| Literature DB >> 31238699 |
Graham N Meadows1,2,3, Ante Prodan4,5, Scott Patten6, Frances Shawyer1, Sarah Francis1, Joanne Enticott1,7, Sebastian Rosenberg8,9, Jo-An Atkinson6,10,11,12, Ellie Fossey13, Ritsuko Kakuma14.
Abstract
A doubling of Australian expenditure on mental health services over two decades, inflation-adjusted, has reduced prevalence of neither psychological distress nor mental disorders. Low rates of help-seeking, and inadequate and inequitable delivery of effective care may explain this partially, but not fully. Focusing on depressive disorders, drawing initially on ideas from the work of philosopher and socio-cultural critic Ivan Illich, we use evidence-based medicine statistics and simulation modelling approaches to develop testable hypotheses as to how iatrogenic influences on the course of depression may help explain this seeming paradox. Combined psychological treatment and antidepressant medication may be available, and beneficial, for depressed people in socioeconomically advantaged areas. But more Australians with depression live in disadvantaged areas where antidepressant medication provision without formal psychotherapy is more typical; there also are urban/non-urban disparities. Depressed people often engage in self-help strategies consistent with psychological treatments, probably often with some benefit to these people. We propose then, if people are encouraged to rely heavily on antidepressant medication only, and if they consequently reduce spontaneous self-help activity, that the benefits of the antidepressant medication may be more than offset by reductions in beneficial effects as a consequence of reduced self-help activity. While in advantaged areas, more comprehensive service delivery may result in observed prevalence lower than it would be without services, in less well-serviced areas, observed prevalence may be higher than it would otherwise be. Overall, then, we see no change. If the hypotheses receive support from the proposed research, then implications for service prioritisation and delivery could include a case for wider application of recovery-oriented practice. Critically, it would strengthen the case for action to correct inequities in the delivery of psychological treatments for depression in Australia so that combined psychological therapy and antidepressant medication, accessible and administered within an empowering framework, should be a nationally implemented standard.Entities:
Keywords: Mental health services; epidemiology; health behaviour; iatrogenesis; side effects (treatment)
Year: 2019 PMID: 31238699 PMCID: PMC6724452 DOI: 10.1177/0004867419857821
Source DB: PubMed Journal: Aust N Z J Psychiatry ISSN: 0004-8674 Impact factor: 5.744
Figure 1.Overlapping waves of action in help-seeking for medical care.
Source: Adapted from Jorm et al. (2004) (Figure 2; p. 298).
Figure 2.Simplified causal loop diagram of the hypothetical nexus.
+/− indicates positive or negative influence through the pathway.
indicates that the left-hand pathway has greater influence than the right.
Estimated population by socioeconomic disadvantage quintiles with very-high Kessler 10 scores.
| IRSD[ | Population (2016) | Estimated population % of very-high K10[ | Estimated number of people with very-high K10[ | % of all Australians with very-high K10[ |
|---|---|---|---|---|
| Capital cities | ||||
| 1 | 2,617,180 | 1.6 | 41,875 | 8 |
| 2 | 2,383,572 | 3.1 | 73,891 | 14 |
| 3 | 1,902,530 | 3.9 | 74,199 | 14 |
| 4 | 1,549,484 | 4.5 | 69,727 | 13 |
| 5 | 1,467,165 | 5.4 | 79,227 | 15 |
| Other areas | ||||
| 1 | 297,417 | 2.5 | 7435 | 1 |
| 2 | 607,052 | 2.9 | 17,605 | 3 |
| 3 | 1,007,006 | 3.5 | 35,245 | 7 |
| 4 | 1,323,931 | 3.6 | 47,662 | 9 |
| 5 | 1,302,272 | 6.1 | 79,439 | 15 |
| Total | 14,457,609 | 526,303 | 100 | |
Index of Relative Socioeconomic Disadvantage.
Australian Bureau of Statistics, 2016 Census of Population and Housing, TableBuilder data available at: www.abs.gov.au/websitedbs/censushome.nsf/home/tablebuilder.
Scores on the Kessler 10 questionnaire above 30, based on 2011 data (Isaacs et al., 2018).
Values calculated by multiplying column 2 with column 3.
Percentages calculated by dividing column 3 by total population provided at bottom of column 2.