Multilevel predictors of cancer clinical trial enrollment among CCOP physicians

Despite the potential benefits, only 3-5% of American adults with cancer participate in cancer clinical trials. One intervention aimed at increasing participation in clinical trials is the Community Clinical Oncology Program (CCOP), a cancer focused provider-based research network administered by the National Cancer Institute (NCI). Although drivers of enrollment at the CCOP level are well understood, no research has exclusively examined enrollment among CCOP physicians. The objective of this dissertation was to understand the factors that predict enrollment of patients in NCI-sponsored cancer clinical trials among CCOP physicians. Data were obtained from the 2011 Annual CCOP Progress Reports, two surveys conducted in 2011 among CCOP administrators and physicians, and the 2012 American Medical Association Physician Masterfile. The sample consisted of 485 CCOP physicians. We used structural equation modeling to analyze three models that predicted physician enrollment. Our first analysis sought to determine the physician characteristics, attitudes, and CCOP factors associated with physician enrollment. Our results demonstrated that physicians' attitudes toward participating in CCOP, and CCOP policies and practices (e.g. trainings offered, expectations instituted, support provided) were both significant in directly predicting enrollment, although neither physician characteristics nor CCOP factors were indirectly associated with enrollment operating through physician attitudes. In the second analysis, we included physicians' perceptions of CCOP, and tested whether fit between CCOP and physicians' values moderated the effect of physicians' perceptions of implementation climate (i.e., a climate that supports, rewards, and expects implementation) on enrollment. Our results demonstrated that both constructs were significantly associated with enrollment and including the moderator improved overall fit of the model. Lastly, we included both CCOP factors and perceptions of context in a single model. Our results confirmed that implementation climate mediated the relationship between organizational policies and practices and enrollment Overall, the results have both theoretical and practice implications. This dissertation extends the setting and unit of analysis in which innovation implementation theories have been tested. In addition, the findings from this dissertation could be used to develop physician directed strategies aimed at increasing involvement in clinical research. These strategies will be increasingly important as the CCOP network continues to evolve.Doctor of Philosoph

Context matters: measuring implementation climate among individuals and groups

Abstract Background It has been noted that implementation climate is positively associated with implementation effectiveness. However, issues surrounding the measurement of implementation climate, or the extent to which organizational members perceive that innovation use is expected, supported and rewarded by their organization remain. Specifically, it is unclear whether implementation climate can be measured as a global construct, whether individual or group-referenced items should be used, and whether implementation climate can be assessed at the group or organizational level. Methods This research includes two cross-sectional studies with data collected via surveys at the individual level. The first study assessed the implementation climate perceptions of physicians participating in the National Cancer Institute’s (NCI) Community Clinical Oncology Program (CCOP), and the second study assessed the perceptions of children’s behavioral health clinicians implementing a treatment innovation. To address if implementation climate is a global construct, we used confirmatory factor analysis. To address how implementation climate should be measured and at what level, we followed a five-step framework outlined by van Mierlo and colleagues. This framework includes exploratory factor analysis and correlations to assess differences between individual and group-referenced items and intraclass correlations, interrater agreements, and exploratory factor analysis to determine if implementation climate can be assessed at the organizational level. Results The confirmatory factor analysis demonstrated that implementation climate is a global construct consisting of items related to expectations, support and rewards. There are mixed results, however, as to whether implementation climate should be measured using individual or group-referenced items. In our first study, where physicians were geographically dispersed and practice independently, there were no differences based on the type of items used, and implementation climate was an individual level construct. However, in the second study, in which clinicians practice in a central location and interact more frequently, group-referenced items may be appropriate. In addition, implementation climate could be considered an organizational level construct. Conclusions The results are context-specific. Researchers should carefully consider the study setting when measuring implementation climate. In addition, more opportunities are needed to validate this measure and understand how well it predicts and explains implementation effectiveness

Organizational readiness for implementing change: a psychometric assessment of a new measure

Abstract Background Organizational readiness for change in healthcare settings is an important factor in successful implementation of new policies, programs, and practices. However, research on the topic is hindered by the absence of a brief, reliable, and valid measure. Until such a measure is developed, we cannot advance scientific knowledge about readiness or provide evidence-based guidance to organizational leaders about how to increase readiness. This article presents results of a psychometric assessment of a new measure called Organizational Readiness for Implementing Change (ORIC), which we developed based on Weiner’s theory of organizational readiness for change. Methods We conducted four studies to assess the psychometric properties of ORIC. In study one, we assessed the content adequacy of the new measure using quantitative methods. In study two, we examined the measure’s factor structure and reliability in a laboratory simulation. In study three, we assessed the reliability and validity of an organization-level measure of readiness based on aggregated individual-level data from study two. In study four, we conducted a small field study utilizing the same analytic methods as in study three. Results Content adequacy assessment indicated that the items developed to measure change commitment and change efficacy reflected the theoretical content of these two facets of organizational readiness and distinguished the facets from hypothesized determinants of readiness. Exploratory and confirmatory factor analysis in the lab and field studies revealed two correlated factors, as expected, with good model fit and high item loadings. Reliability analysis in the lab and field studies showed high inter-item consistency for the resulting individual-level scales for change commitment and change efficacy. Inter-rater reliability and inter-rater agreement statistics supported the aggregation of individual level readiness perceptions to the organizational level of analysis. Conclusions This article provides evidence in support of the ORIC measure. We believe this measure will enable testing of theories about determinants and consequences of organizational readiness and, ultimately, assist healthcare leaders to reduce the number of health organization change efforts that do not achieve desired benefits. Although ORIC shows promise, further assessment is needed to test for convergent, discriminant, and predictive validity

Achieving high cancer control trial enrollment in the community setting: An analysis of the Community Clinical Oncology Program

Determining the factors that lead to successful enrollment of patients in cancer control clinical trials is essential as cancer patients are often burdened with side effects such as pain, nausea, and fatigue. One promising intervention for increasing enrollment in cancer control trials is the National Cancer Institute’s Community Clinical Oncology Program (CCOP). In this article, we examined CCOP staffing, polices, and procedures associated with enrollment in control trials. Data were obtained from three sources: the online CCOP, MB-CCOP, and Research Base Management System, CCOP Annual Progress Reports, and a survey of CCOP Administrators conducted in 2011. We analyzed cancer control trial accrual in 2011 among 46 CCOPs using multivariate regression. Three factors were significant predictors of accrual. First, having a team of staff dedicated to enrolling patients in control and prevention trials, compared to having no dedicated staff, was associated on average with an additional 30 patients enrolled in control trials (p <0.05). Second, CCOPs that recognized physicians for enrolling a large number of patients compared to CCOPs that did not recognize high enrolling physicians enrolled on average an additional 25 patients in control trials (p <0.05). Lastly, the number of cancer control trials available was also associated with enrollment (β = 5.50, p<0.00). Our results indicate that CCOPs looking to increase enrollment in control trials should consider dedicating a team of staff to enroll patients in these types of trials. In addition, CCOPs or other volunteer research systems looking to increase physician participation should consider recognizing high enrolling physicians

Pressing ahead: developing and testing of new measures in implementation science

Measurement forms the foundation of any scientific field; yet, systematic reviews reveal that many available measures of implementation context, process, and outcomes lack reliability or validity. An urgent need exists for psychometrically strong measures in implementation science; without them, the field cannot produce cumulative knowledge about implementation barriers, facilitators, processes, or generate sound evidence about which implementation strategies work best, when, and for whom. In this panel session, three researchers reported on their efforts to develop and test new measures of constructs featured in the Consolidated Framework for Implementation Research (CFIR). Maria Fernandez described the work of the CDC/NCI-funded Cancer Prevention and Control Research Network to create measures for seven constructs in the inner-setting domain of CFIR and assess the psychometric properties of those measures using data from a multi-state sample of community health centers. Shuting Liang reported on the Network’s effort to develop and assess measures of selected constructs in other CFIR domains and discussed the inter-relationships of these constructs at both the individual and clinic level of analysis. Sara Jacobs explored in two different study contexts the psychometric properties of, and measurement issues associated with, a new theory-based measure of implementation climate. Building on the presentations, Stephen Taplin moderated a discussion between panelists and participants about the role of theory in measurement, the challenges of adapting existing measures, the implications of item-wording choices, the effects of context on measurement properties, and the measurement of organization-level constructs using individual-level data. Participants learned about new measures they could use in their own research; in addition, they engaged in dialogue about needs, opportunities, challenges, and recommended practices in measurement in implementation scienc

Fine Particulate Air Pollution and the Progression of Carotid Intima-Medial Thickness: A Prospective Cohort Study from the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Background Fine particulate matter (PM2.5) has been linked to cardiovascular disease, possibly via accelerated atherosclerosis. We examined associations between the progression of the intima-medial thickness (IMT) of the common carotid artery, as an indicator of atherosclerosis, and long-term PM2.5 concentrations in participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Methods and Results MESA, a prospective cohort study, enrolled 6,814 participants at the baseline exam (2000–2002), with 5,660 (83%) of those participants completing two ultrasound examinations between 2000 and 2005 (mean follow-up: 2.5 years). PM2.5 was estimated over the year preceding baseline and between ultrasounds using a spatio-temporal model. Cross-sectional and longitudinal associations were examined using mixed models adjusted for confounders including age, sex, race/ethnicity, smoking, and socio-economic indicators. Among 5,362 participants (5% of participants had missing data) with a mean annual progression of 14 µm/y, 2.5 µg/m3 higher levels of residential PM2.5 during the follow-up period were associated with 5.0 µm/y (95% CI 2.6 to 7.4 µm/y) greater IMT progressions among persons in the same metropolitan area. Although significant associations were not found with IMT progression without adjustment for metropolitan area (0.4 µm/y [95% CI −0.4 to 1.2 µm/y] per 2.5 µg/m3), all of the six areas showed positive associations. Greater reductions in PM2.5 over follow-up for a fixed baseline PM2.5 were also associated with slowed IMT progression (−2.8 µm/y [95% CI −1.6 to −3.9 µm/y] per 1 µg/m3 reduction). Study limitations include the use of a surrogate measure of atherosclerosis, some loss to follow-up, and the lack of estimates for air pollution concentrations prior to 1999. Conclusions This early analysis from MESA suggests that higher long-term PM2.5 concentrations are associated with increased IMT progression and that greater reductions in PM2.5 are related to slower IMT progression. These findings, even over a relatively short follow-up period, add to the limited literature on air pollution and the progression of atherosclerotic processes in humans. If confirmed by future analyses of the full 10 years of follow-up in this cohort, these findings will help to explain associations between long-term PM2.5 concentrations and clinical cardiovascular events. Please see later in the article for the Editors' Summar

Determining the predictors of innovation implementation in healthcare: a quantitative analysis of implementation effectiveness

BackgroundThe failure rates for implementing complex innovations in healthcare organizations are high. Estimates range from 30% to 90% depending on the scope of the organizational change involved, the definition of failure, and the criteria to judge it. The innovation implementation framework offers a promising approach to examine the organizational factors that determine effective implementation. To date, the utility of this framework in a healthcare setting has been limited to qualitative studies and/or group level analyses. Therefore, the goal of this study was to quantitatively examine this framework among individual participants in the National Cancer Institute’s Community Clinical Oncology Program using structural equation modeling.MethodsWe examined the innovation implementation framework using structural equation modeling (SEM) among 481 physician participants in the National Cancer Institute’s Community Clinical Oncology Program (CCOP). The data sources included the CCOP Annual Progress Reports, surveys of CCOP physician participants and administrators, and the American Medical Association Physician Masterfile.ResultsOverall the final model fit well. Our results demonstrated that not only did perceptions of implementation climate have a statistically significant direct effect on implementation effectiveness, but physicians’ perceptions of implementation climate also mediated the relationship between organizational implementation policies and practices (IPP) and enrollment (p <0.05). In addition, physician factors such as CCOP PI status, age, radiological oncologists, and non-oncologist specialists significantly influenced enrollment as well as CCOP organizational size and structure, which had indirect effects on implementation effectiveness through IPP and implementation climate.ConclusionsOverall, our results quantitatively confirmed the main relationship postulated in the innovation implementation framework between IPP, implementation climate, and implementation effectiveness among individual physicians. This finding is important, as although the model has been discussed within healthcare organizations before, the studies have been predominately qualitative in nature and/or at the organizational level. In addition, our findings have practical applications. Managers looking to increase implementation effectiveness of an innovation should focus on creating an environment that physicians perceive as encouraging implementation. In addition, managers should consider instituting specific organizational IPP aimed at increasing positive perceptions of implementation climate. For example, IPP should include specific expectations, support, and rewards for innovation use

A Simple and Practical Approach to Unit Testing: The JML and JUnit Way

Writing unit test code is labor-intensive, hence it is often not done as an integral part of programming. However, unit testing is a practical approach to increasing the correctness and quality of software; for example, the Extreme Programming approach relies on frequent unit testing. In this paper we present a new approach that makes writing unit tests easier. It uses a formal specification language\u27s runtime assertion checker to decide whether methods are working correctly, thus automating the writing of unit test oracles. These oracles can be easily combined with hand-written test data. Instead of writing testing code, the programmer writes formal specifications (e.g., pre- and postconditions). This makes the programmer\u27s task easier, because specifications are more concise and abstract than the equivalent test code, and hence more readable and maintainable. Furthermore, by using specifications in testing, specification errors are quickly discovered, so the specifications are more likely to provide useful documentation and inputs to other tools. We have implemented this idea using the Java Modeling Language (JML) and the JUnit testing framework, but the approach could be easily implemented with other combinations of formal specification languages and unit test tools

Organizational and physician factors associated with patient enrollment in cancer clinical trials

Our purpose was to identify physicians’ individual characteristics, attitudes, and organizational contextual factors associated with higher enrollment of patients in cancer clinical trials among physician participants in the National Cancer Institute’s Community Clinical Oncology Program (CCOP). We hypothesized that physicians’ individual characteristics, such as age, medical specialty, tenure, CCOP organizational factors (i.e., policies and procedures to encourage enrollment), and attitudes towards participating in CCOP would directly determine enrollment. We also hypothesized that physicians’ characteristics and CCOP organizational factors would influence physicians’ attitudes towards participating in CCOP, which in turn would predict enrollment

A Simple and Practical Approach to Unit Testing: The JML and JUnit Way

Writing unit test code is labor-intensive, hence it is often not done as an integral part of programming. However, unit testing is a practical approach to increasing the correctness and quality of software; for example, the Extreme Programming approach relies on frequent unit testing. In this paper we present a new approach that makes writing unit tests easier. It uses a formal specification language\u27s runtime assertion checker to decide whether methods are working correctly, thus automating the writing of unit test oracles. These oracles can be easily combined with hand-written test data. Instead of writing testing code, the programmer writes formal specifications (e.g., pre- and postconditions). This makes the programmer\u27s task easier, because specifications are more concise and abstract than the equivalent test code, and hence more readable and maintainable. Furthermore, by using specifications in testing, specification errors are quickly discovered, so the specifications are more likely to provide useful documentation and inputs to other tools. We have implemented this idea using the Java Modeling Language (JML) and the JUnit testing framework, but the approach could be easily implemented with other combinations of formal specification languages and unit test tools