Hickson, R.I., Rawlinson, Andrew A., Roberts, Melanie E., and Faux, Noel G. (2023) Modelling aspects of the effect of community stigma on the prevalence of anxiety and/or depression. In: Proceedings of the 25th International Congress on Modelling and Simulation. pp. 358-364. From: MODSIM 2023: 25th International Congress on Modelling and Simulation, 9-14 July 2023, Darwin, NT, Australia.

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Abstract

Mental health is an important component of overall well-being, but over two in five Australians will experience a mental disorder in their lifetime. Anxiety and depression compose a large proportion of the mental disorders in Australia, and can substantially affect the lives of those affected. Stigma about these disorders is thought to adversely affect many aspects of treatment, including delaying treatment seeking behaviours, the duration required for treatment to take effect, and withdrawal from treatment. There have been findings showing strong social clustering of anxiety and/or depression. One such postulated reason for this is that contact with people suffering from anxiety and/or depression can increase the risk of otherwise unaffected people, which is a direct analogue to “transmission”. As such, we use a transmission model framework to investigate the changes in long-term prevalence of anxiety and/or depression as a result of stigma in a community affecting model pathways to and from treatment, using strata for those affected by stigma and those unaffected (neutral). The population is divided into states for those unaffected (U ), affected by anxiety and/or depression (A), undergoing treatment (T ), and with managed anxiety and/or depression (M ). Those in the A and T states are considered to be experiencing acute affects of anxiety and/or depression and are able to affect others, whilst those in the M state are considered to still be receiving treatment but not longer able to affect others, and may be re-affected. We first calibrate our model, showing a strong linear relationship between our “transmission” r ate (β) and the rate of spontaneously experiencing the disorders (ν) to capture the reported prevalence of anxiety and/or depression. We explore the effect of stigma on model pathways related to treatment parameters on this prevalence, using univariate and bivariate sweeps. Finally, we conduct a sensitivity analysis to gain insights on how parameter estimates and ranges will affect future prevalence estimates. We found that increasing levels of stigma in a community nonlinearly increased the burden of anxiety and/or depression. This result was consistent for all calibrated parameter combinations explored. We also showed that, as expected, modelled burden was most sensitive to the transmission rate (β), and next most sensitive to the average periods of time spent being actively treated (ω, σn). We further explored the impact of the most sensitive combinations of the effects of stigma on the model parameters. Surprisingly, we found a strong relationship between the calibrated values of the spontaneous rate of experiencing the disorder (ν), and the transmission rate (β). This relationship suggested transmission was always larger, and is further evidence of a transmission framework being appropriate to explore anxiety and/or depression in this framework. It is important to emphasise that the progression of anxiety and depression are nuanced, with a complex array of underlying drivers and risk factors. We have taken a simplified approach, and focus on likely effects of parameter combinations on long-term population prevalence of anxiety and/or depression to mitigate the limitations of our approach. Overall, this helps provide information on the most important parameters needed to better understand how policies might affect the overall mental health of a population with regards to anxiety and/or depression, in the presence of stigma affecting treatment-related model pathways.

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