Development and psychometric testing of the clinical networks engagement tool

<div><p>Background</p><p>Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks.</p><p>Methods</p><p>The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores.</p><p>Results</p><p>Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: <i>global engagement</i>, <i>inform</i> (provided with information), <i>involve</i> (worked together to address concerns), and <i>empower</i> (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that <i>inform</i>, <i>involve</i>, and <i>empower</i> were all significant predictors of <i>global engagement</i>, with <i>involve</i> as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores.</p><p>Conclusions</p><p>This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement.</p></div

Cronbach’s alpha, means, standard deviations, and correlations between subscale scores.

<p>Cronbach’s alpha, means, standard deviations, and correlations between subscale scores.</p

Multivariate and univariate analysis of variance for engagement scores.

<p>Multivariate and univariate analysis of variance for engagement scores.</p

Exploratory factor analysis of final engagement items.

<p>Exploratory factor analysis of final engagement items.</p

Means and standard deviations of engagement scores by governance level.

<p>Means and standard deviations of engagement scores by governance level.</p

Item descriptive statistics.

<p>Item descriptive statistics.</p

Participant characteristics.

<p>Participant characteristics.</p

Selection of the analysis cohorts.

<p>Selection of the analysis cohorts.</p

Increased inflammation is associated with islet autoimmunity and type 1 diabetes in the Diabetes Autoimmunity Study in the Young (DAISY)

<div><p>Background</p><p>Type 1 diabetes (TID) is characterized by a loss of pancreatic islet beta cell function resulting in loss of insulin production. Genetic and environmental factors may trigger immune responses targeting beta cells thus generating islet antibodies (IA). Immune response pathways involve a cascade of events, initiated by cytokines and chemokines, producing inflammation which can result in tissue damage.</p><p>Methods</p><p>A nested case-control study was performed to identify temporal changes in cytokine levels in 75 DAISY subjects: 25 diagnosed T1D, 25 persistent IA, and 25 controls. Serum samples were selected at four time points: (T1) earliest, (T2) just prior to IA, (T3) just after IA, and (T4) prior to T1D diagnosis or most recent. Cytokines (IFN-α2a, IL-6, IL-17, IL-1β, IP-10, MCP-1, IFN-γ, IL-1α, and IL-1ra) were measured using the Meso Scale Discovery system Human Custom Cytokine 9-Plex assay.</p><p>Results</p><p>Multivariate mixed models adjusting for HLA risk, first-degree relative status, age, and gender, showed MCP-1 and IFN-үto be significantly higher at T3 in T1D compared to IA subjects. At T4, IP-10 was significantly higher in IA subjects than controls.</p><p>Conclusions</p><p>This repeated measures nested case-control study identified increased inflammatory markers in IA children who developed T1D compared to IA children who had not progressed to clinical disease. It also showed increased inflammation in both T1D and IA children when compared to controls. Results suggest inflammation may be related to both the development of IA and progression to T1D.</p></div

The Mixed Model Estimate of Change from Time 1.

<p>Pattern of change in serum cytokine or chemokine concentrations over time for the T1D, IA, and control groups. The log mean concentration for each individual cytokine is depicted as the difference from T1, the baseline (T2-T1, T3-T1, T4-T1).</p