Preventing mental illness: closing the evidence-practice gap through workforce and services planning

Abstract

BackgroundMental illness is prevalent across the globe and affects multiple aspects of life. Despite advances in treatment, there is little evidence that prevalence rates of mental illness are falling. While the prevention of cardiovascular disease and cancers are common in the policy dialogue and in service delivery, the prevention of mental illness remains a neglected area. There is accumulating evidence that mental illness is at least partially preventable, with increasing recognition that its antecedents are often found in infancy, childhood, adolescence and youth, creating multiple opportunities into young adulthood for prevention. Developing valid and reproducible methods for translating the evidence base in mental illness prevention into actionable policy recommendations is a crucial step in taking the prevention agenda forward.MethodBuilding on an aetiological model of adult mental illness that emphasizes the importance of intervening during infancy, childhood, adolescence and youth, we adapted a workforce and service planning framework, originally applied to diabetes care, to the analysis of the workforce and service structures required for best-practice prevention of mental illness.ResultsThe resulting framework consists of 6 steps that include identifying priority risk factors, profiling the population in terms of these risk factors to identify at-risk groups, matching these at-risk groups to best-practice interventions, translation of these interventions to competencies, translation of competencies to workforce and service estimates, and finally, exploring the policy implications of these workforce and services estimates. The framework outlines the specific tasks involved in translating the evidence-base in prevention, to clearly actionable workforce, service delivery and funding recommendations.ConclusionsThe framework describes the means to deliver mental illness prevention that the literature indicates is achievable, and is the basis of an ongoing project to model the workforce and service structures required for mental illness prevention.

Risks for Mental Illness in Indigenous Australian Children: A Descriptive Study Demonstrating High Levels of Vulnerability

“…With the far higher rate of multiple adversity, we find 22.5% of indigenous children between the ages of 4 and 10 years report high levels of psychological distress (using the SDQ total problems score), compared with just over 8% of children of the same age in LSAC 29. Other studies put rates of psychological distress in Australian children higher, across a broad age range 21, 44, 45, 46…”

“…It forms part of a larger research program to apply an evidence‐informed needs‐based planning framework to determine the optimal workforce and service structure required to deliver best practice in preventive mental health care29 (complementing recent work by Guy et al30 on rates of risk exposure in predominantly nonindigenous Australian children).…”

The long-term indirect effect of the early Family Check-Up intervention on adolescent internalizing and externalizing symptoms via inhibitory control

“…In addition, by school age, children's environments have expanded considerably to include school and peer networks, adding to the complexity of an intervention that should ideally address the child's entire ecosystem (Coie et al, 1993). Thus, early family-based preventative interventions that target at-risk families are often considered to be optimal for preventing future mental health problems (Furber et al, 2015). For example, one analysis found that parenting interventions aimed at preventing conduct problems in the United Kingdom had a 25-year return on investment of at least eight times the initial costs of intervention in the form of decreased public expenses associated with health care, social services, and the justice system (Knapp, McDaid, & Parsonage, 2011).…”

Regional South Australia Health (RESONATE) survey: study protocol

“…Using the needs-based health workforce planning framework, the healthcare needs of this population can be mapped against best practice care to estimate total health workforce requirements. 44 This model has already been applied to regional populations with diabetes 45 and mental illness, 46 and takes a far more ‘richer perspective on population needs’ than other workforce planning approaches. 44 Given the considerable health workforce maldistribution in regional South Australia, 47–49 this is an important next step for this research.…”