Overnight sleep duration and obesity in 2-5 year-old American Indian children.

BACKGROUND: Sleep has emerged as a potentially modifiable risk factor for obesity in children. OBJECTIVES: The purpose of this investigation was to evaluate the association between overnight sleep duration and obesity among American Indian (AI) children ages 2-5 years. METHODS: Data were examined from the baseline assessment of children enrolling in the Healthy Children, Strong Families study, which is a randomized lifestyle intervention trial in five diverse rural and urban AI communities nationally among children ages 2-5 years. Multivariable models were built to assess the relationship between sleep duration and BMI z-score while controlling for potential sociodemographic and behavioural covariates. RESULTS: Three hundred and ninety-eight children had sufficient data to be included in analysis. In multivariable models controlling for potential covariates, overnight sleep duration was significantly and inversely associated with BMI z-score (B = -0.158, t = -1.774, P = 0.006). Similarly, when controlling for covariates, children who slept 12 or more hours had significantly lower BMI z-scores compared with those who slept 8 to 10 h (P = 0.018) or less than 8 h (P = 0.035); the difference between 12+ hours and 10 to 12-h groups did not reach statistical significance (P = 0.073) but supported a linear relationship between overnight sleep duration and BMI. Weekday-to-weekend variability in overnight sleep duration was not associated with BMI z-score (B = 0.010, t = 0.206, P = 0.837). CONCLUSIONS: Overnight sleep duration is independently and inversely related to BMI z-score among AI children ages 2-5 years, even when controlling for important sociodemographic and obesogenic lifestyle factors. This represents the first report, to our knowledge, of sleep duration as a risk factor for obesity among AI children.

Evaluating the impact of population changes in diet, physical activity, and weight status on population risk for colon cancer (United States).

OBJECTIVE: To estimate the effects of observed population-level changes in risk factors on population risk and incidence of disease. METHODS: Trends in a set of risk factors for colon cancer (vegetable intake, red meat intake, alcohol consumption, physical activity levels, and weight status) were modeled for the US adult population over the years 1975-1995 and combined with relative risk estimates from epidemiologic studies and a probability distribution for the induction period to estimate the percentage change in incidence rates from 1985 to 1995 due to the five risk factors. A sensitivity analysis was performed to account for imprecision related to estimates of trends in behavior and epidemiologic risk. RESULTS: Increased vegetable intake and decreased intakes of red meat and alcohol reduced risk, while reduced physical activity and increased body mass index increased risk for colon cancer. When all five factors were considered together, change in the average population relative risk was small and the risk factors accounted for little of the recently observed decline in incidence. CONCLUSIONS: Although these factors have the potential to greatly affect risk of colon cancer and incidence rates, little of that potential was realized since adverse trends neutralized what progress had been made in the areas of vegetables, red meat, and alcohol consumption.

Can media advocacy influence newspaper coverage of tobacco: measuring the effectiveness of the American stop smoking intervention study's (ASSIST) media advocacy strategies.

OBJECTIVE: To compare the rate and slant of local tobacco control print media coverage in ASSIST (American stop smoking intervention study) states as compared with non-ASSIST states. METHODS: Local tobacco control policy articles, editorials, and letters to the editors published from 1994 to 1998 clipped from all daily local newspapers in the USA were analysed (n = 95 911). The main hypothesis tested for the existence of an interaction between ASSIST intervention and time. This interaction would represent a change in the difference between ASSIST and non-ASSIST states over the course of the intervention. RESULTS: No evidence of an ASSIST-year interaction was found. However, a main effect for ASSIST was significant for the percentage of articles with the model predicting higher rates of articles for ASSIST states. Similarly the rate of letters to the editor expressing protobacco control views was higher in ASSIST states than non-ASSIST states. No main effects or interactions were found for analyses of percentage of protobacco control editorials. Models controlled for a measure of preintervention tobacco control conditions at baseline. CONCLUSIONS: The presence of an ASSIST main effect should be interpreted with caution because of the quasi-experimental design and the lack of information on article rates before the ASSIST intervention. Nonetheless, these preliminary findings suggest some possible effects of the media advocacy activities of ASSIST when controlling for differences in states' initial tobacco control conditions.

Bayesian monitoring of a phase 2 chemoprevention trial in high-risk cohorts for prostate cancer.

The objective of phase 2 cancer chemoprevention trials is to evaluate whether a chemopreventive agent will cause significant modulation of intermediate endpoint biomarkers (IEB) in patients at high risk for the disease. A phase 2 chemoprevention trial of 4-hydroxyphenyl retinamide (4-HPR) versus placebo was conducted in men with a histologic diagnosis of early prostate cancer and scheduled to have radical prostatectomy. A Bayesian monitoring method was used to sequentially monitor this trial for evidence of biological activity or ineffectiveness based on a single IEB variable. Different prior distributions were used and posterior distributions were obtained to calculate the probability that treatment differences are greater than or less than a predetermined clinically significant effect. The interim analysis of transforming growth factor-alpha expression indicated a high probability of insufficient biological activity of 4-HPR on this IEB. This study demonstrates the potential utility of Bayesian methods in the decision-making process in the conduct of phase 2 chemoprevention trials.

Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival.

A common population-based cancer progress measure for net survival (survival in the absence of other causes) of cancer patients is relative survival. Relative survival is defined as the ratio of a population of observed survivors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer-free individuals in the general public, thus giving a measure of excess mortality due to cancer. Relative survival was originally designed to address the question of whether or not there is evidence that patients have been cured. It has proven to be a useful survival measure in several areas, including the evaluation of cancer control efforts and the application of cure models. However, it is not representative of the actual survival patterns observed in a cohort of cancer patients. This paper suggests a measure for cumulative crude (in the presence of other causes) cause-specific probability of death for a population diagnosed with cancer. The measure does not use cause of death information which can be unreliable for population cancer registries. Point estimates and variances are derived for crude cause-specific probability of death using relative survival instead of cause of death information. Examples are given for men diagnosed with localized prostate cancer over the age of 70 and women diagnosed with regional breast cancer using Surveillance, Epidemiology and End Results (SEER) Program data. The examples emphasize the differences in crude and net mortality measures and suggest areas where a crude measure is more informative. Estimates of this type are especially important for older patients as new screening modalities detect cancers earlier and choice of treatment or even 'watchful waiting' become viable options. Published in 2000 by John Wiley & Sons, Ltd.

Bayesian monitoring of phase II trials in cancer chemoprevention.

Early randomized Phase II cancer chemoprevention trials which assess short-term biological activity are critical to the decision process to advance to late Phase II/Phase III trials. We have adapted published Bayesian interim analysis methods (Spiegelhalter et al., J. R. Statist. Soc A, 1994; 157: 357-416) which give greater flexibility and simplicity of inference to the monitoring of randomized controlled Phase II trials using intermediate endpoints. The Bayesian stopping rule is designed to stop the trial more quickly when the evidence suggests ineffectiveness rather than when it suggests biological activity, thus allowing resources to be concentrated on those agents that show the most promise in this early stage of testing. We investigate frequentist performance characteristics of the proposed method through simulation of randomized placebo controlled trials with a growth factor intermediate end-point using mean and variance values derived from the literature. Simulation results show expected error rates and trial size similar to other commonly used group sequential methods for this setting. These results suggest that the Bayesian approach to interim analysis is well suited for monitoring small randomized controlled Phase II chemoprevention trials for early detection of either inactive or promising agents.

Cancer surveillance series: interpreting trends in prostate cancer--part III: Quantifying the link between population prostate-specific antigen testing and recent declines in prostate cancer mortality.

BACKGROUND: The objective of this study was to investigate the circumstances under which dissemination of prostate-specific antigen (PSA) testing, beginning in 1988, could plausibly explain the declines in prostate cancer mortality observed from 1992 through 1994. METHODS: We developed a computer simulation model by use of information on population-based PSA testing patterns, cancer detection rates, average lead time (the time by which diagnosis is advanced by screening), and projected decreased risk of death associated with early diagnosis of prostate cancer through PSA testing. The model provides estimates of the number of deaths prevented by PSA testing for the 7-year period from 1988 through 1994 and projects what prostate cancer mortality for these years would have been in the absence of PSA testing. RESULTS: Results were generated by assuming a level of screening efficacy similar to that hypothesized for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Under this assumption, the projected mortality in the absence of PSA testing continued the increasing trend observed before 1991 only when it was assumed that the mean lead time was 3 years or less. Projected mortality trends in the absence of PSA screening were not consistent with pre-1991 increasing trends for lead times of 5 years and 7 years. CONCLUSIONS: When screening is assumed to be at least as efficacious as hypothesized in the PLCO trial, it is unlikely that the entire decline in prostate cancer mortality can be explained by PSA testing based on current beliefs concerning lead time. Only very short lead times would produce a decline in mortality of the magnitude that has been observed.

Assessing uncertainty in microsimulation modelling with application to cancer screening interventions.

Microsimulation is fast becoming the approach of choice for modelling and analysing complex processes in the absence of mathematical tractability. While this approach has been developed and promoted in engineering contexts for some time, it has more recently found a place in the mainstream of the study of chronic disease interventions such as cancer screening. The construction of a simulation model requires the specification of a model structure and sets of parameter values, both of which may have a considerable amount of uncertainty associated with them. This uncertainty is rarely quantified when reporting micro-simulation results. We suggest a Bayesian approach and assume a parametric probability distribution to mathematically express the uncertainty related to model parameters. First, we design a simulation experiment to achieve good coverage of the parameter space. Second, we model a response surface for the outcome of interest as a function of the model parameters using the simulation results. Third, we summarize the variability in the outcome of interest, including variation due to parameter uncertainty, using the response surface in combination with parameter probability distributions. We illustrate the proposed method with an application of a microsimulator designed to investigate the effect of prostate specific antigen (PSA) screening on prostate cancer mortality rates.

Case-control studies of cancer screening: theory and practice.

This review summarizes methodologic theories for the design of cancer screening case-control studies and examines the methods applied in studies published in English from 1980 through 1996. In addition to summarizing state-of-the-art methodologic approaches, we identify areas where obvious gaps exist between theory and practice, and we recommend potential areas where theory and methodology may need further development. In particular, we focus on three major areas: 1) the selection of case and control subjects, 2) the definition of exposure (i.e., exposure to the screening test), and 3) bias. Each area is considered carefully by summarizing current theory, reviewing cancer screening applications, and linking recommended methodologic approaches to those used in practice to identify areas where inconsistencies exist. In general, we found methodologic theory and practice in this field of research to be consistent. However, discrepancies were identified in the area of exposure definition, including the use of screening frequency and the use of a detectable, curable preclinical phase for case subjects as the exposure measures. Even when recommended methods were followed, a number of difficulties arose in practice. Specific concerns included the ability to carry out the following: identifying all case subjects within a source population, defining eligibility criteria to ensure that case and control subjects had equal access to screening during the exposure period, distinguishing between symptomatic and diagnostic tests, and controlling for self-selection bias. Careful scrutiny is warranted in all aspects of the design of cancer screening case-control studies, and caution is advised in the interpretation of study results.

Teaching mending skills to mentally retarded adolescents.

This experiment presents a model for analyzing community living skills and teaching them to mentally retarded adolescents. A task analysis of three mending skills was developed and validated, aided by consultation with persons having expertise in home economics and mental retardation. The task analysis was modified to compensate for the constraints imposed by the trainees' disabilities. Five moderately retarded youths received training on sewing hems, buttons, and seams. Sewing skills were acquired rapidly and maintained. The behavior generalized from trained to untrained tasks on their common components for all subjects. A multiple baseline across participants combined with a multiple baseline across responses demonstrated the combined effectiveness of an objectively validated, detailed task analysis; graduated sequence of prompts; and response consequences in training and maintaining community living skills with mentally retarded adolescents.