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Administrative and surveillance data are used frequently in healthcare epidemiology and antimicrobial stewardship (HE&AS) research because of their wide availability and efficiency. However, data quality issues exist, requiring careful consideration and potential validation of data. This methods paper presents key considerations for using administrative and surveillance data in HE&AS, including types of data available and potential use, data limitations, and the importance of validation. After discussing these issues, we review examples of HE&AS research using administrative data with a focus on scenarios when their use may be advantageous. A checklist is provided to help aid study development in HE&AS using administrative data.
Surveys are one of the most frequently employed study designs in healthcare epidemiology research. Generally easier to undertake and less costly than many other study designs, surveys can be invaluable to gain insights into opinions and practices in large samples and may be descriptive and/or be used to test associations. In this context, qualitative research methods may complement this study design either at the survey development phase and/or at the interpretation/extension of results stage. This methods article focuses on key considerations for designing and deploying surveys in healthcare epidemiology and antibiotic stewardship, including identification of whether or not de novo survey development is necessary, ways to optimally lay out and display a survey, denominator measurement, discussion of biases to keep in mind particularly in research using surveys, and the role of qualitative research methods to complement surveys. We review examples of surveys in healthcare epidemiology and antimicrobial stewardship and review the pros and cons of methods used. A checklist is provided to help aid design and deployment of surveys in healthcare epidemiology and antimicrobial stewardship.
Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages—such as shorter developmental timelines and opportunities for extensive experimentation—over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship.
Observational studies compare outcomes among subjects with and without an exposure of interest, without intervention from study investigators. Observational studies can be designed as a prospective or retrospective cohort study or as a case-control study. In healthcare epidemiology, these observational studies often take advantage of existing healthcare databases, making them more cost-effective than clinical trials and allowing analyses of rare outcomes. This paper addresses the importance of selecting a well-defined study population, highlights key considerations for study design, and offers potential solutions including biostatistical tools that are applicable to observational study designs.
Quasi-experimental studies evaluate the association between an intervention and an outcome using experiments in which the intervention is not randomly assigned. Quasi-experimental studies are often used to evaluate rapid responses to outbreaks or other patient safety problems requiring prompt, nonrandomized interventions. Quasi-experimental studies can be categorized into 3 major types: interrupted time-series designs, designs with control groups, and designs without control groups. This methods paper highlights key considerations for quasi-experimental studies in healthcare epidemiology and antimicrobial stewardship, including study design and analytic approaches to avoid selection bias and other common pitfalls of quasi-experimental studies.
Randomized controlled trials (RCT) produce the strongest level of clinical evidence when comparing interventions. RCTs are technically difficult, costly, and require specific considerations including the use of patient- and cluster-level randomization and outcome selection. In this methods paper, we focus on key considerations for RCT methods in healthcare epidemiology and antimicrobial stewardship (HE&AS) research, including the need for cluster randomization, conduct at multiple sites, behavior modification interventions, and difficulty with identifying appropriate outcomes. We review key RCTs in HE&AS with a focus on advantages and disadvantages of methods used. A checklist is provided to aid in the development of RCTs in HE&AS.
Research in Healthcare Epidemiology and Antimicrobial Stewardship (HE&AS) is rapidly expanding with the involvement of researchers from varied countries and backgrounds. Researchers must use scientific methods that will provide the strongest evidence to advance healthcare epidemiology, but there are limited resources for information on specific aspects of HE&AS research or easy ways to access examples of studies using specific methods with HE&AS. In response to this need, the SHEA Research Committee has developed a series of white papers on research methods in HE&AS. The objective of this series is to promote rigorous healthcare epidemiology research by summarizing critical components, practical considerations, and pitfalls of commonly used research methods.