Project description:We aimed to characterize the long-term health outcomes of survivors of severe acute respiratory syndrome (SARS) and determine their recovery status and possible immunological basis. Although health outcomes continued to improve, SARS survivors still suffered from physical fatigue, osteoporosis, and necrosis of the femoral head 18 years after discharge, possibly related to plasma metabolic disorders and immunological alterations.
Project description:The study aimed to investigate whether social engagement predicted longitudinally objective and subjective physical health. Measures of social engagement, subjective and objective health were taken at three points in time, 4 years apart (T1, T2, T3). Three questions were examined: does social engagement at T1 predict objective/subjective health at T2, does social engagement at T2 predict objective/subjective health at T3, and does social engagement at T1 predict objective/subjective health at T3? Participants were 359 adults aged 65 and over. A fully cross-lagged structural equation model was examined. Social engagement at T1 was found to significantly predict subjective health at T2. However, social engagement at T1 did not significantly predict subjective health at T3, nor was subjective health at T3 predicted by social engagement at T2. Social engagement never significantly predicted objective health. Unexpectedly, objective health at T2 predicted social engagement at T3. Finally, post-hoc analyses suggest that age has a greater influence on social engagement at T2 than at T1. Social engagement is a useful predictor of subjective physical health. However, objective health was not predicted by social engagement-indeed, the converse was the case. It is suggested that the relationship between social engagement and subjective health is mediated by psychosocial factors which may not be present in the social engagement-objective health relationship. In conclusion, the results reflect the complex interplay of objective and subjective health and social engagement as people age.
Project description:While previous studies have identified a range of potential risk factors for postnatal depression (PND), none have examined a comprehensive set of risk factors at a population-level using data collected prospectively. The aim of this study was to explore the relationship between a range of factors and PND and to construct a model of the predictors of PND.Data came from 5219 women who completed Survey 5 of the Australian Longitudinal Study on Women's Health in 2009 and reported giving birth to a child.Over 15% of women reported experiencing PND with at least one of their children. The strongest positive associations were for postnatal anxiety (OR = 13.79,95%CI = 10.48,18.13) and antenatal depression (OR = 9.23,95%CI = 6.10,13.97). Positive associations were also found for history of depression and PND, low SF-36 Mental Health Index, emotional distress during labour, and breastfeeding for less than six months.Results indicate that understanding a woman's mental health history plays an important role in the detection of those who are most vulnerable to PND. Treatment and management of depression and anxiety earlier in life and during pregnancy may have a positive impact on the incidence of PND.
Project description:This article describes the design and phenotype and genotype data available for sibling pairs with varying genetic relatedness in the National Longitudinal Study of Adolescent Health (Add Health). Add Health is a nationally representative longitudinal study of over 20,000 adolescents in the United States in 1994-1995 who have been followed for 15 years into adulthood. The Add Health design included oversamples of more than 3,000 pairs of individuals with varying genetic resemblance, ranging from monozygotic twins, dizygotic twins, full siblings, half siblings, and unrelated siblings who were raised in the same household. Add Health sibling pairs are therefore nationally representative and followed longitudinally from early adolescence into adulthood with four in-home interviews during the period 1994-2009. Add Health has collected rich longitudinal social, behavioral, environmental, and biological data, as well as buccal cell DNA from all sample members, including sibling pairs. Add Health has an enlightened dissemination policy and to date has released phenotype and genotype data to more than 10,000 researchers in the scientific community.
Project description:The prevalence of ideal cardiovascular health (CVH) among adults in the United States is low and decreases with age. Our objective was to identify specific age windows when the loss of CVH accelerates, to ascertain preventive opportunities for intervention. Data were pooled from 5 longitudinal cohorts (Project Heartbeat!, Cardiovascular Risk in Young Finns Study, The Bogalusa Heart Study, Coronary Artery Risk Development in Young Adults, Special Turku Coronary Risk Factor Intervention Project) from the United States and Finland from 1973 to 2012. Individuals with clinical CVH factors (i.e., body mass index, blood pressure, cholesterol, blood glucose) measured from ages 8 to 55 years were included. These factors were categorized and summed into a clinical CVH score ranging from 0 (worst) to 8 (best). Adjusted, segmented, linear mixed models were used to estimate the change in CVH over time. Among the 18,343 participants, 9,461 (52%) were female and 12,346 (67%) were White. The baseline mean (standard deviation) clinical CVH score was 6.9 (1.2) at an average age of 17.6 (8.1) years. Two inflection points were estimated: at 16.9 years (95% confidence interval: 16.4, 17.4) and at 37.2 years (95% confidence interval: 32.4, 41.9). Late adolescence and early middle age appear to be influential periods during which the loss of CVH accelerates.
Project description:Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.
Project description:Personalized policy represents a paradigm shift from one-decision-rule-for-all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual features are modeled as fixed and random effects, respectively, and synthesized to form the personalized policy. The group lasso type penalty is imposed to avoid overfitting of individual deviations from the population model. We examine the conditions under which the proposed method work in the presence of time-varying endogenous covariates, and provide conditional optimality and marginal consistency results of the expected immediate outcome under the estimated policies. We apply our method to develop personalized push ("prompt") schedules in 294 app users, with the goal to maximize the prompt response rate given past app usage and other contextual factors. The proposed method compares favorably to existing estimation methods including using the R function "glmer" in a simulation study.