The effect of resilience on the relationship between perceived stress and change in alcohol consumption during the COVID-19 pandemic in Queensland,Australia

Abstract

The COVID-19 pandemic has led to increased levels of stress and alcohol consumption. This study examined the effect of resilience on the relationship between stress and changes in alcohol consumption in the context of the COVID-19 pandemic in early 2020. A cross-sectional survey of 502 adults in Queensland, Australia (mean age = 45.68 (16.61)), found 23.9% of individuals had increased their alcohol consumption since the start of the pandemic. Regression modelling demonstrated a significant association between perceived stress and change in alcohol consumption. The study also revealed resilience was a moderating factor, where high levels of resilience buffered against increases in alcohol consumption associated with stress during the COVID-19 pandemic.

Keywords

alcohol use COVID-19 restrictions perceived stress resilience scale

Introduction

First detected in Wuhan, China in December of 2019, coronavirus disease 2019 (COVID-19) continues to have an enormous global impact (Huang et al., 2020). The outbreak was officially declared a pandemic by the World Health Organisation (WHO, 2021a) on March 11, 2020 after spreading to over 190 countries in less than a four-month period. As of July 29, 2021, over 196 million global cases have been recorded and over 4.2 million deaths (World Health Organization, 2021b). Australia has achieved relative success in suppressing COVID-19 outbreaks in the community through various nonpharmacological methods yet, as of August 2, 2021, Australia has recorded over 34,384 cases and 924 deaths (Australian Government Department of Health, 2021).

Australia employed a range of public health measures at the state and national level to control viral transmission. Queensland was the first state or territory in Australia to declare a public health emergency, officially making this announcement the same day the state detected its first case on January 29, 2020 (Queensland Health, 2020). Like the rest of the country, Queensland was an early adopter of lockdown measures designed to ‘flatten the curve’. Measures included but were not limited to the restriction or closure of non-essential businesses and education facilities, limits to the number of individuals allowed in certain areas or households, strict state-border closures, and working from home mandates for non-essential workers (Shakespeare-Finch et al., 2020). These restrictions on movement and activities meant Queenslanders were experiencing long periods of time ‘house-bound’ which disrupted their usual daily activities and social interactions. Public health measures were and continue to be implemented on a rolling and highly fluid basis in response to case numbers at the discretion of individual state governments or as part of national mandates.

The ever-changing restrictions and continued re-emergence of COVID-19 case clusters within communities has undoubtedly been accompanied by a sense of uncertainty and stress amongst the Australian population (Shakespeare-Finch et al., 2020). Studies have found that stay-at-home interventions enforced to prevent disease spread lead to elevated feelings of loneliness and isolation (Killgore et al., 2020). Early insights from the COLLATE (COvid-19 and you: mentaL heaLth in AusTralia now survEy) project, designed to monitor the pandemic’s impact on levels of depression, stress and anxiety in Australian adults, found that levels of these negative emotions were three times higher in April 2020 than existing population norms prior to the COVID-19 pandemic (Rossell et al., 2021). Similarly, an Australian study conducted by Newby et al. (2020) at the height of the outbreak found 78% of adult respondents reported a worsening in their mental health.

The advent of crises and health shocks such as the COVID-19 pandemic can not only influence mental health but can also have impacts on health behaviours (Agüero and Beleche, 2017). Research has demonstrated that the COVID-19 pandemic is impacting the lifestyle behaviours of Australians, including alcohol consumption (Stanton et al., 2020; Tran et al., 2020). Changes in alcohol consumption in a population during or after large-scale crises is not a new phenomenon. Previous research investigating the ramifications of events such as terrorist attacks (DiMaggio et al., 2009; Gonçalves et al., 2020; Vlahov et al., 2004), economic recession (de Goeij et al., 2015), natural disasters (Nordløkken et al., 2013) and infectious disease outbreaks (Brooks et al., 2018; Wu et al., 2008) have found an association between experiencing these events and increases in alcohol consumption in certain populations.

A national poll commissioned by the Foundation for Alcohol Research and Education found one in five Australian households reported purchasing more alcohol than usual since the start of the Australian outbreak of COVID-19, and in these households 34% reported drinking on a daily basis (Foundation for Alcohol Research and Education, 2020). Recent data shows that despite a significant decrease in total spending in March 2020, the closure of restaurants and licensed pubs pushed alcohol-related purchasing behaviours towards bottle shops, which saw an 84% rise in card purchases during this period (Commonwealth Bank of Australia, 2020). Research surveys have also indicated that between 20% and 25% of Australians report an increase in their levels of alcohol consumption since the onset of the pandemic (Stanton et al., 2020; Tran et al., 2020).

This shift in drinking behaviour may be in response to the stress Australians are currently experiencing. Substantial evidence has demonstrated that exposure to many different forms of stress is positively associated with alcohol consumption (Keyes et al., 2012). The theory behind this connection suggests that individuals under stress experience negative emotions and use unhealthy behaviours such as alcohol consumption to bring pleasure or relief and self-manage their mood (Corbin et al., 2013; Ng and Jeffery, 2003). However, increasing alcohol consumption is a maladaptive coping technique, providing only short-term mood regulation which is ultimately detrimental to both mental and physical health (Stanton et al., 2020). Some recent studies support this potential link between stress during the COVID-19 pandemic and increased alcohol consumption (Neill et al., 2020; Rodriguez et al., 2020).

As nations strive to adapt to the new reality created by the COVID-19 pandemic, it is inevitable that some individuals will respond to these stressors more favourably than others. The concept of resilience attempts to understand and explain this heterogeneity amongst individual responses to stress. While various definitions for resilience exist, it is generally agreed to represent the ability to positively adapt in the face of adversity, stress or trauma (Campbell-Sills and Stein, 2007; Luthar et al., 2000). Resilience characterises the set of psychological qualities and internal resources which allow us to thrive despite significant challenges (Connor and Davidson, 2003).

While much of the focus in the field of resilience research has remained on mental health outcomes, there have been some studies investigating its role in alcohol consumption behaviours. Dinsmore et al. (2011), Green et al. (2013) and more recently Diaz-Martinez et al. (2021) reported an inverse relationship between resilience and alcohol consumption or alcohol misuse. However, there are also studies that did not identify an association between these variables (Goldstein et al., 2013; Wingo et al., 2014). A recent study by van Gils et al. (2021) in older adults found those with lower resilience levels reported a positive association between negative affect and hazardous drinking, while those with higher levels of resilience did not. A few additional studies have also included the measurement of stress, exploring the interrelationship between resilience, stress and alcohol consumption (Morgan et al., 2018; Wang and Chen, 2015). Although these studies did suggest resilience may act as a buffer in the relationship, they either did not measure alcohol consumption directly (Morgan et al., 2018) or were conducted in a specific population with limited generalisability (Wang and Chen, 2015).

A recent study by Du et al. (2021) explored the connections between dietary behaviours, alcohol misuse, sleep and resilience during the COVID-19 pandemic. The study included moderation analyses but found that resilience did not influence the relationship between perceived stress and sleep duration or alcohol misuse. However, the study population consisted only of higher education students, the vast majority of which were undergraduate students (mean age = 22.5, SD = 5.5). The authors postulated that the lack of moderation effect was likely due to low levels of alcohol misuse in this young population where socialisation is a driving force behind alcohol consumption and was likely significantly impacted by the COVID-19-related social restrictions.

Thus, despite the knowledge accumulated from earlier research, there is much about the concept of resilience that remains unclear. Further studies are required to understand the nature of the relationship between resilience, stress, and alcohol consumption in a wider variety of contexts and populations. Much of the previous literature has also examined resilience in association with long-term stressors such as exposure to child maltreatment and abuse (Goldstein et al., 2013; Wingo et al., 2014), sexual violence (Catabay et al., 2019) or past army duties (Green et al., 2013) and the resulting long-term impacts on mental health and alcohol consumption.

The current study attempts to address these gaps in the current literature by exploring the relationship between the shorter-term stressors brought on by the COVID-19 pandemic, individual resilience, and the potential short-term changes to alcohol consumption. The insights afforded by this research could assist in identifying strategies to help individuals cope during periods of prolonged lockdown, social isolation, or in other settings involving acute stressors. The ongoing COVID-19 pandemic also provides a unique new context within which to explore and expand the body of resilience research. With this overarching purpose in mind, the study aims to examine the effect of resilience on the relationship between stress and changes in alcohol consumption in the context of the COVID-19 pandemic.

Methods

Study design and participants

This study employed a cross-sectional design using an online survey to collect quantitative data. When participants entered the survey, a page was shown with information including the purpose of the study, what is involved for participants, details regarding confidentiality and consent. A statement was included to inform participants that completion of the survey indicates consent to participate in the study. Ethics approval for this study was granted by the Griffith University Human Research Ethics Committee (GU Ref No: 2020/643).

The target population for the survey included any individuals over the age of 18 currently residing within Queensland, Australia. Exclusion criteria included individuals who were pregnant, living outside of Queensland or under 18 years of age (the legal alcohol drinking age in Australia). Social media, namely Facebook, was used as a primary platform to recruit participants. An advertisement was created on Facebook with a filter set up to reach individuals over the age of 18 and living in Queensland, Australia according to their Facebook profile details. The advertisement included an image that briefly described the study and a link to the survey. The research team also utilised Griffith University’s volunteers for research project broadcast email sent to all staff and students to facilitate recruitment.

Measures

The survey was developed using the online open-source questionnaire application LimeSurvey (LimeSurvey, Version 2.59.1) and was made available during October 2020 for a period of 4 months. The first section consisted of basic demographic questions (e.g. age, gender, education, state of residence, country/region of origin, marital status). This section also covered questions regarding employment and income.

A 10-item version of the Connor Davidson Resilience Scale (CD-RISC) was used to assess the internal qualities contributing to individual resilience (Connor and Davidson, 2003). The CD-RISC is a widely used and tested Likert scale for measuring resilience, often in health-related research (Dinsmore et al., 2011; Green et al., 2013; Wang and Chen, 2015). Campbell-Sills and Stein (2007) conducted a systematic factor structure analysis and synthesised the original scale into a briefer 10-item version, demonstrating good internal consistency and reliability (Cronbach α = 0.85). The 10 statements included in the scale describe various aspects of resilience such as flexibility, self-efficacy, optimism and cognitive focus under stress (Campbell-Sills and Stein, 2007). Participants respond on a 5-point scale based on how well these statements apply to themselves. The sum of responses formed a final resilience score ranging from 0 to 40, where higher scores indicate greater resilience.

The stress level of participants was measured using a modified version of the 10-item Perceived Stress Scale (PSS-10) (Cohen, 1988). Originally a 14-item scale, the PSS is a widely used psychological instrument designed to assess the extent to which an individual appraises their life as stressful (Cohen et al., 1983). Items in the Likert scale include both direct statements regarding experience of stress as well as statements concerning whether respondents find their lives to be uncontrollable or overburdened (Cohen, 1988). A review of research analysing the psychometric properties of the scale found that the PSS-10 is superior to the longer PSS-14 version, with acceptable internal consistency (Cronbach α ⩾ 0.70 in all studies) and test-retest reliability (coefficient value of >0.70 in all studies) (Lee, 2013). The total PSS score ranges from 0 to 40, where higher values reflect greater perceived stress.

A 3-item version of the Alcohol Use Disorders Identification Test (AUDIT) scale was utilised to assess the alcohol consumption of participants. The AUDIT scale, developed under the auspices of the WHO (2001), is an internationally recognised and validated scale often used as a brief tool for assessment of alcohol consumption in primary care settings . The derived 3-item Alcohol Use Disorders Identification Test Concise (AUDIT-C) scale was originally tested in a veteran population (Bush et al., 1998), but has since been validated in many other populations including a large general US sample (Bradley et al., 2007). The score for each item is summed to yield a total score ranging from 0 to 12, where a score equal to or higher than 3 for women or 4 for men signifies hazardous drinking and a potential alcohol use disorder.

Prior to distribution, the survey was pilot tested with a small sample of participants (n = 15). Feedback from pilot testing on comprehension, readability and completion time was used to revise and improve the survey. The AUDIT-C scale required slight modification of wording to evaluate participants’ behaviour both prior to and during the peak period of the COVID-19 pandemic, but the core components and factors evaluated by the scale remained consistent with the validated version. Participants were asked to respond to questions with reference to their usual alcohol consumption prior to the COVID-19 pandemic (e.g., prior to January 2020) and again during the peak period of Queensland restrictions in early 2020.

Statistical analysis

The pre-coded data was exported from LimeSurvey and imported into IBM SPSS Statistics (Version 27.0.1.0) for analysis. Descriptive statistics were generated using frequency and percentage for categorical variables and mean and standard deviation (SD) for continuous variables. Results were scrutinised to ensure data entry accuracy and to check for outlying data points.

The AUDIT-C score prior to the pandemic was subtracted from the score during the pandemic for each participant to yield a figure representing change in consumption. These values were recoded into three categorical groups: decreased drinking (−), unchanged drinking (0) or increased drinking (+). Thus, a change in AUDIT-C score of ±1 or more was considered to represent a change in consumption and was recoded into the relevant decrease or increase in consumption group. Bivariate analysis was performed using Chi-square statistics to compare the sociodemographic characteristics of each group, including the calculation of effect size (phi). Column proportions were then compared using z-tests. The Kruskal-Wallis test was also performed to compare the mean age of participants in each consumption change group.

Normality testing was performed on the data from each of the key study variables. Based on the non-normal distribution, continuous data regarding resilience and perceived stress was converted into categorical data. Participants were split into tertile groups based on their resilience score (T1 = 0–24, T2 = 25–30, T3 = 31–40), consistent with methodology presented in previous studies using the CD-RISC scale (Chamberlain et al., 2016; Ernst et al., 2021; Scali et al., 2012; Tsourtos et al., 2019). Perceived stress scores ranging from 0 to 13 were classified as low perceived stress, 14 to 26 as moderate perceived stress and 27 to 40 as high perceived stress (Alharbi and Alshehry, 2019; Drachev et al., 2020; Gambetta-Tessini et al., 2013; Lane et al., 2020).

Multiple steps were then conducted to determine whether resilience interacted with the relationship between participants’ perceived stress and change in alcohol consumption. Firstly, ordinal logistic regression modelling was employed to determine the association between perceived stress and resilience. The modelling was performed in two parts to predict the odds of low stress compared to moderate stress, as well as low stress compared to high stress. Testing was performed to ensure key variables did not demonstrate collinearity and met model assumptions. The model controlled for key sociodemographic variables including age group, gender, highest level of education, marital status, income, employment type, income change, employment change and primary worksite. Odds ratio was reported with a 95% confidence interval (CI) and a p-value <0.05 indicated statistical significance.

The next step of moderation analysis was conducted using the Hayes PROCESS Macro extension for SPSS statistics (PROCESS v3.5) (Hayes, 2018). Hayes (2018) designed this programme to facilitate regression-based mediation, moderation and conditional process analysis. A conceptual framework was developed based on the previous literature (Keyes et al., 2012; Morgan et al., 2018; Wang and Chen, 2015) indicating the hypothesised interaction between each of the key study variables. Using this framework, a simple moderation model (Model 1) was selected and tested to establish the relationship between variables (Hayes, 2018). In the model, perceived stress was entered as the independent variable ‘X’, change in alcohol consumption was entered as the dependent variable ‘Y’, and resilience was entered as the moderator variable ‘W’. Key sociodemographic variables were set as potential covariates. The programme was also set to probe interactions and thereby demonstrate conditional effects of the focal predictor at values of the moderator. Confidence intervals were set at 95%, with 5000 bootstrap samples.

Results

Descriptive and bivariate

Table 1 details the sociodemographic characteristics of survey respondents, as well as the results of bivariate analysis comparing these characteristics against change in alcohol consumption. A total of 502 participant responses were included for analysis, the majority of which were female (74.9%). The mean age of participants was 45.68 (SD = 16.61), with an acceptable spread across young adulthood (18–24 years, 14.4%), middle adulthood (25–44 years, 30.1%), older adulthood (45–64 years, 41.5%) and retirement age (65+ years, 14.0%). Testing demonstrated that both the PSS and CD-RISC Likert scales had high reliability in the current study (Cronbach α = 0.906 and = 0.918 respectively).

Table 1.

Sociodemographic, employment, income and stress characteristics of study participants by change in alcohol consumption.

Variable	Change in alcohol consumption (AUDIT-C score)			Total	Test statistic (χ²)	Effect size (phi)	p Value
	N (%)			N (%)(N = 502)
	Decrease (N = 114)	Unchanged (N = 268)	Increase (N = 120)	N (%)(N = 502)
Age group					14.324	0.169	0.026*
Mean	43.09	48.10	42.75	45.68	Kruskal-Wallis H = 12.665 (p = 0.002**)
(±SD)	(±17.209)	(±16.196)	(16.214)	(±16.607)
Young adulthood (18–24 years)	21 (29.2)_a	28 (38.9)_b	23 (31.9)_a	72 (14.4)
Middle adulthood (25–44 years)	39 (26.0)_a	71 (47.3)_a	40 (26.7)_a	150 (30.1)
Older adulthood (45–64 years)	39 (18.8)_a	124 (59.9)_b	44 (21.3)_a,b	207 (41.5)
Retirement (65+years)	15 (21.4)_a	43 (61.4)_a	12 (17.1)_a	70 (14.0)
Gender					4.144	0.090	0.126
Male	23 (18.3)	77 (61.1)	26 (20.6)	126 (25.1)
Female	91 (24.2)	191 (50.8)	94 (25.0)	376 (74.9)
Region of origin					9.093	0.131	0.335
Australia/New Zealand	85 (23.2)	186 (50.7)	96 (26.2)	367 (73.3)
Europe	16 (21.6)	44 (59.5)	14 (18.9)	74 (14.8)
Africa/Middle East	2 (22.2)	5 (55.6)	2 (22.2)	9 (1.8)
Asia	7 (18.4)	27 (71.1)	4 (10.5)	38 (7.6)
Americas	4 (30.8)	6 (46.2)	3 (23.1)	13 (2.6)
Highest level of education					8.406	0.124	0.078
School education	20 (25.3)	41 (51.9)	18 (22.8)	79 (15.7)
TAFE diploma or certification	15 (13.2)	68 (59.6)	31 (27.2)	114 (22.7)
University education	79 (25.6)	159 (51.5)	71 (23.0)	309 (61.6)
Marital status					10.375	0.143	0.110
Single	29 (26.4)	59 (53.6)	22 (20.0)	110 (22.0)
De facto/in a relationship	31 (27.4)	48 (42.5)	34 (30.1)	113 (22.6)
Married	38 (18.4)	123 (59.4)	46 (22.2)	207 (41.3)
Widowed/divorced/separated	15 (21.1)	38 (53.5)	18 (25.4)	71 (14.2)
Income^†					6.950	0.121	0.542
$0–$999	40 (21.9)	100 (54.6)	43 (23.5)	183 (36.7)
$1000–$1499	17 (23.3)	41 (56.2)	15 (20.5)	73 (14.7)
$1500–$1999	13 (17.8)	36 (49.3)	24 (32.9)	73 (14.7)
$2000–$2999	18 (20.7)	47 (54.0)	22 (25.3)	87 (17.5)
$3000+	25 (30.5)	41 (50.0)	16 (19.5)	82 (16.5)
Perceived family wealth					5.537	0.098	0.477
Wealthy	6 (27.3)	10 (45.5)	6 (27.3)	22 (4.4)
Quite well-off	60 (23.5)	135 (52.9)	60 (23.5)	255 (51.2)
Not very well off	43 (23.9)	94 (52.2)	43 (23.9)	180 (36.1)
Quite poor	4 (9.8)	26 (63.4)	11 (26.8)	41 (8.2)
Employment type					20.884	0.210	0.022*
Permanent	38 (21.2)_d	94 (52.5)_d	47 (26.3)_d	179 (36.3)
Contract	23 (40.4)_d	25 (43.9)_e	9 (15.8)_e	57 (11.6)
Casual	26 (25.7)_d	48 (47.5)_d	27 (26.7)_d	101 (20.5)
Unemployed, looking for work	6 (14.3)_d	26 (61.9)_d	10 (23.8)_d	42 (8.5)
Unemployed, not looking for work	15 (14.4)_d	67 (64.4)_e	21.2 (22)_d,e	104 (21.1)
Self employed	4 (40.0)_d	4 (40.0)_d	2 (20.0)_d	10 (2.0)
Income change					6.523	0.116	0.163
Decreased	23 (23.2)	44 (44.4)	32 (32.3)	99 (19.9)
Unchanged	74 (21.7)	189 (55.4)	78 (22.9)	341 (68.6)
Increase	16 (28.1)	31 (54.4)	10 (17.5)	57 (11.5)
Employment change					6.626	0.116	0.357
Unchanged	67 (20.8)	185 (57.5)	70 (21.7)	322 (64.1)
Reduction^‡	22 (27.8)	37 (46.8)	20 (25.3)	79 (15.7)
Increase^‡	10 (25.0)	19 (47.5)	11 (27.5)	40 (8.0)
Loss	15 (24.6)	27 (44.3)	19 (31.1)	61 (12.2)
Primary worksite
Home (off-site)	69 (20.6)	180 (53.7)	86 (25.7)	335 (68.0)	3.552	0.086	0.169
Work (on-site)	44 (27.8)	81 (51.3)	33 (20.9)	158 (32.0)
Perceived stress					28.006	0.223	<0.001***
Low	35 (25.0)_g	92 (65.7)_g	13 (9.3)_h	140 (27.9)
Moderate	64 (21.6)_g	148 (50.0)_g	84 (28.4)_h	296 (59.0)
High	15 (22.7)_g,h	28 (42.4)_h	23 (34.8)_g	66 (13.1)

N = 502, change in alcohol consumption calculated based on change in total AUDIT-C score and grouped into decrease, unchanged or increase in total score.

Subscript letters for column proportion z test results: age group (a, b), employment type (d, e, f) and perceived stress (g, h) denote test results for each variable. Different letters within a category (each line) indicate proportions which differ significantly from each other (p value <0.05).

†

Fortnightly income before tax.

‡

Refers to an increase/reduction in days and/or hours of work.

p < 0.0.5. **p < 0.01. ***p < .001.

The proportion of participants who reported decreasing their usual level of alcohol consumption during the pandemic was 22.7% (n = 114) while 23.9% increased consumption (n = 120) and 53.4% did not change their behaviour (n = 268). A total of 393 (78.6%) respondents indicated they consume alcohol and 21.4% (N = 107) answered ‘never’ when asked how often they had a drink containing alcohol prior to the pandemic.

Bivariate analysis revealed age group was significantly associated with change in consumption (p = 0.026). Of note, the proportion of participants in the increased alcohol consumption group decreased with age. The Kruskal-Wallis test on continuous age data also demonstrated there was a statistically significant difference in age between the alcohol consumption groups, where Kruskal-Wallis H = 12.665 (p = 0.002). Age breakdown suggested young adults (aged 18–24) experienced the greatest increase in consumption (31.9%), followed by those in middle adulthood (age 25–44, 26.7%). However, these groups also experienced the greatest decrease in alcohol consumption, while the older adulthood (59.9%) and retirement (61.4%) categories predominated the ‘unchanged’ group. Column proportions testing indicated that the proportion in the ‘unchanged consumption’ group significantly differed to the ‘decrease’ and ‘increase’ consumption groups within the young adulthood (18–24 years) sub-group. However, no clear upward or downward trend was identified (See Table 1, subscript a and b). Testing also indicated that the proportion in the ‘decrease consumption’ group was significantly lower than the ‘unchanged consumption’ group within the older adulthood (45–64 years) sub-group.

The results also demonstrated that those who were unemployed, either looking for work (61.9%) or not looking for work (64.4%) predominated the ‘unchanged’ consumption group compared to those who were casual employees (47.5%). Conversely, casual employees (26.7%) and those with permanent employment (26.3%) had the highest proportion of individuals increasing their alcohol consumption, in comparison to those who were unemployed (22%–23.8%). Column proportions testing suggested that the proportion in the ‘decrease consumption’ group significantly differed from both the ‘unchanged consumption’ and ‘increase consumption’ group for those with contracted employment. Meanwhile, the proportion in the ‘decrease consumption’ group was significantly lower than the proportion from the ‘unchanged consumption’ group for those unemployed but not looking for work (See Table 1, subscript d and e).

Initial bivariate analysis suggested a significant association between change in alcohol consumption and perceived stress level (β = 28.006, p = <0.001). The column proportions testing indicated the proportion in the ‘increase consumption’ group was significantly lower than the proportion in the ‘unchanged consumption’ group at low, moderate and high levels of perceived stress (See Table 1, subscript g and h). Also of note, the increased consumption group had a higher proportion of participants reporting high perceived stress (N = 23, 34.8%), compared to moderate (N = 84, 28.4%) or low perceived stress (N= 13, 9.3%). Thus, the proportion of participants in the increased alcohol consumption group increased as stress level increased.

Logistic regression analysis

Ordinal logistic regression modelling was performed in two parts to predict the odds of both moderate (Part 1) and high stress (Part 2) compared to low stress levels (see Table 2). After controlling for key sociodemographic variables, both Parts 1 and 2 indicated that individuals with higher levels of resilience were at lower risk of moderate (T2 OR = 0.352, CI = 0.173–0.719; T3 OR = 0.092, CI = 0.045–0.187) or high stress levels (T2 OR = 0.036, CI = 0.007–0.174; T3 OR = 0.008, CI = 0.001–0.044) compared to those with lower levels of resilience. Female gender was found to be a significant predictor of moderate levels of stress compared to male gender (OR = 2.335, CI = 1.315–4.145). Whereas those who were in older adulthood or retirement age or earning between $1500 and $2999 per fortnight were found to be significantly less likely to experience higher levels of stress. Those who had no change to their income during the pandemic were also significantly less likely to experience moderate levels of stress compared to those who had a decrease in income (OR = 0.332, CI = 0.130–0.851).

Table 2.

Predictors of moderate and high perceived stress levels.

Model variables	Part 1 (odds of moderate stress)		Part 2 (odds of high stress)
Model variables	Adjusted OR (95% CI)	p Value	Adjusted OR (95% CI)	p Value
Age group
Young adulthood (18–24 years)	1	–	1	–
Middle adulthood (25–44 years)	0.769 (0.242, 2.441)	0.656	0.715 (0.100, 5.098)	0.738
Older adulthood (45–64 years)	0.427 (0.136, 1.342)	0.145	0.052 (0.008, 0.354)	0.003**
Retirement (65+ years)	0.358 (0.097, 1.314)	0.121	0.048 (0.003, 0.744)	0.030*
Gender
Male	1	–	1	–
Female	2.335 (1.315, 4.145)	0.004**	3.061 (0.884, 10.596)	0.077
Highest level of education
School education	1	–	1	–
TAFE diploma or certification	0.614 (0.238, 1.584)	0.314	1.027 (0.184, 5.748)	0.975
University education	0.768 (0.323, 1.828)	0.551	1.330 (2.35, 7.527)	0.747
Marital status
Single	1	–	1	–
De facto/in a relationship	1.124 (0.481, 2.628)	0.787	1.357 (0.322, 5.720)	0.678
Married	1.287 (0.597, 2.774)	0.520	0.481 (0.090, 2.557)	0.390
Widowed/divorced/separated	0.982 (0.380, 2.539	0.970	1.457 (0.209, 10.170)	0.704
Income^†
$0–$999	1	–	1	–
$1000–$1499	0.482 (0.213, 1.088)	0.079	0.276 (0.047, 1.604)	0.152
$1500–$1,999	0.462 (0.185, 1.154)	0.098	0.068 (0.008, 0.593)	0.015*
$2000–$2999	0.467 (0.194, 1.125)	0.090	0.104 (0.012, 0.918)	0.042*
$3000+	0.441 (0.186, 1.047)	0.063	0.189 (0.021. 1/706)	0.138
Employment type
Permanent	1	–	1	–
Contract	1.188 (0.503, 2.806)	0.695	0.547 (0.059, 5.029)	0.594
Casual	0.611 (0.260, 1.436)	0.258	0.751 (0.119, 4.736)	0.761
Unemployed, looking for work	0.749 (0.204, 2.747)	0.663	0.587 (0.046, 7.554)	0.682
Unemployed, not looking for work	0.549 (0.229, 1.320)	0.180	0.323 (0.022, 4.628)	0.405
Self-employed	0.635 (0.095, 4.231)	0.639	7.269 (0.198, 267.347)	0.281
Income change
Decreased	1	–	1	–
Unchanged	0.332 (0.130, 0.851)	0.022*	0.164 (0.027, 0.997)	0.050
Increase	0,.461 (0.130, 1.526)	0.205	0.520 (0.056, 4.865)	0.567
Employment change
Unchanged	1	–	1	–
Reduction^‡	0.883 (0.341, 2.287)	0.798	0.826 (0.156, 4.370)	0.822
Increase^‡	1.304 (0.475, 3.578)	0.606	1.212 (0.146, 10.068)	0.859
Loss of employment	1.495 (0.471, 4.747)	0.496	0.356 (0.042, 2.997)	0.342
Primary worksite
Home (off-site)	1	–	1	–
Work (on-site)	0.773 (0.424, 1.410)	0.402	0.460 (0.110, 1.921)	0.287
Resilience
T1	1	–	1	–
T2	0.352 (0.173, 0.719)	0.004**	0.036 (0.007, 0.174)	<0.001***
T3	0.092 (0.045, 0.187)	<0.001***	0.008 (0.001, 0.044)	<0.001***

N = 502.

OR: odds ratio; CI: confidence interval.

†

Fortnightly income before tax.

‡

Refers to an increase/reduction in days and/or hours of work.

p < 0.0.5. **p < 0.01. ***p < 0.001.

Moderation analysis

The results of PROCESS Macro analysis suggested perceived stress was a significant predictor for change in alcohol consumption (× = 0.0846, SE = 0.0310, p = 0.0066), but resilience and the stress β resilience interaction term did not appear significant (see Table 3). However, the within-group analysis shows significant differences for the resilience tertile groups (T1β = 0.0644, SE = 0.0193, p = 0.0009) (T2 β = 0.0442 SE = 0.0131, p = 0.0008) (see Table 4). When these results are considered together with the previous logistic regression analysis, it becomes apparent resilience is exerting moderation effects on alcohol consumption behaviour, likely through its association with perceived stress.

Table 3.

Predictors of change in alcohol consumption.

Variables	β coefficient	SE	t	p Value	95% CI
Variables	β coefficient	SE	t	p Value	LL	UL
Constant	−2.00496	0.8595	−2.3846	0.0175	−3.7385	−0.3607
Perceived stress	0.0846	0.0310	2.7272	0.0066	0.0237	0.1456
Resilience	0.4069	0.2750	1.4795	0.1396	−0.1335	0.9472
Interaction (perceived stress × resilience)	−0.0202	0.0139	−1.4502	0.1476	−0.0476	0.0072
Age	0.0079	0.0064	1.2425	0.2147	−0.0046	0.0205
Gender	−0.1764	0.1932	−0.9132	0.3616	−0.5560	0.2032
Highest level of education	0.0505	0.1113	0.4534	0.6505	−0.1682	0.2692
Marital status	0.0879	0.0989	0.8881	0.3750	−0.1066	0.2823
Employment type	−0.0093	0.0531	−0.1758	0.8606	−0.1137	0.0950

N = 489, model summary (R = 0.1835, R² = 0.0337, p = 0.0352).

CI: confidence interval; LL: lower limit; UL: upper limit.

Table 4.

Conditional effects of perceived stress on change in alcohol consumption at each tertile of resilience.

Resilience tertile	Effect	SE	t	p Value	95% CI
Resilience tertile	Effect	SE	t	p Value	LL	UL
1	0.0644	0.0193	3.3348	0.0009***	0.0265	0.1024
2	0.0442	0.0131	3.3707	0.0008***	0.0184	0.0699
3	0.0240	0.0190	1.2647	0.2066	−0.0133	0.0612

N = 489. These findings are an extension of the results in Table 3, and the variables listed above are included in the analysis.

CI: confidence interval; LL: lower limit; UL: upper limit.

p < 0.0.5. **p < 0.01. ***p < 0.001.

The effect of resilience can be visualised in Figure 1, which demonstrates that those respondents with the lowest levels of resilience experienced the greatest change in alcohol consumption in response to perceived stress levels. This is illustrated by the differences in the gradient of each slope. Thus, higher levels of resilience had a protective effect, preventing some of the changes in alcohol consumption associated with stress in the context of the pandemic.

Discussion

The purpose of this study was to investigate whether individuals in Queensland, Australia changed their alcohol consumption behaviours during the COVID-19 pandemic and to determine whether resilience had an impact on the relationship between stress and alcohol consumption in this context. This study found that 23.9% of respondents had increased their alcohol consumption since the start of the pandemic. The study also suggested resilience had a moderating effect on the relationship between perceived stress and change in alcohol consumption, where higher levels of resilience had a protective effect against the increases in alcohol consumption associated with stress during the COVID-19 pandemic.

The findings enhance our understanding of drinking behaviours amongst Australians during the COVID-19 pandemic. The finding that 23.9% of respondents increased their consumption of alcohol concurs with earlier Australian studies which have reported between 20% and 25% of adults have increased their usual levels of alcohol consumption since the start of the pandemic (Stanton et al., 2020; Tran et al., 2020). It is also important to note that the proportion of respondents in the current study who report consuming alcohol (78.6%, N = 393), is largely in line with the 77% reported by the most recent National Drug Strategy Household Survey (Australian Institute of Health and Welfare, 2020). This consistency of findings provides some support to the assumption that the participants in the current study provide an accurate reflection of alcohol consumption rates in the general Australian population.

The results also suggest that younger age groups experienced the greatest change to their level of consumption, whether that be an increase or decrease. Callinan et al. (2016) conducted a study investigating where Australians consume their alcohol and found that younger age groups are more likely to drink in pubs, clubs and at events while older adults mostly drink in their homes. It may therefore follow that closure of licensed venues contributed to the decrease in consumption seen in some of the younger participants. Also of note, the type of employment held by individuals was found to be a significant in bivariate analysis χ² = 20.884, p = 0.022). Individuals who were casual employees had the greatest increase in their levels of alcohol consumption (26.7%), this may be due to the distress caused by uncertainty surrounding the future of their employment.

The findings also support the positive relationship between stress and alcohol consumption suggested in previous literature (Keyes et al., 2012). Those who reported higher levels of perceived stress during the COVID-19 pandemic, also predominated the increased alcohol consumption group. Although it is generally agreed that stress is associated with increased drinking, there are many complexities to the relationship. For example, stress might have a different impact on a social drinker as compared to someone who has coping motives (i.e. drinking to cope with or lessen negative emotions) (Cooper, 1994; Cox and Klinger, 2011; Merrill and Thomas, 2013). The relationship is not consistently predictable, and in this study the complexities are difficult to interpret because of factors unique to the pandemic context such as licenced venue closure and social distancing.

In the context of the COVID-19 pandemic, the study suggested resilience acted as a moderator on the relationship between perceived stress and change in alcohol consumption. While the literature differs in how resilience is defined, it is generally agreed to represent positive adaptation despite stress and adversity (Campbell-Sills et al., 2006). However, it is not necessarily an insensitivity to stressors, but rather resilient individuals respond better to stress and adopt positive coping strategies and problem-solving skills (Kalisch et al., 2017). During the COVID-19 pandemic such positive coping strategies might include the maintenance of daily routines, continued physical activity and taking steps to maintain social connectedness despite physical restrictions (Nitschke et al., 2021; Shanahan et al., 2020). The findings of this study suggest those with lower levels of resilience were more likely to turn to maladaptive coping strategies such as alcohol consumption when experiencing stress.

Previous studies have determined that psychological resilience buffers against the development of negative emotions such as anxiety and depression when experiencing stressful events (Bitsika et al., 2013; Wang and Chen, 2015). Additionally, some studies investigating the stress and alcohol consumption relationship indicate stress triggers alcohol consumption in those individuals who use it to cope with negative emotions (Cooper et al., 1995; Corbin et al., 2013). By linking these two concepts, it may therefore be theorised that resilience acted as a moderator in this study by both minimising the stress response and reducing the negative emotions associated with perceived stress during the pandemic, which then buffered against the effects on alcohol consumption.

Limitations

The study had several limitations. While the cross-sectional design can infer relationships between variables, it does not indicate directionality of these associations. Therefore, a casual inference cannot be established. It is also acknowledged that resilience is a dynamic process which builds and evolves over time in response to the adversity experienced by an individual (Kalisch et al., 2017). This study used the CD-RISC scale to measure resilience at a singular point in time, and thus does not capture the dynamicity of the concept.

Further, the recruitment methods of the study involved convenience sampling and required volunteers, both of which have the potential to introduce selection bias. The use of university channels for recruitment has the potential to skew the participant pool towards individuals with certain characteristics (e.g. higher level of education). Similarly, utilising the social media platform Facebook would only allow participation of individuals with access to this site. Certain demographics could therefore be potentially under-represented that is, individuals with lower education levels, those who are less technologically savvy and non-users of Facebook. Therefore, results may underestimate the true levels of risk regarding stress, resilience and alcohol consumption. However, it must be noted the results regarding changes in alcohol consumption in the early stages of the pandemic were in line with previous Australian studies (Stanton et al., 2020; Tran et al., 2020). The mean age (45.68, SD = 16.61), and range of participant age (range = 18–86 years) in this study was also a strength, helping to address some of the limitations of previous research (Du et al., 2021).

The survey also relied on self-report scales which asked past levels of alcohol consumption. Therefore, an element of recall and desirability bias was also possible, which could result in under-reporting of problem drinking. However, literature has suggested that self-report tools are generally a valid and reliable tool for the measurement of alcohol consumption (Del Boca and Darkes, 2003). Web-based surveys have also been shown to elicit higher levels of sensitive question self-disclosure when compared to paper and pencil-based surveys (Gnambs and Kaspar, 2015; Kays et al., 2012).

Future directions

Augmenting resilience research is important as it has the potential to improve public health by shifting focus away from the traditional disease-centred model towards prevention and health promotion. This study has suggested resilience may be an important factor in preventing stress related changes to alcohol consumption. Further research into this field could use a longitudinal approach to help establish directionality of associations and thereby strengthen our understanding of resilience. It may also be of value to explore how crises such as the current pandemic impact and develop the resilience of individuals, shaping their reaction and ability to cope with further stressors.

Future research may also focus on potential health promotion interventions to help individuals improve components of internal resilience to better cope with stressors such as those experienced during the COVID-19 pandemic. From a community perspective, initiatives could focus on providing individuals with the environment and personal resources required to foster resilience. Focus could be placed on the possibility for evidence-based workshops to build resilience for those most in need of these mental resources.

Regarding the pandemic’s impact on lifestyle behaviours, it may also be informative to investigate not just changes to the quantity and frequency of alcohol consumption, but habits surrounding consumption such as drinking location, time and occasion. Further analysis may investigate hazardous levels of drinking, to identify those who are most at risk of unhealthy behavioural change during crises such as the pandemic.

Conclusion

This research has expanded the body of resilience literature and provided evidence to suggest that resilience is an important factor in helping individuals to cope with stress and unprecedented life challenges, buffering against increases in alcohol consumption associated with stress in the context of the COVID-19 pandemic. The urgent need for a focus on resilience has never been more apparent than during the COVID-19 crisis, which signals for a drive in public health policy and investment towards preventative measures.

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Footnotes

Data sharing statement

The current article includes the complete raw dataset collected in the study including the participants’ dataset, syntax file and log files for analysis. These files are all available in the Figshare repository and as Supplemental Material via the SAGE Journals platform.

Declaration of conflicting interests

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

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Lucy Tudehope

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