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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2017)
Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community.
    Cover page: A robust interrupted time series model for analyzing complex health care intervention data
    • Thesis
    • Peer Reviewed
    UC Irvine Electronic Theses and Dissertations (2019)

    Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology that enables researchers to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS data a priori restrict the interruption's effect to a predetermined time point or censor data for which the intervention effects may not be fully realized, and neglect changes in the temporal dependence and variability. In addition, current methods limit the analysis to one hospital unit or entity and are not well specified for discrete outcomes (e.g., patient falls). This dissertation develops novel ITS methods based on segmented regression that address the aforementioned limitations.

    We propose the 'Robust-ITS' model, a single-unit model able to estimate (rather than merely assume) the lagged effect of an intervention on a health outcome. Robust-ITS accounts for plausible differences in the mean, temporal dependence and variability of an outcome pre- and post-intervention. Next, we develop the 'Robust Multiple ITS' model as an extension of Robust-ITS for multi-unit data. Alongside Robust Multiple ITS, we propose the 'supremum Wald test', able to formally test for the existence of a change point across unit specific mean functions. Lastly, we present the 'Generalized Robust ITS' model, appropriate for outcomes whose underlying distribution belongs to the family of exponential distributions. Generalized Robust ITS expands the available methodology to adequately model multi-unit binary, count and rate ITS. The methods proposed allow researchers to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation structure pre- and post-intervention. Throughout, the methodology is illustrated by analyzing patient centered data from a hospital that implemented and evaluated a new care delivery model in multiple units.

      Cover page: Interrupted Time Series Models for Assessing Complex Health Care InterventionsCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2021)

      Aims

      Patients are harmed or die every year because of unsafe, inappropriate or inadequate healthcare delivery. Registered Nurses are a recognized patient safety strategy. However, variability in research findings indicate the relationship is not as simple as "more nurses=better outcomes." Hence, currently there exists no evidence-based frontline nursing care model. One emerging model is the Clinical Nurse Leader care model.

      Design

      This Hybrid Type II Implementation-Effectiveness study will evaluate the effect of the care model on standardized quality and safety outcomes and identify implementation characteristics that are sufficient and necessary to achieve outcomes.

      Methods

      This study leverages a natural experiment in 66 clinical care units in nine hospitals across five states in the United States that have implemented the Clinical Nurse Leader care model.

      Results

      Findings will elucidate Registered Nurse's mechanisms of action as organized into frontline models of care and link actions to improved care quality and safety.
        Cover page: A study protocol to evaluate the implementation and effectiveness of the Clinical Nurse Leader Care Model in improving quality and safety outcomes
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2019)
        The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation, we propose the "Robust Multiple ITS" (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.
          Cover page: Assessing health care interventions via an interrupted time series model: Study power and design considerations
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2012)
          The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved. One contentious issue is whether the settlement occurred by means of a single migration or multiple streams of migration from Siberia. The pattern of dispersals within the Americas is also poorly understood. To address these questions at a higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. Here we show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call 'First American'. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan speakers on both sides of the Panama isthmus, who have ancestry from both North and South America.
            Cover page: Reconstructing Native American population history
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