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Biomedical subjects

P M Odell

Publications and source records attributed to P M Odell.

7 recordsLinked to original sources
PubMed1994

Long-term intraindividual cholesterol variability: natural course and adverse impact on morbidity and mortality--the Framingham Study.

We examined intraindividual variability in serum TC in 2912 men and women having TC measured at each of biennial examinations 2 through 7 of the FHS. RMSE described variability around the linear slope of an individual's TC during the baseline decade. Average biennial difference +/- SD was +3.7 +/- 6.7 mg/dl in men, +6.6 +/- 8.8 mg/dl in women. RMSE was < 7 mg/dl in half the group, but in the teens and twenties in the highest quartile of variability. Age-adjusted analyses showed positive associations with all-cause mortality over a 24-year period in men and a positive relation to cardiovascular and coronary incidence and mortality in both sexes. Risk ratios for highest versus lowest quartile of TC variability ranged up to 1.75. High TC variability portends excess mortality risk, and women in particular must include TC variability among their risk factors for coronary death.

Adult↗
PubMed1994

New models for predicting cardiovascular events.

Data from the Framingham Heart Study are used to derive equations for long-term predicted probabilities for death and a variety of cardiovascular endpoints. An accelerated failure method is employed, first the standard Weibull model and then a useful extension. The extension relaxes the assumption of proportional hazards implied by the standard Weibull model. Models differ markedly in form for the various endpoints, but in every case the varying scale model provided a significantly better fit. The resulting differences in predicted probability may be important in planning community health projects or clinical trials and in carrying out cost-benefit analyses.

Cardiovascular Diseases↗
PubMed1992

Functional limitations and disability among elders in the Framingham Study.

BACKGROUND: The measurement of physical disability as an indication of the impact of disease is commonly seen in research. However, these measures often do not clearly differentiate between functional limitations and daily performance of an activity. METHODS: We measured the differences between self-reported disability and observed functional limitations in six activities of daily living tasks among community-dwelling elders. The value of functional limitations vs disability measures in determining risk factors for disablement was ascertained. RESULTS: Systematic differences were found among the 1453 participants. At least 89% of the time when a difference was identified, the subjects ranked disability greater than the functional limitations observed. For those who were cognitively impaired, discrepancies occurred up to 11% of the time. In determining risk factors for disablement, we found that neurological impairments were associated with both functional limitations and disability, while sociocultural factors were associated with disability only. CONCLUSIONS: Our findings suggest that physical functional limitations and disability in the elderly are two distinct concepts and that the measure of choice should be determined by research objectives and the type of population being studied.

Activities of Daily Living↗
PubMed1992

Maximum likelihood estimation for interval-censored data using a Weibull-based accelerated failure time model.

The accelerated failure time regression model is most commonly used with right-censored survival data. This report studies the use of a Weibull-based accelerated failure time regression model when left- and interval-censored data are also observed. Two alternative methods of analysis are considered. First, the maximum likelihood estimates (MLEs) for the observed censoring pattern are computed. These are compared with estimates where midpoints are substituted for left- and interval-censored data (midpoint estimator, or MDE). Simulation studies indicate that for relatively large samples there are many instances when the MLE is superior to the MDE. For samples where the hazard rate is flat or nearly so, or where the percentage of interval-censored data is small, the MDE is adequate. An example using Framingham Heart Study data is discussed.

Age Factors↗
PubMed1991

Variability of body weight and health outcomes in the Framingham population.

BACKGROUND: Fluctuation in body weight is a common phenomenon, due in part to the high prevalence of dieting. In this study we examined the associations between variability in body weight and health end points in subjects participating in the Framingham Heart Study, which involves follow-up examinations every two years after entry. METHODS: The degree of variability of body weight was expressed as the coefficient of variation of each subject's measured body-mass-index values at the first eight biennial examinations during the study and on their recalled weight at 25 years of age. Using the 32-year follow-up data, we analyzed total mortality, mortality from coronary heart disease, and morbidity due to coronary heart disease and cancer in relation to intraindividual variation in body weight, including only end points that occurred after the 10th biennial examination. We used age-adjusted proportional-hazards regression for the data analysis. RESULTS: Subjects with highly variable body weights had increased total mortality (P = 0.005 for men, P = 0.01 for women), mortality from coronary heart disease (P = 0.009 for men, P = 0.009 for women), and morbidity due to coronary heart disease (P = 0.0009 for men, P = 0.006 for women). Using a multivariate analysis that also controlled for obesity, trends in weight over time, and five indicators of cardiovascular risk, we found that the positive associations between fluctuations in body weight and end points related to mortality and coronary heart disease could not be attributed to these potential confounding factors. The relative risks of these end points in subjects whose weight varied substantially, as compared with those whose weight was relatively stable, ranged from 1.27 to 1.93. CONCLUSIONS: Fluctuations in body weight may have negative health consequences, independent of obesity and the trend of body weight over time.

Adult↗
PubMed1991

Cardiovascular disease risk profiles.

This article presents prediction equations for several cardiovascular disease endpoints, which are based on measurements of several known risk factors. Subjects (n = 5573) were original and offspring subjects in the Framingham Heart Study, aged 30 to 74 years, and initially free of cardiovascular disease. Equations to predict risk for the following were developed: myocardial infarction, coronary heart disease (CHD), death from CHD, stroke, cardiovascular disease, and death from cardiovascular disease. The equations demonstrated the potential importance of controlling multiple risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, glucose intolerance, and left ventricular hypertrophy) as opposed to focusing on one single risk factor. The parametric model used was seen to have several advantages over existing standard regression models. Unlike logistic regression, it can provide predictions for different lengths of time, and probabilities can be expressed in a more straightforward way than the Cox proportional hazards model.

Adult↗