Visualising Q - from the bootstrap to interactive graphics

Introduction

The MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to maximize the use of health & social care data. The goal is to link data sources to support senior managers & policy makers in delivering services. The project includes 4 case studies, in 4 countries. As part of the evaluation, we need to understand the perspectives of the users, developers, and senior managers involved, and to see how these change over time. We employ Q-methodology, an objective mixed method for the study of human perspectives, to do this. In this paper we describe the use of bootstrap methods, and visualizations to assist in the execution and interpretation of the first round of our Q study.

Methods

A concourse of 36 items was developed from the literature, a logic model for the project, and a series of semi-structured interviews with project participants. Sixteen people (3 female, and 13 male) took part in the Q study, six developers, five managers, two health professionals, and 3 others. The 36 statements on the concourse were ranked online, by each participant, using the HTMLQ software, in order of their agreement with each statement. These are then subjected to a form of factor analysis, but by person, not by statement, using the qmethod package in R.

For each Q-sort 1,000 bootstrap replications were done, using sampling with replacement. A range of visualisations were prepared, using ggplot2.

Results

Visualizations of bias and variability showed modest levels of both, suggesting that the Q-method model fitted well. Interactive visualizations of the factors, and respondents, were done. These showed distinct clusters of respondents, with divergent perspectives on the project. These assisted in making final decisions, both on the number of factors to report on, and the interpretation of those factors.

Further use of advance visualisations is recommended for future Q-studies.

Funded by the European commission under contract 727721

Key messages

Q methodology is useful across many areas of public health, and is a valuable way of studying individual perspectives. Modern statistical visualisation tools can enhance the interpretation of Q methodology studies.

Q-Methodology Evaluation of a European Health Data Analytic End User Framework

Background

MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to use a wide range of health and social care data to support better policy making. As part of the project evaluation, we have used Q-methodology, a well established approach, to understand the perspectives of the individual participants on their needs and how the MIDAS system is meeting them, at its current stage of development.

Methods

We defined a concourse of 36 statements relevant to project implementation and goals, by working from a logic model for the evaluation, and structured interviews with project participants. This was delivered online to participants. Analyses were done in the qmethod package. The first q-sort was done at 14 months into the project.

Results

16 people took part, 6 developers, 5 managers, 2 health professionals and 3 others. Three factors were identified in the data. These were tentatively labelled ‘Technical optimism’, ‘End-user focus’ and ‘End-user optimism’. These loaded well onto individuals, and there were few consensus statements. There were significant differences in perspectives between different groups of participants. In particular, two of the developers held opposite perspectives to most other participants on the third perspective identified. This was drawn to the attention of the participants, and a more intensive process of communication was set-up, seeking to reduce the divergence.

Conclusions

A Q-methodological approach to evaluating the implementation of a large and complex health ICT system showed considerable divergence between the perspectives of users, developers, and managers. Such divergences can lead to project failure. Q-methodology is a valuable tool has seldom been used in public health research.

Keywords: Q-Methodology, Public Health, Data Analytics, Decision Support.

What would encourage blood donation in Ireland?

BACKGROUND

Recent changes have resulted in the loss of 4% of the donor panel in the Republic of Ireland and 3% in Northern Ireland. In order to increase the number of donors in these two regions, it is important that transfusion service providers explore and understand the reasons, which prevent individuals from donating. The aim of this study was to explore these issues particularly in non-donors and those who had lapsed.

METHODS

This 7-month all-Ireland study was conducted by computer-assisted telephone interview. Data collected included sociodemographic history, donation status, as well as barriers/deterrents to donation.

RESULTS

There were 4166 completed questionnaires (44% donors; 56% non-donors). Of the donors, 13% had donated blood within the last 2 years. Current donors cited 'awareness of patients needs' (88%), 'trust in the blood transfusion service' (70%), and 'an advertising campaign' (70%) as reasons encouraging them to donate blood. Lapsed donors and non-donors cited 'more frequent mobile clinics/sessions' (30% lapsed donors; 53% non-donors), 'if I was asked' (28% lapsed donors; 53% non-donors), and 'more flexible opening hours' (23% lapsed donors; 44% non-donors) as reasons that would encourage them to donate. The main reasons cited by non-donors for never having donated included 'medical reasons' (41% Republic of Ireland; 43% Northern Ireland), 'lack of information' (20% Republic of Ireland; 22% Northern Ireland), 'fear of needles' (15% Republic of Ireland; 17% Northern Ireland), and 'time constraints' (12% Republic of Ireland; 13% Northern Ireland). Among the non-donor group, 10% (Republic of Ireland) and 6% (Northern Ireland) claimed that they are not permitted to donate.

CONCLUSION

Replacing regular donors is a major challenge for the transfusion service providers. This study shows that by facilitating the general public by introducing more mobile clinics/sessions, more flexible opening hours and having a better level of knowledge in the community about blood donation may encourage lapsed donors and new donors to become regular donors.