Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Optimal Test for Parameter Instability When the Unstable Process and the Error Distribution are Unknown

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does not provide asymptotic power gains. I first derive a test under known error distribution, which is asymptotically equivalent to LR tests for correctly identified unstable parameter processes under suitable conditions. The conditions are weak enough to cover a wide range of unstable processes such as various types of structural breaks and time varying parameter processes. The test is then extended to semiparametric models in which the underlying distribution in unknown but treated as unknown infinite dimensional nuisance parameter. The semiparametric test is adaptive in the sense that its asymptotic power function is equivalent to the power envelope under known error distribution.

Second, do less harm: The state of states' medical error reporting programs

Statement of the problem and public health significance. Hospitals were designed to be a safe haven and respite from disease and illness. However, a large body of evidence points to preventable errors in hospitals as the eighth leading cause of death among Americans. Twelve percent of Americans, or over 33.8 million people, are hospitalized each year. This population represents a significant portion of at risk citizens exposed to hospital medical errors. Since the number of annual deaths due to hospital medical errors is estimated to exceed 44,000, the magnitude of this tragedy makes it a significant public health problem. ^ Specific aims. The specific aims of this study were threefold. First, this study aimed to analyze the state of the states' mandatory hospital medical error reporting six years after the release of the influential IOM report, "To Err is Human." The second aim was to identify barriers to reporting of medical errors by hospital personnel. The third aim was to identify hospital safety measures implemented to reduce medical errors and enhance patient safety. ^ Methods. A descriptive, longitudinal, retrospective design was used to address the first stated objective. The study data came from the twenty-one states with mandatory hospital reporting programs which report aggregate hospital error data that is accessible to the public by way of states' websites. The data analysis included calculations of expected number of medical errors for each state according to IOM rates. Where possible, a comparison was made between state reported data and the calculated IOM expected number of errors. A literature review was performed to achieve the second study aim, identifying barriers to reporting medical errors. The final aim was accomplished by telephone interviews of principal patient safety/quality officers from five Texas hospitals with more than 700 beds. ^ Results. The state medical error data suggests vast underreporting of hospital medical errors to the states. The telephone interviews suggest that hospitals are working at reducing medical errors and creating safer environments for patients. The literature review suggests the underreporting of medical errors at the state level stems from underreporting of errors at the delivery level. ^

Adherence to medication regimen among children with persistent asthma in Houston, Texas

An estimated 5.6 million children under the age of 18 suffer from asthma in the United States making asthma one of the most common chronic diseases in children. Because of the nature of the disease, the treatment regimen necessary to manage asthma has considerably changed as new drugs are developed and improved. The complexity of the treatment regimen recommended by the physician, however, has been linked with problems in adherence. Accordingly, the purpose of this cross-sectional study is to determine the prevalence of adherence to medication regimens among children between 9 to 15 years of age with physician-diagnosed asthma in Houston, Texas. The study used a set of data from a sample of children who participated in a pilot, panel asthma study to investigate the association between exposure to oxygenated air toxics and asthma health outcomes, conducted between 2002 and 2003. Data on daily intake of medication and onset of asthma-related symptoms were collected for each child per 10-day sampling period. Information was gathered through telephone or personal interviews for the 28 study participants who completed the study. The prevalence of adherence was calculated based on the number of times the participant reported taking his or her maintenance medication. The Fisher's Exact test and the Student's t-test were used to compare the level of adherence between the first and the last sampling cycle (Cycle 1 and Cycle 4) among the study participants by age, gender, and ethnicity. ^

Self medication behaviors of illegal immigrants living along the U.S.-Mexico border

Mexican immigrants make up the largest subgroup of Hispanics living in the United States. The largest percentage of illegal immigrants comes from México. As such they are a subpopulation with limited access to health care and social services; their health seeking behaviors including self-medication behaviors that, aside from the intake of antibiotics, have not been studied in depth. The analysis of the data presented sought to document the medication behaviors of illegal immigrants living in El Paso County along the U.S.-México border. Of the 80 participants, 31 were taking medication on a regular basis. Of these, 28 claimed that at least one of the medications had been prescribed by a physician, 13 people had bought at least one of their medications in México, nine participants claimed that they had not paid for at least one of the medications they were taking, ten participants reported that they had skipped the doses of at least one of their medications due to monetary constraints. Participants were also asked if they had purchased medication in México during the year prior to the study, 68 of the 80 (85%) participants had bought 295 pharmaceutical products across the border themselves or through a third party. The most frequently purchased medications were antibiotics (17%), followed by syrups, pomades, creams, eardrops, and cold medicine as a group (15%), followed by analgesics (13%) and other non steroidal anti-inflammatory drugs (12%) and oral hypoglycemic agents (6%). ^

A case-control study of medication-associated acute kidney injuries in hospitalized pediatric patients

Acute kidney Injury (AKI) in hospitalized pediatric patients can be a significant event that can result in increased patient morbidity and mortality. The incidence of medication associated AKI is increasing in the pediatric population. Currently, there are no data to quantify the risks of developing AKI for various potentially nephrotoxic medications. The primary objective of this study was to determine the odds of nephrotoxic medication exposure in hospitalized pediatric patients with AKI as defined by the pediatric modified pRIFLE criteria. A retrospective case-control study was performed with patients that developed AKI, as defined by the pediatric pRIFLE criteria, as cases, and patients without AKI as controls that were matched by age category, gender, and disease state. Patients between 1 day and 18 years of age, admitted to a non-intensive care unit at Texas Children's Hospital for at least 3 days, and had at least 2 serum creatinine values drawn were included. Patient data was analyzed with Student's t test, Mann-Whitney U test, Chi square analysis, ANOVA, and conditional logistic regression. ^ Out of 1,660 patients identified for inclusion, 561 (33.8%) patients had AKI, and 357 cases were matched with 357 controls to become pairs. Of the cases, 441 were category 'R', 117 category 'I', 3 patients were category 'F', and no patient died. Cases with AKI were significantly younger than controls (p < 0.05). Significantly longer hospital length of stays, increased hospital costs, and exposure to more nephrotoxic medications for a longer period of time were characteristics of patients with AKI compared to patient without AKI. Patients with AKI had greater odds of exposure to one or more nephrotoxic medication than patients without AKI (OR 1.3, 95% CI 1.1–1.4, p < 0.05). Percent changes in estimated creatinine clearance (eCCl) from baseline were greatest with increased number of nephrotoxic medication exposures. ^ Exposure to potentially nephrotoxic medications may place pediatric patients at greater risk of acute kidney injury. Multiple nephrotoxic medication exposure may confer a greater risk of development of acute kidney injury, and result in increased hospital costs and patient morbidity. Due to the high percentage of patients that were exposed to potentially nephrotoxic medications, monitoring and medication selection strategies may need to be altered to prevent or minimize risk.^

Evaluation of risk factors for long term changes in refractive error among adult patients at a Dallas Texas private setting A retrospective chart-review study

Background. Over 39.9% of the adult population forty or older in the United States has refractive error, little is known about the etiology of this condition and associated risk factors and their entailed mechanism due to the paucity of data regarding the changes of refractive error for the adult population over time.^ Aim. To evaluate risk factors over a long term, 5-year period, in refractive error changes among persons 43 or older by testing the hypothesis that age, gender, systemic diseases, nuclear sclerosis and baseline refractive errors are all significantly associated with refractive errors changes in patients at a Dallas, Texas private optometric office.^ Methods. A retrospective chart review of subjective refraction, eye health, and self-report health history was done on patients at a private optometric office who were 43 or older in 2000 who had eye examinations both in 2000 and 2005. Aphakic and pseudophakic eyes were excluded as well as eyes with best corrected Snellen visual acuity of 20/40 and worse. After exclusions, refraction was obtained on 114 right eyes and 114 left eyes. Spherical equivalent (sum of sphere + ½ cylinder) was used as the measure of refractive error.^ Results. Similar changes in refractive error were observed for the two eyes. The 5-year change in spherical power was in a hyperopic direction for younger age groups and in a myopic direction for older subjects, P<0.0001. The gender-adjusted mean change in refractive error in right eyes of persons aged 43 to 54, 55 to 64, 65 to 74, and 75 or older at baseline was +0.43D, +0.46 D, -0.09 D, and -0.23D, respectively. Refractive change was strongly related to baseline nuclear cataract severity; grades 4 to 5 were associated with a myopic shift (-0.38 D, P< 0.0001). The mean age-adjusted change in refraction was +0.27 D for hyperopic eyes, +0.56 D for emmetropic eyes, and +0.26 D for myopic eyes.^ Conclusions. This report has documented refractive error changes in an older population and confirmed reported trends of a hyperopic shift before age 65 and a myopic shift thereafter associated with the development of nuclear cataract.^

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.^

Costs and quality of medication reconciliation practice in primary care clinics

Medication reconciliation, with the aim to resolve medication discrepancy, is one of the Joint Commission patient safety goals. Medication errors and adverse drug events that could result from medication discrepancy affect a large population. At least 1.5 million adverse drug events and $3.5 billion of financial burden yearly associated with medication errors could be prevented by interventions such as medication reconciliation. This research was conducted to answer the following research questions: (1a) What are the frequency range and type of measures used to report outpatient medication discrepancy? (1b) Which effective and efficient strategies for medication reconciliation in the outpatient setting have been reported? (2) What are the costs associated with medication reconciliation practice in primary care clinics? (3) What is the quality of medication reconciliation practice in primary care clinics? (4) Is medication reconciliation practice in primary care clinics cost-effective from the clinic perspective? Study designs used to answer these questions included a systematic review, cost analysis, quality assessments, and cost-effectiveness analysis. Data sources were published articles in the medical literature and data from a prospective workflow study, which included 150 patients and 1,238 medications. The systematic review confirmed that the prevalence of medication discrepancy was high in ambulatory care and higher in primary care settings. Effective strategies for medication reconciliation included the use of pharmacists, letters, a standardized practice approach, and partnership between providers and patients. Our cost analysis showed that costs associated with medication reconciliation practice were not substantially different between primary care clinics using or not using electronic medical records (EMR) ($0.95 per patient per medication in EMR clinics vs. $0.96 per patient per medication in non-EMR clinics, p=0.78). Even though medication reconciliation was frequently practiced (97-98%), the quality of such practice was poor (0-33% of process completeness measured by concordance of medication numbers and 29-33% of accuracy measured by concordance of medication names) and negatively (though not significantly) associated with medication regimen complexity. The incremental cost-effectiveness ratios for concordance of medication number per patient per medication and concordance of medication names per patient per medication were both 0.08, favoring EMR. Future studies including potential cost-savings from medication features of the EMR and potential benefits to minimize severity of harm to patients from medication discrepancy are warranted. ^

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.