Dataset Information

Local Decision Making for Implementing Social Distancing in Response to Outbreaks.

ABSTRACT: OBJECTIVES:Social distancing is the practice of restricting contact among persons to prevent the spread of infection. This study sought to (1) identify key features of preparedness and the primary concerns of local public health officials in deciding to implement social distancing measures and (2) determine whether any particular factor could explain the widespread variation among health departments in responses to past outbreaks. METHODS:We conducted an online survey of health departments in the United States in 2015 to understand factors influencing health departments' decision making when choosing whether to implement social distancing measures. We paired survey results with data on area population demographic characteristics and analyzed them with a focus on broad trends. RESULTS:Of 600 health departments contacted, 150 (25%) responded. Of these 150 health departments, 63 (42%) indicated that they had implemented social distancing in the past 10 years. Only 10 (7%) health departments had a line-item budget for isolation or quarantine. The most common concern about social distancing was public health impact (n = 62, 41%). Concerns about law, politics, finances, vulnerable populations, and sociocultural issues were each identified by 7% to 10% of health departments. We were unable to clearly predict which factors would influence these decisions. CONCLUSIONS:Variations in the decision to implement social distancing are likely the result of differences in organizational authority and resources and in the primary concerns about implementing social distancing. Research and current social distancing guidelines for health departments should address these factors.

SUBMITTER: Katz R

PROVIDER: S-EPMC6410485 | biostudies-literature | 2019 Mar/Apr

REPOSITORIES: biostudies-literature

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Local Decision Making for Implementing Social Distancing in Response to Outbreaks.

Katz Rebecca R Vaught Andrea A Simmens Samuel J SJ

Public health reports (Washington, D.C. : 1974) 20190118 2

<h4>Objectives</h4>Social distancing is the practice of restricting contact among persons to prevent the spread of infection. This study sought to (1) identify key features of preparedness and the primary concerns of local public health officials in deciding to implement social distancing measures and (2) determine whether any particular factor could explain the widespread variation among health departments in responses to past outbreaks.<h4>Methods</h4>We conducted an online survey of health de ...[more]

PMID: 30657730

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Project description:The ethical argument that shared decision-making is "the right" thing to do, however laudable, is unlikely to change how healthcare is organized, just as evidence alone will be an insufficient factor: practice change is governed by factors such as cost, profit margin, quality, and efficiency. It is helpful, therefore, when evaluating new approaches such as shared decision-making to conceptualize potential consequences in a way that is broad, long-term, and as relevant as possible to multiple stakeholders. Yet, so far, evaluation metrics for shared decision-making have been mostly focused on short-term outcomes, such as cognitive or affective consequences in patients. The goal of this article is to hypothesize a wider set of consequences, that apply over an extended time horizon, and include outcomes at interactional, team, organizational and system levels, and to call for future research to study these possible consequences.To date, many more studies have evaluated patient decision aids rather than other approaches to shared decision-making, and the outcomes measured have typically been focused on short-term cognitive and affective outcomes, for example knowledge and decisional conflict. From a clinicians perspective, the shared decision-making process could be viewed as either intrinsically rewarding and protective, or burdensome and impractical, yet studies have not focused on the impact on professionals, either positive or negative. At interactional levels, group, team, and microsystem, the potential long-term consequences could include the development of a culture where deliberation and collaboration are regarded as guiding principles, where patients are coached to assess the value of interventions, to trade-off benefits versus harms, and assess their burdens-in short, to new social norms in the clinical workplace. At organizational levels, consistent shared decision-making might boost patient experience evaluations and lead to fewer complaints and legal challenges. In the long-term, shared decision-making might lead to changes in resource utilization, perhaps to reductions in cost, and to modification of workforce composition. Despite the gradual shift to value-based payment, some organizations, motivated by continued income derived from achieving high volumes of procedures and contacts, will see this as a negative consequence.We suggest that a broader conceptualization and measurement of shared decision-making would provide a more substantive evidence base to guide implementation. We outline a framework which illustrates a hypothesized set of proximal, distal, and distant consequences that might occur if collaboration and deliberation could be achieved routinely, proposing that well-informed preference-based patient decisions might lead to safer, more cost-effective healthcare, which in turn might result in reduced utilization rates and improved health outcomes.

Project description:In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Dataset Information

Local Decision Making for Implementing Social Distancing in Response to Outbreaks.

Publications

Local Decision Making for Implementing Social Distancing in Response to Outbreaks.

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