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Abstract

The devastation of the Covid-19 pandemic on society raised the question of how best to gauge society’s psychological adaptation to continually evolving global disruptor events, such as Covid-19. This paper aims to illustrate the use of different approaches to monitor society’s psychological response to Covid-19, in order to argue for a more comprehensive, multi-model, approach. The results from different approaches are presented in two studies employing measures of mental disorders and mental distress, respectively, using South African samples for demonstration. The first study presents findings from repeat administration of measures of common mental disorders (major depressive and generalized anxiety disorders) across three consecutive years, while the second study presents findings from mood response profiles (measured with the Brunel Mood Scale) collected across five time points during the Covid-19 pandemic. Both studies showed that the Covid-19 pandemic was temporally associated with adverse mental health outcomes across the mental health continuum, and that mental health profiles were associated with both time since onset of Covid-19 and subsequent wave occurrence. Elevated prevalence of common mental disorders, as well as fluctuating patterns of mood response profiles, are discussed against the context of Covid-19. The paper concludes that a multi-modal approach, for instance measuring specific mental disorders as well as more general mental distress, is crucial to comprehensively understand society’s psychological adaptation to major disruptor events, and guide health sector responses. The paper serves as a reminder to continue to observe mental health more inclusively to appropriately respond to the psychological needs of communities.

Plain Language Summary

Measuring mental health across Covid-19 waves

The impact of Covid-19 on mental health raised the question of how can we best measure how society adapts, psychologically, to major events that disrupt life. This article looked at two specific ways to measure and monitor society’s psychological response to Covid-19. The first study looked at the prevalence of mental disorders, and the second looked at levels of mental distress. Both studies used South African samples to demonstrate this. The first study presented data from a repeat administration of scales that identify depressive and anxiety disorders, completed across three consecutive years. The second study presented findings from mood response profiles (measured with the Brunel Mood Scale) collected across five time points during the Covid-19 pandemic. The results showed that the pandemic was associated with adverse mental health outcomes—across the mental health continuum—and that mental health profiles were associated with both time since the start of Covid-19 and fluctuated with the subsequent waves across the pandemic time-line. This article demonstrated the importance of using a multi-modal approach to fully understand society’s psychological response to major life disruptions. It also acted as reminder to monitor mental health continuously to be able to respond to the psychological needs of communities.

Keywords

Covid-19 emotional wellbeing mental disorders mental health services psychological adaptation South Africa

Introduction

Orientation

During 2020 and 2021, global society, including South Africa (SA), was ravaged by the double burden brought on by the Covid-19 pandemic. On the one hand, the world faced the devastating health effects of Covid-19, including high infection rates and rapid transmission, high mortality (Coronavirus Resource Centre, 2021), and long-term health consequences for survivors of Covid-19 (Del Rio et al., 2020). This also included the associated psychological effects of its health impacts, including fears for the safety of selves and significant others (Dymecka et al., 2022), and managing loss and grief in respect of friends and loved ones who had succumbed to Covid-19 (Eisma et al., 2021). On the other hand, like the rest of the world, SA also faced the devastating social and socio-economic effects of measures implemented to mitigate the spread of the pandemic (colloquially referred to as “lockdown”), including loss of income and livelihoods and an associated increase in poverty (Futshane, 2021), disruption of social structures and support systems (Mbunge, 2020), increase in domestic violence (Clark, 2020) escalated experiences of isolation and loneliness (Killgore et al., 2020; Laher et al., 2021; Pretorius & Padmanabhanunni, 2021; Tso & Park, 2020), and as shown elsewhere, the profoundly negative impact on psychological health and well-being through reduced access to physical activity (Maugeri et al., 2020; Maugeri & Musumeci, 2021). Similar experiences—from increased poverty to gender-based violence—have been reported from other resource-limited settings, and also highlighted the association between mental stress and socio-economic and socio-political factors (Shammi et al., 2020, 2021). This situation led, early on, to predictions of an impending mental health disaster in the wake of Covid-19, both internationally (Lancet Public Health, 2020; Pfefferbaum & North, 2020), and for sub-Saharan Africa (Semo & Frissa, 2020). The complex interactions between disease and social conditions, highlighted during Covid-19, have also led to calls for the pandemic to be viewed as a syndemic, to more fully appreciate the “inseparable ties between biological and social processes in the health of individuals” (Horton, 2020; Musumeci, 2022; Rudd et al., 2022, p. 33).

As the pandemic unfolded and evolved over multiple surges in the spread of Covid-19 and increasing numbers of hospitalizations (also referred to as waves), national governments responded by alternately easing and increasing restrictions on populations (in SA, these restrictions were collectively referred to as “Alert Levels”) in an attempt to mitigate the effects of such waves. Expectations of an unfolding mental health pandemic raised questions about ways to best gauge the psychological adaptation of broader society, or specific communities in SA, to continually evolving pandemic events like Covid-19. As pandemic(s) evolve through subsequent waves, this question becomes pertinent for understanding society-wide responses, in order to allow for planning of resource allocation for mental healthcare and, on a more granular level, to respond to changing psychological needs of specific communities over time.

Literature Review

Descriptions of mental health impacts of the Covid-19 pandemic already proliferate in local SA literature, with a number of approaches employed for this purpose.

Prevalence of Common Mental Disorders

One approach was to study the prevalence of common mental disorders (CMD), and in this regard, studies—particularly in easily accessible populations like SA university students—abound. Some studies found increased self-reports of depression and anxiety in this population (Pretorius, 2021; Pretorius & Padmanabhanunni, 2021; Visser & Law-van Wyk, 2021). Others reported no significant elevation on scores on mental health measures, but did report elevated concerns around (non-clinical) measures of psychological wellbeing (Laher et al., 2021). The differences in results across SA studies could in part be due to the range of different measures used, together with concerns regarding the validity of some of these measures in the SA multilingual and multicultural setting. More germane to this paper, some of the scales used (e.g., Centre for Epidemiological Studies—Depression scale, CES-D; Patient Health Questionnaire-4, PHQ-4) are associated with CMD and thus designed to identify clinically poor adjustment, rather than (non-clinical) decreased emotional wellbeing or, conversely, increased mental distress.

This distinction between mental disorders and mental distress is an important one. Within the framework of mental health—defined here as psychological adaptation to the environment – the two aspects have different meaning. While mental disorder refers to “dysfunction of psychological, biological, or developmental processes” (American Psychiatric Association, 2013, p. 20), mental distress is viewed as a “normal human response, when it both emerges and persists in proportion with external stressful situations; it is not merely a less severe or more transient version of disorder” (Horwitz, 2007, p. 275). Thus, a sense of decreased emotional wellbeing may be an appropriate response to severely challenging times.

In this regard, the abovementioned study of Laher et al. (2021) was particularly instructive, as it demonstrated that reliance on measures of mental ill-health cannot be a sufficient reflection of general psychological adaptation in the face of global events like the Covid-19 pandemic. Attending only to presentations of CMD would risk neglecting the experiences of the majority of the population, who too may have difficulty adapting to the pandemic, but in different ways. Other non-clinical emotional indicators, as expressions of mental distress, may thus be equally as important to understand the psychological effects of the pandemic.

Endorsement of Items From Word Lists

A second approach, employed by the UJ-HSRC Democracy Survey (UJ-HSRC, 2020a, 2020b) identified emotional responses to the pandemic through participants’ endorsement of items from word lists. The approach followed longstanding international initiatives (e.g., UK; YouGov, 2020), and rather than scaled responses, it relied on the frequency of individual endorsement of specific emotional adjectives to reflect community experiences. To an extent, the results were similar to the findings reported above, with high numbers of respondents describing their emotions during lockdown with words such as “scared,”“irritable,” and “depressed.” This approach had the advantage of allowing participants to express their emotional responses in non-clinical ways, with the disadvantage that the lack of scaling might not allow for reporting finely nuanced changes in feeling states.

Non-Clinical Mood State Scaling

A third approach was used in a community study that employed non-clinical mood state scaling (Van Wijk & Majola, 2021). The Brunel Mood Scale (BRUMS) was used to express responses during Covid-19 across six non-clinical mood states. Similar to what was reported above, elevated levels of anxiety, depression, and fatigue, as well as reduced levels of vigor, were reported. This study followed a number of international initiatives that used the same measure (e.g., Bailon et al., 2020; Terry et al., 2020), and that reported comparable results. Recent studies suggested that the effect of Covid-19 on the mood responses of individuals could be an important indicator of how society is coping with the pandemic, as well as act as an indicator of long-term mental health outcome risks (Terry et al., 2020, Van Wijk & Majola, 2021). Terry et al. (2020) also reported data suggesting that mood responses are sensitive to evolving phases of the pandemic.

It is recognized that all these approaches have inherent limitations: Whether studies use traditional paper-and-pencil type assessments, or more sophisticated online survey platforms, each suffer from barriers of access to representative cross-sections of society. Specific approaches also have more specific limitations. For example, estimates of mental disorder prevalence are higher when using self-report scales as opposed to diagnostic interviews (Thombs et al., 2018), with respondents’ tendency to amplify their symptoms when self-reporting, complicating interpretation. Further, data, irrespective its source, need to be interpreted against comparable reference points, whether from historical data or similar populations, both which may be unavailable for SA samples.

Rationale and Aim

With society’s temporal emotional responses to the pandemic described in different ways and at different points in the pandemic’s evolution, it posed the question how best to monitor society’s psychological adaptation to the Covid-19 pandemic over time. One option would be to regularly repeat clinical screeners to profile the prevalence of mental disorder in communities. To be meaningful, this requires comparable samples, but responses are generally easy to interpret (e.g., by using established threshold cut-points), and appropriate scales are available for at least some segments of SA society.

Another option would be to regularly repeat measures of mood states (as indicators of mental distress, i.e., non-clinical emotional responses), which has been shown to be viable in international samples (e.g., Bailon et al., 2020; Terry et al., 2020). Appropriate scales are available, and are generally less invasive, reducing the burden on respondents. However, there remains a question of whether mood state profiles can be interpreted meaningfully against the larger context of a global pandemic.

It is argued here that a multi-modal approach, including surveillance of both specific mental disorders and more general mental distress, is required to more comprehensively describe psychological adaptation to global disruptor events. The aim of this paper is to illustrate the use of measures of both mental disorders and mental distress to monitor and describe society’s psychological response to Covid-19. It will do this by presenting two studies. The first study will present findings from the repeat administration of clinical measures of CMD across three consecutive years (2019–2021), and then discuss the benefits and limitations of profiling prevalence estimates of mental disorders against the context of major societal disruptions (e.g., the Covid-19 pandemic). The second study will present findings from mood response profiles, as expressions of mental distress, collected across five time points during the Covid-19 pandemic, and discuss the benefits and limitations of mood response profiling in the context of evolving disruptor events.

This paper focussed on the concepts around mental health surveillance during global disruptor events. The data were presented to illustrate concepts, rather than provide detailed descriptions of the impact of Covid-19 on SA society. It used Covid-19 as an example of a global disaster of longer duration that affects large parts of society (as opposed to natural disasters, e.g., earthquakes or floods, which are more typically limited to specific communities or geographical areas). Further, the scales represent specific approaches, with many other measures available that may be equally useful. In the same vein, the samples were used for illustrative purposes, make no claim to represent any specific population or group in SA, and were included primarily because they were comparable, thus facilitating longitudinal reliability.

Study 1

Aim

Study 1 aimed to describe the prevalence of CMD, as determined by repeat administration of clinical measures, across three consecutive years (from 2019 to 2021).

Methods

Participants and Sampling

The primary data came from the archives of an organization offering comprehensive healthcare to workers and their families in specific employment sectors in SA. The data originated from routine mental health monitoring initiatives, offered as part of comprehensive general healthcare. Permission was obtained to access de-identified archived data, from the same sites across the most recent three consecutive years, namely 2019 to 2021. For current purposes, the samples were considered comparable: all three were drawn from the same larger community, from the same geographical sites, and their composition was comparable in terms of age, gender, occupational field stratification, and general health-status. As the data were de-identified before being accessed for the study, the extent of possible overlap (i.e., the same people being recruited to more than one sample) was not known.

Sample 1: Data (N = 1,827) came from routine monitoring as part of comprehensive general healthcare, and were collected over the period May to August 2019. The mean age was 33.9 (±8.3, range 20–60), and 27.3% were women.

Sample 2: Data (N = 1,854) came from routine monitoring as part of comprehensive general healthcare, and were collected over the period May to July 2020. The mean age was 35.9 (±9.4, range 20–60), and 25.9% were women.

Sample 3: Data (N = 1,895) came from routine monitoring as part of comprehensive general healthcare, and were collected over the period July to August 2021. The mean age was 37.4 (±9.6, range 20–60) and 30.6% were women.

Additional data came from a fourth sample that was recruited from a different health center in the same geographical area, and is included here to compare the prevalence of the different mental disorders across different samples and contexts. The 350 participants were recruited from individuals who attended a government sponsored Covid-19 vaccination initiative, and consented to complete the PHQ-9 and GAD-7 during their 15-minute wait after receiving their vaccination. This took place over the period 26 July to 7 August 2021. The self-select bias to this sample is recognized. The mean age was 44.5 (±7.9, range 20–60), and 51.7% were women.

Measures

To illustrate the use of mental disorder prevalence to describe the impact of chronic major global events on mental health, Study 1 reports on two examples of CMD, namely major depressive and generalized anxiety disorders. They were measured by two brief mental health screeners, which had local validation data available.

The Patient Health Questionnaire-9 (PHQ-9) is a 9-item scale that measures the severity of depression in primary care settings (Gilbody et al., 2007). Psychometric validity was previously reported for samples from sub-Saharan Africa, with a range of sensitivities and good specificity noted (Bhana et al., 2015; Cholera et al., 2014; Van Wijk et al., 2021). A score of ≥10 has been recommended as a positive screen for depression in low-and-middle-income contexts (Akena et al., 2012; Van Wijk et al., 2021) and was used here. The locally validated English version of the PHQ-9 was used, and in the present study α = .85.

The Generalized Anxiety Disorder questionnaire (GAD-7) is a 7-item scale that measures the severity of generalized anxiety in primary care settings (Löwe et al., 2008). Psychometric validity was previously reported for SA samples (Van Wijk et al., 2021). High sensitivity and good specificity were reported for also detecting panic disorder, social anxiety disorder, and PTSD (Kroenke et al., 2007). An optimal cut-point for any anxiety disorder was established at ≥10 (Kroenke et al., 2007; Löwe et al., 2008; Van Wijk et al., 2021) and was used here. The locally validated English version of the GAD-7 was used, and in the present study α = .90.

Data Analysis

For this study, prevalence estimates were calculated as follow. Diagnostic likelihood was determined through the self-report psychometric screeners, using total scores that met the established thresholds indicated earlier, and reported here as percentage of the sample. Further, population estimates, using 95% confidence intervals, were calculated and reported. The combined (i.e., total) burden of co-morbid mood and anxiety disorders was also calculated and reported as percentage of the sample.

Results and Discussion

The prevalence of major depressive and generalized anxiety disorders across the 3 years, as well as the total burden of co-morbid mood and anxiety disorders, are graphically represented in Figure 1, and the details presented in Table 1. Table 1 also include the details of the secondary sample recruited during a government sponsored vaccination initiative.

Figure 1.

Prevalence of selected common mental disorders across three consecutive years.

Table 1.

Prevalence of Depressive and General Anxiety Disorders Across 3 Years (2019–2021).

	South African population estimates^a	Primary sample 2019 (N = 1,827)		Primary sample 2020 (n = 1,854)		Primary sample 2021 (n = 1,895)		Secondary sample^b (n = 350)
	%	%	[95% CI]	%	[95% CI]	%	[95% CI]	%	[95% CI]
Depressive disorders	4.9	3.9	[3.1–4.9]	3.9	[3.1–4.9]	4.7	[3.8–5.8]	6.9	[4.4–10.0]
Anxiety disorders	8.1	3.1	[2.2–4.1]	3.1	[2.3–4.0]	4.2	[3.4–5.2]	6.3	[4.0–9.4]
Total burden of co-morbid depression and anxiety^c		40.8		53.6		55.9		43.8

Note. 95%CI = 95% confidence intervals.

From Herman et al. (2009).

Recruited during vaccination initiative.

The percentage of all individuals with identified depressive or anxiety disorders, who were suffering from both.

Given that the data across the three samples are considered comparable, in that they came from the same population, used the same measures, and were collected in the same manner, it provided for a number of interesting observations:

Firstly, there appeared little effect of the Covid-19 pandemic on the prevalence of these disorders by the end of July 2020 (compared to a year earlier), in spite of the harsh lockdown in SA during the preceding 6 to 8 weeks. A number of European studies reported similar stability in mental disorder prevalence in the first 6 months of the pandemic, compared to pre-pandemic levels (Knudsen et al, 2021; Van der Velden et al., 2020), while others (Daly et al., 2022; Ettman et al., 2020; Winkler et al., 2020), as well as earlier cited SA studies, reported often dramatic increases. These discrepancies may be partly due to study designs—those finding stable levels of mental disorders used interview-type data for diagnoses, while those finding increased levels typically used self-report scales, which are known to report higher estimations of disorders (Thombs et al., 2018). Further, the timing of studies is also important, as higher levels of psychiatric symptoms are often reported soon after disasters, but also resolve relatively soon thereafter (cf. Goldmann & Galea, 2014, for review). Aside from methodological issues, the study participants were still working and receiving salaries, and had no expectation that their employment might be terminated, which could be hypothesized to have acted as a protective factor.

Alternatively, the apparent lack of pandemic effect on mental disorders may also speak to the delayed expression of serious mental health effects following major community disasters. While less common, the deferred presentation of psychiatric disorders subsequent to disasters has been described previously (Goldmann & Galea, 2014; Holgersen et al., 2011; Hull et al., 2002; Morganstein & Ursano, 2020; Norris et al., 2002, 2009).

Secondly, by August 2021, increases of close to 1% for both depressive and general anxiety disorders could be observed. Presumably, the cumulative effects of the past 18 months had indeed become visible in psychopathological expressions of distress. This may be supported by the abovementioned delays that had been reported between environmental disasters and presentations of mental illness. Thus, by implication, what is reported in the present may reflect circumstances that already occurred some time ago. However, this may be a partially erroneous assumption. The etiology of CMD is located in complex intersections of biological, environmental, social, and psychological factors, and thus may occur for reasons not associated with temporal events. Further, only one DSM-5 disorder refers to environmental conditions as requirement for diagnosis (namely Post-traumatic Stress Disorder), supporting the principle that mental disorders are contingent on many factors, and not necessarily a response to current environmental events only.

Thirdly, the observed upward trend raises questions regarding future developments. Comparable upward trends have previously been described following natural disasters (e.g., earthquakes, floods), where increases in mental disorder prevalence plateaued up to 24 months after large-scale disruptor events (cf. Morganstein & Ursano, 2020, for review). This could imply that a further upward spread may continue to be observed as the pandemic evolves. Such findings—of increased occurrence of mental disorders—would be pertinent for real-world planning, particularly in the light of the reported disruption of mental health services in many countries (World Health Organization, 2020). The upward trend may suggest an increasing need for mental health interventions, and better awareness and resourcing in the primary healthcare sector, as mental disorders will most likely present itself in primary healthcare and general practice contexts.

Fourthly, high levels of co-morbid major depression and generalized anxiety were reported. While it risks further complicating the interpretation of mental health profiles, it does highlight the need for clinical interventions on a more specialized level (i.e., beyond community support).

Fifthly, Samples 3 and 4, collected during the same period, from the same geographical region, and using the same scales, provided very different prevalence estimates, which speaks to the influence of context. In spite of the limitations of Sample 4 (e.g., small size, etc.), its mental disorder profile stands as a reminder that context matters. Findings, such as prevalence profiles drawn from different subsections of society, cannot easily be extrapolated to represent the experience of any larger section of society.

It needs to be emphasized that, in this study, individuals who reported symptoms consistent with mental disorders could be identified by the service provider and offered appropriate intervention. In general community settings this type of research may present an ethical challenge, and would require careful consideration of appropriate follow-up mechanisms during planning for community application.

In conclusion, the general profiling of CMD in communities offers the benefit of directing the appropriate resourcing of service provision based on specific mental health needs. It also has a number of limitations, among other the real risks of over- or under-reporting, depending on sampling strategies and measures employed, both within and across communities. Pertinent to this paper, the usefulness of profiling CMD in communities for the purpose of monitoring society’s psychological adaptation to the Covid-19 pandemic or any major disruptor events, on its own, remains questionable. One major challenge is the question regarding the extent to which changes in prevalence profiles are causally related to large scale societal disruptor events. Longitudinal research across the span of Covid-19 may eventually provide a more complete answer.

Study 2

Aim

Study 2 aimed to describe mood response profiles, determined by repeat administration of a non-clinical mood-state measure, over the period October 2020 to November 2021.

Methods

Participants and Sampling

This study used anonymous survey data sourced from primary healthcare and family medicine clinics and practices in Cape Town, SA, during five discrete collection periods, from October 2020 to November 2021. The five samples comprised adult participants who accessed health services for either non-clinical purposes (e.g., vaccinations, family planning, occupational health certificates), oral health concerns, or for minor clinical complaints that could be managed within the clinic/practice without the need for further referral. Participants were invited to indicate certain socio-demographic details (age, gender, general health status) and then complete the mood response survey anonymously. Due to the anonymous nature of the survey, the extent of possible overlap (i.e., the same people being recruited to more than one sample) was not known.

Sample 1: Data (N = 403) was collected over the period 26 October to 20 November 2020. This occurred after the return to Alert Level 1, with many government-imposed restrictions eased. The mean age was 37.2 (±13.0, range 18–70), and 35.7% were women.

Sample 2: Data (N = 403) was collected over the period 15 December 2020 to 15 January 2021, during the height of the so-called “second wave,” with many restrictions in place, including restrictions on gatherings and outside activities. At that time the second wave was characterized by high rates of infections, high numbers of mortality, and many people being affected. The sample mean age was 39.4 (±12.5, range 18–70), and 43.4% were women.

Sample 3: Data (N = 402) was collected over the period 1 to 27 March 2021, after the return to Alert Level 1, although many restrictions were still in place, among other the restriction on gatherings (including limitations on the number of people attending funerals). The sample mean age was 37.7 (±13.1, range 18–70), and 35.6% were women.

Sample 4: Data (N = 362) was collected over the period 12 to 31 July 2021, during the third wave (Alert Level 4, with heightened restrictions again in place). The sample mean age was 40.9 (±10.0, range 18–70), and 45.1% were women.

Sample 5: Data (N = 402) was collected over the period 27 September to 5 November 2021, after the third wave has passed. The sample mean age was 33.6 (±18.7, range 18–70), and 38.7% were women.

All five samples were drawn from the same population, and generally comparable with regard to age and context of scale completion. The samples differed with regard to gender composition (χ² = 22.650, p < .05).

Measure

To illustrate the use of mood responses to monitor the impact of chronic major global disruptor events on society, Study 2 reports on the repeat administration of the Brunel Mood Scale (BRUMS).

The BRUMS measures transient affective mood states through a 24-item self-report inventory, with respondents rating a list of adjectives on a 5-point Likert-type scale (Terry, Lane & Fogarty, 2003). It has been validated across diverse cultures and situational contexts (Han et al., 2020; Sties et al., 2014), and has been used, among others, as a marker of mental health (Brandt et al., 2016; Terry & Galambos, 2004). Good psychometric properties have been reported for various versions of the scale, both internationally (Terry, Lane & Fogarty, 2003) and in SA (Terry, Potgieter & Fogarty, 2003). The validated English version of the BRUMS was used, and in the present study α = .89. The standard response timeframe was adapted to include reference to the time of Covid-19.

This administration of the BRUMS included six subscales, namely Tension, Depression, Anger, Vigour, Fatigue, and Confusion. The subscales are not diagnostic indicators, but refer to sub-clinical psychological mood states. For this study, mood was defined as “a set of feelings, ephemeral in nature, varying in intensity and duration, and usually involving more than one emotion” (Lane & Terry, 2000, p. 17).

Using a mood state scale provides for a number of advantages. Among others, it allows for the expression of non-clinical mental distress, and responses are further scaled to provide the opportunity to observe more nuanced changes in mood responses over time. The BRUMS was chosen because it had been used previously for a similar purpose (Bailon et al., 2020; Terry et al., 2020; Van Wijk & Majola, 2021) and is easy to administer, and because respondents experience the mood state adjectives as easy to comprehend. However, other popular mood or affect scales, for example, PANAS (Positive and Negative Affect Scale; Watson et al., 1988), POMS (Profile of Mood States; McNair et al., 1992), and MAACL-R (Multiple Affect Adjective Check List–Revised; Zuckerman & Lubin, 1985) might work equally well.

While electronical versions are available (e.g., Terry et al., 2013), to increase access, the BRUMS was administered in its paper-and-pencil version (with special arrangements in place to manage cross-contamination during the time of Covid-19).

Data Analysis

Subscale total scores were transformed into standardized scores. Cases were included if there were no missing values. For each sample, the collective mood state profile was described, using T-score means. Additionally, differences between individual mood for each sample (i.e., across time points) were calculated using ANOVA, with significance set at p < .05. Data were analyzed with SPSS 27.

Results and Discussion

Figure 2 presents the mood response profiles of the five samples, each representing a discrete time-point of data collection. The vertical axis of the graph is slightly distorted to illustrate the finer differences between individual mood states across time (significance is reported in Table 2). Means and standard deviations of mood state scales, as well as differences between response profiles across consecutive time periods, are presented in Table 2.

Figure 2.

Mean mood profiles across five time-points during the Covid-19 pandemic, expressed as T-scores.

Table 2.

T-Scale Scores for Mood Response Profiles Across Five Time-Points During the Covid-19 Pandemic.

Mood state	November 2020 (N = 403)		15 December 2020 to 15 January 2021 (N = 403)		March 2021 (N = 402)		July 2021 (N = 362)		October 2021 (N = 402)		F	p
Mood state	M ^a	SD	M	SD	M	SD	M	SD	M	SD	F	p
Tension	57.8	10.1	60.9	10.5	57.6	8.3	59.8	10.6	56.5	9.5	13.142	<.001
Depression	54.9	9.6	55.6	9.0	57.8	11.5	55.5	9.5	52.9	6.2	14.656	<.001
Anger	55.8	10.1	56.5	11.2	55.7	9.1	54.9	9.4	53.2	7.4	3.074	.057
Vigor	46.7	9.3	43.8	9.4	47.5	10.2	44.5	10.8	47.2	9.7	15.333	<.001
Fatigue	56.9	9.1	60.1	10.3	60.1	11.3	56.0	8.1	56.4	7.7	18.294	<.001
Confusion	55.0	9.8	55.1	9.7	55.8	10.0	55.7	10.5	54.1	8.1	1.971	.096

Numbers refer to T-scale means for each sample.

Given that the data across the 5 time-point samples were considered comparable—same broader population, same measure, collected in the same manner—it provided for a number of interesting observations:

Firstly, the measures at all time-points differed significantly from the normative mean, supporting the usefulness of a mental distress measure to monitor population responses to major events. Further, significant between-administration changes in Tension, Depression, Vigor, and Fatigue showed them to be the most responsive to different phases of the evolving pandemic.

Secondly, during the waves, Tension was elevated and Vigor suppressed, compared to the troughs in-between. These mood response differences between wave peaks and troughs were significant, and temporally associated with periods of increased infections/mortality (and increased government-imposed restrictions) during the waves, and reduced rates of infection (and easing of restrictions) during the between-wave periods, with the pattern appearing to repeat itself across subsequent waves/troughs. This pattern remained consistent over the available data period, suggesting that these two mood states may be particularly sensitive to wave-associated emotional responses.

Thirdly, during March 2021, endorsement of the Depression subscale peaked after the second wave. This could have been related to the dual effects of high mortality rates during the second wave, where many communities and families experienced multiple losses, combined with remaining restrictions on movement and gatherings, which affected the societal rituals to deal with such bereavement. This absence of societal mechanisms to manage bereavement, particularly restrictions on home gatherings and public funerals—both important mechanisms in bereavement trajectories in SA (Appel, 2011; Kgatle & Segalo, 2021)—deprived individuals and families of the social and practical support typically experienced after such loss. This appears to be an example of disenfranchised grief (Doka, 2002). Disenchanted grief refers to the experience of loss that might not be fully acknowledged or recognized by individuals or communities, in this case by the absence of customary rituals of communities to process such grief. Restrictions on gatherings lead to a minimizing of outward expressions of grief, further disenfranchising individual and community experiences. Such constraints to sharing grief and loss with others can be isolating and induce powerlessness, and have been associated with decreased mood and increased potential for psychopathology (Fisher et al., 2020; Fisher & Kirkman, 2020). It is worth noting that loss may also refer to loss of liberty, autonomy, and agency, as everyday activities were limited by the Covid-19 restrictions (Fisher & Kirkman, 2020).

Fourthly, Fatigue peaked during the second wave and its aftermath, but during the most recent administrations of the BRUMS decreased to its lowest level since the monitoring started. Two potential mechanisms, among others, may be involved, namely habituation and adaptation. Habituation refers to a “behavioural response decrement that results from repeated stimulation and that does not involve sensory adaptation” (Rankin et al., 2009, p. 136). After being exposed to high levels of Fatigue for an extended period, participants may be becoming habituated to it, resulting in a reset of the baseline against which current levels of Fatigue are reported on the BRUMS. Adaptation refers to the “process by which individuals or groups make changes (cognitive, behavioural, and affective) in response to new environmental conditions or demands in order to meet basic needs and maintain quality of life” (Psychology Research and Reference, 2021). Adaptation to changing (and challenging) realities requires effort and resources, which in turn require mental energy to initiate, maintain, and conclude. People tend toward stable levels of well-being and usually adapt back to baseline levels within a period of time after disruptive evens (Lucas et al., 2003). As adaptation to Covid-19 stabilized, a reduced demand on psychological energy may have led to a (relative) lowering of previously high levels of reported Fatigue.

Fifthly, all five samples exhibited an “inverse iceberg” profile, also seen elsewhere during Covid-19 (Terry et al., 2020). This profile has been associated with poorer general mental health (Brandt et al., 2016), as well as with increased risk for adverse long-term outcomes (cf. Terry et al., 2020, for summary), and thus may require continuous monitoring to timeously identify and manage maladaptive responses (i.e., in the form of mental disorders).

In conclusion, the general profiling of mood response profiles in communities, as an expression of mental distress, offers the benefit of observing the more immediate effects of the evolving pandemic, particularly through its sensitivity to peaks and troughs of infection rates and associated mortality and government restrictions. Such real-time description may enable a nimbler response to community experiences in order to facilitate, or at least advocate for, appropriate community level intervention. It also has a number of limitations, among others, that this approach reflects transient mood states, and thus may also reflect other community experiences, not necessarily directly related to the pandemic. A further limitation is that emotional adjectives are culturally interpreted, and responses may thus require careful interpretation against local cultural-linguistic contexts. Pertinent to this paper, the usefulness of profiling more transient mental distress in communities for the purpose of monitoring society’s psychological adaptation to a major disruptor event, was supported. However, the data from the current study was interpreted retrospectively, with the benefit of multiple data points to act as reference. A major challenge remains in the extent to which profiles can be interpreted meaningfully as stand-alone “snap-shot” descriptors of community experiences, where the trajectories of large-scale societal disruptor events are unknown. As with Study 1, longitudinal research across the full span of Covid-19 may eventually provide further answers.

General Discussion

An individual’s psychological adaptation to their environment occurs on a continuum, from minimal disruption, to milder or more severe mental distress, to dysfunction in the form of mental disorders. For psychologists the question is how best to monitor mental health—here defined as psychological adaptation to environment—across the mental health continuum and across time during global disruptor events?

This paper described two approaches, each with its opportunities and obstacles. The description of CMD prevalence in communities is indispensable to guide the resourcing and provisioning of specific clinical services to affected communities. However, selecting a standardized way to screen for mental disorders across communities in the SA multicultural and multilingual context remains a challenge. Further, tenuous causal links between environmental events and the presentation of mental disorders imply that the usefulness of predicting population-based CMD against contexts of major disruptor events remains unclear.

The description of indicators of mental distress in communities offers real-time monitoring of the evolving effects of environmental events, in turn allowing an appropriate response to the needs of communities that do not pathologize their experiences. Again, selecting a standardized way to measure emotional responses for meaningful interpretation across communities in the SA multicultural and multilingual context remains a challenge. Further, this can only be done where appropriate reference data exist against which to interpret responses. The practical usefulness of profiling mental distress during major disruptor events across different cultural-linguistic groups to inform specific community intervention or general support will need further investigation.

It is argued here that, to best monitor society’s psychological adaptation to global disruptor events, a multi-modal approach is required, including description of both specific mental disorders and more general mental distress. It is self-evident that, the greater the span of psychological responses along the continuum that can be tapped, using multiple modalities, the better psychological adaptation can be understood and tracked over time. Narrow focal points, whether dysfunctional responses (i.e., mental disorders) or proportionate mental distress, are by themselves not sufficient to describe or understand a community’s mental health or particular psychological adaptation to major disruptor events. Further, any form of mental health focus would need to be nestled in a larger syndemic understanding of major disruptor events. The Covid-19 pandemic has laid bare deep geographic, racial, and economic inequities on local and global stage, and a syndemic approach may provide a helpful framework for understanding chronic disease disparities (Rudd et al., p.33).

The two studies presented in this paper were for illustration only, to demonstrate different approaches to monitor Covid-19 and future disruptor events. The specific measuring tools were used because they were available, and to some extent validated for the local context, but it is recognized that other measuring or descriptive tools may prove to be equally or even better suited to the task. As mentioned, the challenge remains to select tools that are widely accepted by researchers and practitioners, and that can be fairly and appropriately applied to communities across cultural-linguistic backgrounds in SA. Future research would be helpful to test various tools to meaningfully assess mental health responses to environmental events.

The study further used the context of Covid-19 to illustrate the use of markers of mental health to monitor society’s psychological response to pandemics. This may serve as a useful framework to prepare for other societal-level disasters or events of long duration. It is however recognized that the current pandemic may still be present for a lengthy period of time, and that longitudinal data may eventually present a different understanding of its course and mental health effects across time. Ongoing research in this regard would be essential.

Conclusion

This paper highlighted several issues of clinical and practical relevance. Firstly, the Covid-19 pandemic was temporally associated with adverse mental health outcomes across the mental health continuum. Although causality cannot be directly attributed to any individual factor, mental health profiles were associated with both time since onset of Covid-19 and subsequent wave occurrence. Secondly, a multimodal approach is crucial to a comprehensive understanding of society-wide mental health impacts of the pandemic. Singular-focussed cross-sectional surveys are no longer sufficient to guide scientific understanding, nor to guide inclusive health sector responses. Thirdly, psychologists are challenged to carefully select tools that are not only fair to the applicable populations, but also appropriate, and more importantly, useful to inform comprehensive understanding of mental health responses. Fourthly, to fully understand responses to any measure, it would be critical to have appropriate reference points (e.g., local norms) against which to interpret individual or group responses. And fifthly, given the reported associations between mental stress and socio-economic factors (Shammi et al., 2020), a syndemic understanding may be helpful to inform public health responses.

In summary, this paper argued that the two approaches—of measuring mental disorders as well as mental distress—augment each other, and that a multi-modal approach is required to more comprehensively understand society’s psychological adaptation to major disruptor events. The two studies presented here illustrated the potential shortcomings of single focal points, and served as a reminder to continue to observe mental health more inclusively to ultimately understand—and importantly, appropriately respond to—the psychological needs of society.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Disclaimer

The views and opinions expressed in the article are those of the author and do not necessarily reflect an official policy or position of any affiliated agency of the author.

Ethics

The paper used archived data, with ethical oversight for the analysis provided by the Stellenbosch University Health Research Ethics Committee (Study 1: #N20-07-078; Study 2: #N20-11-070_Covid-19).

ORCID iD

Charles H. Van Wijk

Data Availability

Study 1: The data are not publicly available due to privacy and ethical considerations.

Study 2: The data are available from author upon reasonable request.

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Appraising Psychological Adaptation During Covid-19 in South Africa: A Descriptive Study Illustrating the Need for Multi-Model Monitoring of Mental Health

Abstract

Plain Language Summary

Keywords

Introduction

Orientation

Literature Review

Prevalence of Common Mental Disorders

Endorsement of Items From Word Lists

Non-Clinical Mood State Scaling

Rationale and Aim

Study 1

Aim

Methods

Participants and Sampling

Measures

Data Analysis

Results and Discussion

Study 2

Aim

Methods

Participants and Sampling

Measure

Data Analysis

Results and Discussion

General Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Disclaimer

Ethics

ORCID iD

Data Availability

References