Health Systems' "Surge Capacity": State of the Art and Priorities for Future Research

London School of Hygiene and Tropical Medicine.
Milbank Quarterly (Impact Factor: 3.38). 03/2013; 91(1):78-122. DOI: 10.1111/milq.12003
Source: PubMed


Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems’ ability to prepare for, and cope with, “surges” (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems’ surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.
A systematic review of the literature on concepts of health systems’ surge capacity, with a narrative summary of key concepts relevant to public health.
The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.
The concept of surge capacity is a useful addition to the study of health systems’ disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems’ readiness for, and response to, unpredictable high-consequence surges in demand.

Download full-text


Available from: Richard Coker, Sep 17, 2014
1 Follower
55 Reads
  • Source
    01/2014; Federal Emergency Management Agency.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective: To review the research methods of mass casualty incident (MCI) systematically and introduce the concept and characteristics of complexity science and artificial system, computational experiments and parallel execution (ACP) method. Data sources: We searched PubMed, Web of Knowledge, China Wanfang and China Biology Medicine (CBM) databases for relevant studies. Searches were performed without year or language restrictions and used the combinations of the following key words: "mass casualty incident", "MCI", "research method", "complexity science", "ACP", "approach", "science", "model", "system" and "response". Study selection: Articles were searched using the above keywords and only those involving the research methods of mass casualty incident (MCI) were enrolled. Results: Research methods of MCI have increased markedly over the past few decades. For now, dominating research methods of MCI are theory-based approach, empirical approach, evidence-based science, mathematical modeling and computer simulation, simulation experiment, experimental methods, scenario approach and complexity science. Conclusions: This article provides an overview of the development of research methodology for MCI. The progresses of routine research approaches and complexity science are briefly presented in this paper. Furthermore, the authors conclude that the reductionism underlying the exact science is not suitable for MCI complex systems. And the only feasible alternative is complexity science. Finally, this summary is followed by a review that ACP method combining artificial systems, computational experiments and parallel execution provides a new idea to address researches for complex MCI.
    Chinese medical journal 07/2014; 127(13):2523-30. · 1.05 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: Public health surveillance and epidemiologic investigations are critical public health functions for identifying threats to the health of a community. We conducted a survey of local health departments (LHDs) in California to describe the workforce that supports public health surveillance and epidemiologic functions during routine and emergency infectious disease situations. Methods: The target population consisted of the 61 LHDs in California. The online survey instrument was designed to collect information about the workforce involved in key epidemiologic functions. We also examined how the public health workforce increases its epidemiologic capacity during infectious disease emergencies. Results: Of 61 LHDs in California, 31 (51%) completed the survey. A wide range of job classifications contribute to epidemiologic functions routinely, and LHDs rely on both internal and external sources of epidemiologic surge capacity during infectious disease emergencies. This study found that while 17 (55%) LHDs reported having a mutual aid agreement with at least one other organization for emergency response, only nine (29%) LHDs have a mutual aid agreement specifically for epidemiology and surveillance functions. Conclusions: LHDs rely on a diverse workforce to conduct epidemiology and public health surveillance functions, emphasizing the need to identify and describe the types of staff positions that could benefit from public health surveillance and epidemiology training. While some organizations collaborate with external partners to support these functions during an emergency, many LHDs do not rely on mutual aid agreements for epidemiology and surveillance activities.
    Public Health Reports 10/2014; 129 Suppl 4:114-22. · 1.55 Impact Factor
Show more