Thriving in a pandemic: Determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown; a cross-sectional survey.

OBJECTIVE: The COVID-19 pandemic and associated restrictions are associated with adverse psychological impacts but an assessment of positive wellbeing is required to understand the overall impacts of the pandemic.
METHODS: The NZ Lockdown Psychological Distress Survey is an on-line cross-sectional survey of 3487 New Zealanders undertaken during a strict lockdown for COVID-19. The lockdown extended from 25 March 2020 to 28 April 2020 and the survey was undertaken between 15 April 2020 and 27 April 2020. The survey measured excellent wellbeing categorised by a WHO-Five Well-being Index (WHO-5) score ≥22. The survey also contained demographic and pre-lockdown questions, subjective and objective lockdown experiences, and questions on alcohol use. The proportion of participants with excellent wellbeing is reported with multivariate analysis examining the relative importance of individual factors associated with excellent wellbeing.
RESULTS: Approximately 9% of the overall sample (303 participants) reported excellent wellbeing during the New Zealand lockdown. In the multivariable analysis, excellent wellbeing status was positively associated with increasing age (p<0.001), male gender (p = 0.044), Māori and Asian ethnicity (p = 0.008), and lower levels of education (certificate/diploma level qualification or less) (p<0.001). Excellent wellbeing was negatively associated with smoking (p = 0.001), poor physical (p<0.001) and mental health (p = 0.002), and previous trauma (p = 0.033).
CONCLUSION: Nine percent of New Zealanders reported excellent wellbeing during severe COVID-19 pandemic restrictions. Demographic and broader health factors predicted excellent wellbeing status. An understanding of these factors may help to enhance wellbeing during any future lockdowns.

Introduction

New Zealand identified its first COVID-19 case on 28 February 2020. Case numbers increased during March 2020 and New Zealand (NZ) entered alert level 4 (a stringent lockdown) on 11.59pm, 25 March 2020. All schools and non-essential businesses closed and, except for essential workers, citizens were required to stay at home (except when undertaking essential shopping, health care, and exercise). The lockdown ended on 28 April. The restrictions were considered some of the strictest imposed globally [1] and successfully eliminated COVID-19 from NZ for a period of time.

The lockdown restrictions reduced family and social contact, limited recreation opportunities, caused job losses and financial insecurity; and restricted attendance at educational, religious and social sites. Recent studies have identified that the COVID-19 pandemic and its associated restrictions are associated with negative psychological effects, including increased psychological distress, increased suicidal ideation, and increased risk of mental disorder in those assessed (2–4). The literature also suggests that the impacts of the COVID-19 pandemic are not uniformly distributed. Sub-groups facing particular issues include parents for whom parenting exhaustion is a concern [2], LGBT individuals [3], and students [4]. These findings suggest that sub-group analyses are required to attain a detailed as opposed to population-level understanding of the psycho-social impacts of the pandemic.

Trauma and disasters can also lead to post-traumatic growth and thriving where individuals and groups do well despite adverse experiences [5, 6] including previous epidemics [7]. This suggests that a full understanding of the COVID-19 pandemic’s effects requires assessment of positive as well as negative consequences. Although some studies have reported post-traumatic growth during the COVID-19 pandemic [8, 9], we are unaware of studies examining for the possibility that some people will experience excellent wellbeing despite restrictions. We therefore measured New Zealanders’ wellbeing during the COVID-19 lockdown to identify factors associated with excellent wellbeing status during this period.

Methods

The NZ Lockdown Psychological Distress Survey is an online cross-sectional survey of 3487 New Zealanders undertaken between 15 April 2020 and 27 April 2020 during level 4 lockdown. The study was granted ethical approval by the University of Otago Human Ethics Committee (approval code F20/003) and was reviewed by the Ngāi Tahu Research Committee. All participants were asked to read a participant information sheet and gave written informed consent before they could proceed with the survey.

We aimed to recruit a sample that represented the New Zealand adult population. Recruitment occurred via two pathways. Firstly, we used a commercial survey platform (Dynata) which invited participants from their commercial survey panel and applied target participation quotas by age, sex, and ethnicity [10]. Secondly, we invited participation from New Zealanders who had previously been randomly selected by the NZ Ministries of Health and Justice to participate in large-scale national data surveys [11, 12] and who had consented to further contact. Population-level outcomes for the Dynata sample have been reported previously [10]. The survey was completed on-line. Analytical methods required to accommodate these sampling steps are detailed below.

Survey questionnaire

The WHO-Five Well-being Index (WHO-5) was used to measure wellbeing [13]. The WHO-5 is a 5-item scale; each question evaluates a different measure of positive wellbeing using a six-item Likert scale ranging from 0 (not present) to 5 (constantly present). Although low scores on the WHO-5 are used as a screen for depression, the WHO-5 scale is regarded as a measure of mental wellbeing rather than just the absence of depressive symptoms [14]. Various cut points are reported to define high and excellent wellbeing [15, 16]. We used the cut points of Yallop et al. [17] for poor (score<13), good (score 13–17), very good (score 18–21) and excellent (score 22–25) wellbeing. Our focus is on those who thrived despite the lockdown experience. We therefore compare those with excellent wellbeing compared to those with lower scores (WHO-5 scores <22).

The survey questions are available as a supplementary file (S1 File). The survey assessed demographic and pre-lockdown socio-economic factors, objective and subjective lockdown experiences, substance use, psychological distress, and wellbeing.

Demographic and pre-lockdown factors included age, gender, ethnicity, socio-economic status (education and household income), employment, smoking and alcohol usage, general and mental health, and prior trauma.

We assessed objective lockdown experiences using questions on living circumstances during lockdown, essential worker status, contacts with others, work load during lockdown, and COVID-19 exposure status. These questions explored the participant’s bubble; defined as the people sharing the household with the participant during the lockdown. We examined respondents’ contacts with others outside their bubble via written, electronic, and face to face media, and summarised contact frequency as high, medium, and low contact. We also asked if contact with others outside the bubble had increased, decreased, or stayed the same since the lockdown began.

We explored subjective lockdown experiences through questions on satisfaction with lockdown home environment, personal relationships during lockdown, stressors, and concerns about risk of infection. Respondents were also asked if they had experienced ‘silver linings’ personally or for society as a result of the lockdown experience.

Alcohol use and smoking were assessed by questions on the amount consumed before and during the lockdown.

Further contextual information on wellbeing among adults in NZ is drawn from StatsNZ’s General Social Survey (NZGSS) [18], which collects data on the well-being of New Zealanders aged 15 years and over. Estimates of excellent wellbeing status (score 22–25) were requested from StatsNZ to provide a national baseline of wellbeing data prior to the pandemic. As excellent wellbeing data from the NZGSS is not in the public domain, we report these findings in our results.

Statistical analysis

This paper presents a secondary analysis of data collected during national surveys. The primary purpose of this analysis is to estimate associations between variables and draw inferences that may be applied to wide range of populations. We therefore combined the Dynata and Ministry datasets and used unweighted data to increase the sample size to improve statistical precision of estimates. Prior to combining datasets, we ran the analyses separately to ensure consistency of effects.

The exception to this approach occurred when we compared weighted prevalence estimates of excellent wellbeing in the Dynata and Ministry datasets to pre-COVID pandemic results from the New Zealand General Social Survey [18]. Details of the weighting strategy used for the Dynata and Ministry datasets is described in our parent paper [10]. For this comparison, national prevalence estimates are presented separately for the Dynata and Health and Justice survey panel datasets due to the differing sampling strategies and sampling weights applied to these surveys. Statistical tests were not undertaken for this comparison due to the differences in sampling strategies between the NZGSS and the study groups included in this paper.

We grouped potential explanatory factors into demographic and pre-lockdown factors, objective lockdown factors, and subjective lockdown experiences. The proportion of respondents reporting excellent wellbeing was calculated for each explanatory factor. We assessed differences across levels using chi-squared or Fisher’s exact tests. Unadjusted odds ratios with 95% confidence intervals were calculated for selected pairwise differences versus a nominated reference category. We explored the relative importance of individual factors by creating a series of four nested multivariable logistic regression models, entering variables in four blocks; demographics (age, sex, ethnicity); qualifications and employment; pre-existing risk factors (smoking status, prior mental health diagnosis, self-rated health, past history of trauma); and household composition. We did not incorporate subjective and objective lockdown experiences into this model due to the risk of reverse causality. There were 19 participants did not complete the WHO-5 and are excluded from analysis. A further 106 participants had missing data for some of the variables. When this occurred, participants were excluded from the multivariable analysis but are still included in descriptive and univariable analyses. Analysis was performed using the R 4.0.3 programming language and environment [19].

Results

Demographic and socioeconomic factors

A total of 3,487 participants completed the surveys (2010 from the Dynata survey and 1477 from the Ministries of Health and Justice dataset). Nineteen participants did not provide full WHO-5 data and are excluded from the analysis. 32.8% (n = 1139) of the sample were in the poor wellbeing group (WHO-5<13), 67.2% (n = 2329) were rated good wellbeing or better (WHO-5≥13) and, among the latter, 303 participants (8.7%) were in the excellent wellbeing group. The overall mean WHO-5 score was 14.7 (SD = 5.71).

Females were significantly less likely than males to report excellent wellbeing (OR = 0.73, CI 0.58–0.93, p = 0.010). Older people, particularly those over 65, were more likely than young people to report excellent wellbeing (OR for 65+ years compared to 15–24 years = 2.64, CI 1.63–4.28, p<0.001). People with higher qualification levels (Bachelor’s degree or greater) were less likely to report excellent wellbeing compared people with lower levels (OR = 0.49, CI 0.33–0.73, p<0.001). The retired were more likely to report excellent wellbeing than those who were working (OR = 2.01, CI 1.54–2.62, p<0.001)) but the unemployed were less likely to report excellent wellbeing than workers (OR = 0.64, CI 0.43–0.96, p = 0.031). Excellent wellbeing status was comparable between essential and non-essential workers (OR = 1.16, CI 0.84–1.60, p = 0.402). Self-rated good or better physical health status was strongly associated with excellent wellbeing (OR = 4.84, CI 2.86–8.19, p<0.001). Pre-COVID-19 mental illness (OR = 0.29, CI 0.19–0.47, p<0.001), physical illness (OR = 0.52, CI 0.30–0.91, p = 0.020), and prior trauma (OR = 0.75, CI 0.57–0.99, 0 = 0.042) were all negatively associated with excellent wellbeing. There were no significant effects by ethnicity (Fishers exact test p = 0.510) or income (chi-squared p = 0.780) on excellent wellbeing status.

Objective lockdown experiences

The composition of the bubble was not significantly associated with excellent wellbeing (chi-squared p = 0.057). Connections with others were significantly associated with excellent wellbeing with highest rates of excellent wellbeing being reported by people with a high frequency of connection with others (chi-squared p = 0.012). Reducing the frequency of contact with others was associated with lower rates of excellent wellbeing relative to those whose frequency of contact with others remained unchanged (OR = 0.39, CI 0.28–0.54, p<0.001). There were no significant effects for change in work load (Fishers exact test p = 0.364) or loss of job during COVID-19 lockdown (Fishers exact test p = 0.101) or between COVID-19 exposure or infection status on the likelihood of excellent wellbeing (Fishers exact test p = 0.196).

Subjective lockdown experiences

Those who were extremely satisfied with their ‘bubbles’ were more likely to report excellent wellbeing than those who were not satisfied (OR = 3.17, CI 2.04–4.93, p<0.001). Similarly, getting along very well with others in their ‘bubbles’ was associated with excellent wellbeing compared to those who were not getting on well with others (OR = 6.41, CI 3.62–11.35, p<0.001). Conversely, greater loneliness was associated with a reduced proportion reporting excellent well-being, relative to people who did not feel lonely (OR = 0.11, CI 0.07–0.18, p<0.001). Looking at information on COVID-19 for more than two hours/day was not significantly associated with excellent wellbeing status (OR = 0.73, CI 0.53–1.11, p = 0.165). However, stress about personal health (OR = 0.43, CI 0.28–0.64, p<0.001), the health of loved ones (OR = 0.34, CI 0.25–0.47, p<0.001), finances (OR = 0.29, CI 0.20–0.41, p<0.001), employment security (OR = 0.56, CI 0.39–0.79, p<0.001), and the wider consequences of COVID-19 (OR = 0.42, CI 0.32–0.54, p<0.001) were all associated with reduced likelihood of reporting excellent wellbeing. Reporting ‘silver linings’ during the COVID-19 lockdown, either personally (OR = 1.02, CI 0.80–1.29, p = 0.904) or societal-level silver linings (OR = 0.98, CI 0.77–1.24, p = 0.854), was not significantly associated with excellent wellbeing status.

Substance use

Smokers (current and former) were less likely to report excellent wellbeing than those who never smoked (OR for current compared to never smoked = 0.48, CI 0.32–0.72, p<0.001). There were no significant relationships between pre-lockdown drinking levels and excellent wellbeing status although hazardous drinking during the lockdown (OR = 0.63, CI 0.42–0.96, p = 0.029) and increasing (OR = 0.43, CI 0.31–0.60, p<0.001) or decreasing alcohol (OR 0.70 CI 0.50–0.97, p = 0.031) intake during the lockdown were associated with a reduced likelihood of excellent wellbeing.

Multivariable analysis/logistic regression

Multivariable modelling is reported in Table 1. Following the multivariate analysis; excellent wellbeing was independently associated with older age, male gender, Māori and Asian ethnicity, and certificate/diploma level qualification or less. Excellent wellbeing was also negatively associated with smoking, poor physical and mental health, and previous trauma.

Table 1

Multivariable modelling of independent variables and excellent wellbeing.

		Respondents	Excellent wellbeing	Adjusted
Characteristic	Level	N	% (n)	Odds ratio (95% CI)	p
Age (years)	15–24	322	6.5 (21)	1.00 (Reference)	<0.001
	25–34	582	4.6 (27)	0.79 (0.43, 1.47)
	35–44	588	4.8 (28)	0.93 (0.52, 1.72)
	45–54	611	9.2 (56)	1.83 (1.10, 3.19)
	55–64	593	8.6 (51)	1.73 (1.02, 3.07)
	65+	772	15.5 (120)	3.02 (1.71, 5.53)
Gender	Male	1476	10.2 (150)	1.00 (Reference)	0.044
	Female	1970	7.7 (151)	0.80 (0.65, 0.99)
	Unknown	22	-	-
Ethnicity	European/other	2381	8.5 (202)	1.00 (Reference)	0.008
	Maori	620	8.4 (52)	1.38 (1.00, 1.85)
	Pacific	148	10.8 (16)	1.43 (0.79, 2.32)
	Asian	319	10.3 (33)	1.86 (1.25, 2.68)
Qualification	None	375	11.5 (43)	1.00 (Reference)	<0.001
	High school	960	10.3 (99)	0.84 (0.60, 1.20)
	Certificate or Diploma	871	9.8 (85)	0.86 (0.61, 1.24)
	Bachelors or higher	1262	6 (76)	0.49 (0.34, 0.73)
Employment	Employed	2230	7.9 (176)	1.00 (Reference)	0.703
	Unemployed	576	5.2 (30)	0.85 (0.56, 1.24)
	Retired	661	14.7 (97)	0.98 (0.71, 1.38)
	Unknown	1	-	-
Smoking	Never smoked	1873	10.1 (190)	1.00 (Reference)	0.001
	Past	1047	8.1 (85)	0.70 (0.54, 0.89)
	Current	545	5.1 (28)	0.57 (0.37, 0.84)
	Unknown	3	-	-
Self-reported Health	Poor/fair	652	2.3 (15)	1.00 (Reference)	<0.001
	Good/excellent	2816	10.2 (288)	3.83 (2.33, 6.87)
Prior mental health	No	2763	10 (277)	1.00 (Reference)	0.002
	Yes	629	3.2 (20)	0.53 (0.33, 0.81)
	Unknown	76	-	-
Physical disability	No	3186	9.1 (289)	1.00 (Reference)	0.335
	Yes	282	5 (14)	0.78 (0.43, 1.27)
Prior trauma	No	2432	9.4 (228)	1.00 (Reference)	0.033
	Yes	1036	7.2 (75)	0.77 (0.59, 0.98)
Household compostion	Solo	527	10.2 (54)	1.00 (Reference)	0.636
	Two adults	1103	10.1 (111)	0.91 (0.68, 1.23)
	Multiple adults	685	7.2 (49)	0.85 (0.58, 1.25)
	With children	1149	7.7 (89)	1.04 (0.73, 1.49)
	Unknown	4	-	-

2018 New Zealand General Social Survey (NZGSS) pre-COVID-19 comparison

Table 2 reports excellent wellbeing separately for the Dynata panel and the Health and Justice survey panel compared to the NZGSS. Rates of excellent wellbeing group were 8.7% (Dynata sample) and 7.8% (Health and Justice panel) compared to 7.0% percent of the NZGSS sample. This pattern was largely repeated when data was broken down by gender and ethnicity although Māori participants in the Health and Justice survey panel reported lower rates of excellent wellbeing than the Dynata and NZGSS datasets. Rates of excellent wellbeing for those aged under 45 years were consistently lower in the post-COVID-19 datasets compared to the NZGSS.

Table 2

Excellent wellbeing status: Comparison with WHO-5 data from the 2018 NZGSS.

	Dynata dataset			Health and Justice surveys		NZGSS
Characteristic	Level	%	CI	%	CI	%	CI
Total		8.7	(7.5, 10.1)	7.8	(6.4, 9.5)	7.0	(5.6, 8.4)
Gender	Male	9.5	(7.7, 11.7)	8.1	(6.1, 10.7)	8.1	(5.6, 10.6)
	Female	8.0	(6.3, 9.9)	7.5	(5.7, 9.8)	5.9	(4.1, 7.7)
Age	15–24	6.6	(4.1, 10.4)	2.9	(0.7, 11.9)	8.4	(4.3, 12.5)
	25–34	4.9	(3.1, 7.7)	4.2	(1.9, 8.9)	6.7	(2.2, 11.2)
	35–44	5.1	(3.2, 8.1)	5.4	(2.9, 10.1)	6.3	(2.2, 10.4)
	45–54	9.8	(6.9, 13.7)	8.5	(5.3, 13.4)	4.9	(1.2, 8.6)
	55–64	8.6	(5.8, 12.6)	8.7	(5.6, 13.1)	4.1	(1.9, 6.3)
	65+	16.1	(12.3, 20.9)	15.4	(13.4, 22.0)	10.5	(5.6, 15.4)
Ethnicity	European/Other	7.8	(6.4, 9.5)	8.1	(6.6, 9.8)	5.9	(4.5, 7.3)
	Maori	8.7	(6.2, 12.1)	5.6	(3.3, 9.5)	7.0	(2.9, 11.1)
	Pacific	8.8	(4.7, 15.8)	10.9	(3.7, 28.4)	8.5	(3.2, 13.8)
	Asian	12.6	(8.5, 18.3)	7.6	(3.3, 16.6)	10.6	(4.9, 16.3)

% are weighted according to the NZ population

Discussion

Nine percent of the survey population reported excellent wellbeing during the lockdown. There was substantial pattering across different population groups. Male gender, older age, Māori and Asian ethnicity, lower levels of education, and being a non-smoker were associated with excellent wellbeing status. Males typically score higher on the WHO-5 index than females [14, 20]; our finding is consistent with this and epidemiological studies that report higher rates of mood and anxiety disorders for females (for example [21, 22]). Rates of excellent wellbeing varied across ethnic groups. Māori and Asian participants were more likely to be in the excellent wellbeing group than the NZ European/Other group. This may relate to the importance of family connections for these groups compared to non-Europeans [23]. Further studies are required to see if this finding is repeated or relates to sampling methods specific to this study.

We expected that the elderly might be more negatively affected by COVID-19 restrictions. However, those aged 65+ were 2.64 times more likely to report excellent wellbeing than those aged 15–24. We speculate that the social isolation was particularly difficult for the younger age group and was not compensated for by non-face-to-face social connections. It is also possible that the elderly were relatively protected from the economic consequences of the pandemic. The comparison with pre-COVID-19 NZGSS data suggests that the 65+ age group were also more likely to report excellent wellbeing prior to the COVID-19 pandemic. A study of older individuals in Germany also reported that mental wellbeing was largely unaffected by a COVID-19 lockdown [24]. These findings suggest that younger age groups rather than the elderly require specific attention when planning interventions to mitigate adverse impacts of future lockdowns.

The finding that excellent wellbeing was related to lower levels of education was unexpected. We are aware of studies reporting that poor educational achievement in school is linked to subsequent risk of mental health problems [25] and assumed that this link would be reflected in lower wellbeing among those with less education. We were unable to identify literature reporting on levels of education and wellbeing to see if our findings are reproduced elsewhere.

The relationships between frequency and quality of contact with others, satisfaction with bubbles, and loneliness all point to the importance of quality social connections. These findings are a reminder of the importance of links between social capital (including formal and informal social interactions) and wellbeing [26]. Although our data is cross-sectional in nature (reducing our ability to infer causal links between these factors and wellbeing), bolstering the ability for formal and informal social connections to occur during lockdowns could be considered by governments and support agencies to mitigate adverse effects caused by social restrictions and isolation.

Prior physical and mental health issues, and prior trauma are static risk factors that are difficult to modify. However, the range of stressors (health, finances, employment, and COVID-19) associated with reduced likelihood of excellent wellbeing provides direction for public health initiatives in both preventative and responsive frameworks. Clear communication from governments and appropriate safety nets for those affected by loss of employment may have a role in mitigating adverse effects and improving wellbeing. Similarly, health messaging around stopping smoking and drinking within recommended limits appears relevant during lockdowns as well as other times.

Limitations

We merged data collected by Dynata with data from the NZ Ministries of Health and Justice for the analysis. This approach increased our sample size and allowed us to make more precise estimates of these associations for the larger sample. This strategy means any between dataset differences may not be highlighted although the analyses were run separately prior to merging to minimise the risk of important differences being missed.

Our data are cross-sectional and the associations we report do not allow strong causal inferences to be made. Although our sampling strategy used quotas to achieve a demographically representative sample, our respondents were computer literate, and were both available to participate and consented to complete the survey. Despite being demographically representative, the study population may not therefore be representative of the national population.

The COVID-19 infection rate in NZ was low over the study period. We therefore assessed the impacts of the NZ lockdown and fear of infection as opposed to assessing wellbeing in the presence of high rates of COVID-19 community transmission. We completed data collection in the early stages of the pandemic. We therefore provide a snapshot of wellbeing at that point of time and recognise the importance of repeated measures in order to track progress over time.

Conclusion

Nine percent of the survey population reported excellent wellbeing during the lockdown compared to 7% of those surveyed prior to the lockdown in the NZGSS. This suggests that focussing on the negative consequences of lockdown restrictions does not provide a balanced understanding of the psychosocial impacts of pandemic restrictions. Some of the factors associated with excellent wellbeing were static and not amenable to change. Other factors highlighted the importance of stress, substance use, health, and relationships in determining wellbeing and provide a path for governments and individuals to enhance wellbeing.

The NZ Lockdown Psychological Distress Survey questionnaire.

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6 Dec 2021

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Reviewer #1: The paper entitled: “Thriving in a pandemic: determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown” explores the results of a survey on the wellbeing of New Zealanders during the lockdown. The main results are 9that % of the sample reported excellent wellbeing during the New Zealand lockdown, and was associated with older age, male gender, Māori and Asian ethnicity, and lower levels of education. On the other hand, it is negatively associated with smoking, poor physical and mental health, and previous trauma. The paper concludes that a substantial minority of New Zealanders reported excellent wellbeing during severe COVID-19 pandemic restrictions.

I think the paper makes a nice job in adding a specific contribution to the related literature, giving the scholars’ community some new information about the well-being of New Zealanders during the 2020 lockdown. The nested model and the summary statistic presented are up to the point and make a good job in giving to the scholars an idea of the data. The results are interesting. I would nonetheless like to see some amendments to improve the paper.

First, the authors make use of unweighted data in the merge (line 117). I would like to see more explanation about this choice and caveat about the possible consequences of this in terms of representativeness of the sample and external validity of the results.

Second, I see room for improvement in the literature review. The authors cite in the article relevant articles in the introduction, this can be expanded. First, some articles on the effects of COVID-19 lockdown on mental health seem appropriate, such as Evans et al., 2021, Sharma and Subramanyam, 2021), and Rohr et al., 2020. Moreover, articles discussing the possible impact of other cultural and socio-economic characteristics on lockdown compliance seem to be relevant, such as Alfano, 2021 for work ethics, or Marchetti et al. (2020) for being parents. This would help better frame the article in the literature and enhance its collocation in the literature.

I wish best luck to the authors with this interesting piece of research!

References

Alfano, V. (2021). Work ethics, stay-at-home measures and COVID-19 diffusion. Eur J Health Econ.

Evans, S., Erkan Alkan, Jazmin K. Bhangoo, Harriet Tenenbaum, Terry Ng-Knight, Effects of the COVID-19 lockdown on mental health, wellbeing, sleep, and alcohol use in a UK student sample, Psychiatry Research,

Volume 298, 2021, 113819.

Marchetti, D., Lilybeth Fontanesi, Mazza, C., Di Giandomenico, S., Roma, P., Verrocchio, M.C. (2020). Parenting-Related Exhaustion During the Italian COVID-19 Lockdown, Journal of Pediatric Psychology, Volume 45, Issue 10, Pages 1114–1123.

Röhr, S., Reininghaus, U. & Riedel-Heller, S.G. Mental wellbeing in the German old age population largely unaltered during COVID-19 lockdown: results of a representative survey. BMC Geriatr 20, 489.

Sharma, A. J., Subramanyam, M. A. (2021). A cross-sectional study of psychological wellbeing of Indian adults during the Covid-19 lockdown: Different strokes for different folks.

Reviewer #2: The topic of this study is interesting. I have following comments for the authors.

TITLE:

Please add the study design (i.e. A cross sectional survey) at the end of the title according to the The EQUATOR Network guidelines for reporting survey. Please refer to the 'A Consensus-Based Checklist for Reporting of Survey Studies (CROSS)' available https://www.equator-network.org/reporting-guidelines/a-consensus-based-checklist-for-reporting-of-survey-studies-cross/

ABSTRACT:

Methods: please report the study design, total sample size, sampling methods and how the survey was administered. Also, please could you report the dates/duration (months and year) of lockdown that was studied in this study.

Results: Please report the number of respondents that accounted for 9% of the sample.

Results: Could you please report the age range that refers to 'older age' as well as what was the cut off education level that is reported as lower levels of education.

Results. Please report the evidence i.e. statistics showing significant associations between reported variable.

Conclusion: The authors conclude that substantial minority reported excellent wellbeing. Is 9% a substantial figure? Please revise your conclusion remarks.

INTRODUCTION

Citations: Please merge the citations in the text because they are mostly included as standalone numbers such as (2) (3) (4), which should be (2-4). Please do this in the whole manuscript.

METHODS

Sample: Please double check the number of participants because 2020 (Dynata)+1477 (MoHJ) = 3497. But you have reported total 3487 participants. In addition, you have reported that 120 cases with missing data were excluded which means 3497-120= 3377. But your final figures do not match this calculation.

Results:

Total participants: Please see comment about the number of participants as mentioned in the methods section above and revise your results accordingly.

Please check you number of total respondents and numbers given in Table 1.In there were missing values for example in the gender etc then please report these in Table 1. For example gender (Male and female = 1476+1970=3446. This number does not match with the total respondents reported at the beginning of the results section. Please check for this for all variables and categories.

Please report the number / count of participants along with the % figures.

Please report Standard deviation values along with the mean values and if data was no normally distributed then report the median number too.

Please sign post about the tables in the text.

Please report p -value along with the Odds Ratios.

Please clarify/report whether the lower education level included people who had education less than a Bachelor's degree or something else. Please be specific.

Please report statistics or refer to the relevant table in the sentences where you report associations. for example, there is no such information in this sentence: "Connections with others were significantly associated with excellent wellbeing....".,

Table 2: Not sure why data from the 2018 NZGSS has been included in the manuscript> It could be referred in the discussion only.

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Reviewer #1: No

Reviewer #2: Yes: Syed Ghulam Sarwar Shah

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19 Dec 2021

13.12.21

Response to reviewers

Dear Dr Joshi

Thank-you for the opportunity to revise Thriving in a pandemic: determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown; a cross-sectional survey.

Response to editorial questions

1. We have reviewed the style requirements and believe these are met.

2. We have included the Inclusivity of global research questionnaire as a Supporting Information document.

3. We have amended our acknowledgements statement to make the relationship with Dynata more clear. We were grateful for their support of the project but they did not provide any funding. The study was completed without funding so we do not need to update the Funding Statement.

4. Data availability statement; we will place the data in a repository if accepted for publication but have not done so at this stage.

5. Captions for Supporting Information files are now included.

6. Supporting Information S1 file has now been uploaded.

We thank Reviewers 1 and 2 for their constructive comments. We believe their feedback has improved the paper. Our responses to their comments follows.

Reviewer 1

1. We have expanded on our original explanation relating to the merging of the datasets in the methods. We have also provided additional comment in the limitations section of the discussion to provide context to the decision to merge data.

2. We have expanded the introduction as suggested to provide better context to our research. We have also referenced the Rohr paper in the discussion. The tracked changes version highlights the areas of change.

Reviewer 2

1. Title: We have added ‘cross-sectional survey’ to the study title as per the CROSS checklist.

2. Abstract: Methods; we have added details of the design, sample size, and survey administration to the methods. We have also provided the lockdown dates.

3. Abstract: Results; we have added the number of respondents (303). We have also specified increasing age (this finding was part of the multivariable analysis) and detailed the level of education (certificate/diploma level or under). We have also clarified that the findings relate to multivariable analysis and provided p values.

4. Abstract: Conclusion; we have removed ‘substantial minority’ and replaced this with the more accurate ‘Nine percent’.

5. We have merged the citations as suggested.

6. Methods: Sample; the sample size was 3487 as reported but we made an error in reporting the Dynata sample (this should be 2010). We have corrected this in the results. Nineteen participants did not provide full WHO-5 data and were excluded from the analysis. A total of 106 participants had missing data for some of the other variables. These participants are included in the descriptive and univariable analyses but are excluded from the multivariable analysis. We have clarified this in the methods section and results section.

7. Results: Total participants; see above for details.

8. Results: Missing values now included in Table 1.

9. Results: We have amended the reporting of % figures to include the number of participants.

10. We have now included the SD for the mean WHO-5 score

11. Results: Line 238 signposts Table 1 in the text

12. Results: We have added the p-values for the reported statistics.

13. Results: We have clarified that the lower education level is certificate/diploma level qualification or less.

14. Results: We have now provided the relevant statistics when associations are reported.

15. Results: Table 2. We have provided more explanation in the methods. The data we report from the NZGS is not in the public domain. Further analysis of the NZGSS data was required to report excellent wellbeing. We therefore believe that this data belongs in the results section.

Submitted filename: Response to reviewers PLOS-1.docx

Click here for additional data file.

5 Jan 2022

Thriving in a pandemic: determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown; a cross-sectional survey

PONE-D-21-34860R1

Dear Dr. Ben Beaglehole ,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Rajnish Joshi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: My comments have been addressed, and the paper is much better. I wish to the authors best luck with this interesting piece of research.

Reviewer #2: Thanks for addressing all issues raised in my earlier review report. The manuscript is clear and improved now. Data could be made available or any relevant statement should be included in the manuscript.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Syed Ghulam Sarwar Shah

23 Feb 2022

PONE-D-21-34860R1

Thriving in a pandemic: determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown; a cross-sectional survey

Dear Dr. Beaglehole:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Rajnish Joshi

Academic Editor

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