Translating evidence into practice:
The role & network of the CoE for Public Health.
H. McAneney1, 2, J.F. McCann2, 3, L. Prior3, 4, J. Wilde3, 5 and F. Kee1, 3
1
School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast 2 School of Mathematics and Physics, Queen's University Belfast
3 Centre of Excellence for Public Health (Northern Ireland) 4 School of Sociology, Social Policy and Social Work, Queen's University Belfast
5 The Institute of Public Health in Ireland
Abstract
Over the last five years within the UK, the Research Councils, the Department of Health and major charities such as the Wellcome Trust, have begun to address the need to build
capacity in public health research and to ensure better mechanisms for translating evidence into practice. Following reports such as Public Health Sciences: Challenges and
Opportunities, major new ventures such as the National Prevention Research Initiative, the creation of Public Health Research Centres of Excellence, and the new public health stream
of the National Institute for Health Research, appear to have forged a common purpose to support “better research for better health” [1]. This study has capitalized on the occasion of
the launch of one such Centre to describe the social networks of its stakeholders and investigate the nature and extent of the relationships between them.
Network measures
Background: The NI Health Care System
The NI health care system has gone under major reforms in the last few years. In
November 2005, the Secretary of State for Northern Ireland announced a radical
restructure of public administration structures within the province. The number of public
bodies have been reduced significantly to make the public sector more streamlined and
economically efficient. The impact on health and social care has been significant [2].
The details given below were correct at the time of the launch of the CoE in 2008, prior to
the further reforms initiated on 1st April 2009.
Centrality is a structural attribute of nodes in a network and is a measure of the
contribution of network position to the importance, influence or prominence of an actor in
a network. Centralisation is a network level measure which gives information regarding
the overall network structure.
Table 5: Top 6 nodes by degree, eigenvector and
betweenness centrality measures [4,6] of Figure 3.
See Table 2 for meaning of abbreviations.
Regardless of measure, the same few organisations
are central. Note the elevated position of the RDO
in eigenvector and betweenness centrality.
Centralisation measure
In-Degree
Out-Degree
Eigenvector
Betweenness
Chart 1: Organizational structure of the health service [2].
Table 1: Population sizes of the four health and social services boards, 2002 [2].
QUB_NM
RDO
UU
1.
2.
3.
4.
5.
6.
Percentage
5
16
51
4
Eigenvector
BHSCT
DHSSPS
QUB_CCPS
UU
EHSSB
RDO
Betweenness
DHSSPS
BHSCT
QUB_NM
UU
IPH
RDO
Table 6: Centralisation measures of the network [4,6]. Note that
the eigenvector centralisation, a weighted degree measure,
indicates a cluster of a few dominate organisations, central in the
network structure. Other values indicate a robust network.
Block-modelling (or positional analysis) partitions the nodes into structurally equivalent
or attribute based sets [6,7]. In mathematical terms, the adjacency matrix is rearranged to
form a specified number of blocks, wherein each block contains nodes with the same
attribute. The 193 organisations depicted in Figure 3 were organised according to their
work sector, as were listed in Table 3.
Belfast Health & Social Care Trust
Department of Health, Social Services & Public Safety
Eastern Health & Social Services Board
Health & Social Care Trusts
Institute of Public Health in Ireland
Northern Ireland Cancer Registry
Queen’s University Belfast
Queen’s University Belfast, Centre of Clinical &
Population Sciences
Queen’s University Belfast, School of Nursing &
Midwifery
Research & Development Office
University of Ulster
Table 2: Abbreviations of organisational names
In-Degree
DHSSPS
BHSCT
IPH
HSCT
QUB
UU
Block modelling
Figure 1: The four Health and Social Services Boards [2].
BHSCT
DHSSPS
EHSSB
HSCT
IPH
NICR
QUB
QUB_CCPS
Out-Degree
QUB_CCPS
EHSSB
NICR
DHSSPS
QUB_NM
BHSCT
Figure 4: Reduced block-model of network were nodes have
been partitioned into structurally equivalent sets. The shape of
the node is representative of the type of organisation (see
caption to Figure 3). Node size corresponds to the number of
organisations grouped together within each sector. Numbers
close to each node indicate the number of nominations from one
sector type toward another. Note that the network is
unidirectional between academics and the third sector.
Figure 2: The five Health and Social Care Trusts within NI.
Source http://fgcforumni.org/index.php
3.5
3
CoE for Public Health (NI) Network
Initial work carried out included the creation, coding and analysis of a questionnaire on
those who attended the launch of the UKCRC funded Centre of Excellence for Public
Health (NI) [3]. This was to discern the potential placement of the CoE and the necessary
role it could play within the local health sector. This involved obtaining the necessary
information through questionnaires, of which 98 were returned. From the information
given, a representation of the public health care sector within Northern Ireland was created
and analysed. This involved the use of UCINET, Netdraw and SPSS software packages.
Figure 5: A bubble chart of values attributed to impact and
strength of collaboration. Both measures were rated from high
(1) to low (3). A bubble chart is a two-dimensional scatter plot
where a third variable is represented by the size of the points,
in this case the frequency of choice. The coefficient of
correlation between impact and strength is r = 0.5869.
Therefore both are duly considered in Table 7.
Strength
2.5
2
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
3
3.5
Impact
Table 7: Root mean sum of squares (RMSS),
of impact (x) and strength (y) that participants regarded
their contact with organisations.
Scale of 1(high) - 3 (low), as partitioned/blocked into
sectors. RMSS range of strong (12+12) to (32+32).
Entry (i : j) from row i and column j, gives the RMSS
from block i to block j.
Those tran-sectoral connections with values missing may not be due to a lack of interaction, but rather a lack of data being collected at the CoE
launch. For example, no For-Profit organisation responded to the questionnaire and hence there were no nominations from this to other sectors.
Conclusions
Table 3: Profile of participants of the
questionnaire. 59 respondents were from the
academic sector reflecting the composition of the
CoE centred in the University, and will be
reflected in the network structures.
Figure 3: Network of 193 organisations and research clusters as named by attendees at
launch. The shape of a node is representative of the type of organisation:  = Statutory
Public Health Delivery (53);  = Policy-making, standard setting and professionals (37); 
= Third Sector (27); = Academic (60);  = Commissioners of research (11);  = ForProfit (4); and + = Primary Care (1). The colour of the nodes is then an indication as to
whether that organisation was present at the symposium (blue if present, grey if not) and
whether it has representation within the CoE (red). Lastly, the colour of the ties (edges) is
an indicator of whether the relation is reciprocated or not. The red dashed ties denote
reciprocated nominations (e.g. A B) whilst black solid ties are one-way nominations,
where an organisation has named another but not vice versa (e.g. AB and A B).
Using results obtained from 98 respondents from 44 organizations and research clusters we have been
able to assess the expectations, goals, and network connections of the respondents. Analysis of data on
participant expectations and personal goals suggest that the academic members of the network were
more likely to expect the work of the Centre to produce new knowledge as compared to non-academics,
but less likely to expect the Centre to generate health interventions and influence health policy.
Academics were also less strongly oriented than non-academics to knowledge transfer as a personal
goal, though more confident that research findings would be diffused beyond the immediate network. A
social network analysis of our data suggests that a central core of around 5 nodes is crucial to overall
configuration of the regional public health network in Northern Ireland, and that whilst the overall
network structure is fairly robust, the connections, between some component parts of the network - such
as academics and the third sector - are unidirectional.
References:
Table 4: How academic and non-academic participants personal goals relate to those of the UKCRC Northern Ireland Centre of
Excellence in Public Health Research, with a chi-square test performed to see if these were the same. Note that non-academics’
goals are more strongly aligned with ‘Knowledge brokerage’ when compared to those of the academics (p-value = 0.002).
1. Best Research for Best Health. A new national health research strategy. Department of Health, 2006.
2. Jordan, A., McCall, J., Moore, W., Reid, H., Stewart, D., 2006. Health Systems in Transition:
Northern Ireland. Copenhagen, WHO Regional Office for Europe on behalf of the European
Observatory on Health Systems and Policies.
3. www.qub.ac.uk/coe
4. Borgatti, S. P., Everett, M. G., Freeman, L. C., 2002. Ucinet 6 for Windows: Software for Social
Network Analysis. Harvard: Analytic Technologies.
5. SPSS for Windows, Rel 15.0.1.1. Chicago: SPSS Inc. 2008.
6. Carrington, P. J., Scott, J., Wasserman, S. (Eds.), February 2005. Models and Methods in Social
Network Analysis (Structural Analysis in the Social Sciences). Cambridge University Press.
7. Nelson, R. E., 1986. The use of blockmodelling in the study of organization structure: A
methodological proposal. Organization Studies 7, 75–85.