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  • We conducted four FG discussions attended by a total of 35 individuals from Lenoir County that were organized into two African-American (AA) and two White (W) FGs. We used purposeful sampling to ensure that the predominant racial groups in the county, African-American and White, were equally represented in our sample. Our recruitment goal was to have racially homogenous groups with a balance of men and women. Eligibility criteria included being an adult aged 18 and older, English-speaking, and a current resident of Lenoir County. Participants were recruited by key community members (e.g. the Health Director), or through flyers posted in the community. Interested participants were screened by phone to determine eligibility. Since we were unaware of any previous genomic studies conducted in Lenoir County, we wanted to understand the thoughts, feelings, and concerns both about genomics and heart health from Lenoir residents. Co-investigators with experience in the Lenoir community and community member assistants worked together to develop a semi-structured discussion guide to explore the acceptability of genomic research in Lenoir County based on input from discussions with key community residents. The community member assistants were either referred by our Community Advisory Council or recruited through a job advertisement in the community. The two community member assistants reflected the racial makeup of each FG, either African-American or White. The community residents who helped develop the guide included Lenoir County Health and Human Services agency employees. Based on community input and existing literature, we constructed our guide with the hypothesis that there would be unfamiliarity and mistrust of genomic research in Lenoir County, and reluctance to participate in a genomics study; furthermore, the mistrust and reluctance would be higher in African-Americans. The University of North Carolina Chapel Hill Institutional Review Board reviewed and approved the study protocol (IRB # 10–0395). The FGs were conducted in winter 2011 with each session lasting approximately 90 minutes. Groups were held in a private location at the community hospital. A trained co-investigator with extensive qualitative expertise moderated discussions, assisted by a community member of the research team. At the beginning of each group, the moderator read the consent form aloud and gave participants the opportunity to ask study-related questions before those interested signed the written informed consent form. Next, demographic information was collected via survey (e.g. age, race, and education level). Word association was then used to assess baseline familiarity with the term ‘genomics’. The FG leader then provided an analogy that investigators and community research team members developed to help participants define genomics. Next, participants were asked to verbally rate on a scale from 1 to 10, with 10 being “completely important,” how important genomics is to their health. The discussion then commenced covering the following topics: (1) community concerns about genomics; (2) thoughts and perceptions about genomics and heart health; and (3) community concerns about participation in genomic research. Participants were each paid $25 upon completion of the session. FGs were digitally recorded and reviewed for quality and completeness. Files were transcribed verbatim then verified by listening to the original recordings. To analyze our data, we first created a coding scheme using both deductive and inductive methods [20,21]. A codebook was developed and applied to organize text and assist with interpretation. Using a deductive a priori approach, we developed a codebook based on the discussion topics, the hypothesis, and a preliminary reading of the transcripts before beginning an in-depth analysis of the data. We then incorporated data-driven inductive coding techniques as described by Strauss and Corbin [22], and Crabtree and Miller [23] to explore patterns. We applied the codes from the codebook to each line of transcript text to identify meaningful units of text, connected the codes and identified themes, and confirmed the findings through a process of clustering the themes [23]. While codes were mutually exclusive, lines of text could have been marked with multiple codes if more than one theme was represented. We used a qualitative data analysis software program, ATLAS.ti 6.2, to facilitate analysis. After each transcript was imported into the software and coded, we retrieved text on specific codes or combination of codes to enable thematic analysis of particular topics [24]. From this, we looked at the quotes in the context of the documents and assessed the levels of agreement and saliency of themes. Finally, we summarized our findings and chose quotes representative of each theme for presentation.
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