An Innovative Network Approach to Coordinating a National Effort to Improve Cardiovascular Health: The Case of Million Hearts.

OBJECTIVE: To assess the structure, content, quality, and quantity of partnerships that developed in response to a national cardiovascular health initiative, Million Hearts. DESIGN: This study used a social network analysis (SNA) approach to assess the Million Hearts initiative network partnerships and identify potential implications for policy and practice. SETTING/PARTICIPANTS: The Million Hearts network comprised a core group of federal and private sector partners that participate in Million Hearts activities and align with initiative priorities. To bound the network for the SNA, we used a list of 58 organizations (74% response rate) from a previously completed qualitative analysis of Million Hearts partnerships. MAIN OUTCOME MEASURES: We used the online PARTNER (Program to Analyze Record and Track Networks to Enhance Relationships-www.partnertool.net) survey to collect data on individual organizational characteristics and relational questions that asked organizations to identify and describe their relationships with other partners in the network. Key SNA measures include network density, centralizations, value, and trust. RESULTS: Our analyses show a network that is decentralized, has strong perceptions of trust and value among its members, and strong agreement on intended outcomes. Interestingly, partners report a desire and ability to contribute resources to Million Hearts; however, the perceptions between partners are that resources are not being contributed at the level they potentially could be. The majority of partners reported that being in the network helped them achieve their goals related to cardiovascular disease prevention. The largest barrier to successful activities within the network was cited as lack of targeted funding and staff to support participation in the network. CONCLUSIONS: The Million Hearts network described in this article is unique in its membership at the national level, agreement on outcomes, its powerful information-sharing abilities that require few resources, and its decentralized structure. We identified strategies that could be implemented to strengthen the network and its activities. By examining a national-level public-private partnership formed to address a public health issue, we can identify ways to strengthen the network and provide a framework for developing other initiatives.

Social Connectedness in Family Social Support Networks: Strengthening Systems of Care for Children with Special Health Care Needs.

Current approaches to addressing the problems families face when navigating complex service systems on behalf of their children rely largely on state or nationally driven efforts around the development of systems of care (SOCs). However, operationalizing meaningful family involvement within SOCs remains a challenge, with little attention paid to the role of personal social support networks (PSSNs). Specifically, risk factors related to the variations in the social connectedness of family social support networks are difficult to identify, assess, and track over time. This paper summarizes families' descriptions of their PSSNs and describes the development of a social network analysis tool, the Person-Centered Network App (PCNA), used to measure and monitor the social connectedness of families of children with special health care and developmental needs. Twenty-nine families participated in the project and completed social network surveys, identifying a total of 38 unique types of support partners and 230 partnerships (dyadic relationships). Families identified a range of formal and informal members including primary care providers, medical specialists, family, friends, faith-based organizations, insurance providers, nurses, community organizations, early interventionists, school resources, other families, online support groups, and public resources, rating 61 percent of them as "very important." Informal network members (e.g., family, friends) provided emotional and day-to-day support. Primary care providers, medical specialists, and public resources provided health care services while early intervention and medical specialists provided therapies. PSSNs were characterized by high levels of trust but low levels of coordination. These findings inform providers and case workers that families can readily describe their social connectedness in ways that may affect health care access and utilization. Understanding how PSSNs function in the lives of families of children with complex health care needs provides opportunities for improving systems of care (e.g., medical homes) and ultimately, enhancing health and developmental outcomes.

Patterns in PARTNERing across Public Health Collaboratives.

Inter-organizational networks represent one of the most promising practice-based approaches in public health as a way to attain resources, share knowledge, and, in turn, improve population health outcomes. However, the interdependencies and effectiveness related to the structure, management, and costs of these networks represents a critical item to be addressed. The objective of this research is to identify and determine the extent to which potential partnering patterns influence the structure of collaborative networks. This study examines data collected by PARTNER, specifically public health networks (n = 162), to better understand the structured relationships and interactions among public health organizations and their partners, in relation to collaborative activities. Combined with descriptive analysis, we focus on the composition of public health collaboratives in a series of Exponential Random Graph (ERG) models to examine the partnerships between different organization types to identify the attribute-based effects promoting the formation of network ties within and across collaboratives. We found high variation within and between these collaboratives including composition, diversity, and interactions. The findings of this research suggest common and frequent types of partnerships, as well as opportunities to develop new collaborations. The result of this analysis offer additional evidence to inform and strengthen public health practice partnerships.

New perspectives on the "silo effect": initial comparisons of network structures across public health collaboratives.

OBJECTIVES: We explored to what extent "silos" (preferential partnering) persist in interorganizational boundaries despite advances in working across boundaries. We focused on organizational homophily and resulting silo effects within networks that might both facilitate and impede success in public health collaboratives (PHCs). METHODS: We analyzed data from 162 PHCs with a series of exponential random graph models to determine the influence of uniform and differential homophily among organizations and to identify the propensity for partnerships with similar organizations. RESULTS: The results demonstrated a low presence (8%) of uniform homophily among networks, whereas a greater number (30%) of PHCs contained varying levels of differential homophily by 1 or more types of organization. We noted that the higher frequency among law enforcement, nonprofits, and public health organizations demonstrated a partner preference with similar organizations. CONCLUSIONS: Although we identified only a modest occurrence of partner preference in PHCs, overall success in efforts to work across boundaries might be problematic when public health members (often leaders of PHCs) exhibit the tendency to form silos.

Implications of network structure on public health collaboratives.

Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of these findings for research and practice are discussed.

An Exploratory Analysis of Network Characteristics and Quality of Interactions among Public Health Collaboratives.

ABSTRACT: While the benefits of collaboration have become widely accepted and the practice of collaboration is growing within the public health system, a paucity of research exists that examines factors and mechanisms related to effective collaboration between public health and their partner organizations. The purpose of this paper is to address this gap by exploring the structural and organizational characteristics of public health collaboratives. Design and Methods. Using both social network analysis and traditional statistical methods, we conduct an exploratory secondary data analysis of 11 public health collaboratives chosen from across the United States. All collaboratives are part of the PARTNER (www.partnertool.net) database. We analyze data to identify relational patterns by exploring the structure (the way that organizations connect and exchange relationships), in relation to perceptions of value and trust, explanations for varying reports of success, and factors related to outcomes. We describe the characteristics of the collaboratives, types of resource contributions, outcomes of the collaboratives, perceptions of success, and reasons for success. We found high variation and significant differences within and between these collaboratives including perceptions of success. There were significant relationships among various factors such as resource contributions, reasons cited for success, and trust and value perceived by organizations. We find that although the unique structure of each collaborative makes it challenging to identify a specific set of factors to determine when a collaborative will be successful, the organizational characteristics and interorganizational dynamics do appear to impact outcomes. We recommend a quality improvement process that suggests matching assessment to goals and developing action steps for performance improvement. ACKNOWLEDGEMENTS: the authors would like to thank the Robert Wood Johnson Foundation's Public Health Program for funding for this research.

Social capital and health care access: a systematic review.

There is a growing interest in community-level characteristics such as social capital and its relationship to health care access. To assess the rigor with which this construct has been empirically applied in research on health care access, a systematic review was conducted. A total of 2,396 abstracts were reviewed, and 21 met the criteria of examining some measure of social capital and its effects on health care access. The review found a lack of congruence in how social capital was measured and interpreted and a general inconsistency in findings, which made it difficult to draw firm conclusions about the effects of social capital on health care access. Insights from the social network literature can help improve the conceptual and measurement problems. Future work should distinguish among bonding, bridging, and linking social capital and their sources and benefits, and examine whether three dimensions of social capital actually exist: cognitive, behavioral, and structural.

The growth of partnerships to support patient safety practice adoption.

OBJECTIVES: To document the numbers and types of interorganizational partnerships within the national patient safety domain, changes over time in these networks, and their potential for disseminating patient safety knowledge and practices. DATA SOURCES: Self-reported information gathered from representatives of national-level organizations active in promoting patient safety. STUDY DESIGN: Social network analysis was used to examine the structure and composition of partnership networks and changes between 2004 and 2006. DATA COLLECTION: Two rounds of structured telephone interviews (n=35 organizations in 2004 and 55 in 2006). PRINCIPAL FINDINGS: Patient safety partnerships expanded between 2004 and 2006. The average number of partnerships per interviewed organization increased 40 percent and activities per reported partnership increased over 50 percent. Partnerships increased in all activity domains, particularly dissemination and tools development. Fragmentation of the overall partnership network decreased and potential for information flow increased. Yet network centralization increased, suggesting vulnerability to partnership failure if key participants disengage. CONCLUSIONS: Growth in partnerships signifies growing strength in the capacity to disseminate and implement patient safety advancements in the U.S. health care system. The centrality of AHRQ in these networks of partnerships bodes well for its leadership role in disseminating information, tools, and practices generated by patient safety research projects.

Core dimensions of connectivity in public health collaboratives.

A major challenge facing state and local public health agencies is how to partner with other organizations, agencies, and groups to collaboratively address goals in population health while effectively maximizing resource sharing of the partners involved. Today's public health efforts require multiagency partnerships between both governmental and nongovernmental sectors to achieve this mission. However, the frequent reconfiguration of partnerships among government and nongovernmental agencies has left many public health managers struggling to find ways to both develop public health collaboratives and evaluate their success. In this article, we use network theory and social network analysis to outline the core dimensions of connectivity used to measure progress in public health collaboratives. Connectivity is defined as the measured interactions between partners in a collaborative such as the amount and quality of interactions and how these relationships might change over time. We also articulate how these measures fit into the overall process of measuring progress in public health collaboratives and end the article with suggestions for future research and development.

Strategies to improve global influenza surveillance: a decision tool for policymakers.

BACKGROUND: Global pandemic influenza preparedness relies heavily on public health surveillance, but it is unclear that current surveillance fully meets pandemic preparedness needs. METHODS: We first developed a conceptual framework to help systematically identify strategies to improve the detection of an early case or cluster of novel human influenza disease during the pre-pandemic period. We then developed a process model (flow diagram) depicting nine major pathways through which a case in the community could be detected and confirmed, and mapped the improvement strategies onto this model. Finally, we developed an interactive decision tool by building quantitative measures of probability and time into each step of the process model and programming it to calculate the net probability and time required for case detection through each detection pathway. Input values for each step can be varied by users to assess the effects of different improvement strategies, alone or in combination. We illustrate application of the tool using hypothetical input data reflecting baseline and 12-month follow-up scenarios, following concurrent implementation of multiple improvement strategies. RESULTS: We compared outputs from the tool across detection pathways and across time, at baseline and 12-month follow up. The process model and outputs from the tool suggest that traditional efforts to build epidemiology and laboratory capacity are efficient strategies, as are more focused strategies within these, such as targeted laboratory testing; expedited specimen transport; use of technologies to streamline data flow; and improved reporting compliance. Other promising strategies stem from community detection - better harnessing of electronic data mining and establishment of community-based monitoring. CONCLUSION: Our practical tool allows policymakers to use their own realistic baseline values and program projections to assess the relative impact of different interventions to improve the probability and timeliness of detecting early human cases or clusters caused by a novel influenza virus, a possible harbinger of a new pandemic. Policymakers can use results to target investments to improve their surveillance infrastructure. Multi-national planners can also use the tool to help guide directions in surveillance system improvements more globally. Finally, our systematic approach can also be tailored to help improve surveillance for other diseases.