Expert answer:Choose four policy analysis processes and apply ea
Expert answer:To complete the final assignment you are required to use at least eight peer-reviewed sources with at least two from Ashford University Library. Click to watch Kaiser Permanente Overview of U.S. Health Policy (Links to an external site.)Links to an external site.. Choose four policy analysis processes and apply each one to a policy case (you may use a previous week’s case) or choose from the list below:Policy response to the problem of suicide in Australia (Links to an external site.)Links to an external site.Provide details on the other policy processes that were not utilized in your research. How could they be applied? Why would they be applicable?All applicable information from previous weeks must be included.A conclusion MUST be included at the end of the paper summarizing the key aspects of health policy.Paper:Must be 12 to 15 double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center.Must include a title page with the following:Title of paperStudent’s nameCourse name and numberInstructor’s nameDate submittedMust begin with an introductory paragraph that has a succinct thesis statement.Must address the topic of the paper with critical thought.Must end with a conclusion that reaffirms your thesis.Must use at least eight scholarly sources in addition to the text.Must document all sources in APA style, as outlined in the Ashford Writing Center.Must include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center.
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Atkinson et al. Implementation Science (2015) 10:26
DOI 10.1186/s13012-015-0221-5
Implementation
Science
DEBATE
Open Access
A modelling tool for policy analysis to support
the design of efficient and effective policy
responses for complex public health problems
Jo-An Atkinson1*, Andrew Page2, Robert Wells3, Andrew Milat4 and Andrew Wilson5
Abstract
Background: In the design of public health policy, a broader understanding of risk factors for disease across the life
course, and an increasing awareness of the social determinants of health, has led to the development of more
comprehensive, cross-sectoral strategies to tackle complex problems. However, comprehensive strategies may not
represent the most efficient or effective approach to reducing disease burden at the population level. Rather, they
may act to spread finite resources less intensively over a greater number of programs and initiatives, diluting the
potential impact of the investment. While analytic tools are available that use research evidence to help identify
and prioritise disease risk factors for public health action, they are inadequate to support more targeted and
effective policy responses for complex public health problems.
Discussion: This paper discusses the limitations of analytic tools that are commonly used to support evidence-informed
policy decisions for complex problems. It proposes an alternative policy analysis tool which can integrate diverse
evidence sources and provide a platform for virtual testing of policy alternatives in order to design solutions that are
efficient, effective, and equitable. The case of suicide prevention in Australia is presented to demonstrate the limitations
of current tools to adequately inform prevention policy and discusses the utility of the new policy analysis tool.
Summary: In contrast to popular belief, a systems approach takes a step beyond comprehensive thinking and seeks to
identify where best to target public health action and resources for optimal impact. It is concerned primarily with what
can be reasonably left out of strategies for prevention and can be used to explore where disinvestment may occur
without adversely affecting population health (or equity). Simulation modelling used for policy analysis offers promise in
being able to better operationalise research evidence to support decision making for complex problems, improve
targeting of public health policy, and offers a foundation for strengthening relationships between policy makers,
stakeholders, and researchers.
Background
The use of research evidence to underpin public health
policy arose from a desire to improve the effectiveness
of policy and population-level interventions. It was one
response to criticisms of ineffective policies driven by
crisis management, political objectives, and the lobbying
of organised interest groups [1,2]. Government leaders
in the United States, Canada, United Kingdom, and
Australia have supported the increased use of evidence
in public health policy [3]. However, its use to inform
* Correspondence: jo-an.atkinson@saxinstitute.org.au
1
Research Fellow, The Australian Prevention Partnership Centre, Sax Institute,
PO Box K617, Haymarket, Sydney NSW 1240, Australia
Full list of author information is available at the end of the article
the development of effective policy responses to address
complex public health problems presents both analytic
and design challenges.
Over the past few decades, advances in life course epidemiology and an increasing awareness of the social determinants of health have revealed complex causal
pathways to chronic illness [4], and broadened the range
of factors that need to be considered in order to prevent
non-communicable disease. Quantitative analytic tools
commonly used to synthesise available evidence and assist
with identifying and prioritising risk factors for public
health action include systematic review with meta-analysis
and calculations of population attributable risk. While
these tools have no doubt provided valuable guidance to
© 2015 Atkinson et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Atkinson et al. Implementation Science (2015) 10:26
inform the development of effective public health policy,
their adequacy and accuracy are called into question when
applied to complex problems. This paper highlights the
constraints of the traditional analytic approach (i.e. linear
regression modelling) to accurately determine the strength
of associations between risk factors and conditions that
exhibit the characteristics of complexity, undermining
confidence in the results of meta-analyses and calculations
of population attributable risk that might have assisted in
identifying how to best to target policy responses. Unsurprisingly, these challenges have contributed to the development of more comprehensive, cross-sectoral strategies
to tackle complex public health problems in the hope that
if risk factors are more comprehensively included in strategies for prevention, they are more likely to be effective.
However, comprehensive strategies may not represent the
most efficient or effective approach to reducing disease
burden at the population level. Rather, they may act to
spread finite resources less intensively over a greater number of programs and initiatives, diluting the potential
impact of the investment. In addition to the analytic limitations of traditional tools to prioritise risk factors for optimal public health action, they can lack the ability to
adequately inform policy responses. Policy responses often
require a multidimensional design. This might involve legislation, regulation, enforcement, cross-sectoral cooperation, incentives, attempts at shifting sociocultural norms,
and programs and services made up of multiple packaged
interventions requiring supportive financial mechanisms,
infrastructure, workforce, and governance structures to be
integrated into complex, dynamic health and political environments [5]. Therefore, the synthesis of available evidence on the effectiveness and cost-effectiveness of
interventions may not provide adequate guidance for the
many questions that decision makers encounter as they attempt to design effective policy responses [5].
The case of suicide prevention in Australia is presented to demonstrate the lack of impact of the ‘comprehensive’ strategy on suicide rates over the last two
decades, the limitations of traditional analytic tools, and
to explore the potential benefits of systems science tools
that can support the design of effective policy responses
for suicide prevention and other complex public health
problems.
Discussion
The complex problem of suicide
Suicide remains the leading cause of death in adult
males aged under 44 years and women under 34 years
[6] despite significant declines in young adult males
since the 1990s [7], and represents significant economic
and health service costs to Australia [8]. The rate of suicide in males (16.4 deaths per 100,000 population) was
almost four times higher than in females (4.8 deaths per
Page 2 of 9
100,000 population) in 2010 [6]. Many more females
than males have attempted suicide despite their death
rate being considerably lower [9]. The rate of suicide for
Aboriginal and Torres Strait Islander peoples was twice
that of non-Indigenous people over the period of 2001–
2010 [6]. These figures do not include attempted suicide
or other forms of deliberate self-harm.
Explanation and prediction of suicide remains immensely difficult due to its complex aetiology involving social, economic, cultural, interpersonal, and individual-level
antecedents. Mental disorder is key risk factor for suicide
[10,11] and has been identified as a National Health Priority Area in Australia [12]. Consequently, mental disorders
often are the main focus of suicide prevention strategies
[13]. However, other important determinants of suicide
have been identified and range from proximal causes such
as personal characteristics (i.e. biologic, genetic, cognitive, personality factors, sexual orientation, family history) [14-17], behavioural factors [18,19], and adverse
life circumstances [20,21], to distal causes including
macro-social and economic factors (i.e. segregation, unemployment, educational attainment, media) [11,22,23],
cultural influences [24], and structural factors (e.g. economic policy, regulation of the means of suicide) [25-27].
There is a complex, dynamic interrelation of these factors
across the life course and heterogeneity in their distribution among the population. This represents a significant
challenge for researchers trying to establish causal relationships, and for policy makers at national and state level
to determine how best to direct investment in suicide prevention initiatives.
Five major domains of suicide prevention interventions were identified by an international consortium of
suicide experts as part of the most recent systematic review of the effectiveness of suicide prevention strategies
[28]. These domains included the following: education
and awareness programs for the general public and professionals; screening methods for high-risk persons;
treatment of psychiatric disorders and follow-up care for
suicide attempts (including pharmacotherapy and psychotherapy); restricting access to lethal means; and
media reporting of suicide [28]. The review identified
two suicide prevention approaches that were effective in
reducing suicide rates: restriction of access to lethal
methods, and physician education in depression recognition and treatment [28]. However, the sufficiency of this
information for informing the design of policy responses
for suicide prevention in Australia is limited. Questions
remain as to whether a lack of evidence of effectiveness
of other interventions is due to inadequate intensity and
duration of implementation; the limited timespan for
evaluation follow-up (as longer term trends in suicide
rates are not captured in these studies); and how to interpret contextual variations in outcomes even within
Atkinson et al. Implementation Science (2015) 10:26
the same country [28]. In addition, such reviews can
overlook the impact of targeting factors that may have a
significant, but indirect effect on suicide rates such as
occupational status and educational attainment [11,22].
Policy response to the problem of suicide in Australia
In response to a significant rise in young male suicide
between the 1970s and late 1990s, the Australian
Government implemented the first National Youth
Suicide Prevention Strategy in 1995 [29,30]. This strategy was expanded to all age groups with the launch of
the National Suicide Prevention Strategy (NSPS) in 1999
and subsequent release of the LIFE Framework (Living
Is For Everyone) in 2000 [31]. This framework took a
comprehensive approach to addressing risk factors for
suicide. Based on the LIFE Framework, over the next
6 years most States and Territories also adopted their
own suicide prevention strategies [32]. During the 1999–
2006 phase of the NSPS, over 150 community projects
(mostly small-scale targeted programs with non-recurring
funding) and 27 national initiatives were funded [32]
which appeared not to have a major impact on the youth
suicide rate [29].
In response to growing public and political concern
that mental health reform was failing to achieve impact,
two reports were released on the status of mental health
services in Australia; Not For Service: Experiences of injustice and despair in mental health care in Australian
(2005) [33] by the Mental Health Council of Australia;
and the Senate Inquiry and report; A national approach
to mental health – from crisis to community (2006) [34].
In 2006, the Council of Australian Governments (COAG)
agreed to the National Action Plan on Mental Health
2006–2011 [35], which included a commitment by the
Commonwealth Government to double funding for suicide prevention through the National Suicide Prevention
Program from $62 million to $127 million [30]. In 2008,
the Australian Suicide Prevention Advisory Council was
established to provide national leadership and expert advice to the Australian Government as well as support the
government’s implementation of the National Suicide Prevention Program. In response to further criticism that government efforts had resulted in fragmented services for
those at risk of suicide, a further Senate Committee report
(The Hidden Toll: Suicide in Australia) made recommendations to deliver more comprehensive and effective suicide prevention responses [30]. A sustained multilevel
approach was recommended with efforts ranging from
government to community responses, a combination of
targeted and population-based interventions, underpinned
by best available evidence [30]. Another key recommendation was increased coordination and alignment of suicide
prevention programs and services in order to prevent
overlap, duplication, and access gaps [30]. To implement
Page 3 of 9
these recommendations, the Australian Government released the Mental Health: Taking Action to Tackle Suicide
package providing an additional $274 million over 4 years
commencing 2010–2011 [36]. In addition to national and
state government investments, business, community, and
philanthropic funding sources also support suicide prevention programs and activities in Australia [30].
There is currently significant investment and policy
momentum in suicide prevention in Australia. A large
range of prevention programs and activities are being
implemented at the national, state and local levels. These
initiatives comprehensively address identified risk factors
for suicide and include direct prevention initiatives (e.g.
school-based interventions, targeted risk group initiatives,
primary mental health care, early and crisis interventions,
treatment and follow-up initiatives) and systems-level approaches (e.g. socioeconomic programs to mitigate risk
factors, media education, restricting access to means of
suicide, physical health promotion, and inter-sectoral collaboration) [37-40], but has this investment and activity to
comprehensively address the range of risk factors for suicide had a significant impact on population-level suicide
rates? Figure 1 demonstrates there was a decline in male
suicide from 1997–2007. However, it is argued that these
falls can be explained by a reduction in the availability of
lethal methods of suicide, namely, measures to control the
availability of firearms following the Port Arthur massacre,
the requirement for new cars to be fitted with catalytic
converters, and the decline in the prescription of tricyclic antidepressants (due to availability of a new class of antidepressant compounds with fewer side effects and lower toxicity in
overdose) [41,42]. Therefore, the impact on male suicide was
most likely a consequence of independent policy actions, unrelated to the national strategy for suicide prevention.
Over the same 10-year period, there was no decline in suicidal ideation or rates of attempted suicide [41]. This suggests
that the root causes of suicide were not adequately addressed
by the National Strategy, and while regulation of access to lethal means of suicide can reduce the death rate to a point, as
an ongoing strategy, it may have limited impact due to the
difficulty in regulating to prevent access to other suicide
methods (hanging, sharp objects, jumping from a height).
This raises some important questions. Why is evidence
of impact of the National Strategy for Suicide Prevention
on population-level suicide rates limited? Do we really
understand the complex and dynamic interrelation of
causal factors of suicide over the life course? After almost 20 years of action, why are we still uncertain about
how to effectively prevent suicide in Australia? If the
‘comprehensive approach’ to suicide prevention is not
achieving impact, should we not consider alternative approaches? Unfortunately, current tools for synthesising
and operationalising research evidence are not able to
answer vital question of what the ideal targeting,
Atkinson et al. Implementation Science (2015) 10:26
Page 4 of 9
Figure 1 Suicide rates in Australia (1992–2012)*. *Data in this figure was obtained from Australian Bureau of Statistics (ABS) Catalogue 3303.0
Causes of Death Australia, 2012, released Friday 25th March 2014. For more information on data visit ABS website at www.abs.gov.au.
intensity, consistency, and coordination of programs to
prevent suicide is.
Limitations of traditional analytic tools for supporting
evidence-informed policy
For complex public health problems such as suicide,
there are two important limitations of traditional analytic tools to support the design of effective evidenceinformed policy responses:
(I) Analytic limitations for exploring the impact of
policy options
Evidence of measurable impacts of suicide prevention
policy responses on population-level suicide rates is limited
[28,29]. Uncertainties remain around the type, scope, and
intensity of interventions to implement, and the right place
and right period to implement them. Designing an effective
and efficient policy response for suicide prevention requires
a comprehensive perspective on causation, consideration of
the influence of factors such as access to healthcare and
preventive services, and analytic methods for testing the
range of policy options and their consequences to better
target actions for the Australian context. Numerous
conceptual models of suicide have been developed for
specific populations and stages of the life course [17,43-52],
with varying emphasis on proximal causal factors,
ecological influences, and multilevel determinants. While
conceptual models can convey complexity, and map the
interrelationships of multilevel factors, they cannot
capture the magnitude of their influence (or temporal
changes in influence over the life course) nor quantify
the potential impacts of preventive interventions
implemented individually or in combination, at various
levels, using targeted and/or universal approaches.
(II) Constraints of traditional approaches to data analysis
Dominant analytic methods attempt to identify the
‘determinants’ of an outcome and estimate the effect size
of a given exposure/s on an outcome by controlling for
common causes (confounders). Basic assumptions of this
approach are that exposure variables (or ‘risk factors’)
are independent, and relationships between exposures
and outcome are unidirectional, linear, and constant
through time. This approach does not necessarily
capture health behaviours as being a result of interacting
and interdependent ‘risk factors’ acting at multiple
levels (e.g. individual characteristics, social networks,
economic, and political environments). Nor does it
reflect how these multilevel ‘risk factors’ shape one
another, and in turn are shaped by health and health
behaviours (i.e. relationships between variables can be
characterised by interdependence, nonlinearity and
feedback loops) [53]. These characteristics violate the
conditions for use of traditional analytic methods.
While traditional methods provide valuable data-driven
explanations of simple causal relationships between a
finite range of variables for well-defined problems, and
rigorously take account of variables that can confound
these relationships [53], public health problems that
arise from complex human behaviours makes reliance
on traditional methods problematic and undermines
confidence in …
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