Examining the mental health impact of the COVID-19 pandemic on people living with diabetes in the Western Cape,South Africa

Abstract

People living with diabetes (PLWD) faced heightened biomedical and psychosocial risk during the COVID-19 pandemic, yet little is known about their mental health in South Africa. This study examined whether COVID-19 threat appraisals (fear of COVID-19, perceived vulnerability to disease, and COVID-19-related worry) predicted depression, anxiety, posttraumatic stress, and alcohol use among PLWD, and described the prevalence of these outcomes in a post-peak phase of the pandemic. Participants (n = 266) were recruited from public hospitals in the Western Cape and completed sociodemographic and standardised measures. Grounded in Lazarus and Folkman’s Stress and Coping theory, hierarchical regression analysis showed that fear of COVID-19 was the most consistent predictor of psychological distress, explaining between 9 and 20% of the variance across outcomes. Study participants reported on average elevated levels of psychological distress, suggesting the need to integrate routine mental health assessment and referral into diabetes care.

Keywords

COVID-19 coping diabetes fear mental health south africa

Introduction

The global health ramifications of the COVID-19 pandemic have been profound. Among the understudied implications of the pandemic is mental health, especially among vulnerable groups such as persons living with diabetes (Beran et al., 2021; Moradian et al., 2021). Diabetes mellitus (DM) is a chronic metabolic condition characterised by persistent hyperglycaemia due to insufficient insulin production (type 1 diabetes) or insulin resistance (type 2 diabetes; World Health Organisation (WHO) (2024). As a prevalent and escalating global health concern, diabetes affects an estimated 10.5% of adults aged 20–79, with prevalence projected to rise to 12.2% by 2045 (Sun et al., 2022). The greatest relative increases are expected in middle-income countries such as South Africa (21%), compared to high- (12.2%) and low-income (11.9%) countries, underscoring the importance of understanding psychosocial determinants of health among PLWD in these settings.

While the COVID-19 pandemic adversely affected the health of the general population, PLWD encountered a compounded burden of risk, stemming from their heightened susceptibility to severe COVID-19 complications, elevated psychosocial stressors associated with the pandemic, and the persistent demands of managing a chronic condition under disrupted healthcare and social circumstances (Beran et al., 2021; Singhai et al., 2020). Evidence, for example, suggests that PLWD were two to three times more likely to require intensive care when infected with SARS-CoV-2 and exhibited significantly higher mortality rates compared to people without diabetes (Roncon et al., 2020; Saha et al., 2021). This elevated risk necessitated strict adherence to preventive behaviours, including mask-wearing, hand hygiene, and physical distancing (Weismüller et al., 2021), thereby intensifying psychological demands of daily life.

Various reviews have reported elevated prevalence rates of depression and anxiety among PLWD during the COVID-19 pandemic. For example, García-Lara et al. (2022a) found anxiety prevalence rates of 23% in persons with type 1 diabetes and 20% in those with type 2 diabetes (n = 13,932), across 37 studies. Similarly, García-Lara et al. (2022b) found depression rates ranging from 17% to 33% among PLWD (n = 34,554) across 33 studies, depending on the screening tool used (PHQ-9 or PHQ-8). Although specific data on PTSD prevalence rates among PLWD during the COVID-19 pandemic are limited, Carducci et al. (2021) found that 29.1% of parents of children with type 1 diabetes reported clinically significant posttraumatic stress symptoms. Findings on alcohol use during the pandemic have been inconsistent, with some studies reporting increased consumption among PLWD and others reporting reductions, likely reflecting contextual differences in alcohol access and cultural norms (e.g. Dartora et al., 2023; Yan et al., 2020).

Emerging literature suggests that PLWD reported elevated COVID-19-related threat appraisals during the pandemic, including heightened fear, worry, and perceived vulnerability to infection (Abdelghani et al., 2022; Musche et al., 2021). For example, a German study by Musche et al. (2021) found that patients with diabetes (n = 253) reported significantly higher COVID-19-related fear and risk perception compared to controls sampled from a national study (n = 253; Bäuerle et al., 2020). Similarly, research by Sacre et al. (2021) indicated that a substantial proportion of PLWD in Australia (n = 470) experienced elevated levels of worry about the pandemic, which correlated with changes in behaviour and increased psychological distress. Furthermore, in a study conducted among 2166 participants in the Arab Gulf region (568 with diabetes, 1598 without diabetes), Al-Sofiani et al. (2021) found that among PLWD, fear of COVID-19 infection, running out of diabetes medications, requiring hospitalisation for hypoglycaemia, hyperglycaemia, or diabetic ketoacidosis, and lack of telecommunication with healthcare professionals were associated with significantly higher odds of presenting with symptoms of depression and anxiety. Together, these findings suggest that COVID-19-related primary threat appraisals, such as fear of infection, perceived vulnerability, and pandemic-related worry, may be central mechanisms through which biomedical risk translates into psychological distress among PLWD (see also, Alessi et al., 2020; Joensen et al., 2020; Pettinicchio et al., 2021).

Importantly, PLWD were already at increased risk for mental health difficulties prior to the pandemic. Meta-analytic evidence demonstrates elevated rates of depression and anxiety among PLWD relative to those without the condition (Buchberger et al., 2016; Semenkovich et al., 2015). Poor mental health in this population is particularly concerning, as it is associated with suboptimal glycaemic control, decreased adherence to treatment regimens, increased diabetes-related complications, and premature mortality (Gonzalez et al., 2008; Snoek et al., 2015).

In LMICs such as South Africa, the psychological burden of living with chronic conditions like diabetes is likely compounded by structural inequalities, including widespread poverty, limited access to healthcare, and persistent shortages in mental health services (Malakoane et al., 2020; Sorsdahl et al., 2023). Nevertheless, there remains a paucity of empirical research directly examining the mental health outcomes and their predictors among PLWD in South Africa amidst the COVID-19 pandemic. This gap is especially concerning given the estimated diabetes prevalence rate of 7.2% among South African adults (International Diabetes Federation [IDF], 2026), with rates as high as 14.3% reported in the public health sector, which serves approximately 80% of the population (Sahadew et al., 2016). Concerningly, diabetes is the leading natural cause of death among South African women and the second highest cause of death overall (Stats, 2017).

Existing studies in South Africa have largely relied on pre-pandemic data (e.g. Qubekile et al., 2022; Sifunda et al., 2023), focussed on predicting future diabetes trends (Sibiya et al., 2023), and broadly examined chronic disease management during the pandemic (e.g. Mboweni and Risenga, 2022). For example, Qubekile et al. (2022) found that in 2018, 36% of PLWD (n = 101) in KwaZulu-Natal reported elevated depressive symptoms, while Sifunda et al. (2023), using 2012 national survey data, identified strong associations between diabetes and psychological distress (n = 4598). In a study conducted on data collected between 2015 and 2020, Boake and Mash (2022) further found that COVID-19-related hospital admissions were the third most common cause of admission among PLWD (n = 116,726), with a mental health condition co-morbidity rate of 16.2%.

The present study is grounded in Lazarus and Folkman’s (1984) transactional Stress and Coping theory, which conceptualises psychological distress as arising from individuals’ appraisals of environmental demands and their perceived capacity to cope with those demands. Central to this model is the distinction between primary appraisals, wherein events are evaluated as threatening or challenging, and secondary appraisals, which involve assessments of available coping resources. Although the model distinguishes primary appraisals (evaluations of events as threatening or challenging) from secondary appraisals (perceived coping resources), the present study focuses specifically on primary COVID-19-related threat appraisals.

Within a stress and coping framework, sociodemographic factors may further shape access to coping resources and influence psychological vulnerability. Pandemic research has consistently shown that younger adults (Myers et al., 2022), and women (Lazaridou et al., 2022) report higher levels of anxiety and depressive symptoms, while socioeconomic disadvantage, including unemployment and lower educational attainment (Chlapecka et al., 2022; Weerakoon et al., 2021), is associated with poorer mental health outcomes. Additionally, relational status may function as a stress buffer, with partnered individuals generally demonstrating greater psychological resilience than those who are unpartnered or socially isolated (Umberson and Montez, 2010). However, these dynamics remain underexplored among PLWD in South Africa.

Accordingly, this study had three aims: (1) to examine whether COVID-19 threat appraisals (fear of COVID-19, perceived vulnerability to disease, and COVID-19-related worry) predicted symptoms of depression, anxiety, posttraumatic stress, and alcohol use among PLWD in South Africa; (2) to describe the prevalence of these mental health outcomes within the sample in a post-peak phase of the pandemic; and (3) to examine whether sociodemographic factors (age, gender, marital status, education, and employment status) independently contributed to mental health outcomes when accounting for threat appraisals.

Methods

Participants and procedure

In this cross-sectional investigation, participants were a convenience sample of 266 PLWD from eight public hospitals and clinics situated in the Western Cape province of South Africa. The data were collected between April 2022 and May 2023. We recruited participants by means of notices displayed in the hospitals inviting PLWD to contact the research team via an allocated WhatsApp number. On contacting the team, participants were sent a link to an online consent form and survey through WhatsApp. Participation in the study was voluntary and anonymous. Participants were eligible for inclusion if they were adults aged 18 years or older, had a confirmed diagnosis of either Type 1 or Type 2 diabetes, and were able to complete the survey in English. Individuals were excluded if they did not have a diagnosis of diabetes, were younger than 18 years of age, were unable to complete the survey in English, undergoing psychotherapy, or those admitted to psychiatric hospitals. Participant characteristics are fully delineated in Table S1 in the Supplemental Material.

Instruments

Participants completed a brief demographic survey, as well as various standardised measures, as fully delineated below.

Demographic data were collected on age, gender, marital, employment and education status.

Depression was measured using the 20-item Centre for Epidemiological Studies Depression Scale-Revised (CESD-R; Eaton et al., 2004). The CESD-R is a 20-item measure assessing depressive symptoms aligned with DSM diagnostic criteria (Eaton et al., 2004). Items capture symptom frequency over the past week and are rated on a 5-point scale from 0 (not at all) to 4 (nearly every day for 2 weeks), resulting in total scores between 0 and 80. For example, item one states, “My appetite was poor.” Higher scores indicate greater depressive symptom severity, with a commonly used cut-off of ⩾16 suggesting clinically significant symptoms. The CESD-R has demonstrated excellent internal consistency in its original validation (α = 0.93) and strong convergent validity with other depression measures (Eaton et al., 2004; Van Dam and Earleywine, 2011). The CESD-R has demonstrated reliable results when used in South African studies (e.g. Coetzee et al., 2025; Haine et al., 2025).

Anxiety was measured using the Beck Anxiety Inventory (BAI: Beck et al., 1988). The BAI is a 21-item self-report measure assessing cognitive and somatic symptoms of anxiety (Beck et al., 1988). Items are rated on a 4-point scale from 0 (not at all) to 3 (severely), with total scores ranging from 0 to 63; higher scores indicate more severe anxiety symptoms. For example, item one, asks respondents to rate the following statement “Numbness or tingling.” Standard cut-offs classify anxiety severity as minimal (0–7), mild (8–15), moderate (16–25), or severe (26–63). When used in the context of South Africa, the BAI has shown satisfactory internal consistency reliability, for example, among a sample of nurses (Haine et al., 2025) and individuals living with HIV (Kagee et al., 2015).

Posttraumatic stress was assessed using the 20-item Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Blevins et al., 2015). The PCL-5 consists of 20 items measuring symptoms across the four DSM-5 PTSD symptom clusters: intrusion, avoidance, negative alterations in cognition and mood, and hyperarousal (Blevins et al., 2015). Items are rated on a 5-point scale from 0 (not at all) to 4 (extremely), yielding total scores between 0 and 80, with higher scores indicating greater symptom severity. For example, item two asks respondents to rate the following statement “Repeated, disturbing dreams of the stressful experience?” Prior research suggests that total scores between 31 and 33 are indicative of probable PTSD (Bovin et al., 2016). The PCL-5 demonstrates strong internal consistency, test-retest reliability, and construct validity (Forkus et al., 2023). In South Africa, it has shown excellent reliability, with Haine et al. (2025) reporting a Cronbach’s alpha of 0.96 among nurses. Kagee et al. (2022) identified a cutoff score of 32 as yielding 88% sensitivity and 88% specificity in detecting PTSD among people living with HIV.

Alcohol use was measured with the 10-item Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993). The AUDIT is a 10-item screening tool developed to identify hazardous drinking, harmful alcohol use, and possible dependence (Saunders et al., 1993). While items assess alcohol consumption (Items 1–3), dependence symptoms (Items 4–6), and alcohol-related problems (Items 7–10), the scale is typically scored using a total composite score. Items are rated on a 0–4 scale according to WHO guidelines, yielding total scores from 0 to 40. Item one, for example, asks “How often do you have a drink containing alcohol?.” Standard cut-offs classify scores as low risk (0–7), hazardous (8–15), harmful (16–19), or indicative of possible dependence (⩾20). The original validation reported good internal consistency (α = 0.80) and high test-retest reliability (0.83–0.95), with strong internal consistency (α = 0.82; Chen et al., 2024) and acceptable reliability in South African studies (Morojele et al., 2015; Saal et al., 2020).

Fear of COVID-19 was measured using the seven-item Fear of COVID-19 Scale (FCV-19S; Ahorsu et al., 2022). The FCV-19S is a seven-item instrument designed to assess emotional and physiological reactions associated with fear of COVID-19. Responses are recorded on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5), yielding total scores between 7 and 35, with higher scores reflecting greater fear. For example, item one states, “I am most afraid of Covid-19.” Initial validation studies demonstrated a unidimensional factor structure and good internal reliability (α = 0.82). The FCV-19S has shown strong internal consistency internationally, such as among Spanish healthcare workers (α = 0.89; Peñacoba-Puente et al., 2024) and Turkish students (α = 0.90; Teleş, 2024). In the context of South Africa, acceptable reliability has been demonstrated among teachers (Padmanabhanunni et al., 2022), students (Kagee et al., 2024), and nurses (Coetzee et al., 2025).

Perceived vulnerability to disease was measured using the 15-item Perceived Vulnerability to Disease Questionnaire (PVD-Q; Duncan et al., 2009). The PVD-Q comprises 15 items that assess beliefs regarding susceptibility to infectious illness (Duncan et al., 2009). The measure includes two subscales: Perceived Infectability (seven items) and Germ Aversion (8 items). Items are rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree), producing total scores ranging from 15 to 105, with higher scores indicating greater perceived vulnerability. For example, item one states, “It really bothers me when people sneeze without covering their mouths.” In the original validation, acceptable reliability was reported for both subscales (α = 0.87 and 0.74; Duncan et al., 2009). Similarly, the scale has shown acceptable reliability among South African teachers (Padmanabhanunni et al., 2022), students (Kagee et al., 2024), and nurses (Haine et al., 2025). Notably, the PVD-Q assesses general susceptibility to infectious illness rather than diabetes-specific threat. Given the timing of data collection shortly after the COVID-19 peak, participants likely responded with COVID-19 in mind, although the measure reflects broader trait-like vulnerability appraisals. In the present sample, item 14 demonstrated a low item-total correlation and was removed prior to computing total scores; thus, analyses reflect the sum of the remaining 14 items.

Covid-19-related Worries were assessed using a four-item Visual Analogue Scale (VAS), which asked participants to indicate their level of worry about infection with COVID-19, worry and concern about infecting others, worry about hospitalisation, and fear of death. For example, item one asked “On the scale below, please indicate your level of worry about infection with Covid-19.”

Data analysis

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 28 (IBM Corp., Armonk, NY, USA). With a maximum of eight predictors entered in the final regression models, the sample size of 266 exceeded commonly recommended guidelines of at least 10–15 participants per predictor (Tabachnick and Fidell, 2019), supporting adequate statistical power. Descriptive statistics were used to demonstrate participants’ sociodemographic characteristics and psychological wellbeing scores. Pearson’s correlation coefficients were calculated to explore associations between continuous variables. As certain variables were not normally distributed, non-parametric tests including the Mann-Whitney U test and Kruskal-Wallis H test were employed to assess group differences across gender, marital status, educational attainment, and employment status. Where applicable, post-hoc Dunn-Bonferroni tests were applied for pairwise comparisons, with 95% confidence intervals reported.

Consistent with the study’s focus on primary threat appraisals within Lazarus and Folkman’s transactional model, hierarchical multiple regressions were used to examine COVID-19 threat appraisals (fear of COVID-19, perceived vulnerability to disease, and COVID-19-related worries) as predictors of psychological outcomes (symptoms of depression, anxiety, posttraumatic stress, and alcohol use). COVID-19-related threat appraisals were entered sequentially to examine their incremental explanatory value. Fear of COVID-19 (FOC) was entered in Block 1, perceived vulnerability (PVD-Q) in Block 2, and COVID-19-related worries (VAS) in Block 3. Sociodemographic characteristics (age, gender, marital status, education, and employment status) were entered in Block 4 to account for background contextual influences. Although some variables demonstrated non-normal distributions, inspection of residual plots and tests of regression assumptions indicated that linearity, homoscedasticity, and normality of residuals were adequately met, supporting the use of hierarchical multiple regression. Significance was set at p < 0.05. Effect sizes were evaluated using standardised β coefficients and Cohen’s f². Model-level variance explained was assessed using R² and ΔR². Given the theory-driven nature of the analyses and the conceptual relatedness of outcomes, no formal correction for multiple comparisons was applied. Instead, emphasis was placed on effect sizes and confidence intervals to aid interpretation. Hierarchical regression was used to structure the analyses, and effect sizes (β and f²) are reported to assist interpretation. For each hierarchical regression model, 95% confidence intervals were computed for unstandardised regression coefficients (B).

Ethical considerations

Ethical approval was obtained from Stellenbosch University’s Health Research Ethics Committee (Reference number: N21/05/012-COVID-19). Prior to their participation, respondents were required to provide informed consent on the landing page of the survey. Participants were assured of the voluntary nature of their involvement in the study as well as of their anonymity. Participants were provided with the contact details of free counselling services, in the event of experiencing distress as a consequence of the completing the survey.

Results

Sample characteristics

The final sample consisted of 266 adult patients living with diabetes (see Table S1), with a mean age of 33.70 years (SD = 9.99). Most participants identified as women (73.3%), were married or partnered (50%), employed full-time (69.2%), and nearly half had completed post-school education such as university or college (45.9%).

Clinical symptoms

Clinically significant depressive symptoms (CES-D ⩾ 16) were reported by 61% of participants, with 46% of these participants showing severe depressive symptoms (CES-D ⩾ 27). Severe anxiety symptoms (BAI ⩾ 26) were reported by 39%, and 64% scored at or above the recommended PCL-5 cut-off (⩾31), suggestive of probable PTSD. Scores suggesting alcohol dependence (AUDIT ⩾ 20) were reported by 24% of participants.

Bivariate correlations

Table 1 presents the means, standard deviations, Cronbach’s alpha coefficients, and intercorrelations among study variables. Internal consistency for most scales was high (αs = 0.89–0.97), and acceptable for the PVD-Q (α = 0.68), which falls within the range considered adequate for exploratory research contexts (Taber, 2018).

Table 1.

Intercorrelations between variables, descriptive statistics, and reliabilities of study variables (n = 266).

Variables	1	2	3	4	5	6	7	8
1. Age	—
2.Fear of Covid19	−0.22**	—
3.Anxiety	−0.25**	0.42**	—
4.PVD-Q	0.05	0.23**	−0.15*	—
5.PTSD	−0.17**	0.40**	0.67**	0.02	—
6.Alcohol Use	−0.24**	0.45**	0.61**	−0.12*	0.47**	—
7. COVID-19 Worries	−0.01	0.40**	−0.09	0.53**	0.06	−0.13*	—
8.Depression	−0.19**	0.30**	0.79**	−0.13*	0.58**	0.55**	−0.17**	—
Mean	33.70	23.21	20.50	59.99	35.49	11.24	28.43	25.50
SD	9.99	7.30	17.50	11.08	20.42	10.62	10.79	19.69
Alpha	—	0.90	0.97	0.68	0.97	0.94	0.91	0.96

Note. PVD-Q = Perceived Vulnerability to Disease Questionnaire.

p < 0.01. *p < 0.05.

As expected, depression was strongly and positively correlated with anxiety (r = 0.79, p < 0.01), posttraumatic stress symptoms (r = 0.58, p < 0.01), and alcohol use (r = 0.55, p < 0.01). COVID-19-related worries were significantly associated with increased fear of COVID-19 (r = 0.40, p < 0.01) and PVD-Q scores (r = 0.53, p < 0.01), but inversely related to depression (r = −0.17, p < 0.01) and alcohol use (r = −0.14, p < 0.05).

Demographic factors

Tables S2–S5 in the Supplemental Material present the non-parametric analyses for differences in psychological outcomes by gender, marital status, employment status, and education level. Where Kruskal-Wallis tests were significant, Dunn-Bonferroni post hoc pairwise comparisons identified which groups differed.

Mann-Whitney U tests showed that women reported significantly higher anxiety than men (U = 5677.50, p = 0.037) and were significantly younger than men (U = 5327.50, p = 0.009).

Kruskal-Wallis tests revealed significant group differences for marital status: age (H(3) = 57.44, p < 0.001) and fear of COVID-19 (H(3) = 9.58, p = 0.023) differed by relationship status. Post hoc tests indicated that separated individuals reported the highest fear of COVID-19, and those who had never married were significantly younger than other groups.

Significant group differences were also found for employment status: age (H(4) = 38.34, p < 0.001), alcohol use (H(4) = 16.84, p = 0.002), and depression (H(4) = 11.25, p = 0.024) varied by employment. Post hoc comparisons showed that students were significantly younger than all other groups. Full-time employed participants reported the highest alcohol use levels, while unemployed participants reported the highest levels of depression.

Education level was significantly associated with depressive symptoms (H(3) = 13.76, p = 0.003), with those who had not completed high school reporting the highest depression scores.

Regression diagnostics

The regression diagnostics indicated no concerns. No influential cases were detected (Cook’s distance < 1.00), Mahalanobis distances were within acceptable limits and did not exceed the critical χ² threshold for p < 0.001, and multicollinearity was low (VIFs < 2; tolerance > 0.60). As such, hierarchical multiple regression analyses were conducted to examine the extent to which COVID-19 threat appraisals (FOC, PVD-Q and COVID-related worries) predicted symptoms of depression, anxiety, posttraumatic stress, and alcohol use. Tables 2 and S6–S9 display these results, with notable results narrated below.

Table 2.

Summary of hierarchical regression models predicting symptoms of depression, anxiety, posttraumatic stress, and alcohol use.

Outcome	Model	Predictors	R	R ²	Adj. R²	ΔR²	F	ΔF	p for ΔF
Depressive symptoms	1	FOC	0.29	0.09	0.08	0.09	24.51	24.51	<0.001**
	2	FOC, PVD-Q	0.37	0.14	0.13	0.05	20.71	15.45	<0.001**
	3	FOC, PVD-Q, VAS	0.44	0.19	0.18	0.05	20.40	17.29	<0.001**
	4	FOC, PVD-Q, VAS, Sociodemographic	0.47	0.23	0.20	0.04	9.27	2.26	0.047*
Anxiety symptoms	1	FOC	0.41	0.17	0.17	0.17	53.58	53.58	<0.001**
	2	FOC, PVD-Q	0.49	0.24	0.24	0.07	41.81	25.11	<0.001**
	3	FOC, PVD-Q, VAS	0.52	0.27	0.26	0.03	32.11	9.87	0.002*
	4	FOC, PVD-Q, VAS, Sociodemographic	0.54	0.30	0.27	0.03	13.41	1.87	0.101
Posttraumatic stress symptoms	1	FOC	0.40	0.16	0.15	0.16	49.00	49.00	<0.001**
	2	FOC, PVD-Q	0.41	0.17	0.16	0.01	25.77	2.29	0.132
	3	FOC, PVD-Q, VAS	0.41	0.17	0.16	0.01	17.92	2.03	0.155
	4	FOC, PVD-Q, VAS, Sociodemographic	0.43	0.19	0.16	0.01	7.25	0.87	0.500
Alcohol use	1	FOC	0.44	0.20	0.19	0.20	63.78	63.78	<0.001**
	2	FOC, PVD-Q	0.50	0.25	0.25	0.06	44.03	19.73	<0.001**
	3	FOC, PVD-Q, VAS	0.56	0.32	0.31	0.06	40.09	24.33	<0.001**
	4	FOC, PVD-Q, VAS, Sociodemographic	0.61	0.38	0.36	0.06	19.14	4.81	<0.001**

Note. Sociodemographic variables include age, gender, marital status, education, and employment status.

R²: coefficient of determination; Adj. R²: adjusted coefficient of determination; ΔR²: change in R²; ΔF: change in F statistic; FOC: fear of COVID-19; PVD-Q: Perceived Vulnerability to Disease Questionnaire; VAS: COVID-19-related worries.

p < 0.01, *p < 0.05.

Predictors of depressive symptoms

Fear of COVID-19 significantly predicted depressive symptoms in Block 1 (R² = 0.09, p < 0.001). The addition of PVD-Q and COVID-19-related worries significantly improved model fit, with threat appraisals together explaining 19% of the variance in depression. Sociodemographic variables contributed a small but significant additional 4% of variance in the final step (final R² = 0.23, p < 0.001; n = 264). In the fully adjusted model, greater fear of COVID-19 (β = 0.39, p < 0.001) and lower COVID-19-related worries (β = −0.27, p < 0.001) predicted higher depressive symptoms, while higher education was associated with lower depression (β = −0.15, p = 0.012).

Predictors of anxiety symptoms

Fear of COVID-19 significantly predicted anxiety in Block 1 (R² = 0.17, p < 0.001). Adding PVD-Q and COVID-19-related worries improved model fit, with threat appraisals explaining 27% of the variance in anxiety. Sociodemographic variables did not significantly increase explained variance in the final step (final R² = 0.30, p < 0.001; n = 264). In the fully adjusted model, fear of COVID-19 remained a strong positive predictor (β = 0.48, p < 0.001), whereas greater perceived vulnerability (PVD-Q; β = −0.16, p = 0.011) and higher COVID-19-related worries (β = −0.20, p = 0.004) were associated with lower anxiety. Younger age also predicted higher anxiety (β = −0.17, p = 0.005).

Predictors of posttraumatic stress symptoms

Fear of COVID-19 significantly predicted posttraumatic stress symptoms in Block 1 (R² = 0.16, p < 0.001). Neither PVD-Q, COVID-19-related worries, nor sociodemographic variables significantly improved model fit in subsequent steps (final R² = 0.19, p < 0.001; n = 264). In the fully adjusted model, fear of COVID-19 was the only significant predictor of posttraumatic stress symptoms (β = 0.41, p < 0.001).

Predictors of alcohol use

Fear of COVID-19 significantly predicted alcohol use in Block 1 (R² = 0.20, p < 0.001). The addition of PVD-Q and COVID-19-related worries significantly improved model fit, with threat appraisals explaining 32% of the variance in alcohol use. Sociodemographic variables contributed a further significant 6% of variance in the final step (final R² = 0.38, p < 0.001; n = 264). In the fully adjusted model, greater fear of COVID-19 (β = 0.51, p < 0.001) and lower COVID-19-related worries (β = −0.27, p < 0.001) predicted higher alcohol use. Younger age (β = −0.15, p = 0.007) and lower employment status (β = −0.23, p < 0.001) were also associated with greater alcohol use.

Discussion

This study was conducted during the post-peak phase of the COVID-19 pandemic in South Africa (April 2022–May 2023), a period during which formal lockdown restrictions had largely been lifted and health services were transitioning towards routine functioning. Despite the easing of acute public health measures, PLWD in this study reported substantial psychological distress. Sixty-one percent reported clinically significant depressive symptoms, 39% severe anxiety, 64% reported posttraumatic stress symptoms suggestive of probable PTSD, and 24% reported alcohol use indicative of possible dependence. These rates suggest a sustained mental health burden even as the immediate crisis receded.

Depressive symptoms in this study exceeded both estimates for the general South African population during the pandemic (De Man et al., 2022) and international meta-analytic prevalence rates among PLWD (García-Lara et al., 2022b). Although anxiety levels were lower than those reported in the general South African population during the height of the pandemic (De Man et al., 2022), they remained substantially elevated relative to international cohorts of PLWD (García-Lara et al., 2022a), indicating a considerable and persistent mental health burden among PLWD in South Africa.

Global data on the prevalence of PTSD among PLWD during the COVID-19 pandemic remain limited. However, broader pandemic-related research indicates pooled prevalence estimates of posttraumatic stress symptoms of approximately 22.6% following the pandemic peak (Yuan et al., 2021). The substantially higher rates observed in the present study may reflect cumulative trauma exposure within a resource-constrained South African context. Notably, the majority of participants who identified an index trauma reported events unrelated to COVID-19 (e.g. interpersonal violence or sexual assault), suggesting that pre-existing or concurrent trauma, compounded by pandemic-related stressors, may have contributed to the elevated symptomatology observed (Malakoane et al., 2020; Mboweni and Risenga, 2022).

Alcohol use suggestive of dependence was also elevated relative to estimates from the general South African population during the pandemic (Mapanga et al., 2023). Nevertheless, these findings align with Yan et al. (2020), who reported increased alcohol consumption among PLWD in China during COVID-19. Excessive alcohol use is particularly concerning in the context of diabetes, as it may compromise glycaemic control, increase complications, and undermine treatment adherence (Ahmed et al., 2008). Alcohol use may therefore have functioned as a maladaptive coping response to managing a chronic illness amid disrupted healthcare systems and limited mental health resources (Beran et al., 2021; Singhai et al., 2020; Sorsdahl et al., 2023).

The COVID-19 threat appraisals, namely fear of COVID-19, perceived vulnerability to disease, and COVID-19-related worries emerged as central predictors of mental health outcomes, although their effects differed across symptom domains. Most notably, fear of COVID-19 was the most consistent and robust predictor across all models, significantly predicting symptoms of depression, anxiety, posttraumatic stress, and alcohol use, even after adjusting for sociodemographic factors. This finding supports prior research suggesting that fear-related responses during health crises can substantially increase the risk of psychological morbidity (Alessi et al., 2020; Joensen et al., 2020; Pettinicchio et al., 2021). Within Lazarus and Folkman’s (1984) Stress and Coping framework, fear reflects a primary appraisal of threat. Given evidence that PLWD faced substantially elevated risks of severe COVID-19 outcomes (Roncon et al., 2020; Saha et al., 2021), heightened fear may have taxed or exceeded perceived coping resources, thereby amplifying distress.

COVID-19-related worry demonstrated a more nuanced pattern. In the fully adjusted models, worry significantly predicted depression, anxiety, and alcohol use, but not posttraumatic stress symptoms. Interestingly, greater worry was associated with lower depressive symptoms and alcohol use. One possible explanation is that moderate, cognitively engaged worry may reflect an active monitoring orientation that promotes precautionary behaviour and a sense of control, consistent with research distinguishing functional from dysfunctional worry about COVID-19 (Solymosi et al., 2021).Within a coping framework, such worry may align more closely with problem-focussed processes, distinguishing it from the more affectively overwhelming nature of fear. This distinction may be particularly relevant for PLWD, who are accustomed to ongoing health-related vigilance.

Perceived vulnerability to disease was a less consistent predictor, remaining significant only for anxiety symptoms in the fully adjusted model. This pattern suggests that appraisals of susceptibility to infection may be particularly linked to anxious symptomatology, which is inherently future-oriented and threat-sensitive. Notably, the PVD-Q captures broader, trait-like perceptions of infectious vulnerability rather than diabetes-specific threat, which may explain its more domain-specific associations.

Compared to the strong and consistent effects of fear, sociodemographic variables were generally weaker predictors, though important exceptions emerged. Younger age predicted higher anxiety and alcohol use, aligning with evidence that younger adults with diabetes experienced heightened psychological vulnerability during the pandemic due to disruptions in education, employment, and social connexion (Myers et al., 2022). Lower educational attainment predicted higher depressive symptoms, consistent with research demonstrating that limited educational resources may constrain health literacy, economic stability, and adaptive coping strategies (Chlapecka et al., 2022). Employment status was significantly associated with alcohol use, with unemployment linked to greater consumption, reflecting the established relationship between financial strain, job insecurity, and substance use during crises (Weerakoon et al., 2021). Non-parametric comparisons further contextualised these findings. Younger participants were more likely to be single, women, students, or unemployed, suggesting that intersecting social vulnerabilities may contribute to elevated distress. Women reported higher anxiety than men, consistent with evidence indicating greater emotional reactivity and internalising responses among women during health threats (Lazaridou et al., 2022). Separated individuals reported the highest levels of fear of COVID-19, suggesting that relational stability may buffer threat appraisals (Umberson and Montez, 2010). Taken together, these findings reinforce the role of social determinants, including age, gender, education, employment, and relational stability, in shaping mental health among individuals managing chronic illness.

Research implications

These findings yield several important implications. First, the substantial psychological burden observed among PLWD in South Africa indicates that mental health screening should be systematically incorporated into diabetes care, particularly in resource-constrained settings. Second, interventions should explicitly target maladaptive fear-based threat appraisals. Cognitive-behavioural approaches aimed at recalibrating catastrophic threat interpretations and strengthening coping efficacy may reduce cross-cutting mental health distress. Finally, public health communication strategies should balance risk awareness with empowerment, ensuring that fear does not override adaptive coping and resilience.

Limitations and future directions

This study is not without limitations. First, the cross-sectional design precludes causal inference, and reliance on self-report measures introduces the potential for bias. Second, while the sample size was sufficient for the analytic models employed, the convenience sampling strategy limits the extent to which prevalence estimates can be generalised to the broader population of PLWD in South Africa. Similarly, the relatively young sample limits applicability to older PLWD, who face different risk profiles. Third, although internal consistency for the modified PVD-Q was acceptable, it was marginal, and findings related to this construct should therefore be interpreted with caution. Finally, comorbidities were not examined, though these may interact with diabetes to influence mental health outcomes. Future research should employ longitudinal designs to trace trajectories of distress, investigate intervention efficacy, and differentiate between subgroups within populations of PLWD.

Conclusion

This study provides insights into the mental health burden among people living with diabetes in South Africa in the post-peak phase of the COVID-19 pandemic. Grounded in Lazarus and Folkman’s Stress and Coping framework, the findings demonstrate that primary threat appraisals, particularly fear of COVID-19, emerged as central drivers of psychological distress, whereas other cognitive threat appraisals demonstrated more domain-specific or protective effects. Even as the acute public health crisis receded, threat appraisals remained potent determinants of psychological wellbeing.

Supplemental Material

sj-docx-1-hpq-10.1177_13591053261450005 – Supplemental material for Examining the mental health impact of the COVID-19 pandemic on people living with diabetes in the Western Cape, South Africa

Supplemental material, sj-docx-1-hpq-10.1177_13591053261450005 for Examining the mental health impact of the COVID-19 pandemic on people living with diabetes in the Western Cape, South Africa by Bronwyne Coetzee, Phillipa Haine and Ashraf Kagee in Journal of Health Psychology

Footnotes

Acknowledgements

We would like to thank all our participants for completing the survey.

ORCID iDs

Bronwyne Coetzee

Phillipa Haine

Ashraf Kagee

Ethical Considerations

Consent to Participate

Before commencing the online survey, participants were required to provide informed consent to participate through the survey’s landing page, or through signed written consent forms for those who opted to complete the paper-based version.

Consent for Publication

Before commencing the online survey, participants were required to provide informed consent for publication through the survey’s landing page, or through signed written consent forms for those who opted to complete the paper-based version.

Author Contributions

Prof. Ashraf Kagee and Prof. Bronwyne Coetzee designed and executed this project. They also oversaw conceptual planning and data collection at all stages. Dr Phillipa Haine conducted the data analysis and wrote the first draft of the manuscript. Ms Lindokuhle Shongwe and Mr Marnus Janse Van Vuuren contributed to data collection and data cleaning.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding was obtained from the National Research Foundation (Grant No: 137992) and the SA Medical Research Council.

Declaration of conflicting interests

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

Data availability statement

Data are available on request from the corresponding author.

Disclaimer

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy of any affiliated agency of the authors.

Supplemental material

Supplemental material for this article is available online.

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