Selected practices were divided between 2 trained anthropologists who spent 2 full workdays at each of their assigned sites. Following a prescribed protocol, their task was to observe the office practice, the interpersonal styles of the clinicians, and the office environment and culture. They also observed provider-patient interactions for 3 to 5 patients per practice and interviewed the patients following their examinations to assess their overall satisfaction and experience with the clinic. Also, the team reviewed information obtained from the face-to-face interviews of clinicians and nurses.
A systematic process of review of all observations occurred throughout the data collection to insure the trustworthiness of these data based on techniques used by Silverman and coworkers.35 The first level of review was to discuss all observations among the 3 anthropologists following the collection of observation data of each site. This served to identify gaps in the data collection or areas where observations required some confirmation for accuracy and interpretation. The second level of review was through discussions at the weekly research team meetings for data verification and clarification of interpretation. Finally, the 8 sites were classified into types based on similarities in organizational structure, operational characteristics, and physician and staff philosophy.
Focus Groups
Two focus groups were held with senior adults who were not among the patients participating in the study. One group consisted of 14 African Americans, and the other consisted of 10 whites; both sexes were represented. Participants were recruited through a local senior center, where the focus groups were conducted. Discussion addressed issues of barriers to and facilitators of immunization and recommendations for improving immunization rates.
Statistical Methods
Qualitative data analysis methods such as the creation of code books was used to categorize the provider and staff responses to the interviewer questions and participant observations. These categorized responses were then used in the quantitative analysis in both bivariate and logistic regression models.36
The quantitative analysis of the data must take into account the cluster-correlated nature of the data which results from a complex multistage stratified clustered sampling strategy. For this reason, we used statistical software that can compute standard errors, regression coefficients, and other statistics in accordance with the sample design.37 Frequencies of patient responses to questions were computed using both quantitative and coded qualitative items. Bivariate relationships were examined, followed by logistic regression modeling, with receipt versus nonreceipt of vaccines as the dependent variable. Models were developed for each of the influenza and pneumococcal vaccines as dependent variables.
Discussion
Relevance of This Methodology for Future Interventions
The application of the PRECEDE-PROCEED framework and Awareness to Adherence and Triandis models to the study of provider and patient attitudes, knowledge, beliefs, and practices regarding adult immunizations is ideal. Using these models combined with a multidisciplinary research team and triangulation of data collection, we hope to gain insight into factors associated with adult immunizations. Authorities have suggested that a tailored approach that accounts for the core values, structure, and internal operations of practices, is more likely to raise immunization rates than using the same approach for all practices.11,15,16 This unique study design allows for simultaneous examination of patient, provider, and office culture factors and their relative impact on adult immunization rates. This in turn will facilitate the development of tailored intervention plans to improve those rates.
Acknowledgments
This publication/project was funded by HS09874-01A1 from the Agency for Healthcare Research and Quality. The authors wish to acknowledge Michael J. Fine, MD; Edmund M. Ricci, PhD; Seymour Grufferman, MD; Ilene K. Jewell, MS Hyg; and Mahlon Raymund, PhD, for their significant contributions to the design and implementation of this project or paper.