Trust-building in temporary public health partnerships: a qualitative study of the partnership formation process of a Covid-19 test, trace and protect service.

BACKGROUND: Public health initiatives require coordinated efforts from healthcare, social services and other service providers. Organisational theory tells us that trust is essential for reaching collaborative effectiveness. This paper explores the drivers for initiating and sustaining trust in a temporary public health partnership, in response to a sudden health threat. METHODS: This qualitative study analysed the formation process of a multisector partnership for a Covid-19 contact tracing service. Data was collected through 12 interviews, two focus groups, one feedback workshop, and an online survey with workforce members from all seven partner organisations. Purposive maximum variation sampling was used to capture the reflections and experiences of workforce members from all seven partner organisations. A deductive code scheme was used to identify drivers for building and sustaining trust in inter-organisational collaboration. RESULTS: Relational mechanisms emanating from the commitment to the common aim, shared norms and values, and partnership structures affected trust-building. Shared values and the commitment to the common aim appeared to channel partners' behaviour when interacting, resulting in being perceived as a fair, reliable and supportive partner. Shared values were congruent with the design of the partnership in terms of governance structure and communication lines reflecting flat hierarchies and shared decision-making power. Tensions between partner organisations arose when shared values were infringed. CONCLUSIONS: When managing trust in a collaboration, partners should consider structural components like governance structure, organisational hierarchy, and communication channels to ensure equal power distribution. Job rotation, recruitment of candidates with the desired personality traits and attitudes, as well as training and development, encourage inter-organisational networking among employees, which is essential for building and strengthening relationships with partner organisations. Partners should also be aware of managing relational dynamics, channelling behaviours through shared values, objectives and priorities and fostering mutual support and equality among partner organisations.

Why Resilience in Health Care Systems is More than Coping with Disasters: Implications for Health Care Policy.

Health care systems need to be resilient to deal with disasters like the global spread of the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) on top of serving the changing needs of a multi-morbid, ageing and often dispersed population. This paper identifies, discusses and augments critical dimensions of resilience retrieved from the academic literature. It pulls together an integrated concept of resilience characterised by organisational capabilities. Our concept does not focus on the micro-level like most resilience literature in health care but addresses the system level with many stakeholders involved. Distinguishing exogenous shocks to the health care system into adverse events and planned innovations provides the basis for our conclusions and insights. It becomes apparent only when dealing with planned interventions that transformative capabilities are indispensable to cope with sudden increases in health care pressures. Due to the current focus on absorptive and adaptive resilience, organisations over-rely on management capabilities that cannot generate a lasting increase in functionality. Therefore, reducing the resilience discussion to bouncing back from adverse events could deceive organisations into cultivating a suboptimal mix of organisational capabilities lacking transformative capabilities, which pave the way for a structural change that aims at a sustainably higher functionality.

Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system dynamics.

Introduction: Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts of the healthcare system is often uncertain and not entirely understood. Clinical progression with depression can be equally complex and relates to whether or not a patient is seeking care, the care pathway they are on, and the ability for timely access to healthcare services. Considering both pathways and progression for depression are however rarely studied together in the literature. Methods: This paper presents a hybrid simulation modeling framework that is uniquely able to capture both disease progression, using Agent Based Modeling, and related care pathways, using a System Dynamics. The two simulation paradigms within the framework are connected to run synchronously to investigate the impact of depression progression on healthcare services and, conversely, how any limitations in access to services may impact clinical progression. The use of the developed framework is illustrated by parametrising it with published clinical data and local service level data from Wales, UK. Results and discussion: The framework is able to quantify demand, service capacities and costs across all care pathways for a range of different scenarios. These include those for varying service coverage and provision, such as the cost-effectiveness of treating patients more quickly in community settings to reduce patient progression to more severe states of depression, and thus reducing the costs and utilization of more expensive specialist settings.

Why local air pollution is more than daily peaks: modelling policies in a city in order to avoid premature deaths.

We use a predator-prey representation of an urban system to analyse how policy interventions can prevent the adverse effects of air pollution on people's health. The number of residents is treated as prey variable, and particulate matter that consists of particles with a diameter of up to 10 micrometres (PM10) as predator variable. This representation allows integration of population trends and the effects of environmental interventions on the average level of PM10 concentration (which establishes a baseline for the potential health burden for residents). For the case of Graz, Austria, we illustrate the insights generated regarding the interdependency of market-based and technological pollution controls, and propose an indicator that assesses the cost of delayed interventions by counting additional premature deaths caused by polluted environments.

A model of chaotic drug markets and their control.

This paper explores the idea that drug markets may be chaotic in a mathematical sense by considering a discrete-time model of populations of drug users and drug sellers for which initiation into either population is a function of relative numbers of both populations. The structure of the system follows that considered in an arms control context by Behrens, Feichtinger and Prskawetz (Behrens D.A., Feichtinger G., & Prskawetz A. (1997). Complex Dynamics and Control of Arms Race. European Journal of Operations Research, 100, 192-215). The model presented in this paper summarizes prerequisites for possible chaotic behavior of the number of addicts and drug dealers frequenting a local drug market. Interestingly, even if the uncontrolled market dynamics do not exhibit chaotic patterns, a static intervention like removing a constant fraction of addicts each time period can easily create chaos--but even if static control would create chaos, dynamic controls can be crafted that avoid it. Especially OGY controls seem to work well for this example.

Markov chain modeling of initiation and demand: the case of the U.S. cocaine epidemic.

Everingham and Rydell's Markov chain model of cocaine demand is modified and updated in light of recent data. Key insights continue to hold, e.g., that the proportion of cocaine demand stemming from heavy vs. light users changed dramatically over the 1980s. New insights emerge, e.g., pertaining to the average duration of a career of heavy use (about 12 years) and the negative relationship between levels of heavy use and epidemic "infectivity" or the number of new initiates per current user per year. This illustrates how simple modeling can yield insights directly relevant to managing complex drug control policy questions.