Outcomes assessment of a pediatric practice guideline

Objective. This study examines the structure, processes, and data necessary to assess the outcome variables, length of stay and total cost, for a pediatric practice guideline. The guideline was developed by a group of physicians and ancillary staff members representing the services that most commonly provide treatment for asthma patients at Texas Children's Hospital, as a means of standardizing care. Outcomes have needed to be assessed to determine the practice guideline's effectiveness.^ Data sources and study design. Data for the study were collected retrospectively from multiple hospital data bases and from inpatient chart reviews. All patients in this quasi-experimental study had a diagnosis of Asthma (ICD-9-CM Code 493.91) at the time of admission.^ The study examined data for 100 patients admitted between September 15, 1995 and November 15, 1995, whose physician had elected to apply the asthma practice guideline at the time of the patient's admission. The study examined data for 66 inpatients admitted between September 15, 1995 and November 15, 1995, whose physician elected not to apply the asthma practice guideline. The principal outcome variables were identified as "Length of Stay" and "Cost".^ Principal findings. The mean length of stay for the group in which the practice guideline was applied was 2.3 days, and 3.1 days for the comparison group, who did not receive care directed by the practice guideline. The difference was statistically significant (p value = 0.008). There was not a demonstrable difference in risk factors, health status, or quality of care between the groups. Although not showing statistical significance in the univariate analysis, private insurance showed a significant difference in the logistic regression model presenting an elevated odds ratio (odds ratio = 2.2 for a hospital stay $\le$2 days to an odds ratio = 4.7 for a hospital stay $\le$3 days) showing that patients with private insurance experienced greater risk of a shorter hospital stay than the patients with public insurance in each of the logistic regression models. Public insurance included; Medicaid, Medicare, and charity cases. Private insurance included; private insurance policies whether group, individual, or managed care. The cost of an admission was significantly less for the group in which the practice guideline was applied, with a mean difference between the two groups of $1307 per patient.^ Conclusion. The implementation and utilization of a pediatric practice guideline for asthma inpatients at Texas Children's Hospital has a significant impact in terms of reducing the total cost of the hospital stay and length of the hospital stay for asthma patients admitted to Texas Children's Hospital. ^

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^