Decreased Pain and Improved Dynamic Knee Instability Mediate the Beneficial Effect of Wearing a Soft Knee Brace on Activity Limitations in Patients With Knee Osteoarthritis

OBJECTIVE: To evaluate whether improvement of proprioception, pain or dynamic knee instability mediate the effect of wearing a soft knee brace on activity limitations in persons with knee osteoarthritis (OA). METHODS: Exploratory analysis from 44 participants with knee OA and self-reported knee instability in a laboratory trial evaluating the effect of wearing a commercially available soft knee brace. Activity limitations were assessed with the 10-meter walk test and the Get up and Go test. Knee joint proprioception was assessed by an active joint position sense test; pain was assessed with the Numeric Rating Scale (NRS); pressure pain threshold (PPT) was assessed with a hand-held pressure algometer; dynamic knee instability was expressed by the Perturbation Response, i.e. a measure reflecting a deviation in mean knee varus-valgus angle after a controlled mechanical perturbation on a treadmill, with respect to level walking. Mediation analysis was conducted with the product of coefficients approach. Confidence intervals were calculated with a bootstrap procedure. RESULTS: A decrease of pain (NRS) and a decrease of dynamic knee instability mediated the effect of wearing a soft knee brace on reduction of activity limitations (p < 0.05), while changes of proprioception and PPT did not mediate this effect (p > 0.05). CONCLUSION: This study shows that decreased pain and reduced dynamic knee instability are pathways via which wearing a soft knee brace decreased activity limitations in persons with knee OA. This article is protected by copyright. All rights reserved

Performance of methods to conduct mediation analysis with time-to-event outcomes

Previous studies have discouraged the use of the Cox proportional hazards (PH) model for traditional mediation analysis as it might provide biased results. Accelerated failure time (AFT) models have been proposed as an alternative for Cox PH models. In addition, the use of the potential outcomes framework has been proposed for mediation models with time-to-event outcomes. The aim of this paper is to investigate the performance of traditional mediation analysis and potential outcomes mediation analysis based on both the Cox PH and the AFT model. This is done by means of a Monte Carlo simulation study and the illustration of the methods using an empirical data set. Both the product-of-coefficients method of the traditional mediation analysis and the potential outcomes framework yield unbiased estimates with respect to their own underlying indirect effect value for simple mediation models with a time-to-event outcome and estimated based on Cox PH or AFT

Vitamin D Status and Depressive Symptoms in Older Adults:A Role for Physical Functioning?

Objectives: Depressive symptoms and low vitamin D status are common in older persons and may be associated, but findings are inconsistent. This study investigated whether 25-hydroxyvitamin D (25(OH)D) concentrations are associated with depressive symptoms in older adults, both cross-sectionally and longitudinally. We also examined whether physical functioning could explain this relationship, to gain a better understanding of the underlying mechanisms. Methods: Data from two independent prospective cohorts of the Longitudinal Aging Study Amsterdam were used: an older cohort (≥65 years, n = 1282, assessed from 1995–2002) and a younger-old cohort (55–65 years, n = 737, assessed from 2002–2009). Measurements: Depressive symptoms were measured at baseline and after 3 and 6 years with the Center of Epidemiological Studies Depression Scale. Cross-sectional and longitudinal linear regression techniques were used to examine the relationship between 25(OH)D and depressive symptoms. The mediating role of physical functioning was examined in the longitudinal models. Results: Cross-sectionally, associations were not significant after adjustment for confounders. Longitudinally, women in the older cohort with baseline 25(OH)D concentrations up to 75 nmol/L experienced 175 to 24% more depressive symptoms in the following 6 years, compared with women with 25(OH)D concentrations >75 nmol/L. Reduced physical performance partially mediated this relationship. In men and in the younger-old cohort, no significant associations were observed. Conclusions: Older women showed an inverse relationship between 25(OH)D and depressive symptoms over time, which may partially be explained by declining physical functioning. Replication of these findings by future studies is needed

Trajectories of frailty with aging:Coordinated analysis of five longitudinal studies

BACKGROUND AND OBJECTIVES: There is an urgent need to better understand frailty and its predisposing factors. Although numerous cross-sectional studies have identified various risk and protective factors of frailty, there is a limited understanding of longitudinal frailty progression. Furthermore, discrepancies in the methodologies of these studies hamper comparability of results. Here, we use a coordinated analytical approach in 5 independent cohorts to evaluate longitudinal trajectories of frailty and the effect of 3 previously identified critical risk factors: sex, age, and education. RESEARCH DESIGN AND METHODS: We derived a frailty index (FI) for 5 cohorts based on the accumulation of deficits approach. Four linear and quadratic growth curve models were fit in each cohort independently. Models were adjusted for sex/gender, age, years of education, and a sex/gender-by-age interaction term. RESULTS: Models describing linear progression of frailty best fit the data. Annual increases in FI ranged from 0.002 in the Invecchiare in Chianti cohort to 0.009 in the Longitudinal Aging Study Amsterdam (LASA). Women had consistently higher levels of frailty than men in all cohorts, ranging from an increase in the mean FI in women from 0.014 in the Health and Retirement Study cohort to 0.046 in the LASA cohort. However, the associations between sex/gender and rate of frailty progression were mixed. There was significant heterogeneity in within-person trajectories of frailty about the mean curves. DISCUSSION AND IMPLICATIONS: Our findings of linear longitudinal increases in frailty highlight important avenues for future research. Specifically, we encourage further research to identify potential effect modifiers or groups that would benefit from targeted or personalized interventions

Gait speed as predictor of transition into cognitive impairment: Findings from three longitudinal studies on aging

Objectives: Very few studies looking at slow gait speed as early marker of cognitive decline investigated the competing risk of death. The current study examines associations between slow gait speed and transitions between cognitive states and death in later life. Methods: We performed a coordinated analysis of three longitudinal studies with 9 to 25 years of follow-up. Data were used from older adults participating in H70 (Sweden; n = 441; aged ≥70 years), InCHIANTI (Italy; n = 955; aged ≥65 years), and LASA (the Netherlands; n = 2824; aged ≥55 years). Cognitive states were distinguished using the Mini-Mental State Examination. Slow gait speed was defined as the lowest sex-specific quintile at baseline. Multistate models were performed, adjusted for age, sex and education. Results: Most effect estimates pointed in the same direction, with slow gait speed predicting forward transitions. In two cohort studies, slow gait speed predicted transitioning from mild to severe cognitive impairment (InCHIANTI: HR = 2.08, 95%CI = 1.40–3.07; LASA: HR = 1.33, 95%CI = 1.01–1.75) and transitioning from a cognitively healthy state to death (H70: HR = 3.30, 95%CI = 1.74–6.28; LASA: HR = 1.70, 95%CI = 1.30–2.21). Conclusions: Screening for slow gait speed may be useful for identifying older adults at risk of adverse outcomes such as cognitive decline and death. However, once in the stage of more advanced cognitive impairment, slow gait speed does not seem to predict transitioning to death anymore

Comparison of methods for the analysis of relatively simple mediation models

Background/aims: Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Methods: Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. Results: OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Conclusions: Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them. Keywords: Mediation analysis, Indirect effect, Ordinary least square regression, Structural equation modeling, Potential outcomes framework, Cross-sectional dat

Noncollapsibility and its role in quantifying confounding bias in logistic regression

Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. Methods A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. Results The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. Conclusion In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model

The use of traditional and causal estimators for mediation models with a binary outcome and exposure-mediator interaction

An important recent development in mediation analysis is the use of causal mediation analysis. Causal mediation analysis decomposes the total exposure effect into causal direct and indirect effects in the presence of exposure-mediator interaction. However, in practice, traditional mediation analysis is still most widely used. The aim of this paper is to demonstrate the similarities and differences between the causal and traditional estimators for mediation models with a continuous mediator, a binary outcome, and exposure-mediator interaction. A real-life data example, analytical comparisons, and a simulation study were used to demonstrate the similarities and differences between the traditional and causal estimators. The causal and traditional estimators provide similar indirect effect estimates, but different direct and total effect estimates. Traditional mediation analysis may only be used when conditional direct effect estimates are of interest. Causal mediation analysis is the generally preferred method as its casual effect estimates help unravel causal mechanisms

Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata

Mediation analysis is a methodology used to understand how and why an independent variable (X) transmits its effect to an outcome (Y) through a mediator (M). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of mediation analysis. There are several programs available to estimate causal mediation effects, but these programs differ substantially in data set up, estimation, output, and software platform. To compare these programs, an empirical example is presented, and a single mediator model with treatment-mediator interaction was estimated with a continuous mediator and a continuous outcome in each program. Even though the software packages employ different estimation methods, they do provide similar causal effect estimates for mediation models with a continuous mediator and outcome. A detailed explanation of program similarities, unique features, and recommendations is discussed

Association of a Family Integrated Care Model with Paternal Mental Health Outcomes during Neonatal Hospitalization

Importance: During newborn hospitalization in the neonatal unit, fathers often feel anxious and excluded from their child's caregiving and decision-making. Few studies and interventions have focused on fathers' mental health and their participation in neonatal care. Objective: To study the association of a family integrated care (FICare) model (in single family rooms with complete couplet-care for the mother-newborn dyad) vs standard neonatal care (SNC) in open bay units with separate maternity care with mental health outcomes in fathers at hospital discharge of their preterm newborn and to study whether parent participation was a mediator of the association of the FICare model on outcomes. Design, Setting, and Participants: This prospective, multicenter cohort study was conducted from May 2017 to January 2020 as part of the fAMily Integrated Care in the Neonatal Ward Study, at level-2 neonatal units in the Netherlands (1 using the FICare model and 2 control sites using SNC). Participants included fathers of preterm newborns admitted to participating units. Data analysis was performed from January to April 2021. Exposure: FICare model in single family rooms with complete couplet-care for the mother-newborn dyad during maternity and/or neonatal care. Main Outcomes and Measures: Paternal mental health was measured using the Parental Stress Scale: NICU, Hospital Anxiety and Depression Scale, Post-partum Bonding Questionnaire, Perceived (Maternal) Parenting Self-efficacy Scale, and satisfaction with care (EMpowerment of PArents in THe Intensive Care-Neonatology). Parent participation (CO-PARTNER tool) was assessed as a potential mediator of the association of the FICare model with outcomes with mediation analyses (prespecified). Results: Of 309 families included in the fAMily Integrated Care in the Neonatal Ward Study, 263 fathers (85%) agreed to participate; 126 fathers were enrolled in FICare and 137 were enrolled in SNC. In FICare, 89 fathers (71%; mean [SD] age, 35.1 [4.8] years) responded to questionnaires and were analyzed. In SNC, 93 fathers (68%; mean [SD] age, 36.4 [5.5] years) responded to questionnaires and were analyzed. Fathers in FICare experienced less stress (adjusted β, -10.02; 95% CI, -15.91 to -4.13; P =.001) and had higher participation scores (adjusted odds ratio, 3.424; 95% CI, 0.860 to 5.988; P =.009) compared with those in SNC. Participation mediated the beneficial association of the FICare model with fathers' depressive symptoms (indirect effect, -0.051; 95% CI, -0.133 to -0.003) and bonding with their newborns (indirect effect, -0.082; 95% CI, -0.177 to -0.015). Conclusions and Relevance: These findings suggest that the FICare model is associated with decreased paternal stress at discharge and enables fathers to be present and participate more than SNC, thus improving paternal mental health. Supporting fathers to actively participate in all aspects of newborn care should be encouraged regardless of architectural design of the neonatal unit.