Using natural experiments to evaluate population health interventions: new Medical Research Council guidance

Craig, Peter; Cooper, Cyrus; Gunnell, David; Haw, Sally; Lawson, Kenny; Macintyre, Sally; Ogilvie, David; Petticrew, Mark; Reeves, Barnaby C; Sutton, Matt; Thompson, Simon G.

doi:10.1136/jech-2011-200375

Cited by 768 publications

(732 citation statements)

References 38 publications

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“…11,12 In addition, natural experimental studies may be the only option when it is impossible to manipulate exposure to the intervention. 13 As with other quasi-experimental designs, natural experiments cannot definitively determine causation and are subject to bias; however, they can provide a useful inferential tool that may not be obtainable in any other way.…”

Section: Evidence Of Effectivenessmentioning

confidence: 99%

A guide to scaling up population health interventions

Milat¹,

Newson²,

King³

et al. 2016

Public Health Research & Prac

130

174

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The 'how to' of scaling up public health interventions for maximum reach and outcomes is receiving greater attention; however, there remains a paucity of practical tools to guide those actively involved in scaling up processes in high-income countries. To fill this gap, the New South Wales Ministry of Health developed Increasing the scale of population health interventions: a guide (2014). The guide was informed by a systematic review of scaling up models and methods, and a two-round Delphi process with a sample of senior policy makers, practitioners and researchers actively involved in scaling up processes.Although it is a practical guide to assist health policy makers, health practitioners and others responsible for scaling up effective population health interventions, it can also be used by researchers in the design of research studies that are potentially suitable for scaling up, particularly where research-practice collaborations are involved. The guide is divided into four steps: step 1, 'scalability assessment', aims to determine if an intervention is scalable; step 2, 'developing a scale up plan', aims to develop a practical and workable scaling up plan that can be used to convince stakeholders there is a compelling case for action.Step 3, 'preparing for scale up', aims to identify ways of securing resources needed for going to scale, operating at scale, and building a foundation of legitimacy and support to sustain the scaling up effort through the implementation stage; and step 4, 'scaling up the intervention', involves putting the plan developed in step 2 into place.Although the guide is written as though the user is starting from the point of assessing the scalability of an intervention, later steps can be used by those already involved in scaling up to review their implementation processes. The guide is not intended to be prescriptive. Its purpose is to help policy makers, practitioners, researchers and other decision makers decide on appropriate methodological and practical choices, and balance what is desirable with what is feasible.

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Section: Evidence Of Effectivenessmentioning

confidence: 99%

A guide to scaling up population health interventions

Milat¹,

Newson²,

King³

et al. 2016

Public Health Research & Prac

130

174

View full text Add to dashboard Cite

show abstract

“…1 Several methods already exist that can be used to evaluate interventions within complex contexts. [19][20][21][22][23][24][25][26] For example, the UK's Medical Research Council has produced guidance on natural experimental evaluations, 27 studies in which the differences between experimental and control contexts are not determined by researchers, but result from policy or other interventions outside their control. Statistical methods, such as interrupted time-series analysis, can be used effectively to evaluate the impacts of such interventions over time, 28 and simulation approaches, such as agent-based modelling, can integrate diverse evidence sources, allow for non-independence and feedback, and simulate emergence.…”

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confidence: 99%

The need for a complex systems model of evidence for public health

et al. 2017

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Despite major investment in both research and policy, many pressing contemporary public health challenges remain. To date, the evidence underpinning responses to these challenges has largely been generated by tools and methods that were developed to answer questions about the effectiveness of clinical interventions, and as such are grounded in linear models of cause and effect. Identification, implementation, and evaluation of effective responses to major public health challenges require a wider set of approaches 1,2 and a focus on complex systems. 3,4 A complex systems model of public health conceptualises poor health and health inequalities as outcomes of a multitude of interdependent elements within a connected whole. These elements affect each other in sometimes subtle ways, with changes potentially reverberating throughout the system. 5 A complex systems approach uses a broad spectrum of methods to design, implement, and evaluate interventions for changing these systems to improve public health.Complex systems are defined by several properties, including emergence, feedback, and adaptation. 3 Emergence describes the properties of a complex system that cannot be directly predicted from the elements within it and are more than just the sum of its parts. For example, the changing distribution of obesity across the population can be conceptualised as an emergent property of the food, employment, transport, economic, and other systems that shape the energy intake and expenditure of individuals. Feedback describes the situation in which a change reinforces or balances further change. For example, if a smoking ban in public places reduces the visibility and convenience of smoking, and this makes it less appealing, fewer young people might then start smoking, further reducing its visibility, and so on in a reinforcing loop. Adaptation refers to adjustments in behaviour in response to interventions, such as a tobacco company lowering the price of cigarettes in response to a public smoking ban.Rhetoric urging complex systems approaches to public health is only rarely operationalised in ways that generate relevant evidence or effective policies. 1,6 Public health problems that emerge as a property of a complex system cannot necessarily be solved with a simple, single intervention, but the interacting factors within the system can potentially be reshaped to generate a more desirable set of outcomes. 7,8 Achievement of meaningful impacts on complex multicausal problems, like obesity, requires more than single interventions, such as traffic light food labelling or exercise on prescription, many of which require high levels of individual agency, have low reach and impact, and tend to widen health inequalities. 9-11 Shifts within multiple elements across the many systems that influence obesity are required, some of which might only have small effects on individuals but can drive large changes when aggregated at population level. 12 Although randomised controlled trials of individual-level interventions are relatively strai...

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“…Social desirability was not relevant in the current study, as the increased access to alcohol among adolescents was induced exogenously 43. However, the exposure is based on aggregated data with increased alcohol availability used as a proxy for increased alcohol consumption.…”

Section: Discussionmentioning

confidence: 99%

Effects of increased alcohol availability during adolescence on the risk of all‐cause and cause‐specific disability pension: a natural experiment

et al. 2017

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AimTo test if being exposed to increased alcohol availability during adolescence is associated with an increased risk of receiving disability pension due to all‐cause, alcohol use disorders and mental disorders.DesignRegister‐based population‐based study using a natural experiment setting, the alcohol policy change in Sweden (1967–68), with increased access to strong beer in a narrow time window and geographical area. The individuals exposed to the policy change were compared with non‐exposed individuals living in the rest of Sweden, excluding a border area.SettingSweden.ParticipantsA total of 518 810 individuals (70 761 in the intervention group; 448 049 in the control group) born 1948–1953, aged 14–20 years during the policy change.MeasurementsDate and diagnosis of the outcome variable of disability pension due to all‐cause, alcohol use disorders and mental disorders were obtained from the Swedish National Social Insurance Agency database from 1971 to 2013. Individual and family level socio‐demographic and health‐related covariates, as well as a regional level covariate, were included.FindingsCompared with the control group, adolescents exposed to the alcohol policy change were at an increased risk of receiving disability pension due to all‐causes [hazard ratio (HR) = 1.09, 95% confidence interval (CI) = 1.07–1.11], alcohol use disorders (HR = 1.17, 95% CI = 1.05–1.30) and mental disorders (HR = 1.19, 95% CI = 1.15–1.23).ConclusionIn Sweden, a natural experiment with a 43‐year follow‐up suggests that exposure to increased alcohol availability during adolescence is associated with an increased risk of receiving a disability pension due to all‐cause, alcohol use disorder and mental disorder diagnoses.

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Using natural experiments to evaluate population health interventions: new Medical Research Council guidance

Cited by 768 publications

References 38 publications

A guide to scaling up population health interventions

A guide to scaling up population health interventions

The need for a complex systems model of evidence for public health

Effects of increased alcohol availability during adolescence on the risk of all‐cause and cause‐specific disability pension: a natural experiment

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