Health indicators and poor health dynamics during COVID-19 pandemic.
Adeola OyenubiChijioke O NwosuUmakrishnan KollamparambilPublished in: Current psychology (New Brunswick, N.J.) (2022)
It is expected that the coronavirus pandemic will exacerbate inequality in wellbeing compared to the pre-pandemic situation. However, there are theories (e.g., the Conservation of Resource (COR) theory) that acknowledge situation-specific lower wellbeing for individuals who typically have more resources. The argument is that perception of loss might occur differently across the socioeconomic spectrum such that individuals with higher socioeconomic status perceive that they experience more loss. Therefore, given the pandemic situation, it is possible that indicators of poor wellbeing (e.g., depression) becoming less concentrated among the poor, contrary to expectation. Given the above, we examine income-related inequality in self-assessed health and depressive symptoms in South Africa. This is done using both pre-pandemic data (i.e. National Income Dynamic Study) and data collected during the pandemic (National Income Dynamic Study-Coronavirus Rapid Mobile Survey). Consistent with expectation, we find that poor self-assessed health is not only disproportionately concentrated amongst the poor, but this concentration has increased compared to the pre-pandemic period. However, contrary to expectation, depressive symptoms have become less concentrated amongst the poor compared to the pre-pandemic period. We note that while there may be an alternative explanation for this change in trend, it may also be due to situation-specific lower wellbeing for individuals who typically have more resources. We argue that this has implication for tracking population health in a crisis.
Keyphrases
- sars cov
- coronavirus disease
- depressive symptoms
- public health
- mental health
- healthcare
- respiratory syndrome coronavirus
- south africa
- health information
- physical activity
- social support
- electronic health record
- health promotion
- sleep quality
- machine learning
- cross sectional
- deep learning
- social media
- climate change
- drug induced
- sensitive detection