Why doctors use or do not use ethics consultation.

BACKGROUND: Ethics consultation is used regularly by some doctors, whereas others are reluctant to use these services. AIM: To determine factors that may influence doctors to request or not request ethics consultation. METHODS: A survey questionnaire was distributed to doctors on staff at the University Community Hospital in Tampa, Florida, USA. The responses to the questions on the survey were arranged in a Likert Scale, from strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree to strongly agree. Data were analysed with the Wilcoxon test for group comparisons, the chi2 test to compare proportions and a logistic regression analysis. RESULTS: Of the 186 surveys distributed, 121 were returned, giving a 65% response rate. Demographic data were similar between the groups saying yes (I do/would use ethics consultation when indicated) and no (I do not/would not use ethics consultation when indicated). No statistically significant differences were observed between the user and non-user groups in terms of opinions about ethics consultants having extensive training in ethics or participating in ethics educational opportunities. On the issue "Ethics committee members or consultants cannot grasp the full picture from the outside", the non-users were neutral, whereas the users somewhat disagreed (p=0.012). Even more significant was the difference between surgeons and non-surgeons, where, by logistic regression analysis, surgeons who believed that ethics consultants could not grasp the full picture from the outside were highly likely to not use (p=0.0004). Non-users of ethics consultations thought that it was their responsibility to resolve issues with the patient or family (72.2% agree, p<0.05). Users of ethics consultation believed in shared decision making or the importance of alternate points of view (90.8% agree, p<0.05). IMPLICATIONS: Ethics consultations are used by doctors who believe in shared decision making. Doctors who did not use ethics consultation tended to think that it was their responsibility to resolve issues with patients and families and that they were already proficient in ethics.

Repeated antenatal corticosteroid treatments. Do they reduce neonatal morbidity?

OBJECTIVE: To compare the effectiveness of single, as compared to multiple, courses of antenatal steroid treatments in reducing neonatal morbidity. STUDY DESIGN: A retrospective chart review of 204 patients who delivered preterm and received antenatal corticosteroids between June 1996 and December 1998 was performed and data extracted. All patients who delivered prior to 35 weeks of gestation and received corticosteroids were included. The number of antenatal treatments was chosen by the obstetrician caring for the patient as well as by the opportunity to administer steroids. RESULTS: There were 61 patients in the multiple-course group. Gestational age was greater in the multiple-course group (31.6 vs. 30.6, P = 0.3), but Apgar scores, neonatal hospital stay, specific neonatal morbidity and combined morbidity were not different between the two groups. Combined neonatal morbidity was higher after three courses of antenatal steroids (29% vs. 50%). Logistic regression analyses adjusting for gestational age suggest a possible adverse effect of multiple courses, development of respiratory distress syndrome (odds ratio 1.3; confidence interval 1.02; 1.8; P = .02) and combined neonatal morbidity (odds ratio 1.3; confidence interval 1.04; 1.7; P = .02). CONCLUSION: Weekly antenatal steroids do not improve neonatal morbidity.

Evaluating the forecast accuracy and bias of alternative population projections for states.

"A common perception among producers (and users) of population projections is that complex and/or sophisticated techniques produce more accurate forecasts than simple and/or naive techniques. In this paper we test the validity of that perception by evaluating the forecast accuracy and bias of eight commonly used projection techniques drawn from...four categories [trend extrapolation, ratio extrapolation, cohort-component, and structural]. Using data for [U.S.] state population projections from a number of different time periods, we find no evidence that complex and/or sophisticated techniques produce more accurate or less biased forecasts than simple, naive techniques."

An empirical analysis of the effect of length of forecast horizon on population forecast errors.

Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States. We find that in most instances there is a linear or nearly linear relationship between forecast accuracy and the length of the forecast horizon, but no consistent relationship between bias and the length of the horizon. We believe that these results provide useful information regarding the nature of population forecast errors.

The relationship between the length of the base period and population forecast errors.

"The base period of a population forecast is the time period from which historical data are collected for the purpose of forecasting future population values. The length of the base period is one of the fundamental decisions made in preparing population forecasts, yet very few studies have investigated the effects of this decision on population forecast errors. In this article the relationship between the length of the base period and population forecast errors is analyzed, using three simple forecasting techniques and data from 1900 to 1980 for states in the United States. It is found that increasing the length of the base period up to 10 years improves forecast accuracy, but that further increases generally have little additional effect. The only exception to this finding is long-range forecasts of rapidly growing states, in which a longer base period substantially improves forecast accuracy for two of the forecasting techniques."

Stability over time in the distribution of population forecast errors.

A number of studies in recent years have investigated empirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of time periods during the 20th century, comparing these projections with census enumerations to determine forecast errors, and analyzing the stability of the resulting error distributions over time. These data are then used to construct and test empirical confidence limits. We find that in this sample the distribution of absolute percentage errors remained relatively stable over time and data on past forecast errors provided very useful predictions of future forecast errors.