Limitations of the Randomized Controlled Trial in Evaluating Population-Based Health Interventions

Sanson‐Fisher, Rob; Bonevski, Billie; Green, Lawrence W.; D’Este, Cate

doi:10.1016/j.amepre.2007.04.007

Cited by 434 publications

(346 citation statements)

References 49 publications

Supporting

Mentioning

324

Contrasting

Unclassified

Order By: Relevance

“…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

135

190

View full text Add to dashboard Cite

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.

show abstract

Section: Evidence Of Effectivenessmentioning

confidence: 99%

A guide to scaling up population health interventions

Milat¹,

Newson²,

King³

et al. 2016

Public Health Research & Prac

135

190

View full text Add to dashboard Cite

show abstract

“…22 A recent review of literature in this area identified several major challenges including the standardisation of interventions, differences in approaches by those carrying out interventions and determining appropriate evaluation measures. 23 Since this study began, there have been additional suggestions about how to develop effective family communication tools using the Theory of Planned Behaviour, 24 although these are yet to be evaluated.…”

Section: Limitationsmentioning

confidence: 99%

Outcomes of a randomised controlled trial of a complex genetic counselling intervention to improve family communication

et al. 2015

View full text Add to dashboard Cite

When an inherited genetic condition is diagnosed in an individual it has implications for other family members. Privacy legislation and ethical considerations can restrict health professionals from communicating directly with other family members, and so it is frequently the responsibility of the first person in a family to receive the diagnosis (the proband) to share this news. Communication of genetic information is challenging and many at-risk family members remain unaware of important information that may be relevant to their or their children's health. We conducted a randomised controlled trial in six public hospitals to assess whether a specifically designed telephone counselling intervention improved family communication about a new genetic diagnosis. Ninety-five probands/parents of probands were recruited from genetics clinics and randomised to the intervention or control group. The primary outcome measure was the difference between the proportion of at-risk relatives who contacted genetics services for information and/or genetic testing. Audit of the family genetic file after 18 months revealed that 25.6% of intervention group relatives compared with 20.9% of control group relatives made contact with genetic services (adjusted odds ratio (OR) 1.30, 95% confidence interval 0.70-2.42, P = 0.40). Although no major difference was detected overall between the intervention and control groups, there was more contact in the intervention group where the genetic condition conferred a high risk to offspring (adjusted OR 24.0, 95% confidence interval 3.4-168.5, P = 0.001). The increasing sophistication and scope of genetic testing makes it imperative for health professionals to consider additional ways of supporting families in communicating genetic information. INTRODUCTIONThe diagnosis of a new genetic condition in an individual or his/her child is likely to have implications for other family members who may themselves be at risk of developing the condition and/or passing it on to their children. Under these circumstances successful communication is usually dependent on the ability of the newly diagnosed patient (proband), or parents of the proband, to communicate complex genetic information to their family members. Many factors have been identified that may have an impact on successful family communication about genetic risk including existing family relationship characteristics, feelings of anxiety or guilt, understanding of inheritance and/or the genetic condition and the ability of lay individuals to effectively explain complex genetic information. 1 Effective family communication occurs less frequently than is optimal with some estimates suggesting that only 15-20% at-risk relatives become aware of relevant and important information. [2][3][4] Family members usually express a desire to pass on this information themselves 5,6 with support from health professionals, if necessary, to explain more complicated concepts. 7 It seems likely that most relatives would intend to pass on

show abstract

“…Recognizing the methodological, pragmatic, and theoretical limitations of randomized trials in evaluating complex health interventions,8 a comprehensive mixed‐method evaluation was designed at the outset to ensure that the trial's findings would be understood in depth. The aim of this article is to report qualitative findings of the evaluation framework that were collected during the trial to understand factors that affected the trial's implementation and outcomes.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Evaluation of a Complex Intervention to Improve Rheumatic Heart Disease Secondary Prophylaxis

Read

Mitchell

Dassel

et al. 2018

JAHA

View full text Add to dashboard Cite

BackgroundRheumatic heart disease is a high‐burden condition in Australian Aboriginal communities. We evaluated a stepped‐wedge, community, randomized trial at 10 Aboriginal communities from 2013 to 2015. A multifaceted intervention was implemented using quality improvement and chronic care model approaches to improve delivery of penicillin prophylaxis for rheumatic heart disease. The trial did not improve penicillin adherence. This mixed‐methods evaluation, designed a priori, aimed to determine the association between methodological approaches and outcomes.Methods and ResultsAn evaluation framework was developed to measure the success of project implementation and of the underlying program theory. The program theory posited that penicillin delivery would be improved through activities implemented at clinics that addressed elements of the chronic care model. Qualitative data were derived from interviews with health‐center staff, informants, and clients; participant observation; and project officer reports. Quantitative data comprised numbers and types of “action items,” which were developed by participating clinic staff with project officers to improve delivery of penicillin injections. Interview data from 121 health‐center staff, 22 informants, and 72 clients revealed barriers to achieving the trial's aims, including project‐level factors (short trial duration), implementation factors (types of activities implemented), and contextual factors (high staff turnover and the complex sociocultural environment). Insufficient actions were implemented addressing “self‐management support” and “community linkage” streams of the chronic care model. Increased momentum was evident in later stages of the study.ConclusionsThe program theory underpinning the study was sound. The limited impact made by the study on adherence was attributable to complex implementation challenges.

show abstract

Limitations of the Randomized Controlled Trial in Evaluating Population-Based Health Interventions

Cited by 434 publications

References 49 publications

A guide to scaling up population health interventions

A guide to scaling up population health interventions

Outcomes of a randomised controlled trial of a complex genetic counselling intervention to improve family communication

Qualitative Evaluation of a Complex Intervention to Improve Rheumatic Heart Disease Secondary Prophylaxis

Contact Info

Product

Resources

About