sex, socioeconomic, and racial dist

sex, socioeconomic, and racial distributions. They include vital statistics, such as selected mortality and morbidity data. Another source is surveys, such as the Behavior Risk Factor Surveillance System (BRFSS) developed and conducted to monitor state-level prevalence of the major behavioral risks associated with premature morbidity and mortality in adults (see the Did You Know box on p 404 for the BRFSS website). Conducted by the National Center for Chronic Disease Pre-vention and Health Promotion (NCCDPHP), these survey data, from sources such as the BRFSS, are important because they also provide data about trends. Trends are impor¬tant because they show whether a community problem has existed for a long time or whether it is new. Other resources include data from community institutions, including health care organizations and the services they provide, and the characteristics of health care personnel. Often these data have been collected by others via structured interviews, question¬naires, or surveys and are available in published reports. State health departments gather extensive epidemiological data in the form of rates, which are generally published at both county and state levels in the form of vital health statistics. Table 18-4, shown on p 420, provides an example of exist¬ing epidemiological data gathered as part of the Bernalillo County assessment.
The USDHHS Health Resources and Services Administra¬tion has placed its Community Health Status Indicators proj¬ect on the Internet (accessible through the WebLinks on this book's Efolve site). This site provides an excellent example of how data can be gathered to provide this part of the community assessment composite database. It also provides county-level data as well as a list of peer counties for almost every county in the United States.
Data Generation. Data generation is the process of devel oping data that do not already exist through interaction with community members or groups. This type of information is harder to obtain and is generally not statistical in nature. Data that often must be generated include information about a com¬munity's knowledge and beliefs, values and sentiments, goals and perceived needs, norms, problem-solving processes, power, leadership, and influence structures. These data, called quali¬tative data, are more likely to be collected by interviews and observation.
Several methods to generate or collect data are needed. Methods that encourage the nurse to consider the community's perception of its health problems and abilities are as important as methods structured to identify knowledge that the nurse con¬siders essential. Methods of collecting data rely either on what is directly observed by the data collector or on what is reported to the data collector (Richards et al, 2008; Severance and Zinnah, 2009).
Informant interviews, focus groups, participant observa¬tion, and windshield surveys are four methods of generating direct data. All four methods require sensitivity, openness, curiosity, and the ability to listen, taste, touch, smell, and see life as it is lived in a community (Cashman et al, 2008; Sever¬ance and Zinnah, 2009). Either informant interviews (see the How To Identify a Key Informant box) or focus groups, which
consist of directed talks with selected members of a commu¬nity about community members or groups and events are basic to effective data generation (Adams and Canclini, 2008; Levine et al, 2008; Severance and Zinnah, 2009). Also basic is participant observation, the deliberate sharing in the life of a community, to the extent that conditions permit. Infor¬mant interviews, focus groups, and participant observation are good ways to generate information about community beliefs, norms, values, power and influence structures