COSTS AND EFFECTS OF ARKANSAS' LONG-TERM CARE DEMONSTRATION PROJECT (MEDICAID, CHANELLING, COMMUNITY ASSESSMENT)

The purpose of this study was to assess the impact of the Arkansas Long-Term Care Demonstration Project upon Arkansas' Medicaid expenditures and upon the clients it serves. A Retrospective Medicaid expenditure study component used analyses of variance techniques to test for the Project's effects upon aggregated expenditures for 28 demonstration and control counties representing 25 percent of the State's population over four years, 1979-1982.^ A second approach to the study question utilized a 1982 prospective sample of 458 demonstration and control clients from the same 28 counties. The disability level or need for care of each patient was established a priori. The extent to which an individual's variation in Medicaid utilization and costs was explained by patient need, presence or absence of the channeling project's placement decision or some other patient characteristic was examined by multiple regression analysis. Long-term and acute care Medicaid, Medicare, third party, self-pay and the grand total of all Medicaid claims were analyzed for project effects and explanatory relationships.^ The main project effect was to increase personal care costs without reducing nursing home or acute care costs (Prospective Study). Expansion of clients appeared to occur in personal care (Prospective Study) and minimum care nursing home (Retrospective Study) for the project areas. Cost-shifting between Medicaid and Medicare in the project areas and two different patterns of utilization in the North and South projects tended to offset each other such that no differences in total costs between the project areas and demonstration areas occurred. The project was significant ((beta) = .22, p < .001) only for personal care costs. The explanatory power of this personal care regression model (R('2) = .36) was comparable to other reported health services utilization models. Other variables (Medicare buy-in, level of disability, Social Security Supplemental Income (SSI), net monthly income, North/South areas and age) explained more variation in the other twelve cost regression models. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

UNDERSTANDING INTERRUPTIONS IN HEALTHCARE: DEVELOPING A MODEL

Developing a Model Interruption is a known human factor that contributes to errors and catastrophic events in healthcare as well as other high-risk industries. The landmark Institute of Medicine (IOM) report, To Err is Human, brought attention to the significance of preventable errors in medicine and suggested that interruptions could be a contributing factor. Previous studies of interruptions in healthcare did not offer a conceptual model by which to study interruptions. As a result of the serious consequences of interruptions investigated in other high-risk industries, there is a need to develop a model to describe, understand, explain, and predict interruptions and their consequences in healthcare. Therefore, the purpose of this study was to develop a model grounded in the literature and to use the model to describe and explain interruptions in healthcare. Specifically, this model would be used to describe and explain interruptions occurring in a Level One Trauma Center. A trauma center was chosen because this environment is characterized as intense, unpredictable, and interrupt-driven. The first step in developing the model began with a review of the literature which revealed that the concept interruption did not have a consistent definition in either the healthcare or non-healthcare literature. Walker and Avant’s method of concept analysis was used to clarify and define the concept. The analysis led to the identification of five defining attributes which include (1) a human experience, (2) an intrusion of a secondary, unplanned, and unexpected task, (3) discontinuity, (4) externally or internally initiated, and (5) situated within a context. However, before an interruption could commence, five conditions known as antecedents must occur. For an interruption to take place (1) an intent to interrupt is formed by the initiator, (2) a physical signal must pass a threshold test of detection by the recipient, (3) the sensory system of the recipient is stimulated to respond to the initiator, (4) an interruption task is presented to recipient, and (5) the interruption task is either accepted or rejected by v the recipient. An interruption was determined to be quantifiable by (1) the frequency of occurrence of an interruption, (2) the number of times the primary task has been suspended to perform an interrupting task, (3) the length of time the primary task has been suspended, and (4) the frequency of returning to the primary task or not returning to the primary task. As a result of the concept analysis, a definition of an interruption was derived from the literature. An interruption is defined as a break in the performance of a human activity initiated internal or external to the recipient and occurring within the context of a setting or location. This break results in the suspension of the initial task by initiating the performance of an unplanned task with the assumption that the initial task will be resumed. The definition is inclusive of all the defining attributes of an interruption. This is a standard definition that can be used by the healthcare industry. From the definition, a visual model of an interruption was developed. The model was used to describe and explain the interruptions recorded for an instrumental case study of physicians and registered nurses (RNs) working in a Level One Trauma Center. Five physicians were observed for a total of 29 hours, 31 minutes. Eight registered nurses were observed for a total of 40 hours 9 minutes. Observations were made on either the 0700–1500 or the 1500-2300 shift using the shadowing technique. Observations were recorded in the field note format. The field notes were analyzed by a hybrid method of categorizing activities and interruptions. The method was developed by using both a deductive a priori classification framework and by the inductive process utilizing line-byline coding and constant comparison as stated in Grounded Theory. The following categories were identified as relative to this study: Intended Recipient - the person to be interrupted Unintended Recipient - not the intended recipient of an interruption; i.e., receiving a phone call that was incorrectly dialed Indirect Recipient – the incidental recipient of an interruption; i.e., talking with another, thereby suspending the original activity Recipient Blocked – the intended recipient does not accept the interruption Recipient Delayed – the intended recipient postpones an interruption Self-interruption – a person, independent of another person, suspends one activity to perform another; i.e., while walking, stops abruptly and talks to another person Distraction – briefly disengaging from a task Organizational Design – the physical layout of the workspace that causes a disruption in workflow Artifacts Not Available – supplies and equipment that are not available in the workspace causing a disruption in workflow Initiator – a person who initiates an interruption Interruption by Organizational Design and Artifacts Not Available were identified as two new categories of interruption. These categories had not previously been cited in the literature. Analysis of the observations indicated that physicians were found to perform slightly fewer activities per hour when compared to RNs. This variance may be attributed to differing roles and responsibilities. Physicians were found to have more activities interrupted when compared to RNs. However, RNs experienced more interruptions per hour. Other people were determined to be the most commonly used medium through which to deliver an interruption. Additional mediums used to deliver an interruption vii included the telephone, pager, and one’s self. Both physicians and RNs were observed to resume an original interrupted activity more often than not. In most interruptions, both physicians and RNs performed only one or two interrupting activities before returning to the original interrupted activity. In conclusion the model was found to explain all interruptions observed during the study. However, the model will require an even more comprehensive study in order to establish its predictive value.

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^

Model - data comparison of western African rainfall evolutions during the Last Interglacial (130-115 ka)

This dataset characterizes the evolution of western African precipitation indicated by marine sediment geochemical records in comparison to transient simulations using CCSM3 global climate model throughout the Last Interglacial (130-115 ka). It contains (1) defined tie-points (age models), newly published stable isotopes of benthic foraminifera and Al/Si log-ratios of eight marine sediment cores from the western African margin and (2) annual and seasonal rainfall anomalies (relative to pre-industrial values) for six characteristic latitudinal bands in western Africa simulated by CCSM3 (two transient simulations: one non-accelerated and one accelerated experiment).

Planktonic and benthic stables Isotopes, age model and carbon analysis of HUD91/039 from the northernmost Baffin Bay

A multiproxy study of palaeoceanographic and climatic changes in northernmost Baffin Bay shows that major environmental changes have occurred since the deglaciation of the area at about 12 500 cal. yr BP. The interpretation is based on sedimentology, benthic and planktonic foraminifera and their isotopic composition, as well as diatom assemblages in the sedimentary records at two core sites, one located in the deeper central part of northernmost Baffin Bay and one in a separate trough closer to the Greenland coast. A revised chronology for the two records is established on the basis of 15 previously published AMS 14C age determinations. A basal diamicton is overlain by laminated, fossil-free sediments. Our data from the early part of the fossiliferous record (12 300 - 11 300 cal. yr BP), which is also initially laminated, indicate extensive seasonal sea-ice cover and brine release. There is indication of a cooling event between 11 300 and 10 900 cal. yr BP, and maximum Atlantic Water influence occurred between 10 900 and 8200 cal. yr BP (no sediment recovery between 8200 and 7300 cal. yr BP). A gradual, but fluctuating, increase in sea-ice cover is seen after 7300 cal. yr BP. Sea-ice diatoms were particularly abundant in the central part of northernmost Baffin Bay, presumably due to the inflow of Polar waters from the Arctic Ocean, and less sea ice occurred at the near-coastal site, which was under continuous influence of the West Greenland Current. Our data from the deep, central part show a fluctuating degree of upwelling after c. 7300 cal. yr BP, culminating between 4000 and 3050 cal. yr BP. There was a gradual increase in the influence of cold bottom waters from the Arctic Ocean after about 3050 cal. yr BP, when agglutinated foraminifera became abundant. A superimposed short-term change in the sea-surface proxies is correlated with the Little Ice Age cooling.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.