Recruitment of heterosexual couples in public health research: a study protocol

• Main Authors: Pouget Enrique R, Torres Leilani, Tortu Stephanie, McMahon James M, Hamid Rahul
• Format: Article
• Language: English
• Published: BMC 2003-10-01
• Series: BMC Medical Research Methodology
• Online Access: http://www.biomedcentral.com/1471-2288/3/24

<p>Abstract</p> <p>Background</p> <p>Public health research involving social or kin groups (such as sexual partners or family members), rather than samples of unrelated individuals, has become more widespread in response to social ecological approaches to disease treatment and prevention. This approach requires the development of innovative sampling, recruitment and screening methodologies tailored to the study of related individuals.</p> <p>Methods</p> <p>In this paper, we describe a set of sampling, recruitment and screening protocols developed to enlist urban, drug-using, heterosexual couples into a public health research study. This population is especially hard to reach because they are engaged in illegal and/or stigmatized behaviors. The protocols were designed to integrate adaptive sampling, street- and referral-based recruitment, and screening procedures to verify study eligibility and relationship status.</p> <p>Discussion</p> <p>Recruitment of heterosexual couples through one partner, preferably the female, can be an effective enlistment technique. Verification of relationship status is an important component of dyadic research. Comparison of parallel questionnaires administered to each member of a dyad can aid in the assessment of relationship status. However, multiple independent sources of information should be used to verify relationship status when available. Adaptive sampling techniques were effective in reaching drug-using heterosexual couples in an urban setting, and the application of these methods to other groups of related individuals in clinical and public health research may prove to be useful. However, care must be taken to consider potential sources of sampling bias when interpreting and generalizing study results.</p>