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Global initiatives have raised awareness of the need for cross-departmental and cross-sectoral activities to support urban health, sustainability, and equity, with respective indicators routinely used as a way to catalyze and monitor action toward pre-defined goals. Despite the existence of at least 145 urban health indicator (UHI) tools globally, there has been very little research on the use of indicators by policy- and decision-makers; more attention has been devoted to their development and validation. This paper describes the second part of a two-part systematic review of the characteristics (part A) and use (part B, this part) of UHI tools by municipal built environment policy- and decision-makers. Part B is a narrative synthesis of studies on the use of UHI tools. This PRISMA-P compliant review follows a mixed methods sequential explanatory design. The search was conducted using seven bibliographic databases, grey literature searches, and key journal hand searches. Ten studies describing the use of ten UHI tools in seven countries were included in the narrative synthesis, resulting in development of a theory of change (ToC). We found that both expert-led and participatory indicator projects can be underpinned by research evidence and residents' knowledge. Our findings contradict the dominant view of indicator use in policy-making as a linear process, highlighting a number of technical, organizational, political, knowledge, and contextual factors that affect their use. Participatory UHI tools with community involvement were generally more effective at supporting "health in all policies" and "whole-of-society" approaches to governing healthy cities than expert-led processes. UHI tool producers proposed a range of techniques to address urban health complexity characteristics. Finally, in combining data from both parts of the review, we found that potentially important UHI tool features, such as neighbourhood-scale data, were influential in the use of indicators by built environment policy- and decision-makers. [ABSTRACT FROM AUTHOR]

Background: Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes.Objective: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models.Data Sources: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process.Results: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors.Conclusions/implications: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients. [ABSTRACT FROM AUTHOR]

Accurately describing treatment effects using plain language and narrative statements is a critical step in communicating research findings to end users. However, the process of developing these narratives has not been historically guided by a specific framework. The Agency for Healthcare Research and Quality Evidence-based Practice Center Program developed guidance for narrative summaries of treatment effects that identifies five constructs. We explicitly identify these constructs to facilitate developing narrative statements: (1) direction of effect, (2) size of effect, (3) clinical importance, (4) statistical significance, and (5) strength or certainty of evidence. These constructs clearly overlap. It may not always be feasible to address all five constructs. Based on context and intended audience, investigators can determine which constructs will be most important to address in narrative statements. [ABSTRACT FROM AUTHOR]

Background: Over the last decade, various pay-for-performance (P4P) programs have been implemented to improve quality in health systems, including the VHA. P4P programs are complex, and their effects may vary by design, context, and other implementation processes. We conducted a systematic review and key informant (KI) interviews to better understand the implementation factors that modify the effectiveness of P4P.Methods: We searched PubMed, PsycINFO, and CINAHL through April 2014, and reviewed reference lists. We included trials and observational studies of P4P implementation. Two investigators abstracted data and assessed study quality. We interviewed P4P researchers to gain further insight.Results: Among 1363 titles and abstracts, we selected 509 for full-text review, and included 41 primary studies. Of these 41 studies, 33 examined P4P programs in ambulatory settings, 7 targeted hospitals, and 1 study applied to nursing homes. Related to implementation, 13 studies examined program design, 8 examined implementation processes, 6 the outer setting, 18 the inner setting, and 5 provider characteristics. Results suggest the importance of considering underlying payment models and using statistically stringent methods of composite measure development, and ensuring that high-quality care will be maintained after incentive removal. We found no conclusive evidence that provider or practice characteristics relate to P4P effectiveness. Interviews with 14 KIs supported limited evidence that effective P4P program measures should be aligned with organizational goals, that incentive structures should be carefully considered, and that factors such as a strong infrastructure and public reporting may have a large influence.Discussion: There is limited evidence from which to draw firm conclusions related to P4P implementation. Findings from studies and KI interviews suggest that P4P programs should undergo regular evaluation and should target areas of poor performance. Additionally, measures and incentives should align with organizational priorities, and programs should allow for changes over time in response to data and provider input. [ABSTRACT FROM AUTHOR]