Comparing the impact of high versus low lockdown severity on the mental health of young people in Australia during the COVID-19 pandemic

Abstract

Young Australians have been differentially affected by lockdowns and social restrictions during the COVID-19 pandemic. This study compared the mental health impacts of the COVID-19 pandemic and associated restrictions for young people in two Australian states, Victoria and Queensland, with Victoria experiencing more days in lockdown and greater infection rates. An online survey was completed between 01/04/2021 and 31/07/2021 by 687 young people, aged 16 to 24 years; 337 from Victoria and 350 from Queensland. Levels of negative emotion feelings (as measured by the Depression Anxiety Stress Scale), and COVID-19 risk factors for negative emotions (such as financial hardship, education disruption, loneliness and household conflict), as well as protective factors (resilience and self-esteem) were compared between the Victorian and Queensland samples, also considering some early pandemic data and pre-pandemic norms. No significant differences in negative emotions were found between young people living in the two states, despite substantial differences in pandemic restrictions. The results indicated that young people in Queensland and Victoria had experienced similarly high levels of negative emotions, at levels also seen at the start of the pandemic in Victoria. This is of grave concern, requiring urgent attention as the pandemic continues.

1. Introduction

The COVID-19 pandemic has had a negative impact on mental health globally (Santomauro et al., 2021; Rossell et al., 2021). This impact may be exacerbated in young people aged 16-24, owing to several youth-relevant pandemic factors, including economic insecurities about the future, disrupted education, and mandated isolation at home during a time of development that is typically characterised by increased peer group socialisation (Santomauro et al., 2021; AIHW, 2021a). Studies focused on youth mental health during the pandemic are sparse compared to that of adult cohorts (Marchini et al. 2020). In particular, there are limited data on youth-relevant risk and protective factors. Moreover, there were concerns regarding youth mental health, across Australia and elsewhere, even prior to COVID-19 (Butterworth et al., 2020; Twenge et al., 2019; Weinberger et al., 2018), highlighting the critical need to identify modifiable factors that can be harnessed to better manage youth mental health during the pandemic and beyond.

To this end, the focus of the current study was on the experience of young people aged between 16 and 24, residing in the Australian states of Queensland and Victoria during the COVID-19 pandemic. These two states were subjected to very different lockdown conditions during the first year of the pandemic, with Victoria experiencing one of the longest lockdowns in the world (181 days by 31/7/2021 for Victoria, versus 17 for Queensland (AIHW, 2021b)). There were also marked differences in COVID-19 infection and mortality rates in these two states, with Queensland having only 1,800 cases and 7 deaths by 31/7/2021 versus 20,944 cases and 820 deaths in Victoria (AIHW, 2021a). Decreasing human mobility and increasing infection and mortality rates have been identified as significant predictors of major depressive and anxiety disorders during the pandemic (Santomauro et al. 2021), suggesting that the mental health impacts of the pandemic may have been greater for people residing in Victoria than in Queensland. Several recent studies indicate that the effects of lockdowns are particularly deleterious for younger people (e.g. Justo-Alonso et al., 2020), especially in the case of longer lockdowns (Barendse et al., 2021; Panchal et al., 2021).

1.1. COVID-19 impacts on young people

The identification of risk and protective factors is an important consideration when studying COVID-19 impacts on population mental health, because the pandemic is not over. This information is needed to better inform approaches, policies and strategies to mitigate the risks of deteriorating mental health among young people, at this time and during future catastrophic events.

Financial insecurity is one possible contributor to poor youth mental health during the COVID-19 pandemic (Santomauro et al. 2021). Levels of unemployment have been much higher for younger than older adults in recent years, and this has been linked to high rates of casual youth employment (Adams-Prassi et al., 2020). Around 76% of Australian employees aged 15-19 years and 41% of employees aged 20-24 years were casually employed in 2016—well above the all-age average of 25% (Parliament of Australia, 2018). Casual employment areas, such as hospitality and retail, were heavily impacted by social restrictions during the pandemic (Inanc, 2020). In 2019 the Australian youth unemployment rate was 12%, rising to 16.4% in July 2020, and, if underemployment was included, this percentage increased to 37% (Verrender, 2020). AIHW (2021c) attributed this change in part to the COVID-19 pandemic. Job loss is thought to have serious implications for mental health that are not limited to income insecurity. The latent benefits of work, such as time structure, social contact, collective purpose, status and activity are basic human needs that are assumed to be essential for mental health (Thill et al., 2019). However, there is evidence in a longitudinal South African study (Posel, Oyenubi & Kollamparambi, 2021) that paid leave can offset the mental health impacts of losing paid employment during the COVID-19 pandemic.

Household conflict is another possible contributor to deteriorating youth mental health during the pandemic. COVID-19 lockdowns forced household members to be together for extended time periods, often with limited personal space (Behar-Zusman et al., 2020). Overcrowding is particularly likely for young people living in share houses (Raynor & Panza, 2020), making household conflict of special relevance in this context. There is also evidence of increased family conflict for young people living in or returning to the family home. Evans et al. (2020) reported that Australian parents with children aged 0-18 years old living at home found that the pandemic had changed the nature and context of their parenting, increasing their experience of stress and providing reduced opportunities for support and respite in many cases, sometimes putting a strain on parent-child relationships. A recent American study also found that adolescents who perceived greater increases in family conflict during the COVID-19 pandemic reported higher depressive symptoms (Rogers et al., 2021).

The social isolation imposed during lockdowns has resulted in a higher prevalence of loneliness among young people during the COVID-19 pandemic (Wang et al. 2020). In a large UK study (N=15,530), Li and Wang (2020) reported that being young and female carried a higher risk of developing a psychiatric disorder and loneliness during the pandemic, while having a job and living with a partner were protective factors. A recent systematic review (Loades et al, 2020) reported high levels of loneliness in more than one-third of adolescents and almost half of 18–24 year olds during COVID-19 lockdowns, with duration of loneliness more strongly correlated with mental health symptoms than intensity of loneliness.

The effects of social isolation during the COVID-19 pandemic have been of particular concern for people with mental health conditions (Karantonis et al., 2021; Van Rheenen, 2020; Panchal et al., 2021; Chapman et al., 2021). Mental illness has also been associated with family conflict, exacerbated by lack of social support and economic burden (Caqueo-Urizar, 2009). The pandemic has worsened these problems for young people with existing mental illnesses and their carers alike (Panchal et al., 2021).

There is also a need to consider the impacts of the COVID-19 pandemic on protective trait factors for young people, such as resilience and self-esteem. The negative relationship between self-esteem and negative emotions (i.e. depression, anxiety and stress) has been demonstrated for young people during the pandemic in a sample of 571 Japanese university students (Arima et al., 2020). In addition, studies have shown a relationship between reduced self-esteem and increased loneliness in young people during the pandemic (e.g. Pizarro-Ruiz & Ordonez-Camblor, 2021), suggesting that self-esteem can serve as a buffer against loneliness as well as negative emotions during this time. The negative relationship between resilience and negative emotions has also been demonstrated for Chinese students during pandemic lockdowns (YueYi et al., 2022). Resilience and self-esteem have also been identified as being particularly important for people with a mental illness (Perlman et al., 2017), because these protective factors tend to be lower in those with mental illness (Corrigan Watson and Barr, 2006). For example, Marchini et al. (2020) found that young people in Italy and Belgium who had mental healthcare needs, particularly those with an increase in these needs following lockdown measures, tended to have lower resilience than those who had never sought mental healthcare. However, as explained by Kilgore et al. (2020), some individuals have been more psychologically resilient during pandemic lockdowns than others. In their study of 18-35 year old's they found that more social support from family and significant others as well as better sleep and more exercise and prayer were associated with greater resilience.

1.2. The current study

The current study sought to examine the impacts of COVID-19 on young people in Australia using an online convenience survey designed to collect information specific to the issues facing young people. The primary aim of the study was to compare the mental health of young people who lived through high and low lockdown intensities/durations, as well as their pandemic experiences, including financial stressors, employment, and education. The states of Victoria and Queensland had substantially different lockdown trajectories up to the point of the survey and were therefore chosen for this purpose. It was hypothesised that the harsher and longer lockdown experienced in Victoria in the previous year, together with larger numbers of COVID-19 infections, would result in greater negative mental health impacts in Victoria than Queensland, and that these negative impacts would be significantly increased compared to pre-pandemic norms and unpublished Australian data collected at the start of the pandemic (Rossell et al., 2021).

2. Methods

2.1. Data collection

The “Understanding the iMpact of the COVID-19 pandemic on yOuth MENtal healTh in Victoria and Queensland (MOMENT)” online survey was completed between 1^st April 2021 and 31^st July 2021. The survey was advertised on Facebook, Instagram, and Twitter to young people aged 16-24 years old. It received ethics approval from the Swinburne University Human Research Ethics Committee (approval number: 20215451-6504) and complied with the Declaration of Helsinki. Invitations to complete the survey were placed on digital and physical university noticeboards, digital community noticeboards and social media, and mental health participant registries held within Swinburne University of Technology and Queensland University of Technology, as well as with organisations supporting youth in the community. A high school lecture series targeting 16-17-year-olds was also used to promote the survey in Queensland. Exponential non-discriminative snowball sampling was used, with participants asked to pass the invitation on to their networks.

2.2. Measures

2.2.1. Demographic variables

Data for a range of self-report demographic variables were obtained, including gender, age, highest educational attainment, country of birth, living situation (single person or couple living with family/parents/carer vs. not living with family/parents/carer), partner status, current student and/or employment status.

2.2.2. Negative Emotions – primary outcome (past week)

Negative emotions in the past week were measured using the well validated self-report Depression, Anxiety and Stress Scale (DASS-21; Lovibond and Lovibond, 1995), with 21 items coded using a 4-point Likert scale, ranging from 0 = Never to 3 = Almost always. Higher scores for this scale indicate great symptoms of depression, anxiety or stress (rather than any specific psychiatric diagnosis involving depression, anxiety or stress). Excellent reliability was achieved for this scale in this study (α=0.94) and in many other studies involving young people (e.g. Shaw et al., 2016). Only the total DASS score was used because several studies have shown that although the DASS-21 supports a general factor in adolescent populations, the subscales lack specificity (e.g. Shaw et al., 2016).

2.2.3. Mental Illness (lifetime)

A self-reported endorsement of mental illness obtained as a “Yes/No” response to the question “Are you a person with a mental illness?” was also included in this study, and for almost all of the people who reported a mental illness (99%), a formal diagnosis of at least one of 21 common mental illnesses by a general practitioner (GP), psychiatrist or psychologist was identified during their lifetimes. See Table 2.

Table 2.

Diagnosis frequencies from a GP, psychiatrist or psychologist for MOMENT survey participants reporting mental illness

(Percentages below add to more than 100% due to comorbidities)

	Frequency (% for those with mental illness)
Formal Mental Health Diagnosis	Victoria	Queensland	Total
Depression	90 (79.6)	95 (72.5)	185 (75.8)
Generalised Anxiety Disorder	73 (64.6)	93 (71.0)	166 (68.0)
Social Anxiety Disorder	48 (42.5)	53 (40.5)	101 (41.4)
Panic Disorder	23 (20.4)	22 (16.8)	45 (18.4)
Post-traumatic Stress Disorder	17 (15.0)	21 (16.0)	38 (15.6)
Attention Deficit Hyperactivity Disorder	13(11.5)	19 (14.5)	32 (13.1)
Obsessive Compulsive Disorder	12 (10.6)	18 (13.7)	30 (12.3)
Body Dysmorphic Disorder	10 (8.8)	17 (13.0)	27 (11.1)
Autism Spectrum Disorder	8 (7.1)	13 (9.9)	21 (8.6)
Anorexia Nervosa	7 (6.2)	14 (10.7)	21 (8.6)
Binge Eating	7 (6.2)	9 (6.9)	16 (6.6)
Bulimia Nervosa	5 (4.4)	8 (6.1)	13 (5.3)
Other Eating Disorder	5 (4.4)	15 (11.5)	20 (8.2)
Bipolar Disorder	5 (4.4)	7 (5.3)	12 (4.9)
Alcohol Use Disorder	3 (2.7)	1 (0.8)	4 (1.6)
Substance Use Disorder	2 (1.8)	2 (1.5)	4 (1.6)
Psychotic Disorder (e.g. Schizophrenia)	2 (1.8)	1 (0.8)	3 (1.2)
Number with mental illness	113 (100)	131 (100)	244 (100)

2.2.4. Measures of Educational and Financial Disruption (past year, i.e., experienced between 1/4/2020 and 1/4/2021)

Disrupted education (e.g. deferred studies but excluding a shift to remote learning), and various measures for financial insecurity were collected, including job loss, lost hours, financial assistance from government, and money issues (e.g. issues with affording mortgage, rent, going out, basic necessities, transport-related and study costs). These were dichotomised as “Yes/No” variables.

2.2.5. Loneliness (past week)

Loneliness was measured using the 20-item Revised UCLA Loneliness scale (Russell et al., 1980) with item responses on a 4-point Likert scale ranging from 1=never to 4=often. Higher scores indicate greater loneliness in the past week. This scale has shown excellent reliability in this study (α=.94), and many other studies (e.g. Russell et al., 1980).

2.2.6. Household Conflict (past year, i.e., household conflict experienced between 1/4/2020 and 1/4/2021)

Household conflict during the COVID-19 pandemic was measured using the 15-item COVID-19 Household Environment Scale (CHES, Behar‐Zusman et al., 2020). The CHES contains item responses on a 5-point Likert scale, 1=much less than before the pandemic to 5=much more than before the pandemic. Higher scores indicate more household conflict. Due to non-applicability of some items for some respondents, this scale was computed as the mean of completed items. This scale has shown good reliability in this (α=.87), and the original study (Behar‐Zusman et al., 2020).

2.2.7. Self-esteem (trait)

The 10-item Rosenberg (1965) Self-Esteem Scale (RSES) assesses self-esteem traits and is scored on a 4-point Likert scale ranging from 1=strongly disagree to 4=strongly agree. Higher scores indicate higher global self-esteem, with a normal range of 15-25. This scale has shown good reliability in this study (α=0.91) and many others (e.g. Rosenberg, 1965).

2.2.8. Resilience (trait)

The Brief Resilience Scale (BRS; Smith et al., 2008) is an averaged six-item scale; each response recorded from 1=strongly agree to 5=strongly disagree. Higher scores indicate higher resilience based on lifetime experience. This is a trait measure and the normal range is 3 to 4.3. This scale measures the ability to bounce back and has shown good reliability in this study (α=0.86) and many others (e.g. Fung, 2020).

2.3. Statistical analysis

The Queensland and Victorian samples were compared in terms of demographics, financial insecurity, employment and changes in education plans, using chi-squared (χ²) tests of association. Then, controlling for gender, age, mental illness, highest education level, analysis of covariance (ANCOVA) models were used to compare the two states in terms of their mean scores for current negative emotions (primary outcome) and other factors contributing to negative emotions, namely resilience, self-esteem, loneliness and household conflict. Using 95% confidence intervals these mean scores were compared with normative data, and, in the case of negative emotions and resilience, with unpublished data collected in Victoria at the start of the pandemic in April 2020, as described by Rossell (2021) and Tan et al. (2020).

3. Results

3.1. Characterisation of the Victorian and Queensland Samples

A total of 687 young Australians completed the MOMENT survey. As shown in Table 1 , there were no significant demographic differences between survey participants from the two states (n=337 for Victoria and n=350 for Queensland), except for a weak relationship for highest education level (Cramer's V = 0.096), with school completions significantly more common for Queensland (67.7%) than Victoria (62.6%), and Trade/Diploma completions significantly more common for Victoria (15.4%) than Queensland (9.1%). For both states, there was a strong participation bias in favour of females and students (particularly university students), with an under-representation of young people aged 16-17 years old (6.3% for Victoria and 7.7% for Queensland). As shown in Table 2 , depression was the most commonly diagnosed mental health condition amongst respondents reporting a mental illness, with no significant differences observed between the states.

Table 1.

Demographic Comparison of Victorian and Queensland Samples

		Victoria		Queensland		Chi-Squared test of Association
		N	%	N	%	χ2	df	p-value	Cramer's V
Gender	Female	246	73.0	264	75.4	0.667	2	.717	.031
	Male	78	23.1	72	20.6
	Non-binary or Transgender	13	3.9	14	4.0
Age Group	16-17	21	6.3	27	7.7	0.565	1	.459	.029
	18-24	315	93.8	323	92.3
Self-Reported Mental Illness(a)	No	224	66.5	219	62.6	1.139	1	.301	.041
	Yes	113	33.5	131	37.4
Highest Level Education(b)	School	211	62.6	237	67.7	6.343	2	.042	.096
	Trade/ Diploma	52	15.4	32	9.1
	University	74	22.0	81	23.1
Australian Born	No	56	16.7	67	19.1	0.714	1	.426	.032
	Yes	280	83.3	283	80.9
Current Living Situation	Not with family	115	34.6	140	40.1	2.180	1	.154	.057
	Living with family	217	65.4	209	59.9
Current Partner	Couple	38	11.3	53	15.2	2.278	1	.144	.058
	Single	299	88.7	296	84.8
Current Student Status	Part-time	40	11.9	37	10.6	0.856	2	.652	.035
	Full-time	256	76.0	263	75.1
	Not a student	41	12.2	50	14.3
Current Employment Status	Unemployed	74	22.0	60	17.1	5.912	4	.206	.093
	Employed Full-time	37	11.0	46	13.1
	Volunteer/Stay-home	18	5.4	15	4.3
	Employed Part-time	73	21.7	65	18.6
	Employed Casual	134	39.9	164	46.9

^(a)Similar to the proportion of young people (18-25 years old) experiencing high or very high levels of distress, as reported in Australian community surveys in 2018 (36.0%) and 2020 (35.5%) (Headspace, 2020).

^(b)Significant differences bolded (p<.05). School (Primary/Secondary).

Table 3 provides a comparison of young people in Queensland and Victoria for job- and education-related variables between 1/4/2020 and 1/4/2021, with the high number of participants (59.4%) reporting 2 or more jobs, pointing to the casualisation of work for young people in both states rather than job loss (only 8.2% of participants lost jobs). Young people in Victoria were significantly less likely to have received government assistance (50.7%) between 1/4/2020 and 1/4/2021 than young people in Queensland (62.9%), while more young people in Queensland had started a casual job (37.1%) than those in Victoria (29.7%). These were only weak associations and no other significant differences were found. However, there were important relationships between current employment status and the changes in employment status experienced during this period (See Supplementary Table S1). Although there was no significant relationship between job loss during this period and current employment status (χ²(1)=5.15, p=.398), young people who had lost hours of work during this period were significantly more likely to be currently casually employed (61%) than those who had not lost any hours (32%). In addition, young people who had lost hours of work during this period were significantly less likely to be currently unemployed (7.5%) than those who had not lost hours of work during this period (27%), illustrating the dynamics of casual work for young people during the pandemic in Victoria (Cramer's V = .398) and Queensland (Cramer's V = .325).

Table 3.

Descriptive Statistics for Job and Education Related Risk Factors

Time Interval		Victoria		Queensland		Chi-Squared/Fisher Exact test
1/4/2020 to 1/4/2021		N	%	N	%	χ2	df	p-value	Cramer's V
Number of jobs	None	51	15.5	33	9.6	7.715	3	.052	.107
	One	157	47.7	157	45.6
	Two	84	25.5	111	32.3
	≥3	37	11.2	43	12.5
Borrowed money	No	74	22.0	79	22.6	.037	1	.855	.007
	Yes	263	78.0	271	77.4
Money issues	No	154	45.7	171	48.9	.688	1	.445	.032
	Yes	183	54.3	179	51.1
Work Hours Lost	No	196	58.2	224	64.0	2.464	1	.118	.060
	Yes	141	41.8	126	36.0
At least one job lost	No	310	92.0	320	91.4	.071	1	.890	.010
	Yes	27	8.0	30	8.6
Started a full-time job	No	309	91.7	326	93.1	.517	1	.564	.027
	Yes	28	8.3	24	6.9
Started a part-time job	No	301	89.3	310	88.6	.097	1	.808	.012
	Yes	36	10.7	40	11.4
Started a casual job	No	237	70.3	220	62.9	4.301	1	.043	.079
	Yes	100	29.7	130	37.1
Started a volunteer job	No	322	95.5	335	95.7	.011	1	1.00	.004
	Yes	15	4.5	15	4.3
Started any job	No	167	49.6	191	54.6	1.731	1	.195	.050
	Yes	170	50.4	159	45.4
Job Seeker	No	304	90.2	325	92.9	1.559	1	.212	.048
	Yes	33	9.8	25	7.1
Job Keeper	No	29	87.5	314	89.7	.809	1	.369	.034
	Yes	42	12.5	36	10.3
Coronavirus Supplement	No	281	83.4	301	86.0	.908	1	.341	.036
	Yes	56	16.6	49	14.0
Youth/Study Allowance	No	248	73.6	273	78.0	1.822	1	.177	.051
	Yes	89	26.4	77	22.0
Government Payments	None	166	49.3	130	37.1	10.28	1	.001	.122
	Yes, at least one	171	50.7	220	62.9
Change in income	Significant increase	46	13.7	40	11.5	3.063	4	.547	.067
	Slight increase	70	20.8	85	24.4
	No change	113	33.6	123	35.1
	Slight decrease	65	19.3	55	15.8
	Significant decrease	42	12.5	46	13.2
Change in Study Plans	No	184	55.4	217	62.0	3.04	1	.081	.067
	Yes	148	44.6	133	38.0

*Significant differences bolded (p<.05)

3.2. Mental Health and Other Related Measures for the Victorian and Queensland Subsamples

Table 4 provides a comparison of young people in Queensland and Victoria in terms of negative emotions (DASS-21) experienced in the last week, showing no significant difference. However, the level of negative emotion for both states was very high, significantly higher than pre-pandemic normative data for negative emotions (DASS-21) in a large sample (n=4258) of Victorian university students (Larcombe et al., 2016). Table 4 also includes results for other risk factors (loneliness, household conflict) and protective factors (self-esteem, resilience). Similarly to negative emotions, there were no significant differences observed between young Queenslanders versus Victorians in terms of these measures and their correlations with negative emotions (See Supplementary Table S2). However, a comparison with pre-pandemic normative data for these measures did show some significant differences, with lower levels for resilience and self-esteem and higher levels for household conflict observed for our study (see Appendix and Figure 1 ).

Table 4.

Marginal means for young people in Victoria and Queensland controlling for demographic factors^a

	Victoria (N=337)			Queensland (N=350)			ANOVA Test Comparing Mean Values for Victoria and Queensland			Comparative Mean Valuesj Pre-pandemic Norms (Unbolded) Start of Pandemic (Bolded)
		95% Confidence Interval (CI)			95% Confidence Interval(CI)
	Mean	LL	UL	Mean	LL	UL	F(1,df) df=637	p-value	η2	Mean(SD)	95% CI
Negative Emotion (0-118)	41.2	36.9	45.5	42.8	38.4	47.3	0.73	.392	.001	32.10 (np)b 4.28 (25.40)c	(np) (41.56,47.00)
Resilience (1-5)	2.91	2.78	3.04	2.99	2.86	3.13	2.14	.144	.003	3.31(0.55)d 2.57(0.79)c	(3.26,3.36).(2.48,2.66)
Loneliness (20-78)	46.1	44.0	48.2	45.8	43.7	47.9	0.13	.723	<.001	47.86(10.10)e 48.3(np)f	(47.19,48.53)
Self-esteem (10-40)	25.2	24.2	26.1	24.9	23.9	25.8	0.43	.512	.001	31.04(5.04)g 29.76(5.69)h	(30.57,31.51) (29.29,30.23)
Household Conflict (1-6)	3.64	3.53	3.72	3.57	3.45	3.65	3.34	.068	.005	2.59 (0.416)I	(2.58,2.60)

^agender, age, mental illness, highest level of education;

^bLarcombe et al. (2016);

^cCOLLATE Victorian data at the start of the pandemic: Rossell et al. (2021)

^dFung (2020)

^eLim et al. (2019);

^fCigna (2018)

^gBagley et al. (1997);

^hBagley & Mallick (2001);

ⁱBehar-Zusman et al. (2020) – adult sample (All other data refers to samples of young people or students). (np) = not provided.

Figure 1.

Outcome Measures Compared by State and Pre-pandemic Normative Values

^aLarcombe et al. (2016); ^bFung (2020); ^cLim et al. (2019); ^dBagley & Mallick (2001); ^eBehar-Zusman et al. (2020) – adult sample, All normative data except for Household Conflict refers to samples of young people or university students

Additionally, Table 4 investigates changes in the level of negative emotions and resilience since the start of the pandemic using the unpublished COLLATE data described in the Appendix. These data for 18-24 year-olds were collected for Victorian young people responding to a separate survey in the first three days of April 2020 (Rossell et al., 2021). Only the COLLATE results for Victoria are reported here (N=335) because the sample size for Queensland was too small for reporting purposes (N=21). A significant Victorian increase since the start of the pandemic was found for resilience but not for negative emotions (see Appendix and Table 4).

4. Discussion

The primary aim of this study was to compare the experience of the COVID-19 pandemic between young people living in Victoria (high lockdown intensity/duration) and Queensland (low lockdown intensity/duration), particularly regarding its impacts on mental health. It was hypothesised that the harsher and longer lockdowns experienced in Victoria in the previous year, together with larger numbers of COVID-19 infections, would result in greater negative mental health impacts in Victoria than Queensland, and that these negative impacts would be significantly increased compared to pre-pandemic norms and unpublished Australian data collected at the start of the pandemic in Australia (Rossell et al., 2021).

No support was found for differences in the mental health impacts in Victoria and Queensland, nor were significant increases in negative emotions observed since the start of the pandemic. However, as expected, there were high levels of negative emotions (DASS-21) in both states, well above pre-pandemic levels (Larcombe et al., 2016). However, pre-COVID studies in the past 5 years have indicated increases in psychological distress over time in young people that date back to well before the pandemic (Brennan et al. 2021), Butterworth et al., 2020; Larcombe et al, 2016), suggesting that these pre-pandemic norms should be treated with caution.

The similarity in levels of negative emotions observed for Victoria and Queensland was unexpected, given the difference in lockdown duration and the number of COVID-19 infections and mortality rates observed in these two states. In addition, the lack of a significant increase in negative emotions in Victoria since the start of the pandemic, confirms that the effects of lockdowns on negative emotions were not as large as expected. This contrasted with studies by Barendse et al. (2021) for America, the Netherlands and Peru, and the Panchal et al. (2021) systematic review covering 22 countries, which found that the effects of the pandemic on negative emotions were significantly moderated by government restrictions and lockdown duration.

Indicators of financial stress and education/employment disruptions in the two states were similar and the other variables predicting negative emotions (including loneliness, self-esteem, resilience and household conflict) were also alike; perhaps helping to explain this lack of difference in negative emotions experienced between the two states. In particular, leave with pay has been shown to lessen the impacts of work losses in South Africa (Posel et al., 2021), suggesting that the government payments such as Job Seeker and Job Keeper initiated at the start of the pandemic may have helped ease the financial pressures experienced by young people during the pandemic in both of these states. Nevertheless, there was a little evidence to suggest that when overall government funding was considered, young Queenslanders benefited more than young Victorians.

However, the levels for negative emotion were still noticeably high in both states. This can perhaps be attributed to certain issues that were common across both states, such as unaffordable housing, uncertainty about the effects of COVID and vaccines, restricted travel, remote learning for students and remote working for employees, as well as uncertainty about when and how the pandemic will end. However, the potential experience of vicarious trauma, in terms of the general threat of the COVID-19 pandemic, could also be a factor, perhaps exacerbated by the continuous stream of COVID-19 news (Liu & Liu, 2020). Vicarious trauma has been observed in several countries. For example, a Chinese study showed that scores for vicarious trauma have been significantly higher in the general population than for front-line nursing staff (Li et al. 2020), while a Brazilian study focused on the growing demand for mental health services as a result of vicarious trauma (Serafim et al., 2020). In addition, an American-Canadian study has shown that vicarious trauma was correlated with pre-existing psychopathology and problems with excessive COVID-19-related avoidance, panic buying and problems coping with isolation (Taylor, 2020).

Unexpectedly, levels of loneliness were also similar in Victoria and Queensland, but not at levels higher than those seen for young people pre-COVID (Cigna, 2018, Lim et al., 2019). This is not the first study to suggest that the pandemic and lockdowns have not increased loneliness. A systematic review including a comparison of loneliness before and during the pandemic has produced inconclusive results (Pai & Vella, 2021), with various studies finding no change, an increase or a decrease in loneliness. Folk (2020) found that college students’ sense of social connection remained largely intact during the pandemic (perhaps because they discovered new opportunities for connection and social bonding online); however, for those who had not been able to stay connected, higher scores for negative emotions were observed by Knopf (2020).

Levels of self-esteem were similar for Victoria and Queensland with mean values in the pre-pandemic normal range (15-25), but significantly lower than levels recorded by Bagley et al. (1997) and Bagley et al. (2001). Mean resilience levels were also similar for participants from these two Australian states, with values slightly below the bottom end of the normal range (3 to 4.3). The relatively low levels for both these protective factors and their significant relationships with negative emotions may help to explain the high levels of negative emotions seen in both states.

Finally, levels of household conflict were also similar for the two states and higher than the levels observed by Behar-Zusman et al. (2020) in adult samples for 81 countries, possibly also contributing to the high levels of negative emotion seen in young Australians during the pandemic.

This study is not without its limitations. Convenience sampling was used, with study recruitment conducted using social media. The sample was biased in favour of women, university students, and likely toward respondents with an interest in mental health and mental health research. It is also possible that those more adversely affected by the Victorian lockdowns may have chosen not to participate in the survey. However, the percentage of participants reporting a mental illness was similar to the percentage of young people reporting high/very high levels of distress in the Australian Community Headspace (2020) survey, suggesting that the sample was representative in this respect.

The consideration of only two of the Australian states (Victoria and Queensland) in this study was a deliberate strategy to allow for a comparison of extreme ends of the lockdown spectrum. However, this can also be seen as a limitation in that there were other differences between these states that did not relate to lockdown experiences, such as climate (warmer in Queensland) and public health response capacity, despite a national response to the pandemic co-ordinated by the National Cabinet.

Many of the measures were retrospective, requiring participants to remember what they experienced in the previous year. The accuracy of this recall may not be reliable, given the changes in lockdown status that occurred over this period, especially in Victoria. Also, this was a cross- sectional study, which meant that causal relationships could not be assumed. A longitudinal study with multiple timepoints would provide greater certainty about the relationships that have been observed in this study, as well as allowing for detailed exploration of potential mediating and moderating pathways.

In addition, the measures related to a range of periods. Negative emotions and loneliness were based on perceptions for the last week, financial and other stressors were identified for the first year of the pandemic, while resilience and self-esteem were trait measures not related to any specific time period. The extent to which pandemic hardships experienced previously have affected current levels of loneliness and negative emotion are likely to vary between individuals, depending on factors such as the above trait measures. Nonetheless, the pandemic is ongoing (Pietrabissa & Simpson, 2020), making it likely that current negative emotions have some relationship with previous pandemic experiences.

Finally, given the complexity and severity of the impacts of the pandemic a more nuanced study of the mechanisms of negative mental health impact of severe lockdowns is required for young people. The ecological model of Windle and Bennett (2011) provides a possible theoretical framework for better understanding this impact and better informing public health policy in similar settings.

In conclusion, no significant differences in negative emotions were found between young Victorians and Queenslanders after the first year of the COVID-19 pandemic, despite significant differences in the intensity/duration of lockdown periods and the number of COVID-19 cases across these two states. To some extent, this can be explained by similar experiences regarding employment loss and education disruption, with similar benefits from government actions taken to support youth in both states. This suggests that the pandemic restrictions (lockdowns and social distancing) and infection rates per se did not impact youth mental health to a greater extent in Victoria than Queensland. That being said, the high level of negative emotions in young people in Australia is of significant concern, and must be monitored and addressed as the pandemic continues.

CRediT authorship contribution statement

Denny Meyer: Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. Philip J. Sumner: Data curation, Investigation, Methodology, Writing – review & editing. Eric J. Tan: Investigation, Methodology, Writing – review & editing. Erica Neill: Investigation, Methodology, Writing – review & editing. Emily Hielscher: Investigation, Methodology, Writing – review & editing. Julie A. Blake: Investigation, Methodology, Writing – review & editing. James G. Scott: Writing – review & editing. Andrea Phillipou: Writing – review & editing. Wei Lin Toh: Writing – review & editing. Tamsyn E. Van Rheenen: Writing – review & editing. Susan L. Rossell: Funding acquisition, Writing – review & editing.

Declaration of Competing Interests

There are no conflicts of interest for any of the authors.

Appendix. Pre-Pandemic Normative Data and Early Pandemic Data for Table 4 and Figure 1

Negative Emotions.Larcombe et al. (2016) provided 2013 data for the DASS-21 for 4258 Victorian students from a single metropolitan university with 91% of the students under the ae of 25.

Resilience (BRS).Fung (2020) provided a pre-COVID norm for 511 Chinese university students with an average age of 20.41 years (SD=2.49). Normative data was also provided for young Victorians (18-24) at the start of the pandemic (Rossell, 2021), suggesting a significant increase in resilience since the start of the pandemic.

Loneliness (UCLA-R). The pre-COVID mean values were taken for a sample of 870 18-25 year old Australians living in Victoria (Lim et al., 2019) and a sample of 18-22 year old Americans (N=20,000, Cigna, 2018). The results suggested no significant differences in loneliness compared to these pre-pandemic normative values despite levels of above 43, which provides evidence of loneliness, according to Cigna (2018).

Self-esteem (RSES). The pre-COVID values for the RSES were taken from a sample of 560 British young people aged 16-19 (Bagley et al., 2001) and a sample of 436 Canadians aged 16-19 (Bagley et al., 1997), suggesting significantly lower self-esteem compared to these pre-pandemic normative values.

Household Conflict (CHES). There were no pre-COVID data for the CHES scale because this scale relates specifically to the COVID-19 pandemic. Therefore, only the mean values for the 3965 adults from 81 countries included in the original study were provided in Table 4 and Figure 1. These results suggest significantly higher values for household conflict in our sample of young people than observed at the start of the pandemic in an adult sample.

COLLATE data for Victoria collected at the start of the pandemic at the beginning of April 2020 was utilised in Table 4 but not in Figure 1. The sample included 335 young Victorians aged 18-24. The data reported in Table 4 has not been published previously. Details of the COLLATE study can be found in Tan et al. (2020) and Rossell et al. (2021). These data were used to provide early pandemic data for negative emotions and resilience using the same MOMENT scales described previously.