Danielle M. Varda

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.

A systematic review of collaboration and network research in the public affairs literature: implications for public health practice and research.

OBJECTIVES We explored and analyzed how findings from public affairs research can inform public health research and practice, specifically in the area of interorganizational collaboration, one of the most promising practice-based approaches in the public health field. METHODS We conducted a systematic review of the public affairs literature by following a grounded theory approach. We coded 151 articles for demographics and empirical findings (n = 258). RESULTS Three primary findings stand out in the public affairs literature: network structure affects governance, management strategies exist for administrators, and collaboration can be linked to outcomes. These findings are linked to priorities in public health practice. CONCLUSIONS Overall, we found that public affairs has a long and rich history of research in collaborations that offers unique organizational theory and management tools to public health practitioners.

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. Todays 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.

Community Carrying Capacity: A Network Perspective

Scholars have explored the idea of the determinants of the size of the nonprofit sector as a linear relationship between supply of resources and the demand for nonprofit services. This in turn has fueled debate about whether there are too many nonprofits for available resources. In this article, we propose that the scarcity (or abundance) of resources does not inherently determine the limits of a communitys nonprofit “carrying capacity”. Rather, network exchanges between nonprofits and other organizations may exhibit positive synergistic effects that are associated with diverse outcomes. We therefore propose a model of nonprofit carrying capacity that shifts the discussion to the ability of a community to support network exchanges among independent agents.

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.

A Network Perspective on State-Society Synergy to Increase Community-Level Social Capital

“Can state–society synergy be created in the short run, or does it require historically deep institutional and normative foundations?” In other words, what role can an outside party—such as a government or state actor—play in constructing social capital when it is not a permanent fixture of the existing interrelationships within a community? Taking a network perspective, this exploratory research examines community-level social capital outcomes of a government-led intervention. Operationalized as social networks, social capital is measured as an increase to the strength of weak ties and reduction in redundancy among exchange relationships. Findings suggest that state–society synergy has the potential to increase bridging social capital in communities. In addition, communities with higher levels of cohesion and connectivity pre-intervention results in greater increases to social capital, and although trust plays a crucial role in development of social capital, the influence organizations are perceived to have does not.

Collaborative Performance as a Function of Network Members’ Perceptions of Success

Abstract: Interorganizational networks are a common collaborative approach to tackle complex issues such as public health, national security, education, and poverty. While there is a consensus that networks are a viable approach to these issues, it is unclear what factors lead to effective collaborative performance. One issue for assessing performance is the lack of sufficient evaluation/assessment methods and, subsequently, of empirical data. Applying a conceptual model based in the literature, this study examines characteristics of network members and their perceptions of success in order to ascertain the degree to which members’ agreement on outcomes varies among networks and the characteristics of members of networks that report greater levels of success or of disagreement about success. This study contributes to the collaborative performance literature by analyzing an unprecedentedly large N (n = 98) dataset of interorganizational (whole) networks to test empirically the conceptual model. The results show that higher trust and greater resource contributions predicted higher levels of perceived success among members of a network. A second model, with disagreement about success as the dependent variable, more resources, and higher amounts of diversity, predicted higher levels of disagreement about success. We conclude that the literature on interorganizational networks overemphasizes the benefits of network diversity, and that diversity may, in fact, hinder perceptions of success.

An exploratory analysis of network characteristics and quality of interactions among public health collaboratives

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.

Data-driven management strategies in public health collaboratives.

Objectives: The objective of this article is to demonstrate a data-driven management approach to effectively implement quality improvement (QI) in public health collaboratives. Using a modeled simulation, this article utilizes network data to demonstrate strategic management approaches. Design: This article uses simulated data to demonstrate the application of data-driven management strategies. This simulation was developed using examples from real-world data on public health collaboratives. Setting and Participants: The simulation represents a community that is just getting started working collaboratively on a public health issue. In this urban community, a number of organizations have been working both individually and in partnerships with one another for years to address the social and economic needs of its growing homeless population, led in large part by the efforts of local public health department. Main Outcome Measure: The main outcome measure is the “network” of organizational partners. Operationalizing networks as the outcome measures allows managers to think about how to implement action strategies to improve the outcome (networks as collaboration). Methods: These data are analyzed in PARTNER, a social network analysis program designed for use by managers and facilitators of public health collaboratives. Social Network Analysis is the study of the structural relationships among interacting units and the resulting effect on the network. Results: Network data provide a data-driven methodology for engaging in Strategic Collaborative Management. Such data can inform strategy for improving connectivity, trust, resource distribution, and increase successful strategic planning of action steps for QI. Conclusions: Data-driven strategic approaches to practical decision-making and program implementation are currently lacking in public health systems improvement. Such an approach leads to QI

Strategies to improve global influenza surveillance: A decision tool for policymakers

BackgroundGlobal pandemic influenza preparedness relies heavily on public health surveillance, but it is unclear that current surveillance fully meets pandemic preparedness needs.MethodsWe 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.ResultsWe 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.ConclusionOur 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.