Guide Best Evidence Structural Interventions for HIV Prevention

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Best evidence structural interventions for HIV prevention

Public policy may play a role in shaping environmental outcomes to stem HIV transmission. A recent study identified strategies for HIV prevention by collecting the opinions of behavioral scientists who have conducted research in the area of HIV prevention in the U. This process incorporates views and suggestions about structural interventions from a large number of experts in the field of HIV prevention.

The identification of interventions was based on a specific definition of structural intervention. Structural interventions are those that address physical, social, cultural, organizational, community, economic, legal, or policy aspects of the environment. The methodology allowed the experts to contribute to this process and facilitated the inclusion of a wide range of interventions.

The analyses typically include multidimensional scaling MDS of the sort data, hierarchical cluster analysis of the MDS coordinates, and computation of average ratings for each statement and cluster of statements. The resulting maps show the individual statements in two-dimensional x,y space with more similar statements located closer to each other and grouped into clusters. Participants are actively involved in interpreting the results to ensure that the maps are understandable and labeled in a meaningful way.


Concept mapping has been used effectively to address substantive issues across a wide range of fields. Data were collected in a number of steps. First, a working group was formed that included representatives from CDC and a number of subject-matter experts affiliated with various research institutes. These experts had conducted research in this area and had been promoting the need for developing and implementing structural interventions for HIV prevention. Second, the working group identified stakeholders and subject-matter experts from a broad range of disciplines and regions of the U.

The stakeholders and subject-matter experts were identified and selected based on their knowledge, expertise, and involvement in HIV prevention research and program activities and structural interventions. As the stakeholders were identified based on their knowledge and expertise, many of them came from outside the U. It was also decided that most participants needed to be representatives of the HIV prevention community, such as state and local health departments, community-based organizations, academic and research institutes, and state and federal government agencies, including CDC.

It was expected that individuals outside HIV prevention would bring new perspectives to the field.

Best evidence structural interventions for HIV prevention (eBook, ) []

Third, the working group developed a focused prompt, namely: To address these structural factors, a specific action e. Fourth, the stakeholders and subject-matter experts were invited to participate in the project and contribute to its development and implementation. They were asked to generate as many statements as possible and enter them into the system. The participants had eight weeks to respond to our request. During this eight-week period, they had the option of generating and entering statements in more than one session.

Recognizing that the participants' locations and access to technology varied, the project enabled multiple ways to submit ideas—for example, using a fax-back form or mail. This process resulted in the generation of statements from an estimated 75 different participants.

Because the brainstorming was anonymous, the estimate was based on Web traffic patterns. Each statement represented an action, an intervention, or a project.

Finally, after receiving the statements, a smaller number of working group members met and reviewed each statement to edit, consolidate, eliminate redundancy, and minimize any confusion in meaning. Editing and consolidation were necessary to sort and rate the statements. The following criteria were used to generate a final set of statements:. The goal was to have a set of mutually exclusive statements, with only one main idea in each and with no loss of content from the original list. This process generated a final list of statements. Statements that were considered too broad, nonspecific, and general in terms of their relevance to the focus statement e.

These statements identified a social injustice without identifying a strategy by which to address that social injustice.

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In addition, such goals may not be seen by the public as falling within the appropriate range of action for public health. The individual interventions were grouped into 11 categories based on similarity of concepts. Next, stakeholders from the original list of volunteered to rate the statements on a 1-to-5 scale of both feasibility and impact upon HIV incidence that these structural interventions might have if implemented. Impact and feasibility scores were perceived by the project participants and not necessarily based on any empirical findings.

The mean impact scores varied widely from 2. The stakeholders had the option of completing the task using the dedicated website, or by faxing back a form sent to them. We used Concept Systems, Inc. MDS is a statistical technique for displaying differences between items, as if they were points on a map or in a three-dimensional space. The greater the distance, the more different the items are in the opinions of people who rate them. This statistical technique is used for perceptual mapping.

Identification of Structural Interventions for HIV/AIDS Prevention: The Concept Mapping Exercise

Hierarchical cluster analysis is a statistical method for finding relatively homogeneous clusters of cases based on measured characteristics. This data-reduction technique starts with each case in a separate cluster and then combines the clusters sequentially, reducing the number of clusters at each step until only one cluster is left. The analysis used the sorted information, discussed previously, to construct an NxN binary, symmetric matrix of similarities, for all sorting participants.

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We analyzed the total similarity matrix using non-metric MDS analysis with a two-dimensional solution.