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Results for “longitudinal data”

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PubMed1999

Smoking and the occurrence of Alzheimer's disease: cross-sectional and longitudinal data in a population-based study.

The authors tested the hypothesis that smoking exerts a protective effect on Alzheimer's disease or dementia in a population-based cohort of 668 people aged 75-101 years (Sweden). Smoking was negatively associated with prevalent Alzheimer's disease (adjusted odds ratio = 0.6, 95% confidence interval 0.4-1.1) and dementia (adjusted odds ratio = 0.6, 95% confidence interval 0.4-1.0). Over 3-year follow-up (1989-1992), the hazard ratios of incident Alzheimer's disease and dementia due to smoking were 1.1 (95% confidence interval 0.5-2.4) and 1.4 (95% confidence interval 0.8-2.7). Mortality over 5-year follow-up was greater among smokers in demented (hazard ratio = 3.4) than nondemented (hazard ratio = 0.8) subjects. Smoking does not seem protective against Alzheimer's disease or dementia, and the cross-sectional association might be due to differential mortality.

Aged↗
PubMed1990

The use of residuals for longitudinal data analysis: the example of child growth.

Health impact evaluations often measure changes in health status over part of a total life experience. The effects on health up to and including the start of the evaluation, which are embodied in the measure of initial health status, need to be removed while examining the effects that other variables exert during the evaluation period on final health status. Statistical models, which include initial health status as a covariate while examining the effects of other variables during the evaluation, confound the effects of a determinant during evaluation with preevaluation effects, because they do not differentiate between effects produced at different times by the same determinant. A residual model removes the preevaluation effects by regressing final health status on initial health status. The residuals from this regression are then regressed on the other predictor variables. In this paper, standard covariate adjustment, which includes all effects simultaneously, is compared with a two-part residual model using child growth as an example. The simultaneous model over- and underpredicts growth relative to the residual model depending on the age and initial body size of a child. In general, whenever initial (preintervention) and final (postintervention) measures of health outcome exist, the residual model should be considered on the basis of biologic and epidemiologic consideration, not solely on statistical optimality.

Anthropometry↗
PubMed1979

Timing and duration effects in residence histories and other longitudinal data: Part 1. Stochastic and statistical models; Part 2. Studies of duration effects in Norway, 1965-1971.

The author describes an attempt to fit stochastic models to individual life histories. "In particular, [he] is concerned with the application of semi-Markov processes to the study of the timing of moves and duration of residence effects in the migration histories of Norwegian men over the period 1965-1971." In the second part of the article, the results of 11 studies using data on Norwegian men are presented

Demography↗
PubMed1990

The relationship between wages and income and the timing and spacing of births: evidence from Swedish longitudinal data.

"This paper estimates semiparametric reduced-form neoclassical models of life-cycle fertility in Sweden.... The estimated model integrates aspects of life cycle fertility that have previously been studied in isolation of each other: completed fertility, childlessness, interbirth intervals, and the time series of annual birth rates. The main objective of this paper is to determine which aspects of life cycle fertility, if any, are sensitive to male income and female wages."

Birth Intervals↗
PubMed2006

Monitoring outcomes of arthritis and longitudinal data collection using patient questionnaires in routine care.

Though quantitative data might lead to improved information for clinical decisions, at the present time decisions in routine rheumatology practice generally are based largely on qualitative impressions, rather than on data. Patient questionnaires are readily accessible tools that the rheumatologist can use to go beyond impressions and to institute evidence-based guidelines appropriate to his or her own patient population and practice style. The Health Assessment Questionnaire (HAQ) and its derivatives have been shown to be the best predictors of functional and work disability, costs, joint replacement surgery, and mortality. Such questionnaires are at least as good as joint counts, radiographs, and laboratory tests in predicting these outcomes. Every encounter of a patient with a rheumatologist provides an opportunity to collect data. Based on experience with the Brooklyn Outcomes of Arthritis Registry Database, the author advocates distributing a waiting-room questionnaire to every patient who comes for an office visit. Potential benefits of recording questionnaire-based information include identifying trends or important changes in a patient's pain or physical function, providing a baseline for success with various treatment strategies for conditions of the rheumatologist's own practice, allowing patients an opportunity to express concerns, encouraging patients to disclose information they may feel is too minor to mention, and providing control data for research studies. A short questionnaire designed specifically for clinical, rather than research, use does not create a burden for office staff. Consistent use of patient questionnaires and systematic storage of the information gained can help document, track, and improve patient care in routine rheumatology practice.

Arthritis, Rheumatoid↗
PubMed1980

Growth in British Asians: longitudinal data in the first year.

Weight, crown-heel length and head circumference were measured at birth and at three, six, nine and 12 months of age in 80 British Asians of predominantly Indian origin. At birth, all measurements were reduced compared with Europeans, but catch-up growth occurred during the first three months, suggesting that there had been intra-uterine growth restriction. Subsequently, linear growth remained comparable with British standards but weight-gain velocity declined, so that by one year of age mean weight was about 1 kg less than the standards. Throughout the measurement period it was more usual for infants to cross centile lines, either up or down, than to grow along them.

Anthropometry↗