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PubMed2004

A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data.

We propose a transmission model to estimate the main characteristics of influenza transmission in households. The model details the risks of infection in the household and in the community at the individual scale. Heterogeneity among subjects is investigated considering both individual susceptibility and infectiousness. The model was applied to a data set consisting of the follow-up of influenza symptoms in 334 households during 15 days after an index case visited a general practitioner with virologically confirmed influenza. Estimating the parameters of the transmission model was challenging because a large part of the infectious process was not observed: only the dates when new cases were detected were observed. For each case, the data were augmented with the unobserved dates of the start and the end of the infectious period. The transmission model was included in a 3-levels hierarchical structure: (i) the observation level ensured that the augmented data were consistent with the observed data, (ii) the transmission level described the underlying epidemic process, (iii) the prior level specified the distribution of the parameters. From a Bayesian perspective, the joint posterior distribution of model parameters and augmented data was explored by Markov chain Monte Carlo (MCMC) sampling. The mean duration of influenza infectious period was estimated at 3.8 days (95 per cent credible interval, 95 per cent CI [3.1,4.6]) with a standard deviation of 2.0 days (95 per cent CI [1.1,2.8]). The instantaneous risk of influenza transmission between an infective and a susceptible within a household was found to decrease with the size of the household, and established at 0.32 person day(-1) (95 per cent CI [0.26,0.39]); the instantaneous risk of infection from the community was 0.0056 day(-1) (95 per cent CI [0.0029,0.0087]). Focusing on the differences in transmission between children (less than 15 years old) and adults, we estimated that the former were more likely to transmit than adults (posterior probability larger than 99 per cent), but that the mean duration of the infectious period was similar in children (3.6 days, 95 per cent CI [2.3,5.2]) and adults (3.9 days, 95 per cent CI [3.2,4.9]). The posterior probability that children had a larger community risk was 76 per cent and the posterior probability that they were more susceptible than adults was 79 per cent.

Adolescent↗
PubMed2003

A common methylenetetrahydrofolate reductase (C677T) polymorphism is associated with low bone mineral density and increased fracture incidence after menopause: longitudinal data from the Danish osteoporosis prevention study.

A polymorphism in the gene encoding methylenetetrahydrofolate reductase (MTHFR) has recently been associated with bone mineral density (BMD) in postmenopausal Japanese women. It is not known whether this effect is also present in European populations and whether it is caused by lower peak bone mass or accelerated postmenopausal bone loss. MTHFR genotyping was done in 1748 healthy postmenopausal Danish women participating in a prospective study of risk factors for osteoporosis. At the time of enrollment, 3-24 months after last menstrual period, the less prevalent genotype (TT, 8.7% of the population) was associated with significantly lower BMD at the femoral neck (ANOVA, p < 0.05), total hip (p < 0.01), and spine (p < 0.05 adjusted for lifestyle covariates, p = 0.06 without adjustment). The mean difference was between 0.1 and 0.3 SD, depending on measurement site. MTHFR genotype added significantly to prediction of BMD by weight and age. Fracture incidence was increased more than 2-fold in subjects with the TT genotype (risk ratio [RR], 2.6; 95% CI 1.2-5.6). This remained significant when the Cox analysis was controlled for BMD (RR, 2.4; 95% CI 1.1-5.2). No differences in serum osteocalcin, bone-specific alkaline phosphatase, and 25-OH-vitamin D were found between genotypes. The response to hormone replacement therapy (HRT) did not differ, but the association of the TT genotype with reduced BMD was maintained at the total hip after 5 years of HRT. The MTHFR TT genotype is associated with low BMD and increased fracture incidence in early postmenopausal women.

Alleles↗
PubMed2006

Change in height, weight and body mass index: Longitudinal data from the HUNT Study in Norway.

OBJECTIVE: The aim of this study was to analyse changes in body weight and height, and the changes in the prevalence of overweight and obesity. DESIGN: Prospective population based study with 11-year follow-up. SUBJECTS: Norwegian men (n=21565) and women (n=24337) aged 20 years or more who participated in two health surveys, the first in 1984-1986 and the other in 1995-1997. MEASUREMENTS: Height and weight were measured by using standardised procedures at both surveys, and we computed body mass index (BMI) as weight in kilo divided by the squared value of height in meters. RESULTS: Participants who were younger than 50 years at the first survey showed a large increase in body weight, and men and women aged 20-29 years increased their weight with an average of 7.9 kg and 7.3 kg, respectively. Contradictory, participants who were 70 years or older had on average a weight loss. The prevalence of overweight (BMI=25.0-29.9 kg/m(2)) and obesity (BMI>/=30 kg/m(2)) increased between the surveys, especially in the youngest age groups. Overall, the proportion classified as obese increased from 6.7 to 15.5% among men and from 11.0 to 21.0% among women. Some of this increase was due to a reduction in height, which was most pronounced in the oldest age groups. CONCLUSION: During approximately 10 years, body weight increased in all age groups below 70 years, and the prevalence of overweight and obese persons was approximately 20% higher at the second survey compared with the first survey.

Adult↗
PubMed1997

Blood pressure and performance on the Mini-Mental State Examination in the very old. Cross-sectional and longitudinal data from the Kungsholmen Project.

The authors examined the association of blood pressure with cognitive function as assessed by the Mini-Mental State Examination (MMSE) in a community-based Swedish cohort of 1,736 people aged 75-101 years. Age, sex, education, antihypertensive medication use, heart disease, and stroke were considered as covariates. Multiple linear regression analysis indicated that both systolic and diastolic blood pressure, measured in 1987-1989, were positively and significantly related to baseline MMSE score; baseline systolic pressure was also positively and significantly related to follow-up MMSE score, measured after an average period of 40.5 months among subjects who were not taking antihypertensive medication at baseline. Furthermore, in the nontreated group, multiple logistic regression showed that individuals with a baseline systolic pressure less than 130 mmHg had an odds ratio of 1.88 (p = 0.05) for follow-up cognitive impairment (MMSE score < 24) compared with those whose systolic pressure was 130-159 mmHg. An increased but not statistically significant risk of cognitive impairment was associated with high blood pressure (systolic pressure > or = 180 mmHg or diastolic pressure > or = 95 mmHg) only in persons taking antihypertensive medication at baseline. Subjects with systolic pressure of 160-179 mmHg tended to be at lower risk of cognitive impairment. These results may support the view that a certain blood pressure level, particularly a systolic pressure of at least 130 mmHg, is important to the maintenance of cognitive functioning in the very old. They also suggest that severe hypertension that is not well controlled (systolic pressure > or = 180 mmHg or diastolic pressure > or = 95 mmHg) is still a threat to cognitive function in this age group. However, the use of blood pressure measurements made at a single visit and the relatively short follow-up period should be considered when interpreting these results.

Aged↗
PubMed1998

Can cardiovascular risk be predicted by newborn, childhood, and adolescent body size? An examination of longitudinal data in urban African Americans.

OBJECTIVE: Recent retrospective studies of older adults have demonstrated a correlation between lower birth weight and hypertension and insulin resistance. We tested this finding in our sample of urban African Americans with prospective data on growth and blood pressure and also tested other variables (in addition to birth weight) for their relationship to adult cardiovascular risk. STUDY DESIGN: A prospective study of birth weight, growth, and blood pressure (Philadelphia Perinatal Collaborative Project) followed a sample of 137 African Americans, with nine examinations from birth through 28.0 +/- 2.7 years. Metabolic measurements (oral glucose tolerance testing, euglycemic hyperinsulinemic clamp, and plasma lipid concentration) were performed on the subjects as adults. Bivariate correlations among parameters were computed using the Pearson r. The chi-squared statistic was used to determine associations of outcomes with birth weight. Stepwise multiple linear regressions were computed using newborn, early childhood, adolescent, and young adult parameters to predict adult outcomes. RESULTS: Birth weight and blood pressure at age 28 years are not correlated (Pearson r = 0.06). Birth weight is also unrelated to adult obesity. However, weight at 0.3 years and after and body mass index at 7 years and after are correlated with adult weight. Furthermore, weight at age 14 years is significantly negatively correlated with measures of insulin-stimulated glucose use, indicating that obese adolescents may be at greater risk than nonobese adolescents for development of non-insulin dependent diabetes in adulthood. CONCLUSIONS: We found no relationship between birth weight and adult outcomes pertaining to cardiovascular risk in this sample of adult African Americans. However, we did find evidence that somatic growth (body weight and body mass index) is significantly related to obesity and attenuated insulin-stimulated glucose utilization in adulthood. These findings indicate that the origins of adult cardiovascular disease are related to somatic growth, but not intrauterine growth, and are evident during childhood.

Adolescent↗
PubMed1995

The implications of age of onset for delinquency risk. II: Longitudinal data.

The role of age of onset in the level of involvement in delinquent behavior as marked by seriousness and chronicity of involvement continues to draw extensive attention from researchers. This issue bears on some of the key causal contentions about the dynamism of involvement and the validity of a developmental model of antisocial behavior risk. Five waves of the National Youth Survey were utilized here to determine if, among a nationally representative sample, there was evidence of onset age influence on later involvement. Results suggest that early onset (before age 12) relates to higher rates of more serious acts over a longer period of time for boys and girls. Overall, the results suggest support for early onset spurring on later involvement, but the contribution is small once psychosocial predictors are considered. Onset age seems most important in understanding involvement in serious crime over several years. Involvement is explained best by peer variables for males and school and family variables for females. Onset age is explained by a wider range of variables than involvement and there is greater similarity of the psychosocial variables that explain onset for both genders. The interaction of involvement and predictors was noted, suggesting a dynamic model of risk. Implications for prediction and prevention are discussed.

Adolescent↗
PubMed2005

Methods to account for attrition in longitudinal data: do they work? A simulation study.

Attrition threatens the internal validity of cohort studies. Epidemiologists use various imputation and weighting methods to limit bias due to attrition. However, the ability of these methods to correct for attrition bias has not been tested. We simulated a cohort of 300 subjects using 500 computer replications to determine whether regression imputation, individual weighting, or multiple imputation is useful to reduce attrition bias. We compared these results to a complete subject analysis. Our logistic regression model included a binary exposure and two confounders. We generated 10, 25, and 40% attrition through three missing data mechanisms: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR), and used four covariance matrices to vary attrition. We compared true and estimated mean odds ratios (ORs), standard deviations (SDs), and coverage. With data MCAR and MAR for all attrition rates, the complete subject analysis produced results at least as valid as those from the imputation and weighting methods. With data MNAR, no method provided unbiased estimates of the OR at attrition rates of 25 or 40%. When observations are not MAR or MCAR, imputation and weighting methods may not effectively reduce attrition bias.

Bias↗
PubMed2004

[Joint modeling of quantitative longitudinal data and censored survival time].

BACKGROUND: In epidemiology, we are often interested in the association between the evolution of a quantitative variable and the onset of an event. The aim of this paper is to present a joint model for the analysis of Gaussian repeated data and survival time. Such models allow, for example, to perform survival analysis when a time-dependent explanatory variable is measured intermittently, or to study the evolution of a quantitative marker conditionally to an event. METHODS: They are constructed by combining a mixed model for repeated Gaussian variables and a survival model which can be parametric or semi-parametric (Cox model). RESULTS: We discuss the hypotheses underlying the different joint models proposed in the literature and the necessary assumptions for maximum likelihood estimation. The interest of these methods is illustrated with a study of the natural history of dementia in a cohort of elderly persons.

Biometry↗
PubMed2001

Preventive intervention effects on developmental progression in drug use: structural equation modeling analyses using longitudinal data.

This study examined the plausibility of the gateway hypothesis to account for drug involvement in a sample of middle school students participating in a drug abuse, prevention trial. Analyses focused on a single prevention approach to exemplify intervention effects on drug progression. Improvements to social competence reduced multiple drug use at 1- and 2-year follow-ups. Specific program effects disrupted drug progression by decreasing alcohol and cigarette use over 1 year and reducing cigarette use over a 2-year period. Controlling for previous drug use, alcohol was integrally involved in the progression to multiple drug use. Subgroup analyses based on distinctions of pretest use/nonuse of alcohol and cigarettes provided partial support for the gateway hypothesis. However, evidence also supported alternate pathways including cigarette use as a starting point for later alcohol and multiple drug use. Findings underscore the utility of targeting more than one gateway substance to prevent escalation of drug involvement and reinforce the importance of social competence enhancement as an effective deterrent to early-stage drug use.

Adolescent↗
PubMed2005

A latent markov model for the analysis of longitudinal data collected in continuous time: states, durations, and transitions.

Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent Markov models to allow for the analysis of rating data that are collected at arbitrary points in time. This extension offers new ways of investigating change processes by focusing explicitly on the durations that are spent in latent states. In an experience sampling application the author shows that such duration analyses can provide valuable insights about chronometric features of emotions.

Humans↗