A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

A COMPARISON OF TWO METHODS FOR THE DETECTION OF FECAL POLLUTION IN DRINKING WATER (HYDROGEN SULFIDE, INDICATOR, MODIFICATION)

A new technique for the detection of microbiological fecal pollution in drinking and in raw surface water has been modified and tested against the standard multiple-tube fermentation technique (most-probable-number, MPN). The performance of the new test in detecting fecal pollution in drinking water has been tested at different incubation temperatures. The basis for the new test was the detection of hydrogen sulfide produced by the hydrogen sulfide producing bacteria which are usually associated with the coliform group. The positive results are indicated by the appearance of a brown to black color in the contents of the fermentation tube within 18 to 24 hours of incubation at 35 (+OR-) .5(DEGREES)C. For this study 158 water samples of different sources have been used. The results were analyzed statistically with the paired t-test and the one-way analysis of variance. No statistically significant difference was noticed between the two methods, when tested 35 (+OR-) .5(DEGREES)C, in detecting fecal pollution in drinking water. The new test showed more positive results with raw surface water, which could be due to the presence of hydrogen sulfide producing bacteria of non-fecal origin like Desulfovibrio and Desulfomaculum. The survival of the hydrogen sulfide producing bacteria and the coliforms was also tested over a 7-day period, and the results showed no significant difference. The two methods showed no significant difference when used to detect fecal pollution at a very low coliform density. The results showed that the new test is mostly effective, in detecting fecal pollution in drinking water, when used at 35 (+OR-) .5(DEGREES)C. The new test is effective, simple, and less expensive when used to detect fecal pollution in drinking water and raw surface water at 35 (+OR-) .5(DEGREES)C. The method can be used for qualitative and/or quantitative analysis of water in the field and in the laboratory. ^

Comparison of financial trends in small rural and urban hospitals in Texas, 1983--1985

The purpose of this study was to compare the financial performance of small rural hospitals to that of small urban hospitals in Texas. Hospital-specific and environmental factors were studied as control variables.^ Small rural hospitals were found to be financially stronger on measures of liquidity but weaker on measures of profitability. Small urban hospitals performed better on measures of profitability and long-range solvency. When all measures in the five dimensions of financial performance were analyzed, no significant difference was found between the two groups of hospitals. None of the control variables included in the study was significantly associated with financial performance both for rural and urban hospitals. Conclusions were that small rural hospitals in Texas are experiencing a deterioration in financial condition but small, rural hospitals are not doing any worse than small urban hospitals; and that the financial hardship which rural hospitals suffer may be inherent in the nature of the institutions themselves, and not as a result of their smallness nor their rural settings. ^

International comparison of wait times and quality of care: A pilot study

During the healthcare reform debate in the United States in 2009/2010, many health policy experts expressed a concern that expanding coverage would increase waiting times for patients to obtain care. Many complained that delays in obtaining care in turn would compromise the quality of healthcare in the United States. Using data from The Commonwealth Fund 2010 International Health Policy Survey in Eleven Countries, this study explored the relationship between wait times and quality of care, employing a wait time scale and several quality of care indicators present in the dataset. The impact of wait times on quality was assessed. Increased wait time was expected to reduce quality of care. However, this study found that wait times correlated with better health outcomes for some measures, and had no association with others. Since this is a pilot study and statistical significance was not achieved for any of the correlations, further research is needed to confirm and deepen the findings. However, if future studies confirm this finding, an emphasis on reducing wait times at the expense of other health system level performance variables may be inappropriate. ^

COMPARISON OF THE ORGANIZATIONAL STRUCTURES AND EFFECTIVENESS OF THE INVESTOR-OWNED AND VOLUNTARY MULTIHOSPITAL SECTORS

Objectives. The central objective of this study was to systematically examine the internal structure of multihospital systems, determining the management principles used and the performance levels achieved in medical care and administrative areas.^ The Universe. The study universe consisted of short-term general American hospitals owned and operated by multihospital corporations. Corporations compared were the investor-owned (for-profit) and the voluntary multihospital systems. The individual hospital was the unit of analysis for the study.^ Theoretical Considerations. The contingency theory, using selected aspects of the classical and human relations schools of thought, seemed well suited to describe multihospital organization and was used in this research.^ The Study Hypotheses. The main null hypotheses generated were that there are no significant differences between the voluntary and the investor-owned multihospital sectors in their (1) hospital structures and (2) patient care and administrative performance levels.^ The Sample. A stratified random sample of 212 hospitals owned by multihospital systems was selected to equally represent the two study sectors. Of the sampled hospitals approached, 90.1% responded.^ The Analysis. Sixteen scales were constructed in conjunction with 16 structural variables developed from the major questions and sub-items of the questionnaire. This was followed by analysis of an additional 7 structural and 24 effectiveness (performance) measures, using frequency distributions. Finally, summary statistics and statistical testing for each variable and sub-items were completed and recorded in 38 tables.^ Study Findings. While it has been argued that there are great differences between the two sectors, this study found that with a few exceptions the null hypotheses of no difference in organizational and operational characteristics of non-profit and for-profit hospitals was accepted. However, there were several significant differences found in the structural variables: functional specialization, and autonomy were significantly higher in the voluntary sector. Only centralization was significantly different in the investor owned. Among the effectiveness measures, occupancy rate, cost of data processing, total manhours worked, F.T.E. ratios, and personnel per occupied bed were significantly higher in the voluntary sector. The findings indicated that both voluntary and for-profit systems were converging toward a common hierarchical corporate management approach. Factors of size and management style may be better descriptors to characterize a specific multihospital group than its profit or nonprofit status. (Abstract shortened with permission of author.) ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^