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. Author manuscript; available in PMC: 2013 Nov 6.
Published in final edited form as: J Prev Interv Community. 2012;40(2):10.1080/10852352.2012.660123. doi: 10.1080/10852352.2012.660123

Programmatic capacity and HIV structural change interventions: Influences on coalitions’ success and efficiency in accomplishing intermediate outcomes

Sarah J Reed 1, Robin Lin Miller 1, Vincent T Francisco 2; the Adolescent Medicine Trials Network for HIV/AIDS Interventions
PMCID: PMC3818714  NIHMSID: NIHMS351958  PMID: 24188353

Abstract

This paper assesses how programmatic capacity affects coalitions’ ability to achieve structural HIV prevention interventions. The focus of the analysis was on the structural changes developed (N = 304) at all coalitions involved in Connect to Protect between early 2006 through the end of 2008. Data included records of coalitions’ structural change objectives and the progress made towards their accomplishment. For the current study, we divided objectives into two periods: those created before 2008 (N = 201) and those created from January 2008 through December 2008 (N = 103). In addition to becoming more structurally focused, C2P coalitions are becoming more efficient and most individual coalitions becoming more ‘successful.’ Findings highlight the benefit of creating high quality, strategic structural change objectives. Future research should investigate other influences that impede or facilitate the implementation of structural change HIV prevention interventions.

Keywords: Coalitions, Structural Interventions, Programmatic Capacity, HIV Prevention

Introduction

Having been in and out of favor over the past two decades (Green & Kreuter, 2002), coalitions and community collaboratives continue to be used as vehicles to promote structural interventions to improve community health (Clark et al., 2010). Structural interventions decrease individual vulnerability to health problems by changing the environmental, political, and economic conditions that influence risk exposure (Blankenship, Bray, & Merson, 2000; Blankenship, Friedman, Dworkin, & Mantell, 2006; Frieden, 2010). Structural interventions present promising strategies for HIV prevention (Blankenship et al., 2000), including interventions targeting adolescents (Rotheram-Borus, 2000). While community coalitions and collaborative focused on achieving health-related structural changes are becoming more popular (Wynn et al., 2006), few studies describe the structural changes that these coalitions are able to achieve, and fewer still report factors associated with achievement (Clark et al., 2010).

Coalitions that focus on achieving structural change will encounter the same developmental challenges that face all coalitions. Coalitions must develop four types of collaborative capacity to be sustained and effective (Foster-Fishman, Berkowitz, Lounsbury, Jacobson, & Allen, 2001). Member capacity entails the knowledge and skills required of members to collaborate. Relational capacity is contingent upon fostering positive internal and external relationships which facilitate the achievement of goals and organizational capacity relates to operational functioning. Lastly, programmatic capacity refers to coalitions’ ability to identify local needs and develop clear, meaningful, ecologically valid solutions to needs (Foster-Fishman et al., 2001). Programmatic capacity may thus be evident in what coalitions specify they wish to accomplish.

Connect to Protect

Connect to Protect® (C2P) is an Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) supported research initiative with the objective of reducing HIV rates for adolescents through the implementation of structural change. For C2P, structural changes are defined as “new or modified programs, policies, or practices that are logically linked to HIV acquisition and transmission and can be sustained over time” (Ziff et al., 2006, p. 513). To this end, C2P mobilized a network of coalitions in urban areas where there are high rates of HIV infection among youth (Ziff et al., 2006). There are 13 coalitions operating across the mainland United States and Puerto Rico and coalitions target populations that epidemiological data suggest are at high risk in each respective city (young men who have sex with men or MSM, young Black and Latino heterosexual females, or injection drug-users).

The C2P coalitions have achieved structural changes that have the potential to affect community risk-conditions influencing local HIV epidemics (Chutuape et al., 2010). Coalitions’ ability to succeed may have increased with time because of efforts on behalf of the coordinating body to increase coalition programmatic and member capacity (Willard, Chutuape, Stines, & Ellen, this issue). This study assesses coalitions’ creation and achievement of structural change (i.e., intermediate outcomes) and identifies ways in which programmatic capacity affects success. Better understanding of the relationship between programmatic capacity and attainment of objectives helps clarify its influence on one intermediate measure of coalition success—achieving structural change.

Each of the C2P coalitions create a strategic plan, detail an action plan that serves as a blueprint for moving forward, and identify what they believe are root causes of HIV transmission among youth in their target population. Members of the coalition then participate in the completion of action steps that are necessary for the completion of structural change objectives, which are the strategies that members have proposed for addressing their identified root causes. The following is an example of a structural change objective that was ultimately completed: “By the year 2007, Law 81 will be amended allowing health professionals to perform HIV testing to youth under 21 without parental consent.” Coalitions are given leniency to develop as many or as few structural change objectives as they desire, though the objectives are regularly reviewed by the coordinating body.

Method

The focus of the current analysis is on the structural change objectives developed (N = 304) at all coalitions between their inception in early 2006 through the end of 2008. Individual coalitions formed between 14 and 44 structural change objectives within this time (M = 23.38). Data for the current analysis included records of coalitions’ structural change objectives and the progress made towards their accomplishment. C2P project staff regularly record progress made towards structural change objectives on action plan worksheets that chart when structural change objectives are first proposed and accomplished, detail modifications made to structural change objectives, list the completion of action steps necessary to accomplish the objectives, and keep track of objectives which the coalition has decided to discontinue. From these documents we were able to track how long each objective took from the time it was created until it was completed or discontinued; the number of action steps, or the activities needing to take place prior to accomplishment, for each objective; and the number of modifications made to structural change objectives that were required when the specific aim of the objective was unclear, the anticipated time to completion needed prolonged, or the objective did not meet the requirements of the coordinating body. These variables were entered into an SPSS database that tracked each individual structural change objective.

For the current study, we divided objectives into two periods: those created in 2006 or 2007 (N = 201) and those created in 2008 (N = 103). This time frame allowed all coalitions to function long enough to make strategic decisions in response to previous successes and feedback from the coordinating center. Additionally, by the beginning of 2008, coalitions were taught a new approach to root cause analysis (Willard et al., this issue) which was thought to have impacted the objectives that were created (and thus accomplished) thereafter. We also categorized coalitions into two success groupings based on their rate of completion of objectives across both time periods; seven coalitions were categorized as high success coalitions (>50% of objectives accomplished) and six were categorized as low success coalitions (<50% of objectives accomplished). This classification allowed us to take into consideration what the coalitions hoped to accomplish, as well as what they were able to accomplish.

Coding of objectives

All structural change objectives were coded along multiple dimensions related to objective status, quality, purpose, and scope. Coding was done using QSR International's (2008) NVIVO 8. The following codes were applied:

Status of objective

At the time of data collection, coalitions were still in operation and working towards completion of objectives and as such, objectives were coded as being completed (N = 139), discontinued (N = 103), or still active (N = 62). Objective status was entered as a categorical variable into the SPSS database.

Quality of objectives

We coded the original objective (i.e., the non-modified objective) when they were first proposed by coalition members for their quality. We defined quality using SMART criteria (specific, measurable, achievable, realistic, time-bound objectives) (Drucker, 1954). Objectives needed to meet all criteria to be classified as SMART. Objectives that were not SMART most often did not specify a date for completion, identify a precise agency as the target of the change, or were fragments (as opposed to sentences) in which the strategy being proposed could not be discerned. Objective quality was entered as a categorical variable into the SPSS database.

Type of risk

We coded the objectives for the kind of change that was proposed. Individual risk factors address individual level determinants of risk and essential are objective which essentially propose doing more or less of something which is already occurring. In contrast, objectives seeking to alter community risk conditions attend to systems and structures that impact individuals; these objectives seek to change widely shared assumptions, reframe social systems, and require doing something fundamentally different than what was done before. This variable was entered as a categorical variable into the SPSS database.

Sector and Strategy

We also coded the structural change objectives for the sectors that were the targets of change (e.g., the ball community, faith-based organizations, HIV service centers) and for the strategies that were being employed to influence change (e.g., changing physical design, changing consequences, facilitating support, increasing access). Strategy and sector codes were not quantified.

Analysis

Coding was planned a priori, as we knew we wanted to track the quality, scope, and purpose of the structural change objectives. Emergent codes were not developed. However, the coding scheme developed iteratively in conjunction with analysis, as codes which were conceptually similar were at times collapsed. To ascertain the clarity of the codes and assure they could be reliably applied, two research assistants, working independently, doubly coded 100% of the text for each code. Approximately ¼ of the coding was completed for each code and then a meeting arranged to discuss discrepancies in the interpretation of code definitions. At this time, coders discussed the coding scheme and these discussions occasionally resulted in an addition to or clarification of the code definition or a collapsing of conceptually similar codes. After these discussions, the new code definitions were used to code the remaining text and if changes to the coding scheme were made, all the structural change objectives were recoded. After these discussions and the coding of all text, inter-rater agreement was calculated using Cohen's Kappa. Kappa coefficients of 0.87 or higher were obtained for each code (from a high of 0.97 for the SMART codes to a low of 0.87 for the strategy codes), indicating the codes could reliably be applied.

We used Erickson's (1986) analytic induction method to interpret and evaluate the validity of our research questions. Analytic induction is an iterative form of analysis that consists of the development, testing, and reformation of a set of assertions based on exploration of the data. After creating assertions, each was systematically examined at the level of the group (e.g., across case). As noted by Smith (1997) in her discussion of using a modified analytic induction approach, the form of the data (e.g., whether words or numbers) does not affect the meaning derived from them. As such, where we were able, across case evidence was examined using the quantified data; Chi Square analyses were used for categorical data and independent groups t-tests were used to test mean differences. Across case confirming evidence suggested more nuanced investigation at the level of the individual coalition (e.g., within case). In this analytic process, within case disconfirming evidence is of particular interest, as these cases provide an opportunity to refine initial assertions. We revised or eliminated assertions based on their evidentiary adequacy and routine revisions to the assertions ensured that they provided a reasonable fit to the data. We verified the assertions through member checks (Miles & Huberman, 1994) with representatives of C2P during face-to-face meetings at the biannual ATN meetings. By explaining our findings to the larger study team, we were able to assess the degree to which they perceived that we had correctly and reasonably represented the data.

Results

Results first describe the variation in structural change accomplishment and discontinuation over the two time periods, and then describe the assertions that we investigated in order to account for this variation in accomplishment.

Completion Patterns

We first proposed that coalitions’ patterns of completing objectives would differ across periods because of coalitions’ ability to learn from their previous successes and mistakes and because time would be needed to gain a better awareness of the coalitions’ abilities and the community context (i.e., evidence of gaining programmatic capacity). The data, not accounting for still active objectives, show that coalitions improved their ability to complete objectives over time. The objectives coalitions created earlier were more often abandoned than completed when compared with the objectives that they created later, X2 (1, N = 242) = 34.40, p < .001. Table 1 depicts the across-case evidence for this effect of time. As displayed in the table, the vast majority (90%) of the discontinued objectives were created in period 1. High and low achieving coalitions also have distinct completion patterns. During period 1, objectives were more likely completed than discontinued at the high achieving coalitions; as would be expected, this pattern is reversed at low achieving coalitions. During time 1, high success coalitions were significantly more likely than low success coalitions to complete objectives, X2 (1, N = 173) = 4.50, p < .05. During period 2, high success and low success sites did not significantly differ in their ability to complete objectives, X2 (1, N = 69) = 0.12, p = 0.73; both high and low achieving coalitions were unlikely to discontinue objectives during period 2, suggesting that early accomplishment at the high success coalitions and early discontinuation at the low achieving coalitions may be largely influencing rates of completion.

Table 1.

Current Status of Objectives by the Period in which they were Created

Period 1 Period 2
Coalitions Completed Discontinued Completed Discontinued
Percentage at all coalitions 39.3% (n = 79) 46.8% (n = 94) 58.3% (n = 60) 8.7% (n = 9)
Percentage at low achieving coalitions 31.0% (n = 36) 50.0% (n = 58) 46.0% (n = 23) 8.0% (n = 4)
Percentage at high achieving coalitions 50.6% (n = 43) 42.4% (n = 36) 69.8% (n = 37) 9.4% (n = 5)
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Note. Percentages may not add up to 100% because objectives at each time are still active.

Looking within case, seven coalitions completed more objectives from period 2 than period 1 and abandoned more objectives from period 1 than period 2; these coalitions conformed to the dominant pattern. Among the remaining six coalitions, at high achieving coalitions the difference in completion over the two periods was slight (the completion of one more objective will make them conform to the dominant pattern) and at low achieving coalitions, over half of their objectives from period 2 were still active at the close of 2008 (thus, the ultimate status of the majority of their objectives from period 2 was indeterminate). Overall the data suggest that the coalitions were becoming better able to complete objectives.

While the coalitions were becoming more successful, they were also becoming more efficient in achieving structural changes. We hypothesized that objectives completed early in the coalitions’ mobilization would take longer and require more action steps and modifications than objectives completed later because coalitions were acclimating to the environment and their task. We found that at all coalitions objectives required fewer steps to complete in period 2 (see Table 2). All but one coalition took less time to complete objectives created during period 1 compared with period 2; six of the coalitions cut their completion times in half. Additionally, all but one coalition modified objectives created during period 2 fewer times when compared with the objectives created in period 1.

Table 2.

Efficiency of Completed Objectives by the Period in which they were Created

Variables Period 1 Period 2 t
Mean number of months 10.87* (9.50) 4.33 (3.28) 5.69
Mean number of action steps 4.38* (3.67) .95 (.81) 8.05
Mean number of modifications 1.39* (1.40) .45 (.83) 4.94
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Note.

*

p < .001. df = 137 for all analyses. Standard deviations are in parentheses below means.

How is programmatic capacity evident in the structural change objectives coalitions created and how might the quality, scope, and strategic development of objectives facilitate success?

Quality of Objective Formation

Coalitions received extensive training in creating objectives and feedback on the objectives they crafted (Ziff et al., 2006); as such, we hypothesized that success was associated with increasing quality of objective setting. Across coalitions, objectives which were SMART were more likely to be completed, X2 (1, N = 304) = 8.57; p < .01, than those that were not SMART. Coalitions with higher completion rates also had a higher proportion of objectives that met SMART criteria, X2 (1, N = 304) = 8.57; p < .01 (range: 52.2%-83.3%; compared to 21.4%-74.2%), suggesting that the ability to create SMART objectives contributed to success. Objectives that were SMART also required fewer action steps (M = 2.28, SD = 2.89) to complete than those that were not SMART (M = 4.23, SD = 3.70), t (137) = -3.07, p < .01 and fewer modifications (M = 0.78, SD = 1.09) to complete than those that were not SMART (M = 1.43, SD = 1.52), t (137) = -2.57, p < .05). As anticipated, the ability to set SMART objectives improved from period 1 to period 2, X2 (1, N = 304) = 21.04; p < .001. Fewer than half (48.3%) the objectives created during period 1 were SMART, whereas approximately ¾ of those developed during period 2 were SMART. The only coalitions that did not improve in their ability to set SMART objectives were coalitions mobilized at later dates and whose objectives at both time periods were likely to meet SMART criteria; notably, these coalitions may have received more intensive training in objective setting, as coordinators could use objectives from the other coalitions as examples of well formed structural changes.

Strategic Development of Objectives

Coalitions varied in the strategies that were successful and in the arenas or sectors in which they succeeded. We hypothesized that coalitions would focus on successful strategies and arenas over time and would tailor their objectives to involve strategies and sectors that more often led to accomplishment. We found that coalitions engaged fewer sectors over time, but continued to employ diverse intervention strategies. In period 2, coalitions avoided sectors they had experienced limited success in during period 1. Avoidance of sectors in which few or no structural changes were achieved was most evident at coalitions targeting young MSM.

Scope of Objectives

We also hypothesized that objectives created during the coalitions’ initial period of operation might be more ambitious or far-reaching than those created during the later period, accounting, in part, for the longer times to completion; we also thought that performance pressure at later dates may compel coalitions to create objectives more easily achievable and that objectives addressing individual risk factors would more often led to success. However, across case, we did not find evidence that created objectives became less complex. Objectives developed during period 1 were more likely to address individual risk behaviors (65.2%) than they were to address community risk conditions (34.8%). Objectives developed during period 2 were about as likely to address individual risk behaviors (49.5%), as they were to address community risk conditions (50.5%), X2 (1, N = 304) = 6.95; p < .01. This hypothesis was not supported; success was not being achieved at the expense of complexity.

Discussion

This study has identified ways in which C2P coalitions are building programmatic capacity through an analysis of the ways in which coalitions create and achieve structural change objectives. Members became more adept at creating higher quality objectives and more competently created objectives that aligned with the priorities of the coordinating body and the overall project goals. The strategic development of high quality objectives facilitated coalitions’ success and efficiency even as objectives became increasingly structural. As coalitions’ programmatic capacity increased, so too did they become better able to accomplish objectives; they also spent less time on objectives that ultimately were discontinued. Discontinuing fewer objectives may save the coalition resources, the coalition members’ time, and positively impact member morale. Yet discontinuing objectives early may be a necessary part of the learning process, as members learn how to create attainable objectives within their particular community contexts. As such, it may be beneficial for coalition staff and coordinators to frame the discontinuation of objectives as part of the learning process.

One of the most straightforward indicators of coalition success in achieving structural change objectives was coalitions’ ability to set high quality objectives. The contribution of a clear mission statement and strategic plan to coalition success is well documented; clear mission statements generate awareness of a partnership, reduce conflicts over goals, and lessen the time and cost required to complete goals (Butterfoss, Goodman, & Wandersman, 1993; Florin, Mitchell, Stevenson, & Klein, 2000; Roussos & Fawcett, 2000; Zakocs & Edwards, 2006). In our data, coalitions’ ability to set SMART objectives from the outset influenced success and as coalitions gained the capacity to set SMART objectives, became more successful and efficient. Setting SMART structural change objectives may have similar benefits to developing clear mission statements and strategic plans, as SMART objectives provide a strategic focus for coalition members. These data underscore the value of training coalition members to use SMART criteria when identifying structural changes.

At later dates, coalitions took into consideration where they were likely to experience success, showing they were flexible and strategic. Patterns of accomplishment and discontinuation and the sectors targeted for change suggest that coalitions avoided sectors where they had marginal success and targeted sectors where they were more likely to accomplish objectives. This was particularly true in coalitions focusing on young MSM. Understandably, if objectives targeting a particular sector are repeatedly discontinued because of a lack of buy-in or external barriers to implementation, then coalition members may learn to avoid those sectors for fear that their efforts will be in vain. They may start out optimistic that change can occur in these sectors, only to find their resources sapped, morale drained, and optimism dissipated. While strategically targeting sectors may facilitate the success of structural change objectives, strategic sector engagement may also be a barrier to systemic change. If coalitions systematically target sectors where objectives are likely to be completed, sectors less amenable to change (e.g., churches at young MSM focused coalitions) may be overlooked and changes oversaturated in particular sectors. Interventions may thus be more structural, although less systemic.

Our findings corroborate Willard and colleague's (this issue) that objectives formulated later are more likely to address community risk conditions. Objectives formulated early were less likely to address underlying community conditions that put youth at risk whereas later objectives were more apt to do so. With time, coalitions focused on changes that have the potential to impact community conditions, structures, and deeply entrenched beliefs regarding HIV that might increase youths’ risk (Blankenship et al., 2000; Blankenship et al., 2006; Friedan 2010). However, the capacity to develop, let alone achieve, structural interventions did not come easily. To train coalition members to create structural objectives, the C2P coordinating body developed capacity building trainings, gave regular feedback on objectives, and required that coalition members use the ‘VMOSA’ planning process (Ziff et al., 2006), as well as root cause analysis (Willard et al., this issue). In the absence of these strategies to develop programmatic capacity, it is not clear how long coalitions may have needed to demonstrate the ability to set sound objectives. Although the coalitions are increasing in their programmatic capacity to create structural interventions, many later objectives did still address individual risk behavior, evidence of how difficult it may be for coalition members to make a paradigmatic shift and forsake more traditional, behavioral notions of HIV prevention in exchange for structural approaches.

Strengths and Limitations

This study provides only an interim assessment of the coalitions’ objective setting and success. Whether still active objectives will be completed or discontinued remains to be seen. Another limitation concerns the time periods selected for analysis. Coalitions were established at different points in time and given freedom to develop objectives when they felt ready. As a result, some of the coalitions may have been in different stages of development (Butterfoss et al., 1993; Clark et al., 2006; & Fawcett, Paine, Francisco, & Villet, 1993). This seems particularly likely, as the coalitions that started later often had patterns of success and discontinuation that were anomalous. Further, coalitions develop in stages and revert to earlier stages as new members join, plans are reevaluated, and new priorities identified (Butterfoss & Kegler, 2002). Thus, our simple 2-period classification may mask some the more nuanced patterns of learning in the coalitions. Third, these coalitions receive ongoing technical and administrative support in developing SMART objectives, identifying root causes, and crafting structural change objectives. The programmatic capacity manifest in these coalitions may only be possible under these conditions, which limits the generalizability of research findings to coalitions operating under different conditions. Additionally, our focus was limited to how the formation of objectives impacts the success of the coalitions; further organizational (e.g., coalition functioning and processes) and contextual elements that facilitate and impede the success of the C2P coalitions has yet to be explored.

However, our focus on intermediate outcomes provides one of the few investigations of influences on proximal measures of success in accomplishing structural changes (Clark et al., 2010). Further, coalitions have been challenged to demonstrate the longer-term impacts of their efforts (e.g., Green & Kreuter, 2002). Evidence from this investigation indicates that improving the efficiency of these coalitions in developing structural change objectives is indeed possible.

Conclusion

These findings highlight the benefit of creating high quality, strategic structural change objectives. In addition to becoming more structural, C2P coalitions as a whole are becoming more efficient and most individual coalitions are becoming better able to accomplish structural changes. In less than 2 years time, C2P coalitions are gaining the programmatic capacity to create and achieve structural change interventions that have the propensity for meaningful community impact. Considering the broad impacts and benefits that structural interventions hold for HIV prevention, future research should investigate other influences that impede or facilitate the implementation of structural change HIV prevention interventions.

ACKNOWLEDGEMENTS

The Adolescent Trials Network for HIV/AIDS Interventions (ATN) is funded by Grant Nos. 2 U01 HD040533 and 2 U01 HD 040474 from the National Institutes of Health through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (B. Kapogiannis, MD, Sonia Lee, PhD), with supplemental funding from the National Institute on Drug Abuse (N. Borek, PhD), and the National Institute on Mental Health (P. Brouwers, PhD, Susannah Allison, PhD). The study was scientifically reviewed by the ATN's Community Prevention Leadership Group. Network scientific and logistical support was provide by the ATN Coordinating Center (C. Wilson, C. Partlow), at the University of Alabama at Birmingham. The ATN 079 Protocol Team members are Vincent Francisco (University of North Carolina-Greensboro), Robin Lin Miller (Michigan State University), Jonathan Ellen (John Hopkins University), Peter Freeman (Children's Memorial Hospital), Lawrence B. Friedman (University of Miami School of Medicine), Grisel-Robles Schrader (University of California-San Francisco), Jessica Roy (Children's Diagnostic and Treatment Center), Nancy Willard (John Hopkins University), and Jennifer Huang (Westat, Inc.). Research assistantship was provided by Sarah J. Reed (Michigan State University) and Greer Cook (University of North Carolina-Greensboro).

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