Estimating health care savings associated with alcoholism treatment.
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Alcoholism↗
Biomedical subjects
R H Shachtman
Publications and source records attributed to R H Shachtman.
A method for constructing case-mix indexes, with application to hospital length of stay.
This article presents the methodological development of an index for case-mix adjustment of hospital data exemplified by our construction of an index for studying length of stay. We describe the development and evaluation of this index, including internal and external validation procedures, and show an example of its use in a policy-relevant context by applying it to the analysis of length-of-stay differences between investor-owned and voluntary hospitals. Some advantages of this approach to adjusting for case mix are applicability to many hospital or patient output measurements/diagnostic scheme situations; usefulness in reducing heterogeneity in other case-mix adjustments, e.g., the Diagnosis-Related Group (DRG) approach; interpretation possibilities; production of a single score for each patient/hospital; statistical approach allowing more accurate and reliable interpretation of hospital and patient output measurements, ability to deal with hospital deaths; and consideration of the complete set of secondary diagnoses. We also suggest other possible uses of this approach.
Diagnosis-Related Groups↗
Analysis of length-of-stay differences between investor-owned and voluntary hospitals.
In contrast to assertions that investor-owned (I-O) hospitals are more efficient than voluntary hospitals, this study finds no significant difference between I-Os and voluntaries where the efficiency measure is length of hospitalization (LOH). The data base used is a national probability sample of hospitals and patients. The analysis accounts for variation in LOH by controlling for hospital characteristics other than ownership, and in particular it utilizes a new case-mix index to control for the case-mix portion of heretofore suggested differences.
Bed Occupancy↗
A method to determine a non-economic personal value of life.
This paper presents a method for estimating a non-monetary personal value for death in the context of a decision problem. The method evolved from a case study of a personal decision strategy for choosing whether to receive the swine influenza vaccine, based on the predicted epidemic in the United States in the fall of 1976. Rather than dealing with the decision-maker's assessments of utilities associated with extreme outcomes such as one's own death, the basic approach considers probabilities representing marginal reductions in the probability of death. An application to the swine influenza decision is included.
Community Participation↗
Nosocomial infections in U.S. hospitals, 1975-1976: estimated frequency by selected characteristics of patients.
To obtain estimates of the frequency of nosocomial infections nationwide, those occurring at the four major sites--urinary tract, surgical wound, lower respiratory tract and bloodstream--were diagnosed in a stratified random sample of 169,526 adult, general medical and surgical patients selected from 338 hospitals representative of the "mainstream" of U.S. hospitals. We estimate that in the mid-1970s one or more infections developed in 5.23 percent (+/- 0.16) of the patients and that 6.62 (+/- 0.24) infections occurred among every 100 admissions. Risks were significantly related to age, sex, service, duration of total and of preoperative hospitalization, presence of previous nosocomial or community-acquired infection, types of underlying illnesses and operations, duration of surgery, and treatment with urinary catheters, continuous ventilatory support or immunosuppressive medications. Seventy-one percent of the nosocomial infections occurred in the 42 percent of patients undergoing surgery and 56 percent in the 38 percent financed by Medicare, Medicaid or other public health care plans.
Adolescent↗
The SENIC sampling process: design for choosing hospitals and patients and results of sample selection.
To achieve its primary objectives, the Study on the Efficacy of Nosocomial Infection Control (SENIC Project) focused its attention on a target population of patients referred to as SENIC-eligible admissions in a target population of hospitals referred to as the "SENIC Universe." SENIC thus required a design for sampling hospitals and patients within these hospitals and a valid procedure for projecting sample results to the target population. This paper presents the details of the sampling design used, describes the actual process of selecting hospitals and patients for the surveys, explains the procedure used to project sample results to the target population, and examines the possibility of bias in the design and hospital selection process. As with most large-scale sample surveys, the design and sample selection processes for the surveys in Phases II and III of SENIC were complicated by incomplete frame, nonresponse and measurement problems. Nevertheless, adjustments to reduce the effects of some of these problems have been made through the development of a valid procedure for projecting sample results to the target population, and it appears unlikely that practically important nonsampling biases will result from the estimation procedures applied to this sample of hospitals.
Cross Infection↗
The accuracy of retrospective chart review in measuring nosocomial infection rates. Results of validation studies in pilot hospitals.
To measure the accuracy and consistency of a standardized method--retrospective chart review (RCR)--for estimating nosocomial infection rates (NIRs) in individual hospitals, the authors performed a series of pilot studies in four hospitals of different types. In comparison with a standard based on diagnoses made by physician-epidemiologists supervising intensive prospective data collection teams, the RCR method was found to have an average sensitivity of 0.74 (+/- 0.02 SE; range 0.69-0.78) and an average specificity of 0.964 (+/- 0.002; 0.945-0.991). These values were comparable to those of the physician-epidemiologists' diagnoses and varied less among the hospitals. Two independent teams of chart reviewers were found to have similar levels of sensitivity and specificity, and the reliability of diagnosis at the level of the individual chart reviewer averaged 0.94. In a restudy at one of the pilot hospitals at the midpoint of the actual Medical Records Survey (MRS), there was a substantial increase in sensitivityand a slight increase in specificity as a result of improvements made in the RCR method after the original pilot studies.
Cross Infection↗
The emergence of infection surveillance and control programs in US hospitals: an assessment, 1976.
To assess the current state of hospitals' infection surveillance and control programs (ISCPs) nationwide and to provide a sampling frame for selecting hospitals for later phases of the SENIC Project, the authors mailed a screening questionnaire in March, 1976, to virtually all US hospitals; 86% of those in the SENIC target universe responded. Of these, 64% (2299) reported that their ISCPs were being supervised by a physician or a microbiologist with special interest in infection control, and 42% had an infection control nurse (ICN), or equivalent, working at least half time. In contrast to the supervisors, most of the ICN's had recieved special training in hospital infection epidemiology and spent the majority of their time doing surveillance. Almost all hospitals (87%) had practiced some form of infection surveillance. Almost all hospitals (87%) had practiced some form of infection surveillance, with half reporting very active programs. Larger hospitals with ISCP staff tended to use active clinical casefinding methods, while smaller hospitals tended to use passive techniques. Most hospitals (76%) were collecting relatively large numbers of environmental cultures routinely, although a growing number (about 25%) had reduced or discontinued this practice. Routine culturing was more often performed in hospitals employing passive surveillance methods. Although the adoption of selected infection control policies and practices has varied widely, chronological data indicate that a major infection control movement has emerged since 1970.
Cross Infection↗
A decision analysis approach to the swine influenza vaccination decision for an individual.
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Adult↗
The interactive effects of induced abortion, inter-pregnancy interval and contraceptive use on subsequent pregnancy outcome.
Prior induced abortion and outcome of the next pregnancy are investigated, allowing for two intervening and potentially confounding variables: 1) length of interval between the termination of the first pregnancy and the conception of the next (inter-pregnancy interval) and 2) the utilization of contraception during this interval. Results show that non-contracepting (susceptibility) intervals which immediately precede a subsequent pregnancy are significantly shorter following an induced abortion than those following a spontaneous abortion or delivery. A life table analysis of all susceptibility intervals confirmed this finding. To investigate outcome of subsequent pregnancy as influenced by preceding pregnancy outcome, inter-pregnancy interval and contraceptive use in the interval, a categorical linear model has been developed. Among non-contraceptors, the model indicates no differences in proportions of succeeding adverse outcomes (spontaneous abortion or low birth weight) regardless of inter-pregnancy interval and whether or not the preceding pregnancy had been terminated by an induced abortion. For the contraceptive users, however, proportions of adverse outcomes increased with length of inter-pregnancy interval, and, within each interval category, proportion of adverse outcomes was higher when the preceding pregnancy had terminated in an induced abortion.
Abortion, Induced↗
Special issue on health care. Preface.
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Delivery of Health Care↗
Conditional rate derivation in the presence of intervening variables using a Markov chain.
When conducting inferential and epidemiologic studies, researchers are often interested in the distribution of time until the occurrence of some specified event, a form of incidence calculation. Furthermore, this interest often extends to the effects of intervening factors on this distribution. In this paper we impose the assumption that the phenomena being investigated are governed by a stationary Markov chain and review how one may estimate the above distribution. We then introduce and relate two different methods of investigating the effects of intervening factors. In particular, we show how an investigator may evaluate the effect of potential intervention programs. Finally, we demonstrate the proposed methodology using data from a population study.
Abortion, Induced↗
A stochastic model to measure patient effects stemming from hospital-acquired infections.
We introduce a Markov chain model to represent a patient's path in terms of the number and type of infections s/he may have acquired during a hospitalization period. The model allows for categories of patient diagnosis, surgery, the four major types of nosocomial (hospital-acquired) infections, and discharge or death. Data from a national medical records survey including 58,647 patients enable us to estimate transition probabilities and, ultimately, perform statistical tests of fit, including a validation test. Novel parameterizations (functions of the transition matrix) are introduced to answer research questions on time-dependent infection rates, time to discharge or death as a function of patient diagnostic groups and conditional infection rates reflecting intervening variables (e.g., surgery).
Cross Infection↗
Does workplace health promotion decrease medical claims?
We examined the relationship between workplace health promotion and medical claims in 38 textile plants, considering also the effects of demographic and contextual variables (i.e., average worker age, sex ratio, racial composition, plant product, and access to medical services). Number of claims per worker varied threefold among plants but was independent of plant workforce's sex ratio, racial composition, and access to medical services. Worker age predicted claims; in a linear regression model, age, sex, race, plant product, and access explained 23% of variance in claims. Health promotion was also related to claims, and its inclusion in the model (with interaction terms involving plant product) explained 54% of variance in claims, with the deletion of race, sex, and access from the reduced model. We concluded that effective health promotion must address the contexts of different types of plant product.
Age Factors↗