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Biomedical subjects

Paul R Rosenbaum

Publications and source records attributed to Paul R Rosenbaum.

8 recordsLinked to original sources
PubMed2005

Changes in prognosis after the first postoperative complication.

BACKGROUND: Postoperative complications are common in the Medicare population, yet no study has formally quantified the change in prognosis that occurs after a broad range of first complications. OBJECTIVE: We sought to estimate the relative severity of 24 first postoperative complications. RESEARCH DESIGN: We undertook a multivariate matched, population-based, case-control study of death after surgery in a sample of 1362 Pennsylvania Medicare patients. SUBJECTS: Cases (681 deaths) were selected randomly using claims from 1995-1996. Models were developed to scan all Pennsylvania claims, looking for similar controls that did not die. MEASURES: Charts were abstracted, complications identified, and models were constructed to estimate the odds of dying after any 1 of 24 first postoperative complications. RESULTS: The odds of dying within 60 days increased 3.4-fold (95% confidence interval [CI] 2.5-4.7) in patients with complications as compared with those without complications. A first complication of respiratory compromise was associated with a 7.2-fold increase in the odds of dying (95% CI 4.5-11.6). The first complications of pneumonia or congestive heart failure were associated with, respectively, 5-fold (95% CI 2.1-12.1) and 5.1-fold (2.3-11.1) increases in odds of dying as compared with no complication. CONCLUSIONS: First complications after surgery, even seemingly mild ones, may radically alter the patient's risk of death. First complications often begin the cascade of complications that end in death. Caregivers should consider the first complication as a timely signal of a changed clinical situation demanding a reevaluation of the patient's care. Researchers may use these estimates to determine the relative severity of a broad range of first or early complications.

Aged↗
PubMed2005

Preoperative antibiotics and mortality in the elderly.

OBJECTIVE AND BACKGROUND: It is generally thought that the use of preoperative antibiotics reduces the risk of postoperative infection, yet few studies have described the association between preoperative antibiotics and the risk of dying. The objective of this study was to determine whether preoperative antibiotics are associated with a reduced risk of death. METHODS: We performed a multivariate matched, population-based, case-control study of death following surgery on 1362 Pennsylvania Medicare patients between 65 and 85 years of age undergoing general and orthopedic surgery. Cases (681 deaths within 60 days from hospital admission) were randomly selected throughout Pennsylvania using claims from 1995 and 1996. Models were developed to scan Medicare claims, looking for controls who did not die and who were the closest matches to the previously selected cases based on preoperative characteristics. Cases and their controls were identified, and charts were abstracted to define antibiotic use and obtain baseline severity adjustment data. RESULTS: For general surgery, the odds of dying within 60 days were less than half in those treated with preoperative antibiotics within 2 hours of incision as compared with those without such treatment: (odds ratio = 0.44; 95% confidence interval, 0.32-0.60), P < 0.0001). For orthopedic surgery, no significant mortality reduction was observed (OR = 0.85; 95% confidence interval, 0.54-1.32; P < 0.464). INTERPRETATION: Preoperative antibiotics are associated with a substantially lower 60-day mortality rate in elderly patients undergoing general surgery. In patients who appear to be comparable, the risk of death was half as large among those who received preoperative antibiotics.

Age Factors↗
PubMed2005

Attributable effects in case2-studies.

In an effort to determine whether a particular treatment causes a particular outcome event, data are obtained from a database system that records events when they occur, and for such events, the system records exposure to the treatment. That is, the system records information about cases. The system provides no information about events that might have occurred but did not, that is, about units which are not cases. Roughly speaking, we know the number of successes for two proportions, treated and control, but not the numbers of trials or units for these proportions; indeed, the concept of a "trial" may be somewhat vague. With no further information, the situation is quite hopeless. However, an interesting strategy that is sometimes used entails identifying two types of cases whose origin is entirely different so that it is known the cases of the second type were definitely not affected by the treatment under study. This strategy--the case-case or case2-study--seems to have been reinvented independently many times, and has recently been offered as a general strategy for infectious disease epidemiology by McCarthy and Giesecke (1999, International Journal of Epidemiology 28, 764-768). Can this strategy permit estimation of the number of cases caused by the treatment? Using attributable effects in a new way, a method of exact inference is proposed, along with a large sample approximation. Two examples are discussed: one concerning the effects of daytime running lights (DRLs) on the risk of multivehicle accidents; the other concerning the origin of a Salmonella infection. A counterexample with superficially similar appearance is also discussed concerning suicide rates following the publication of Final Exit; here, the treatment may alter the outcome, or it may alter the type, and the attributable effect cannot be estimated.

Accidents, Traffic↗
PubMed2004

The case-only odds ratio as a causal parameter.

In the simplest case-only design, cases of a disease are cross-classified into a 2 x 2 table describing a genotype attribute and exposure to some environmental agent. In some instances, the genetic attribute has described inherited genes; in other instances, it has described mutations, for instance, damage to proto-oncogenes or tumor suppressor genes leading to cancer. Here, the population case-only odds ratio is written as a causal parameter in terms of potential outcomes with and without exposure to the agent. It is shown that the case-only odds ratio makes sense as a causal parameter with inherited genes, but its magnitude does not have a causal interpretation with mutations, although deviations from 1 do provide information. The difference is that the environmental agent certainly did not cause an individual to inherit particular genes, but it may have caused the mutation.

Biometry↗
PubMed2004

Equivalent lengths of stay of pediatric patients hospitalized in rural and nonrural hospitals.

BACKGROUND: Many children receive their care at local hospitals outside of a large urban area. There may be differences in the length of stay (LOS) between children hospitalized in rural versus urban hospitals. This study compared the differences in LOS, conditional LOS (CLOS), odds of prolonged stay, and 21-day readmission rates for children with 19 medical conditions and 9 surgical procedures admitted to rural, community, and large urban hospitals. METHODS: Discharge records for the hospitalizations of children 1 to 17 years of age were obtained from the New York Department of Public Health Statewide Planning and Research Cooperative System and the Pennsylvania Health Care Cost Containment Council for April 1996 to July 1998. The 19 medical and 9 surgical conditions were identified with the principal condition and procedure codes. Hospitals were classified into 1 of 5 geographic categories on the basis of United States rural-urban continuum codes, ie, large urban, suburban, moderate urban, small urban, or rural. LOS was defined as the period of time between hospital admission and discharge. Readmission rates were calculated for 21 days after discharge from the hospital. A prolonged stay for each condition was defined as any admission lasting beyond the prolongation point, or the day at which the rate of discharge began to decline, as determined with the Hollander-Proschan statistic. This aspect of LOS describes the ability of providers to treat uncomplicated cases of that specific principle diagnosis. CLOS, as a marker for the management of complicated cases, was defined as the LOS beyond the prolongation point. Cox and logistic regression models were developed to describe the geographic effects on the 4 outcome variables, after severity adjustment with 32 demographic and 11 comorbidity variables and adjustment for hospital clustering. RESULTS: Medical (N = 114,787) and surgical (N = 29,156) admissions to rural hospitals (N = 12,367) had similar outcomes, compared with all geographic categories except the large urban category. Medical patients admitted to rural hospitals had a shorter LOS (12% increase in discharge rate), a shorter CLOS (12% increase in discharge rate), and lower odds of prolonged stay (odds ratio: 0.80), compared with those in large urban hospitals. Surgical patients admitted to rural hospitals had a shorter LOS (12% increase in discharge rate) and lower odds of prolonged stay (odds ratio: 0.81), compared with those in large urban hospitals. For individual conditions, rural hospitals in general had similar or improved LOS, compared with all other hospitals in the 2 states. The addition of hospital-level variables failed to change the results of the primary models. CONCLUSIONS: In their treatment of pediatric hospitalized patients, rural hospitals were not significantly different from hospitals in all geographic regions other than large urban areas. Rural hospitals appear to deliver similar care, compared with nonrural hospitals, for many of the common pediatric conditions included in this study. Additional research is needed to apply these results to other regions or states with different geographic distributions of hospitals and children, in order to determine the overall impact on the regionalization of pediatric care.

Adolescent↗
PubMed2003

Does a dose-response relationship reduce sensitivity to hidden bias?

It is often said that an important consideration in judging whether an association between treatment and response is causal is the presence or absence of a dose-response relationship, that is, larger ostensible treatment effects when doses of treatment are larger. This criterion is widely discussed in textbooks and is often mentioned in empirical papers. At the same time, it is well known through both important examples and elementary theory that a treatment may cause dramatic effects with no dose-response relationship, and hidden biases may produce a dose-response relationship when the treatment is without effect. What does a dose-response relationship say about causality? It is observed here that a dose-response relationship may or may not reduce sensitivity to hidden bias, and whether it has or has not can be determined by a suitable analysis using the data at hand. Moreover, a study without a dose-response relationship may or may not be less sensitive to hidden bias than another study with such a relationship, and this, too, can be determined from the data at hand. An example concerning cytogenetic damage among professional painters is used to illustrate.

Adolescent↗
PubMed2003

Length of stay, conditional length of stay, and prolonged stay in pediatric asthma.

OBJECTIVE: To understand differences in length of stay for asthma patients between New York State and Pennsylvania across children's and general hospitals in order to better guide policy. DATA SOURCES/STUDY SETTING: All pediatric admissions for asthma in the states of Pennsylvania and New York using claims data obtained from each state for the years 1996-1998, n = 38,310. STUDY DESIGN: A retrospective cohort design to model length of stay (LOS), the probability of prolonged stay, conditional length of stay (CLOS or the LOS after stay is prolonged), and the probability of readmission, controlling for patient factors, state, location and hospital type. ANALYTIC METHODS: Logit models were used to estimate the probability of prolonged stay and readmission. The LOS and the CLOS were estimated with Cox regression. Model variables included comorbidities, income, race, distance from hospital, and insurance type. Prolonged stay was based on a Hollander-Proschan "New-Worse-Than-Used" test, corresponding to a three-day stay. PRINCIPAL FINDINGS: The LOS was longer in New York than Pennsylvania, and the probabilities of prolonged stay and readmission were much higher in New York than Pennsylvania. However, once an admission was prolonged, there were no differences in CLOS between states (when readmissions were not added to the LOS calculation). In both states, children's hospitals and general hospitals had similar adjusted LOS. CONCLUSIONS: Management of asthma appears more efficient in Pennsylvania than New York: Less severe patients are discharged faster in Pennsylvania than New York; once discharged, patients are less likely to be readmitted in Pennsylvania than New York. However, once a stay is prolonged, there is little difference between New York and Pennsylvania, suggesting medical care for severely ill patients is similar across states. Differences between children's and general hospitals were small as compared to differences between states. We conclude that policy initiatives in New York, and other states, should focus their efforts on improving the care provided to less severe patients in order to help reduce overall length of stay.

Adolescent↗