Does a physical activity program in the nursing home impact on depressive symptoms? A generalized linear mixed-model approach

OBJECTIVES

Physical activity (PA) may counteract depressive symptoms in nursing home (NH) residents considering biological, psychological, and person-environment transactional pathways. Empirical results, however, have remained inconsistent. Addressing potential shortcomings of previous research, we examined the effect of a whole-ecology PA intervention program on NH residents' depressive symptoms using generalized linear mixed-models (GLMMs).

METHOD

We used longitudinal data from residents of two German NHs who were included without any pre-selection regarding physical and mental functioning (n = 163, M_age = 83.1, 53-100 years; 72% female) and assessed on four occasions each three months apart. Residents willing to participate received a 12-week PA training program. Afterwards, the training was implemented in weekly activity schedules by NH staff. We ran GLMMs to account for the highly skewed depressive symptoms outcome measure (12-item Geriatric Depression Scale-Residential) by using gamma distribution.

RESULTS

Exercising (n = 78) and non-exercising residents (n = 85) showed a comparable level of depressive symptoms at pretest. For exercising residents, depressive symptoms stabilized between pre-, posttest, and at follow-up, whereas an increase was observed for non-exercising residents. The intervention group's stabilization in depressive symptoms was maintained at follow-up, but increased further for non-exercising residents.

CONCLUSION

Implementing an innovative PA intervention appears to be a promising approach to prevent the increase of NH residents' depressive symptoms. At the data-analytical level, GLMMs seem to be a promising tool for intervention research at large, because all longitudinally available data points and non-normality of outcome data can be considered.

Longitudinal Determinants of Married Women's Autonomy in Egypt

This article explores determinants of women's autonomy in Egypt around the beginning of the 'Arab Spring' in 2011. We show that women's autonomy over time is a product not only of their individual characteristics, but also of the household and community environment in which they live. Using the 2006 and 2012 Egyptian Labor Market Panel Survey (ELMPS) and multilevel models, results demonstrate that women's autonomy changes over time. There are large and consistent variations in women's autonomy by household region of residence and wealth. For example, women in the rural and urban Upper Egypt region are less autonomous than women in the Cairo region, and women in wealthier households are less autonomous compared to the poorest households. Programs aiming to increase women's autonomy focus exclusively or primarily on women's own characteristics. These results indicate that strategies to improve women's autonomy should be mindful of the multiple dimensions of autonomy and have a programmatic focus on changing household and social environments.

Multilevel correlates of household anthropometric typologies in Colombian mothers and their infants

Background

The aim of this study was to establish the association of maternal, family, and contextual correlates of anthropometric typologies at the household level in Colombia using 2005 Demographic Health Survey (DHS/ENDS) data.

Methods

Household-level information from mothers 18-49 years old and their children <5 years old was included. Stunting and overweight were assessed for each child. Mothers were classified according to their body mass index. Four anthropometric typologies at the household level were constructed: normal, underweight, overweight, and dual burden. Four three-level [households (n = 8598) nested within municipalities (n = 226), nested within states (n = 32)] hierarchical polytomous logistic models were developed. Household log-odds of belonging to one of the four anthropometric categories, holding 'normal' as the reference group, were obtained.

Results

This study found that anthropometric typologies were associated with maternal and family characteristics of maternal age, parity, maternal education, and wealth index. Higher municipal living conditions index was associated with a lower likelihood of underweight typology and a higher likelihood of overweight typology. Higher population density was associated with a lower likelihood of overweight typology.

Conclusion

Distal and proximal determinants of the various anthropometric typologies at the household level should be taken into account when framing policies and designing interventions to reduce malnutrition in Colombia.

Longitudinal Relations Between Childhood Maltreatment, Maltreatment-Specific Shame, and Postpartum Psychopathology

The persistence of shame-based reactions to child maltreatment (CM) has been associated with poor posttraumatic adjustment. Despite evidence that the postpartum period is a vulnerable time for women with CM histories, little is known about the consequences of maltreatment-specific (MS) shame for postpartum functioning. The current study examined individual differences in MS shame among a sample of women during the postpartum period ( n = 100) as well as prospective relations from MS shame to postpartum psychopathology at 6-, 12-, 15-, and 18-month postpartum. Linear growth curve (LGC) analyses showed that MS shame predicted higher levels of depression symptoms but not post-traumatic stress disorder (PTSD) symptoms at all time points whereas path analyses showed that shame mediated the relations from multi-maltreatment to both depression and PTSD symptoms at all time points. Results point to the long-term consequences of MS shame during postpartum and the importance of attending to shame in clinical care of maltreatment survivors who present with postpartum psychopathology.

Trends and predictors of appropriate complementary feeding practices in Nepal: An analysis of national household survey data collected between 2001 and 2014

There is evidence that suboptimal complementary feeding contributes to poor child growth. However, little is known about time trends and determinants of complementary feeding in Nepal, where the prevalence of child undernutrition remains unacceptably high. The objective of the study was to examine the trends and predictors of suboptimal complementary feeding in Nepali children aged 6–23 months using nationally representative data collected from 2001 to 2014. Data from the 2001, 2006, and 2011 Nepal Demographic and Health Surveys and the 2014 Multiple Indicator Cluster Survey were used to estimate the prevalence, trends and predictors of four WHO‐UNICEF complementary feeding indicators: timely introduction of complementary foods (INTRO), minimum meal frequency (MMF), minimum dietary diversity (MDD), and minimum acceptable diet (MAD). We used multilevel logistic regression models to identify independent factors associated with these indicators at the individual, household and community levels. In 2014, the weighted proportion of children meeting INTRO, MMF, MDD, and MAD criteria were 72%, 82%, 36% and 35%, respectively, with modest average annual rate of increase ranging from 1% to 2%. Increasing child age, maternal education, antenatal visits, and community‐level access to health care services independently predicted increasing odds of achieving MMF, MDD, and MAD. Practices also varied by ecological zone and sociocultural group. Complementary feeding practices in Nepal have improved slowly in the past 15 years. Inequities in the risk of inappropriate complementary feeding are evident, calling for programme design and implementation to address poor feeding and malnutrition among the most vulnerable Nepali children.

Sedentariness and weight status related to SES and family characteristics in Italian adults: exploring geographic variability through multilevel models

AIM

In this study, our aim was to assess the prevalence of sedentariness and overweight/obesity, two modifiable risk factors for non-communicable diseases (NCDs), and to investigate the geographic variability in their association with socio-economic status (SES) and family characteristics in Italian adults.

METHODS

The Multipurpose Survey on Health Conditions and the Recourse to Health Services (MSHC), 2012/2013 edition, conducted by the National Institute of Statistics was used as data source. The sample for this study included 99,479 interviewed people aged 18 and over, which are representative of about 50 million persons. For the scope of this analysis, data were considered as individuals nested within families within regions and analysed through multilevel models.

RESULTS

It was estimated that 39.8% of Italian adults are sedentary, 38.1% are partially active and 22.1% are physically active; 11.3% of Italian adults are obese and the 34.5% are overweight. There was evidence of inverse socio-economic gradient for both sedentariness and body mass index (BMI). There was higher risk of sedentariness for one-parent (odds ratio (OR) = 1.10; 95% confidence interval (CI) = (1.02; 1.20)) and other family types (OR = 1.34; 95% CI = (1.20; 1.48 )) compared with couples with children. Also, the relative variation of BMI was statistically significant for one-parent, one-person and other families ( p < 0.05). An increasing north-south gradient was suggested for BMI, but not for sedentariness.

CONCLUSIONS

Policy interventions could be addressed to reduce BMI levels in the southern area and to encourage physical activity in regions with high sedentariness. The Italian family is the key driver to promote virtuous healthy behaviours.

The Relationship of Repeated Technical Assistance Support Visits to the Delivery of Positive Health, Dignity, and Prevention (PHDP) Messages by Healthcare Providers in Mozambique: A Longitudinal Multilevel Analysis

BACKGROUND

Positive health, dignity, and prevention (PHDP) is Mozambique's strategy to engage clinicians in the delivery of prevention messages to their HIV-positive clients. This national implementation strategy uses provider trainings on offering key messages and focuses on intervening on 9 evidence-based risk reduction areas. We investigated the impact of longitudinal technical assistance (TA) as an addition to this basic training.

METHODS

We followed 153 healthcare providers in 5 Mozambican provinces over 6 months to evaluate the impact of on-site, observation-based TA on PHDP implementation. Longitudinal multilevel models were estimated to model change in PHDP message delivery over time among individual providers.

RESULTS

With each additional TA visit, providers delivered about 1 additional PHDP message ( P < .001); clinicians and nonclinicians started at about the same baseline level, but clinicians improved more quickly ( P = .004). Message delivery varied by practice sector; maternal and child health sectors outperformed other sectors.

CONCLUSION

Longitudinal TA helped reach the programmatic goals of the PHDP program in Mozambique.

Community and Individual Risk Factors for Physical Child Abuse and Child Neglect: Variations by Poverty Status

Families are impacted by a variety of risk and protective factors for maltreatment at multiple levels of the social ecology. Individual- and neighborhood-level poverty has consistently been shown to be associated with higher risk for child abuse and neglect. The current study sought to understand the ways in which individual- and neighborhood-level risk and protective factors affect physical child abuse and child neglect and whether these factors differed for families based on their individual poverty status. Specifically, we used a three-level hierarchical linear model (families nested within census tracts and nested within cities) to estimate the relationships between physical child abuse and child neglect and neighborhood structural factors, neighborhood processes, and individual characteristics. We compared these relationships between lower and higher income families in a sample of approximately 3,000 families from 50 cities in the State of California. We found that neighborhood-level disadvantage was especially detrimental for families in poverty and that neighborhood-level protective processes (social) were not associated with physical child abuse and child neglect for impoverished families, but that they had a protective effect for higher income families.

Examining the Performance of the Metropolis-Hastings Robbins-Monro Algorithm in the Estimation of Multilevel Multidimensional IRT Models

The purpose of this study was to examine the performance of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm in the estimation of multilevel multidimensional item response theory (ML-MIRT) models. The accuracy and efficiency of MH-RM in recovering item parameters, latent variances and covariances, as well as ability estimates within and between clusters (e.g., schools) were investigated in a simulation study, varying the number of dimensions, the intraclass correlation coefficient, the number of clusters, and cluster size, for a total of 24 conditions. Overall, MH-RM performed well in recovering the item, person, and group-level parameters of the model. Ratios of the empirical to analytical standard errors indicated that the analytical standard errors reported in flexMIRT were somewhat overestimated for the cluster-level ability estimates, a little too large for the person-level ability estimates, and essentially accurate for the other parameters. Limitations of the study, implications for educational measurement practice, and directions for future research are offered.

Invited Commentary: Beyond Individuals-Area Poverty and Health, or the Search for an Impactful Epidemiology

In this commentary, I reflect on the paper "Poverty and Health: Prospective Evidence From the Alameda County Study" by Haan et al. (Am J Epidemiol. 1987;125(6):989-998), which together with supporting work ushered in a surge of epidemiologic interest in the impact of social contexts on health. I discuss why the paper was influential and how the premise of this type of work influenced theories and methods in epidemiology. The paper is placed in the broader context of the nature of evidence in epidemiology and the ultimate purpose of epidemiologic inquiry.

A Multilevel Multiset Time-Series Model for Describing Complex Developmental Processes

The authors sought to develop an analytical platform where multiple sets of time series can be examined simultaneously. This multivariate platform capable of testing interaction effects among multiple sets of time series can be very useful in empirical research. The authors demonstrated that the multilevel framework can readily accommodate this analytical capacity. Given their intention to use the multilevel multiset time-series model to pursue complicated research purposes, their resulting model is relatively simple to specify, to run, and to interpret. These advantages make the adoption of their model relatively effortless as long as researchers have the basic knowledge and skills in working with multilevel growth modeling. With multiple potential extensions of their model, the establishment of this analytical platform for analysis of multiple sets of time series can inspire researchers to pursue far more advanced research designs to address complex developmental processes in reality.

Intermediate and advanced topics in multilevel logistic regression analysis

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R² measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

A Comparison of Population-Averaged and Cluster-Specific Approaches in the Context of Unequal Probabilities of Selection

Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights). The goal of this study was to determine under what conditions single-level and multilevel estimators outperform one another in the context of a two-stage sampling design with unequal probabilities of selection. Monte Carlo simulation methods were used to evaluate the impact of several factors, including population model, informativeness of the design, distribution of the outcome variable, intraclass correlation coefficient, cluster size, and estimation method. Results indicated that the unweighted estimators performed similarly across conditions, whereas the weighted single-level estimators tended to outperform the weighted multilevel estimators, particularly under nonideal sample conditions. Multilevel weight approximation methods did not perform well when the design was informative. An empirical example is provided to demonstrate how researchers might investigate the implications of the simulation results in practice.

Many-level multilevel structural equation modeling: An efficient evaluation strategy

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

Nonindependence and sensitivity analyses in ecological and evolutionary meta-analyses

Meta-analysis is an important tool for synthesizing research on a variety of topics in ecology and evolution, including molecular ecology, but can be susceptible to nonindependence. Nonindependence can affect two major interrelated components of a meta-analysis: (i) the calculation of effect size statistics and (ii) the estimation of overall meta-analytic estimates and their uncertainty. While some solutions to nonindependence exist at the statistical analysis stages, there is little advice on what to do when complex analyses are not possible, or when studies with nonindependent experimental designs exist in the data. Here we argue that exploring the effects of procedural decisions in a meta-analysis (e.g. inclusion of different quality data, choice of effect size) and statistical assumptions (e.g. assuming no phylogenetic covariance) using sensitivity analyses are extremely important in assessing the impact of nonindependence. Sensitivity analyses can provide greater confidence in results and highlight important limitations of empirical work (e.g. impact of study design on overall effects). Despite their importance, sensitivity analyses are seldom applied to problems of nonindependence. To encourage better practice for dealing with nonindependence in meta-analytic studies, we present accessible examples demonstrating the impact that ignoring nonindependence can have on meta-analytic estimates. We also provide pragmatic solutions for dealing with nonindependent study designs, and for analysing dependent effect sizes. Additionally, we offer reporting guidelines that will facilitate disclosure of the sources of nonindependence in meta-analyses, leading to greater transparency and more robust conclusions.

Duration in Poverty-Related Programs and Number of Child Maltreatment Reports

This study examined the relationship of a family's duration in poverty-related programs (i.e., Aid to Families with Dependent Children/Temporary Assistance for Needy Families and Medicaid) to the subject child's number of maltreatment reports while considering race and baseline neighborhood poverty. Children from a large Midwestern metropolitan area were followed through a linked cross-sector administrative database from birth to age 15. Generalized multilevel models were employed to account for the multilevel structure of the data (i.e., nesting of families within neighborhoods). The data showed a unique and significant contribution of duration in poverty-related programs to the number of maltreatment reports. The predicted number of maltreatment reports increased by between 2.5 and 3.7 times, as duration in poverty-related programs increased from 0 to 9 years. This relationship was consistent between Whites and non-Whites (over 98% Black), but non-Whites showed a significantly lower number of total maltreatment reports while controlling for duration in poverty-related programs. We were unable to find a significant association between child maltreatment reports and baseline neighborhood poverty.

From Heterogeneity to Concentration: Latino Immigrant Neighborhoods and Collective Efficacy Perceptions in Los Angeles and Chicago

Latino immigrant presence in urban neighborhoods has been linked with reduced neighborhood cohesion in social disorganization-based ethnic heterogeneity hypotheses and enhanced cohesion in immigration revitalization approaches. Using the 2000-2002 Los Angeles Family and Neighborhood Survey and the 1994-1995 Project on Human Development in Chicago Neighborhoods Community Survey, we explore the association between Latino immigrant concentration and both levels of, and agreement about, neighborhood collective efficacy. Findings from multilevel models with heteroskedastic variance indicate that Latino immigrant concentration exhibits a nonlinear association with collective efficacy. At low levels, increases in Latino immigrant concentration diminish collective efficacy, consistent with a heterogeneity hypothesis. The negative association between Latino immigrant concentration and collective efficacy declines in magnitude as immigrant concentration increases and, particularly in LA, becomes positive beyond a threshold, consistent with an immigration revitalization effect. We also find an inverse nonlinear pattern of association with the variance of collective efficacy. At low levels, increasing Latino immigrant concentration increases the variance of collective efficacy (reflecting more disagreement), but beyond a threshold, this association becomes negative (reflecting increasing agreement). This pattern is observed in both LA and Chicago. The prevalence of social interaction and reciprocated exchange within neighborhoods explains a modest proportion of the Latino immigrant concentration effect on mean levels of collective efficacy in Chicago, but does little to explain effects on the mean in LA or effects on the variance in either LA or Chicago. These findings offer insight into the complex role Latino immigrant presence plays in shaping neighborhood social climate.

Association of volunteering with mental well-being: a lifecourse analysis of a national population-based longitudinal study in the UK

OBJECTIVES

The association of volunteering with well-being has been found in previous research, but mostly among older people. The aim of this study was to examine the association of volunteering with mental well-being among the British population across the life course.

DESIGN

British Household Panel Survey, a population-based longitudinal study.

SETTING

UK.

PARTICIPANTS

66 343 observations (person-years).

MAIN OUTCOME MEASURES

Mental well-being was measured by using the General Health Questionnaire (GHQ-12 or GHQ); high values denote high mental disorder. Four groups of volunteering participation were created: frequent (once a week), infrequent (once a month/several times a year), rare (once or less a year) and never. Multilevel linear models were used to analyse variations in mental well-being over the life course by levels of volunteering.

RESULTS

When not considering age, those who engaged in volunteering regularly appeared to experience higher levels of mental well-being than those who never volunteered. To explore the association of volunteering with the GHQ across the life course, interaction terms were fitted between age and volunteering. The interactions were significant, demonstrating that these associations vary by age. The association between volunteering and well-being did not emerge during early adulthood to mid-adulthood, instead becoming apparent above the age of 40 years and continuing up to old age. Moreover, in early adulthood, the absence of engagement in voluntary activity was not related to mental well-being, but GHQ scores for this group increased sharply with age, levelling off after the age of 40 and then increasing again above the age of 70 years. The study also indicates variation in GHQ scores (65%) within individuals across time, suggesting evidence of lifecourse effects.

CONCLUSIONS

We conclude that volunteering may be more meaningful for mental well-being at some points of time in the life course.

Can You Be Hit by Your Partner and Be Intensely in Love?

This study examines, in a multilevel context, the influence of intensity of love at an individual level on couples' mean reports of psychological and physical aggression in a sample of 2,988 adult couples of both sexes from the Region of Madrid. The percentages of intimate partner aggression considering the highest report of aggression in the couple were around 60% of psychological aggression and 15% of physical aggression. Multilevel models confirm that individuals who were less intensely in love reported a higher level of psychological aggression. Concerning physical aggression, men who declared they were less intensely in love reported a higher level of physical aggression by their partners, but this pattern was not found in the women. Therefore, psychological aggression plays a more relevant role in the intensity of love than physical aggression.

Division of Household and Childcare Labor and Relationship Conflict Among Low-Income New Parents

We examine the relationships among the division of housework and childcare labor, perceptions of its fairness for two types of family labor (housework and childcare), and parents' relationship conflict across the transition to parenthood. Perceived fairness is examined as a mediator of the relationships between change in the division of housework and childcare and relationship conflict. Working-class, dual-earner couples (n = 108) in the U.S Northeast were interviewed at five time points from the third trimester of pregnancy and across the first year of parenthood. Research questions addressed whether change in the division of housework and childcare across the transition to parenthood predicted mothers' and fathers' relationship conflict, with attention to the mediating role of perceived fairness of these chores. Findings for housework indicated that perceived fairness was related to relationship conflict for mothers and fathers, such that when spouses perceived the change in the division of household tasks to be unfair to either partner, they reported more conflict, However, fairness did not significantly mediate relations between changes in division of household tasks and later relationship conflict. For childcare, fairness mediated relations between mothers' violated expectations concerning the division of childcare and later conflict such that mothers reported less conflict when they perceived the division of childcare as less unfair to themselves; there was no relationship for fathers. Findings highlight the importance of considering both childcare and household tasks independently in our models and suggest that the division of housework and childcare holds different implications for mothers' and fathers' assessments of relationship conflict.

When parsimony is not enough: considering dual processes and dual levels of influence in sexual decision making

The literature on sexual decision making that has been used to understand behaviors relevant to HIV and STI risk has relied primarily on cognitive antecedents of behavior. In contrast, several prominent models of decision making outside of the sexual behavior literature rely on dual process models, in which both affective and cognitive processing are considered as important precursors to behavior. Moreover, much of the literature on sexual behavior utilizes individual-level traits and characteristics to predict aggregated sexual behavior, despite decision making itself being a situational or event-level process. This article proposes a framework for understanding sexual decision making as the result of dual processes (affective and cognitive) operating at dual level of influence (individual and situational). Finally, this article ends with a discussion of the conceptual and methodological benefits and challenges to its use and future directions for research.

Databases for multilevel biophysiology research available at Physiome.jp

Physiome.jp (http://physiome.jp) is a portal site inaugurated in 2007 to support model-based research in physiome and systems biology. At Physiome.jp, several tools and databases are available to support construction of physiological, multi-hierarchical, large-scale models. There are three databases in Physiome.jp, housing mathematical models, morphological data, and time-series data. In late 2013, the site was fully renovated, and in May 2015, new functions were implemented to provide information infrastructure to support collaborative activities for developing models and performing simulations within the database framework. This article describes updates to the databases implemented since 2013, including cooperation among the three databases, interactive model browsing, user management, version management of models, management of parameter sets, and interoperability with applications.

Feel like you belong: on the bidirectional link between emotional fit and group identification in task groups

Three studies investigated the association between members’ group identification and the emotional fit with their group. In the first study, a cross-sectional study in a large organization, we replicated earlier research by showing that group identification and emotional fit are positively associated, using a broader range of emotions and using profile correlations to measure group members’ emotional fit. In addition, in two longitudinal studies, where groups of students were followed at several time points during their collaboration on a project, we tested the directionality of the relationship between group identification and emotional fit. The results showed a bidirectional, positive link between group identification and emotional fit, such that group identification and emotional fit either mutually reinforce or mutually dampen each other over time. We discuss how these findings increase insights in group functioning and how they may be used to change group processes for better or worse.

Incorporating the sampling design in weighting adjustments for panel attrition

We review weighting adjustment methods for panel attrition and suggest approaches for incorporating design variables, such as strata, clusters, and baseline sample weights. Design information can typically be included in attrition analysis using multilevel models or decision tree methods such as the chi-square automatic interaction detection algorithm. We use simulation to show that these weighting approaches can effectively reduce bias in the survey estimates that would occur from omitting the effect of design factors on attrition while keeping the resulted weights stable. We provide a step-by-step illustration on creating weighting adjustments for panel attrition in the Galveston Bay Recovery Study, a survey of residents in a community following a disaster, and provide suggestions to analysts in decision-making about weighting approaches.

Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction

Training set size is an important determinant of genomic prediction accuracy. Plant breeding programs are characterized by a high degree of structuring, particularly into populations. This hampers the establishment of large training sets for each population. Pooling populations increases training set size but ignores unique genetic characteristics of each. A possible solution is partial pooling with multilevel models, which allows estimating population-specific marker effects while still leveraging information across populations. We developed a Bayesian multilevel whole-genome regression model and compared its performance with that of the popular BayesA model applied to each population separately (no pooling) and to the joined data set (complete pooling). As an example, we analyzed a wide array of traits from the nested association mapping maize population. There we show that for small population sizes (e.g., <50), partial pooling increased prediction accuracy over no or complete pooling for populations represented in the training set. No pooling was superior; however, when populations were large. In another example data set of interconnected biparental maize populations either partial or complete pooling was superior, depending on the trait. A simulation showed that no pooling is superior when differences in genetic effects among populations are large and partial pooling when they are intermediate. With small differences, partial and complete pooling achieved equally high accuracy. For prediction of new populations, partial and complete pooling had very similar accuracy in all cases. We conclude that partial pooling with multilevel models can maximize the potential of pooling by making optimal use of information in pooled training sets.

Childhood Conditions and Multimorbidity Among Older Adults

OBJECTIVES

This research tests whether childhood conditions are associated with trajectories of chronic conditions among older adults.

METHODS

Using data from the Health and Retirement Study (1992-2008), a series of hierarchical linear models are used to estimate number of chronic conditions at survey midpoint and the rate of increase in chronic conditions across 18 years of data.

RESULTS

Results suggest that lower childhood socioeconomic status (SES) and poor childhood health are associated with increased number of chronic conditions; however, childhood SES is no longer associated with chronic conditions after adjustment for adult SES and adult health. Poor childhood health continues to be associated with total number of chronic conditions after adjustment for adult SES and health. Rate of change in chronic conditions was not associated with childhood conditions. Results from a multinomial logistic regression model further indicated that the association between childhood conditions and adult multimorbidity increased at higher levels of multimorbidity.

DISCUSSION

This research adds to the evidence that early life conditions have a lasting influence on adult health, and that their influence may be independent of adult health and SES.

Who is left behind on the road to universal facility delivery? A cross-sectional multilevel analysis in rural Tanzania

OBJECTIVES

To examine factors associated with home delivery among women in Pwani Region, Tanzania, which has experienced a rapid rise in facility delivery coverage.

METHODS

Cross-sectional data from a population-based survey of women residing in rural areas of Pwani Region were linked to health facility locations. We fitted multilevel logistic models to examine individual and community factors associated with home delivery.

RESULTS

A total of 752 (27.95%) of the 2691 women who completed the survey delivered their last child at home. Women were less likely to deliver at home if they had any primary education [odds ratio (OR) 0.62; 95% confidence interval (CI): 0.50, 0.79], were primiparous (OR: 0.52; 95% CI: 0.37, 0.73), had more exposure to media (OR: 0.80; 95% CI: 0.66, 0.96) or had received more (OR: 0.78; 95% CI: 0.63, 0.96) or better quality antenatal care (ANC) services (OR: 0.48; 95% CI: 0.34, 0.67). Increased wealth was strongly associated with lower odds of home delivery (OR: 0.27; 95% CI: 0.18, 0.39), as was living in a village that grew cash crops (OR: 0.56; 95% CI: 0.35, 0.88). Farther distance to hospital, but not to lower level facilities, was associated with higher likelihood of home delivery (OR 2.49; 95% CI: 1.60, 3.88).

CONCLUSIONS

Poverty, multiparity, weak ANC and distance to hospital were associated with persistence of home delivery in a region with high coverage of facility delivery. A pro-poor path to universal coverage of safe delivery requires a greater focus on quality of care and more intensive outreach to poor and multiparous women.

Does population density and neighborhood deprivation predict schizophrenia? A nationwide Swedish family-based study of 2.4 million individuals

People living in densely populated and socially disorganized areas have higher rates of psychiatric morbidity, but the potential causal status of such factors is uncertain. We used nationwide Swedish longitudinal registry data to identify all children born 1967-1989 (n = 2361585), including separate datasets for all cousins (n = 1715059) and siblings (n = 1667894). The nature of the associations between population density and neighborhood deprivation and individual risk for a schizophrenia diagnosis was investigated while adjusting for unobserved familial risk factors (through cousin and sibling comparisons) and then compared with similar associations for depression. We generated familial pedigree structures using the Multi-Generation Registry and identified study participants with schizophrenia and depression using the National Patient Registry. Fixed-effects logistic regression models were used to study within-family estimates. Population density, measured as ln(population size/km(2)), at age 15 predicted subsequent schizophrenia in the population (OR = 1.10; 95% CI: 1.09; 1.11). Unobserved familial risk factors shared by cousins within extended families attenuated the association (1.06; 1.03; 1.10), and the link disappeared entirely within nuclear families (1.02; 0.97; 1.08). Similar results were found for neighborhood deprivation as predictor and for depression as outcome. Sensitivity tests demonstrated that timing and accumulation effects of the exposures (mean scores across birth, ages 1-5, 6-10, and 11-15 years) did not alter the findings. Excess risks of psychiatric morbidity, particularly schizophrenia, in densely populated and socioeconomically deprived Swedish neighborhoods appear, therefore, to result primarily from unobserved familial selection factors. Previous studies may have overemphasized the etiological importance of these environmental factors.

To center or not to center? Investigating inertia with a multilevel autoregressive model

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

The role of partnership status on late-life physical function

This study examined the socioeconomic pathways linking partnership status to physical functioning, assessed using objective measures of late life physical functioning, including peak flow and grip strength. Using Wave 4 of the Survey of Health, Ageing and Retirement in Europe (SHARE), we ran multilevel models to examine the relationship between partnership status and physical function in late life, adjusting for social-network characteristics, socioeconomic factors, and health behaviours. We found a robust relationship between partnership status and physical function. Incorporating social-network characteristics, socioeconomic factors, and health behaviours showed independent robust relationships with physical function. Co-variates attenuated the impact of cohabitation, separation, and widowhood on physical function; robust effects were found for singlehood and divorce. Sex-segregated analyses suggest that associations between cohabitation, singlehood, divorce, and widowhood were larger for men than for women. Results suggest that social ties are important to improved physical function.

Associations of neighborhood concentrated poverty, neighborhood racial/ethnic composition, and indoor allergen exposures: a cross-sectional analysis of los angeles households, 2006-2008

Although racial/ethnic, socioeconomic, and neighborhood factors have been linked to asthma, and the association between indoor allergens and asthma is well documented, few studies have examined the relationship between these factors and indoor allergens. We examined the frequency of reported indoor allergens and differences by racial/ethnic, socioeconomic, and neighborhood characteristics among a diverse sample of Los Angeles households. Multilevel logistic regression models were used to analyze the data from 723 households from wave 2 of the Los Angeles Family and Neighborhood Survey. The reported presence of rats, mice, cockroaches, mold, pets, and tobacco smoke were the primary outcomes of interest. Hispanic and Asian households had a nearly threefold increase in the odds of reporting cockroaches compared to non-Hispanic Whites (OR, 2.85; 95 % CI 1.38-5.88 and OR, 2.62; 95 % CI 1.02-6.73, respectively) even after adjusting for socioeconomic factors. Primary caregivers who had obtained a high school degree were significantly less likely to report the presence of mice and cockroaches compared to primary caregivers with less than a high school degree (OR, 0.19; 95 % CI 0.08-0.46 and OR, 0.39; 95 % CI 0.23-0.68, respectively). Primary caregivers with more than a high school degree were also less likely to report the presence of rats, mice, and cockroaches within their households, compared to those with less than a high school degree. Compared to renters, home owners were less likely to report the presence of mice, cockroaches, and mold within their households. At the neighborhood level, households located within neighborhoods of high concentrated poverty (where the average poverty rate is at least 50 %) were more likely to report the presence of mice and cockroaches compared to households in low concentrated poverty neighborhoods (average poverty rate is 10 % or less), after adjusting for individual race/ethnicity and socioeconomic characteristics. Our study found evidence in support of neighborhood-level racial/ethnic and socioeconomic influences on indoor allergen exposure, above and beyond individual factors. Future studies should continue to explore individual and neighborhood-level racial/ethnic and socioeconomic differences in household allergen exposures across diverse contexts.

Obesity (Sometimes) Matters: The Importance of Context in the Relationship between Obesity and Life Satisfaction

Previous research has established the negative influence of obesity on subjective well-being. In the present work, the authors use multilevel modeling and Behavior Risk Factor Surveillance System data (N = 1,319,340) to examine how this relationship is influenced by the prevalence of obesity in the contexts in which individuals are living and how such relationships vary by gender. The results suggest that some of the influence of obesity on life satisfaction is the result of relative comparison. Implications for both our understanding of the growing "obesity epidemic" and the study of subjective well-being are discussed.

Combining information from two data sources with misreporting and incompleteness to assess hospice-use among cancer patients: a multiple imputation approach

Combining information from multiple data sources can enhance estimates of health-related measures by using one source to supply information that is lacking in another, assuming the former has accurate and complete data. However, there is little research conducted on combining methods when each source might be imperfect, for example, subject to measurement errors and/or missing data. In a multisite study of hospice-use by late-stage cancer patients, this variable was available from patients' abstracted medical records, which may be considerably underreported because of incomplete acquisition of these records. Therefore, data for Medicare-eligible patients were supplemented with their Medicare claims that contained information on hospice-use, which may also be subject to underreporting yet to a lesser degree. In addition, both sources suffered from missing data because of unit nonresponse from medical record abstraction and sample undercoverage for Medicare claims. We treat the true hospice-use status from these patients as a latent variable and propose to multiply impute it using information from both data sources, borrowing the strength from each. We characterize the complete-data model as a product of an 'outcome' model for the probability of hospice-use and a 'reporting' model for the probability of underreporting from both sources, adjusting for other covariates. Assuming the reports of hospice-use from both sources are missing at random and the underreporting are conditionally independent, we develop a Bayesian multiple imputation algorithm and conduct multiple imputation analyses of patient hospice-use in demographic and clinical subgroups. The proposed approach yields more sensible results than alternative methods in our example. Our model is also related to dual system estimation in population censuses and dual exposure assessment in epidemiology.

Multilevel sparse functional principal component analysis

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

The aftermath of public housing relocations: relationships between changes in local socioeconomic conditions and depressive symptoms in a cohort of adult relocaters

USA is experiencing a paradigm shift in public housing policy: while policies used to place people who qualified for housing assistance into spatially concentrated housing complexes, they now seek to geographically disperse them, often to voucher-subsidized rental units in the private market. Programs that relocate residents from public housing complexes tend to move them to neighborhoods that are less impoverished and less violent. To date, studies have reached conflicting findings about the relationship between public housing relocations and depression among adult relocaters. The present longitudinal multilevel analysis tests the hypothesis that pre-/postrelocation improvements in local economic conditions, social disorder, and perceived community violence are associated with declines in depressive symptoms in a cohort of African-American adults; active substance misusers were oversampled. We tested this hypothesis in a cohort of 172 adults who were living in one of seven public housing complexes scheduled for relocation and demolition in Atlanta, GA; by design, 20% were dependent on substances and 50% misused substances but were not dependent. Baseline data captured prerelocation characteristics of participants; of the seven census tracts where they lived, three waves of postrelocation data were gathered approximately every 9 months thereafter. Surveys were administered at each wave to assess depressive symptoms measured using the Center for Epidemiologic Studies Depression Scale (CES-D), perceived community violence, and other individual-level covariates. Participants' home addresses were geocoded to census tracts at each wave, and administrative data sources were used to characterize tract-level economic disadvantage and social disorder. Hypotheses were tested using multilevel models. Between waves 1 and 2, participants experienced significant improvements in reported depressive symptoms and perceived community violence and in tract-level economic disadvantage and social disorder; these reductions were sustained across waves 2-4. A 1 standard deviation improvement in economic conditions was associated with a 1-unit reduction in CES-D scores; the magnitude of this relationship did not vary by baseline substance misuse or gender. Reduced perceived community violence also predicted lower CES-D scores. Our objective measure of social disorder was unrelated to depressive symptoms. We found that relocaters who experienced greater pre-/postrelocation improvements in economic conditions or in perceived community violence experienced fewer depressive symptoms. Combined with past research, these findings suggest that relocation initiatives should focus on the quality of the places to which relocaters move; future research should also identify pathways linking pre-/postrelocation changes in place characteristics to changes in mental health.

Does neighborhood collective efficacy for families change over time? The Boston Neighborhood Survey

There is an increased interest in how neighborhood social processes, such as collective efficacy, may protect mental health. Yet little is known about how stable these neighborhood processes are over time, or how to change them to influence other downstream factors. We used a population-based, repeat cross-sectional study of adults (n=5135) to assess stability of collective efficacy for families in 38 Boston neighborhoods across 4 years (2006, 2008, 2010) (the Boston Neighborhood Survey). We test temporal stability of collective efficacy for families across and within neighborhoods using 2-level random effects linear regression, fixed effects linear regression, T-tests, and Wilcoxon rank tests. Across the different methods, neighborhood collective efficacy for families remained stable across 4 years, after adjustment for neighborhood composition. If neighborhood collective efficacy is measured within 4 years of the exposure period of interest, assuming temporal stability may be valid.

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data.

Multilevel and spatial-time trend analyses of the prevalence of hypertension in a large urban city in the USA

We aimed to test two hypotheses that (1) there were significant variations in the prevalence of hypertension (HBP) across neighborhoods in the city of Philadelphia and (2) these variations were significantly explained by the variations in the neighborhood physical and socioeconomic environment (PSE). We used data from the Southeastern Pennsylvania Household Health Surveys in 2002-2004 (study period 1, n = 8,567), and in 2008-2010 (period 2, n = 8,747). An index of neighborhood PSE was constructed using multiple specific measures. The associations of HBP with PSE at the neighborhood level and other risk factors at the individual level were examined using multilevel regression analysis. The results show that age-adjusted prevalence of HBP increased from 30.33 to 33.04 % from study periods 1 to 2 (p < 0.001). An estimate of 44 and 53 % of the variations in the prevalence of HBP could be explained by the variations in neighborhood PSE in study periods 1 and 2, respectively. In conclusion, prevalence of HBP significantly increased from 2002-2004 to 2008-2010. Individuals living in neighborhoods with disadvantaged PSE have significantly higher risk of the prevalence of HBP.

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts

Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models.

Assessing interchangeability at cluster levels with multiple-informant data

Studies examining the relationship between neighborhood social disorder and health often rely on multiple informants. Such studies assume interchangeability of the latent constructs derived from multiple-informant data. Existing methods examining this assumption do not clearly delineate the uncertainty at individual levels from that at neighborhood levels. We propose a multilevel variance component factor model that allows this delineation. Data come from a survey of a representative sample of children born between 1983 and 1985 in the inner city of Detroit and nearby middle-class suburbs. Results indicate that the informant-level models tend to exaggerate the effect of places because of differences between persons. Our evaluations of different methodologies lead to the recommendation of the multilevel variance component factor model whenever multiple-informant reports can be aggregated at a neighborhood level.

People, Land, and Context: Multilevel Determinants of Off-farm Employment in the Ecuadorian Amazon

This paper investigates the factors that motivate decisions of settler colonists to engage in off-farm employment (OFE) in the Northern Ecuadorian Amazon (NEA). Overall, OFE, as a type of population mobility, may increasingly become a dominant demographic factor in rural frontier regions. Although OFE decisions are primarily a matter of individual choice, factors associated with the farm household and the local community also play key roles in this decision-making. This paper applies a multilevel conceptual framework and uses a multinomial, multilevel statistical model to study OFE in the NEA in 1999 as a result of factors at the individual, farm household, and community levels. The results show important differences between OFE participation choices in personal characteristics, human capital, farm household life cycle, land use, land management, farm environmental conditions, transportation accessibility, community size, and structure of local labor markets. The paper also identifies the effects of policy-relevant variables on choices to engage in OFE in local community, other rural, or urban areas of destination.

Mediation and spillover effects in group-randomized trials: a case study of the 4Rs educational intervention

Peer influence and social interactions can give rise to spillover effects in which the exposure of one individual may affect outcomes of other individuals. Even if the intervention under study occurs at the group or cluster level as in group-randomized trials, spillover effects can occur when the mediator of interest is measured at a lower level than the treatment. Evaluators who choose groups rather than individuals as experimental units in a randomized trial often anticipate that the desirable changes in targeted social behaviors will be reinforced through interference among individuals in a group exposed to the same treatment. In an empirical evaluation of the effect of a school-wide intervention on reducing individual students' depressive symptoms, schools in matched pairs were randomly assigned to the 4Rs intervention or the control condition. Class quality was hypothesized as an important mediator assessed at the classroom level. We reason that the quality of one classroom may affect outcomes of children in another classroom because children interact not simply with their classmates but also with those from other classes in the hallways or on the playground. In investigating the role of class quality as a mediator, failure to account for such spillover effects of one classroom on the outcomes of children in other classrooms can potentially result in bias and problems with interpretation. Using a counterfactual conceptualization of direct, indirect and spillover effects, we provide a framework that can accommodate issues of mediation and spillover effects in group randomized trials. We show that the total effect can be decomposed into a natural direct effect, a within-classroom mediated effect and a spillover mediated effect. We give identification conditions for each of the causal effects of interest and provide results on the consequences of ignoring "interference" or "spillover effects" when they are in fact present. Our modeling approach disentangles these effects. The analysis examines whether the 4Rs intervention has an effect on children's depressive symptoms through changing the quality of other classes as well as through changing the quality of a child's own class.

Describing differences in weight and length growth trajectories between white and Pakistani infants in the UK: analysis of the Born in Bradford birth cohort study using multilevel linear spline models

OBJECTIVE

To describe the growth pattern from birth to 2 years of UK-born white British and Pakistani infants.

DESIGN

Birth cohort.

SETTING

Bradford, UK.

PARTICIPANTS

314 white British boys, 383 Pakistani boys, 328 white British girls and 409 Pakistani girls.

MAIN OUTCOME MEASURES

Weight and length trajectories based on repeat measurements from birth to 2 years.

RESULTS

Linear spline multilevel models for weight and length with knot points at 4 and 9 months fitted the data well. At birth Pakistani boys were 210 g lighter (95% CI -290 to -120) and 0.5 cm shorter (-1.04 to 0.02) and Pakistani girls were 180 g lighter (-260 to -100) and 0.5 cm shorter (-0.91 to -0.03) than white British boys and girls, respectively. Pakistani infants gained length faster than white British infants between 0 and 4 months (+0.3 cm/month (0.1 to 0.5) for boys and +0.4 cm/month (0.2 to 0.6) for girls) and gained more weight per month between 9 and 24 months (+10 g/month (0 to 30) for boys and +30 g/month (20 to 40) for girls). Adjustment for maternal height attenuated ethnic differences in weight and length at birth, but not in postnatal growth. Adjustment for other confounders did not explain differences in any outcomes.

CONCLUSIONS

Pakistani infants were lighter and had shorter predicted mean length at birth than white British infants, but gained weight and length quicker in infancy. By age 2 years both ethnic groups had similar weight, but Pakistani infants were on average taller than white British infants.

The Division of Household Labor: Longitudinal Changes and Within-Couple Variation

This study examined how the division of household labor changed as a function of marital duration and whether within-couple variation in spouses' relative power and availability were linked to within-couple variation in the division of labor. On 4 occasions over 7 years, 188 stably married couples reported on their housework activities using daily diaries. Multilevel models revealed that wives' portions of household responsibilities declined over time, and that changes in spouses' relative income and work hours were linked to changes in housework allocation. Wives with husbands who perceived greater marital control, on average, did proportionally more housework, and for couples with husbands who had highly autonomous jobs, changes in spouses' relative psychological job involvement were linked to changes in housework allocation. Findings highlight the importance of understanding household division of labor as a lifespan phenomenon, the distinction between within- versus between-couple associations, and the multidimensional nature of power and availability.

The effect of stability and change in health behaviors on trajectories of body mass index in older Americans: a 14-year longitudinal study

BACKGROUND

Obesity is increasingly prevalent among older adults, yet little is known about the impact of health behaviors on the trajectories of body weight in this age group.

METHODS

We examined the effect of time-varying smoking, physical activity (PA), alcohol use, and changes thereof, on the 14-year (1992-2006) trajectory of body- mass index (BMI) in a cohort of 10,314 older adults from the Health and Retirements Study, aged 51-61 years at baseline. Hierarchical linear modeling (HLM) quantifies the effect of smoking, PA, and alcohol use (user status, initiation and cessation) on intercept and rate-of-change in BMI trajectory, and tests for variations in the strength of association between each behavior and BMI.

RESULTS

Over 14 years (82,512 observations), BMI increased approximated by a quadratic function. Smoking and PA (user status and initiation) were associated with significantly lower BMI trajectories over time. Cessation of smoking and PA resulted in higher BMI trajectories over time. The weight-gaining effect of smoking cessation increased, while the strength of association between BMI trajectories and PA or alcohol use were constant over time. Socio-economic and health status differences explained the effects of alcohol use on BMI trajectory.

CONCLUSIONS

In older adults, smoking and PA, and changes thereof, vary in their long-term effect on trajectories of BMI. Barring increases in PA levels, older smokers who quit today are expected to gain significantly more weight than two decades ago. This knowledge is essential for the design of smoking cessation, physical activityPA, and weight-control interventions in older adults.

An Examination of Exposure to Traumatic Events and Symptoms and Strengths for Children Served in a Behavioral Health System of Care

The present study examined how exposure to traumatic events impacts children with severe emotional disturbance who are being served in a school-based system of care. Multilevel growth curve models were used to examine the relationships between a child's history of traumatic events (physical abuse, sexual abuse, or domestic violence) and behavioral and emotional strengths, internalizing problem behaviors, or externalizing problem behaviors over 18 months. Results indicate that children receiving services (N = 134) exhibited increased emotional and behavioral strengths and decreased internalizing and externalizing problem behaviors from enrollment to 18 months follow-up. Children with a history of traumatic events improved more slowly than children without such a history on both strengths and internalizing problem behaviors, even after controlling for dosage of services received and other characteristics previously found to predict outcomes. Gender was also related to improvement in internalizing symptoms. Results highlight the continued need to assess the impact of exposure to traumatic events for children served in a system of care.

Linking neighborhood characteristics to food insecurity in older adults: the role of perceived safety, social cohesion, and walkability

Among the 14.6% of American households experiencing food insecurity, approximately 2 million are occupied by older adults. Food insecurity among older adults has been linked to poor health, lower cognitive function, and poor mental health outcomes. While evidence of the association between individual or household-level factors and food insecurity has been documented, the role of neighborhood-level factors is largely understudied. This study uses data from a representative sample of 1,870 New York City senior center participants in 2008 to investigate the relationship between three neighborhood-level factors (walkability, safety, and social cohesion) and food insecurity among the elderly. Issues relating to food security were measured by three separate outcome measures: whether the participant had a concern about having enough to eat this past month (concern about food security), whether the participant was unable to afford food during the past year (insufficient food intake related to financial resources), and whether the participant experienced hunger in the past year related to not being able to leave home (mobility-related food insufficiency). Unadjusted and adjusted logistic regression was performed for each measure of food insecurity. Results indicate that neighborhood walkability is an important correlate of mobility-related food insufficiency and concern about food insecurity, even after controlling the effects of other relevant factors.

Variation in the Relationship Between Education and Marriage: Marriage Market Mismatch?

Educational expansion has led to greater diversity in the social backgrounds of college students. We ask how schooling interacts with this diversity to influence marriage formation among men and women. Relying on data from the 1979 National Longitudinal Survey of Youth (N = 3208), we use a propensity score approach to group men and women into social strata and multilevel event history models to test differences in the effects of college attendance across strata. We find a statistically significant, positive trend in the effects of college attendance across strata, with the largest effects of college on first marriage among the more advantaged and the smallest-indeed, negative-effects among the least advantaged men and women. These findings appear consistent with a mismatch in the marriage market between individuals' education and their social backgrounds.

Social environment and asthma: associations with crime and No Child Left Behind programmes

BACKGROUND

The relationship between asthma and socio-economic status remains unclear. The authors investigated how neighbourhood, school and community social environments were associated with incident asthma in Southern California schoolchildren.

METHODS

New-onset asthma was measured over 3 years of follow-up in the Children's Health Study cohort. Multilevel random-effects models assessed associations between social environments and asthma, adjusted for individual risk factors. At baseline, subjects resided in 274 census tracts (ie, neighbourhoods) and attended kindergarten or first grade in one of 45 schools distributed in 13 communities throughout Southern California. Neighbourhoods and communities were characterised by measures of deprivation, income inequality and racial segregation. Communities were further described by crime rates. Information on schools included whether a school received funding related to the Title 1 No Child Left Behind programme, which aims to reduce academic underachievement in disadvantaged populations.

RESULTS

Increased risk for asthma was observed in subjects attending schools receiving Title I funds compared with those from schools without funding (adjusted HR 1.71, 95% CI 1.14 to 2.58), and residing in communities with higher rates of larceny crime (adjusted HR 2.02, 95% CI 1.08 to 3.02 across the range of 1827 incidents per 100,000 population).

CONCLUSIONS

Risk for asthma was higher in areas of low socio-economic status, possibly due to unmeasured risk factors or chronic stress.

Work and high-risk alcohol consumption in the Canadian workforce

This study examined the associations between occupational groups; work-organization conditions based on task design; demands, social relations, and gratifications; and weekly high-risk alcohol consumption among Canadian workers. A secondary data analysis was performed on Cycle 2.1 of the Canadian Community Health Survey conducted by Statistics Canada in 2003. The sample consisted of 76,136 employees 15 years of age and older nested in 2,451 neighbourhoods. High-risk alcohol consumption is defined in accordance with Canadian guidelines for weekly low-risk alcohol consumption. The prevalence of weekly high-risk alcohol consumption is estimated to be 8.1% among workers. The results obtained using multilevel logistic regression analysis suggest that increased work hours and job insecurity are associated with elevated odds of high-risk alcohol consumption. Gender female, older age, being in couple and living with children associated with lower odds of high-risk drinking, while increased education, smoking, physical activities, and, and economic status were associated with higher odds. High-risk drinking varied between neighbourhoods, and gender moderates the contribution of physical demands. The results suggest that work made a limited contribution and non-work factors a greater contribution to weekly high-risk alcohol consumption. Limits and implications of these results are discussed.