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Personalised risk communication for informed decision making about taking screening tests

  1. Adrian GK Edwards1,*,
  2. Gurudutt Naik1,
  3. Harry Ahmed1,
  4. Glyn J Elwyn1,
  5. Timothy Pickles2,
  6. Kerry Hood2,
  7. Rebecca Playle2

Editorial Group: Cochrane Consumers and Communication Group

Published Online: 28 FEB 2013

Assessed as up-to-date: 24 MAR 2012

DOI: 10.1002/14651858.CD001865.pub3


How to Cite

Edwards AGK, Naik G, Ahmed H, Elwyn GJ, Pickles T, Hood K, Playle R. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database of Systematic Reviews 2013, Issue 2. Art. No.: CD001865. DOI: 10.1002/14651858.CD001865.pub3.

Author Information

  1. 1

    Cardiff University, Cochrane Institute of Primary Care and Public Health, School of Medicine, Cardiff, Wales, UK

  2. 2

    Cardiff University, South East Wales Trials Unit, Institute of Translation, Innovation, Methodology and Engagement, Cardiff, Wales, UK

*Adrian GK Edwards, Cochrane Institute of Primary Care and Public Health, School of Medicine, Cardiff University, 2nd Floor, Neuadd Meirionnydd, Heath Park, Cardiff, Wales, CF14 4YS, UK. edwardsag@cardiff.ac.uk. adriangkedwards@btinternet.com.

Publication History

  1. Publication Status: New search for studies and content updated (conclusions changed)
  2. Published Online: 28 FEB 2013

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Summary of findings    [Explanations]

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

 
Summary of findings for the main comparison. Personalised risk communication versus general risk information for informed decision making about taking screening tests

Patient or population: patients with informed decision making about taking screening tests
Settings: screening test in primary and secondary care setting
Intervention: personalised risk communication versus general risk information

OutcomesIllustrative comparative risks* (95% CI)Relative effect
(95% CI)
No of Participants
(studies)
Quality of the evidence
(GRADE)
Comments

Assumed risk8Corresponding risk

ControlPersonalised risk communication versus general risk information

Informed decision - Numerical risk and categorised risk combined
MMIC1
202 per 1000480 per 1000
(350 to 612)
OR 3.65
(2.13 to 6.23)
2444
(3 studies)
⊕⊕⊕⊕
high2,3,4,7
Random-effects estimate presented due to significant heterogeneity

Uptake of screening test - Numerical and categorised risk combined
Number of participants taking up the test
532 per 1000566 per 1000
(537 to 594)
OR 1.15
(1.02 to 1.29)
6442
(12 studies)
⊕⊕⊝⊝
low5,6,7
Fixed-effect estimate

*The basis for the assumed risk (e.g. the median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).
CI: Confidence interval; OR: Odds ratio;

GRADE Working Group grades of evidence (we have not assessed publication bias for any of the groups)
High quality: Further research is very unlikely to change our confidence in the estimate of effect.
Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate.
Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate.
Very low quality: We are very uncertain about the estimate.

 1 MMIC: Multi-dimensional Measure of Informed Choice
2 Significant heterogeneity among studies but all studies have same direction of effect and hence not down graded.
3Good quality randomised studies with low risk of bias.
4All studies consistently demonstrating an odds ratio of > 2 and quality upgraded by one point.
5Moderate or low heterogeneity among studies and not down graded for inconsistency.
6Out of 12 studies: 5 mentioned generation of random sequence, only 2 studies mentioned concealing allocation, blinding of participants, personnel and outcome assessors was mostly unclear across studies, 9 studies were low risk for attrition bias and 5 studies were high risk for reporting bias. Quality was downgraded by 1 point.
7Personalised risk communication is delivered as a part of the interventions. Informed choice and uptake are promoted by influencing many other elements such as knowledge, perceived risk etc leading to indirectness of evidence and hence down graded by a point.
8Control risk was used as baseline risk, due to lack of studies that measure this in detail to be presented as baseline risk for the population.

 Summary of findings 2 Personalised risk communication versus general risk information for informed decision making about taking screening tests

 

Background

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

Risk communication is an integral part of modern healthcare practice. It is important in various strategies to help individuals make the right choices. Be it a public screening programme or a matter of starting a new treatment, providing evidence-based risk and benefit information to patients, and ensuring they understand it, forms the cornerstone of informed decision making (Naik 2012). However, understanding is still limited of how best to present and discuss risks and benefits of health care in general, and screening in particular, for an individual.

 

Screening: what are the issues around policies and practice?

Screening is defined as "the systematic application of a test or inquiry, to identify individuals at sufficient risk of a specific disorder to warrant further investigation or direct preventive action, amongst persons who have not sought medical attention on account of symptoms of that disorder"(UKNSC 1998). 

Many tests and procedures form part of screening programs - programs with the objective of sorting out apparently well persons who probably have a disease, from those who probably do not. They may only highlight a risk of disease, and are not intended to definitely diagnose a disease or condition. Screening is a complex event that can have various outcomes for the participant, some of which can cause unnecessary distress (Collins 2011). Although screening programmes are generally seen as an effective measure to reduce mortality and morbidity in the target populations, there are several issues surrounding them. The evolution of evidence regarding certain screening programmes has led to fierce debate about actual benefits and harms (Bewley 2011; Hackshaw 2012). A recent review of breast cancer screening for example, suggests that it is unclear whether such screening does more harm than good, and that balanced, evidence-based risk-benefit information should be provided to help women make an informed decision about undergoing screening (Gøtzsche 2011). Directly harmful effects of screening include: complications arising from the investigation, adverse effects of treatment, unnecessary treatment of persons with true-positive test results who have inconsequential disease, adverse effects of labelling or early diagnosis, anxiety generated by the investigations and treatment, and the costs or inconvenience incurred during investigations and treatment (Barratt 1999). Other harms, such as failure to detect disease, only apply to those with the condition. In either of these scenarios, Raffle 2001 warns that "harm to uninformed participants leads to anger, bitterness, and potentially to litigation". Furthermore screening can have significant social and psychological costs which are difficult to evaluate and quantify (Stewart-Brown 1997).

These well recognised potential harms have led to increasing debate about the policy and practice of screening. A review of breast cancer screening policy has been proposed in the United Kingdom (UK), prompted by concerns about the accuracy and transparency of information provided to women about the benefits and harms of screening (Richards 2011). The American Cancer Society has also highlighted the confusion created by screening guidelines, and proposes providing the public with clear guidance about the benefits, limitations, and harms associated with taking a screening test (Brawley 2012). 

 

Informed decision making

An informed decision is defined as one where "a reasoned choice is made by a reasonable individual using relevant information about the advantages and disadvantages of all the possible courses of action, in accord with the individual’s beliefs" (Bekker 1999).

The UK national guidance confirms that the purpose of information about screening is to ensure that participants can make a fully informed choice about whether to take the test (GMC 1998). The UK National Screening Committee some time ago proposed a new definition for screening to incorporate the importance of informed choice in screening:

"a public health service in which members of a defined population, who do not necessarily perceive they are at risk of, or are already affected by, a disease or its complications, are asked a question or offered a test to identify those individuals who are more likely to be helped than harmed by further tests or treatment to reduce the risk of disease or its complications" (UKNSC 2000)

Jepson and colleagues reiterate this policy and recommend evaluating screening programmes to ensure that people invited for screening are genuinely making informed choices, and explore how comprehensive information affects other variables such as uptake, cost effectiveness, and satisfaction (Jepson 2005).

 

How can informed decisions be measured?

Briss 2004 provides a conceptual framework for informed decision making which includes: knowledge, participation in decision making at a personally desirable level, and consistency between the decision and individual preferences or values. A systematic review by Mullen et al, on measures of informed decision making in cancer screening, observed no single study measuring all of these three aspects (Mullen 2006). They recommended the development and use of validated measures of informed decision making and the use of theoretical frameworks for such screening studies.

Marteau and colleagues have proposed the multi-dimensional measure of informed choice, which has been validated for Down’s syndrome screening (Michie 2002; Marteau 2001). This is based on assessing the consistency between knowledge, attitudes to tests and choices of uptake of tests. To date, few studies in screening have used this scale as a measure for informed choice (Nagle 2008; Smith 2010; Steckelberg 2011). This scale is promising and could form the basis for development of a universal measure of informed choice for uptake of screening tests.  Further research is needed to validate this scale in other areas. Furthermore, increasingly the outcomes described in the literature include 'affective' measures such as satisfaction with the decision made, and 'decisional conflict'. This includes whether an individual feels a decision is consistent with their personal values, and certainty about making the right decision (Edwards 1999a; Holmes-Rovner 1996; Llewelyn-Thomas 1995).

 

Description of the intervention

 

What is tailoring and why is personalising risk information important?

Tailored health communication refers to providing information to someone based on characteristics that are unique to that person. Kreuter et al define tailoring as: "Any combination of information or change strategies intended to reach one specific person, based on characteristics that are unique to that person, related to the outcome of interest and have been derived from an individual assessment" (Kreuter 1999a; Kreuter 1999b).

Tailoring could address any characteristics such as the person’s educational background, cultural orientation, and general level of comprehension. Many studies have adopted a theoretically-driven approach and have tailored their interventions on behavioural constructs that are based on behaviour change models  (Noar 2007), for example the Transtheoretical Model (Prochaska 1992; Prochaska 1997) and Heath Belief Model (Becker 1974; Rosenstock 1988; Rosenstock 2000).

A high rate of participation is expected in most established screening programmes to ensure that they run effectively. Health authorities may employ a variety of strategies to promote screening and enhance uptake amongst the target populations. Some screening programmes provide information which is personally tailored and more relevant to the person in question. Others provide information about population or 'average' risks of contracting a disease as a basis for discussion or decision making about undergoing screening, or perhaps simply to try to motivate people to attend for tests which are perceived by authorities to be in the person's or population's best interests (Slaytor 1998). Such tactics may be seen as questionable, working against the spirit of informed decision making and consumer autonomy, and amounting to a disregard for the principles of medical ethics and good practice (Beauchamp 2001). Foster and Anderson (Foster 1998) criticise the implementers of the UK's National Cervical Screening Programme, including general practitioners, for their persuasive tactics in getting patients to comply.

According to the Health Belief Model one of the key determinants of health-related behaviour is perceived threat (Becker 1974). This is made up of two components: the individuals perceiving themselves as susceptible to the disease, and the perceived severity of the disease. Tailoring information using an individual’s specific risk factors is what we describe as 'personalised risk communication', sometimes also called 'individualised risk communication'. This may be based on the individual's own risk factors for a condition (such as age or family history). In some cases it is calculated from an individual's risk factors using formulae derived from epidemiological data, with the information presented as an absolute risk or as a risk score, for example the Gail score for the risk of breast cancer (Gail 1989) and Q-risk score for cardiovascular risk (Hippisley-Cox 2008). Risk information may also be categorised into, for example, high, medium or low risk groups. It may be less detailed, involving a listing, for example, of a person's risk factors as a focus for discussion and intervention.

 

How the intervention might work

Evidence suggests that the format in which risk information is presented affects patients’ understanding and perception of risk (Ahmed 2012). It is assumed that tailored messages are perceived as more relevant to an individual, and are therefore better processed and understood (Barratt 2005). Foster and Anderson (Foster 1998) have emphasised the importance of providing good-quality information to those undergoing cervical screening procedures. They stress that women should be made aware of the risks (discomfort, over-treatment of abnormal smears) and limitations (false positives; false negatives), as well as the benefits of the test. Further, they also point out that people have different risks of contracting the cervical cancer, based on factors such as age, sexual activity, social class and smoking.  A systematic review  of studies with interventions designed to provide tailored information on cancer risk and screening method has shown that personally-tailored risk information improved subjects' knowledge and realistic perception of cancer risk, compared to the provision of generic risk information only (Albada 2009). Noar et al found in their meta-analytic review that tailored messages outperformed comparison messages in affecting health behaviour change, lending support to claims made by other authors of narrative reviews that tailoring does in fact 'work' (Noar 2007). 

There is recognition that uptake of tests per se is not necessarily desirable (for example, prostate specific antigen screening and antenatal cystic fibrosis screening are contentious areas). Also some screening tests are only recommended for those who are at high risk for disease susceptibility, for example genetic testing for breast and ovarian cancer (US PSTF 2005). Making an informed choice about taking screening tests, and adherence to the consumer's chosen option, are widely regarded as more desirable goals for risk communication (Edwards 1999a; Liao 1996; Sarfati 1998).

In this light, providing individualised risk information may seem to be more pertinent than providing generic risk information about the disease for which the screening intervention is designed. Personalised risk communication is more likely to be useful in decision making about whether or not to participate in screening, and individuals who perceive their personal risk as high may feel more susceptible to the disease in question. They may weigh this risk information with the harmful effects of screening, leading to an informed choice to take up the test. Similarly, those who consider themselves at low or no risk may opt out of the screening programme.

 

Why it is important to do this review

 

Public health impact, the scope and limitations

The UK National Health Service incorporates 11 different screening activities within its national health programme (UK National Screening Portal). In England alone, 2.24 million women were sent invitations to attend breast cancer screening in 2010-11 (NHS Information Centre 2011a). Invitations to attend cervical cancer screening were sent to 1.79 million women (NHS Information Centre 2011b). Significant volumes of screening procedures can also be expected to be taking place within breast and cervical cancer programmes and other screening programmes globally. People invited for such screening procedures should make informed and balanced choices. However, focusing narrowly on this outcome may ignore conflicting implications for uptake, cost-effectiveness and other important public health goals. Stakeholders and policy makers must carefully consider these factors. We attempt to organise such evidence, wherever available, in this systematic review. Further research would certainly be necessary to place the findings of this review in context with existing or planned screening programmes.

 

Other reviews in the field

Risk communication itself may be subdivided by the dyad involved (for example between agencies and the public, or from individual communicators), as well as according to the nature of the risk, which may be familiar or non-familiar (Vlek 1987). Reviews of 'mass' risk communication, primarily from the environmental health discipline (Covello 1986; Fischhoff 1979; Keeney 1986), and from narrow clinical fields such as familial cancer (Bottorff 1996), are available. Briss 2004 also reviewed interventions that sought to promote informed decisions about cancer screening, but not through 'personalising' risk information. In addressing the effects of personalised methods of risk communication, this review focuses on risk communication which is provided, used and evaluated in healthcare encounters with individuals, couples, or immediate families (for example, parents of young children). As such, the review is broader than previous clinical risk communication reviews (Bottorff 1996) but still aims to be relevant to the community and the clinical needs of healthcare professionals across a range of disciplines. It focuses on the domain of healthcare screening. In contrast, Albada and colleagues (Albada 2009) in their systematic review have reviewed interventions that provided tailored information specifically about cancer risk and screening.  Information in the included studies was tailored to the personal characteristics related to behaviour change theories or constructs, and to the participant’s individualised risks. Their main outcome measures were risk perception, cancer knowledge and screening behaviour. A recent systematic review (Stacey 2011) evaluated randomised controlled trials (RCTs) of decision aids that provided information about treatment or screening options and their associated outcomes, compared to standard care. Decision attributes, behavioural, health, and health system effects were studied as outcome measures. Several other reviews have sought to consolidate evidence on the most effective means of increasing screening uptake (Bonfill 2001; Everett 2011; Flight 2004).

This review updates the 2006 version, which identified 22 relevant studies (Edwards 2006).

 

Objectives

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

We primarily compare the effects of personalised and general risk communication interventions in promoting an informed decision about participating in health screening. We also compare the effects of these interventions on people's cognitive, affective and behavioural outcomes. These include their knowledge, risk perception, satisfaction with decision making and decisional conflict, emotional well-being and behaviour (i.e. taking or intending to take screening tests).

 

Methods

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
 

Criteria for considering studies for this review

 

Types of studies

Randomised controlled trials (RCTs).

 

Types of participants

Participants were people facing real life decisions (not hypothetical exercises) about whether to undergo screening. They were individuals making decisions alone or on another's behalf (for example, for a young child), or couples making decisions together.

The screening activities involved an investigation performed by a health professional. Examples of these include:

  • mammography;
  • cervical 'Papanicolaou' smears;
  • colorectal cancer screening;
  • prostatic cancer screening (PSA test);
  • antenatal screening (including Down's syndrome, neural tube defects and other fetal anomalies);
  • genetic screening (including breast cancer gene testing);
  • high cholesterol/cardiovascular risk screening;
  • neonatal screening (including cystic fibrosis and Duchenne testing);
  • skin cancer screening;
  • lung cancer screening.

We excluded studies if they described only:

  • mass communication; or
  • military or school or prison-based interventions (where people are less free to choose than in other healthcare settings).

 

Types of interventions

The interventions comprise one of the three levels of personalised risk information:

  • individualised risk score or individual actual risk information (i.e. absolute or relative risk information); or
  • categorisations of risk status based on these estimates (for example, high, medium or low risk status); or
  • discussion of personal risk factors relevant to the screening decision (that is, the individual's own characteristics are taken into account in assessing their actual risk or elevated risk status relative to others).

The interventions address decision making about screening tests; that is, testing whose objective is the presumptive identification of unrecognised disease or defect (including genetic markers for disease) by the application of tests or other procedures, which can be applied rapidly. These tests are intended to sort out apparently well people who may have a disease from those who probably do not, and they are not intended to be diagnostic (Rose 1978; Wilson 1968).

The risk communication intervention could come before screening, at the time of the screening intervention, or at the time of counselling or promotion of screening. Personalised risk communication could be delivered via oral, written, video, or electronic (e.g. Internet or CD-ROM) media.

Personalised risk information is compared to generalised risk communication interventions, including: population risk estimates, general information on risk factors, or general encouragement to acknowledge risks or change risk behaviour.

We excluded studies which simply evaluated health education/promotion to reduce risk factors or increase adherence to screening, without discussion of risks and benefits of undergoing or not undergoing screening; or if general rather than personalised risk communication was the main basis of the intervention.

 

Types of outcome measures

 

Primary outcomes

Measures of an informed decision. An informed decision is defined as one where "a reasoned choice is made by a reasonable individual using relevant information about the advantages and disadvantages of all the possible courses of action, in accord with the individual’s beliefs" (Bekker 1999). An example is the Multidimensional Measure of Informed Choice (MMIC), proposed by Marteau and colleagues, based on assessing the consistency between knowledge, attitudes to tests and choices of uptake of tests. This has been validated for Down's syndrome screening (Michie 2002; Marteau 2001).

 

Secondary outcomes

Other outcome measures focus on identifying changes in any of the following key areas of informed decision making (Llewelyn-Thomas 1995; Edwards 1999a), if appropriate data were available:

  • 'cognitive' outcomes: knowledge of risk, accurate risk perception;
  • 'affective' outcomes: anxiety/emotional well-being, satisfaction with decisions made, decisional conflict, anxiety, intention to take up screening;
  • behavioural outcomes: uptake of tests, adherence to choice regarding screening test, 'appropriate' uptake;
  • health status outcomes: specific status measures or quality of life measures such as SF-36;
  • economic outcomes: cost of intervention.

 

Search methods for identification of studies

 

Electronic searches

Two authors searched:

  • the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 3 2012),
  • MEDLINE (OvidSP) (2005 to March 2012),
  • EMBASE (OvidSP) (2005 to March 2012),
  • CINAHL (EbscoHOST) (2005 to March 2012), and
  • PsycINFO (OvidSP) (2005 to March 2012).

We developed the search strategies from earlier work by Matthews et al (Matthews 1999) and the previous versions of this review (Edwards 2006; Edwards 2003b; Edwards 2003c), as well as that used by Jepson et al (Jepson 2000) in their systematic review. The strategies comprised three layers of search terms - keywords and medical subject headings (MeSH) - aimed at identifying articles about screening involving counselling/education or risk specifically. The goal was for high recall (sensitivity) of literature in the field of risk communication in screening (which may therefore be at the expense of precision (specificity). We searched from 2006 to March 2012 for all databases. We tailored search strategies to the relevant databases, and as appropriate used mainly subject headings (for example, MEDLINE, CINAHL) or the equivalent translated text words (for example, EMBASE). We used 'explode' functions on all subject headings. We have not applied language restrictions to the searches.

The date ranges for the previous searches (Edwards 2006) were from 1989 to December 2005 for PsycINFO and from 1985 to December 2005 for all other databases.

Overall the search date range for the CENTRAL, MEDLINE, EMBASE and CINAHL databases was from 1985 to March 2012. For PsycINFO the overall date range was from 1989 to March 2012.

For the first publication of this review (Edwards 2003b; Edwards 2003c) we also searched CancerLit (1985 to 2001) and Science Citation Index Expanded (searched March 2002). Strategies are available from the lead author upon request.

We present all search strategies in Appendices.

 

Searching other resources

We also reviewed the reference lists and conducted citation searches of included studies and other systematic reviews in the field (Albada 2009), to identify other potentially-relevant studies.

 

Data collection and analysis

 

Selection of studies

Two authors (GN and HA) independently made an initial selection of potentially-eligible studies from search outputs (titles and abstracts). Disagreements were resolved by discussion. In cases of doubt about relevance to this review, we retrieved papers in full for final decision making. Papers rejected at this stage were circulated to other review authors to review the decision, and if finally excluded, were listed in the table 'Characteristics of Excluded Studies' with a reason for exclusion given.

 

Data extraction and management

Two authors (GN and HA) independently extracted and collated on a template key information from all included studies. Data extracted included country of origin, health professional group involved, screening program and patient group involved, setting, sample size, and key outcomes. We also extracted data on differences in baseline risk, for assessment of effect modifiers. We checked consistency of assessment by weighted kappa agreement scores.

We also collected data on the nature/design of the intervention. This included: the type of personalised risk communication in the main intervention: estimate or calculation of numerical risk or risk score; estimated or calculated risk categorised as, for example, high, medium or low level of risk for consumer; or listing of personal risk factors without estimate or calculation of risk level. The nature of the intervention also covered whether specific counselling or behavioural change strategies, or both, were included in the risk communication intervention.

 

Assessment of risk of bias in included studies

We extracted data to assess the risk of bias of the included studies. Two authors independently assessed all studies against the Risk of Bias tool derived from the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We resolved the differences in assessment of the risk of bias included studies by discussion.

The principal elements used for this review are:

  • Random sequence generation (selection bias): Studies that describe the random sequence generation were marked as low risk. If there was no mention of this then the studies were marked as unclear. Quasi-randomised trials were marked as high risk.
  • Allocation concealment (selection bias): Studies that describe allocation concealment were marked as low risk. If there was no mention of this then the studies were marked as unclear. Studies were marked as high risk if an allocation concealment procedure was clearly not followed.
  • Blinding of participants and personnel (performance bias): Studies that describe blinding in detail were marked as low risk. If there was no mention of this then the studies were marked as unclear. Studies were marked as high risk if a blinding procedure was clearly not followed or the trialists described the participants and personnel in the study as unblinded.
  • Blinding of outcome assessment (detection bias): Studies that describe blinding in detail were marked as low risk. If there was no mention of this then the studies were marked as unclear. Studies were marked as high risk if a blinding procedure was clearly not followed or the trialists described the outcome assessment procedure as unblinded.
  • Incomplete outcome data (attrition bias): Studies were marked as low risk if there was a low attrition rate (less than 20%) or if an intention-to-treat principle was adopted. If there was no description of attrition in the study, this element was marked as unclear. If no adjustments were made despite a significant drop out rate studies were marked as high risk for attrition bias.
  • Selective reporting (reporting bias): Studies were marked as low risk if the protocols were available and all outcomes listed in it were addressed. They were marked as unclear if protocols were not available. High risk status was assigned if there was clear evidence of selective reporting based on the outcomes mentioned in the methods section of the included studies.
  • Other biases, e.g. baseline comparability, measure against contamination and funding for the screening test: Studies that describe the components in detail were marked as low risk. If there was no mention of this then the studies were marked as unclear. Studies were marked as high risk if the components were clearly not addressed, or demonstrated biases.

We report the risk of bias assessment in Figures and the table Characteristics of included studies. We conducted the sensitivity analysis by excluding studies that were considered as high risk for each risk of bias item mentioned above and then studying how this affected the results. We have also incorporated these assessments whilst rating the quality of the evidence using the GRADE working group's recommendations (Guyatt 2008).

 

Measures of treatment effect

 

Dichotomous data

We present dichotomous data as odds ratios (ORs) with 95% confidence intervals (CIs), calculated from absolute change in numbers between the intervention and control groups.

 

Continuous data

We present continuous data as mean differences (MDs) with 95% CIs, calculated from the mean change (and the standard deviation) between the intervention and control groups. When the same outcomes were measured on different scales we used standardised mean differences (SMDs) when combining data in the meta-analysis.

 

Unit of analysis issues

We included studies randomised at individual and cluster level. For studies that were cluster randomised, we used outcome measures adjusted for clustering in the analysis.

 

Dealing with missing data

We contacted authors to procure missing data. If data were still unavailable, these studies were still included and reported in the narrative. We did not conduct an intention-to-treat (ITT) analysis and data were analysed as reported. We documented loss to follow-up and assessed it as a source of potential bias. We conducted sensitivity analysis by excluding studies that were considered at high risk for attrition bias.

 

Assessment of heterogeneity

We used the I2 statistic to assess heterogeneity (Deeks 2011). We expected heterogeneity of the studies in terms of the screening tests addressed, participants and design of intervention. We used a fixed-effect model to present pooled data where heterogeneity was of low or moderate significance i.e. I2 < 60%. Results from a random-effects model (which gives a more conservative confidence interval on the estimate of effect size) were examined and presented in cases (in addition to fixed-effect estimates), where this was of substantial significance i.e. I2 = 60% to 79%. We did not pool the data if there was considerable heterogeneity between the studies i.e. I2 = / > 79%.

 

Data synthesis

Studies in this review derive from a heterogeneous group of screening programmes. Nevertheless the essential characteristic of the risk communication interventions (personalised elements) rendered data combination appropriate. We first examined studies for narrative synthesis. We then used standard statistical methods described in the Cochrane Handbook (Deeks 2011) to combine data. Data to be studied were entered and meta-analysed in the statistical software package incorporated within RevMan 5. Outcome data extracted comprised the absolute changes in numbers between groups (for dichotomous variables), and the mean change and standard deviation of the mean change (for continuous variables). From these, we calculated the odd ratios (ORs) for dichotomous variables, and mean differences (MDs) for continuous outcomes. When the same outcomes were measured on different scales we used SMDs when combining data in the meta-analysis. For cluster randomised studies, we used the generic inverse variance method to incorporate the adjusted ORs and the MDs. We also used the generic inverse variance method to combine studies where the raw data were not available, despite contacting the authors for further information. We recorded the statistical significance of results for all analysed outcomes. Insufficient data were provided in many studies to enable (raw) data entry into RevMan 5.

 

Subgroup analysis and investigation of heterogeneity

The categorisation of personalised risk communication into three levels of detail given to consumers (numerical risk, categorised risk and listing of personal risk factors) was examined for evidence of a different effect at each level. We also tested for heterogeneity within subgroups.We expected the interventions to be spread across a wide range of screening programmes and populations. Hence we also examined important effect modifiers on the changes in outcomes demonstrated, examining in particular, if the data were available, for the influence of: screening program or condition, setting, professional group involved, type of intervention, differences in baseline risk, high risk status of target population for screening, age, gender, educational level and the funding for the screening procedure. We then analysed different sections of the data, for example according to different conditions for screening, or studies based only on ‘high risk’ participants rather than people at ‘average risk’.

 

Sensitivity analysis

We included all studies in the meta-analysis where possible. We performed a series of sensitivity analyses to investigate how the results changed when studies with greater risk of bias were excluded in each risk of bias category.

 

Results

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
 

Description of studies

See: Characteristics of included studies; Characteristics of excluded studies; Characteristics of ongoing studies.

 

Results of the search

Twenty-two studies were already included in the last version of the review (Edwards 2006). From the new searches (2006 onwards), we identified 8371 publications. From these, we excluded 8025 publications after we examined the titles and abstracts. We retrieved 346 potentially-relevant studies in full-text for detailed evaluation. Of these, 15 new publications met our inclusion criteria. We also undertook a manual follow-up of references from key publications and journals, and of key authors. We identified a systematic review by Albada and colleagues (Albada 2009) as being highly relevant to this review. Four studies were added by hand searching reference lists of included studies in the Albada review, bringing the total of newly-included studies to 19. In total this updated review includes 41 studies. (See Figure 1).

 FigureFigure 1. Study flow diagram. Search dates from 2006 to March 2012.

 

Included studies

We describe the main characteristics of the 41 studies, including participants, interventions and outcomes measured, in the table Characteristics of included studies. All were published in English. Thirty-four were from the USA, four from Australia, one from Canada, one from Germany and one from the UK. There appeared to be increasing interest in the subject over time, with no studies published in the 1980s, 14 studies published in the 1990s and 27 published after 1999. The studies varied in size, with 4 studies involving less than 200 patients, 19 studies involving between 200 and 500 patients, 9 studies involving 500 to 1000 patients and 9 studies involving over 1000 patients.

All included studies were RCTs. Five studies used cluster randomisation (Geller 2006; Glazebrook 2006; Manne 2009; Manne 2010; Nagle 2008), of which one study reported an insignificant cluster effect (Manne 2009). We contacted six authors of included studies, mainly for further clarification of data and to gather information on how the risks were personalised. Three authors responded.

 

Study setting, recruitment and duration

The patient population and screening programme varied. Most of the studies related to breast cancer screening followed by colorectal cancer screening. They are as illustrated in the table below:

Most of the studies (35 out of 41) were based in the community, 2 were in an out-patient department setting and 4 were in primary care settings.

Time of outcome assessment varied from immediately after the intervention to 18 months post intervention. The mean time in months for outcome measurement was 7.26 and the median was 6 months.

 

Participants

The 41 studies in this review included 28,700 people. The number of people in each study varied from 140 to 3152 (median: 435). Recruitment of participants in most studies was done through clinic lists/GP lists/tumour registries (15 studies). Bowen 2002 had two methods of recruitment. The following table shows the method of recruitment for all studies included in the review.


RecruitmentStudy IDsNumber of studies

Clinic/GP listsBodurtha 2009; Campbell 1997; Champion 2002; Champion 2003; Champion 2007; Glazebrook 2006; Helmes 2006; Hutchison 1998; Kreuter 1996; Myers 1999; Nagle 2008; Saywell 1999; Sequist 2011; Skinner 1994; Trevena 200815

Newspaper adverts/community networksBowen 2002; Bowen 2006; Lipkus 2007b3

Community health centre/GP co-operatives/Lists from agencies/insurance companiesBowen 2010; Champion 2000a; Curry 1993; Jibaja-Weiss 2003; Lee 1991; Lipkus 2005; Marcus 2005; Rimer 2002; Smith 2010; Steckelberg 201110

First degree relatives of index cancer patients/patients with cancer identified from tumour registriesBastani 1999; Bloom 2006; Bowen 2002; Geller 2006; Glanz 2007; Lerman 1995; Lerman 1997; Manne 2009; Manne 2010; Rawl 2008; Schwartz 1999; Skinner 200212

Random digit diallingChampion 1994; Champion 19952



 

Healthcare group involved

Healthcare groups delivering/developing the interventions were as follows:


Healthcare GroupStudy IDsNumber of studies

General practitioners/Family care physicians/Community cliniciansCampbell 1997; Jibaja-Weiss 2003; Nagle 2008; Sequist 2011; Skinner 20025

Trained counsellors/genetic counsellors/health educators/health advisorsBloom 2006; Bowen 2002; Bowen 2006; Bowen 2010; Champion 2007; Geller 2006; Glanz 2007; Helmes 2006; Lerman 1997*; Lipkus 2005; Manne 2009; Manne 2010; Myers 1999; Rimer 200214

Nursing professionalsChampion 1994; Champion 1995; Champion 2000a; Champion 2002; Champion 2003; Lerman 1995; Lerman 1997*; Saywell 19998

Information specialistsMarcus 20051

Multidisciplinary teams (involving primary care, public health, secondary care professionals, psychologists and/or researchers)Bodurtha 2009; Glazebrook 2006; Smith 20103

Not statedBastani 1999; Curry 1993; Hutchison 1998; Kreuter 1996; Lee 1991; Lipkus 2007b; Rawl 2008; Schwartz 1999; Skinner 1994; Steckelberg 2011; Trevena 200811

* Both nursing professionals and counsellors delivered the interventions



 

Interventions

The personalised risk communication formed components of a wide range of interventions, with some studies using multiple or combined interventions of varying complexity. In general, the interventions used educational counselling (oral), written material such as tailored print material, computer/web-based interventions, and decision aids. Many studies tailored the interventions using constructs from behaviour change theories. Several studies used more than one theoretical construct to base their interventions. The table Characteristics of included studies provides further information on the interventions. With regard to the interventions targeted at patients, 19 studies reported on single component interventions and 22 reported on multiple component/complex interventions. They are illustrated in the table below:


Intervention typeStudy IDsNumber of studies

Studies assessing single component interventions for patients

Written material/Tailored printBastani 1999; Campbell 1997; Curry 1993; Hutchison 1998; Jibaja-Weiss 2003; Kreuter 1996; Lee 1991; Lipkus 2007b; Marcus 2005; Rawl 2008; Skinner 2002; Skinner 199412 

Face to face education/CounsellingBowen 2002; Schwartz 1999  2

Telephone counsellingBloom 2006; Geller 2006  2

Computer/web basedGlazebrook 2006; Sequist 2011; Steckelberg 2011  3

Studies assessing multiple component/complex interventions

Written material with face to face or telephone education/counsellingBodurtha 2009; Bowen 2006; Bowen 2010; Champion 1994; Champion 1995; Champion 2000a; Champion 2002; Champion 2003; Champion 2007; Glanz 2007; Helmes 2006; Lerman 1995; Lerman 1997; Lipkus 2005; Manne 2009; Manne 2010; Myers 1999; Rimer 2002; Saywell 1999  19

Decision aidsNagle 2008; Smith 2010; Trevena 2008  3



Studies examined interventions based on several behaviour change theoretical constructs. Some studies had a combination approach, incorporating two or more behaviour change theories in their interventions. They are illustrated in the table below:

Personalised risk was delivered to the participants in three basic methods. Ten studies had interventions that included a calculated numerical risk estimate, such as from the Gail model for breast cancer risk (Gail 1989). Seven studies had interventions that categorised risk level, such as into 'high’, 'medium’ or 'low’ strata. The other 24 studies examined interventions that included the most basic level of ’personalised risk’, such as the listing of risk factors pertinent to an individual.

 

Comparison

In 20 studies, the control participants received general risk information intended to be similar in length to the personal risk intervention being studied, and in 16 studies they received only the usual care. In five studies the interventions for control participants were delayed until the completion of the study.


Comparison/control groupStudy IDsNumber of studies

Received general risk information intended to be similar in length to the personal risk intervention being studiedBastani 1999; Bodurtha 2009; Curry 1993; Glanz 2007; Hutchison 1998; Lee 1991; Lerman 1995; Lipkus 2005; Lipkus 2007b; Manne 2009; Manne 2010; Marcus 2005; Myers 1999; Nagle 2008; Rawl 2008; Schwartz 1999; Skinner 1994; Skinner 2002; Smith 2010; Steckelberg 201120

Usual careCampbell 1997; Champion 1994; Champion 1995; Champion 2000a; Champion 2002; Champion 2003; Champion 2007; Geller 2006; Glazebrook 2006; Jibaja-Weiss 2003; Kreuter 1996; Lerman 1997; Rimer 2002; Saywell 1999; Sequist 2011; Trevena 200816

Interventions for control participants were delayed until the completion of the studyBloom 2006; Bowen 2002; Bowen 2006; Bowen 2010; Helmes 20065



 

Outcomes

We extracted data on all reported outcomes specified in the review. The primary focus was on measures of an informed decision. We also list below studies reporting other cognitive, affective and behavioural outcomes.

Informed decision

  • Three newly-identified studies reported informed decision as an outcome measure in their study (Nagle 2008; Smith 2010; Steckelberg 2011). All of these studies used outcome measures based on Marteau’s multi-dimensional measure of informed choice (Marteau 2001). 

Knowledge regarding the screening test

Improvement in risk comprehension

  • Lerman 1995 reported improvement in risk comprehension as an outcome measure.

Perceived risk

  • One study (Bowen 2002) reported perceiving self as an appropriate candidate for the test, and three studies (Lerman 1995; Rimer 2002; Skinner 2002) reported accurately perceived risk as outcome measures. Another study (Helmes 2006) measured perceived risk as the perceived chance of developing the disease, which was not included in the meta-analysis.

Anxiety

  • Six studies measured anxiety. Different scales were used, with two studies (Nagle 2008; Smith 2010) using the State Trait Anxiety Inventory (STAI), one study (Lerman 1995) using the IES breast cancer distress scale, and three studies (Bloom 2006; Helmes 2006; Skinner 2002) using the breast cancer worry score, on different scales where higher scores correspond to increased anxiety or worry.

Satisfaction with decision

  • Smith 2010 measured satisfaction with the decision made.

Decision conflict

  • This was used by two studies. Smith 2010 measured the proportion of participants with low decision conflict and Nagle 2008 used the decision conflict scale as a continuous measure.

Intention to take the screening test

Uptake of the test

 Appropriate use of cholesterol test

Stages of change for uptake of test

Making a recommended behaviour change

 

Excluded studies

Several studies were considered closely for inclusion but were eventually excluded. We excluded 29 studies from this review. The primary reason for exclusion (14 studies) was that the intervention did not involve a personalised estimate of the participant's risk. Six studies designed the intervention around participants' health beliefs rather than their risks, and we excluded four studies for not containing a control group. Of the remaining five studies, one was a longitudinal study rather than a randomised trial, one was a study protocol, and in three studies, the outcome could not be regarded as a screening test. See Characteristics of excluded studies. Two studies were still on-going and could not be included, see Characteristics of ongoing studies.

 

Risk of bias in included studies

As described in the Characteristics of included studies, six main risk of bias criteria (random sequence generation, allocation concealment, blinding: participants and personnel, blinding: outcome assessors, incomplete outcome data, selective reporting) and three other risk of bias items (baseline comparability, measures against contamination and funding for screening tests) were applied to each study. Only nine studies met four or more of the nine criteria for low risk of bias (Bodurtha 2009; Glazebrook 2006; Manne 2009; Manne 2010; Nagle 2008; Sequist 2011; Smith 2010; Steckelberg 2011; Trevena 2008). The three studies (Nagle 2008; Smith 2010; Steckelberg 2011) reporting informed decision, our primary outcome measures, met six, eight and all of the nine criteria respectively (Figure 2; Figure 3).

 FigureFigure 2. Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
 FigureFigure 3. Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

 

Allocation

The allocation sequence was adequately generated in 10 studies (Bodurtha 2009; Champion 2000a; Champion 2003; Glazebrook 2006; Jibaja-Weiss 2003; Lee 1991; Nagle 2008; Smith 2010; Steckelberg 2011; Trevena 2008), whereas 2 studies (Campbell 1997; Geller 2006) described a quasi-random component in the sequence generation process. Other (29) studies did not report sufficient information to determine this risk of bias.

The allocation was adequately concealed only in four studies (Nagle 2008; Smith 2010; Steckelberg 2011; Trevena 2008). The majority of the studies (37) did not describe the allocation concealment in sufficient detail to permit evaluation.

 

Blinding

The majority of the interventions provided to clients were difficult to blind for clients, providers and outcomes assessors. Therefore, only two studies (Smith 2010; Steckelberg 2011) could blind clients, providers and outcome assessors.

 

Incomplete outcome data

Twenty-six studies adequately addressed incomplete outcome data (Bloom 2006; Bodurtha 2009; Bowen 2002; Bowen 2006; Bowen 2010; Campbell 1997; Champion 1994; Champion 1995; Champion 2007; Curry 1993 Glanz 2007; Helmes 2006; Lee 1991; Lipkus 2005; Lipkus 2007b; Manne 2009; Manne 2010; Marcus 2005; Myers 1999; Nagle 2008; Schwartz 1999; Sequist 2011; Skinner 2002; Smith 2010; Steckelberg 2011; Trevena 2008) whereas six studies did not (Bastani 1999; Champion 2002; Geller 2006; Hutchison 1998; Rimer 2002; Saywell 1999). The principal reason for the attrition bias was that missing outcomes are enough to induce clinically-relevant bias in observed effect estimates. Nine studies reported insufficient information to evaluate this criterion. 

 

Selective reporting

Only five studies provided trial registration numbers, for which protocols were available. Of these five studies, one (Trevena 2008) did not contain adequate details for outcome measures in the protocol. The other four studies (Nagle 2008; Sequist 2011; Smith 2010; Steckelberg 2011) reported all planned outcomes and were definitely free of reporting bias. Ten studies (Bloom 2006; Bowen 2006; Bowen 2010; Champion 1995; Champion 2002; Glazebrook 2006; Jibaja-Weiss 2003; Lee 1991; Manne 2009; Skinner 1994) did not report all the outcomes mentioned in the paper. The other 26 studies reported all outcomes mentioned in the paper, but were marked as unclear due to unavailability of study protocol.

 

Other potential sources of bias

In 28 studies (Bodurtha 2009; Bowen 2002; Bowen 2006; Bowen 2010; Campbell 1997; Champion 2007; Geller 2006; Glanz 2007; Glazebrook 2006; Helmes 2006; Hutchison 1998; Jibaja-Weiss 2003; Kreuter 1996; Lerman 1995; Lerman 1997; Lipkus 2005; Lipkus 2007b; Manne 2009; Manne 2010; Marcus 2005; Nagle 2008; Rawl 2008; Rimer 2002; Saywell 1999; Sequist 2011; Skinner 1994; Skinner 2002; Smith 2010; Steckelberg 2011; Trevena 2008) the baseline characteristics of participants in all study arms were comparable to each other. In five studies (Bastani 1999; Champion 1994; Champion 2002; Champion 2003; Hutchison 1998) there were differences in the baseline characteristics of participants between the study arms. The remaining eight studies did not provide adequate information to assess this source of bias.

Nine studies (Bastani 1999; Campbell 1997; Geller 2006; Glazebrook 2006; Hutchison 1998; Manne 2009; Manne 2010; Nagle 2008; Steckelberg 2011) had adequate protection against contamination. We assessed a further seven studies (Bloom 2006; Bowen 2002; Glanz 2007; Lerman 1995; Lerman 1997; Rawl 2008; Schwartz 1999) as being at high risk due to the potential for contamination within recruited members, and without mention of protection against contamination. We assessed as unclear the other 25 studies in which there was no mention of protection against contamination.

We included funding for the screening test as one of the risk of bias elements, as this can influence the intentions and the uptake of screening tests. Only 13 studies (Bodurtha 2009; Glanz 2007; Glazebrook 2006; Helmes 2006; Lipkus 2005; Lipkus 2007b; Manne 2009; Manne 2010; Rawl 2008; Saywell 1999; Sequist 2011; Smith 2010; Steckelberg 2011) reported how participants would have had their screening tests funded; i.e. if tests were provided free of charge by the researchers, if participants themselves had to pay for these tests, or if they were provided by the health organisation or insurance companies.  

 

Effects of interventions

See:  Summary of findings for the main comparison Personalised risk communication versus general risk information for informed decision making about taking screening tests;  Summary of findings 2 Personalised risk communication versus general risk information for informed decision making about taking screening tests

 

Informed decision

Our primary outcome of interest was 'informed decision'. This was measured in three newly-included studies, with the measures based on Marteau’s Multidimensional Measure of Informed choice (Marteau 2001). One study provided personalised risk to the consumers undergoing screening, within interventions that used a calculated risk score (Nagle 2008). The other two studies (Steckelberg 2011; Smith 2010) listed out the personal risk factors to the participants. Overall there is strong evidence from these three trials that personalised risk communication, when presented as a part of complex interventions such as decision aids, promotes informed uptake of screening tests. Overall 45.2% (592/1309) of participants who received personalised risk information made informed choices, compared to 20.2% (229/1135) of participants who received generic risk information. The odds ratio (OR) for the study that provided numerical risk in its intervention (Nagle 2008) was OR 2.08 (95% CI 1.14 to 3.81). The pooled OR for the two studies that used listing of risk factors in their intervention was 4.98 (95% CI 3.97 to 6.24). The overall ORs were 4.48 (95% CI 3.62 to 5.53) for fixed-effect and 3.65 (95% CI 2.13 to 6.23) for random-effects. The effect measures showed significant heterogeneity (I2 = 77%), probably due to differences in the type of interventions and the populations within each study. However all studies consistently demonstrated a similar direction of effect, favouring personalised risk communication (see  Analysis 1.1 and  Summary of findings for the main comparison).

 

Knowledge

Nine studies measured knowledge regarding the screening test. Four studies measured this on continuous outcome scales. The scales used varied with studies and we present these as standardised mean differences (SMDs). There were considerable subgroup differences, but all studies showed a significant increase in knowledge with personalised risk communication. We present the SMDs (by fixed-effects model) in each subgroup below in the table (also see  Analysis 1.2 subcategories 1-3 and  Summary of findings 2):


 Knowledge measured on continuous scales

 Subgroup

 
  Standardised Mean Difference (SMD)

Fixed-effect model

Calculated risk score (numerical) versus general information0.40 (95% CI 0.23 to 0.56)

Calculated risk score (categorised) versus general information0.57 (95% CI 0.32 to 0.82)

Personal risk factor list versus general information0.89 (95% CI 0.75 to 1.04)



Five studies measured the proportion of participants with good knowledge in each of their study arms. Again, there were significantly higher proportions of participants with good knowledge within the intervention arms of the studies, when personalised risk communication was delivered:


Knowledge - measured as proportion with good knowledge

Subgroup

 
Odds Ratios (OR)

Fixed-effect modelRandom-effects model

Calculated risk score (numerical) versus general information (see  Analysis 1.3)2.11 (95% CI 1.52 to 2.91)2.60 (95% CI 1.27 to 5.34)

Personal risk factor list v general information (see  Analysis 1.4)7.13 (95% CI 5.79 to 8.79)-



 

Risk perception/comprehension

In three studies the interventions showed a trend towards more accurate risk perception (fixed-effect OR 1.46 (95% CI 1.13 to 1.88); random-effects OR 1.65 (95% CI 0.96 to 2.81), see  Analysis 1.5 ). However the evidence from these studies was of low quality (see  Summary of findings 2). Another study (Bowen 2002) examined whether people perceived themselves as appropriate for the tests after the intervention, with a non-significant trend towards fewer people perceiving appropriateness of tests after the intervention(see  Analysis 1.6). Skinner 2002 studied the effect of tailored print material on the decision to take up genetic screening in women with personal history of breast and ovarian cancer. This study showed that women were more knowledgeable (SMD 0.57 (95% CI 0.32 to 0.82)), and had more accurate perceptions of risk (OR 2.50 (95% CI 1.48 to 4.20)), but fewer intended to take tests after the personalised risk intervention (OR 0.21 (95% CI 0.07 to 0.64)), see  Analysis 1.2;  Analysis 1.5;  Analysis 1.11. Lerman 1995 reported a small (non-significant) improvement in risk comprehension after personalised risks were delivered (see  Analysis 1.17).

 

Anxiety

Four out of six studies reported non-significant changes in anxiety following personalised risk communication to the participants. Overall there was a small, non-significant decrease in anxiety scores in the intervention group (fixed-effect SMD -0.13 (95% CI -0.22 to -0.03); random-effects SMD -0.13 (95% CI -0.29 to 0.03); negative values indicate a decrease in anxiety and favouring personalised risk communication), see  Analysis 1.7 and  Summary of findings 2.

 

Uptake of test

Most studies (32/41) measured the uptake of screening tests following personalised risk communication interventions. There was significant heterogeneity (I2 > 80%) within these studies. Subgroup analysis revealed this was less significant when studies were examined by the levels of risk communication that was provided within their interventions. Heterogeneity was least significant in studies where risk was provided in a more detailed manner, i.e. as a numerical score. There were six studies in this group, with personalised risk communication having little effect on the uptake of screening tests (fixed-effect OR 0.95 (95% CI 0.78 to 1.15)), see  Analysis 1.14 subcategory 01 and  Summary of findings for the main comparison). For risk estimates or calculations which were categorised into high, medium or low strata of risk (six studies), the results were significant ( OR 1.29 (95% CI 1.11 to 1.51) (see  Analysis 1.14 subcategory 02 and  Summary of findings for the main comparison). Overall, pooling of these two subgroups yielded a combined OR of 1.15 (95% CI 1.02 to 1.29) – indicative of weak evidence, consistent with a small effect, that personalised risk communication where a risk score was provided or the participants were given their categorised risk, increases uptake of screening tests. 

For studies that listed personal risk factors in their interventions (20 studies), there was considerable heterogeneity between studies for uptake of test, and we do not present a pooled estimate for this subgroup. However 10/20 studies (50%) showed a significant increase in the uptake of the screening tests following personalised risk communication interventions (see  Analysis 1.15 subcategory 01). 

One study (Hutchison 1998) also assessed 'appropriate uptake' of tests (externally-defined appropriateness: that is, uptake for people at higher risk, and non-uptake for people at lower risk) and found more appropriate uptake after the personalised risk intervention (OR 1.32 (95% CI 1.14 to 1.55) ( Analysis 1.16).

 

Intention to take up screening test

Seven studies measured the intention to take screening tests, and had considerably heterogeneous results (see  Analysis 1.11). Three other studies measured the intention to take up genetic testing in normal or low risk participants. These tests (as discussed earlier in this review) are not necessarily indicated in consumers with such low or average risks. In one study (Bowen 2002) the results were inconclusive (see  Analysis 1.12). The other two studies (Helmes 2006; Bowen 2010) demonstrated a significant decrease in participants' intentions to take up the genetic tests following personalised risk communication interventions. The mean difference (MD) for fixed-effect was -0.53 (95% CI -0.63 to -0.44) and for random-effects was -0.77 (95% CI -1.42 to -0.13) (negative values indicate a decrease in intention and favour personalised risk communication; see  Analysis 1.13).

 

Decision conflict

Nagle 2008 measured decision conflict regarding prenatal testing for foetal abnormality. The results of this study showed a non-significant increase in decision conflict following personalised risk communication (see  Analysis 1.9).

 

Satisfaction with decision

Smith 2010 measured satisfaction with decision in interventions to support informed choice about bowel cancer screening and found no change after personalised risk communication (see  Analysis 1.10).

 

Stages of change

Three studies looked at the stages of change based on the Transtheoretical model (Prochaska 1992; Prochaska 1997). Champion 2007 found all interventions were effective (compared to usual care) in moving women forward in mammography stage (versus same or backward). However, the combination of tailored telephone and print interventions (versus usual care) had the largest effect (OR 1.95 (95% CI 1.36 to 2.81)). Rawl 2008 found that the forward movement of stage was slightly higher in the risk tailored group for colorectal cancer screening. Skinner 1994 found that 71% of participants did not change; 14% advanced one stage; 12% 'regressed'; no significant differences between tailored message and control groups were noticed (see  Analysis 1.18).

 

Making a recommended behaviour change

One study (Kreuter 1996) measured the proportion of participants making a recommended behaviour change after personalised risks were communicated. There were slight, non-significant differences between the groups (OR 0.98 95% CI 0.76 to 1.28).

 

Quality of life (SF-36)

One study (Bowen 2010) measured quality of life scores on the SF-36 scale after interventions to promote breast health behaviours. There was significant improvement on this measure following personalised risk communication(MD 7.10 (95% CI 5.90 to 8.30), see  Analysis 1.20).

 

Subgroup analysis

The results for the studies addressing mammography showed similar effects with uptake of screening and to the overall data set. The numerical calculated risk estimates were associated with lower ORs for uptake of tests (OR 0.84 (95% CI 0.68 to 1.03)) (See  Analysis 3.5 and subcategories). The subgroups for categorised risk and listing of risk factors showed considerable heterogeneity with uptake of the tests and it was not appropriate to pool these results (see  Analysis 3.6 and  Analysis 3.7).

The 11 studies examining risk communication in high-risk individuals showed trends towards larger ORs for uptake of tests than the other studies. The OR for numerical estimates of risk was 1.05 by fixed-effect (95% CI 0.79 to 1.40) and 1.08 by random-effects (95% CI 0.63 to 1.84). The OR for categorised estimates of risk was 1.38 (95%CI 1.06 to1.80). Overall, pooling of these two subgroups yielded a combined OR of 1.22 (95% CI 1.00 to 1.48),  indicative of a higher uptake of screening tests when only the high-risk group studies were analysed ( Analysis 5.6).

For studies whose interventions listed personal risk factors, there was considerable heterogeneity between studies for uptake of test, and we do not present a pooled estimate for this subgroup (See  Analysis 5.7).

Ten studies measured uptake of tests in colorectal cancer screening programmes. The overall OR for uptake was 1.02 by fixed-effect (95% CI 0.90 to 1.16) and 1.06 by random-effects (95% CI 0.82 to 1.37) (see  Analysis 6.8).

 

Sensitivity analysis

We conducted a sensitivity analysis for the uptake of screening tests, by excluding studies that were either at either unclear or high risk of bias for four risk of bias domains that we considered to be most important for this topic. The results were as follows:

 
Random sequence generation (selection bias)

In this analysis we included only low risk studies. The analysis for this risk of bias domain yielded a lower uptake of tests, with an OR of 1.10 (95% CI 0.88 to 1.39) when the two groups with limited heterogeneity (i.e. those which provided either numerical or categorised risks) were pooled. Five low risk studies accounted for this analysis (Bodurtha 2009; Glazebrook 2006; Lee 1991; Nagle 2008; Trevena 2008). The studies that provided listing of risk factors remained considerably heterogeneous, even after studies with high risk of bias for the above domain were excluded.

 
Incomplete outcome data (attrition)

In this analysis we included only low risk studies. The analysis for this risk of bias domain yielded similar uptake of tests, with an OR of 1.15 (95% CI 0.99 to 1.35) when the two groups with limited heterogeneity (i.e. those which provided either numerical or categorised risks) were pooled. Nine low risk studies accounted for this analysis (Bloom 2006; Bodurtha 2009; Bowen 2010; Glanz 2007; Lee 1991; Nagle 2008; Schwartz 1999; Sequist 2011 Trevena 2008). The studies that provided listing of risk factors remained considerably heterogeneous, even after studies with high risk of bias for the above domain were excluded.

Selective reporting (reporting bias)

In this analysis we only excluded studies that were at high risk for selective reporting. We included low risk studies and the 'unclear' studies which reported all outcomes mentioned in their methods sections, but were marked 'unclear' due to unavailability of study protocol. The analysis for this risk of bias domain reduced the uptake of tests, with an OR of 1.03 (95% CI 0.90 to 1.18) when the two groups with limited heterogeneity (i.e. those which provided either numerical or categorised risks) were pooled. Seven studies accounted for this analysis (Bastani 1999; Bodurtha 2009; Glanz 2007; Nagle 2008; Rimer 2002; Schwartz 1999; Sequist 2011). The studies that provided listing of risk factors remained considerably heterogeneous, even after studies with high risk of bias for the above domain were excluded.

 
Baseline comparability

The analysis for this risk of bias domain again yielded a similar measure of uptake of tests, with an OR of 1.15 (95% CI 1.00 to 1.33) for fixed-effect and 1.22 (95% CI 0.94 to 1.57) for random-effects model (I2 = 63%), when the two groups with limited heterogeneity (i.e. those which provided either numerical or categorised risks) were pooled. Eight studies accounted for this analysis (Bodurtha 2009; Bowen 2010; Glanz 2007; Glazebrook 2006; Nagle 2008; Rimer 2002; Sequist 2011; Trevena 2008). The studies that provided listing of risk factors remained considerably heterogeneous, even after studies with high risk of bias for the above domain were excluded.

The sensitivity analyses for selection bias, attrition bias and baseline comparability did not alter the uptake measure significantly. By excluding the studies that had a high risk for selective reporting bias, the OR for uptake was lower, probably due to the exclusion of studies that may have reported only significant results.

 

Discussion

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
 

Summary of main results

This review updates the second (2006) publication of this review (Edwards 2006).  Risk communication, particularly in the field of screening, is evolving with time. We have identified 19 new studies with elements of personalised risk communication in screening, which takes the total number of studies in this review to 41. The earlier two versions of this review (Edwards 2003c; Edwards 2006) included studies that measured outcomes that form elements of an informed decision, but identified no studies measuring informed decision as such. This updated review includes three studies (Nagle 2008; Smith 2010; Steckelberg 2011) that specifically have measured informed decision for the uptake of screening tests after personalised risk communication. From these three studies, there was strong evidence that personalised risk communication enhanced informed decision making, although with heterogeneous results. We expected this heterogeneity due to the varied nature of the interventions and the level of risk communication that was used in each of these studies. However, all studies had significant effect sizes, all favouring personalised risk communication.

Other recent studies have sought to address knowledge, perceived risk, and other elements of informed decision making. Overall these studies showed an increase in knowledge and a trend towards more accurate risk perception when personalised risk was included in their interventions. This field of risk communication in screening mirrors other clinical areas of health care, where there is an increasing trend to involve consumers in their own healthcare choices. In the same way that screening recommendations have developed in recent years from simply seeking to maximise uptake, to promotion of informed choice about participation in screening programmes (Holland 2005), the trial literature also now shows some development towards this.

Further, there was weak evidence to suggest that such interventions promote uptake of screening tests, although effects were small.

Some data also show that when the personalised risk information disclosed is more detailed, the uptake of tests is lower than when the risk communication is less detailed or numerically unspecific (i.e. when categorised into risk groups). Significant heterogeneity was noted among studies that merely listed the personal risk factors in their intervention. Test uptake thus becomes unpredictable when less specific ways of communicating personal risk factors are used. This may reflect the differences in perception of actual risk among participants between studies when such less specific methods of personalised risk communication are employed, but may also reflect the different nature of screening programme where some are more controversial (e.g. mammography and PSA testing) that the others (e.g. colorectal cancer screening).

 

High risk status an effect modifier

The effects of interventions on screening uptake appeared greater among consumers or patients deemed to be at higher risk of a disease than average. In this group of studies, most favoured the intervention, irrespective of the level of risk communication. This suggests that the 'high risk status' of the consumer may be an important effect modifier for personalised risk communication. There appears to be potential for substantial modification of choices made among this group of consumers. It may be appropriate that the scope for interventions to influence decisions is large in this group of consumers. Equally, however, interventions could be harmful if they are not introduced carefully. Healthcare professionals must take steps to ensure that they are achieving informed decision making by these consumers.

 

Overall completeness and applicability of evidence

We have used a highly-sensitive search strategy (Matthews 1999) to identify as many papers as possible in this field, from all relevant databases. The search covers studies published until March 2012, in an effort to outline the most contemporary evidence at the time of publication. To further increase the completeness of this search, we have followed this up by handsearching journals and reference lists, and also have looked at other similar reviews in the field to identify any papers missed in the initial search. This updated review presents a significant increase in the number of included studies, compared to the previous versions. Notably, however, the interventions used in these studies cover a range of methods to deliver the personalised element of risk communication.  In addition, the measures of components of informed decision vary widely across studies. The settings in which the studies were conducted include a diverse range of screening programmes. Therefore, data from this review do not allow us to draw a firm conclusion about the best interventions to deliver personalised risk st= in enhancing informed decisions. The results are dominated by findings from the topic area of mammography and colorectal cancer. Caution is therefore required in generalising from these results, particularly for clinical topics other than mammography and colorectal cancer screening.

Further, in all studies the effects are a measure of the ('complex') intervention as a whole rather than that of personalised risk communication per se. However, isolating the effect of personalised risk communication on informed decisions or the uptake of tests, and drawing firm conclusions based on this, poses a great challenge to researchers working in this field.

 

Quality of the evidence

We included 41 studies in this review. It is difficult to draw general conclusions from this review because these studies varied widely according to interventions provided, outcomes measured, the screening programme, and duration of interventions and follow-up. A substantial number of people are included in this review (28,700), and the number of participants within each study varied widely, from 140 to 3152 people.

The studies that reported direct measures of informed decision were few (n = 3) in number, but were of very good methodological quality. Most other studies that measured other outcomes (i.e. components of informed decision) were not explicit in reporting elements that could introduce biases into their design, although studies reporting several of the knowledge outcomes were also of high quality. Only five studies (Lerman 1995; Lerman 1997; Manne 2010; Nagle 2008; Smith 2010) provided information of the validity of scales used to measure the outcomes of interest.

We have illustrated the quality of evidence for our main outcomes using GRADE Working Group recommendations (Guyatt 2008) in  Summary of findings for the main comparison and  Summary of findings 2.

Overall, the outcome ‘informed decision’ and several of the knowledge outcomes were subject to less risk of bias and provide better quality of evidence than the other reported outcomes (in particular, screening uptake, accuracy of perceived risk, and anxiety).

 

Potential biases in the review process

Despite the extensive search strategy, we may have missed some studies of interventions including personalised risk communication for screening tests if they were not indexed in the bibliographic databases appropriately to be identified by our search terms. We made significant efforts to contact trial authors for further data, where these were not available, and for further details of how the risks were personalised. Some data that were not extractable or were unclear were not included, if we did not receive further clarification from the authors.

As expected, the other main limitation of this review lies in the fact that we did not find any studies that examined the effect of personalised risk communication in isolation. All studies studied the effect of their interventions which included personalised risk communication. The interventions as such were diverse in nature, and it is hard to draw firm conclusions on the weight of effect that personalised risk communication has contributed to. There was considerable heterogeneity in some of the groups, in which case we did not pool these data. Many studies that were unclear on the risk of bias items are still included within the overall meta-analyses, and we present a sensitivity analysis by excluding such studies. Lastly, ways to measure the outcomes were also diverse which may have biased the review. Common validated methods of measuring informed decision are yet to be developed for wider use.

 

Agreements and disagreements with other studies or reviews

We are not aware of any other systematic reviews that assessed specifically the effect of personalised risk communication on measures of informed decision. Albada 2009 was the most similar review assessing the effects of tailored information based on theoretical (behavioural) constructs and providing cancer risk for individuals undergoing screening. The authors assessed the effects on risk perception, knowledge and screening behaviour. They concluded that interventions based on theoretical constructs increased risk perception and knowledge of cancer as compared to generic interventions, and information tailored to individual risk specifically increased realistic risk perceptions. As with our review, they found limited evidence for enhancing screening behaviour. A recent review (Stacey 2011) also found that decision aids that are tailored to support individual healthcare decisions increased people's involvement, and improved knowledge and realistic perception of outcomes, but had variable effect on choice behaviour.

Noar et al presented the first meta-analytic review of tailored print interventions for health behaviour change. In contrast with our review, their findings suggest that tailored print information focusing on preventive health behaviours out-perform generic interventions. However a host of behaviour change studies other than screening are included in their review (Noar 2007).

Briss 2004 studied interventions that were generally consistent in improving individuals’ knowledge about the disease, accuracy of risk perceptions, or knowledge and beliefs about the pros and cons of screening and treatment options. They reported insufficient evidence that such interventions promote informed decisions, due to a paucity of studies that fully measured this outcome. They recommended further research in this area, providing a conceptual framework to evaluate this outcome. We have found only three studies that used informed decision as an outcome measure, reiterating the recommendations proposed by Briss 2004.

 

Authors' conclusions

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

 

Implications for practice

There is strong evidence, from three trials, that personalised risk estimates incorporated within communication interventions for screening programmes enhance informed choice. However the evidence for similar interventions increasing the uptake of such screening tests is weak and it is not clear if this increase is associated with informed choice. Focusing on the achievement and demonstration of informed choice in isolation, would, nonetheless, fail to address the wider context. This context concerns an apparent tension between public health and individual health policy. Policies of greater patient involvement in decisions are being pursued, and this is justified from both ethical perspectives and some (though perhaps more limited) evidence of improved health outcomes (Guadagnoli 1998). There is a need to evaluate whether such policies mean that those people invited for screening are genuinely making informed choices, and how comprehensive information affects other variables such as uptake, cost effectiveness, and satisfaction (Jepson 2005). Appropriate risk communication has an integral role in this (Elwyn 2000). But as this is followed through, it is possible that public health objectives might be compromised.

An example may be seen in breast cancer screening.  Younger women are at lower risk of developing cancer (Nelson 2009), but may perceive themselves as at higher risk due to screening activity, publicity and public health messages about the importance of screening for the population.  Some women may choose not to have regular screening when informed of the absolute levels of risk and benefit from screening programmes. But the public health message, aided sometimes by financial incentives for clinicians to achieve high uptake figures, means that informed decision making by patients is still a major challenge (Thomson 2005). Further, recent literature demonstrates uncertainties surrounding actual estimates of risks and benefits associated with breast cancer screening.  McPherson 2010 analyses such evidence in a recent article, concluding "Arguments that polarise are unhelpful and render women, many with strong preferences, more helpless (to make informed decisions). For too long they have been misled and confused by too much agenda driven analyses of these data. What is required now is a full examination of all the data, preferably individual patient data, from all the recent studies, by dispassionate epidemiologists to get the best estimates in the UK screening setting.”

The same tension exists in relation to cardiovascular screening (Marteau 2002). These tensions between public health goals and individual choices must be recognised (Rogers 2002). An informed debate should be conducted among the relevant interest groups - government, public health, clinicians, and particularly patients and patient representative groups. There are some signs that this debate is now taking place (Marteau 2002; Stefanek 2011). Policy developments could explore whether individually appropriate choices, which vary from those recommended by standards and guidelines, can be accommodated in assessments of achieving best practice (i.e. meeting standards) at population level. One solution may be for care to be viewed as 'adherent to guidelines' or 'satisfactory' if adequate steps have been made to achieve and demonstrate informed decision making by patients - even if it deviates from the conventional (bio-medical) recommendations of those guidelines. However it is also important to consider ethical implications whilst trying to achieve informed choice specially in certain disadvantaged populations. The question is whether this deters such individuals from undergoing the immediate inconveniences of screening, especially if the diagnosis necessitates a change in their behaviour or lifestyle and thus potentially widening the inequities in healthcare provision. Kellar and colleagues in their study conclude that in such circumstances efforts to enhance informed choice may require that the immediate benefits are communicated, and efforts to address the apparent barriers are made, if the potential for inequity is to be avoided (Kellar 2011).

Taking the example of cervical cancer screening, a full discussion of the pros and cons of screening for cervical cancer may be viewed as a satisfactory goal for the healthcare provider, even if an individual woman chooses not to undergo screening, thus against public health advice. Counting such scenarios as satisfactory or adherent to guidelines (for example for purposes of audit) may allow healthcare professionals to enhance their communication with patients. If this is incorporated into the goals of health care, then improvements in care can be achieved without falling foul of requirements for governance, audit, and payment targets. An example was evident in the criteria for Fellowship by Assessment (FBA) of the Royal College of General Practitioners (UK). In the case of vaccination targets, the criteria stated that "candidates who are unable to demonstrate these standards must exceed the average level of vaccination and show how patients/ parents/ care givers are enabled to make an informed choice about vaccination" (RCGP 2002). The FBA scheme has now been revised, but the issue of informed decision making in preventive activities could equally apply to screening choices. The need for measures to demonstrate informed choice across a number of healthcare decisions is further reiterated.

 
Implications for research

In future, studies that use standardised and validated measures of informed decision are needed. Further, studies designed to measure the effect of personalised risk communication more accurately still need to be developed. We discuss three main areas in which research should be focused in risk communication for screening:

Calculating risk scores

The relative paucity of data in the screening area reflects the difficulties for researchers or clinicians in communicating personalised risk to consumers. The range of clinical topics for which risk scores in specified populations are available is narrow. Calculation or estimation of risk is dependent on adequate epidemiological background data, and feasible means of converting these into information for consumers based on their individual risk factors. Examples include the Gail model (Gail 1989) and methods for calculating cardiovascular risk (Hippisley-Cox 2008; Heart 1999), but only the latter has permeated into routine clinical practice. In some cases, risk scores can only be calculated after the screening test - an example is the FRAX® score (Kanis 2009) used to calculate risk of osteoporotic fractures. In clinical practice patients are offered screening if they have known risk factors for developing osteoporosis. Currently treatment recommendations are not exclusively based on FRAX® assessment scores. This remains a matter of clinical judgement. However the scores can provide patients with a personalised estimate of the risk of sustaining an osteoporotic fracture, and help them make informed decisions about adopting preventive measures or starting prophylactic medications. There is a need for equivalent epidemiological research into an extended range of clinical topics. This could provide a basis for models that allow calculation of personalised risk estimates for individuals about other conditions, such as cervical, prostate or colorectal cancer. Then further evaluation of the effects of providing such personalised risk information to consumers would be possible.

Presenting risk information

Further challenges for clinicians arise from limitations in the available literature. Despite the large body of evidence, there seems to be a lack of consensus concerning the most appropriate method with which to communicate medical risk (Ghosh 2005). Lipkus states that the available evidence has yet to suggest a best practice approach. Reasons for this are the lack of consistency in testing communication formats using the same outcome measure, the relative lack of randomised controlled trials, and the lack of emphasis on developing communication strategies that are built upon a solid theoretical background (Lipkus 2007a). Of the studies that are available, very few have used shared decision making or informed decision making as the outcome measure. Most studies look at risk perception, knowledge, or understanding and these may not be the most appropriate measures of effective risk communication. Future research must address this in an attempt to involve patients in decision making, promoting informed choices in health care in general and in screening in particular.

Demonstrating informed choices

There may also be an onus on clinicians to demonstrate this informed decision making with evidence. Further research is required into how such informed decision making by consumers can be demonstrated validly. There are some outcome measures in this field such as the multi-dimensional measure of informed choice in Down's syndrome screening (Marteau 2001). This is based on assessing the consistency between knowledge, attitudes to tests and choices of uptake of tests. This measure is specific to Down's screening at present. Further research should explore the development of measures for other screening choices, or whether a generic measure of informed choice for screening decisions is feasible. This would require more general cognitive measures than the condition-specific knowledge items. Other scales currently available offer the scope to derive such a cognitive component for a generic measure (O'Connor 1995; Barry 1997; Edwards 2003a) but at the present time, there is no validated measure of informed choice in cancer screening (Jepson 2005). There would be value in developing a generic measure of informed decision making for screening choices by consumers. Using such a measure widely would also be important in assessing the true effect of personalised risk communication on people's use of screening tests.

 

Acknowledgements

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

The review authors thank the many people who commented on the project protocol and Cochrane protocol and review at various stages, helping to make them feasible. In particular this includes the 1998 consumer advisory group established for this review by the Cochrane Consumers and Communication Review Group (comprising Hilda Bastian and Genny Nolan (Australia), Teenah Handiside (New Zealand), and Christine Brunswick (USA)); Professors Theresa Marteau (then University of London) and Alex Barratt (University of Sydney) for discussions about screening programmes; Silvana Unigwe and Rhodri Evans for work on the previous versions of the review; the original Contact Editor - the late Dominique Broclain, the current Contact Editor Sandy Oliver, the editors of the Cochrane Consumers and Communication Review Group, Managing Editor Megan Prictor, Coordinating Editor Sophie Hill, former Trials Search Coordinator Judy Stoelwinder, and external peer reviewers. We also thank current Trials Search Coordinator John Kis-Rigo for his help in amending our search strategy, and the UK Cochrane Centre for their support through well-organised review courses for our authors. We gratefully acknowledge the financial support from the Cochrane Health Promotion and Public Health Field which enabled completion of the first published version of this review in 2003 (Edwards 2003c) and the UK Department of Health for its support (via the Review Group) to speed up completion of the updated review in 2006. Finally we thank the members of the Support Unit for Research Evidence (SURE), Cardiff University for all the support extended during the process of this review update.

 

Data and analyses

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
Download statistical data

 
Comparison 1. personalised risk communication versus general risk information

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Informed decision32444Odds Ratio (Fixed, 95% CI)4.48 [3.62, 5.53]

    1.1 calculated risk score (numerical) v general information
1338Odds Ratio (Fixed, 95% CI)2.08 [1.14, 3.81]

    1.2 personal risk factor list v general information
22106Odds Ratio (Fixed, 95% CI)4.98 [3.97, 6.24]

 2 knowledge regarding screening test / condition concerned4Std. Mean Difference (Fixed, 95% CI)Subtotals only

    2.1 calculated risk score (numerical) v general information
1588Std. Mean Difference (Fixed, 95% CI)0.40 [0.23, 0.56]

    2.2 calculated risk score (categorised) v general information
1260Std. Mean Difference (Fixed, 95% CI)0.57 [0.32, 0.82]

    2.3 personal risk factor list v general information
2838Std. Mean Difference (Fixed, 95% CI)0.89 [0.75, 1.04]

 3 knowledge regarding screening test / condition concerned- proportion with good knowledge3Odds Ratio (Random, 95% CI)Subtotals only

    3.1 calculated risk score (numerical) v general information
31413Odds Ratio (Random, 95% CI)2.60 [1.27, 5.34]

 4 knowledge regarding screening test / condition concerned- proportion with good knowledge22107Odds Ratio (Fixed, 95% CI)7.13 [5.79, 8.79]

    4.1 personal risk factor list v general information
22107Odds Ratio (Fixed, 95% CI)7.13 [5.79, 8.79]

 5 accurately perceived risk31264Odds Ratio (M-H, Random, 95% CI)1.65 [0.96, 2.81]

    5.1 calculated risk score (numerical) v general information
21004Odds Ratio (M-H, Random, 95% CI)1.22 [0.91, 1.64]

    5.2 calculated risk score (categorised) v general information
1260Odds Ratio (M-H, Random, 95% CI)2.50 [1.48, 4.20]

 6 perceived risk - perceiving self as appropriate candidate for test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    6.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 7 Anxiety (Cancer related anxiety and helplessness scale; IES breast cancer distress)61848Std. Mean Difference (Random, 95% CI)-0.13 [-0.29, 0.03]

    7.1 calculated risk score (numerical) v general information
41058Std. Mean Difference (Random, 95% CI)-0.19 [-0.42, 0.04]

    7.2 calculated risk score (categorised) v general information
1260Std. Mean Difference (Random, 95% CI)0.0 [-0.24, 0.24]

    7.3 personal risk factor list v general information
1530Std. Mean Difference (Random, 95% CI)-0.02 [-0.20, 0.16]

 8 decision conflict (proportion with lower scores)1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    8.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 9 decision conflict1Mean Difference (Fixed, 95% CI)Totals not selected

    9.1 calculated risk score (numerical) v general information
1Mean Difference (Fixed, 95% CI)0.0 [0.0, 0.0]

 10 satisfaction with decision1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    10.1 personal risk factor list v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 11 intention to take screening test7Odds Ratio (Random, 95% CI)Totals not selected

    11.1 calculated risk score (numerical) v general information
1Odds Ratio (Random, 95% CI)0.0 [0.0, 0.0]

    11.2 calculated risk score (categorised) v general information
3Odds Ratio (Random, 95% CI)0.0 [0.0, 0.0]

    11.3 personal risk factor list v general information
3Odds Ratio (Random, 95% CI)0.0 [0.0, 0.0]

 12 intention to take genetic screening test in normal risk patients1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    12.1 calculated risk score (numerical)
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 13 intention to take genetic screening test in normal risk patients2Mean Difference (Random, 95% CI)-0.77 [-1.42, -0.13]

    13.1 calculated risk score (numerical)
1Mean Difference (Random, 95% CI)-0.52 [-0.61, -0.43]

    13.2 calculated risk score (categorised)
1Mean Difference (Random, 95% CI)-1.2 [-1.88, -0.52]

 14 uptake of screening test126442Odds Ratio (Fixed, 95% CI)1.15 [1.02, 1.29]

    14.1 calculated risk score (numerical) v general information
62569Odds Ratio (Fixed, 95% CI)0.95 [0.78, 1.15]

    14.2 calculated risk score (categorised) v general information
63873Odds Ratio (Fixed, 95% CI)1.29 [1.11, 1.51]

 15 uptake of screening test20Odds Ratio (Random, 95% CI)Totals not selected

    15.1 personal risk factor list v general information
20Odds Ratio (Random, 95% CI)0.0 [0.0, 0.0]

 16 appropriate use of cholesterol test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    16.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 17 improvement in risk comprehension/perception1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    17.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 18 stages of changeOther dataNo numeric data

    18.1 personal risk factor list v general information
Other dataNo numeric data

 19 making a recommended behaviour change1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    19.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 20 Quality of life (SF-36)1Mean Difference (Fixed, 95% CI)Totals not selected

    20.1 calculated risk score (categorised) v general information
1Mean Difference (Fixed, 95% CI)0.0 [0.0, 0.0]

 
Comparison 2. personalised risk communication versus general risk information for PAP SMEARS

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 intention to take screening test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    1.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 2 uptake of screening test3Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    2.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

    2.2 personal risk factor list v general information
2Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 
Comparison 3. personalised risk communication versus general risk information for MAMMOGRAPHY

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 knowledge regarding screening test / condition concerned1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    1.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 2 accuracy of perceived risk1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    2.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 3 anxiety (Cancer related anxiety and helplessness scale; IES breast cancer distress)1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    3.1 calculated risk score (numerical) v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 4 intention to take screening test2Odds Ratio (Fixed, 95% CI)Totals not selected

    4.1 calculated risk score (categorised) v general information
1Odds Ratio (Fixed, 95% CI)0.0 [0.0, 0.0]

    4.2 personal risk factor list v general information
1Odds Ratio (Fixed, 95% CI)0.0 [0.0, 0.0]

 5 uptake of screening test41595Odds Ratio (M-H, Fixed, 95% CI)0.84 [0.68, 1.03]

    5.1 calculated risk score (numerical) v general information
41595Odds Ratio (M-H, Fixed, 95% CI)0.84 [0.68, 1.03]

 6 uptake of screening test3Odds Ratio (M-H, Random, 95% CI)Totals not selected

    6.1 calculated risk score (categorised) v general information
3Odds Ratio (M-H, Random, 95% CI)0.0 [0.0, 0.0]

 7 uptake of screening test8Odds Ratio (M-H, Random, 95% CI)Totals not selected

    7.1 personal risk factor list v general information
8Odds Ratio (M-H, Random, 95% CI)0.0 [0.0, 0.0]

 8 stages of changeOther dataNo numeric data

    8.1 personal risk factor list v general information
Other dataNo numeric data

 
Comparison 4. personalised risk communication versus general risk information for CHOLESTEROL TESTS

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 uptake of screening test2Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    1.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

   1.2 calculated risk score (categorised) v general information
0Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

    1.3 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 2 appropriate use of cholesterol test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    2.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 
Comparison 5. personalised risk communication versus general risk information for 'HIGH RISK' PEOPLE

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 knowledge regarding screening test / condition concerned31156Std. Mean Difference (Fixed, 95% CI)0.59 [0.47, 0.71]

    1.1 calculated risk score (numerical) v general information
1588Std. Mean Difference (Fixed, 95% CI)0.40 [0.23, 0.56]

    1.2 calculated risk score (categorised) v general information
1260Std. Mean Difference (Fixed, 95% CI)0.57 [0.32, 0.82]

    1.3 personal risk factor list v general information
1308Std. Mean Difference (Fixed, 95% CI)1.02 [0.78, 1.26]

 2 perceived risk - perceiving self as appropriate candidate for test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    2.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 3 accurately perceived risk2460Odds Ratio (M-H, Fixed, 95% CI)2.25 [1.44, 3.53]

    3.1 calculated risk score (numerical) v general information
1200Odds Ratio (M-H, Fixed, 95% CI)1.69 [0.70, 4.06]

    3.2 calculated risk score (categorised) v general information
1260Odds Ratio (M-H, Fixed, 95% CI)2.50 [1.48, 4.20]

 4 anxiety (Cancer related anxiety and helplessness scale; IES breast cancer distress)3662Mean Difference (IV, Fixed, 95% CI)0.07 [-0.14, 0.28]

    4.1 calculated risk score (numerical) v general information
2402Mean Difference (IV, Fixed, 95% CI)0.16 [-0.16, 0.48]

    4.2 calculated risk score (categorised) v general information
1260Mean Difference (IV, Fixed, 95% CI)0.0 [-0.28, 0.28]

 5 intention to take screening test3854Odds Ratio (Fixed, 95% CI)1.29 [0.97, 1.72]

    5.1 calculated risk score (categorised) v general information
1260Odds Ratio (Fixed, 95% CI)0.21 [0.07, 0.64]

    5.2 personal risk factor list v general information
2594Odds Ratio (Fixed, 95% CI)1.47 [1.09, 1.97]

 6 uptake of screening test52085Odds Ratio (Fixed, 95% CI)1.22 [1.00, 1.48]

    6.1 calculated risk score (numerical) v general information
31156Odds Ratio (Fixed, 95% CI)1.05 [0.79, 1.40]

    6.2 calculated risk score (categorised) v general information
2929Odds Ratio (Fixed, 95% CI)1.38 [1.06, 1.80]

 7 uptake of screening test7Odds Ratio (Random, 95% CI)Totals not selected

    7.1 personal risk factor list v general information
7Odds Ratio (Random, 95% CI)0.0 [0.0, 0.0]

 8 stages of changeOther dataNo numeric data

    8.1 personal risk factor list v general information
Other dataNo numeric data

 
Comparison 6. personalised risk communication versus general risk information for COLORECTAL SCREENING

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Informed decision22107Odds Ratio (Fixed, 95% CI)4.99 [3.98, 6.25]

    1.1 personal risk factor list v general information
22107Odds Ratio (Fixed, 95% CI)4.99 [3.98, 6.25]

 2 knowledge regarding screening test / condition concerned32378Odds Ratio (M-H, Fixed, 95% CI)6.81 [5.57, 8.34]

    2.1 calculated risk score (numerical) v general information
1271Odds Ratio (M-H, Fixed, 95% CI)4.26 [1.86, 9.74]

    2.2 personal risk factor list v general information
22107Odds Ratio (M-H, Fixed, 95% CI)7.04 [5.72, 8.67]

 3 knowledge regarding screening test / condition concerned1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    3.1 personal risk factor list v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 4 anxiety (Cancer related anxiety and helplessness scale; IES breast cancer distress)1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    4.1 personal risk factor list v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 5 satisfaction with decision1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    5.1 personal risk factor list v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 6 Decision conflict (proportion with low scores)1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    6.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 7 intention to take screening test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    7.1 calculated risk score (numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 8 uptake of screening test105567Odds Ratio (M-H, Random, 95% CI)1.06 [0.82, 1.37]

    8.1 calculated risk score (numerical) v general information
1271Odds Ratio (M-H, Random, 95% CI)0.78 [0.28, 2.17]

    8.2 calculated risk score (categorised) v general information
31557Odds Ratio (M-H, Random, 95% CI)1.34 [1.01, 1.77]

    8.3 personal risk factor list v general information
63739Odds Ratio (M-H, Random, 95% CI)0.96 [0.68, 1.35]

 9 stages of changeOther dataNo numeric data

    9.1 personal risk factor list v general information
Other dataNo numeric data

 
Comparison 7. personalised risk communication versus general risk information for PROSTATE CANCER SCREENING

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 uptake of screening test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    1.1 personal risk factor list v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 
Comparison 8. Personalised risk communication versus general risk information for SKIN CANCER SCREENING

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 knowledge regarding screening test / condition concerned (Generic Inverse Variance)1Std. Mean Difference (Fixed, 95% CI)Totals not selected

    1.1 calculated risk score(numerical) v general information
1Std. Mean Difference (Fixed, 95% CI)0.0 [0.0, 0.0]

 2 intention to take screening test1Odds Ratio (Fixed, 95% CI)Totals not selected

    2.1 Personal risk factor list v general information
1Odds Ratio (Fixed, 95% CI)0.0 [0.0, 0.0]

 3 uptake of screening test31284Odds Ratio (Fixed, 95% CI)1.69 [1.32, 2.18]

    3.1 calculated risk score(numerical) v general information
1587Odds Ratio (Fixed, 95% CI)1.67 [1.04, 2.69]

    3.2 personal risk factor list v general information
2697Odds Ratio (Fixed, 95% CI)1.70 [1.26, 2.29]

 
Comparison 9. Personalised risk communication versus general risk information for PRENATAL TESTING FOR FOETAL ABNORMALITY

Outcome or subgroup titleNo. of studiesNo. of participantsStatistical methodEffect size

 1 Informed decision1338Odds Ratio (Fixed, 95% CI)2.08 [1.14, 3.81]

    1.1 calculated risk score(numerical) v general information
1338Odds Ratio (Fixed, 95% CI)2.08 [1.14, 3.81]

 2 knowledge regarding screening test / condition concerned1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    2.1 calculated risk score(numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 3 uptake of screening test1Odds Ratio (M-H, Fixed, 95% CI)Totals not selected

    3.1 calculated risk score(numerical) v general information
1Odds Ratio (M-H, Fixed, 95% CI)0.0 [0.0, 0.0]

 4 anxiety1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    4.1 calculated risk score(numerical) v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 5 decision conflict1Mean Difference (IV, Fixed, 95% CI)Totals not selected

    5.1 calculated risk score(numerical) v general information
1Mean Difference (IV, Fixed, 95% CI)0.0 [0.0, 0.0]

 

Appendices

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
 

Appendix 1. Search Strategy: CENTRAL

The Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library Issue 3, 2012)

#1        MeSH descriptor Risk explode all trees

#2        MeSH descriptor Disease Susceptibility explode all trees

#3        (tailor* near counsel*) or (tailor* near message*) or (tailor* near material*) or (tailor* near intervention*) or (personal* near counsel*) or (personal* near message*) or (personal* near material*) or (personal* near intervention*) or (individual* near counsel*) or (individual* near message*) or (individual* near material*) or (individual* near intervention*)

#4        tailored or tailoring or individualised or personalised

#5        risk* or susceptib*

#6        (#1 OR #2 OR #3 OR #4 OR #5)

#7        MeSH descriptor Communication, this term only

#8        MeSH descriptor Persuasive Communication, this term only

#9        MeSH descriptor Counseling, this term only

#10      MeSH descriptor Genetic Counseling, this term only

#11      MeSH descriptor Health Promotion, this term only

#12      MeSH descriptor Health Education, this term only

#13      MeSH descriptor Patient Education as Topic, this term only

#14      MeSH descriptor Health Knowledge, Attitudes, Practice, this term only

#15      MeSH descriptor Attitude to Health, this term only

#16      MeSH descriptor Informed Consent explode all trees

#17      MeSH descriptor Patient Acceptance of Health Care, this term only

#18      MeSH descriptor Decision Making, this term only

#19      (informed near consent) or (informed near choice) or (informed near decision*)

#20      (#7 OR #8 OR #9 OR #10 OR #11 OR #12 OR #13 OR #14 OR #15 OR #16 OR #17 OR #18 OR #19)

#21      (#6 AND #20)

#22      (risk* near notifi*) or (risk* near inform*) or (risk* near communicat*) or (risk* near counsel*) or (risk* near appraisal*) or (risk* near assesment*) or (risk* near perception*) or (risk* near perciev*)

#23      (#21 OR #22)

#24      MeSH descriptor Mass Screening explode all trees

#25      (screen* or test* or testing or detection or early diagnosis)

#26      MeSH descriptor Early Diagnosis explode all trees

#27      MeSH descriptor Diagnostic Techniques and Procedures explode all trees

#28      MeSH descriptor Laboratory Techniques and Procedures explode all trees

#29      MeSH descriptor Tumor Markers, Biological explode all trees

#30      MeSH descriptor Predictive Value of Tests, this term only

#31      (mammography or mammogram* or colonoscopy or sigmoidoscopy or occult blood or vaginal smear* or pap* smear* or prostate specific antigen* or brca* or chest x-ray or tomograph* or bronchoscop*)

#32      prostate* and psa

#33      (#24 OR #25 OR #26 OR #27 OR #28 OR #29 OR #30 OR #31 OR #32)

#34      (#23 AND #33)

#35      (#34), from 2006 to 2011

 

Appendix 2. Search Strategy: CINAHL

CINAHL (EBSCO) (2006 to March 2012)


#QueryLimiters/Expanders

S49S36 and S46Limiters - Published Date from: 20060101-20111231; Exclude MEDLINE records
Search modes - Boolean/Phrase

S48S36 and S46Limiters - Published Date from: 20060101-20111231
Search modes - Boolean/Phrase

S47S36 and S46Search modes - Boolean/Phrase

S46S37 or S38 or S39 or S40 or S41 or S42 or S43 or S44 or S45Search modes - Boolean/Phrase

S45TI (singl* or doubl* or tripl* or trebl*) and TI (blind* or mask*)Search modes - Boolean/Phrase

S44AB (singl* or doubl* or tripl* or trebl*) and AB (blind* or mask*)Search modes - Boolean/Phrase

S43AB (random* or trial or placebo*) or TI (random* or trial or placebo*)Search modes - Boolean/Phrase

S42MH Quantitative StudiesSearch modes - Boolean/Phrase

S41MH PlacebosSearch modes - Boolean/Phrase

S40MH Random AssignmentSearch modes - Boolean/Phrase

S39MH Clinical Trials+Search modes - Boolean/Phrase

S38PT Clinical TrialSearch modes - Boolean/Phrase

S37"randomi?ed controlled trial*"Search modes - Boolean/Phrase

S36S27 and S35Search modes - Boolean/Phrase

S35S28 or S29 or S30 or S31 or S32 or S33 or S34Search modes - Boolean/Phrase

S34TX prostate* and psaSearch modes - Boolean/Phrase

S33TX (mammography or mammogram* or colonoscopy or sigmoidoscopy or occult blood or vaginal smear* or pap* smear* or prostate specific antigen* or brca* or chest x-ray or tomograph* or bronchoscop*)Search modes - Boolean/Phrase

S32(MH "Predictive Value of Tests")Search modes - Boolean/Phrase

S31(MH "Tumor Markers, Biological+")Search modes - Boolean/Phrase

S30TX (screen* or test? or testing or detection or early diagnosis)Search modes - Boolean/Phrase

S29(MH "Early Diagnosis+")Search modes - Boolean/Phrase

S28(MH "Cancer Screening") OR (MH "Health Screening+") OR (MH "Genetic Screening") OR (MH "Health Screening (Iowa NIC)")Search modes - Boolean/Phrase

S27S25 or S26Search modes - Boolean/Phrase

S26TX (risk* n3 notifi*) or (risk* n3 inform*) or (risk* n3 communicat*) or (risk* n3 counsel*) or (risk* n3 appraisal*) or (risk* n3 assesment*) or (risk* n3 perception*) or (risk* n3 perciev*)Search modes - Boolean/Phrase

S25S6 and S24Search modes - Boolean/Phrase

S24S7 or S8 or S9 or S10 or S11 or S12 or S13 or S14 or S15 or S16 or S17 or S18 or S19 or S20 or S21 or S22 or S23Search modes - Boolean/Phrase

S23TX health behaviour model or trans theoretical model or trans-theoretical model or transtheoretical modelSearch modes - Boolean/Phrase

S22(MH "Health Behavior+")Search modes - Boolean/Phrase

S21(MH "Health Beliefs") OR (MH "Health Belief Model")Search modes - Boolean/Phrase

S20TX (informed n2 consent) or (informed n2 choice) or (informed n2 decision*)Search modes - Boolean/Phrase

S19(MH "Consent+")Search modes - Boolean/Phrase

S18(MH "Decision Making") OR "decision making" OR (MH "Decision Making, Patient") OR (MH "Decision-Making Support (Iowa NIC)")Search modes - Boolean/Phrase

S17(MH "Patient Attitudes")Search modes - Boolean/Phrase

S16"health practice"Search modes - Boolean/Phrase

S15(MH "Attitude to Health")Search modes - Boolean/Phrase

S14(MH "Health Knowledge")Search modes - Boolean/Phrase

S13(MH "Patient Education")Search modes - Boolean/Phrase

S12(MH "Health Education")Search modes - Boolean/Phrase

S11(MH "Health Promotion")Search modes - Boolean/Phrase

S10(MH "Genetic Counseling")Search modes - Boolean/Phrase

S9(MH "Counseling")Search modes - Boolean/Phrase

S8TX (persuasive communication)Search modes - Boolean/Phrase

S7(MH "Communication")Search modes - Boolean/Phrase

S6S1 or S2 or S3 or S4 or S5Search modes - Boolean/Phrase

S5TX (risk* or susceptib*)Search modes - Boolean/Phrase

S4TX (tailored or tailoring or individuali?ed or personali?ed)Search modes - Boolean/Phrase

S3TX (tailor*n2 counsel*) or (tailor* n2 message*) or (tailor* n2 material*) or (tailor* n2 intervention*) or (personal* n2 counsel*) or (personal* n2 message*) or (personal* n2 material*) or (personal* n2 intervention*) or (individual* n2 counsel*) or (individual* n2 message*) or (individual* n2 material*) or (individual* n2 intervention*)Search modes - Boolean/Phrase

S2(MH "Disease Susceptibility")Search modes - Boolean/Phrase

S1"risk"Search modes - Boolean/Phrase



 

 

Appendix 3. Search Strategy: EMBASE

EMBASE (OvidSP) (2006 to March 2012)

1. risk/ or cancer risk/ or cardiovascular risk/ or coronary risk/ or genetic risk/ or high risk patient/ or high risk population/ or population risk/ or risk assessment/ or risk benefit analysis/ or risk factor/

2. exp disease predisposition/

3. ((tailor* or personal* or individual*) adj2 (counsel* or message* or material* or intervention*)).tw.

4. (tailored or tailoring or individuali#ed or personali#ed).tw.

5. or/1-4

6. interpersonal communication/

7. patient information/

8. medical information/

9. persuasive communication/

10. counseling/

11. genetic counseling/

12. parent counseling/

13. patient counseling/

14. exp health education/

15. attitude to health/

16. patient attitude/

17. patient compliance/

18. informed consent/

19. decision making/

20. (informed adj2 (consent or choice or decision*)).tw.

21. or/6-20

22. 5 and 21

23. (risk* adj3 (notif* or inform* or communicat* or counsel* or apprais* or assessment or perception* ot perceiv*)).tw.

24. 22 or 23

25. exp screening/

26. (screen* or test? or testing or detection or early diagnosis).tw.

27. exp diagnosis/

28. exp tumor marker/

29. (mammography or mammogram* or colonoscopy or sigmoidoscopy or occult blood or vaginal smear* or pap* smear* or pap* test* or prostate specific antigen* or (prostat* and psa) or brca* or chest x-ray or tomograph* or bronchoscop* or bone density or absorptiometry).tw.

30. or/25-29

31. 24 and 30

32. randomized controlled trial/

33. controlled clinical trial/

34. single blind procedure/ or double blind procedure/

35. crossover procedure/

36. random*.tw.

37. ((singl* or doubl*) adj (blind* or mask*)).tw.

38. (crossover or cross over or factorial* or latin square).tw.

39. ((allocat* or assign*) adj4 (experiment* or intervention* or control* or group*)).tw.

40. or/32-39

41. nonhuman/ not (human/ and nonhuman/)

42. 40 not 41

43. 31 and 42

44. limit 43 to yr="2006 -Current"

 

Appendix 4. Search Strategy: MEDLINE

MEDLINE (OvidSP) (2006 to March 2012)

1. exp risk/

2. exp disease susceptibility/

3. ((tailor* or personal* or individual*) adj2 (counsel* or message* or material* or intervention*)).tw.

4. (tailored or tailoring or individuali#ed or personali#ed).tw.

5. (risk* or susceptib*).tw.

6. or/1-5

7. communication/

8. persuasive communication/

9. counseling/

10. genetic counseling/

11. health promotion/

12. health education/

13. patient education as topic/

14. health knowledge, attitudes, practice/

15. attitude to health/

16. exp informed consent/

17. patient acceptance of health care/

18. decision making/

19. (informed adj2 (consent or choice or decision*)).tw.

20. or/7-19

21. 6 and 20

22. (risk* adj3 (notif* or inform* or communicat* or counsel* or appraisal or assessment or perception* or perceiv*)).tw.

23. 21 or 22

24. exp mass screening/

25. (screen* or test? or testing or detection or early diagnosis).tw.

26. exp early diagnosis/

27. exp "diagnostic techniques and procedures"/

28. exp "laboratory techniques and procedures"/

29. exp tumor markers biological/

30. predictive value of tests/

31. (mammography or mammogram* or colonoscopy or sigmoidoscopy or occult blood or vaginal smear* or pap* smear* or prostate specific antigen* or (prostat* and psa) or brca* or chest x-ray or tomograph* or bronchoscop*).tw.

32. or/24-31

33. 23 and 32

34. randomized controlled trial.pt.

35. controlled clinical trial.pt.

36. randomized.ab.

37. placebo.ab.

38. clinical trials as topic.sh.

39. randomly.ab.

40. trial.ti.

41. or/34-40

42. exp animals/ not humans.sh.

43. 41 not 42

44. 33 and 43

45. limit 44 to yr=2006 to 2011

 

Appendix 5. Search Strategy: PsycINFO

PsycINFO (OvidSP) (2006 to March 2012)

1. risk factors/

2. risk assessment/

3. risk perception/

4. at risk populations/

5. "susceptibility (disorders)"/

6. predisposition/

7. (risk* or susceptib*).ti,ab,id.

8. personalization/

9. ((tailor* or personal* or individual*) adj2 (counsel* or message* or material* or intervention*)).ti,ab,id.

10. (tailored or tailoring or individuali#ed or personali#ed).ti,ab,id.

11. or/1-10

12. communication/

13. persuasive communication/

14. information/

15. messages/

16. counseling/

17. genetic counseling/

18. health promotion/

19. health education/

20. client education/

21. individualized instruction/

22. health knowledge/

23. health attitudes/

24. informed consent/

25. treatment compliance/

26. decision making/

27. (informed adj2 (consent or choice or decision*)).ti,ab,id.

28. or/12-27

29. 11 and 28

30. (risk* adj3 (notif* or inform* or communicat* or counsel* or apprais* or assessment or perception* ot perceiv*)).ti,ab,id.

31. 29 or 30

32. screening/

33. exp health screening/

34. exp screening tests/

35. (screen* or test? or testing or detection or early diagnosis).ti,ab,id.

36. exp diagnosis/

37. biological markers/

38. (mammography or mammogram* or colonoscopy or sigmoidoscopy or occult blood or vaginal smear* or pap* smear* or pap* test* or prostate specific antigen* or (prostat* and psa) or brca* or chest x-ray or tomograph* or bronchoscop* or bone density or absorptiometry).ti,ab,id.

39. or/32-38

40. 31 and 39

41. random*.ti,ab,hw,id.

42. trial*.ti,ab,hw,id.

43. control*.ti,ab,hw,id.

44. placebo*.ti,ab,hw,id.

45. ((singl* or doubl* or trebl* or tripl*) and (blind* or mask*)).ti,ab,hw,id.

46. (cross over or crossover or factorial* or latin square).ti,ab,hw,id.

47. (assign* or allocat* or volunteer*).ti,ab,hw,id.

48. treatment effectiveness evaluation/

49. mental health program evaluation/

50. exp experimental design/

51. "2000".md.

52. or/41-51

53. 40 and 52

54. limit 53 to yr="2006 -Current"

 

What's new

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

Last assessed as up-to-date: 24 March 2012.


DateEventDescription

24 January 2013New citation required and conclusions have changedThis review updates the issue 4 2006 version of the Cochrane review (Edwards 2006). We identified 19 new studies, bringing the total number of included studies to 41. The review conclusions were revised particularly in light of three new studies which specifically measured the principal outcome of informed decision (not just the key components that form informed decision).

The byline has changed for this update.

24 March 2012New search has been performedNew searches run.



 

History

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

Protocol first published: Issue 4, 1999
Review first published: Issue 1, 2003


DateEventDescription

21 July 2006New citation required and conclusions have changedSubstantive amendment.

The review was updated on Issue 4 2006 of The Cochrane Library. Nine new studies were identified, bringing the total number of included studies to 22. The review's conclusions were amended in light of the new studies included in the review.

The title of the review also changed slightly. It was first published on Issue 1 2003 of The Cochrane Library as "Personalised risk communication for informed decision making about entering screening programs".



 

Contributions of authors

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

AE developed the protocol for the review, with input from GE; GN and HA conducted the literature searches; GN and HA appraised all studies with input from AE, for inclusion of exclusion and for data extraction; KH, TP and RP gave statistical advice regarding data extraction and synthesis; all authors contributed to discussions and drafting of the review report. AE is the guarantor for the review.

 

Declarations of interest

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

None known.

 

Sources of support

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms
 

Internal sources

  • Welsh Assembly Government, UK.
    GN's post as an 'Associate Academic Fellow' in Cardiff University was funded by the Welsh Assembly Government

 

External sources

  • No sources of support supplied

 

Differences between protocol and review

  1. Top of page
  2. Summary of findings    [Explanations]
  3. Background
  4. Objectives
  5. Methods
  6. Results
  7. Discussion
  8. Authors' conclusions
  9. Acknowledgements
  10. Data and analyses
  11. Appendices
  12. What's new
  13. History
  14. Contributions of authors
  15. Declarations of interest
  16. Sources of support
  17. Differences between protocol and review
  18. Index terms

Differences between the protocol and the review are as follows:

  • We have included two quasi-randomised studies as we felt this was appropriate, considering a significant number of included studies did not have clear information on the process of randomisation. They were rated as having a high risk for selection bias.
  • We considered measures of informed decision as our primary outcome, as we had studies that specifically measured this in this review update.
  • Other outcome measures focus on identifying changes in any of the key components of informed decision making
  • Risk of Bias assessment was based on the new guidance derived from the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).
  • Unit of analysis was defined - We will include studies randomised at individual and cluster level. For studies that were cluster randomised, outcome measures adjusted for clustering will be used in the analysis.
  • We have used standardised mean differences (SMDs) to pool data that measured the same outcome/effect on different scales.
  • Tests for assessment of heterogeneity were defined. We used the I2 statistic to assess heterogeneity (Deeks 2011). We expected heterogeneity of the studies in terms of the screening tests addressed, participants, and design of the intervention. We used a fixed-effect model to present pooled data where heterogeneity was of low or moderate significance i.e. I2 < 60%. Results from a random-effects model (which gives a more conservative confidence interval on the estimate of effect size) were examined and presented in cases (in addition to fixed-effect estimates) where there was heterogeneity of substantial significance i.e. I2= 60% to 79%. We have not pooled the data if there was considerable heterogeneity between the studies i.e. I2 > 79%.

* Indicates the major publication for the study

References

References to studies included in this review

  1. Top of page
  2. AbstractRésumé
  3. Summary of findings
  4. Background
  5. Objectives
  6. Methods
  7. Results
  8. Discussion
  9. Authors' conclusions
  10. Acknowledgements
  11. Data and analyses
  12. Appendices
  13. What's new
  14. History
  15. Contributions of authors
  16. Declarations of interest
  17. Sources of support
  18. Differences between protocol and review
  19. Characteristics of studies
  20. References to studies included in this review
  21. References to studies excluded from this review
  22. References to ongoing studies
  23. Additional references
  24. References to other published versions of this review
Bastani 1999 {published data only}
Bloom 2006 {published data only}
Bodurtha 2009 {published data only}
  • Bodurtha J, Quillin JM, Tracy KA, Borzelleca J, McClish D, Wilson DB, et al. Mammography screening after risk-tailored messages: the Women Improving Screening through Education and Risk assessment (WISER) randomized controlled trial. Journal of Women's Health 2009;18(1):41-7.
Bowen 2002 {published data only}
  • Bowen D, Burke W, Yasui Y, McTiernan A, McLeran D. Effects of risk counseling on interest in breast cancer genetic testing for lower risk women. Genetics in Medicine 2002;4(5):359-65.
Bowen 2006 {published data only}
  • Bowen DJ, Powers D, Greenlee H. Effects of breast cancer risk counseling for sexual minority women. Health Care for Women International 2006;27(1):59-74.
Bowen 2010 {published data only}
Campbell 1997 {published data only}
  • Campbell E, Peterkin D, Abbott R, Rogers J. Encouraging underscreened women to have cervical cancer screening: the effectiveness of a computer strategy. Preventive Medicine 1997;26(6):801-7.
Champion 1994 {published data only}
Champion 1995 {published data only}
Champion 2000a {published data only}
  • Champion VL, Ray DW, Heilman DK, Springston J. A tailored intervention for mammography among low-income African-American women. Journal of Psychosocial Oncology 2000;18(4):1-13.
Champion 2002 {published data only (unpublished sought but not used)}
  • Champion V, Skinner C, Menon U, Seshadri R, Anzalone D, Rawl S. Comparisons of tailored mammography interventions at two months postintervention. Annals of Behavioral Medicine 2002;24(3):211-8.
Champion 2003 {published data only}
  • Champion V, Maraj M, Hui S, Perkins AJ, Tierney W, Menon U, et al. Comparison of tailored interventions to increase mammography screening in nonadherent older women. Preventive Medicine 2003;36(2):150-8.
Champion 2007 {published data only}
  • Champion V, Skinner CS, Hui S, Monahan P, Juliar B, Daggy J, Menon U. The effect of telephone versus print tailoring for mammography adherence. Patient Education and Counseling 2007;65(3):416-23.
Curry 1993 {published data only}
  • Curry SJ, Taplin SH, Anderman C, Barlow WE, McBride C. A randomized trial of the impact of risk assessment and feedback on participation in mammography screening. Preventive Medicine 1993;22(3):350-60.
Geller 2006 {published data only}
Glanz 2007 {published data only}
Glazebrook 2006 {published data only}
  • Glazebrook C, Garrud P, Avery A, Coupland C, Williams H. Impact of multimedia intervention "Skinsafe" on patients' knowledge and protective behaviours. Preventive Medicine 2006;42:449-54.
Helmes 2006 {published data only}
Hutchison 1998 {published data only}
  • Hutchison B, Birch S, Evans C, Goldsmith L, Markham B, Frank J, Paterson M. Screening for hypercholesterolaemia in primary care: randomised controlled trial of postal questionnaire appraising risk of coronary heart disease. BMJ 1998;316(7139):1208-13.
Jibaja-Weiss 2003 {published data only}
  • Jibaja-Weiss M, Volk R, Kingery P, Smith Q, Holcomb J. Tailored messages for breast and cervical cancer screening of low-income and minority women using medical records data. Patient Education and Counseling 2003;50(2):123-32.
  • Jibaja-Weiss M, Volk R, Smith Q, Holcomb J, Kingery P. Differential effects of messages for breast and cervical cancer screening. Journal of Health Care for the Poor and Underserved 2005;16(1):42-52.
Kreuter 1996 {published data only}
Lee 1991 {published data only}
  • Lee CY. A randomized controlled trial to motivate work site fecal occult blood testing. Yonsei Medical Journal 1991;32(2):131-8.
Lerman 1995 {published data only}
  • Lerman C, Lustbader E, Rimer B, Daly M, Miller S, Sands C, et al. Effects of individualized breast cancer risk counseling: a randomised trial. Journal of the National Cancer Institute 1995;87(4):286-92.
  • Lerman C, Schwartz MD, Miller SM, Daly M, Sands C, Rimer BK. A randomized trial of breast cancer risk counseling: interacting effects of counseling, educational level, and coping style. Health Psychology 1996;15(2):75-83.
Lerman 1997 {published data only}
  • Lerman C, Biesecker B, Bekendorf JL, Kerner J, Gomez-Caminero A, Hughes C, et al. Controlled trial of pretest education approaches to enhance informed decision-making for BRCA1 gene testing. Journal of the National Cancer Institute 1997;89(2):148-57.
Lipkus 2005 {published data only}
  • Lipkus I, Skinner C, Dement J, Pompeii L, Moser B, Samsa G, et al. Increasing colorectal cancer screening among individuals in the carpentry trade: test of risk communication interventions. Preventive Medicine 2005;40(5):489-501.
  • Lipkus I, Skinner C, Green L, Dement J, Samsa G, Ransohoff D. Modifying attributions of colorectal cancer risk. Cancer Epidemiology Biomarkers and Prevention 2004;13(4):560–6.
Lipkus 2007b {published data only}
  • Lipkus IM, Klien WM. Effects of communicating social comparison information on risk perceptions for colorectal cancer. Journal of Health Communication 2007;11(4):391-407.
Manne 2009 {published data only}
  • Manne S, Coups EJ, Markowitz A, Meropol NJ, Haller D, Jacobsen PB, et al. A randomized trial of generic versus tailored interventions to increase colorectal cancer screening among intermediate risk siblings. Annals of Behavioral Medicine 2009;37(2):207-17.
Manne 2010 {published data only}
  • Manne S, Jacobsen PB, Ming ME, Winkel G, Dessureault S, Lessin SR. Tailored versus generic interventions for skin cancer risk reduction for family members of melanoma patients. Health Psychology 2010;29(6):583-93.
Marcus 2005 {published data only}
  • Marcus AC, Mason M, Wolfe P, Rimer BK, Lipkus I, Strecher V, et al. The efficacy of tailored print materials in promoting colorectal cancer screening: results from a randomized trial involving callers to the National Cancer Institute's cancer information service. Journal of Health Communication 2005;10(1):83-104.
Myers 1999 {published data only}
Nagle 2008 {published data only}
Rawl 2008 {published data only}
  • Rawl SM, Champion VL, Scott LL, Zhou H, Monahan P, Ding Y, et al. A randomized trial of two print interventions to increase colon cancer screening among first degree relatives. Patient Education and Counseling 2008;71(2):215-27.
Rimer 2002 {published data only}
  • Rimer B, Halabi S, Skinner C, Lipkus I, Strigo T, Kaplan E, et al. Effects of a mammography decision-making intervention at 12 and 24 months. American Journal of Preventive Medicine 2002;22(4):247-57.
  • Rimer B, Halabi S, Sugg Skinner C, Kaplan E, Crawford Y, Samsa G, et al. The short-term impact of tailored mammography decision-making interventions. Patient Education and Counseling 2001;43(3):269-85.
Saywell 1999 {published data only}
  • Saywell RM Jr, Champion VL, Skinner CS, McQuillen D, Martin D, Maraj M. Cost-effectiveness comparison of five interventions to increase mammography screening. Preventive Medicine 1999;29(5):374-82.
Schwartz 1999 {published data only}
  • Schwartz M, Rimer B, Daly M, Sands C, Lerman C. A randomized trial of breast cancer risk counseling: the impact on self-reported mammography use. American Journal of Public Health 1999;89(6):924-6.
Sequist 2011 {published data only}
  • Sequist T, Zaslavsky A, Ayanian J, Colditz G. Electronic patient messages and personalized risk assessments to promote colorectal cancer screening: a randomised controlled trial. Journal of General Internal Medicine 2011;25:636-41.
Skinner 1994 {published data only (unpublished sought but not used)}
Skinner 2002 {published data only}
  • Skinner C, Schildkraut J, Berry D, Calingaert B, Marcom P, Sugarman J, et al. Pre-counseling education materials for BRCA testing: does tailoring make a difference?. Genetic Testing 2002;6(2):93-105.
Smith 2010 {published data only}
  • Smith SK, Trevena L, Simpson JM, Barratt A, Nutbeam D, McCaffery KJ. A decision aid to support informed choices about bowel cancer screening among adults with low education: randomised controlled trial. BMJ (Clinical research ed.) 2010;341:c5370.
Steckelberg 2011 {published data only}
  • Steckelberg A, Hulfenhaus C, Haastert B, Muhlhauser I. Effect of evidence based risk information on "informed choice" in colorectal cancer screening: randomised controlled trial. BMJ 2011;342:d3193.
Trevena 2008 {published data only}

References to studies excluded from this review

  1. Top of page
  2. AbstractRésumé
  3. Summary of findings
  4. Background
  5. Objectives
  6. Methods
  7. Results
  8. Discussion
  9. Authors' conclusions
  10. Acknowledgements
  11. Data and analyses
  12. Appendices
  13. What's new
  14. History
  15. Contributions of authors
  16. Declarations of interest
  17. Sources of support
  18. Differences between protocol and review
  19. Characteristics of studies
  20. References to studies included in this review
  21. References to studies excluded from this review
  22. References to ongoing studies
  23. Additional references
  24. References to other published versions of this review
Alexander 1996 {published data only}
  • Alexander NE, Ross J, Sumner W, Nease RF Jr, Littenberg B. The effect of an educational intervention on the perceived risk of breast cancer. Journal of General Internal Medicine 1996;11(2):92-7.
Arimori 2006 {published data only}
  • Arimori N. Randomized controlled trial of decision aids for women considering prenatal testing: the effect of the Ottawa Personal Decision Guide on decisional conflict. Japan Journal of Nursing Science 2006;3:119-30.
Champion 2000b {published data only}
  • Champion V, Skinner C, Foster J. The effects of standard care counseling or telephone/in-person counseling on beliefs, knowledge, and behavior related to mammography screening. Oncology Nursing Forum 2000;27(10):1565-71.
Champion 2006b {published data only}
  • Champion VL, Springston JK, Zollinger TW, Saywell Jr RM, Monahan PO, Zhao Q, et al. Comparison of three interventions to increase Mammography screening in low income African American women. Cancer Detection and Prevention 2006;30:535-44.
Chan 2011 {published data only}
  • Chan ECY, McFall SL, Byrd TL, Mullen PD, Volk RJ, Ureda J, et al. A community-based intervention to promote informed decision making for prostate cancer screening among Hispanic American men changed knowledge and role preferences: a cluster RCT. Patient Education and Counseling 2011;84:e44-51.
Costanza 2007 {published data only}
  • Costanza ME, Luckmann R, Stoddard AM, White M, Stark JR, Avrunin JS, et al. Using tailored telephone counseling to accelerate the adoption of colorectal cancer screening. Cancer Detection and Prevention 2007;31:191-8.
Dignan 1996 {published data only}
  • Dignan M, Michielutte R, Blinson K, Wells HB, Case LD, Sharp P, et al. Effectiveness of health education to increase screening for cervical cancer among eastern band Cherokee Indian women in North Carolina. Journal of the National Cancer Institute 1996;88(22):1670-6.
Engelstad 2005 {published data only}
  • Engelstad L, Stewart S, Otero-Sabogal R, Leung M, Davis P, Pasick R. The effectiveness of a community outreach intervention to improve follow-up among underserved women at highest risk for cervical cancer. Preventive Medicine 2005;41(3):741-8.
Gagnon 1996 {published data only}
Giles 2001 {published data only}
Jerant 2007 {published data only}
  • Jerant A, Kravitz RL, Rooney M, Amerson S, Kreuter M, Franks P. Effects of a tailored interactive multimedia computer program on determinants of colorectal cancer screening: a randomized controlled pilot study in physician offices. Patient Education and Counseling 2007;66:67-74.
Kadison 1998 {published data only}
  • Kadison P, Pelletier EM, Mounib EL, Oppedisano P, Harry T. Improved screening for breast cancer associated with a telephone-based risk assessment. Preventive Medicine 1998;27(3):493-501.
Kreuter 2005 {published data only}
  • Kreuter M, Sugg-Skinner C, Holt C, Clark E, Haire-Joshu D, Fu Q, et al. Cultural tailoring for mammography and fruit and vegetable intake among low-income African-American women in urban public health centers. Preventive Medicine 2005;41(1):53-62.
Leigh 1991 {published data only}
  • Leigh J, Harrison J. Reduction of ischaemic heart disease risk factors following direct probabilistic risk communication in the workplace. Journal of Occupational Health & Safety 1991;7:467-72.
Lipkus 2000 {published data only}
Miller 2005 {published data only}
  • Miller S, Fleisher L, Roussi P, Buzaglo J, Schnoll R, Slater E, et al. Facilitating informed decision making about breast cancer risk and genetic counseling among women calling the NCI's Cancer Information Service. Journal of Health Communication 2005;10(Suppl 1):119-36.
Misra 2011 {published data only}
  • Misra S, Lairson DR, Chan W, Chang Y, Bartholomew LK, Greisinger A, et al. Cost effectiveness of interventions to promote screening for colorectal cancer: a randomized trial. Journal of Preventative Medicine and Public Health 2011;44(3):101-10.
Myers 2007 {published data only}
Ockhuysen-Vermey 2008 {published data only}
  • Ockhuysen-Vermey CF, Henneman L, Van Asperen CJ, Oosterwijk JC, Menko FH, Timmermans DRM. Design of the BRISC study: a multicentre controlled clinical trial to optimize the communication of breast cancer risks in genetic counseling. BioMed Central Cancer 2008;8:283.
Pye 1988 {published data only}
  • Pye G, Christie M, Chamberlain JO, Moss SM, Hardcastle JD. A comparison of methods for increasing compliance within a general practitioner based screening project for colorectal cancer and the effect on practitioner workload. Journal of Epidemiology and Community Health 1988;42(1):66-71.
Rakowski 1998 {published data only}
  • Rakowski W, Ehrich B, Goldstein MG, Rimer BK, Pearlman DN, Clark MA, et al. Increasing mammography among women aged 40-74 by use of a stage-matched, tailored intervention. Preventive Medicine 1998;27(5 pt 1):748-56.
Rhodes 2001 {published data only}
  • Rhodes K, Lauderdale D, Stocking C, Howes D, Roizen M, Levinson W. Better health while you wait: a controlled trial of a computer-based intervention for screening and health promotion in the emergency department. Annals of Emergency Medicine 2001;37(3):284-91.
Rimer 1999 {published data only}
  • Rimer BK, Conaway M, Lyna P, Glassman B, Yarnall KSH, Lipkus I, et al. The impact of tailored interventions on a community health center population. Patient Education and Counseling 1999;37(2):125-40.
Russell 2010 {published data only}
  • Russell KM, Champion VL, Monahan PO, Millon-Underwood S, Zhao Q, Spacey N, et al. Randomized trial of a lay health adviser and computer intervention to increase mammography screening in African American women. Cancer Epidemiology Biomarkers and Prevention 2010;19:201-10.
Schroy 2008 {published data only}
  • Schroy PC, Glick JT, Wilson S, Robinson PA, Heeran TC. An effective educational strategy for improving knowledge, risk perception and risk communication among colorectal adenoma patients. Journal of Clinical Gastroenterology 2008;42:708-14.
Vernon 2011 {published data only}
  • Vernon SW, Bartholomew LK, McQueen A, Bettencourt JL, Greisinger A, Coan SP, et al. A randomized controlled trial of a tailored interactive computer-delivered intervention to promote colorectal cancer screening: sometimes more is just the same. Annals of Behavioral Medicine 2011;41(3):284-99.
Walker 2008 {published data only}
  • Walker EA, Schechter CB, Caban A, Basch CE. Telephone intervention to promote diabetic retinopathy screening among the urban poor. American Journal of Preventative Medicine 2008;34(3):185-91.
Walsh 2010 {published data only}
  • Walsh JME, Salazar R, Nguyen TT, Kaplan C, Nguyen L, Hwang J, et al. Healthy Colon, Healthy Life: a novel colorectal cancer screening intervention. American Journal of Preventative Medicine 2010;39(1):1-14.
Weber 1997 {published data only}
  • Weber B, Reilly B. Enhancing mammography use in the inner city: a randomized trial of intensive case management. Archives of Internal Medicine 1997;157:2345-9.

References to ongoing studies

  1. Top of page
  2. AbstractRésumé
  3. Summary of findings
  4. Background
  5. Objectives
  6. Methods
  7. Results
  8. Discussion
  9. Authors' conclusions
  10. Acknowledgements
  11. Data and analyses
  12. Appendices
  13. What's new
  14. History
  15. Contributions of authors
  16. Declarations of interest
  17. Sources of support
  18. Differences between protocol and review
  19. Characteristics of studies
  20. References to studies included in this review
  21. References to studies excluded from this review
  22. References to ongoing studies
  23. Additional references
  24. References to other published versions of this review
Glenn 2011 {published data only}
  • Glenn BA, Herrmann AK, Crespi CM, Mojica CM, Chang LC, Maxwell AE, et al. Changes in risk perceptions in relation to self-reported colorectal cancer screening among first-degree relatives of colorectal cancer cases enrolled in a randomized trial. Health Psychology 2011;30(4):481-91.
Lowery 2012 {published data only}
  • Lowery JT, Marcus AL, Kinney A, Bowen D, Finklestein DM, Horick N, et al. The family health promotion project (FHPP): Design and baseline data from a randomized trial to increase colonoscopy screening in high risk families. Contemporary Clinical Trials 2012;33:426-35.

Additional references

  1. Top of page
  2. AbstractRésumé
  3. Summary of findings
  4. Background
  5. Objectives
  6. Methods
  7. Results
  8. Discussion
  9. Authors' conclusions
  10. Acknowledgements
  11. Data and analyses
  12. Appendices
  13. What's new
  14. History
  15. Contributions of authors
  16. Declarations of interest
  17. Sources of support
  18. Differences between protocol and review
  19. Characteristics of studies
  20. References to studies included in this review
  21. References to studies excluded from this review
  22. References to ongoing studies
  23. Additional references
  24. References to other published versions of this review
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References to other published versions of this review

  1. Top of page
  2. AbstractRésumé
  3. Summary of findings
  4. Background
  5. Objectives
  6. Methods
  7. Results
  8. Discussion
  9. Authors' conclusions
  10. Acknowledgements
  11. Data and analyses
  12. Appendices
  13. What's new
  14. History
  15. Contributions of authors
  16. Declarations of interest
  17. Sources of support
  18. Differences between protocol and review
  19. Characteristics of studies
  20. References to studies included in this review
  21. References to studies excluded from this review
  22. References to ongoing studies
  23. Additional references
  24. References to other published versions of this review
Edwards 1999b
  • Edwards A, Hood K, Matthews E, Russell I, Wilkinson C. Personalised risk communication in health screening programs. Cochrane Database of Systematic Reviews 1999, Issue 4.
Edwards 2003b
Edwards 2003c
  • Edwards A, Unigwe S, Elwyn G, Hood K. Personalised risk communication for informed decision making about entering screening programs. Cochrane Database of Systematic Reviews 2003, Issue 1. [DOI: 10.1002/14651858.CD001865]
Edwards 2006