Background
Healthcare delivery and outcomes vary enormously within many developed healthcare systems [
1]. Even accounting for the “irreducible uncertainty” [
2] associated with healthcare, much of this variation can be explained by the attitudes, clinical judgements and decisions, and consequent behaviours of health professionals [
3]. One of the most powerful means of shaping these influential variables is the opinions of other healthcare professionals – particularly peers [
4,
5].
Particularly influential practitioners can be thought of as “opinion leaders” (OLs) [
6‐
9]. Despite their widespread use, OLs aren’t universally effective. In a Cochrane review of 18 opinion leader studies, OLs were found to be associated with a median adjusted 12 per cent absolute increase in health professionals’ compliance with recommendations [
5]. However, the range across studies varied from a 15 per cent decrease to a 72 per cent increase in compliance. The mechanisms by which they operate are only just beginning to be demonstrated and understood [
10] but common features include generating consensus [
11], increasing the observability and reducing the potential risk of new clinical behaviours [
12], and producing more efficient learning [
13‐
15].
Opinion leaders have been widely used in a variety of primary and community health care contexts; for example, to influence patient behaviour [
13]; and as a change mechanism in health professional [
16] and online [
17,
18] communities. In “diffusion of innovation” based theories [
6,
19,
20], opinion leaders play a key role in increasing the uptake of recommendations and adoption of innovations - for a classic example see [
21], and more recently [
22‐
24].
Identifying opinion leaders
Despite a range of tested techniques [
13,
25], identifying OLs is challenging. Methods need to be robust, reliable, attract low resources and identify high quality opinion leaders; but to be useful in implementation programmes, they also need to be able to be used alongside other research tools. Identification of OLs for use in behaviour change interventions is often informal [
26], but more formal techniques used include:
-
Key-informant - asking a smaller number of individuals who are knowledgeable about a network to identify influential individuals;
-
Self-designating - self-reporting of own opinion leader status. This can be limiting as it does not guarantee that the OL is credible within the community and does not ensure that the OL shares the agenda of community members and researchers;
-
Direct observation;
-
Sociometric (analysis of leadership nominations) [
19,
21];
-
Selection based on an individuals’ “objective” position or status, for example a celebrity or elected official [
27] or someone who has published on a topic or held a key position in the organisation or geographical area [
28].
Identification methods recognise the role of social networks in opinion leadership. Sociometric techniques, in which community members nominate opinion leaders, are valid, reliable and “sophisticated” (sic.) [
13,
15] methods of collecting opinion leader information [
13]. Other methods, such as self-designating, do not always identify individuals perceived as opinion leaders by their peers. Sociometric techniques are also associated with higher response rates [
26,
29]. Network analysis can help identify who is most central to a community and therefore more influential [
15]. All individuals are ‘actors’ within a communication network, and through the nomination of influential individuals, connections between actors can be described [
30,
31].
Opinion leaders in implementation and “coverage”
A recent review of the use of social networks in healthcare implementation studies examined 52 papers [
32] and revealed a growing interest in use of the technique but that the application to implementation work was limited [
32]. Network data collected is often of insufficient quantity (i.e. too few contacts named) or insufficient quality (i.e. contacts identified are not suitable for use in information dissemination). Insufficient quantity can be captured and described using the ‘coverage statistic’ [
6]: a rate describing the degree to which identified OLs have contact with (and, therefore, influence over) the adopter population. Coverage rates are the proportion of the (whole) population that name at least one nominated opinion leader [
6]. Coverage rates are rarely and idiosyncratically reported in intervention studies [
6].
Capturing more than just an OL name requires longer, and more costly, questionnaires. Typical extensions of the most basic network capture methods include multiple full name nominations of influential peers and additional information, such as the frequency of contact [
33] and the direction in which the information flows between the respondent and the nominated peers [
34]. Thus, the researcher seeking richer network data, with potential utility in an implementation strategy faces a trade-off: better quality data versus potentially lower response rates (longer questionnaires are less likely to be completed) and higher production and transaction costs.
Objectives
As part of a larger implementation programme (see Hanbury et al. [
35] for details of this study) to improve the provision of support for families of people with a diagnosis of psychosis. This was a three phase study. In the first, the study mapped multiple dimensions from Greenhalgh’s framework [
8] as part of a diagnostic analysis to identify barriers to the use of family interventions for service-users with schizophrenia. In the subsequent implementation phase, five interventions were developed to specifically target each barrier. These interventions were an education event, promotion of outreach sessions to generate interest in the topic, promotion of the relevant clinical pathway, development of a register of co-workers). We sought to identify opinion leaders for use in an intervention to change professional behaviour. We aimed to capture good quality data without sacrificing coverage and quantity. The final phase aimed to evaluate the impact of the interventions on process of care measures [
35].
Accordingly, we asked two questions:
1.
Does requesting additional information about nominees affect the response rates to questionnaires collecting opinion leader information?
2.
Is it possible to collect sufficient respondent ‘coverage’ rates for exploitation in implementation programmes?
Ethical approval was granted for this study by Leeds (West) Research Ethics Committee (Reference 10/H1311/1).
Discussion
Comparing two questionnaires, we found that response rates to questions about opinion leaders did not differ regardless of the approach used. Our second finding was that the opinion leaders identified via these questionnaires reached only a small proportion of the population. These results reflect those of Grimshaw and colleagues [
6] but coverage is far lower than those of Cosens et al. [
29] who achieved a 58% population coverage rate. With low response rates, it is unsurprising that population coverage rates were also very low. Questionnaire length and the amount of information requested did not affect response rates. Optimistically, this means that implementation strategy designers seeking to identify opinion leaders
and ask for information of value to designing behaviour change interventions should feel comfortable doing so. The value that this additional information can add to implementation may be worthwhile if this approach does not incur any extra cost.
Less optimistically however, response rates were low to
both approaches. Low response rates may be attributed to the length of the questionnaire overall: that our response rate was lower than some similar studies (Doumit et al., [
37] achieved a 38% response rate whilst Cosens et al. [
29] achieved 61%) may be due to the opinion leader section forming just one component of a longer questionnaire exploring multiple determinants of innovation adoption. Questionnaire approaches that focus
only on gathering opinion leader data may achieve higher response rates, for example, Cosens et al. [
29] achieved 40% response rate to the opinion leader section in their study. We hypothesise that these better response rates may be due to the reduced burden on participants. If implementation intervention designers are wedded to questionnaire based approaches to OL identification then this trade-off between the need to reduce burden and simultaneously collect data on several factors (such as attitudes, norms and intentions, as well as OL nominations) will be unavoidable. Those health professionals who were motivated to respond to the questionnaire were less willing to respond to questions about their contacts; a pattern also identified by Grimshaw and colleagues, who found some respondents perceived the notion of opinion leaders as too “abstract”, making OL identification questions difficult to answer [
6]. A reluctance to provide names may present significant challenge to collecting opinion leader information from community nominations, regardless of the techniques used.
Increased response rates with questionnaires could be possible [
36]. Whilst proven techniques were applied in this study (for example, an incentive of entry into a prize draw was offered to all respondents, respondents had the opportunity to complete either a paper or online copy of the survey, reminder emails and letters were sent to non-respondents) we still had a poor response. It is likely then that the questionnaire length together with the reluctance to name individual colleagues reduced response rates.
These findings should lead implementation experts to ask whether gathering opinion leader information using questionnaires is a suitable approach; especially given the burden placed on respondents by multidimensional questionnaires. Social networks and opinion leaders operate within a wider implementation context [
38]. By focussing on systematically collecting questionnaire based opinion leader data this wider context may be missed. Whilst individual actors and organisational context are both important, it is increasingly recognised that the interplay between these is critical [
38]. Face to face collection of social network data permits a better understanding of the context in which peer communication takes place and of the precise nature of the communication and relationships.
Low response and coverage rates mean that data on opinion leaders cannot be usefully applied to implementation efforts. Even with high quality data and high coverage rates, subsequent utilisation of opinion leaders in implementation strategies is challenging. For example, administrative delays in implementation work can make it difficult to harness the support of opinion leaders [
11]; identifying individuals is not sufficient if they do not endorse the innovation [
26], those identified may not view the innovation to be adopted positively [
6], and the opinion leader status may be temporary [
37]. Thus, the implementation designer must face the challenge of collecting sufficient information about opinion leaders; which in turn requires a more comprehensive tool and thus induces greater sense of burden in respondents.
Limitations
The Trust’s own contact list was of out of date and inaccurate, this seriously hampered the construction of a good quality sampling frame. As an external team of researchers we were reliant upon the information provided by the NHS Trust. Our misplaced assumption that this information was comprehensive and up-to-date meant that our questionnaire did not reach a proportion of the population which in turn affected our sample size and response rates.
We did not collect economic data on the time and financial resources consumed by both approaches. This was an omission and prevented a more informed assessment of the relative cost effectiveness of each approach.
The study took place within a changing organisational context, which may have influenced response rates to the questionnaire, and makes generalising these findings to more stable organisations difficult. The relationship between response rates and coverage rates is unknown: higher response rates may have generated different coverage rates. Lastly, using a questionnaire that focused solely on gathering opinion leader data may have provided better response rates.
Conclusions
Theoretically and empirically, informal communication channels are a valuable means of diffusing innovation [
12,
19,
20,
37]. However, collecting sufficient information using social network techniques is resource intensive and requires high levels of engagement from both researcher and respondents. Our results suggest that questionnaire based approaches to OL identification lead to information that is almost unusable in the context of designing a theoretically-informed behaviour change intervention. The study reinforces Grimshaw and colleagues’ [
6] assertion that there is limited empirical evidence to support the collection of opinion leader data. Other researchers may wish to consider future studies which compare, and assess the cost-effectiveness of, OL-only questionnaires vs. embedded approaches, or questionnaire vs. qualitative/observational techniques.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
KF participated in the design of the project protocol, developed the questionnaire, conducted the analysis presented here, and drafted the manuscript. AH designed the project protocol, developed the questionnaire, and drafted the manuscript. CT designed the project protocol, developed the questionnaire and reviewed the manuscript. All authors read and approved the final manuscript.