Abstract
Background
Previous studies have reported different patterns of change in alcohol consumption during the COVID-19 pandemic, varying across countries and time periods. While some studies report an overall decrease in alcohol use, others describe an increase. This study aimed to investigate within-person changes in alcohol consumption specifically in the French population between 2018 and 2020 during the COVID-19 pandemic, and to identify the factors associated with these changes.
Methods
We used longitudinal data from the French TEMPO cohort, established in 2009 among young adults, to examine within-person changes in alcohol consumption between 2018 and 2020. Data collected in 2018 and during the 2020 lockdown periods were analyzed for 607 participants using a multinomial mixed-effects regression model.
Results
Approximately one-third of the study sample did not change their alcohol consumption between 2018 and 2020 (31.7%). While 24.4% experienced a moderate increase, 21.4% had a moderate decrease, 12.4% a large increase, and 10.0% a large decrease. Men had nearly three times higher odds of experiencing a large decrease in alcohol consumption, rather than maintaining a stable level of consumption, compared to women (95%CI = 1.88–5.49). Regular smokers had lower odds of experiencing a large decrease in alcohol consumption rather than maintaining a stable consumption compared to non-smokers (OR = 0.57, 95%CI = 0.35–0.93).
Conclusions
Our study revealed different trends in alcohol consumption between 2018 and 2020, with most participants maintaining a consistent level of alcohol consumption, while some modestly increased it. This study enabled us to identify vulnerable groups which require particular attention and monitoring in the event of other health, economic, social, or climate-related crises.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27104-x.
Keywords: COVID-19, Alcohol consumption, Risk factors, Adult
Background
The COVID-19 pandemic led to large-scale restrictive measures such as lockdowns, social isolation, and preventive measures. With a large portion of the population experiencing disruptions in their daily routines and relationships, lifestyle behavior modifications are inevitable as individuals seek to adapt to the pandemic on both individual and collective levels [1, 2]. Past studies have reported that these modifications to health behaviors include changes in sleep [3], physical activity, dietary habits [4], as well as psychoactive substance consumption such as alcohol, tobacco, and opioids [5–7]. In addition, restrictive measures related to COVID-19 have been associated with increased levels of psychological distress, including stress, anxiety, and depression in the general population [2, 8–10]. It is also well established that individuals with mental health disorders are at a significantly higher risk of developing alcohol use disorder, even in non-pandemic settings [11]. Therefore, it is plausible that pandemic-related psychological distress contributed to the observed changes in alcohol consumption patterns. Additionally, the COVID-19 pandemic may have impacted patterns of alcohol consumption, with some individuals drinking more and others less than before the pandemic [12, 13]. Those who drank more were mentally less able to cope [12] and likely used alcohol as a coping mechanism. The ranking of motives for increased smoking varied, with the most common being “conviviality,” followed by “reward after a hard-working day”, “lack of social contacts” and the “loss of daily structure” [14].
However, changes in alcohol consumption during the COVID-19 pandemic varied across countries and may have depended on the timing of data collection and the severity of restrictions. In France, periods of lockdown and social distancing measures may have influenced drinking behaviors in different ways [15, 16]. Rehm et al. proposed two hypotheses to describe changes in alcohol consumption during the pandemic [17] : (i) there was a reduction or a maintenance of alcohol consumption due to restricted social interactions and limited availability, (ii) alcohol consumption increased as a means of coping with pandemic-related stress and fear, i.e., to alleviate the effects of the pandemic, as the relationship between stress and alcohol consumption is widely recognized [18–20]. These scenarios were both supported by the literature: in some studies, decreased alcohol consumption was reported during the pandemic when compared to the situation before [21]. For instance, a longitudinal study in Canada found a decrease in heavy drinking among emerging adults during the early phase of the pandemic (June–July 2020) compared to pre-pandemic levels, likely due to social restrictions [22]. Conversely, an international cross-sectional survey reported that while overall alcohol consumption declined during quarantine, 36% of participants increased their drinking, particularly in the UK, among essential workers, individuals with children, and those experiencing high levels of stress, anxiety, and depression [23]. Although the decrease was significant in Wang’s study [24], the overall reduction in alcohol consumption during COVID-19 was minimal, from an average of 3.5 to 3.4 drinks (p = 0.035). This study, conducted in China through a retrospective online survey, also highlighted persistent gender disparities, with men consuming more alcohol, engaging riskier drinking behaviors, and experiencing higher rates of heavy drinking than women both before and during the pandemic. Another longitudinal study in Japan observed a reduction in alcohol intake during the state of emergency in early 2020, particularly among individuals working from home [25]. These variations emphasize the role of contextual factors, such as lockdown policies and work environment, in shaping drinking behaviors. In an Australian study assessing the effects of pandemic-related restrictions on alcohol consumption through wastewater analysis, Bade et al. reported that in 2020, alcohol consumption reached the lowest level compared to previous years, dating back to 2016 [26]. In a French study, 24.4% of respondents in an online cross-sectional survey reported a decrease in their alcohol consumption during COVID-19, while it remained stable for 64.8% [27].
Simultaneously, several other studies suggested an overall increase in alcohol consumption. For example, a nationwide survey in South Korea found a rise in high-risk drinking among men between 2019 and 2020, particularly among those aged 40–49, urban residents, and higher-income groups [28]. In addition, Grossman et al. reported that 60% of respondents in an American online survey indicated increased alcohol consumption in May 2020, compared to pre-COVID-19, with stress and boredom being common reasons for this increase [29]. Similarly, Barbosa et al., through an American online survey conducted between February and April 2020, found that respondents consumed more drinks per day in April 2020 (+ 29%, P < 0.001) compared to February 2020 [30]. These findings highlight the variability in alcohol consumption patterns during the pandemic and underscore the need for longitudinal studies to assess their long-term public health implications.
Factors associated with these changes in alcohol consumption during the COVID-19 pandemic should also be identified to better understand which populations are vulnerable and should be targeted in public health policies. The existing literature has identified several risk factors such as gender, age, income, living situation, anxiety, loneliness [23, 31, 32], but also tobacco smoking [33]. However, other risks factors, such as comorbidities, COVID-19-like symptoms, and social support from family and friends, should be investigated to fully assess the factors associated with changes in alcohol consumption.
Existing studies are predominantly based on cross-sectional surveys, which are unable to accurately assess changes in alcohol use. Therefore, this study aims to investigate within-person changes in alcohol consumption between 2018 and 2020 during COVID-19 and to identify the factors associated with these changes among participants in the French TEMPO cohort.
Methods
Study population
This study included 607 participants from the French TEMPO (Trajectoires EpidéMiologiques en POpulation) cohort, established in 2009 to evaluate the determinants of mental health difficulties and addictive behaviors [34]. This cohort was initially composed of young adults aged 22 to 35 years old, who had previously participated in a study of children’s mental health in 1991 and in 1999 [35], and whose parents participate in the GAZEL cohort study [36]. TEMPO participants were followed-up through self-completed questionnaires in 2009, 2011, 2015, 2018, and in 2020 and 2021 in the context of the COVID-19 pandemic (Supplementary Fig. 1). From March 2020 to May 2021, participants completed nine questionnaires on their mental health and addictive behaviors during the COVID-19 pandemic and lockdowns. Weekly surveys were sent out for the first five waves, followed by surveys every 15 days in the 6th and 7th questionnaires. The last two surveys were sent out respectively in June-July 2020, and between December 2020 and May 2021. The questionnaires for the first 8 waves were completed online, while the last survey was sent both by post and via e-mail. The first waves of data collection during the COVID-19 pandemic (March–May 2020) took place during the strictest lockdown period in France, while later waves (June 2020-May 2021) captured behaviors changes as COVID-19 restrictions evolved.
Measures
Outcome: within-person change in alcohol consumption
In each questionnaire, participants reported their usual consumption of alcoholic drinks in terms of drinking frequency and severity. Self-reported alcohol consumption has previously been shown to be reliable and valid [37]. Based on this, we first assessed the number of drinks per week (DPW) in 2018 and in 2020. During the COVID-19 period, repeated TEMPO data collections were conducted at short intervals (eight waves between March and July 2020). Given the limited number of participants who completed all waves (n = 130), and in order to maximize the use of available data while accounting for intra-individual correlation, we analyzed the dataset in long format, including all 2020 measurements for each participant. This approach allowed us to examine the direction and magnitude of changes over time, while accounting for repeated measures and individual-level heterogeneity. The number of DPW was calculated based on the formula derived from Dawson [38]:
 |
Here, n represents the number of drinks consumed on the last occasion, Fn is the number of drinking occasions per week, and F6 is the drinking frequency of 6 drinks or more on a single occasion per week. Additionally, we multiplied by 10, in order to represent the category “6 drinks or more” and to avoid underestimating the number of drinks.
The study outcome was defined as within-person change in alcohol consumption, measured by comparing the number of drinks per week (DPW) reported in each 2020 wave to the participant’s baseline level in 2018. We categorized the change into 5 groups based on the distribution of the change [39]: a large decrease corresponding to fewer than − 4 drinks per week, a moderate decrease of greater than − 1 to less than − 4 drinks per week, no change of − 1 to 1 drink per week, a moderate increase of greater than 1 to less than 4 drinks per week, and a large increase of 4 drinks per week or more.
Covariates
Participants’ socio-demographic characteristics included biological sex (“Female”, “Male”), age and Socio-Economic Position (SEP). SEP was determined based on participants’ highest level of education, occupational category, employment status and type of work contract. If participants did not have a diploma higher than high school diploma, or were an employee or manual worker, or were unemployed or had a precarious contract type, they were categorized in the “Low SEP” group [40]. Living situation was also available and was coded as “Lives with a partner and children”, “Lives with a partner but no children”, “Other”.
Participants’ anxiety/depression status during COVID-19 (“No”, “Yes”) was also assessed using items from the Anxious/Depressed subscale of the Adult Self Report (ASR) [41] in each questionnaire sent during the pandemic. According to ASR guidelines, the 85th percentile serves to identify persons with clinically significant symptoms [42]. The frequency of tobacco use was categorized into 3 classes: “Non-smoker”, “Ex-smoker” and “Regular smoker or occasional smoker”. Factors related to COVID-19 were also considered. Participants self-reported the occurrence of COVID-19-like symptoms (fever, cough, muscle soreness, respiratory problems, loss of taste, loss of smell, or fatigue) in each questionnaire sent during the pandemic. Participants were also asked if they were concerned about the COVID-19 situation (“Not concerned”, “Moderately concerned”, and “Very concerned”). The presence of comorbidities (“No”, “Yes”) was also self-reported by participants, including conditions such as asthma, cancer, cardiovascular disease, chronic digestive diseases, diabetes, migraines, musculoskeletal disorders, nervous breakdowns, or obesity. Information on social support received from friends and family during the COVID-19 pandemic (“No”, “Yes”) was also collected in each wave.
Statistical analyses
Participants’ characteristics were described based on the within-person change in alcohol consumption (“large decrease”, “moderate decrease”, “no change”, “moderate increase”, “large increase”). Comparisons between the different categories of alcohol change were performed using the Chi-Square test, Kruskal-Wallis rank sum test or Fisher’s exact test. To examine the factors associated with within-person changes in alcohol consumption between 2018 and 2020, we used a multinomial mixed-effects regression model with a random intercept for individuals, with ‘no change’ as the reference category. To account for potential confounding factors, the multivariate model included key variables identified a priori based on clinical relevance and previous literature, as well as additional covariates with a p-value < 0.20 in univariate analyses [43–45] in order to achieve balanced adjustment without overfitting while retaining potentially important predictors. These covariates included biological sex, SEP, living situation, anxiety and/or depression during COVID-19, tobacco use, COVID-like symptoms, concern about COVID-19, comorbidities and social support.
In our study, the percentage of missing data was less than 5% for all covariates and data were deemed to be missing at random. To handle missing data, a random forest-based imputation method was applied exclusively for the multinomial mixed-effects regression model. All other analyses, including descriptive and univariate comparisons, were conducted on non-imputed data. Imputations were performed on all covariates included in the multinomial mixed-effects model; the outcome was not imputed. The procedure used 100 trees per forest, four parallel cores, and a fixed random seed to ensure reproducibility. Random forest imputation has been validated as a robust method for handling missing data, particularly in complex datasets with non-linear relationships [46, 47]. All analyses were performed using SAS® (version 9.4) and R (version 4.0.2, missForest package [46]).
Results
Table 1 describes the characteristics of our study population. For descriptive purposes, each participant contributed only once to the table, based on their first non-stable observation among the eight 2020 waves. Only slightly less than one-third of the study sample did not change their alcohol consumption between 2018 and 2020 (31.7%), while 24.4% experienced a moderate increase, 21.4% had a moderate decrease, 12.4% saw a large increase, and 10.0% had a large decrease. Participants were mostly female (66.2%), had an average age of 40 years, had an intermediate or high SEP, and lived with a partner and children. Participants who were men, regular or occasional smokers, or who received no social support during COVID-19 had higher odds of exhibiting more drastic changes, such as a large increase or decrease in alcohol consumption. The mean number of drinks per week (DPW) evolved from 4.46 (SD = 6.94) in 2018 to values ranging between 3.90 (SD = 6.04) and 6.34 (SD = 8.11) across the eight waves conducted in 2020, with medians increasing from 2.19 to 3.75, reflecting substantial heterogeneity in alcohol consumption changes (Supplementary Table 1).
Table 1.
Characteristics of TEMPO cohort participants and changes in alcohol consumption between 2018 and 2020 (% or mean ± standard deviation (SD))
| Total (n=607) | Large decrease¤ (n=61) | Moderate decrease¤(n=130) | No change(n=193) | Moderate increase¤(n=148) | Large increase¤(n=75) | p-value* | |
|---|---|---|---|---|---|---|---|
| Biological sex | 0.001 | ||||||
| Female | 402 (66.2%) | 31 (7.7%) | 78 (19.4%) | 145 (36.1%) | 103 (25.6%) | 45 (11.2%) | |
| Male | 205 (33.8%) | 30 (14.6%) | 52 (25.4%) | 48 (23.4%) | 45 (22.0%) | 30 (14.6%) | |
| Missing information | 0 | 0 | 0 | 0 | 0 | 0 | |
| Age | 39.8 (3.6) | 39.7 (3.6) | 40.1 (3.8) | 39.6 (3.8) | 39.7 (3.6) | 40.1 (2.9) | 0.674 |
| Missing information | 0 | 0 | 0 | 0 | 0 | 0 | |
| SEP (Socio-Economic Position) | 0.075 | ||||||
| Low | 179 (29.6%) | 15 (8.4%) | 39 (21.8%) | 70 (39.1%) | 34 (19.0%) | 21 (11.7%) | |
| Intermediate or High | 426 (70.4%) | 46 (10.8%) | 91 (21.4%) | 121 (28.4%) | 114 (26.8%) | 54 (12.7%) | |
| Missing information | 2 | 0 | 0 | 2 | 0 | 0 | |
| Living situation | 0.106 | ||||||
| Lives with partner andchildren | 411 (67.7%) | 43 (10.5%) | 81 (19.7%) | 124 (30.2%) | 110 (26.8%) | 53 (12.9%) | |
| Lives with a partner but nochildren | 57 (9.4%) | 8 (14.0%) | 10 (17.5%) | 18 (31.6%) | 11 (19.3%) | 10 (17.5%) | |
| Other | 139 (22.9%) | 10 (7.2%) | 39 (28.1%) | 51 (36.7%) | 27 (19.4%) | 12 (8.6%) | |
| Missing information | 0 | 0 | 0 | 0 | 0 | 0 | |
| Anxiety/depression during COVID-19 | 0.584 | ||||||
| No | 493 (81.6%) | 49 (9.9%) | 109 (22.1%) | 149 (30.2%) | 122 (24.7%) | 64 (13.0%) | |
| Yes | 111 (18.4%) | 11 (9.9%) | 21 (18.9%) | 42 (37.8%) | 26 (23.4%) | 11 (9.9%) | |
| Missing information | 3 | 1 | 0 | 2 | 0 | 0 | |
| Tobacco | <0.001 | ||||||
| Non-smoker | 376 (61.9%) | 31 (8.2%) | 73 (19.4%) | 144 (38.3%) | 94 (25.0%) | 34 (9.0%) | |
| Ex-smoker | 122 (20.1%) | 15 (12.3%) | 24 (19.7%) | 29 (23.8%) | 36 (29.5%) | 18 (14.8%) | |
| Regular or occasional smoker | 109 (18.0%) | 15 (13.8%) | 33 (30.3%) | 20 (18.3%) | 18 (16.5%) | 23 (21.1%) | |
| Missing information | 0 | 0 | 0 | 0 | 0 | 0 | |
| Covid-like symptoms | 0.259 | ||||||
| No | 562 (92.7%) | 54 (9.6%) | 123 (21.9%) | 177 (31.5%) | 135 (24.0%) | 73 (13.0%) | |
| Yes | 44 (7.3%) | 7 (15.9%) | 7 (15.9%) | 16 (36.4%) | 12 (27.3%) | 2 (4.5%) | |
| Missing information | 1 | 0 | 0 | 0 | 1 | 0 | |
| Concern about Covid-19 | 0.227 | ||||||
| Not concerned | 275 (45.4%) | 29 (10.5%) | 63 (22.9%) | 82 (29.8%) | 64 (23.3%) | 37 (13.5%) | |
| Moderately concerned | 148 (24.4%) | 19 (12.8%) | 22 (14.9%) | 46 (31.1%) | 43 (29.1%) | 18 (12.2%) | |
| Very concerned | 183 (30.2%) | 13 (7.1%) | 45 (24.6%) | 65 (35.5%) | 40 (21.9%) | 20 (10.9%) | |
| Missing information | 1 | 0 | 0 | 0 | 1 | 0 | |
| Comorbidities | 0.376 | ||||||
| No | 393 (65.7%) | 39 (9.9%) | 80 (20.4%) | 120 (30.5%) | 104 (26.5%) | 50 (12.7%) | |
| Yes | 205 (34.3%) | 20 (9.8%) | 49 (23.9%) | 71 (34.6%) | 40 (19.5%) | 25 (12.2%) | |
| Missing information | 9 | 2 | 1 | 2 | 4 | 0 | |
| Social support from familyand friends during Covid-19 | 0.034 | ||||||
| No | 500 (83.2%) | 56 (11.2%) | 100 (20.0%) | 160 (32.0%) | 117 (23.4%) | 67 (13.4%) | |
| Yes | 101 (16.8%) | 4 (4.0%) | 29 (28.7%) | 31 (30.7%) | 29 (28.7%) | 8 (7.9%) | |
| Missing information | 6 | 1 | 1 | 2 | 2 | 0 |
*Pearson’s Chi-squared test; Kruskal-Wallis rank sum test; Fisher’s exact test
Each participant contributed only once to the descriptive summary, based on their first non-stable observation among the eight 2020 waves
¤Large decrease: Reduction of ≥4 drinks per week, Moderate decrease: Reduction of 1 to <4 drinks per week, Moderate increase: Increase of 1 to <4 drinks per week, Large increase: Increase of ≥4 drinks per week
Percentages in the "Total" column are column percentages. Percentages in all other columns are row percentages (i.e., within-group prevalence of each alcohol consumption change category)
Table 2 displays the results of the multivariate multinomial mixed-effects model examining factors associated with changes in alcohol consumption between 2018 and 2020, after adjusting for covariates. Compared to women, men had 3.16 times higher odds of reporting a large decrease in alcohol consumption rather than remaining stable (95%CI = 1.88–5.49). They also had 2.53 times higher odds (95%CI = 1.46–4.37) of reporting a moderate increase in alcohol consumption. Additionally, participants with low SEP had lower odds of reporting a large decrease rather than remaining stable compared to those with high SEP (OR = 0.53, 95%CI = 0.30–0.94).
Table 2.
Factors associated with within-person changes in alcohol consumption in the TEMPO cohort between 2018 and 2020, using multinomial multivariate mixed regression on imputed data (France, n=607; odds ratio (OR) with 95% Confidence Interval (CI))
| Large decrease¤ vs. Stable |
Moderate decrease¤ Vs Stable |
Moderate increase¤ vs. Stable |
Large increase¤ vs. Stable |
|||||
|---|---|---|---|---|---|---|---|---|
| OR [95% CI] | p | OR [95% CI] | p | OR [95% CI] | p | OR [95% CI] | p | |
| Sex | ||||||||
| Women | 1 | 1 | 1 | 1 | ||||
| Men | 3.16 [1.88–5.49] | < 0.001 | 0.09 [0.02–0.42] | 0.004 | 2.53 [1.46–4.37] | < 0.001 | 0.99 [0.49–1.99] | 0.978 |
| Socio-economic Position | ||||||||
| Intermediate or high | 1 | 1 | 1 | 1 | ||||
| Low | 0.53 [0.30–0.94] | 0.030 | 0.90 [0.17–5.17] | 0.905 | 1.65 [0.92–3.03] | 0.100 | 1.01 [0.48–2.18] | 0.975 |
| Living situation | ||||||||
| Lives with partner and children | 1 | 1 | 1 | 1 | ||||
| Lives with a partner but no children | 1.65 [0.93–2.94] | 0.091 | 3.29 [1.04–10.28] | 0.046 | 1.48 [0.82–2.75] | 0.200 | 1.91 [0.91–4.05] | 0.082 |
| Other | 1.17 [0.79–1.76] | 0.434 | 3.28 [1.44–7.81] | 0.005 | 0.77 [0.51–1.16] | 0.215 | 1.07 [0.65–1.77] | 0.797 |
| Anxiety/depression during COVID-19 | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 1.49 [0.87–2.53] | 0.142 | 2.34 [0.79–6.74] | 0.121 | 1.48 [0.83–2.65] | 0.185 | 3.69 [1.98–6.96] | < 0.001 |
| Tobacco | ||||||||
| Non-smoker | 1 | 1 | 1 | 1 | ||||
| Ex-smoker | 0.47 [0.29–0.77] | 0.002 | 0.70 [0.26–1.87] | 0.483 | 1.31 [0.78–2.17] | 0.306 | 0.83 [0.46–1.50] | 0.524 |
| Regular or occasional smoker | 0.57 [0.35–0.93] | 0.025 | 0.48 [0.16–1.49] | 0.202 | 1.02 [0.60–1.74] | 0.955 | 0.87 [0.46–1.64] | 0.658 |
| Covid-like symptoms | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 1.26 [0.79–2.00] | 0.330 | 1.77 [0.36–9.14] | 0.493 | 0.44 [0.25–0.74] | 0.003 | 0.34 [0.17–0.64] | 0.001 |
| Concern about Covid-19 | ||||||||
| Not concerned | 1 | 1 | 1 | 1 | ||||
| Moderately | 1.63 [1.03–2.62] | 0.038 | 1.29 [0.54–3.07] | 0.565 | 1.54 [0.97–2.47] | 0.071 | 1.24 [0.72–2.12] | 0.439 |
| Very concerned | 1.29 [0.82–2.03] | 0.270 | 1.46 [0.57–3.84] | 0.446 | 1.02 [0.63–1.61] | 0.935 | 0.91 [0.53–1.58] | 0.741 |
| Comorbidities | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 1.40 [0.82–2.42] | 0.228 | 1.77 [0.36–9.14] | 0.493 | 0.71 [0.41–1.22] | 0.225 | 0.99 [0.49–2.07] | 0.971 |
| Social support from family and friends during Covid-19 | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 0.91 [0.56–1.46] | 0.704 | 0.44 [0.15–1.25] | 0.141 | 1.28 [0.79–2.14] | 0.333 | 1.32 [0.73–2.45] | 0.385 |
¤Large decrease: Reduction of ≥4 drinks per week, Moderate decrease: Reduction of 1 to <4 drinks per week, Moderate increase: Increase of 1 to <4 drinks per week, Large increase: Increase of ≥4 drinks per week
Significant p values are presented in bold
Participants who experienced anxiety and/or depression during the COVID-19 period had higher odds of reporting a large increase in alcohol consumption (OR = 3.69, 95% CI = 1.98–6.96). Regarding tobacco use, we found that regular or occasional smokers had lower odds of reporting a large decrease in alcohol use (OR = 0.57, 95% CI = 0.35–0.93) compared to non-smokers. A similar pattern was observed for ex-smokers compared to non-smokers (OR = 0.47, 95%CI = 0.29–0.77). No significant associations were found for other categories of change in alcohol consumption.
COVID-like symptoms were also associated with alcohol use: participants reporting such symptoms had significantly lower odds of reporting a moderate increase compared to remaining stable (OR = 0.44, 95% CI = 0.25–0.74), and lower odds of a large increase (OR = 0.34, 95% CI = 0.17–0.65). Concern about COVID-19 was also associated with changes in alcohol consumption: individuals who were moderately concerned had higher odds of reporting a large decrease (OR = 1.63, 95% CI = 1.03–2.62). No significant associations were observed for comorbidities or social support.
Discussion
Summary of findings
Summary of findings
This study highlights the potential impact of the COVID-19 pandemic on intra-personal changes in alcohol consumption in France, using longitudinal data from before and during the pandemic. The results reveal that the changes were not uniform, as participants experienced large or moderate increases, but also large or moderate decreases. Our results show that several background factors, including sex, socio-economic position, and tobacco status, were associated with variability in alcohol consumption, influencing both increases and decreases. Rather than predicting a specific direction of change, these factors were linked to overall fluctuations in drinking behavior, highlighting their broader role in shaping alcohol use dynamics during the pandemic.
Comparison to past research
This study contributes to the literature investigating changes in alcohol consumption during the COVID-19 pandemic [14, 48–50], which has yielded mixed results regarding trends in alcohol consumption. In this study, 34.7% of the participants did not change their alcohol consumption; a total of 37% participants had an increased alcohol consumption, while approximately 31% experienced a decrease. In past studies, these percentages varied slightly. For instance, a cross-sectional survey conducted in France (January–March 2021) found that 53.7% of respondents reported no change in alcohol consumption, while 27.6% reported an increase. This study, covering a longer period after the onset of the pandemic, highlights that changes in alcohol consumption persisted beyond the initial lockdown phases [15]. Another online survey conducted in France during the first lockdown (April-May 2020) reported that 22.7% of respondents noted an increase in alcohol consumption, compared to only 12.2% who reported a decrease [51]. Other recent studies have reported a greater decrease in alcohol consumption than an increase during the COVID-19 pandemic [27, 52]. Overall, discrepancies in findings across studies may stem from methodological differences, including study design (cross-sectional vs. longitudinal), type of alcohol consumption measure (self-reported retrospective changes vs. direct pre- and post-pandemic assessments), timing of data collection, and country-specific restrictions. Andersen et al. have shown that there is a large discrepancy between perceived changes in smoking behavior and actual changes [53]. There is no reason to assume this would be different for alcohol consumption. It is likely that the accuracy of assessing changes in alcohol consumption also depends on the type of measurement used, potentially leading to similar mismatches. We can hypothesize that if the data collection occurred during the first lockdowns [27, 52], the percentage of participants reporting decreased alcohol consumption would be higher than the ones reporting increased consumption. However, if data were collected well after the pandemic, when restrictions were lifted, alcohol consumption would be higher among the general population.
Different factors that coexist must be considered in order to comprehend the disparities concerning trends in alcohol consumption during COVID-19 in the global literature. First, the quantity and severity of pandemic lockdowns and restrictions, such as restrictions on social gatherings at home and stay-at-home orders, should be taken into account based on the country. Notably, a study revealed a significant association between the duration of lockdowns and increased alcohol consumption. Participants who had experienced two lockdowns reported significantly higher consumption compared to those who had experienced only one lockdown [54]. This suggests that the severity of restrictions may result in higher alcohol consumption compared to the pre-COVID-19 period. People may resort to purchasing alcohol between lockdowns and shifting their places of consumption from bars to home [15, 55]. However, this dimension alone cannot fully explain trends in alcohol consumption. Another dimension to consider is the availability of alcohol. For instance, in India, after the first COVID-19 cases emerged, a forced period of alcohol abstinence lead to a sudden unavailability of alcohol for the population, resulting in decreased alcohol consumption [56]. In contrast, the UK, which was also under lockdown during the same period, saw a growth of over 50% in online alcohol sales in the first week after lockdown compared to 2019 [57]. This increase could account for the higher relative rise in consumption compared to other countries during lockdown [23, 58].
Furthermore, changes in alcohol consumption within specific individuals appear to be dependent on subpopulations and their characteristics. In our study, male participants had higher odds of either largely reducing or largely increasing their alcohol consumption between 2018 and 2020, rather than remaining unchanged. This finding is consistent with a Slovakian study that reported men tended to alter their alcohol use more than women during the COVID-19 pandemic [59]. Nevertheless, once again, the results in the literature are mixed when it comes to the impact of gender on changes in alcohol use. In a systematic review from 2021, Schmidt et al. reported that among thirty studies investigating the effect of gender on changes in alcohol use during the pandemic, nineteen observed gender differences [60]. Regarding socio-economic position (SEP), we found that individuals with lower SEP had lower odds of reporting a large decrease in alcohol consumption, which is in line with the findings of Smith et al. [61]. However even in non-pandemic settings, SEP is known to be a risk factor for alcohol-related outcomes, with individuals of higher SEP typically consuming greater amounts of alcohol [62]. However, it is also possible to hypothesize that a lack of statistical power in our study might obscure other associations with largely changes in alcohol, as the majority of our sample consists of individuals with intermediate or high SEP.
Lastly, psychological and contextual factors were also associated with changes in alcohol consumption. Participants reporting anxiety and/or depression had higher odds of showing large increases in consumption, while those with COVID-like symptoms had lower odds of experiencing moderate or large increases in alcohol consumption, possibly reflecting health-related reductions in drinking. Participants who were moderately concerned about COVID-19 had higher odds of reporting a large decrease in alcohol use. These associations suggest that emotional states and health perceptions during the pandemic may have influenced self-regulation of alcohol use.
In terms of substance use, tobacco use was another important correlate: ex-smokers and regular or occasional smokers had lower odds of reporting large decreases in alcohol consumption compared to non-smokers. However, only regular or occasional smokers showed an elevated, though not statistically significant, tendency to report large increases, a finding consistent with Villette et al. [15] who reported that increased alcohol consumption was associated with tobacco smoking. It is widely recognized that alcohol consumption and tobacco smoking often go hand in hand, and some studies have noted a correlation between the amount of tobacco smoked and the quantity of alcohol consumed [63]. The initial level of alcohol consumption has also been shown to influence any changes in alcohol use that occurred during the pandemic. Rossow et al. reported that, similarly across all countries, individuals in the highest 10% for pre-pandemic alcohol consumption increased their drinking during the pandemic, while no significant change was observed for those with low pre-pandemic alcohol consumption levels [64].
Limitations and strengths
This study has certain limitations that require attention. First, the TEMPO cohort comprises participants whose parents were employed by the EDF-GDF energy company and have been part of the longitudinal GAZEL study since 1989 [36]. Consequently, they have a higher socio-economic status, are mostly women (66.2%), and enjoy better overall health compared to the general population [34]. This limits the generalizability of the results. Nevertheless, the sample exhibit sufficient diversity in participant characteristics, allowing for meaningful comparisons across socio-demographic groups [65, 66]. Moreover, the sample consists of a highly selected group of participants as the original sample was initiated in 2009, expanded in 2011, and continued follow-up in 2018. While participant retention over time may introduce some selection bias, the high follow-up rate in 2020 (87% of 2018 respondents) strengthens the reliability of within-person comparisons between 2018 and 2020.
Furthermore, alcohol measurements were obtained through self-reported questionnaires, which may lead to potential reporting bias [67]. However, the asked questions and the assessment of the number of drinks per week are based on the quantity/frequency (QF) approach previously employed in the literature [38] and demonstrate to yield the most reliable and valid assessment of alcohol consumption in population studies [68]. Finally, our measure of drinks per week (DPW) does not account for potential changes in the type or strength of alcoholic beverages consumed. While DPW is widely used in epidemiological research due to its simplicity, reliability, and comparability across studies, particularly in longitudinal analyses, it does not capture shifts in beverage choice. Individuals who maintained the same drinking frequency but switched to stronger beverages may not be classified as having increased their consumption, despite a higher ethanol intake. Nevertheless, standardized definitions exist for drink equivalences based on alcohol content. The World Health Organization (WHO) defines a standard drink as containing 10 g of pure alcohol [69], providing equivalences across different beverages types, including beer, wine, vodka, whisky, rum, cognac, and cocktails. This definition is used in TEMPO. While DPW remains a practical and standardized metric, future research could refine this approach by incorporating total ethanol intake assessments and beverage-specific consumption patterns.
Our study also possesses several strengths that mitigate the previously mentioned limitations. First, the study, carried out among participants in an ongoing community-based cohort study, conducted surveys before, during, and after the course of the COVID-19 pandemic, which enabled us to monitor individual changes in alcohol consumption, limiting the recall bias observed in cross-sectional studies. This, in turn, allowed for a comprehensive examination of risk factors associated with changes in alcohol consumption, including socio-demographic factors or substance use. Furthermore, this study was conducted in a country with a high prevalence of alcohol consumption owing to its wine-producing culture. This allowed us to gain a more comprehensive understanding of any changes in consumption and the factors associated with it.
Conclusions
This study will offer a more comprehensive understanding of potential shifts in alcohol consumption and the populations that may be vulnerable to such changes. It will enable us to identify which demographic groups require a particular focus on prevention and monitoring in the event of other health, economic, social, or climate-related crises.
Supplementary Information
Supplementary Material 1: Supplementary Table 1. Distribution of the number of drinks per week (DPW) across all waves between 2018 and 2020. Supplementary Figure 1: Timeline of the TEMPO cohort study.
Acknowledgements
We thank TEMPO and GAZEL study participants who provided data for this project.
Abbreviations
- TEMPO
Trajectoires EpidéMiologiques en Population
- DPW
Drinks per week
- SEP
Socio-economic position
- ASR
Adult Self-Report
- QF
Quantity/frequency
Authors’ contributions
MM conceptualized, designed the TEMPO cohort and found funds. MMK designed the study. SW coordinated administratively the study, conducted the data collection and the investigations. MMK designed the methodology and statistical analysis protocol. FE conducted the statistical analyses under the supervision of MMK. FE wrote the first draft of the manuscript and all authors contributed to and have approved the final manuscript.
Funding
The TEMPO cohort received funding from the French National Research Agency (ANR); the French Institute for Public Health Research-IReSP (TGIR Cohorts); the French Inter-departmental Mission for the Fight against Drugs and Drug Addiction (MILDeCA); the French Institute of Cancer (INCa); and the Pfizer Foundation. The funders had no role in the design, data collection, data analysis, and reporting of this study.
Data availability
Research participants were guaranteed that the raw data they provided will remain confidential. Anonymized data can only be shared after explicit approval of the French national committee for data protection (Commission Nationale de l’Informatique et des Libertés, CNIL).
Declarations
Ethics approval and consent to participate
The TEMPO cohort received approval of bodies supervising ethical data collection in France, the Advisory Committee on the Treatment of Information for Health Research (Comité consultatif sur le traitement de l’information en matière de recherche dans le domaine de la santé, CCTIRS) and the French data protection authority (Commission Nationale de l’Informatique et des Libertés, CNIL, n°908,163). All methods were performed in accordance with the relevant guidelines and regulation. Participants provided informed consent for study participation at registration to the TEMPO cohort.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Supplementary Table 1. Distribution of the number of drinks per week (DPW) across all waves between 2018 and 2020. Supplementary Figure 1: Timeline of the TEMPO cohort study.
Data Availability Statement
Research participants were guaranteed that the raw data they provided will remain confidential. Anonymized data can only be shared after explicit approval of the French national committee for data protection (Commission Nationale de l’Informatique et des Libertés, CNIL).