Risk of depression and socio-economic status
The South African Government declared October as Mental Health Awareness month. Recent studies in South Africa show that the risk of depression varies across socio-economic strata, with low-income individuals being more likely to have depressive symptoms than high-income individuals (Igboeli et al., 2021; Mungai and Bayat, 2019). In raising awareness about mental health and the socio-economic crisis in South Africa, this Map of the Month uses the GCRO Quality of Life (QoL) Survey dataset to analyse and map the spatial association between high risk of depression and socio-economic status in Gauteng. The mapping was done in geographic units known as mesozones and was based on two measures: the Patient Health Questionnaire-2 (PHQ-2) score, which is a screening tool for risk of depression, and a socio-economic status index. See Table 1 in the methods section below for more information on these two indicators.
South Africa experienced slow economic growth since 2014 (Strauss et al., 2020). Pandemic-related lockdowns from March 2020 deepened South Africa’s existing economic crisis, which included stalling economic growth, growing inflation, food insecurity and high unemployment. Research conducted during COVID-19 suggests that the pandemic and the associated economic challenge affected people’s mental health and that the risk of depression is consistently higher than before the pandemic (De Kadt et al., 2021; Hunt et al., 2021). Overall in Gauteng, the percentage of respondents at high risk of depression increased slightly from 36.6% in 2017/18 to 37.3% in 2020/21. However, the risk of depression varies considerably by socio-economic status. Figure 1 illustrates how the percentage of respondents at risk of depression varies by socio-economic status before and after the onset of the COVID-19 pandemic. Respondents with low and medium socio-economic status are more likely to be at high risk of depression when compared to those with high socio-economic status. Figure 1 also shows that these groups are diverging when we compare our two most recent surveys (between 2017/18 and 2020/21). While the percentages of respondents at high risk of depression increased for those with low and medium socio-economic status, the rates decreased for those with high socio-economic status.
Figure 1: Percentage of respondents at high risk of depression by socio-economic status index, in 2017/18 and 2020/21.
Figure 2 shows the relationship between high risk of depression on the vertical axis and the socio-economic status index mean per mesozone on the horizontal axis. Although the analysis below cannot tell us whether the change in socio-economic circumstances leads to a change in the risk of depression (causality), the analysis confirms that a mesozone’s lower socio-economic status (red dots) is associated with higher proportions of respondents being at high risk of depression, often above 30% of respondents. This is evident from the negative sloping correlation line, the black dotted line in Figure 2. On the opposite end, higher socio-economic status in a mesozone (green dots) is associated with lower proportions of respondents being at high risk of depression, often below 30% of respondents.
Figure 2: Relationship between the risk of depression and socio-economic status per mesozone
Figure 3 shows the spatial pattern of the relationship between high risk of depression and socio-economic status. The map suggests that mesozones in which more than 30% of respondents are at risk of depression (dark red shading) are also mesozones with low socio-economic status (red dots). This is clear in mesozones that are economically disadvantaged, including township areas around Soweto, Tembisa, Khutsong, Sebokeng, Katlegong, Soshanguve and Hammanskraal, where the percentages of respondents at high risk of depression are, at least in most parts, above 30%. In contrast, in the areas where a smaller percentage of respondents are at high risk of depression (less than 30% or light red shading), the average socio-economic status of respondents tends to be higher (green dots). These include areas in the wealthiest parts of Gauteng such as those around Bryanston, Sandton, Roodepoort, and Centurion.
Figure 3: Risk of depression and socio-economic indicator per mesozone in Gauteng
This Map of the Month is part of further academic analysis of the relationship between the risk of depression and socio-economic status among youth in Gauteng. The research emphasises how important it is to promote mental health and well-being through settling a long-term path to economic growth for the population, especially those who come from areas with low- and middle socioeconomic status.
Table 1: Description of measures
Note: According to various studies, the PHQ-2 score of 2 or higher has better sensitivity but weaker specificity in screening for depression, than the older convention of using a cut-off of 3 or more. This means that using a PHQ-2 threshold score of 2 or higher results in identifying more respondents that are at risk of depression, but some of these respondents might be “false positive” (American Psychological Association, 2020). For this reason, the purpose of the PHQ-2 is not to establish a final diagnosis or to monitor depression severity, but rather to screen for depression as a “first-step” approach. If respondents score positive on the PHQ-2 (if they score 2 or more), ideally the respondents should then complete the PHQ-9 in order to fully understand their mental health.
American Psychological Association (2020). Patient Health Questionnaire (PHQ-9 & PHQ-2). Available online at: https://www.apa.org/pi/about/publications/caregivers/practice-settings/assessment/tools/patient-health.
Anand, P., Bhurji, N., Williams, N., and Desai, N. (2021). Comparison of PHQ-9 and PHQ-2 as screening tools for depression and school related stress in inner city adolescents. Journal of Primary Care & Community Health, 12, 1-8. https://doi.org/10.1177/21501327211053750
Bhana, A., Rathod, S. D., Selohilwe, O., Kathree, T., and Petersen, I. (2015). The validity of the Patient Health Questionnaire for screening depression in chronic care patients in primary health care in South Africa. BMC Psychiatry, 15(1), 1-9. https://doi.org/10.1186/s12888-015-0503-0
CSIR (Council for Scientific and Industrial Research). (2018). South African Council for Scientific and Industrial Research MesoZone 2018v1 Dataset. Available online at http://stepsa.org/socio_econ.html.
De Kadt, J., Hamann, C., Mkhize, S.P. and Parker, A. 2021. Quality Of Life Survey 6 (2020/21): Overview Report. Johannesburg: Gauteng City-Region Observatory. https://doi.org/10.36634/2021.db.1
GCRO (Gauteng City-Region Observatory). (2018). Quality of Life Survey 2017-2018, Round 5. Johannesburg and Cape Town: GCRO & DataFirst. https://doi.org/10.25828/8yf7-9261
GCRO (Gauteng City-Region Observatory). (2021). Quality of Life Survey 2020-2021, Round 6. Johannesburg and Cape Town: GCRO & DataFirst. https://doi.org/10.25828/wemz-vf31
Giuliani, M., Gorini, A., Barbieri, S., Veglia, F., and Tremoli, E. (2021). Examination of the best cut-off points of PHQ-2 and GAD-2 for detecting depression and anxiety in Italian cardiovascular inpatients. Psychology & Health, 36(9), 1088-1101. https://doi.org/10.1080/08870446.2020.1830093
Igboeli, E. E., Ajaero, C. K., Anazonwu, N. P., and Onuh, J. C. (2021). Geographical variations and determinants of depression status in urban South Africa. Journal of Public Health, 1-10. https://doi.org/10.1007/s10389-021-01510-4
Mungai, K., and Bayat, A. (2019). An overview of trends in depressive symptoms in South Africa. South African Journal of Psychology, 49(4), 518-535. https://doi.org/10.1177/0081246318823580
Naidoo, Y., and De Kadt. (2021). Quality of Life Survey 6 (2020/21): Quality of Life Index methodology. Johannesburg: Gauteng City-Region Observatory (GCRO).
Strauss, I., Isaacs, G., Rosenberg, J., and Passoni, P. (2020). The impacts from a COVID-19 shock to South Africa’s economy and labour market. Geneva: International Labour Organization (ILO)
Link to projects: Quality of Life 2020/21 Survey
Edits and Inputs: Richard Ballard, Pedzisai Ndagurwa, and Graeme Götz
Mkhize, S.P. and Hamann, C. (2022). Risk of depression and socio-economic status. Map of the Month. Gauteng City-Region Observatory. October 2022. https://doi.org/10.36634/CJIG5885.