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. 2021 Apr 16;16(4):e0250269.
doi: 10.1371/journal.pone.0250269. eCollection 2021.

Behavioural response to the Covid-19 pandemic in South Africa

Umakrishnan Kollamparambil  1 , Adeola Oyenubi  1
Affiliations

Behavioural response to the Covid-19 pandemic in South Africa

Umakrishnan Kollamparambil et al. PLoS One. .

Abstract

Background: Given the economic and social divide that exists in South Africa, it is critical to manage the health response of its residents to the Covid-19 pandemic within the different socio-economic contexts that define the lived realities of individuals.

Objective: The objective of this study is to analyse the Covid-19 preventive behaviour and the socio-economic drivers behind the health-response behaviour.

Data: The study employs data from waves 1 and 2 of South Africa's nationally representative National Income Dynamics Study (NIDS)-Coronavirus Rapid Mobile Survey (CRAM). The nationally representative panel data has a sample of 7073 individuals in Wave 1 and 5676 individuals in Wave 2.

Methods: The study uses bivariate statistics, concentration indices and multivariate estimation techniques, ranging from a probit, control-function approach, special-regressor method and seemingly unrelated regression to account for endogeneity while identifying the drivers of the response behaviour.

Findings: The findings indicate enhanced behavioural responsiveness to Covid-19. Preventive behaviour is evolving over time; the use of face mask has overtaken handwashing as the most utilised preventive measure. Other measures, like social distancing, avoiding close contact, avoiding big groups and staying at home, have declined between the two periods of the study. There is increased risk perception with significant concentration among the higher income groups, the educated and older respondents. Our findings validate the health-belief model, with perceived risk, self-efficacy, perceived awareness and barriers to preventive strategy adoption identified as significant drivers of health-response behaviour. Measures such as social distancing, avoiding close contact, and the use of sanitisers are practised more by the rich and educated, but not by the low-income respondents.

Conclusion: The respondents from lower socio-economic backgrounds are associated with optimism bias and face barriers to the adoption of preventive strategies. This requires targeted policy attention in order to make response behaviour effective.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Behavioural change across Wave 1 and Wave 2.
Source: NIDS-CRAM, Wave 1 (2020) and NIDS-CRAM, Wave 2 (2020). Data are weighted. The bars show the reported change in behaviour. 95% confidence intervals are shown.
Fig 2
Fig 2. Types of preventive measures.
Source: NIDS-CRAM, Wave 1 (2020) and NIDS-CRAM, Wave 2 (2020). Data are weighted. The bars show the use of different preventive measures in percentage. 95% confidence intervals are shown.
Fig 3
Fig 3. Covid-19 risk perception over Wave 1 and Wave 2.
Source: NIDS-CRAM, Wave 1 (2020) and NIDS-CRAM, Wave 2 (2020) Data are weighted. The bars show the risk perception in percentage. 95% confidence intervals are shown.
Fig 4
Fig 4. Risk perception across income quintiles.
Source: NIDS-CRAM, Wave 1 (2020) and NIDS-CRAM, Wave 2 (2020). Data are weighted. The bars show the risk perception across income quintiles. 95% confidence intervals are shown.
Fig 5
Fig 5. Self-efficacy across Wave 1 and Wave 2.
Source: NIDS-CRAM, Wave 1 (2020) and NIDS-CRAM, Wave 2 (2020). Data are weighted. The bars show the reported self-efficacy. 95% confidence intervals are shown.
See this image and copyright information in PMC

References

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