Cebu Longitudinal Health and Nutrition Survey (CLHNS)
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ABSTRACT:
SECONDARY ACCESSION(S): PRJNA171954PRJNA171955
REPOSITORIES: dbGaP
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Project description:Add Health (National Longitudinal Study of Adolescent to Adult Health)
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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.
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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.
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Project description:Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.
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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.
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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.
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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.
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Project description:BACKGROUND:The health selection hypothesis suggests that poor health leads to low educational attainment during the life course. Adolescence is an important period as poor health might prevent students from making the best educational choices. We test if health in adolescence is associated with educational aspirations and whether these associations persist over and above sociodemographic background and academic achievement. METHODS:Using classroom surveys, a cohort of students (n = 5.614) from the Helsinki Metropolitan Region was followed from the 7th (12-13 years,) up to the 9th grade (15-16 years) when the choice between the academic and the vocational track is made in Finland. Health factors (Strengths and Difficulties Questionnaire (SDQ), self-rated health, daily health complaints, and long-term illness and medicine prescribed) and sociodemographic background were self-reported by the students. Students' educational aspirations (applying for academic versus vocational track, or both) and their academic achievement were obtained from the Joint Application Registry held by the Finnish National Agency for Education. We conducted multilevel multinomial logistic regression analyses, taking into account that students are clustered within schools. RESULTS:All studied health factors were associated with adolescents' educational aspirations. For the SDQ, daily health complaints, and self-rated health these associations persisted over and above sociodemographic background and academic achievement. Students with better health in adolescence were more likely to apply for the academic track, and those who were less healthy were more likely to apply for the vocational track. The health in the group of those students who had applied for both educational tracks was in between. Inconsistent results were observed for long-term illness. We also found robust associations between educational aspirations and worsening health from grade 7 to grade 9. CONCLUSIONS:Our findings show that selection by health factors to different educational trajectories takes place at early teenage much before adolescents choose their educational track, thus supporting the health selection hypothesis in the creation of socioeconomic health inequalities. Our findings also show the importance of adolescence in this process. More studies are needed to reveal which measures would be effective in helping students with poor health to achieve their full educational potential.
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Project description:In adulthood, excess BMI is associated with cardiovascular disease (CVD); it is unknown whether risk differs by BMI trajectories from adolescence to adulthood.The National Longitudinal Study of Adolescent Health, a nationally representative, longitudinal adolescent cohort (mean age: 16.9 years) followed into adulthood (mean age: 28.8 years) [n = 13,984 individuals (41,982 observations)] was examined. Separate logistic regression models for diabetes, hypertension, and inflammation were used to examine odds of risk factors at given adult BMI according to varying BMI trajectories from adolescence to adulthood.CVD risk factor prevalence at follow-up ranged from 5.5% (diabetes) to 26.4% (hypertension) and 31.3% (inflammation); risk differed across BMI trajectories. For example, relative to men aged 27 years (BMI = 23 kg/m(2) maintained over full study period), odds for diabetes were comparatively higher for men of the same age and BMI ? 30 kg/m(2) with ?8 BMI unit gain between 15 and 20 years (OR = 2.35; 95% CI, 1.51, 3.66) or in those who maintained BMI ? 30 kg/m(2) across the study period (OR = 2.33; 1.92, 2.83) relative to the same ?8 BMI unit gain, but between 20 and 27 years (OR = 1.44; 1.10, 1.87).Specific periods and patterns of weight gain in the transition from adolescence to adulthood might be critical for CVD preventive efforts.
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