Biomedical subjects

S R Lipsitz

Publications and source records attributed to S R Lipsitz.

5 recordsLinked to original sources

PubMed1992

Socioeconomic status and risk for substandard medical care.

OBJECTIVE: To assess whether the socioeconomic status of the patient was associated with the risk of adverse events, defined as medical injuries caused by medical management, and the proportion of these events that resulted from substandard care. SETTING: 51 hospitals in New York State. METHODS: Rates of medical injury and substandard care by gender, race, income, and payer status were developed from reviews of 30,195 medical records in New York in 1984. We evaluated these socioeconomic parameters in a multivariate analysis, while controlling for hospital-level factors. RESULTS: We found that uninsured patients (odds ratio, 2.35; 95% confidence interval, 1.40 to 3.95) were at greater risk for substandard care. The characteristics of the hospitals to which patients were admitted did not affect this result. Race, gender, and income were not independently associated with risk for medical injury or substandard care in multivariate analyses. CONCLUSION: Our findings suggest that the uninsured are at greater risk for suffering medical injury due to substandard medical care.

Black or African American↗

PubMed1992

Methods for estimating the parameters of a linear model for ordered categorical data.

In many empirical analyses, the response of interest is categorical with an ordinal scale attached. Many investigators prefer to formulate a linear model, assigning scores to each category of the ordinal response and treating it as continuous. When the covariates are categorical, Haber (1985, Computational Statistics and Data Analysis 3, 1-10) has developed a method to obtain maximum likelihood (ML) estimates of the parameters of the linear model using Lagrange multipliers. However, when the covariates are continuous, the only method we found in the literature is ordinary least squares (OLS), performed under the assumption of homogeneous variance. The OLS estimates are unbiased and consistent but, since variance homogeneity is violated, the OLS estimates of variance can be biased and may not be consistent. We discuss a variance estimate (White, 1980, Econometrica 48, 817-838) that is consistent for the true variance of the OLS parameter estimates. The possible bias encountered by using the naive OLS variance estimate is discussed. An estimated generalized least squares (EGLS) estimator is proposed and its efficiency relative to OLS is discussed. Finally, an empirical comparison of OLS, EGLS, and ML estimators is made.

Abnormalities, Drug-Induced↗

PubMed1991

Hospital characteristics associated with adverse events and substandard care.

To explore the epidemiology of adverse events (AEs), which were defined as injuries due to medical treatment, and that subset of AEs caused by negligence, we studied interhospital variation in these outcomes in a sample of 31,000 medical records drawn from a random selection of 51 hospitals in New York in 1984. We found a substantial variation in both AE rates (0.2% to 7.9%; mean, 3.2%) and the percentage of AEs due to negligence (1% to 60%; mean, 24.9%) among hospitals. Univariate analyses of AEs revealed that primary teaching institutions had significantly higher rates (4.1%) and rural hospitals had significantly lower ones (1.0%). The percentage of AEs due to negligence was lower in primary teaching (10.7%) and for-profit (9.5%) hospitals and was significantly higher in hospitals with predominantly (greater than 80%) minority patients who had been discharged (37%). These findings were corroborated by multivariate analysis. Our results suggest that AEs and negligence are not randomly distributed and that certain types of hospitals have significantly higher rates of injuries due to substandard care. These observations may represent an important improvement on existing measures of quality because they take into account the fact that some hospitals' populations may be at risk of suffering a poor outcome.

Hospital Records↗

PubMed1990

Maximum likelihood regression methods for paired binary data.

We discuss maximum likelihood methods for analysing binary responses measured at two times, such as in a cross-over design. We construct a 2 x 2 table for each individual with cell probabilities corresponding to the cross-classification of the responses at the two times; the underlying likelihood for each individual is multinomial with four cells. The three dimensional parameter space of the multinomial distribution is completely specified by the two marginal probabilities of success of the 2 x 2 table and an association parameter between the binary responses at the two times. We examine a logistic model for the marginal probabilities of the 2 x 2 table for individual i; the association parameters we consider are either the correlation coefficient, the odds ratio or the relative risk. Simulations show that the parameter estimates for the logistic regression model for the marginal probabilities are not very sensitive to the parameters used to describe the association between the binary responses at the two times. Thus, we suggest choosing the measure of association for ease of interpretation.

Humans↗

PubMed1990

Analyzing correlated binary data using SAS.

We discuss methods for analyzing repeated binary measurements on the same individual. In spite of the fact that the repeated measurements on the same individual are correlated, the ordinary logistic regression maximum likelihood estimates (which assume that the repeated measures are independent) are consistent and asymptotically normal (K. Y. Liang and S. L. Zeger, Biometrika 73, 13 (1986]. However, the inverse of the estimated information matrix (assuming independence) can give inconsistent estimates of the asymptotic variance of estimated parameters. We describe how to obtain the logistic regression estimates, as well as a consistent estimate of their covariance matrix, in SAS, with minimal matrix manipulations.

Analysis of Variance↗