Prediction of Postpartum Weight in Low-Income Mexican-Origin Women From Childhood Experiences of Abuse and Family Conflict

OBJECTIVES

The postpartum period represents a crucial transition period in which weight gain or loss can affect lifetime obesity risk. This study examined the prevalence of obesity and the influence of childhood abuse and family conflict on postpartum weight among low-income Mexican-origin women. Depressive symptoms and partner support were evaluated as mediators.

METHODS

At a prenatal assessment, low-income Mexican-origin women (N = 322; mean [SD] age, 27.8 [6.5]) reported on childhood abuse and family conflict. Weight was measured 7 times between 6 weeks and 2 years postpartum and calculated as body mass index. Regression and growth models were used to estimate the impact of childhood abuse, childhood family conflict, partner support, and depressive symptoms on weight and weight change.

RESULTS

Higher family conflict predicted higher weight across the first (β = .12; p = .037) and second (β = .16; p = .012) postpartum years. Family conflict (β = .17; p = .018) and low partner support (β = -.16; p = .028) also predicted increasing weight in the first year. Partner support partially mediated the effect of childhood abuse on weight change in the first year (p = .031). Depressive symptomatology mediated the effects of childhood abuse and family conflict on weight status in the second year (abuse: p = .005; conflict: p = .023).

CONCLUSIONS

For low-income Mexican-origin women with a history of childhood abuse or high family conflict, depression and low partner support may be important targets for obesity prevention efforts in the postpartum period.

Comparing models of change to estimate the mediated effect in the pretest-posttest control group design

Models to assess mediation in the pretest-posttest control group design are understudied in the behavioral sciences even though it is the design of choice for evaluating experimental manipulations. The paper provides analytical comparisons of the four most commonly used models used to estimate the mediated effect in this design: Analysis of Covariance (ANCOVA), difference score, residualized change score, and cross-sectional model. Each of these models are fitted using a Latent Change Score specification and a simulation study assessed bias, Type I error, power, and confidence interval coverage of the four models. All but the ANCOVA model make stringent assumptions about the stability and cross-lagged relations of the mediator and outcome that may not be plausible in real-world applications. When these assumptions do not hold, Type I error and statistical power results suggest that only the ANCOVA model has good performance. The four models are applied to an empirical example.

Maternal acculturation and the growth of impoverished Mexican American infants

OBJECTIVE

Identification of early life risk factors that predispose low-income Hispanic children to obesity is critical. For low-income Mexican American mothers, the cultural context may influence maternal experience and behaviors relevant to infant weight and growth.

METHODS

In a longitudinal study of 322 low-income Mexican American mother-infant dyads, linear growth modeling examined the relation of maternal acculturation to infant weight gain across the first year and evaluated birth outcomes, breastfeeding, and maternal BMI as mediators.

RESULTS

There was a high prevalence (36% >95th percentile) of infants with obesity at 1 year. Higher maternal acculturation was associated with lower birth weight, higher infant weight at 6 weeks, and a lower prevalence of breastfeeding. Mediation analyses supported formula-feeding as a mediator of the relation between higher maternal acculturation and an increasing slope of infant weight gain across the first year.

CONCLUSIONS

Breastfeeding may have measurable benefits for Mexican American child obesity status in this high-risk population, particularly among those with more acculturated mothers.

Power in Bayesian Mediation Analysis for Small Sample Research

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

SAS® Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design

Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effect estimates. Cause-and-effect inferences are particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator (MacKinnon, 2008). The focus of this paper is how incorporating longitudinal measures of the mediating and outcome variables aides in the causal interpretation of mediated effects. This paper provides useful SAS® tools for designing adequately powered studies to detect the mediated effect. Three SAS macros were developed using the powerful but easy-to-use REG, CALIS, and SURVEYSELECT procedures to do the following: (1) implement popular statistical models for estimating the mediated effect in the pretest-posttest control group design; (2) conduct a prospective power analysis for determining the required sample size for detecting the mediated effect; and (3) conduct a retrospective power analysis for studies that have already been conducted and a required sample to detect an observed effect is desired. We demonstrate the use of these three macros with an example.

A Note on Testing Mediated Effects in Structural Equation Models: Reconciling Past and Current Research on the Performance of the Test of Joint Significance

Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method to detect the significance of the mediated effect is the bias-corrected bootstrap method. Both these methods are used for testing the significance of mediated effects in structural equation models (SEMs). A recent study by Leth-Steensen and Gallitto 2015 provided evidence that the test of joint significance was more powerful than the bias-corrected bootstrap method for detecting mediated effects in SEMs, which is inconsistent with previous research on the topic. The goal of this article was to investigate this surprising result and describe two issues related to testing the significance of mediated effects in SEMs which explain the inconsistent results regarding the power of the test of joint significance and the bias-corrected bootstrap found by Leth-Steensen and Gallitto 2015. The first issue was that the bias-corrected bootstrap method was conducted incorrectly. The bias-corrected bootstrap was used to estimate the standard error of the mediated effect as opposed to creating confidence intervals. The second issue was that the correlation between the path coefficients of the mediated effect was ignored as an important aspect of testing the significance of the mediated effect in SEMs. The results of the replication study confirmed prior research on testing the significance of mediated effects. That is, the bias-corrected bootstrap method was more powerful than the test of joint significance, and the bias-corrected bootstrap method had elevated Type 1 error rates in some cases. Additional methods for testing the significance of mediated effects in SEMs were considered and limitations and future directions were discussed.

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings.

A Bifactor Approach to Model Multifaceted Constructs in Statistical Mediation Analysis

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to an outcome. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an outcome simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to parse variance and represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help determine the properties of mediated effect estimation when the mediator has a bifactor structure and a specific facet of a construct is the true mediator. This study also investigates the conditions when researchers can detect the mediated effect when the multidimensionality of the mediator is ignored and treated as unidimensional. Simulation results indicated that the mediation model with a bifactor mediator measurement model had unbiased and adequate power to detect the mediated effect with a sample size greater than 500 and medium a- and b-paths. Also, results indicate that parameter bias and detection of the mediated effect in both the data-generating model and the misspecified model varies as a function of the amount of facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis.

The Combined Effects of Measurement Error and Omitting Confounders in the Single-Mediator Model

Mediation analysis requires a number of strong assumptions be met in order to make valid causal inferences. Failing to account for violations of these assumptions, such as not modeling measurement error or omitting a common cause of the effects in the model, can bias the parameter estimates of the mediated effect. When the independent variable is perfectly reliable, for example when participants are randomly assigned to levels of treatment, measurement error in the mediator tends to underestimate the mediated effect, while the omission of a confounding variable of the mediator-to-outcome relation tends to overestimate the mediated effect. Violations of these two assumptions often co-occur, however, in which case the mediated effect could be overestimated, underestimated, or even, in very rare circumstances, unbiased. To explore the combined effect of measurement error and omitted confounders in the same model, the effect of each violation on the single-mediator model is first examined individually. Then the combined effect of having measurement error and omitted confounders in the same model is discussed. Throughout, an empirical example is provided to illustrate the effect of violating these assumptions on the mediated effect.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration.

Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings.

Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.

Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

Design approaches to experimental mediation

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., "measurement-of-mediation" designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable.

Effects of prenatal factors and temperament on infant cortisol regulation in low-income Mexican American families

Prenatal psychosocial exposures can significantly affect infant health and development. Infants with higher temperamental negativity are theorized to be more susceptible to environmental exposures. We evaluated the interaction of prenatal maternal exposures and infant temperamental negativity to predict infant cortisol response to mildly challenging mother-infant interaction tasks. Participants included 322 Mexican American mother-infant dyads (mother age 18-42; 82% Spanish-speaking; modal family income $10,000-$15,000). Mothers reported depressive symptoms and social support prenatally and infant temperamental negativity at 6 weeks postpartum. Salivary cortisol was collected from infants before and after mother-infant interaction tasks at 12 weeks. Higher prenatal maternal depressive symptoms and lower social support predicted higher cortisol among infants with higher temperamental negativity. Higher infant temperamental negativity predicted an increase in maternal distress and a decrease in social support from prenatal to 12 weeks postpartum. Interactive influences of maternal social-contextual factors and infant temperament may influence the development of infant neurobiological regulation and promote or strain maternal and infant adaptation over time.

Role transitions and young adult maturing out of heavy drinking: evidence for larger effects of marriage among more severe premarriage problem drinkers

BACKGROUND

Research has shown a developmental process of "maturing out" of problem drinking beginning in young adulthood. Perhaps surprisingly, past studies suggest that young adult drinking reductions may be particularly pronounced among those exhibiting relatively severe forms of problem drinking earlier in emerging adulthood. This may occur because more severe problem drinkers experience stronger ameliorative effects of normative young adult role transitions like marriage.

METHODS

The hypothesis of stronger marriage effects among more severe problem drinkers was tested using 3 waves of data from a large ongoing study of familial alcohol disorder (N = 844; 51% children of alcoholics).

RESULTS

Longitudinal growth models characterized (i) the curvilinear trajectory of drinking quantity from ages 17 to 40, (ii) effects of marriage on altering this age-related trajectory, and (iii) moderation of this effect by premarriage problem drinking levels (alcohol consequences and dependence symptoms). Results confirmed the hypothesis that protective marriage effects on drinking quantity trajectories would be stronger among more severe premarriage problem drinkers. Supplemental analyses showed that results were robust to alternative construct operationalizations and modeling approaches.

CONCLUSIONS

Consistent with role incompatibility theory, findings support the view of role conflict as a key mechanism of role-driven behavior change, as greater problem drinking likely conflicts more with demands of roles like marriage. This is also consistent with the developmental psychopathology view of transitions and turning points. Role transitions among already low-severity drinkers may merely represent developmental continuity of a low-risk trajectory, whereas role transitions among higher-severity problem drinkers may represent developmentally discontinuous "turning points" that divert individuals from a higher- to a lower-risk trajectory. Practically, findings support the clinical relevance of role-related "maturing out processes" by suggesting that they often reflect natural recovery from clinically significant problem drinking. Thus, understanding these processes could help clarify the nature of pathological drinking and inform interventions.

Mediation from multilevel to structural equation modeling

BACKGROUND/AIMS

The purpose of this article is to outline multilevel structural equation modeling (MSEM) for mediation analysis of longitudinal data. The introduction of mediating variables can improve experimental and nonexperimental studies of child growth in several ways as discussed throughout this article. Single-mediator individual-level and multilevel mediation models illustrate several current issues in the estimation of mediation with longitudinal data. The strengths of incorporating structural equation modeling (SEM) with multilevel mediation modeling are described. SUMMARY AND KEY MESSAGES: Longitudinal mediation models are pervasive in many areas of research including child growth. Longitudinal mediation models are ideally modeled as repeated measurements clustered within individuals. Further, the combination of MSEM and SEM provides an ideal approach for several reasons, including the ability to assess effects at different levels of analysis, incorporation of measurement error and possible random effects that vary across individuals.

Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis

Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.

The safety and health improvement: enhancing law enforcement departments study: feasibility and findings

This randomized prospective trial aimed to assess the feasibility and efficacy of a team-based worksite health and safety intervention for law enforcement personnel. Four-hundred and eight subjects were enrolled and half were randomized to meet for weekly, peer-led sessions delivered from a scripted team-based health and safety curriculum. Curriculum addressed: exercise, nutrition, stress, sleep, body weight, injury, and other unhealthy lifestyle behaviors such as smoking and heavy alcohol use. Health and safety questionnaires administered before and after the intervention found significant improvements for increased fruit and vegetable consumption, overall healthy eating, increased sleep quantity and sleep quality, and reduced personal stress.

The Distribution of the Product Explains Normal Theory Mediation Confidence Interval Estimation

The distribution of the product has several useful applications. One of these applications is its use to form confidence intervals for the indirect effect as the product of 2 regression coefficients. The purpose of this article is to investigate how the moments of the distribution of the product explain normal theory mediation confidence interval coverage and imbalance. Values of the critical ratio for each random variable are used to demonstrate how the moments of the distribution of the product change across values of the critical ratio observed in research studies. Results of the simulation study showed that as skewness in absolute value increases, coverage decreases. And as skewness in absolute value and kurtosis increases, imbalance increases. The difference between testing the significance of the indirect effect using the normal theory versus the asymmetric distribution of the product is further illustrated with a real data example. This article is the first study to show the direct link between the distribution of the product and indirect effect confidence intervals and clarifies the results of previous simulation studies by showing why normal theory confidence intervals for indirect effects are often less accurate than those obtained from the asymmetric distribution of the product or from resampling methods.

Increasing Statistical Power in Mediation Models Without Increasing Sample Size

Inadequate statistical power to detect treatment effects in health research is a problem that is compounded when testing for mediation. In general, the preferred strategy for increasing power is to increase the sample size, but there are many situations where additional participants cannot be recruited, necessitating the use of other methods to increase statistical power. Many of these other strategies, commonly applied to analysis of variance and multiple regression models, can be applied to mediation models with similar results. Additional predictors or blocking variables will increase or decrease statistical power, however, depending on whether these variables are related to the mediator, the outcome, or both. The effect of these two methods on the power for tests of mediation is illustrated through the use of simulations. Implications for health researchers using these methods are discussed.

Prenatal stress, partner support, and infant cortisol reactivity in low-income Mexican American families

Maternal exposure to significant prenatal stress can negatively affect infant neurobiological development and increase the risk for developmental and health disturbances. These effects may be pronounced in low SES and ethnic minority families. We explored prenatal partner support as a buffer of the impact of prenatal stress on cortisol reactivity of infants born to low-income Mexican American women. Women (N=220; age 18-42; 84% Spanish-speaking; 89% foreign born; modal family income $10,000-$15,000) reported on economic stress and satisfaction with spousal/partner support during the prenatal period (26-38 weeks gestation), and infant salivary cortisol reactivity to mildly challenging mother-infant interaction tasks was assessed at women's homes at six weeks postpartum. Multilevel models estimated the interactive effect of prenatal stress and partner support on cortisol reactivity, controlling for covariates and potential confounds. Infants born to mothers who reported high prenatal stress and low partner support exhibited higher cortisol reactivity relative to those whose mothers reported high support or low stress. The effects did not appear to operate through birth outcomes. For low-income Mexican American women, partner support may buffer the impact of prenatal stress on infant cortisol reactivity, potentially promoting more adaptive infant health and development.

Mediators of longitudinal changes in measures of adiposity in teenagers using parallel process latent growth modeling

OBJECTIVE

The aim of the study was to evaluate mediating effects of energy balance-related behaviors on measures of adiposity in the Dutch Obesity Intervention in Teenagers-study (DOiT).

DESIGN AND METHODS

DOiT was an 8-month behavioral intervention program consisting of educational and environmental components and evaluated in 18 prevocational secondary schools in the Netherlands (n = 1,108, baseline age 12.7 years, 50% girls). Outcome measures were changes in body mass index (BMI), waist circumference, and sum of skinfold thickness. Self-reported consumption of sugar-containing beverages and high caloric snacks, active transport to/from school, and screen-viewing behaviors were the hypothesized mediators. Data were collected at 0, 8, 12, and 20 months. For the data analysis, parallel process latent growth modeling was used.

RESULTS

Total sugar-containing beverages consumption mediated the intervention effects on BMI (ab = -0.01, 95%CI = -0.20, -0.001). The intervention group lowered their sugar-containing beverages consumption more than controls (B = -0.14, 95%CI = -0.22, -0.11) and this, in turn, led to smaller increases in BMI. No significant mediated effect by the targeted behaviors was found for waist circumference or sum of skinfolds.

CONCLUSIONS

Future school-based overweight prevention interventions may target decreasing sugar-containing beverages consumption.

Sensitivity plots for confounder bias in the single mediator model

BACKGROUND

Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable.

OBJECTIVES

This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods.

DESIGN

Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect.

SUBJECTS

The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely.

CONCLUSIONS

We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.

Some methodological considerations in theory-based health behavior research

As this special issue shows, much research in social and personality psychology is directly relevant to health psychology. In this brief commentary, we discuss three topics in research methodology that may be of interest to investigators involved in health-related psychological research. The first topic is statistical analysis of mediated and moderated effects. The second is measurement of latent constructs. The third is the Multiphase Optimization Strategy, a framework for translation of innovations from social and personality psychology into behavioral interventions.

Multilevel mediation analysis: The effects of omitted variables in the 1-1-1 model

Multilevel mediation analysis examines the indirect effect of an independent variable on an outcome achieved by targeting and changing an intervening variable in clustered data. We study analytically and through simulation the effects of an omitted variable at level 2 on a 1-1-1 mediation model for a randomized experiment conducted within clusters in which the treatment, mediator, and outcome are all measured at level 1. When the residuals in the equations for the mediator and the outcome variables are fully orthogonal, the two methods of calculating the indirect effect (ab, c - c') are equivalent at the between- and within-cluster levels. Omitting a variable at level 2 changes the interpretation of the indirect effect and will induce correlations between the random intercepts or random slopes. The equality of within-cluster ab and c - c' no longer holds. Correlation between random slopes implies that the within-cluster indirect effect is conditional, interpretable at the grand mean level of the omitted variable.