey Abstract Background The outbreak of coronavirus disease 19 (COVID-19) has led to measures of social distancing and quarantine worldwide. This stressful period may lead to psychological problems, including increases in substance use. Objective To investigate changes in alcohol, tobacco, and cannabis consumption before and during COVID-19 lockdown and motives for these changes in substance use. Method A web-based survey was filled out by an unselected population during the social distancing measures of the COVID-19 pandemic in Belgium that assessed changes in alcohol, tobacco, and cannabis consumption in the period before and during the COVID-19 lockdown and also asked about reasons for change. Results A total of 3,632 respondents (mean age 42.1 ± 14.6 years; 70% female) filled out the survey. Overall, respondents reported consuming more alcohol (d = 0.21) and smoking more cigarettes (d = 0.13) than before the COVID-19 pandemic (both p < 0.001), while no significant changes in the consumption of cannabis were noted. The odds of consuming more alcohol during the lockdown were associated with younger age (OR = 0.981, p < 0.001), more children at home (OR = 1.220, p < 0.001), non-healthcare workers (p < 0.001), and being technically unemployed related to COVID-19 (p = 0.037). The odds of smoking more cigarettes during the lockdown were associated with younger age (OR = 0.988, p = 0.027), current living situation (p < 0.001), lower education (p = 0.015), and working situation related to COVID-19 (p = 0.018). Boredom, lack of social contacts, loss of daily structure, reward after a hard-working day, loneliness, and conviviality were the main reasons for consuming more of the various substances. Conclusions During the lockdown, individuals consumed slightly more alcohol and smoked marginally more cigarettes compared to the period before the lockdown. Further research focussing on follow-up of individuals at risk may be useful to provide appropriate care in post-COVID times. Introduction COVID-19 (coronavirus disease 2019) is an infectious disease caused by the virus SARS-CoV-2. The infection is highly contagious and causes severe pneumonia, acute respiratory distress syndrome, and death particularly in vulnerable populations including older adults and people with chronic medical conditions such as cardiovascular disease, diabetes, respiratory disease, hypertension, and malignancy [1]. In an attempt to control the COVID-19 pandemic, physical distancing and hygiene measures, including regular hand washing and covering of nose and mouth when sneezing or coughing, were implemented worldwide. In several countries, sudden and dramatic social changes were implemented to slow contagion and “flatten the curve” of the pandemic to preserve capacity of healthcare resources, which included asking people to stay at home as much as possible [2]. Quarantine or social isolation is stressful and associated with psychological problems, especially in older adults, females, individuals from lower socio-economic groups, and those with underlying mental health conditions [3, 4]. Psychological problems can include anxiety, depression, acute stress disorder, substance abuse, insomnia, and post-traumatic stress disorder [3, 5]. A review on the impact of the major infectious disease outbreak of SARS in 2003 shows that the psychological impact is mainly associated with occupational role, high-risk work environment, quarantine, social support, and the impact on our personal and professional life [6]. Having been quarantined and working in hospital or wards during the 2003 SARS outbreak were linked with the development of alcohol abuse and dependence symptoms 3 years later [7]. Drinking was self-reported by hospital employees as a coping method and was found significantly related with hyperarousal [7]. The main predictors of alcohol consumption after a massive traumatic experience were alcohol consumption before the event and the rationale for drinking during the event, where drinking as a coping mechanism and drinking for pleasure were the main predictors for long-term alcohol consumption up to 10 years later [8]. The almost global lockdown is unique, and little is known about the consumption of substances during social isolation. We hypothesized that individuals in social isolation due to lockdown measures would consume more substances. Mapping the changes in substance use and the reasons for these changes during large-scale general social isolation is important. It reveals the response of the general population in times of stress and may, therefore, be of valuable help to provide appropriate care in post-COVID times and indicate possible preventive actions in case of similar events. In this study, we report on changes in alcohol, tobacco, and cannabis use in the midst of the COVID-19 lockdown in Belgium and report on the major motives for the change in consumption. Materials and Methods Study Population and Data Collection A cross-sectional survey was sent out over a brief period of time (20 days; between April 9, 2020, and April 29, 2020) during the full COVID-19 lockdown measures in Belgium. The survey was distributed by the communication services of the University Hospital of Brussels (UZ Brussel) and the university (Vrije Universiteit Brussel) and was circulated through online social media (Facebook and Twitter). The target population included adult Belgian residents, irrespective of age, living area, or current substance use. To reach out to an unselected population, the survey was made available by a link to the survey on Qualtrics online and the link was shared through social media, including Facebook (targeting a broad range of individuals), the websites of the university (targeting students) and the university hospital (targeting patients and hospital co-workers), and the website SeniorenNet.be (targeting the senior generation). Individuals older than 18 years and living or residing in Belgium during the COVID-19 lockdown measures were asked to fill in the survey online. There were no formal exclusion criteria. The ethics committee of the University Hospital Brussels, Belgium, approved this study on April 9, 2020 (No. 143202043). Measures The questionnaire consisted of 5 different parts with 20 questions regarding (1) demographics, (2) smoking cigarettes, (3) alcohol use, (4) cannabis use, and (5) factors that influenced substance use during the COVID-19 lockdown measures. The questionnaire was made available in Dutch, French, and English. Demographic questions included age, gender, and postal code. Age was asked as an age category (per 5 years). Postal code was recoded into the variables “urban” and “rural.” Current living situation (with partner, with children, with others, or alone), social situation (age and number of children and cared for at home or outside of home), and professional situation (profession, education, and currently home-working) were also asked. Smoking prior to the lockdown measures was noted as the number of cigarettes smoked on an average day in the period before the COVID-19 lockdown. Response options included “not smoking” (0 cigarettes per day), smoking 1–5 cigarettes per day, smoking 6–10 cigarettes per day, smoking 11–19 cigarettes per day, or smoking 20 or more cigarettes per day. Smoking during the current COVID-19 lockdown was assessed as the average number of cigarettes smoked now and by asking whether they currently smoke more, less, or about the same compared to before the lockdown. In case they smoked more or less, they were questioned about the average number of additional or fewer cigarettes on a normal day, using the same response options regarding smoking prior to the lockdown measures. For questions regarding alcohol use, reference was made to alcohol units per day. A standard drink (one alcohol unit) was defined as 10 g of pure alcohol. This corresponds to one 25 cL glass of beer (5% alcohol), 10 cL of wine (12% alcohol), 5 cL of aperitif (15% alcohol), or 3.5 cL of distilled spirits (35% alcohol). Respondents were asked how much alcohol he/she drank on an average day before the COVID-19 measures and how much he/she is drinking now (during the lockdown measures), with additionally specifying whether he/she drank more, less, or about the same than before. Response options referred to the average quantity of alcohol consumption during the prior week (rather than the frequency) and included the options “I do not drink alcohol” (mean of 0 alcohol units per day) and “on average 1 alcohol unit per day” to “on average >12 alcohol units per day,” with specifically denoting whether they drank more, less, or about the same as usual. Cannabis use was assessed as mean amount of cannabis joints smoked per day, both before and during the COVID-19 lockdown measures. The questions concerned the average amount of cannabis consumption per day and specifically asked whether the respondent smoked less, more, or about the same. The final question asked about motives for increased use of any substance during the lockdown with several options (listed further in Table 4). Those options were not mutually exclusive; several options could be chosen together, and there was opportunity for personal additions from the respondents, as “others, please specify: ___.” Statistical Analyses In the survey, age, amount of alcohol units, smoking cigarettes, and cannabis use were recorded in categories. For making these outcomes linear, we applied a transformation of the outcomes by using the mean value of the upper and the lower bound of the categories. Profession was recoded taking into account the variables “do you work in healthcare” and “profession,” thereby regrouping free text into 6 categories: working healthcare, working non-healthcare, student, retired, job seeking, and others. Linear variables were presented as mean ± SD where applicable. Categorical variables were reported using the number of observations and the percentage of observations. Comparison between the amounts of use in both periods (before and during the COVID-19 lockdown measures) was performed using a Wilcoxon signed-rank test. Cohen's d were calculated and represent the effect size. McNemar tests were used to compare substance use before and during the lockdown. In these McNemar tests, a categorization of “using more” or “using less or equal” was used. When >5% of the respondents showed to consume more during the lockdown compared to before, a binary logistic regression was executed. Here, a backwards regression model building technique was used with variables (age, sex, living in a city, family situation, number of children, professional occupation, educational background, and whether or not you work more from home during the lockdown). Removal of a predictor variable or factor was considered appropriate when having a significance of >0.10. Furthermore, a significance level of 0.05 was considered statistically significant for all tests. In order to examine the motives for substance use and the reason why respondents were using more during the COVID-19 measures than before, a descriptive analysis was executed. After reporting the frequencies, the motives were ranked from highest present motive to lowest present motive for each of the substances. Statistics were performed using SPSS version 26. Results Respondents' Characteristics A total of 4,098 respondents participated, with 3,632 (89%) completing the survey. The mean age was 42.1 ± 14.6 years. The characteristics of the n = 3,632 respondents are specified in Table 1. Most respondents were female (70%), between 23 and 47 years old (70%), mostly from rural areas (76%), with higher education (78%), and living with a partner (69%). The vast majority of the participants (93%) filled out the questionnaire in Dutch. Alcohol Use 2,243 respondents (61.8%) were consuming alcohol before the lockdown. Of these, 341 quit drinking during the lockdown (9.4% of the total sample). Of the 1,389 non-consuming respondents before the lockdown, 211 started consuming alcohol during the lockdown (5.8% of the total sample; p < 0.001). Of all respondents, 30.3% stated that they drank more and 13.7% stated to drink less during than before the lockdown. A statistically significant, but small (d = 0.21), difference was found between the number of drinks per day before and during the lockdown (1.0 ± 1.4, range 0–15, and 1.4 ± 2.1, range 0–21, respectively; p < 0.001). Logistic regression analysis (see Table 2) shows that the odds of consuming more alcohol decreased during the lockdown with 1.9% on average per age year of the respondent (OR = 0.981; p < 0.001). Also, a higher number of children at home increased the odds of consuming more alcohol during lockdown measures (p < 0.001): per extra child in the family, the odds increased with 22% (OR = 1.220; 95% CI: 1.146–1.298). A 3rd variable of influence was whether a respondent now works more from home compared to before the lockdown (p = 0.037). Technical unemployed respondents are 36% more likely to drink more (OR = 1.357; p = 0.013) compared to respondents at home due to tele-working. Also, the professional occupation of the respondent played a role in drinking more during the lockdown (p < 0.001), with non-healthcare workers being 40% more likely to drink compared to healthcare workers during the lockdown (OR = 1.404; p < 0.001). Students, however, were on average 46% less likely to drink more compared to healthcare workers (OR = 0.537; p < 0.001). Gender, living in a city, family situation, and educational background were variables without significant effect on the change in alcohol use. Cigarettes 558 respondents (15.4%) smoked cigarettes before the lockdown. Of these, 36 quit smoking during the lockdown (1.0% of the total sample). Of the 3,074 non-smokers before the lockdown, 34 started smoking during the lockdown (0.9% of the total sample). Of all respondents, 7.4% stated smoking more and 2.5% stated smoking less during than before the lockdown. A statistically significant, but negligible (d = 0.13), difference was found between the number of cigarettes per day before and during the lockdown (1.5 ± 4.5, range 0–30, and 1.7 ± 5.5, range 0–60, respectively; p < 0.001). Table 3 shows the results from the logistic regression analysis. Age was inversely associated with the odds of smoking cigarettes being 12% per 10 years more during than before the lockdown (OR = 0.988; p = 0.027). Also, living situation had a statistically significant relationship with increased smoking during the lockdown (p < 0.001): living alone more than doubled the odds of smoking more cigarettes (OR = 2.159; 95% CI: 1.587–2.938) during the lockdown compared to living together with a partner and/or child. Educational background also had a significant effect on the change in cigarette smoking (p = 0.015), with lower education being associated with more cigarette smoking: participants with a vocational education had about double odds to smoke more compared to those with a higher education (OR = 2.085; p = 0.042), whereas those having a doctorate were 76% less likely to smoke more compared to those with a higher education (OR = 0.245; p = 0.017). Finally, technical unemployed respondents were 64% more likely to smoke more cigarettes compared to respondents that were tele-working during the lockdown (p = 0.010). Gender, living in a city, number of children, and professional occupation were variables without significant effect on the change in smoking cigarettes. Cannabis Use Of the 105 respondents that used cannabis before the lockdown, 26 respondents (0.7% of the total sample) stopped using during the lockdown. Of the 3,527 respondents not using cannabis before the lockdown, 31 started using cannabis during the lockdown (0.9% of the total sample; p = 0.597). Of all respondents, only 2.1% stated that they used more cannabis than before and only 1.1% stated to consume less during the lockdown. There was no statistically significant difference between the number of joints per day before and during the lockdown (0.1 ± 0.5 joints per day, range 0–8, and 0.1 ± 0.4 joints per day, range 0–5; p = 0.508). As a result, no regression analysis was performed. Motives for Use of Alcohol, Cigarettes, and Cannabis during Lockdown Measures When observing the motives that drove respondents to consume more cigarettes, alcohol, and cannabis during the COVID-19 measures compared to before, the top reasons were (1) boredom, (2) lack of social contacts, (3) loss of daily structure, (4) reward after a hard-working day, (5) loneliness, and (6) conviviality (see Table 4). Discussion and Conclusion During the COVID-19 lockdown, people self-reported drinking slightly more alcohol compared to before the lockdown, which was negatively associated with age and positively associated with the number of children, unemployment, and homework. The top motive for drinking more was conviviality. The number of cigarettes smoked per day only marginally increased during the lockdown, again with younger respondents having increased odds of smoking more during the lockdown. Similarly, living alone, having a lower educational background, and being technically unemployed increased the odds of smoking more cigarettes during the lockdown as well. The top motive for smoking more cigarettes was boredom. Cannabis use did not change in times of social isolation. This may indicate a true similarity in the point estimates before and during the lockdown, but it may also reflect a zero net-effect of increased need and reduced availability of cannabis in times of social lockdown (borders are closed, dealers have a harder time to go around unnoticed, and prices increase due to increased demand). Similar to smoking, the top for an increased use of cannabis was boredom. The similarities between ranked motives of cannabis and cigarette smoking are most probably an artefact because most cannabis users are also cigarette smokers. This explains why the ranking of motives is almost the same for smoking cigarettes and cannabis use, and is the result of the fact that no separate questions about motives were asked for the 3 separate substances. It might be an interesting future research question to investigate what drives certain individuals to consume more, less, or equal during a pandemic, and individual assessment of different motives for use, per substance, would here be valuable. Also, focussing on more positive factors might reveal now underreported factors for better resilience and consequently more adequate coping mechanisms than substance use. This study sample is not optimally representative of the overall Belgian population with 50% men, less urban people, an average lower education, an estimate of 12% active in healthcare, and different jobs (possibly with higher technical unemployment rates). In this survey, we mainly note highly educated females (70%), with 36.8% being healthcare workers. We found no effect of gender and living in a city on changes in substance use and no effect of educational background for changes in alcohol use. However, our data show that non-healthcare workers were 40% more likely to consume more alcohol compared to healthcare workers during the lockdown. The respondents from this survey included more healthcare workers as would be expected from a true representative sample, which may infer that, in a true representative population, the differences in alcohol use may be larger than presented here. This needs to be confirmed in larger surveys. This study has limitations, including the very brief time during which this web-based survey was made accessible (20 days), which yielded a relatively small sample size (N = 3,632), while being representative for a uniform period of full lockdown measures. In addition, we did not follow-up on the self-reported substance use after this 3-week period, and use may stabilize or increase further with increased duration of lockdown. Another limitation was that we only assessed alcohol, cigarettes, and cannabis and the mean use of alcohol per day, rather than per week, such that individuals consuming only a few drinks per week may have over- or underreported their drinking. This was counterbalanced by the possibility to add “drinking more, or drinking less than usual, or about the same” in the next question regarding their change in alcohol use. As a whole, these data show that specific groups of individuals, especially younger individuals with lower educational background or those technically unemployed, have a higher risk of increasing their alcohol and/or cigarette use during times of social isolation. It is therefore of importance that care providers and especially general practitioners discuss the use of alcohol, tobacco, and cannabis with their patients, as this often has a beneficial effect on behaviour. Statement of Ethics Ethical approval was obtained. Because this was an anonymous web-based survey, patients were exempt from our Ethical Committee for given written informed consent. Research complied with the appropriate guidelines for human studies and animal welfare regulations and was approved as such by the Medical Ethics Committee of the University Hospital Brussels (UZ Brussel), under reference number 143202043. Conflict of Interest Statement C.L.C. has received lecture fees from Takeda and GME. F.M. received honoraria from Lundbeck, Eli Lilly, and Johnson & Johnson. None were related with this research. The other authors have nothing to declare. Funding Sources The authors did not receive any funding. Author Contributions N.V. started the project and C.L.C. prepared the 1st draft. S.V.L. performed the analyses. F.M., D.Z., L.S., and S.V.D.A. provided comments and revised the manuscript. All authors were involved in the study setup and approved the final manuscript in its current form. Table 1 Characteristics of respondents Study respondents (total N = 3,632) N % Age, years (mean ± SD) 42.1±14.6 Gender Female 2,541 70.0 Male 1,083 29.8 Gender-neutral 8 0.2 Language used to fill in survey Dutch 3,361 92.5 French 200 5.5 English 71 2.0 Area of residence Urban 865 23.8 Rural 2,767 76.2 Profession In healthcare 1,370 36.8 Not in healthcare 2,295 63.2 Educational background Primary education 28 0.8 Vocational education 72 2.0 Secondary education 562 15.5 Higher education 2,762 76.0 Doctorate 189 5.2 Missing 19 0.5 Current living situation Living alone 556 15.3 Living with partner and/or children 2,500 68.8 With children 0–5 years old 525 21.0* With children 6–12 years old 550 22.0* With children 13–18 years old 457 18.3* With children older than 18 years 565 22.6* Living with others 576 15.9 Household with children 1,609 44.3 Cared for at home 1,358 37.4 Cared for outside of home 190 5.2 Part of the children cared for at home 61 1.7 Characteristics of respondents (N = 3,632) in the Belgian ALCOVID study. SD, standard deviation. * Percentage of respondents living with partner and/or children. Table 2 Logistic regression regarding alcohol use OR 95% CI for OR p value lower upper Age, years 0.981 0.974 0.987 <0.001 Children, n 1.220 1.146 1.298 <0.001 Are you more at home during the lockdown? 0.037 Yes, with tele-work Ref. − Yes, due to technical unemployment 1.357 1.065 1.728 0.013 Yes, due to illness 0.842 0.541 1.311 0.447 Yes, due to illness related to COVID-19 0.813 0.434 1.526 0.520 No 0.882 0.727 1.069 0.201 Professional occupation <0.001 Healthcare worker Ref. − Non-healthcare worker 1.404 1.176 1.676 <0.001 Student 0.537 0.389 0.740 <0.001 Job seeker 1.086 0.468 2.521 0.848 Retired 1.312 0.879 1.957 0.184 Others (e.g., disabled person) 0.810 0.40 1.527 0.516 Logistic regression explaining relationships to why respondents consumed more alcohol during the lockdown compared to before. In total, 1,102 respondents out of 3,632 (30.3%) noted drinking more during the lockdown compared to before. Table 3 Logistic regression regarding nicotine use OR 95% CI for OR p value lower upper Age, years 0.988 0.978 0.999 0.027 Current living situation <0.001 Living with partner and/or child Ref. − Living alone 2.159 1.587 2.938 <0.001 Living with others 1.081 0.739 1.580 0.690 Educational background 0.015 Higher education Ref. − Primary education 1.114 0.258 4.817 0.885 Vocational education 2.085 1.025 4.240 0.042 Secondary education 1.312 0.944 1.823 0.106 Doctorate 0.245 0.077 0.777 0.017 Do you work more at home now? 0.018 Yes, with tele-work Ref. Yes, due to technical unemployment 1.638 1.125 2.386 0.010 Yes, due to illness 1.431 0.758 2.702 0.269 Yes, due to illness related to COVID-19 1.069 0.413 2.766 0.890 No 0.820 0.598 1.123 0.260 Logistic regression explaining relationships to why respondents smoke more cigarettes during the lockdown compared to before. In total, 264 respondents out of 3,632 (7.3%) noted they smoked more during the lockdown compared to before. Table 4 Ranking of motives for smoking more, drinking more, and using more cannabis Reason for more consuming of … Nicotine Alcohol Cannabis N % rank N % rank N % rank Has nothing to do with lockdown 16 6.1 12 26 2.4 13 2 2.6 12 Fear of contamination 10 3.8 14 35 3.2 12 2 2.6 12 Reward after a hard-working day 98 37.1 3 513 46.6 2 33 43.4 3 Increased tension (family, work, etc.) 59 22.3 7 270 24.5 6 17 22.4 8 Decreased tension (traffic, more sleep time, etc.) 27 10.2 11 123 11.2 11 16 21.1 9 Stress from negative reporting 50 18.9 8 70 15.4 8 19 25.0 7 Loneliness 75 28.4 5 168 15.2 9 25 32.9 5 Lack of social contacts 108 40.9 2 383 34.8 3 37 48.7 2 Lack of activities or sports 46 17.4 9 194 17.6 7 22 28.9 6 More tension due to permanent presence of housemates 38 14.4 10 157 14.2 10 9 11.8 10 Boredom 116 43.9 1 298 27.0 5 41 53.9 1 Conviviality 75 28.4 5 572 51.9 1 25 32.9 5 Increased social control 2 0.8 15 9 0.8 15 2 2.6 12 Loss of daily structure 98 37.1 3 332 30.1 4 33 43.4 3 Seeing smoking and drinking in movies and series 15 5.7 13 25 2.3 14 5 6.6 11 Total N: 264 (7.3%) Total N: 1,102 (30.3%) Total N: 76 (2.1%) Descriptive analysis of the motives for smoking more, drinking more, and using more cannabis. N represents the number of respondents using more. % represents the relative frequency of respondents using more indicating that motive. Rank defines the order of how often the motive was reported, ranging from 1 (highly reported motive) to 15 (lowest reported motive). If an equal number of respondents reported the motive, motives were given the same rank.

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article-title	Self-Reported Alcohol, Tobacco, and Cannabis Use during COVID-19 Lockdown Measures: Results from a Web-Based Survey
abstract	Background The outbreak of coronavirus disease 19 (COVID-19) has led to measures of social distancing and quarantine worldwide. This stressful period may lead to psychological problems, including increases in substance use. Objective To investigate changes in alcohol, tobacco, and cannabis consumption before and during COVID-19 lockdown and motives for these changes in substance use. Method A web-based survey was filled out by an unselected population during the social distancing measures of the COVID-19 pandemic in Belgium that assessed changes in alcohol, tobacco, and cannabis consumption in the period before and during the COVID-19 lockdown and also asked about reasons for change. Results A total of 3,632 respondents (mean age 42.1 ± 14.6 years; 70% female) filled out the survey. Overall, respondents reported consuming more alcohol (d = 0.21) and smoking more cigarettes (d = 0.13) than before the COVID-19 pandemic (both p < 0.001), while no significant changes in the consumption of cannabis were noted. The odds of consuming more alcohol during the lockdown were associated with younger age (OR = 0.981, p < 0.001), more children at home (OR = 1.220, p < 0.001), non-healthcare workers (p < 0.001), and being technically unemployed related to COVID-19 (p = 0.037). The odds of smoking more cigarettes during the lockdown were associated with younger age (OR = 0.988, p = 0.027), current living situation (p < 0.001), lower education (p = 0.015), and working situation related to COVID-19 (p = 0.018). Boredom, lack of social contacts, loss of daily structure, reward after a hard-working day, loneliness, and conviviality were the main reasons for consuming more of the various substances. Conclusions During the lockdown, individuals consumed slightly more alcohol and smoked marginally more cigarettes compared to the period before the lockdown. Further research focussing on follow-up of individuals at risk may be useful to provide appropriate care in post-COVID times.
sec	Background The outbreak of coronavirus disease 19 (COVID-19) has led to measures of social distancing and quarantine worldwide. This stressful period may lead to psychological problems, including increases in substance use.
title	Background
p	The outbreak of coronavirus disease 19 (COVID-19) has led to measures of social distancing and quarantine worldwide. This stressful period may lead to psychological problems, including increases in substance use.
sec	Objective To investigate changes in alcohol, tobacco, and cannabis consumption before and during COVID-19 lockdown and motives for these changes in substance use.
title	Objective
p	To investigate changes in alcohol, tobacco, and cannabis consumption before and during COVID-19 lockdown and motives for these changes in substance use.
sec	Method A web-based survey was filled out by an unselected population during the social distancing measures of the COVID-19 pandemic in Belgium that assessed changes in alcohol, tobacco, and cannabis consumption in the period before and during the COVID-19 lockdown and also asked about reasons for change.
title	Method
p	A web-based survey was filled out by an unselected population during the social distancing measures of the COVID-19 pandemic in Belgium that assessed changes in alcohol, tobacco, and cannabis consumption in the period before and during the COVID-19 lockdown and also asked about reasons for change.
sec	Results A total of 3,632 respondents (mean age 42.1 ± 14.6 years; 70% female) filled out the survey. Overall, respondents reported consuming more alcohol (d = 0.21) and smoking more cigarettes (d = 0.13) than before the COVID-19 pandemic (both p < 0.001), while no significant changes in the consumption of cannabis were noted. The odds of consuming more alcohol during the lockdown were associated with younger age (OR = 0.981, p < 0.001), more children at home (OR = 1.220, p < 0.001), non-healthcare workers (p < 0.001), and being technically unemployed related to COVID-19 (p = 0.037). The odds of smoking more cigarettes during the lockdown were associated with younger age (OR = 0.988, p = 0.027), current living situation (p < 0.001), lower education (p = 0.015), and working situation related to COVID-19 (p = 0.018). Boredom, lack of social contacts, loss of daily structure, reward after a hard-working day, loneliness, and conviviality were the main reasons for consuming more of the various substances.
title	Results
p	A total of 3,632 respondents (mean age 42.1 ± 14.6 years; 70% female) filled out the survey. Overall, respondents reported consuming more alcohol (d = 0.21) and smoking more cigarettes (d = 0.13) than before the COVID-19 pandemic (both p < 0.001), while no significant changes in the consumption of cannabis were noted. The odds of consuming more alcohol during the lockdown were associated with younger age (OR = 0.981, p < 0.001), more children at home (OR = 1.220, p < 0.001), non-healthcare workers (p < 0.001), and being technically unemployed related to COVID-19 (p = 0.037). The odds of smoking more cigarettes during the lockdown were associated with younger age (OR = 0.988, p = 0.027), current living situation (p < 0.001), lower education (p = 0.015), and working situation related to COVID-19 (p = 0.018). Boredom, lack of social contacts, loss of daily structure, reward after a hard-working day, loneliness, and conviviality were the main reasons for consuming more of the various substances.
sec	Conclusions During the lockdown, individuals consumed slightly more alcohol and smoked marginally more cigarettes compared to the period before the lockdown. Further research focussing on follow-up of individuals at risk may be useful to provide appropriate care in post-COVID times.
title	Conclusions
p	During the lockdown, individuals consumed slightly more alcohol and smoked marginally more cigarettes compared to the period before the lockdown. Further research focussing on follow-up of individuals at risk may be useful to provide appropriate care in post-COVID times.
body	Introduction COVID-19 (coronavirus disease 2019) is an infectious disease caused by the virus SARS-CoV-2. The infection is highly contagious and causes severe pneumonia, acute respiratory distress syndrome, and death particularly in vulnerable populations including older adults and people with chronic medical conditions such as cardiovascular disease, diabetes, respiratory disease, hypertension, and malignancy [1]. In an attempt to control the COVID-19 pandemic, physical distancing and hygiene measures, including regular hand washing and covering of nose and mouth when sneezing or coughing, were implemented worldwide. In several countries, sudden and dramatic social changes were implemented to slow contagion and “flatten the curve” of the pandemic to preserve capacity of healthcare resources, which included asking people to stay at home as much as possible [2]. Quarantine or social isolation is stressful and associated with psychological problems, especially in older adults, females, individuals from lower socio-economic groups, and those with underlying mental health conditions [3, 4]. Psychological problems can include anxiety, depression, acute stress disorder, substance abuse, insomnia, and post-traumatic stress disorder [3, 5]. A review on the impact of the major infectious disease outbreak of SARS in 2003 shows that the psychological impact is mainly associated with occupational role, high-risk work environment, quarantine, social support, and the impact on our personal and professional life [6]. Having been quarantined and working in hospital or wards during the 2003 SARS outbreak were linked with the development of alcohol abuse and dependence symptoms 3 years later [7]. Drinking was self-reported by hospital employees as a coping method and was found significantly related with hyperarousal [7]. The main predictors of alcohol consumption after a massive traumatic experience were alcohol consumption before the event and the rationale for drinking during the event, where drinking as a coping mechanism and drinking for pleasure were the main predictors for long-term alcohol consumption up to 10 years later [8]. The almost global lockdown is unique, and little is known about the consumption of substances during social isolation. We hypothesized that individuals in social isolation due to lockdown measures would consume more substances. Mapping the changes in substance use and the reasons for these changes during large-scale general social isolation is important. It reveals the response of the general population in times of stress and may, therefore, be of valuable help to provide appropriate care in post-COVID times and indicate possible preventive actions in case of similar events. In this study, we report on changes in alcohol, tobacco, and cannabis use in the midst of the COVID-19 lockdown in Belgium and report on the major motives for the change in consumption. Materials and Methods Study Population and Data Collection A cross-sectional survey was sent out over a brief period of time (20 days; between April 9, 2020, and April 29, 2020) during the full COVID-19 lockdown measures in Belgium. The survey was distributed by the communication services of the University Hospital of Brussels (UZ Brussel) and the university (Vrije Universiteit Brussel) and was circulated through online social media (Facebook and Twitter). The target population included adult Belgian residents, irrespective of age, living area, or current substance use. To reach out to an unselected population, the survey was made available by a link to the survey on Qualtrics online and the link was shared through social media, including Facebook (targeting a broad range of individuals), the websites of the university (targeting students) and the university hospital (targeting patients and hospital co-workers), and the website SeniorenNet.be (targeting the senior generation). Individuals older than 18 years and living or residing in Belgium during the COVID-19 lockdown measures were asked to fill in the survey online. There were no formal exclusion criteria. The ethics committee of the University Hospital Brussels, Belgium, approved this study on April 9, 2020 (No. 143202043). Measures The questionnaire consisted of 5 different parts with 20 questions regarding (1) demographics, (2) smoking cigarettes, (3) alcohol use, (4) cannabis use, and (5) factors that influenced substance use during the COVID-19 lockdown measures. The questionnaire was made available in Dutch, French, and English. Demographic questions included age, gender, and postal code. Age was asked as an age category (per 5 years). Postal code was recoded into the variables “urban” and “rural.” Current living situation (with partner, with children, with others, or alone), social situation (age and number of children and cared for at home or outside of home), and professional situation (profession, education, and currently home-working) were also asked. Smoking prior to the lockdown measures was noted as the number of cigarettes smoked on an average day in the period before the COVID-19 lockdown. Response options included “not smoking” (0 cigarettes per day), smoking 1–5 cigarettes per day, smoking 6–10 cigarettes per day, smoking 11–19 cigarettes per day, or smoking 20 or more cigarettes per day. Smoking during the current COVID-19 lockdown was assessed as the average number of cigarettes smoked now and by asking whether they currently smoke more, less, or about the same compared to before the lockdown. In case they smoked more or less, they were questioned about the average number of additional or fewer cigarettes on a normal day, using the same response options regarding smoking prior to the lockdown measures. For questions regarding alcohol use, reference was made to alcohol units per day. A standard drink (one alcohol unit) was defined as 10 g of pure alcohol. This corresponds to one 25 cL glass of beer (5% alcohol), 10 cL of wine (12% alcohol), 5 cL of aperitif (15% alcohol), or 3.5 cL of distilled spirits (35% alcohol). Respondents were asked how much alcohol he/she drank on an average day before the COVID-19 measures and how much he/she is drinking now (during the lockdown measures), with additionally specifying whether he/she drank more, less, or about the same than before. Response options referred to the average quantity of alcohol consumption during the prior week (rather than the frequency) and included the options “I do not drink alcohol” (mean of 0 alcohol units per day) and “on average 1 alcohol unit per day” to “on average >12 alcohol units per day,” with specifically denoting whether they drank more, less, or about the same as usual. Cannabis use was assessed as mean amount of cannabis joints smoked per day, both before and during the COVID-19 lockdown measures. The questions concerned the average amount of cannabis consumption per day and specifically asked whether the respondent smoked less, more, or about the same. The final question asked about motives for increased use of any substance during the lockdown with several options (listed further in Table 4). Those options were not mutually exclusive; several options could be chosen together, and there was opportunity for personal additions from the respondents, as “others, please specify: ___.” Statistical Analyses In the survey, age, amount of alcohol units, smoking cigarettes, and cannabis use were recorded in categories. For making these outcomes linear, we applied a transformation of the outcomes by using the mean value of the upper and the lower bound of the categories. Profession was recoded taking into account the variables “do you work in healthcare” and “profession,” thereby regrouping free text into 6 categories: working healthcare, working non-healthcare, student, retired, job seeking, and others. Linear variables were presented as mean ± SD where applicable. Categorical variables were reported using the number of observations and the percentage of observations. Comparison between the amounts of use in both periods (before and during the COVID-19 lockdown measures) was performed using a Wilcoxon signed-rank test. Cohen's d were calculated and represent the effect size. McNemar tests were used to compare substance use before and during the lockdown. In these McNemar tests, a categorization of “using more” or “using less or equal” was used. When >5% of the respondents showed to consume more during the lockdown compared to before, a binary logistic regression was executed. Here, a backwards regression model building technique was used with variables (age, sex, living in a city, family situation, number of children, professional occupation, educational background, and whether or not you work more from home during the lockdown). Removal of a predictor variable or factor was considered appropriate when having a significance of >0.10. Furthermore, a significance level of 0.05 was considered statistically significant for all tests. In order to examine the motives for substance use and the reason why respondents were using more during the COVID-19 measures than before, a descriptive analysis was executed. After reporting the frequencies, the motives were ranked from highest present motive to lowest present motive for each of the substances. Statistics were performed using SPSS version 26. Results Respondents' Characteristics A total of 4,098 respondents participated, with 3,632 (89%) completing the survey. The mean age was 42.1 ± 14.6 years. The characteristics of the n = 3,632 respondents are specified in Table 1. Most respondents were female (70%), between 23 and 47 years old (70%), mostly from rural areas (76%), with higher education (78%), and living with a partner (69%). The vast majority of the participants (93%) filled out the questionnaire in Dutch. Alcohol Use 2,243 respondents (61.8%) were consuming alcohol before the lockdown. Of these, 341 quit drinking during the lockdown (9.4% of the total sample). Of the 1,389 non-consuming respondents before the lockdown, 211 started consuming alcohol during the lockdown (5.8% of the total sample; p < 0.001). Of all respondents, 30.3% stated that they drank more and 13.7% stated to drink less during than before the lockdown. A statistically significant, but small (d = 0.21), difference was found between the number of drinks per day before and during the lockdown (1.0 ± 1.4, range 0–15, and 1.4 ± 2.1, range 0–21, respectively; p < 0.001). Logistic regression analysis (see Table 2) shows that the odds of consuming more alcohol decreased during the lockdown with 1.9% on average per age year of the respondent (OR = 0.981; p < 0.001). Also, a higher number of children at home increased the odds of consuming more alcohol during lockdown measures (p < 0.001): per extra child in the family, the odds increased with 22% (OR = 1.220; 95% CI: 1.146–1.298). A 3rd variable of influence was whether a respondent now works more from home compared to before the lockdown (p = 0.037). Technical unemployed respondents are 36% more likely to drink more (OR = 1.357; p = 0.013) compared to respondents at home due to tele-working. Also, the professional occupation of the respondent played a role in drinking more during the lockdown (p < 0.001), with non-healthcare workers being 40% more likely to drink compared to healthcare workers during the lockdown (OR = 1.404; p < 0.001). Students, however, were on average 46% less likely to drink more compared to healthcare workers (OR = 0.537; p < 0.001). Gender, living in a city, family situation, and educational background were variables without significant effect on the change in alcohol use. Cigarettes 558 respondents (15.4%) smoked cigarettes before the lockdown. Of these, 36 quit smoking during the lockdown (1.0% of the total sample). Of the 3,074 non-smokers before the lockdown, 34 started smoking during the lockdown (0.9% of the total sample). Of all respondents, 7.4% stated smoking more and 2.5% stated smoking less during than before the lockdown. A statistically significant, but negligible (d = 0.13), difference was found between the number of cigarettes per day before and during the lockdown (1.5 ± 4.5, range 0–30, and 1.7 ± 5.5, range 0–60, respectively; p < 0.001). Table 3 shows the results from the logistic regression analysis. Age was inversely associated with the odds of smoking cigarettes being 12% per 10 years more during than before the lockdown (OR = 0.988; p = 0.027). Also, living situation had a statistically significant relationship with increased smoking during the lockdown (p < 0.001): living alone more than doubled the odds of smoking more cigarettes (OR = 2.159; 95% CI: 1.587–2.938) during the lockdown compared to living together with a partner and/or child. Educational background also had a significant effect on the change in cigarette smoking (p = 0.015), with lower education being associated with more cigarette smoking: participants with a vocational education had about double odds to smoke more compared to those with a higher education (OR = 2.085; p = 0.042), whereas those having a doctorate were 76% less likely to smoke more compared to those with a higher education (OR = 0.245; p = 0.017). Finally, technical unemployed respondents were 64% more likely to smoke more cigarettes compared to respondents that were tele-working during the lockdown (p = 0.010). Gender, living in a city, number of children, and professional occupation were variables without significant effect on the change in smoking cigarettes. Cannabis Use Of the 105 respondents that used cannabis before the lockdown, 26 respondents (0.7% of the total sample) stopped using during the lockdown. Of the 3,527 respondents not using cannabis before the lockdown, 31 started using cannabis during the lockdown (0.9% of the total sample; p = 0.597). Of all respondents, only 2.1% stated that they used more cannabis than before and only 1.1% stated to consume less during the lockdown. There was no statistically significant difference between the number of joints per day before and during the lockdown (0.1 ± 0.5 joints per day, range 0–8, and 0.1 ± 0.4 joints per day, range 0–5; p = 0.508). As a result, no regression analysis was performed. Motives for Use of Alcohol, Cigarettes, and Cannabis during Lockdown Measures When observing the motives that drove respondents to consume more cigarettes, alcohol, and cannabis during the COVID-19 measures compared to before, the top reasons were (1) boredom, (2) lack of social contacts, (3) loss of daily structure, (4) reward after a hard-working day, (5) loneliness, and (6) conviviality (see Table 4). Discussion and Conclusion During the COVID-19 lockdown, people self-reported drinking slightly more alcohol compared to before the lockdown, which was negatively associated with age and positively associated with the number of children, unemployment, and homework. The top motive for drinking more was conviviality. The number of cigarettes smoked per day only marginally increased during the lockdown, again with younger respondents having increased odds of smoking more during the lockdown. Similarly, living alone, having a lower educational background, and being technically unemployed increased the odds of smoking more cigarettes during the lockdown as well. The top motive for smoking more cigarettes was boredom. Cannabis use did not change in times of social isolation. This may indicate a true similarity in the point estimates before and during the lockdown, but it may also reflect a zero net-effect of increased need and reduced availability of cannabis in times of social lockdown (borders are closed, dealers have a harder time to go around unnoticed, and prices increase due to increased demand). Similar to smoking, the top for an increased use of cannabis was boredom. The similarities between ranked motives of cannabis and cigarette smoking are most probably an artefact because most cannabis users are also cigarette smokers. This explains why the ranking of motives is almost the same for smoking cigarettes and cannabis use, and is the result of the fact that no separate questions about motives were asked for the 3 separate substances. It might be an interesting future research question to investigate what drives certain individuals to consume more, less, or equal during a pandemic, and individual assessment of different motives for use, per substance, would here be valuable. Also, focussing on more positive factors might reveal now underreported factors for better resilience and consequently more adequate coping mechanisms than substance use. This study sample is not optimally representative of the overall Belgian population with 50% men, less urban people, an average lower education, an estimate of 12% active in healthcare, and different jobs (possibly with higher technical unemployment rates). In this survey, we mainly note highly educated females (70%), with 36.8% being healthcare workers. We found no effect of gender and living in a city on changes in substance use and no effect of educational background for changes in alcohol use. However, our data show that non-healthcare workers were 40% more likely to consume more alcohol compared to healthcare workers during the lockdown. The respondents from this survey included more healthcare workers as would be expected from a true representative sample, which may infer that, in a true representative population, the differences in alcohol use may be larger than presented here. This needs to be confirmed in larger surveys. This study has limitations, including the very brief time during which this web-based survey was made accessible (20 days), which yielded a relatively small sample size (N = 3,632), while being representative for a uniform period of full lockdown measures. In addition, we did not follow-up on the self-reported substance use after this 3-week period, and use may stabilize or increase further with increased duration of lockdown. Another limitation was that we only assessed alcohol, cigarettes, and cannabis and the mean use of alcohol per day, rather than per week, such that individuals consuming only a few drinks per week may have over- or underreported their drinking. This was counterbalanced by the possibility to add “drinking more, or drinking less than usual, or about the same” in the next question regarding their change in alcohol use. As a whole, these data show that specific groups of individuals, especially younger individuals with lower educational background or those technically unemployed, have a higher risk of increasing their alcohol and/or cigarette use during times of social isolation. It is therefore of importance that care providers and especially general practitioners discuss the use of alcohol, tobacco, and cannabis with their patients, as this often has a beneficial effect on behaviour. Statement of Ethics Ethical approval was obtained. Because this was an anonymous web-based survey, patients were exempt from our Ethical Committee for given written informed consent. Research complied with the appropriate guidelines for human studies and animal welfare regulations and was approved as such by the Medical Ethics Committee of the University Hospital Brussels (UZ Brussel), under reference number 143202043. Conflict of Interest Statement C.L.C. has received lecture fees from Takeda and GME. F.M. received honoraria from Lundbeck, Eli Lilly, and Johnson & Johnson. None were related with this research. The other authors have nothing to declare. Funding Sources The authors did not receive any funding. Author Contributions N.V. started the project and C.L.C. prepared the 1st draft. S.V.L. performed the analyses. F.M., D.Z., L.S., and S.V.D.A. provided comments and revised the manuscript. All authors were involved in the study setup and approved the final manuscript in its current form.
sec	Introduction COVID-19 (coronavirus disease 2019) is an infectious disease caused by the virus SARS-CoV-2. The infection is highly contagious and causes severe pneumonia, acute respiratory distress syndrome, and death particularly in vulnerable populations including older adults and people with chronic medical conditions such as cardiovascular disease, diabetes, respiratory disease, hypertension, and malignancy [1]. In an attempt to control the COVID-19 pandemic, physical distancing and hygiene measures, including regular hand washing and covering of nose and mouth when sneezing or coughing, were implemented worldwide. In several countries, sudden and dramatic social changes were implemented to slow contagion and “flatten the curve” of the pandemic to preserve capacity of healthcare resources, which included asking people to stay at home as much as possible [2]. Quarantine or social isolation is stressful and associated with psychological problems, especially in older adults, females, individuals from lower socio-economic groups, and those with underlying mental health conditions [3, 4]. Psychological problems can include anxiety, depression, acute stress disorder, substance abuse, insomnia, and post-traumatic stress disorder [3, 5]. A review on the impact of the major infectious disease outbreak of SARS in 2003 shows that the psychological impact is mainly associated with occupational role, high-risk work environment, quarantine, social support, and the impact on our personal and professional life [6]. Having been quarantined and working in hospital or wards during the 2003 SARS outbreak were linked with the development of alcohol abuse and dependence symptoms 3 years later [7]. Drinking was self-reported by hospital employees as a coping method and was found significantly related with hyperarousal [7]. The main predictors of alcohol consumption after a massive traumatic experience were alcohol consumption before the event and the rationale for drinking during the event, where drinking as a coping mechanism and drinking for pleasure were the main predictors for long-term alcohol consumption up to 10 years later [8]. The almost global lockdown is unique, and little is known about the consumption of substances during social isolation. We hypothesized that individuals in social isolation due to lockdown measures would consume more substances. Mapping the changes in substance use and the reasons for these changes during large-scale general social isolation is important. It reveals the response of the general population in times of stress and may, therefore, be of valuable help to provide appropriate care in post-COVID times and indicate possible preventive actions in case of similar events. In this study, we report on changes in alcohol, tobacco, and cannabis use in the midst of the COVID-19 lockdown in Belgium and report on the major motives for the change in consumption.
title	Introduction
p	COVID-19 (coronavirus disease 2019) is an infectious disease caused by the virus SARS-CoV-2. The infection is highly contagious and causes severe pneumonia, acute respiratory distress syndrome, and death particularly in vulnerable populations including older adults and people with chronic medical conditions such as cardiovascular disease, diabetes, respiratory disease, hypertension, and malignancy [1].
p	In an attempt to control the COVID-19 pandemic, physical distancing and hygiene measures, including regular hand washing and covering of nose and mouth when sneezing or coughing, were implemented worldwide. In several countries, sudden and dramatic social changes were implemented to slow contagion and “flatten the curve” of the pandemic to preserve capacity of healthcare resources, which included asking people to stay at home as much as possible [2].
p	Quarantine or social isolation is stressful and associated with psychological problems, especially in older adults, females, individuals from lower socio-economic groups, and those with underlying mental health conditions [3, 4]. Psychological problems can include anxiety, depression, acute stress disorder, substance abuse, insomnia, and post-traumatic stress disorder [3, 5].
p	A review on the impact of the major infectious disease outbreak of SARS in 2003 shows that the psychological impact is mainly associated with occupational role, high-risk work environment, quarantine, social support, and the impact on our personal and professional life [6]. Having been quarantined and working in hospital or wards during the 2003 SARS outbreak were linked with the development of alcohol abuse and dependence symptoms 3 years later [7]. Drinking was self-reported by hospital employees as a coping method and was found significantly related with hyperarousal [7]. The main predictors of alcohol consumption after a massive traumatic experience were alcohol consumption before the event and the rationale for drinking during the event, where drinking as a coping mechanism and drinking for pleasure were the main predictors for long-term alcohol consumption up to 10 years later [8].
p	The almost global lockdown is unique, and little is known about the consumption of substances during social isolation. We hypothesized that individuals in social isolation due to lockdown measures would consume more substances. Mapping the changes in substance use and the reasons for these changes during large-scale general social isolation is important. It reveals the response of the general population in times of stress and may, therefore, be of valuable help to provide appropriate care in post-COVID times and indicate possible preventive actions in case of similar events. In this study, we report on changes in alcohol, tobacco, and cannabis use in the midst of the COVID-19 lockdown in Belgium and report on the major motives for the change in consumption.
sec	Materials and Methods Study Population and Data Collection A cross-sectional survey was sent out over a brief period of time (20 days; between April 9, 2020, and April 29, 2020) during the full COVID-19 lockdown measures in Belgium. The survey was distributed by the communication services of the University Hospital of Brussels (UZ Brussel) and the university (Vrije Universiteit Brussel) and was circulated through online social media (Facebook and Twitter). The target population included adult Belgian residents, irrespective of age, living area, or current substance use. To reach out to an unselected population, the survey was made available by a link to the survey on Qualtrics online and the link was shared through social media, including Facebook (targeting a broad range of individuals), the websites of the university (targeting students) and the university hospital (targeting patients and hospital co-workers), and the website SeniorenNet.be (targeting the senior generation). Individuals older than 18 years and living or residing in Belgium during the COVID-19 lockdown measures were asked to fill in the survey online. There were no formal exclusion criteria. The ethics committee of the University Hospital Brussels, Belgium, approved this study on April 9, 2020 (No. 143202043). Measures The questionnaire consisted of 5 different parts with 20 questions regarding (1) demographics, (2) smoking cigarettes, (3) alcohol use, (4) cannabis use, and (5) factors that influenced substance use during the COVID-19 lockdown measures. The questionnaire was made available in Dutch, French, and English. Demographic questions included age, gender, and postal code. Age was asked as an age category (per 5 years). Postal code was recoded into the variables “urban” and “rural.” Current living situation (with partner, with children, with others, or alone), social situation (age and number of children and cared for at home or outside of home), and professional situation (profession, education, and currently home-working) were also asked. Smoking prior to the lockdown measures was noted as the number of cigarettes smoked on an average day in the period before the COVID-19 lockdown. Response options included “not smoking” (0 cigarettes per day), smoking 1–5 cigarettes per day, smoking 6–10 cigarettes per day, smoking 11–19 cigarettes per day, or smoking 20 or more cigarettes per day. Smoking during the current COVID-19 lockdown was assessed as the average number of cigarettes smoked now and by asking whether they currently smoke more, less, or about the same compared to before the lockdown. In case they smoked more or less, they were questioned about the average number of additional or fewer cigarettes on a normal day, using the same response options regarding smoking prior to the lockdown measures. For questions regarding alcohol use, reference was made to alcohol units per day. A standard drink (one alcohol unit) was defined as 10 g of pure alcohol. This corresponds to one 25 cL glass of beer (5% alcohol), 10 cL of wine (12% alcohol), 5 cL of aperitif (15% alcohol), or 3.5 cL of distilled spirits (35% alcohol). Respondents were asked how much alcohol he/she drank on an average day before the COVID-19 measures and how much he/she is drinking now (during the lockdown measures), with additionally specifying whether he/she drank more, less, or about the same than before. Response options referred to the average quantity of alcohol consumption during the prior week (rather than the frequency) and included the options “I do not drink alcohol” (mean of 0 alcohol units per day) and “on average 1 alcohol unit per day” to “on average >12 alcohol units per day,” with specifically denoting whether they drank more, less, or about the same as usual. Cannabis use was assessed as mean amount of cannabis joints smoked per day, both before and during the COVID-19 lockdown measures. The questions concerned the average amount of cannabis consumption per day and specifically asked whether the respondent smoked less, more, or about the same. The final question asked about motives for increased use of any substance during the lockdown with several options (listed further in Table 4). Those options were not mutually exclusive; several options could be chosen together, and there was opportunity for personal additions from the respondents, as “others, please specify: ___.” Statistical Analyses In the survey, age, amount of alcohol units, smoking cigarettes, and cannabis use were recorded in categories. For making these outcomes linear, we applied a transformation of the outcomes by using the mean value of the upper and the lower bound of the categories. Profession was recoded taking into account the variables “do you work in healthcare” and “profession,” thereby regrouping free text into 6 categories: working healthcare, working non-healthcare, student, retired, job seeking, and others. Linear variables were presented as mean ± SD where applicable. Categorical variables were reported using the number of observations and the percentage of observations. Comparison between the amounts of use in both periods (before and during the COVID-19 lockdown measures) was performed using a Wilcoxon signed-rank test. Cohen's d were calculated and represent the effect size. McNemar tests were used to compare substance use before and during the lockdown. In these McNemar tests, a categorization of “using more” or “using less or equal” was used. When >5% of the respondents showed to consume more during the lockdown compared to before, a binary logistic regression was executed. Here, a backwards regression model building technique was used with variables (age, sex, living in a city, family situation, number of children, professional occupation, educational background, and whether or not you work more from home during the lockdown). Removal of a predictor variable or factor was considered appropriate when having a significance of >0.10. Furthermore, a significance level of 0.05 was considered statistically significant for all tests. In order to examine the motives for substance use and the reason why respondents were using more during the COVID-19 measures than before, a descriptive analysis was executed. After reporting the frequencies, the motives were ranked from highest present motive to lowest present motive for each of the substances. Statistics were performed using SPSS version 26.
title	Materials and Methods
sec	Study Population and Data Collection A cross-sectional survey was sent out over a brief period of time (20 days; between April 9, 2020, and April 29, 2020) during the full COVID-19 lockdown measures in Belgium. The survey was distributed by the communication services of the University Hospital of Brussels (UZ Brussel) and the university (Vrije Universiteit Brussel) and was circulated through online social media (Facebook and Twitter). The target population included adult Belgian residents, irrespective of age, living area, or current substance use. To reach out to an unselected population, the survey was made available by a link to the survey on Qualtrics online and the link was shared through social media, including Facebook (targeting a broad range of individuals), the websites of the university (targeting students) and the university hospital (targeting patients and hospital co-workers), and the website SeniorenNet.be (targeting the senior generation). Individuals older than 18 years and living or residing in Belgium during the COVID-19 lockdown measures were asked to fill in the survey online. There were no formal exclusion criteria. The ethics committee of the University Hospital Brussels, Belgium, approved this study on April 9, 2020 (No. 143202043).
title	Study Population and Data Collection
p	A cross-sectional survey was sent out over a brief period of time (20 days; between April 9, 2020, and April 29, 2020) during the full COVID-19 lockdown measures in Belgium. The survey was distributed by the communication services of the University Hospital of Brussels (UZ Brussel) and the university (Vrije Universiteit Brussel) and was circulated through online social media (Facebook and Twitter). The target population included adult Belgian residents, irrespective of age, living area, or current substance use. To reach out to an unselected population, the survey was made available by a link to the survey on Qualtrics online and the link was shared through social media, including Facebook (targeting a broad range of individuals), the websites of the university (targeting students) and the university hospital (targeting patients and hospital co-workers), and the website SeniorenNet.be (targeting the senior generation).
p	Individuals older than 18 years and living or residing in Belgium during the COVID-19 lockdown measures were asked to fill in the survey online. There were no formal exclusion criteria. The ethics committee of the University Hospital Brussels, Belgium, approved this study on April 9, 2020 (No. 143202043).
sec	Measures The questionnaire consisted of 5 different parts with 20 questions regarding (1) demographics, (2) smoking cigarettes, (3) alcohol use, (4) cannabis use, and (5) factors that influenced substance use during the COVID-19 lockdown measures. The questionnaire was made available in Dutch, French, and English. Demographic questions included age, gender, and postal code. Age was asked as an age category (per 5 years). Postal code was recoded into the variables “urban” and “rural.” Current living situation (with partner, with children, with others, or alone), social situation (age and number of children and cared for at home or outside of home), and professional situation (profession, education, and currently home-working) were also asked. Smoking prior to the lockdown measures was noted as the number of cigarettes smoked on an average day in the period before the COVID-19 lockdown. Response options included “not smoking” (0 cigarettes per day), smoking 1–5 cigarettes per day, smoking 6–10 cigarettes per day, smoking 11–19 cigarettes per day, or smoking 20 or more cigarettes per day. Smoking during the current COVID-19 lockdown was assessed as the average number of cigarettes smoked now and by asking whether they currently smoke more, less, or about the same compared to before the lockdown. In case they smoked more or less, they were questioned about the average number of additional or fewer cigarettes on a normal day, using the same response options regarding smoking prior to the lockdown measures. For questions regarding alcohol use, reference was made to alcohol units per day. A standard drink (one alcohol unit) was defined as 10 g of pure alcohol. This corresponds to one 25 cL glass of beer (5% alcohol), 10 cL of wine (12% alcohol), 5 cL of aperitif (15% alcohol), or 3.5 cL of distilled spirits (35% alcohol). Respondents were asked how much alcohol he/she drank on an average day before the COVID-19 measures and how much he/she is drinking now (during the lockdown measures), with additionally specifying whether he/she drank more, less, or about the same than before. Response options referred to the average quantity of alcohol consumption during the prior week (rather than the frequency) and included the options “I do not drink alcohol” (mean of 0 alcohol units per day) and “on average 1 alcohol unit per day” to “on average >12 alcohol units per day,” with specifically denoting whether they drank more, less, or about the same as usual. Cannabis use was assessed as mean amount of cannabis joints smoked per day, both before and during the COVID-19 lockdown measures. The questions concerned the average amount of cannabis consumption per day and specifically asked whether the respondent smoked less, more, or about the same. The final question asked about motives for increased use of any substance during the lockdown with several options (listed further in Table 4). Those options were not mutually exclusive; several options could be chosen together, and there was opportunity for personal additions from the respondents, as “others, please specify: ___.”
title	Measures
p	The questionnaire consisted of 5 different parts with 20 questions regarding (1) demographics, (2) smoking cigarettes, (3) alcohol use, (4) cannabis use, and (5) factors that influenced substance use during the COVID-19 lockdown measures. The questionnaire was made available in Dutch, French, and English.
p	Demographic questions included age, gender, and postal code. Age was asked as an age category (per 5 years). Postal code was recoded into the variables “urban” and “rural.” Current living situation (with partner, with children, with others, or alone), social situation (age and number of children and cared for at home or outside of home), and professional situation (profession, education, and currently home-working) were also asked.
p	Smoking prior to the lockdown measures was noted as the number of cigarettes smoked on an average day in the period before the COVID-19 lockdown. Response options included “not smoking” (0 cigarettes per day), smoking 1–5 cigarettes per day, smoking 6–10 cigarettes per day, smoking 11–19 cigarettes per day, or smoking 20 or more cigarettes per day. Smoking during the current COVID-19 lockdown was assessed as the average number of cigarettes smoked now and by asking whether they currently smoke more, less, or about the same compared to before the lockdown. In case they smoked more or less, they were questioned about the average number of additional or fewer cigarettes on a normal day, using the same response options regarding smoking prior to the lockdown measures.
p	For questions regarding alcohol use, reference was made to alcohol units per day. A standard drink (one alcohol unit) was defined as 10 g of pure alcohol. This corresponds to one 25 cL glass of beer (5% alcohol), 10 cL of wine (12% alcohol), 5 cL of aperitif (15% alcohol), or 3.5 cL of distilled spirits (35% alcohol). Respondents were asked how much alcohol he/she drank on an average day before the COVID-19 measures and how much he/she is drinking now (during the lockdown measures), with additionally specifying whether he/she drank more, less, or about the same than before. Response options referred to the average quantity of alcohol consumption during the prior week (rather than the frequency) and included the options “I do not drink alcohol” (mean of 0 alcohol units per day) and “on average 1 alcohol unit per day” to “on average >12 alcohol units per day,” with specifically denoting whether they drank more, less, or about the same as usual.
p	Cannabis use was assessed as mean amount of cannabis joints smoked per day, both before and during the COVID-19 lockdown measures. The questions concerned the average amount of cannabis consumption per day and specifically asked whether the respondent smoked less, more, or about the same.
p	The final question asked about motives for increased use of any substance during the lockdown with several options (listed further in Table 4). Those options were not mutually exclusive; several options could be chosen together, and there was opportunity for personal additions from the respondents, as “others, please specify: ___.”
sec	Statistical Analyses In the survey, age, amount of alcohol units, smoking cigarettes, and cannabis use were recorded in categories. For making these outcomes linear, we applied a transformation of the outcomes by using the mean value of the upper and the lower bound of the categories. Profession was recoded taking into account the variables “do you work in healthcare” and “profession,” thereby regrouping free text into 6 categories: working healthcare, working non-healthcare, student, retired, job seeking, and others. Linear variables were presented as mean ± SD where applicable. Categorical variables were reported using the number of observations and the percentage of observations. Comparison between the amounts of use in both periods (before and during the COVID-19 lockdown measures) was performed using a Wilcoxon signed-rank test. Cohen's d were calculated and represent the effect size. McNemar tests were used to compare substance use before and during the lockdown. In these McNemar tests, a categorization of “using more” or “using less or equal” was used. When >5% of the respondents showed to consume more during the lockdown compared to before, a binary logistic regression was executed. Here, a backwards regression model building technique was used with variables (age, sex, living in a city, family situation, number of children, professional occupation, educational background, and whether or not you work more from home during the lockdown). Removal of a predictor variable or factor was considered appropriate when having a significance of >0.10. Furthermore, a significance level of 0.05 was considered statistically significant for all tests. In order to examine the motives for substance use and the reason why respondents were using more during the COVID-19 measures than before, a descriptive analysis was executed. After reporting the frequencies, the motives were ranked from highest present motive to lowest present motive for each of the substances. Statistics were performed using SPSS version 26.
title	Statistical Analyses
p	In the survey, age, amount of alcohol units, smoking cigarettes, and cannabis use were recorded in categories. For making these outcomes linear, we applied a transformation of the outcomes by using the mean value of the upper and the lower bound of the categories. Profession was recoded taking into account the variables “do you work in healthcare” and “profession,” thereby regrouping free text into 6 categories: working healthcare, working non-healthcare, student, retired, job seeking, and others.
p	Linear variables were presented as mean ± SD where applicable. Categorical variables were reported using the number of observations and the percentage of observations. Comparison between the amounts of use in both periods (before and during the COVID-19 lockdown measures) was performed using a Wilcoxon signed-rank test. Cohen's d were calculated and represent the effect size. McNemar tests were used to compare substance use before and during the lockdown. In these McNemar tests, a categorization of “using more” or “using less or equal” was used. When >5% of the respondents showed to consume more during the lockdown compared to before, a binary logistic regression was executed. Here, a backwards regression model building technique was used with variables (age, sex, living in a city, family situation, number of children, professional occupation, educational background, and whether or not you work more from home during the lockdown). Removal of a predictor variable or factor was considered appropriate when having a significance of >0.10. Furthermore, a significance level of 0.05 was considered statistically significant for all tests. In order to examine the motives for substance use and the reason why respondents were using more during the COVID-19 measures than before, a descriptive analysis was executed. After reporting the frequencies, the motives were ranked from highest present motive to lowest present motive for each of the substances. Statistics were performed using SPSS version 26.
sec	Results Respondents' Characteristics A total of 4,098 respondents participated, with 3,632 (89%) completing the survey. The mean age was 42.1 ± 14.6 years. The characteristics of the n = 3,632 respondents are specified in Table 1. Most respondents were female (70%), between 23 and 47 years old (70%), mostly from rural areas (76%), with higher education (78%), and living with a partner (69%). The vast majority of the participants (93%) filled out the questionnaire in Dutch. Alcohol Use 2,243 respondents (61.8%) were consuming alcohol before the lockdown. Of these, 341 quit drinking during the lockdown (9.4% of the total sample). Of the 1,389 non-consuming respondents before the lockdown, 211 started consuming alcohol during the lockdown (5.8% of the total sample; p < 0.001). Of all respondents, 30.3% stated that they drank more and 13.7% stated to drink less during than before the lockdown. A statistically significant, but small (d = 0.21), difference was found between the number of drinks per day before and during the lockdown (1.0 ± 1.4, range 0–15, and 1.4 ± 2.1, range 0–21, respectively; p < 0.001). Logistic regression analysis (see Table 2) shows that the odds of consuming more alcohol decreased during the lockdown with 1.9% on average per age year of the respondent (OR = 0.981; p < 0.001). Also, a higher number of children at home increased the odds of consuming more alcohol during lockdown measures (p < 0.001): per extra child in the family, the odds increased with 22% (OR = 1.220; 95% CI: 1.146–1.298). A 3rd variable of influence was whether a respondent now works more from home compared to before the lockdown (p = 0.037). Technical unemployed respondents are 36% more likely to drink more (OR = 1.357; p = 0.013) compared to respondents at home due to tele-working. Also, the professional occupation of the respondent played a role in drinking more during the lockdown (p < 0.001), with non-healthcare workers being 40% more likely to drink compared to healthcare workers during the lockdown (OR = 1.404; p < 0.001). Students, however, were on average 46% less likely to drink more compared to healthcare workers (OR = 0.537; p < 0.001). Gender, living in a city, family situation, and educational background were variables without significant effect on the change in alcohol use. Cigarettes 558 respondents (15.4%) smoked cigarettes before the lockdown. Of these, 36 quit smoking during the lockdown (1.0% of the total sample). Of the 3,074 non-smokers before the lockdown, 34 started smoking during the lockdown (0.9% of the total sample). Of all respondents, 7.4% stated smoking more and 2.5% stated smoking less during than before the lockdown. A statistically significant, but negligible (d = 0.13), difference was found between the number of cigarettes per day before and during the lockdown (1.5 ± 4.5, range 0–30, and 1.7 ± 5.5, range 0–60, respectively; p < 0.001). Table 3 shows the results from the logistic regression analysis. Age was inversely associated with the odds of smoking cigarettes being 12% per 10 years more during than before the lockdown (OR = 0.988; p = 0.027). Also, living situation had a statistically significant relationship with increased smoking during the lockdown (p < 0.001): living alone more than doubled the odds of smoking more cigarettes (OR = 2.159; 95% CI: 1.587–2.938) during the lockdown compared to living together with a partner and/or child. Educational background also had a significant effect on the change in cigarette smoking (p = 0.015), with lower education being associated with more cigarette smoking: participants with a vocational education had about double odds to smoke more compared to those with a higher education (OR = 2.085; p = 0.042), whereas those having a doctorate were 76% less likely to smoke more compared to those with a higher education (OR = 0.245; p = 0.017). Finally, technical unemployed respondents were 64% more likely to smoke more cigarettes compared to respondents that were tele-working during the lockdown (p = 0.010). Gender, living in a city, number of children, and professional occupation were variables without significant effect on the change in smoking cigarettes. Cannabis Use Of the 105 respondents that used cannabis before the lockdown, 26 respondents (0.7% of the total sample) stopped using during the lockdown. Of the 3,527 respondents not using cannabis before the lockdown, 31 started using cannabis during the lockdown (0.9% of the total sample; p = 0.597). Of all respondents, only 2.1% stated that they used more cannabis than before and only 1.1% stated to consume less during the lockdown. There was no statistically significant difference between the number of joints per day before and during the lockdown (0.1 ± 0.5 joints per day, range 0–8, and 0.1 ± 0.4 joints per day, range 0–5; p = 0.508). As a result, no regression analysis was performed. Motives for Use of Alcohol, Cigarettes, and Cannabis during Lockdown Measures When observing the motives that drove respondents to consume more cigarettes, alcohol, and cannabis during the COVID-19 measures compared to before, the top reasons were (1) boredom, (2) lack of social contacts, (3) loss of daily structure, (4) reward after a hard-working day, (5) loneliness, and (6) conviviality (see Table 4).
title	Results
sec	Respondents' Characteristics A total of 4,098 respondents participated, with 3,632 (89%) completing the survey. The mean age was 42.1 ± 14.6 years. The characteristics of the n = 3,632 respondents are specified in Table 1. Most respondents were female (70%), between 23 and 47 years old (70%), mostly from rural areas (76%), with higher education (78%), and living with a partner (69%). The vast majority of the participants (93%) filled out the questionnaire in Dutch.
title	Respondents' Characteristics
p	A total of 4,098 respondents participated, with 3,632 (89%) completing the survey. The mean age was 42.1 ± 14.6 years. The characteristics of the n = 3,632 respondents are specified in Table 1. Most respondents were female (70%), between 23 and 47 years old (70%), mostly from rural areas (76%), with higher education (78%), and living with a partner (69%). The vast majority of the participants (93%) filled out the questionnaire in Dutch.
sec	Alcohol Use 2,243 respondents (61.8%) were consuming alcohol before the lockdown. Of these, 341 quit drinking during the lockdown (9.4% of the total sample). Of the 1,389 non-consuming respondents before the lockdown, 211 started consuming alcohol during the lockdown (5.8% of the total sample; p < 0.001). Of all respondents, 30.3% stated that they drank more and 13.7% stated to drink less during than before the lockdown. A statistically significant, but small (d = 0.21), difference was found between the number of drinks per day before and during the lockdown (1.0 ± 1.4, range 0–15, and 1.4 ± 2.1, range 0–21, respectively; p < 0.001). Logistic regression analysis (see Table 2) shows that the odds of consuming more alcohol decreased during the lockdown with 1.9% on average per age year of the respondent (OR = 0.981; p < 0.001). Also, a higher number of children at home increased the odds of consuming more alcohol during lockdown measures (p < 0.001): per extra child in the family, the odds increased with 22% (OR = 1.220; 95% CI: 1.146–1.298). A 3rd variable of influence was whether a respondent now works more from home compared to before the lockdown (p = 0.037). Technical unemployed respondents are 36% more likely to drink more (OR = 1.357; p = 0.013) compared to respondents at home due to tele-working. Also, the professional occupation of the respondent played a role in drinking more during the lockdown (p < 0.001), with non-healthcare workers being 40% more likely to drink compared to healthcare workers during the lockdown (OR = 1.404; p < 0.001). Students, however, were on average 46% less likely to drink more compared to healthcare workers (OR = 0.537; p < 0.001). Gender, living in a city, family situation, and educational background were variables without significant effect on the change in alcohol use.
title	Alcohol Use
p	2,243 respondents (61.8%) were consuming alcohol before the lockdown. Of these, 341 quit drinking during the lockdown (9.4% of the total sample). Of the 1,389 non-consuming respondents before the lockdown, 211 started consuming alcohol during the lockdown (5.8% of the total sample; p < 0.001). Of all respondents, 30.3% stated that they drank more and 13.7% stated to drink less during than before the lockdown. A statistically significant, but small (d = 0.21), difference was found between the number of drinks per day before and during the lockdown (1.0 ± 1.4, range 0–15, and 1.4 ± 2.1, range 0–21, respectively; p < 0.001).
p	Logistic regression analysis (see Table 2) shows that the odds of consuming more alcohol decreased during the lockdown with 1.9% on average per age year of the respondent (OR = 0.981; p < 0.001). Also, a higher number of children at home increased the odds of consuming more alcohol during lockdown measures (p < 0.001): per extra child in the family, the odds increased with 22% (OR = 1.220; 95% CI: 1.146–1.298). A 3rd variable of influence was whether a respondent now works more from home compared to before the lockdown (p = 0.037). Technical unemployed respondents are 36% more likely to drink more (OR = 1.357; p = 0.013) compared to respondents at home due to tele-working. Also, the professional occupation of the respondent played a role in drinking more during the lockdown (p < 0.001), with non-healthcare workers being 40% more likely to drink compared to healthcare workers during the lockdown (OR = 1.404; p < 0.001). Students, however, were on average 46% less likely to drink more compared to healthcare workers (OR = 0.537; p < 0.001). Gender, living in a city, family situation, and educational background were variables without significant effect on the change in alcohol use.
sec	Cigarettes 558 respondents (15.4%) smoked cigarettes before the lockdown. Of these, 36 quit smoking during the lockdown (1.0% of the total sample). Of the 3,074 non-smokers before the lockdown, 34 started smoking during the lockdown (0.9% of the total sample). Of all respondents, 7.4% stated smoking more and 2.5% stated smoking less during than before the lockdown. A statistically significant, but negligible (d = 0.13), difference was found between the number of cigarettes per day before and during the lockdown (1.5 ± 4.5, range 0–30, and 1.7 ± 5.5, range 0–60, respectively; p < 0.001). Table 3 shows the results from the logistic regression analysis. Age was inversely associated with the odds of smoking cigarettes being 12% per 10 years more during than before the lockdown (OR = 0.988; p = 0.027). Also, living situation had a statistically significant relationship with increased smoking during the lockdown (p < 0.001): living alone more than doubled the odds of smoking more cigarettes (OR = 2.159; 95% CI: 1.587–2.938) during the lockdown compared to living together with a partner and/or child. Educational background also had a significant effect on the change in cigarette smoking (p = 0.015), with lower education being associated with more cigarette smoking: participants with a vocational education had about double odds to smoke more compared to those with a higher education (OR = 2.085; p = 0.042), whereas those having a doctorate were 76% less likely to smoke more compared to those with a higher education (OR = 0.245; p = 0.017). Finally, technical unemployed respondents were 64% more likely to smoke more cigarettes compared to respondents that were tele-working during the lockdown (p = 0.010). Gender, living in a city, number of children, and professional occupation were variables without significant effect on the change in smoking cigarettes.
title	Cigarettes
p	558 respondents (15.4%) smoked cigarettes before the lockdown. Of these, 36 quit smoking during the lockdown (1.0% of the total sample). Of the 3,074 non-smokers before the lockdown, 34 started smoking during the lockdown (0.9% of the total sample). Of all respondents, 7.4% stated smoking more and 2.5% stated smoking less during than before the lockdown. A statistically significant, but negligible (d = 0.13), difference was found between the number of cigarettes per day before and during the lockdown (1.5 ± 4.5, range 0–30, and 1.7 ± 5.5, range 0–60, respectively; p < 0.001).
p	Table 3 shows the results from the logistic regression analysis. Age was inversely associated with the odds of smoking cigarettes being 12% per 10 years more during than before the lockdown (OR = 0.988; p = 0.027). Also, living situation had a statistically significant relationship with increased smoking during the lockdown (p < 0.001): living alone more than doubled the odds of smoking more cigarettes (OR = 2.159; 95% CI: 1.587–2.938) during the lockdown compared to living together with a partner and/or child. Educational background also had a significant effect on the change in cigarette smoking (p = 0.015), with lower education being associated with more cigarette smoking: participants with a vocational education had about double odds to smoke more compared to those with a higher education (OR = 2.085; p = 0.042), whereas those having a doctorate were 76% less likely to smoke more compared to those with a higher education (OR = 0.245; p = 0.017). Finally, technical unemployed respondents were 64% more likely to smoke more cigarettes compared to respondents that were tele-working during the lockdown (p = 0.010). Gender, living in a city, number of children, and professional occupation were variables without significant effect on the change in smoking cigarettes.
sec	Cannabis Use Of the 105 respondents that used cannabis before the lockdown, 26 respondents (0.7% of the total sample) stopped using during the lockdown. Of the 3,527 respondents not using cannabis before the lockdown, 31 started using cannabis during the lockdown (0.9% of the total sample; p = 0.597). Of all respondents, only 2.1% stated that they used more cannabis than before and only 1.1% stated to consume less during the lockdown. There was no statistically significant difference between the number of joints per day before and during the lockdown (0.1 ± 0.5 joints per day, range 0–8, and 0.1 ± 0.4 joints per day, range 0–5; p = 0.508). As a result, no regression analysis was performed.
title	Cannabis Use
p	Of the 105 respondents that used cannabis before the lockdown, 26 respondents (0.7% of the total sample) stopped using during the lockdown. Of the 3,527 respondents not using cannabis before the lockdown, 31 started using cannabis during the lockdown (0.9% of the total sample; p = 0.597). Of all respondents, only 2.1% stated that they used more cannabis than before and only 1.1% stated to consume less during the lockdown. There was no statistically significant difference between the number of joints per day before and during the lockdown (0.1 ± 0.5 joints per day, range 0–8, and 0.1 ± 0.4 joints per day, range 0–5; p = 0.508). As a result, no regression analysis was performed.
sec	Motives for Use of Alcohol, Cigarettes, and Cannabis during Lockdown Measures When observing the motives that drove respondents to consume more cigarettes, alcohol, and cannabis during the COVID-19 measures compared to before, the top reasons were (1) boredom, (2) lack of social contacts, (3) loss of daily structure, (4) reward after a hard-working day, (5) loneliness, and (6) conviviality (see Table 4).
title	Motives for Use of Alcohol, Cigarettes, and Cannabis during Lockdown Measures
p	When observing the motives that drove respondents to consume more cigarettes, alcohol, and cannabis during the COVID-19 measures compared to before, the top reasons were (1) boredom, (2) lack of social contacts, (3) loss of daily structure, (4) reward after a hard-working day, (5) loneliness, and (6) conviviality (see Table 4).
sec	Discussion and Conclusion During the COVID-19 lockdown, people self-reported drinking slightly more alcohol compared to before the lockdown, which was negatively associated with age and positively associated with the number of children, unemployment, and homework. The top motive for drinking more was conviviality. The number of cigarettes smoked per day only marginally increased during the lockdown, again with younger respondents having increased odds of smoking more during the lockdown. Similarly, living alone, having a lower educational background, and being technically unemployed increased the odds of smoking more cigarettes during the lockdown as well. The top motive for smoking more cigarettes was boredom. Cannabis use did not change in times of social isolation. This may indicate a true similarity in the point estimates before and during the lockdown, but it may also reflect a zero net-effect of increased need and reduced availability of cannabis in times of social lockdown (borders are closed, dealers have a harder time to go around unnoticed, and prices increase due to increased demand). Similar to smoking, the top for an increased use of cannabis was boredom. The similarities between ranked motives of cannabis and cigarette smoking are most probably an artefact because most cannabis users are also cigarette smokers. This explains why the ranking of motives is almost the same for smoking cigarettes and cannabis use, and is the result of the fact that no separate questions about motives were asked for the 3 separate substances. It might be an interesting future research question to investigate what drives certain individuals to consume more, less, or equal during a pandemic, and individual assessment of different motives for use, per substance, would here be valuable. Also, focussing on more positive factors might reveal now underreported factors for better resilience and consequently more adequate coping mechanisms than substance use. This study sample is not optimally representative of the overall Belgian population with 50% men, less urban people, an average lower education, an estimate of 12% active in healthcare, and different jobs (possibly with higher technical unemployment rates). In this survey, we mainly note highly educated females (70%), with 36.8% being healthcare workers. We found no effect of gender and living in a city on changes in substance use and no effect of educational background for changes in alcohol use. However, our data show that non-healthcare workers were 40% more likely to consume more alcohol compared to healthcare workers during the lockdown. The respondents from this survey included more healthcare workers as would be expected from a true representative sample, which may infer that, in a true representative population, the differences in alcohol use may be larger than presented here. This needs to be confirmed in larger surveys. This study has limitations, including the very brief time during which this web-based survey was made accessible (20 days), which yielded a relatively small sample size (N = 3,632), while being representative for a uniform period of full lockdown measures. In addition, we did not follow-up on the self-reported substance use after this 3-week period, and use may stabilize or increase further with increased duration of lockdown. Another limitation was that we only assessed alcohol, cigarettes, and cannabis and the mean use of alcohol per day, rather than per week, such that individuals consuming only a few drinks per week may have over- or underreported their drinking. This was counterbalanced by the possibility to add “drinking more, or drinking less than usual, or about the same” in the next question regarding their change in alcohol use. As a whole, these data show that specific groups of individuals, especially younger individuals with lower educational background or those technically unemployed, have a higher risk of increasing their alcohol and/or cigarette use during times of social isolation. It is therefore of importance that care providers and especially general practitioners discuss the use of alcohol, tobacco, and cannabis with their patients, as this often has a beneficial effect on behaviour.
title	Discussion and Conclusion
p	During the COVID-19 lockdown, people self-reported drinking slightly more alcohol compared to before the lockdown, which was negatively associated with age and positively associated with the number of children, unemployment, and homework. The top motive for drinking more was conviviality. The number of cigarettes smoked per day only marginally increased during the lockdown, again with younger respondents having increased odds of smoking more during the lockdown. Similarly, living alone, having a lower educational background, and being technically unemployed increased the odds of smoking more cigarettes during the lockdown as well. The top motive for smoking more cigarettes was boredom.
p	Cannabis use did not change in times of social isolation. This may indicate a true similarity in the point estimates before and during the lockdown, but it may also reflect a zero net-effect of increased need and reduced availability of cannabis in times of social lockdown (borders are closed, dealers have a harder time to go around unnoticed, and prices increase due to increased demand). Similar to smoking, the top for an increased use of cannabis was boredom. The similarities between ranked motives of cannabis and cigarette smoking are most probably an artefact because most cannabis users are also cigarette smokers. This explains why the ranking of motives is almost the same for smoking cigarettes and cannabis use, and is the result of the fact that no separate questions about motives were asked for the 3 separate substances. It might be an interesting future research question to investigate what drives certain individuals to consume more, less, or equal during a pandemic, and individual assessment of different motives for use, per substance, would here be valuable. Also, focussing on more positive factors might reveal now underreported factors for better resilience and consequently more adequate coping mechanisms than substance use.
p	This study sample is not optimally representative of the overall Belgian population with 50% men, less urban people, an average lower education, an estimate of 12% active in healthcare, and different jobs (possibly with higher technical unemployment rates). In this survey, we mainly note highly educated females (70%), with 36.8% being healthcare workers. We found no effect of gender and living in a city on changes in substance use and no effect of educational background for changes in alcohol use. However, our data show that non-healthcare workers were 40% more likely to consume more alcohol compared to healthcare workers during the lockdown. The respondents from this survey included more healthcare workers as would be expected from a true representative sample, which may infer that, in a true representative population, the differences in alcohol use may be larger than presented here. This needs to be confirmed in larger surveys.
p	This study has limitations, including the very brief time during which this web-based survey was made accessible (20 days), which yielded a relatively small sample size (N = 3,632), while being representative for a uniform period of full lockdown measures. In addition, we did not follow-up on the self-reported substance use after this 3-week period, and use may stabilize or increase further with increased duration of lockdown. Another limitation was that we only assessed alcohol, cigarettes, and cannabis and the mean use of alcohol per day, rather than per week, such that individuals consuming only a few drinks per week may have over- or underreported their drinking. This was counterbalanced by the possibility to add “drinking more, or drinking less than usual, or about the same” in the next question regarding their change in alcohol use.
p	As a whole, these data show that specific groups of individuals, especially younger individuals with lower educational background or those technically unemployed, have a higher risk of increasing their alcohol and/or cigarette use during times of social isolation. It is therefore of importance that care providers and especially general practitioners discuss the use of alcohol, tobacco, and cannabis with their patients, as this often has a beneficial effect on behaviour.
sec	Statement of Ethics Ethical approval was obtained. Because this was an anonymous web-based survey, patients were exempt from our Ethical Committee for given written informed consent. Research complied with the appropriate guidelines for human studies and animal welfare regulations and was approved as such by the Medical Ethics Committee of the University Hospital Brussels (UZ Brussel), under reference number 143202043.
title	Statement of Ethics
p	Ethical approval was obtained. Because this was an anonymous web-based survey, patients were exempt from our Ethical Committee for given written informed consent. Research complied with the appropriate guidelines for human studies and animal welfare regulations and was approved as such by the Medical Ethics Committee of the University Hospital Brussels (UZ Brussel), under reference number 143202043.
sec	Conflict of Interest Statement C.L.C. has received lecture fees from Takeda and GME. F.M. received honoraria from Lundbeck, Eli Lilly, and Johnson & Johnson. None were related with this research. The other authors have nothing to declare.
title	Conflict of Interest Statement
p	C.L.C. has received lecture fees from Takeda and GME. F.M. received honoraria from Lundbeck, Eli Lilly, and Johnson & Johnson. None were related with this research. The other authors have nothing to declare.
sec	Funding Sources The authors did not receive any funding.
title	Funding Sources
p	The authors did not receive any funding.
sec	Author Contributions N.V. started the project and C.L.C. prepared the 1st draft. S.V.L. performed the analyses. F.M., D.Z., L.S., and S.V.D.A. provided comments and revised the manuscript. All authors were involved in the study setup and approved the final manuscript in its current form.
title	Author Contributions
p	N.V. started the project and C.L.C. prepared the 1st draft. S.V.L. performed the analyses. F.M., D.Z., L.S., and S.V.D.A. provided comments and revised the manuscript. All authors were involved in the study setup and approved the final manuscript in its current form.
table-wrap	Table 1 Characteristics of respondents Study respondents (total N = 3,632) N % Age, years (mean ± SD) 42.1±14.6 Gender Female 2,541 70.0 Male 1,083 29.8 Gender-neutral 8 0.2 Language used to fill in survey Dutch 3,361 92.5 French 200 5.5 English 71 2.0 Area of residence Urban 865 23.8 Rural 2,767 76.2 Profession In healthcare 1,370 36.8 Not in healthcare 2,295 63.2 Educational background Primary education 28 0.8 Vocational education 72 2.0 Secondary education 562 15.5 Higher education 2,762 76.0 Doctorate 189 5.2 Missing 19 0.5 Current living situation Living alone 556 15.3 Living with partner and/or children 2,500 68.8 With children 0–5 years old 525 21.0* With children 6–12 years old 550 22.0* With children 13–18 years old 457 18.3* With children older than 18 years 565 22.6* Living with others 576 15.9 Household with children 1,609 44.3 Cared for at home 1,358 37.4 Cared for outside of home 190 5.2 Part of the children cared for at home 61 1.7 Characteristics of respondents (N = 3,632) in the Belgian ALCOVID study. SD, standard deviation. * Percentage of respondents living with partner and/or children.
label	Table 1
caption	Characteristics of respondents
p	Characteristics of respondents
table	Study respondents (total N = 3,632) N % Age, years (mean ± SD) 42.1±14.6 Gender Female 2,541 70.0 Male 1,083 29.8 Gender-neutral 8 0.2 Language used to fill in survey Dutch 3,361 92.5 French 200 5.5 English 71 2.0 Area of residence Urban 865 23.8 Rural 2,767 76.2 Profession In healthcare 1,370 36.8 Not in healthcare 2,295 63.2 Educational background Primary education 28 0.8 Vocational education 72 2.0 Secondary education 562 15.5 Higher education 2,762 76.0 Doctorate 189 5.2 Missing 19 0.5 Current living situation Living alone 556 15.3 Living with partner and/or children 2,500 68.8 With children 0–5 years old 525 21.0* With children 6–12 years old 550 22.0* With children 13–18 years old 457 18.3* With children older than 18 years 565 22.6* Living with others 576 15.9 Household with children 1,609 44.3 Cared for at home 1,358 37.4 Cared for outside of home 190 5.2 Part of the children cared for at home 61 1.7
tr	Study respondents (total N = 3,632)
th	Study respondents (total N = 3,632)
tr	N %
th	N
th	%
tr	Age, years (mean ± SD) 42.1±14.6
td	Age, years (mean ± SD)
td	42.1±14.6
tr	Gender
td	Gender
tr	Female 2,541 70.0
td	Female
td	2,541
td	70.0
tr	Male 1,083 29.8
td	Male
td	1,083
td	29.8
tr	Gender-neutral 8 0.2
td	Gender-neutral
td	8
td	0.2
tr	Language used to fill in survey
td	Language used to fill in survey
tr	Dutch 3,361 92.5
td	Dutch
td	3,361
td	92.5
tr	French 200 5.5
td	French
td	200
td	5.5
tr	English 71 2.0
td	English
td	71
td	2.0
tr	Area of residence
td	Area of residence
tr	Urban 865 23.8
td	Urban
td	865
td	23.8
tr	Rural 2,767 76.2
td	Rural
td	2,767
td	76.2
tr	Profession
td	Profession
tr	In healthcare 1,370 36.8
td	In healthcare
td	1,370
td	36.8
tr	Not in healthcare 2,295 63.2
td	Not in healthcare
td	2,295
td	63.2
tr	Educational background
td	Educational background
tr	Primary education 28 0.8
td	Primary education
td	28
td	0.8
tr	Vocational education 72 2.0
td	Vocational education
td	72
td	2.0
tr	Secondary education 562 15.5
td	Secondary education
td	562
td	15.5
tr	Higher education 2,762 76.0
td	Higher education
td	2,762
td	76.0
tr	Doctorate 189 5.2
td	Doctorate
td	189
td	5.2
tr	Missing 19 0.5
td	Missing
td	19
td	0.5
tr	Current living situation
td	Current living situation
tr	Living alone 556 15.3
td	Living alone
td	556
td	15.3
tr	Living with partner and/or children 2,500 68.8
td	Living with partner and/or children
td	2,500
td	68.8
tr	With children 0–5 years old 525 21.0*
td	With children 0–5 years old
td	525
td	21.0*
tr	With children 6–12 years old 550 22.0*
td	With children 6–12 years old
td	550
td	22.0*
tr	With children 13–18 years old 457 18.3*
td	With children 13–18 years old
td	457
td	18.3*
tr	With children older than 18 years 565 22.6*
td	With children older than 18 years
td	565
td	22.6*
tr	Living with others 576 15.9
td	Living with others
td	576
td	15.9
tr	Household with children 1,609 44.3
td	Household with children
td	1,609
td	44.3
tr	Cared for at home 1,358 37.4
td	Cared for at home
td	1,358
td	37.4
tr	Cared for outside of home 190 5.2
td	Cared for outside of home
td	190
td	5.2
tr	Part of the children cared for at home 61 1.7
td	Part of the children cared for at home
td	61
td	1.7
table-wrap-foot	Characteristics of respondents (N = 3,632) in the Belgian ALCOVID study. SD, standard deviation. * Percentage of respondents living with partner and/or children.
footnote	Characteristics of respondents (N = 3,632) in the Belgian ALCOVID study. SD, standard deviation.
p	Characteristics of respondents (N = 3,632) in the Belgian ALCOVID study. SD, standard deviation.
footnote	* Percentage of respondents living with partner and/or children.
label	*
p	Percentage of respondents living with partner and/or children.
table-wrap	Table 2 Logistic regression regarding alcohol use OR 95% CI for OR p value lower upper Age, years 0.981 0.974 0.987 <0.001 Children, n 1.220 1.146 1.298 <0.001 Are you more at home during the lockdown? 0.037 Yes, with tele-work Ref. − Yes, due to technical unemployment 1.357 1.065 1.728 0.013 Yes, due to illness 0.842 0.541 1.311 0.447 Yes, due to illness related to COVID-19 0.813 0.434 1.526 0.520 No 0.882 0.727 1.069 0.201 Professional occupation <0.001 Healthcare worker Ref. − Non-healthcare worker 1.404 1.176 1.676 <0.001 Student 0.537 0.389 0.740 <0.001 Job seeker 1.086 0.468 2.521 0.848 Retired 1.312 0.879 1.957 0.184 Others (e.g., disabled person) 0.810 0.40 1.527 0.516 Logistic regression explaining relationships to why respondents consumed more alcohol during the lockdown compared to before. In total, 1,102 respondents out of 3,632 (30.3%) noted drinking more during the lockdown compared to before.
label	Table 2
caption	Logistic regression regarding alcohol use
p	Logistic regression regarding alcohol use
table	OR 95% CI for OR p value lower upper Age, years 0.981 0.974 0.987 <0.001 Children, n 1.220 1.146 1.298 <0.001 Are you more at home during the lockdown? 0.037 Yes, with tele-work Ref. − Yes, due to technical unemployment 1.357 1.065 1.728 0.013 Yes, due to illness 0.842 0.541 1.311 0.447 Yes, due to illness related to COVID-19 0.813 0.434 1.526 0.520 No 0.882 0.727 1.069 0.201 Professional occupation <0.001 Healthcare worker Ref. − Non-healthcare worker 1.404 1.176 1.676 <0.001 Student 0.537 0.389 0.740 <0.001 Job seeker 1.086 0.468 2.521 0.848 Retired 1.312 0.879 1.957 0.184 Others (e.g., disabled person) 0.810 0.40 1.527 0.516
tr	OR 95% CI for OR p value
th	OR
th	95% CI for OR
th	p value
tr	lower upper
th	lower
th	upper
tr	Age, years 0.981 0.974 0.987 <0.001
td	Age, years
td	0.981
td	0.974
td	0.987
td	<0.001
tr	Children, n 1.220 1.146 1.298 <0.001
td	Children, n
td	1.220
td	1.146
td	1.298
td	<0.001
tr	Are you more at home during the lockdown? 0.037
td	Are you more at home during the lockdown?
td	0.037
tr	Yes, with tele-work Ref. −
td	Yes, with tele-work
td	Ref.
td	−
tr	Yes, due to technical unemployment 1.357 1.065 1.728 0.013
td	Yes, due to technical unemployment
td	1.357
td	1.065
td	1.728
td	0.013
tr	Yes, due to illness 0.842 0.541 1.311 0.447
td	Yes, due to illness
td	0.842
td	0.541
td	1.311
td	0.447
tr	Yes, due to illness related to COVID-19 0.813 0.434 1.526 0.520
td	Yes, due to illness related to COVID-19
td	0.813
td	0.434
td	1.526
td	0.520
tr	No 0.882 0.727 1.069 0.201
td	No
td	0.882
td	0.727
td	1.069
td	0.201
tr	Professional occupation <0.001
td	Professional occupation
td	<0.001
tr	Healthcare worker Ref. −
td	Healthcare worker
td	Ref.
td	−
tr	Non-healthcare worker 1.404 1.176 1.676 <0.001
td	Non-healthcare worker
td	1.404
td	1.176
td	1.676
td	<0.001
tr	Student 0.537 0.389 0.740 <0.001
td	Student
td	0.537
td	0.389
td	0.740
td	<0.001
tr	Job seeker 1.086 0.468 2.521 0.848
td	Job seeker
td	1.086
td	0.468
td	2.521
td	0.848
tr	Retired 1.312 0.879 1.957 0.184
td	Retired
td	1.312
td	0.879
td	1.957
td	0.184
tr	Others (e.g., disabled person) 0.810 0.40 1.527 0.516
td	Others (e.g., disabled person)
td	0.810
td	0.40
td	1.527
td	0.516
table-wrap-foot	Logistic regression explaining relationships to why respondents consumed more alcohol during the lockdown compared to before. In total, 1,102 respondents out of 3,632 (30.3%) noted drinking more during the lockdown compared to before.
footnote	Logistic regression explaining relationships to why respondents consumed more alcohol during the lockdown compared to before. In total, 1,102 respondents out of 3,632 (30.3%) noted drinking more during the lockdown compared to before.
p	Logistic regression explaining relationships to why respondents consumed more alcohol during the lockdown compared to before. In total, 1,102 respondents out of 3,632 (30.3%) noted drinking more during the lockdown compared to before.
table-wrap	Table 3 Logistic regression regarding nicotine use OR 95% CI for OR p value lower upper Age, years 0.988 0.978 0.999 0.027 Current living situation <0.001 Living with partner and/or child Ref. − Living alone 2.159 1.587 2.938 <0.001 Living with others 1.081 0.739 1.580 0.690 Educational background 0.015 Higher education Ref. − Primary education 1.114 0.258 4.817 0.885 Vocational education 2.085 1.025 4.240 0.042 Secondary education 1.312 0.944 1.823 0.106 Doctorate 0.245 0.077 0.777 0.017 Do you work more at home now? 0.018 Yes, with tele-work Ref. Yes, due to technical unemployment 1.638 1.125 2.386 0.010 Yes, due to illness 1.431 0.758 2.702 0.269 Yes, due to illness related to COVID-19 1.069 0.413 2.766 0.890 No 0.820 0.598 1.123 0.260 Logistic regression explaining relationships to why respondents smoke more cigarettes during the lockdown compared to before. In total, 264 respondents out of 3,632 (7.3%) noted they smoked more during the lockdown compared to before.
label	Table 3
caption	Logistic regression regarding nicotine use
p	Logistic regression regarding nicotine use
table	OR 95% CI for OR p value lower upper Age, years 0.988 0.978 0.999 0.027 Current living situation <0.001 Living with partner and/or child Ref. − Living alone 2.159 1.587 2.938 <0.001 Living with others 1.081 0.739 1.580 0.690 Educational background 0.015 Higher education Ref. − Primary education 1.114 0.258 4.817 0.885 Vocational education 2.085 1.025 4.240 0.042 Secondary education 1.312 0.944 1.823 0.106 Doctorate 0.245 0.077 0.777 0.017 Do you work more at home now? 0.018 Yes, with tele-work Ref. Yes, due to technical unemployment 1.638 1.125 2.386 0.010 Yes, due to illness 1.431 0.758 2.702 0.269 Yes, due to illness related to COVID-19 1.069 0.413 2.766 0.890 No 0.820 0.598 1.123 0.260
tr	OR 95% CI for OR p value
th	OR
th	95% CI for OR
th	p value
tr	lower upper
th	lower
th	upper
tr	Age, years 0.988 0.978 0.999 0.027
td	Age, years
td	0.988
td	0.978
td	0.999
td	0.027
tr	Current living situation <0.001
td	Current living situation
td	<0.001
tr	Living with partner and/or child Ref. −
td	Living with partner and/or child
td	Ref.
td	−
tr	Living alone 2.159 1.587 2.938 <0.001
td	Living alone
td	2.159
td	1.587
td	2.938
td	<0.001
tr	Living with others 1.081 0.739 1.580 0.690
td	Living with others
td	1.081
td	0.739
td	1.580
td	0.690
tr	Educational background 0.015
td	Educational background
td	0.015
tr	Higher education Ref. −
td	Higher education
td	Ref.
td	−
tr	Primary education 1.114 0.258 4.817 0.885
td	Primary education
td	1.114
td	0.258
td	4.817
td	0.885
tr	Vocational education 2.085 1.025 4.240 0.042
td	Vocational education
td	2.085
td	1.025
td	4.240
td	0.042
tr	Secondary education 1.312 0.944 1.823 0.106
td	Secondary education
td	1.312
td	0.944
td	1.823
td	0.106
tr	Doctorate 0.245 0.077 0.777 0.017
td	Doctorate
td	0.245
td	0.077
td	0.777
td	0.017
tr	Do you work more at home now? 0.018
td	Do you work more at home now?
td	0.018
tr	Yes, with tele-work Ref.
td	Yes, with tele-work
td	Ref.
tr	Yes, due to technical unemployment 1.638 1.125 2.386 0.010
td	Yes, due to technical unemployment
td	1.638
td	1.125
td	2.386
td	0.010
tr	Yes, due to illness 1.431 0.758 2.702 0.269
td	Yes, due to illness
td	1.431
td	0.758
td	2.702
td	0.269
tr	Yes, due to illness related to COVID-19 1.069 0.413 2.766 0.890
td	Yes, due to illness related to COVID-19
td	1.069
td	0.413
td	2.766
td	0.890
tr	No 0.820 0.598 1.123 0.260
td	No
td	0.820
td	0.598
td	1.123
td	0.260
table-wrap-foot	Logistic regression explaining relationships to why respondents smoke more cigarettes during the lockdown compared to before. In total, 264 respondents out of 3,632 (7.3%) noted they smoked more during the lockdown compared to before.
footnote	Logistic regression explaining relationships to why respondents smoke more cigarettes during the lockdown compared to before. In total, 264 respondents out of 3,632 (7.3%) noted they smoked more during the lockdown compared to before.
p	Logistic regression explaining relationships to why respondents smoke more cigarettes during the lockdown compared to before. In total, 264 respondents out of 3,632 (7.3%) noted they smoked more during the lockdown compared to before.
table-wrap	Table 4 Ranking of motives for smoking more, drinking more, and using more cannabis Reason for more consuming of … Nicotine Alcohol Cannabis N % rank N % rank N % rank Has nothing to do with lockdown 16 6.1 12 26 2.4 13 2 2.6 12 Fear of contamination 10 3.8 14 35 3.2 12 2 2.6 12 Reward after a hard-working day 98 37.1 3 513 46.6 2 33 43.4 3 Increased tension (family, work, etc.) 59 22.3 7 270 24.5 6 17 22.4 8 Decreased tension (traffic, more sleep time, etc.) 27 10.2 11 123 11.2 11 16 21.1 9 Stress from negative reporting 50 18.9 8 70 15.4 8 19 25.0 7 Loneliness 75 28.4 5 168 15.2 9 25 32.9 5 Lack of social contacts 108 40.9 2 383 34.8 3 37 48.7 2 Lack of activities or sports 46 17.4 9 194 17.6 7 22 28.9 6 More tension due to permanent presence of housemates 38 14.4 10 157 14.2 10 9 11.8 10 Boredom 116 43.9 1 298 27.0 5 41 53.9 1 Conviviality 75 28.4 5 572 51.9 1 25 32.9 5 Increased social control 2 0.8 15 9 0.8 15 2 2.6 12 Loss of daily structure 98 37.1 3 332 30.1 4 33 43.4 3 Seeing smoking and drinking in movies and series 15 5.7 13 25 2.3 14 5 6.6 11 Total N: 264 (7.3%) Total N: 1,102 (30.3%) Total N: 76 (2.1%) Descriptive analysis of the motives for smoking more, drinking more, and using more cannabis. N represents the number of respondents using more. % represents the relative frequency of respondents using more indicating that motive. Rank defines the order of how often the motive was reported, ranging from 1 (highly reported motive) to 15 (lowest reported motive). If an equal number of respondents reported the motive, motives were given the same rank.
label	Table 4
caption	Ranking of motives for smoking more, drinking more, and using more cannabis
p	Ranking of motives for smoking more, drinking more, and using more cannabis
table	Reason for more consuming of … Nicotine Alcohol Cannabis N % rank N % rank N % rank Has nothing to do with lockdown 16 6.1 12 26 2.4 13 2 2.6 12 Fear of contamination 10 3.8 14 35 3.2 12 2 2.6 12 Reward after a hard-working day 98 37.1 3 513 46.6 2 33 43.4 3 Increased tension (family, work, etc.) 59 22.3 7 270 24.5 6 17 22.4 8 Decreased tension (traffic, more sleep time, etc.) 27 10.2 11 123 11.2 11 16 21.1 9 Stress from negative reporting 50 18.9 8 70 15.4 8 19 25.0 7 Loneliness 75 28.4 5 168 15.2 9 25 32.9 5 Lack of social contacts 108 40.9 2 383 34.8 3 37 48.7 2 Lack of activities or sports 46 17.4 9 194 17.6 7 22 28.9 6 More tension due to permanent presence of housemates 38 14.4 10 157 14.2 10 9 11.8 10 Boredom 116 43.9 1 298 27.0 5 41 53.9 1 Conviviality 75 28.4 5 572 51.9 1 25 32.9 5 Increased social control 2 0.8 15 9 0.8 15 2 2.6 12 Loss of daily structure 98 37.1 3 332 30.1 4 33 43.4 3 Seeing smoking and drinking in movies and series 15 5.7 13 25 2.3 14 5 6.6 11 Total N: 264 (7.3%) Total N: 1,102 (30.3%) Total N: 76 (2.1%)
tr	Reason for more consuming of … Nicotine Alcohol Cannabis
th	Reason for more consuming of …
th	Nicotine
th	Alcohol
th	Cannabis
tr	N % rank N % rank N % rank
th	N
th	%
th	rank
th	N
th	%
th	rank
th	N
th	%
th	rank
tr	Has nothing to do with lockdown 16 6.1 12 26 2.4 13 2 2.6 12
td	Has nothing to do with lockdown
td	16
td	6.1
td	12
td	26
td	2.4
td	13
td	2
td	2.6
td	12
tr	Fear of contamination 10 3.8 14 35 3.2 12 2 2.6 12
td	Fear of contamination
td	10
td	3.8
td	14
td	35
td	3.2
td	12
td	2
td	2.6
td	12
tr	Reward after a hard-working day 98 37.1 3 513 46.6 2 33 43.4 3
td	Reward after a hard-working day
td	98
td	37.1
td	3
td	513
td	46.6
td	2
td	33
td	43.4
td	3
tr	Increased tension (family, work, etc.) 59 22.3 7 270 24.5 6 17 22.4 8
td	Increased tension (family, work, etc.)
td	59
td	22.3
td	7
td	270
td	24.5
td	6
td	17
td	22.4
td	8
tr	Decreased tension (traffic, more sleep time, etc.) 27 10.2 11 123 11.2 11 16 21.1 9
td	Decreased tension (traffic, more sleep time, etc.)
td	27
td	10.2
td	11
td	123
td	11.2
td	11
td	16
td	21.1
td	9
tr	Stress from negative reporting 50 18.9 8 70 15.4 8 19 25.0 7
td	Stress from negative reporting
td	50
td	18.9
td	8
td	70
td	15.4
td	8
td	19
td	25.0
td	7
tr	Loneliness 75 28.4 5 168 15.2 9 25 32.9 5
td	Loneliness
td	75
td	28.4
td	5
td	168
td	15.2
td	9
td	25
td	32.9
td	5
tr	Lack of social contacts 108 40.9 2 383 34.8 3 37 48.7 2
td	Lack of social contacts
td	108
td	40.9
td	2
td	383
td	34.8
td	3
td	37
td	48.7
td	2
tr	Lack of activities or sports 46 17.4 9 194 17.6 7 22 28.9 6
td	Lack of activities or sports
td	46
td	17.4
td	9
td	194
td	17.6
td	7
td	22
td	28.9
td	6
tr	More tension due to permanent presence of housemates 38 14.4 10 157 14.2 10 9 11.8 10
td	More tension due to permanent presence of housemates
td	38
td	14.4
td	10
td	157
td	14.2
td	10
td	9
td	11.8
td	10
tr	Boredom 116 43.9 1 298 27.0 5 41 53.9 1
td	Boredom
td	116
td	43.9
td	1
td	298
td	27.0
td	5
td	41
td	53.9
td	1
tr	Conviviality 75 28.4 5 572 51.9 1 25 32.9 5
td	Conviviality
td	75
td	28.4
td	5
td	572
td	51.9
td	1
td	25
td	32.9
td	5
tr	Increased social control 2 0.8 15 9 0.8 15 2 2.6 12
td	Increased social control
td	2
td	0.8
td	15
td	9
td	0.8
td	15
td	2
td	2.6
td	12
tr	Loss of daily structure 98 37.1 3 332 30.1 4 33 43.4 3
td	Loss of daily structure
td	98
td	37.1
td	3
td	332
td	30.1
td	4
td	33
td	43.4
td	3
tr	Seeing smoking and drinking in movies and series 15 5.7 13 25 2.3 14 5 6.6 11
td	Seeing smoking and drinking in movies and series
td	15
td	5.7
td	13
td	25
td	2.3
td	14
td	5
td	6.6
td	11
tr	Total N: 264 (7.3%) Total N: 1,102 (30.3%) Total N: 76 (2.1%)
td	Total N: 264 (7.3%)
td	Total N: 1,102 (30.3%)
td	Total N: 76 (2.1%)
table-wrap-foot	Descriptive analysis of the motives for smoking more, drinking more, and using more cannabis. N represents the number of respondents using more. % represents the relative frequency of respondents using more indicating that motive. Rank defines the order of how often the motive was reported, ranging from 1 (highly reported motive) to 15 (lowest reported motive). If an equal number of respondents reported the motive, motives were given the same rank.
footnote	Descriptive analysis of the motives for smoking more, drinking more, and using more cannabis. N represents the number of respondents using more. % represents the relative frequency of respondents using more indicating that motive. Rank defines the order of how often the motive was reported, ranging from 1 (highly reported motive) to 15 (lowest reported motive). If an equal number of respondents reported the motive, motives were given the same rank.
p	Descriptive analysis of the motives for smoking more, drinking more, and using more cannabis. N represents the number of respondents using more. % represents the relative frequency of respondents using more indicating that motive. Rank defines the order of how often the motive was reported, ranging from 1 (highly reported motive) to 15 (lowest reported motive). If an equal number of respondents reported the motive, motives were given the same rank.

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