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Evaluation of a Longitudinal Family Stress Model in a Population-Based Cohort.

ABSTRACT: The Family Stress Model (FSM) is an influential family process model that posits that socioeconomic disadvantage impacts child outcomes via its effects on parents. Existing evaluations of the FSM are constrained by limited measures of socioeconomic disadvantage, cross-sectional research designs, and reliance on non-population-based samples. The current study tested the FSM in a subsample of the Fragile Families and Child Wellbeing Study (N = 2,918), a large population-based study of children followed from birth through age 9. We employed a longitudinal framework and used measures of socioeconomic disadvantage beyond economic resources. Although the hypothesized FSM pathways were identified in the longitudinal model (e.g., economic pressure at age 1 was associated with maternal distress at age 3, maternal distress at age 3 was associated with parenting behaviors at age 5), the effects of socioeconomic disadvantage at childbirth on youth socioemotional outcomes at age 9 did not operate through all of the hypothesized mediators. In longitudinal change models that accounted for the stability in constructs, multiple indicators of socioeconomic disadvantage at childbirth were indirectly associated with youth externalizing behaviors at age 9 via either economic pressure at age 1 or changes in maternal warmth from ages 3 to 5. Greater economic pressure at age 1, increases in maternal distress from ages 1 to 3, and decreases/increases in maternal warmth/harshness from ages 3 to 5 were also directly associated with increases in externalizing behaviors from ages 5 to 9. Results provide partial support for the FSM across the first decade of life.

SUBMITTER: Gard AM

PROVIDER: S-EPMC8094580 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveWe aimed to assess the trends in body mass index (BMI) and body fat percentage (BF%) from the age of 13 to 17 years and to evaluate how sociodemographic and behavioural characteristics at the age of 13 impact changes in BMI and BF%.SettingPorto, Portugal.ParticipantsWe evaluated 1451 adolescents in a community-based cohort.Outcome measuresBMI z-scores were calculated according to CDC references. BF% was assessed by bioelectrical impedance. Variables with a significant effect in adiposity changes were identified through linear regression models. In girls, estimates were controlled for duration of follow-up, parental education, baseline BMI z-score, age at menarche and the interaction term baseline BMI z-score×age at menarche; in boys, adjustments were performed for duration of follow-up, parental education, baseline BMI z-score and the interaction term baseline BMI z-score×duration of follow-up.ResultsOn average, BMI z-score decreased from the age of 13 to 17 years (mean difference -0.20, 95% CI -0.23 to -0.16 among girls and -0.15, 95% CI -0.19 to -0.11 among boys). Accordingly, 12.4% of girls and 13% of boys moved to a lower BMI category and 2.2% of girls and 5.5% of boys to a higher category. There were sex differences in the significant determinants of adiposity trends. Among girls, BMI z-score significantly decreased with baseline BMI z-score (β=-0.163, 95% CI -0.204 to -0.122) and significantly increased with age at menarche (β=0.078, 95% CI 0.050 to 0.107). Results were similar for BF%. Among boys, BMI z-score significantly increased with higher parental BMI, and BF% decreased among those who wished to look larger at the age of 13 (β=-1.367, 95% CI -2.174 to -0.560), compared with those who were satisfied with their image.ConclusionsIn adolescents, ageing resulted in a decrease in BMI z-scores and BF%. BMI and BF% at the age of 13 were the major determinants of the observed trends. Our results suggest that adolescence is a possible specific time window for intervention.

Project description:Background/objectivesFew studies have rigorously examined the magnitude of changes in well-being after a transition into sustained and substantial caregiving, especially in population-based studies, compared with matched noncaregiving controls.DesignWe identified individuals from a national epidemiological investigation who transitioned into caregiving over a 10- to 13-year follow-up and provided continuous in-home care for at least 18 months and at least 5 hours per week. Individuals who did not become caregivers were individually matched with caregivers on age, sex, race, education, marital status, self-rated health, and history of cardiovascular disease at baseline. Both groups were assessed at baseline and follow-up.SettingREasons for Geographic And Racial Differences in Stroke study.ParticipantsA total of 251 incident caregivers and 251 matched controls.MeasurementsPerceived Stress Scale (PSS), 10-Item Center for Epidemiological Studies-Depression (CES-D), and 12-item Short-Form Health Survey quality-of-life mental (MCS) and physical (PCS) component scores.ResultsCaregivers showed significantly greater worsening in PSS, CES-D, and MCS, with standardized effect sizes ranging from 0.676 to 0.796 compared with changes in noncaregivers. A significant but smaller effect size was found for worsening PCS in caregivers (0.242). Taking on sustained caregiving was associated with almost a tripling of increased risk of transitioning to clinically significant depressive symptoms at follow-up. Effects were not moderated by race, sex, or relationship to care recipient, but younger caregivers showed greater increases in CES-D than older caregivers.ConclusionPersons who began substantial, sustained family caregiving had marked worsening of psychological well-being, and relatively smaller worsening of self-reported physical health, compared with carefully matched noncaregivers. Previous estimates of effect sizes on caregiver well-being have had serious limitations due to use of convenience sampling and cross-sectional comparisons. Researchers, public policy makers, and clinicians should note these strong effects, and caregiver assessment and service provision for psychological well-being deserve increased priority.

Project description:BackgroundStroke has been the leading cause of death and disability in the world. Early recognition and treatment of stroke could effectively limit brain damage and vastly improve outcomes. This study aims to develop a highly accurate prediction model of stroke with a list of lifestyle behaviors and clinical characteristics to distinguish high-risk groups in the community-dwelling population.MethodsParticipants in this longitudinal cohort study came from the community-dwelling population in Suzhou between November 2018 and June 2019. A total of 4,503 residents participated in the study, while stroke happened to 22 participants in the 2-year follow-up period. Baseline information of each participant was acquired and enrolled in this study. T-test, Chi-square test, and Fisher's exact test were used to examine the relationship of these indexes with stroke, and a prediction scale was constructed by multivariate logistic regression afterward. Receiver operating characteristic analysis was applied to testify to the prediction accuracy.ResultsA highly accurate prediction model of stroke was constructed by age, gender, exercise, meat and vegetarian diet, BMI, waist circumference, systolic blood pressure, Chinese visceral adiposity index, and waist-height ratio. Two additional prediction models for overweight and non-overweight individuals were formulated based on crucial risk factors, respectively. The stroke risk prediction models for community-dwelling and overweight populations had accuracies of 0.79 and 0.82, severally. Gender and exercise were significant predictors (χ2 > 4.57, p < 0.05) in the community-dwelling population model, while homocysteine (χ2 = 4.95, p < 0.05) was significant in the overweight population model.ConclusionThe predictive models could predict 2-year stroke with high accuracy. The models provided an effective tool for identifying high-risk groups and supplied guidance for improving prevention and treatment strategies in community-dwelling population.

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Evaluation of a Longitudinal Family Stress Model in a Population-Based Cohort.

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