A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.

Local government spending and mental health: Untangling the impacts using a dynamic modelling approach

This study investigated the impact of local government spending on mental health in England between 2013 and 2019. Guided by the "Health in All Policies" vision, which encourages the integration of health in all decision-making areas, we explored how healthcare and multiple nonmedical budgeting decisions related to population mental health. We used random curve general cross-lagged modelling to dynamically partition effects into the short-run (from t to t + 1) and long-run (from t to t + 2) impacts, account for unobserved area-level heterogeneity and reverse causality from health outcomes to financial investments, and comprehensive modelling of budget items as an interconnected system. Our findings revealed that spending in adult social care, healthcare, and law & order predicted long-term mental health gains (0.004-0.081 SDs increase for each additional 10% in expenditure). However, these sectors exhibited negative short-term impulses (0.012-0.077 SDs decrease for each additional 10% in expenditure), markedly offsetting the long-term gains. In turn, infrastructural and environmental spending related to short-run mental health gains (0.005-0.031 SDs increase for each additional 10% in expenditure), while the long-run effects were predominantly negative (0.005-0.028 SDs decrease for each additional 10% in expenditure). The frequent occurrence of short-run and long-run negative links suggested that government resources may not be effectively reaching the areas that are most in need. In the short-term, negative effects could also imply temporary disruptions to service delivery largely uncompensated by later mental health improvements. Nonetheless, some non-health spending policies, such as law & order and infrastructure, can be related to long-lasting positive mental health impacts.

Clarifications on the intersectional MAIHDA approach: A conceptual guide and response to Wilkes and Karimi (2024)

Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) has been welcomed as a new gold standard for quantitative evaluation of intersectional inequalities, and it is being rapidly adopted across the health and social sciences. In their commentary "What does the MAIHDA method explain?", Wilkes and Karimi (2024) raise methodological concerns with this approach, leading them to advocate for the continued use of conventional single-level linear regression models with fixed-effects interaction parameters for quantitative intersectional analysis. In this response, we systematically address these concerns, and ultimately find them to be unfounded, arising from a series of subtle but important misunderstandings of the MAIHDA approach and literature. Since readers new to MAIHDA may share confusion on these points, we take this opportunity to provide clarifications. Our response is organized around four important clarifications: (1) At what level are the additive main effect variables defined in intersectional MAIHDA models? (2) Do MAIHDA models have problems with collinearity? (3) Why does the Variance Partitioning Coefficient (VPC) tend to be small, and the Proportional Change in Variance (PCV) tend to be large in MAIHDA? and (4) What are the goals of MAIHDA analysis?

Mapping socio-geographical disparities in the occurrence of teenage maternity in Colombia using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

BACKGROUND

The prevalence of teenage pregnancy in Colombia is higher than the worldwide average. The identification of socio-geographical disparities might help to prioritize public health interventions.

AIM

To describe variation in the probability of teenage maternity across geopolitical departments and socio-geographical intersectional strata in Colombia.

METHODS

A cross-sectional study based on live birth certificates in Colombia. Teenage maternity was defined as a woman giving birth aged 19 or younger. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was applied using multilevel Poisson and logistic regression. Two different approaches were used: (1) intersectional: using strata defined by the combination of health insurance, region, area of residency, and ethnicity as the second level (2) geographical: using geopolitical departments as the second level. Null, partial, and full models were obtained. General contextual effect (GCE) based on the variance partition coefficient (VPC) was considered as the measure of disparity. Proportional change in variance (PCV) was used to identify the contribution of each variable to the between-strata variation and to identify whether this variation, if any, was due to additive or interaction effects. Residuals were used to identify strata with potential higher-order interactions.

RESULTS

The prevalence of teenage mothers in Colombia was 18.30% (95% CI 18.20-18.40). The highest prevalence was observed in Vichada, 25.65% (95% CI: 23.71-27.78), and in the stratum containing mothers with Subsidized/Unaffiliated healthcare insurance, Mestizo, Rural area in the Caribbean region, 29.08% (95% CI 28.55-29.61). The VPC from the null model was 1.70% and 9.16% using the geographical and socio-geographical intersectional approaches, respectively. The higher PCV for the intersectional model was attributed to health insurance. Positive and negative interactions of effects were observed.

CONCLUSION

Disparities were observed between intersectional socio-geographical strata but not between geo-political departments. Our results indicate that if resources for prevention are limited, using an intersectional socio-geographical approach would be more effective than focusing on geopolitical departments especially when focusing resources on those groups which show the highest prevalence. MAIHDA could potentially be applied to many other health outcomes where resource decisions must be made.

Profiles of vulnerability for suicide and self-harm in UK prisoners: Neurodisability, mood disturbance, substance use, and bullying

Screening for vulnerability factors associated with historic suicidality and self-harm on entry to prison is critical to help prisons understand how to allocate extremely limited mental health resources. It has been established that having previous suicide attempts increases odds of future suicidality and self-harm in prison. We utilised administrative screening data from 665 adult male prisoners on entry to a category B prison in Wales, UK, collected using the Do-IT Profiler. This sample represents 16% of all prisoners who entered that prison during a 26-month period. 12% of prisoners reported a history of attempted suicide, 11% reported historic self-harm, and 8% reported a history of both. Historic traumatic brain injury and substance use problems were associated with a 3.3- and 1.9- times increased odds of a historic suicide attempt, respectively, but no significant increased risk of historic self-harm (95% CI: 1.51-6.60 and 1.02-3.50). However, those who were bullied at school had 2.7 times increased odds of reporting a history of self-harm (95% CI: 1.63-6.09). The most salient risk factors associated with both historic suicide and self-harm were higher levels of functional neurodisability (odds ratio 0.6 for a 1 standard deviation change in score, 95% CI: 0.35-0.75), and mood disturbance (odds ratio 2.1 for a 1 standard deviation change in score, 95% CI: 1.26-3.56). Therefore, it could be beneficial for prisons to screen for broader profiles of needs, to better understand how to provide appropriate services to prisoners vulnerable to suicide and self-harm. Multidisciplinary care pathways for prisoner mental health interventions are important, to account for complex multimorbidity. Adaptations may be needed for mental health interventions to be appropriate for, for example, a prisoner with a brain injury. Understanding this broad profile of vulnerability could also contribute to more compassionate responses to suicide and self-harm from prison staff.

Geographical and sociodemographic differences in statin dispensation after acute myocardial infarction in Sweden: a register-based prospective cohort study applying analysis of individual heterogeneity and discriminatory accuracy (AIHDA) for basic comparisons of healthcare quality

BACKGROUND

In Sweden, as in many other countries, official monitoring of healthcare quality is mostly focused on geographical disparities in relation to a desirable benchmark. However, current evaluations could be improved by considering: (1) The intersection of other relevant axes of inequity like age, sex, income and migration status; and (2) The existence of individual heterogeneity around averages. Therefore, using an established quality indicator (ie, dispensation of statins after acute myocardial infarction, AMI), we valuate both geographical and sociodemographic inequalities and illustrate how the analysis of individual heterogeneity and discriminatory accuracy (AIHDA) enhances such evaluations.

POPULATION AND METHODS

We applied AIHDA and calculated the area under the receiver operating characteristics curve (AUC) of regional and sociodemographic differences in the statin dispensations of 35 044 patients from 21 Swedish regions and 24 sociodemographic strata who were discharged from the hospital with an AMI diagnosis between January 2011 and December 2013. Following the Swedish National Board of Health and Welfare, we used a benchmark value of 90%.

RESULTS

Dispensation of stains after AMI in Sweden did not reach the desired target of 90%. Regional differences were absent/very small (AUC=0.537) while sociodemographic differences were small (AUC=0.618). Women, especially those with immigrant background and older than 65 years, have the lowest proportions of statin dispensations after AMI.

CONCLUSIONS

As the AUC statistics are small, interventions trying to achieve the benchmark value should be universal. However, special emphasis should nevertheless be directed towards women, especially older women with immigrant backgrounds.

Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia

BACKGROUND

Measuring multiple and higher-order interaction effects between multiple categorical variables proves challenging.

OBJECTIVES

To illustrate a multilevel modelling approach to studying complex interactions.

METHODS

We apply a two-level random-intercept linear regression to a binary outcome for individuals (level-1) nested within strata (level-2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010.

RESULTS

The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two- and higher-way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction.

CONCLUSIONS

Multilevel modelling is an innovative tool for identifying and analysing higher-order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences.

Looked after children in prison as adults: life adversity and neurodisability

PURPOSE

Looked after children (LAC) are criminalised at five times the rate of children in the general population. Children in contact with both child welfare and child justice systems have higher rates of neurodisability and substance use problems, and LAC in general have high rates of school exclusion, homelessness and unemployment. This study aims to understand whether these factors persist in LAC who are in prison as adults.

DESIGN/METHODOLOGY/APPROACH

Administrative data collected by the Do-IT profiler screening tool in a prison in Wales, UK, were analysed to compare sentenced prisoners who were LAC (n = 631) to sentenced prisoners who were not LAC (n = 2,201). The sample comprised all prisoners who were screened on entry to prison in a two-year period.

FINDINGS

Prisoners who were LAC scored more poorly on a functional screener for neurodisability (effect size = 0.24), and on four self-report measures capturing traits of dyslexia (0.22), attention-deficit hyperactivity disorder (0.40), autism spectrum disorders (0.34) and developmental co-ordination disorder (0.33). Prisoners who were LAC were more likely to have been to a pupil referral unit (0.24), have substance use problems (0.16), be homeless or marginally housed (0.18) and be unemployed or unable to work due to disability (0.13).

ORIGINALITY/VALUE

This study uniquely contributes to our understanding of prisoners who were LAC as a target group for intervention and support with re-integration into the community upon release. LAC in prison as adults may require additional interventions to help with employment, housing and substance use. Education programmes in prison should screen for neurodisability, to develop strategies to support engagement.

What makes for responsive family interactions? Disentangling individual, family-level, and socioeconomic contributions

Families function best, and children benefit the most, when familial interactions are characterized by responsivity-an understanding and consideration of other people's thoughts and feelings. How responsive people are during interactions with others is a product of individual propensities, observed family norms, and unique relationship patterns, though these influences are often hard to disentangle. In the current study, we used a Social Relations Model (SRM) to parse out the extent to which being responsive during family interactions is attributable to individual traits or familial tendencies. Mothers, fathers, and two children each interacted with every other person in the family (N = 198 families) and each person's behavior was coded for the level of responsivity they displayed toward their interactional partner. Data were modeled using a multilevel formulation of the SRM. Between 15% and 30% of the variance in individual's responsivity was attributable to stable traits, with parents tending to be more consistent across all interactional partners than their children. On average, 14% of the variance in responsivity was shared across all members of a given family. Income explained 28% of family-level variance, while other family characteristics, including parent education, parent mental health, interparental conflict, and household chaos, explained little to no variance. Furthermore, it was found that parents contributed more to the family tone of responsivity than did their children. These results provide new insights into what makes family members responsive toward one other and suggest there are likely benefits of providing supports across individual-, family-, and financial-levels to enhance family responsivity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A review and evaluation of secondary school accountability in England: Statistical strengths, weaknesses and challenges for 'Progress 8' raised by COVID-19

School performance measures are published annually in England to hold schools to account and to support parental school choice. This article reviews and evaluates the 'Progress 8' secondary school accountability system for state-funded schools. We assess the statistical strengths and weaknesses of Progress 8 relating to: choice of pupil outcome attainment measure; potential adjustments for pupil input attainment and background characteristics; decisions around which schools and pupils are excluded from the measure; presentation of Progress 8 to users, choice of statistical model, and calculation of statistical uncertainty; and issues related to the volatility of school performance over time, including scope for reporting multi-year averages. We then discuss challenges for Progress 8 raised by the COVID-19 pandemic. Six simple recommendations follow to improve Progress 8 and school accountability in England.

Population heterogeneity in associations between hormonal contraception and antidepressant use in Sweden: a prospective cohort study applying intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

OBJECTIVES

From a reproductive justice framework, we aimed to investigate how a possible association between hormonal contraceptive (HC) and antidepressants use (as a proxy for depression) is distributed across intersectional strata in the population. We aimed to visualise how intersecting power dynamics may operate in combination with HC use to increase or decrease subsequent use of antidepressants. Our main hypothesis was that the previously observed association between HC and antidepressants use would vary between strata, being more pronounced in more oppressed intersectional contexts. For this purpose, we applied an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy approach.

DESIGN

Observational prospective cohort study using record linkage of national Swedish registers.

SETTING

The population of Sweden.

PARTICIPANTS

All 915 954 women aged 12-30 residing in Sweden 2010, without a recent pregnancy and alive during the individual 1-year follow-up.

PRIMARY OUTCOME MEASURE

Use of any antidepressant, meaning being dispensed at least one antidepressant (ATC: N06A) during follow-up.

RESULTS

Previously mentally healthy HC users had an OR of 1.79 for use of antidepressants compared with non-users, whereas this number was 1.28 for women with previous mental health issues. The highest antidepressant use were uniformly found in strata with previous mental health issues, with highest usage in women aged 24-30 with no immigrant background, low income and HC use (51.4%). The largest difference in antidepressant use between HC users and non-users was found in teenagers, and in adult women of immigrant background with low income. Of the total individual variance in the latent propensity of using antidepressant 9.01% (healthy) and 8.16% (with previous mental health issues) was found at the intersectional stratum level.

CONCLUSIONS

Our study suggests teenagers and women with immigrant background and low income could be more sensitive to mood effects of HC, a heterogeneity important to consider moving forward.

Joint Modeling of Individual Trajectories, Within-Individual Variability, and a Later Outcome: Systolic Blood Pressure Through Childhood and Left Ventricular Mass in Early Adulthood

Within-individual variability of repeatedly measured exposures might predict later outcomes (e.g., blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP). Because 2-stage methods, known to introduce bias, are typically used to investigate such associations, we introduce a joint modeling approach, examining associations of mean BP and BPV across childhood with left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allowed BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroskedasticity). We further distinguished within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher body weights, and in female participants and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21%, 95% credible interval: -0.23, 0.69), but this association became negative (-0.78%, 95% credible interval: -2.54, 0.22) once the effect of mean BP on LVMI was adjusted for. This joint modeling approach offers a flexible method of relating repeatedly measured exposures to later outcomes.

Antidepressant use in Sweden: an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

INTRODUCTION

Antidepressants are among the most commonly prescribed drugs in Sweden. However, we lack detailed knowledge on the socioeconomic and demographic distribution of antidepressant use in the population. To fill this gap, we performed an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy.

METHODS

Analysing all Swedish residents older than 10 years (n=8,190,990), we measured the absolute risk of antidepressant use across 144 intersectional strata defined by combinations of age, gender, income, country of birth and psychiatric diagnosis. We calculated the strata-specific absolute risk of antidepressant use in a series of multilevel logistic regression models. By means of the variance partitioning coefficient and the area under the receiver operating characteristic curve, we quantified the discriminatory accuracy of the intersectional contexts (i.e. strata) for discerning those who use antidepressants from those who do not.

RESULTS

The absolute risk of antidepressant use ranged between 0.93% and 24.78% among those without a psychiatric diagnosis, and between 21.41% and 77.56% among those with a psychiatric diagnosis. Both the variance partitioning coefficient of 41.88% and the area under the receiver operating characteristic curve of 0.81 were considerable.

CONCLUSIONS

Besides overt psychiatric diagnoses, our study shows that antidepressant use is mainly conditioned by age, which might express the embodiment of socioeconomic conditions across the individual life course. Our analysis provides a detailed and highly discriminatory mapping of the heterogeneous distribution of antidepressant use in the Swedish population, which may be useful in public health management.

Evaluating the "Dove Confident Me" Five-Session Body Image Intervention Delivered by Teachers in Schools: A Cluster Randomized Controlled Effectiveness Trial

PURPOSE

Body dissatisfaction is common during adolescence and predicts poor psychological and physical health. Interventions have traditionally overrelied on delivery by external providers (e.g., researchers and psychologists), preventing scalability. This study evaluated the acceptability and effectiveness of a school-based body image intervention delivered by schoolteachers.

METHODS

Six British schools participated in a pragmatic cluster randomized controlled trial. Girls and boys aged 11-13 years received the five-session intervention delivered by their teachers (n = 848) or lessons-as-usual control (n = 647) and were assessed at baseline, postintervention, and 2-, 6-, 12-, 24- and 36-month follow-up. The primary outcome was body image (body esteem), secondary outcomes included risk factors for body image (internalization of appearance ideals, sociocultural pressures, social comparisons, appearance-related teasing, and conversations), and tertiary outcomes included psychosocial well-being (negative affect, self-esteem, dietary restraint, and life engagement).

RESULTS

Compared with the control group, intervention students demonstrated improvements in the primary outcome of body esteem at postintervention (Cohen's d = .15), 2-month (d = .26), and 6-month follow-up (d = .15). For girls, there was also a significant reduction in experienced appearance-related teasing at 6-month (d = .24) and 12-month (d = .30) follow-up. No other significant intervention effects were observed. The intervention was acceptable to students.

CONCLUSIONS

These findings present the longest sustained improvements in a cognitive-affective body image outcome observed among girls and boys during a teacher-led universal body image program to date. Intervention refinement and improved teacher training may further improve outcomes. Task-shifting intervention delivery to community providers to scale up interventions is a promising strategy.

Precision-weighted estimates of neonatal, post-neonatal and child mortality for 640 districts in India, National Family Health Survey 2016

BACKGROUND

The conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12-59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level.

METHODS

Data on 259 627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman's rank correlation were assessed for the three mortality estimates, and the Moran's I measure was used to detect spatial clustering of high burden districts for each outcome.

RESULTS

The majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation = 0.47-0.58, Spearman's rank correlation = 0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran's I = 0.61-0.76).

CONCLUSIONS

Sufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival.

Cross-classified Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance: the case of hospital differences in patient survival after acute myocardial infarction

OBJECTIVE

To describe a novel strategy, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance, by analysing differences in 30-day mortality after a first-ever acute myocardial infarction (AMI) in Sweden.

DESIGN

Cross-classified study.

SETTING

68 Swedish hospitals.

PARTICIPANTS

43 247 patients admitted between 2007 and 2009, with a first-ever AMI.

PRIMARY AND SECONDARY OUTCOME MEASURES

We evaluate hospital performance by analysing differences in 30-day mortality after a first-ever AMI using a cross-classified multilevel analysis. We classified the patients into 10 categories according to a risk score (RS) for 30-day mortality and created 680 strata defined by combining hospital and RS categories.

RESULTS

In the cross-classified multilevel analysis the overall RS adjusted hospital 30-day mortality in Sweden was 4.78% and the between-hospital variation was very small (variance partition coefficient (VPC)=0.70%, area under the curve (AUC)=0.54). The benchmark value was therefore achieved by all hospitals. However, as expected, there were large differences between the RS categories (VPC=34.13%, AUC=0.77) CONCLUSIONS: MAIHDA is a useful tool to evaluate hospital performance. The benefit of this novel approach to adjusting for patient RS is that it allowed one to estimate separate VPCs and AUC statistics to simultaneously evaluate the influence of RS categories and hospital differences on mortality. At the time of our analysis, all hospitals in Sweden were performing homogeneously well. That is, the benchmark target for 30-day mortality was fully achieved and there were not relevant hospital differences. Therefore, possible quality interventions should be universal and oriented to maintain the high hospital quality of care.

Geographical and sociodemographic differences in discontinuation of medication for Chronic Obstructive Pulmonary Disease - A Cross-Classified Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

BACKGROUND

While discontinuation of COPD maintenance medication is a known problem, the proportion of patients with discontinuation and its geographical and sociodemographic distribution are so far unknown in Sweden. Therefore, we analyse this question by applying an innovative approach called multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

PATIENTS AND METHODS

We analysed 49,019 patients categorized into 18 sociodemographic contexts and 21 counties of residence. All patients had a hospital COPD diagnosis and had been on inhaled maintenance medication during the 5 years before the study baseline in 2010. We defined "discontinuation" as the absolute lack of retrieval from a pharmacy of any inhaled maintenance medication during 2011. We performed a cross-classified MAIHDA and obtained the average proportion of discontinuation, as well as county and sociodemographic absolute risks, and compared them with a proposed benchmark value of 10%. We calculated the variance partition coefficient (VPC) and the area under the receiver operating characteristics curve (AUC) to quantify county and sociodemographic differences. To summarize the results, we used a framework with 15 scenarios defined by the size of the differences and the level of achievement in relation to the benchmark value.

RESULTS

Around 18% of COPD patients in Sweden discontinued maintenance medication, so the benchmark value was not achieved. There were very small county differences (VPC=0.35%, AUC=0.54). The sociodemographic differences were small (VPC=4.98%, AUC=0.57).

CONCLUSION

Continuity of maintenance medication among COPD patients in Sweden could be improved by reducing the unjustifiably high prevalence of discontinuation. The very small county and small sociodemographic differences should motivate universal interventions across all counties and sociodemographic groups. Geographical analyses should be combined with sociodemographic analyses, and the cross-classified MAIHDA is an appropriate tool to assess health-care quality.

Disentangling the contribution of hospitals and municipalities for understanding patient level differences in one-year mortality risk after hip-fracture: A cross-classified multilevel analysis in Sweden

Background

One-year mortality after hip-fracture is a widely used outcome measure when comparing hospital care performance. However, traditional analyses do not explicitly consider the referral of patients to municipality care after just a few days of hospitalization. Furthermore, traditional analyses investigates hospital (or municipality) variation in patient outcomes in isolation rather than as a component of the underlying patient variation. We therefore aimed to extend the traditional approach to simultaneously estimate both case-mix adjusted hospital and municipality comparisons in order to disentangle the amount of the total patient variation in clinical outcomes that was attributable to the hospital and municipality level, respectively.

Methods

We determined 1-year mortality risk in patients aged 65 or above with hip fractures registered in Sweden between 2011 and 2014. We performed cross-classified multilevel analysis with 54,999 patients nested within 54 hospitals and 290 municipalities. We adjusted for individual demographic, socioeconomic and clinical characteristics. To quantify the size of the hospital and municipality variation we calculated the variance partition coefficient (VPC) and the area under the receiver operator characteristic curve (AUC).

Results

The overall 1-year mortality rate was 25.1%. The case-mix adjusted rates varied from 21.7% to 26.5% for the 54 hospitals, and from 18.9% to 29.5% for the 290 municipalities. The VPC was just 0.2% for the hospital and just 0.1% for the municipality level. Patient sociodemographic and clinical characteristics were strong predictors of 1-year mortality (AUC = 0.716), but adding the hospital and municipality levels in the cross-classified model had a minor influence (AUC = 0.718).

Conclusions

Overall in Sweden, one-year mortality after hip-fracture is rather high. However, only a minor part of the patient variation is explained by the hospital and municipality levels. Therefore, a possible intervention should be nation-wide rather than directed to specific hospitals or municipalities.

Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously

Provider profiling entails comparing the performance of hospitals on indicators of quality of care. Many common indicators of healthcare quality are binary (eg, short‐term mortality, use of appropriate medications). Typically, provider profiling examines the variation in each indicator in isolation across hospitals. We developed Bayesian multivariate response random effects logistic regression models that allow one to simultaneously examine variation and covariation in multiple binary indicators across hospitals. Use of this model allows for (i) determining the probability that a hospital has poor performance on a single indicator; (ii) determining the probability that a hospital has poor performance on multiple indicators simultaneously; (iii) determining, by using the Mahalanobis distance, how far the performance of a given hospital is from that of an average hospital. We illustrate the utility of the method by applying it to 10 881 patients hospitalized with acute myocardial infarction at 102 hospitals. We considered six binary patient‐level indicators of quality of care: use of reperfusion, assessment of left ventricular ejection fraction, measurement of cardiac troponins, use of acetylsalicylic acid within 6 hours of hospital arrival, use of beta‐blockers within 12 hours of hospital arrival, and survival to 30 days after hospital admission. When considering the five measures evaluating processes of care, we found that there was a strong correlation between a hospital's performance on one indicator and its performance on a second indicator for five of the 10 possible comparisons. We compared inferences made using this approach with those obtained using a latent variable item response theory model.

Partitioning variation in multilevel models for count data

A first step when fitting multilevel models to continuous responses is to explore the degree of clustering in the data. Researchers fit variance-component models and then report the proportion of variation in the response that is due to systematic differences between clusters. Equally they report the response correlation between units within a cluster. These statistics are popularly referred to as variance partition coefficients (VPCs) and intraclass correlation coefficients (ICCs). When fitting multilevel models to categorical (binary, ordinal, or nominal) and count responses, these statistics prove more challenging to calculate. For categorical response models, researchers appeal to their latent response formulations and report VPCs/ICCs in terms of latent continuous responses envisaged to underly the observed categorical responses. For standard count response models, however, there are no corresponding latent response formulations. More generally, there is a paucity of guidance on how to partition the variation. As a result, applied researchers are likely to avoid or inadequately report and discuss the substantive importance of clustering and cluster effects in their studies. A recent article drew attention to a little-known exact algebraic expression for the VPC/ICC for the special case of the two-level random-intercept Poisson model. In this article, we make a substantial new contribution. First, we derive exact VPC/ICC expressions for more flexible negative binomial models that allows for overdispersion, a phenomenon which often occurs in practice. Then we derive exact VPC/ICC expressions for three-level and random-coefficient extensions to these models. We illustrate our work with an application to student absenteeism. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Use of Instrumental Variables in Peer Effects Models

Instrumental variables are often used to identify peer effects. This paper shows that instrumenting the 'peer average outcome' with 'peer average characteristics' requires the researcher to include the instrument at the individual level as an explanatory variable. We highlight the bias that occurs when failing to do this.

Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

BACKGROUND

In light of the opioid epidemic in the United States, there is growing concern about the use of opioids in Sweden as it may lead to misuse and overuse and, in turn, severe public health problems. However, little is known about the distribution of opioid use across different demographic and socioeconomic dimensions in the Swedish general population. Therefore, we applied an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), to obtain an improved mapping of the risk heterogeneity of and socioeconomic inequalities in opioid prescription receipt.

METHODS AND FINDINGS

Using data from 6,846,106 residents in Sweden aged 18 and above, we constructed 72 intersectional strata from combinations of gender, age, income, cohabitation status, and presence or absence of psychological distress. We modelled the absolute risk (AR) of opioid prescription receipt in a series of multilevel logistic regression models distinguishing between additive and interaction effects. By means of the Variance Partitioning Coefficient (VPC) and the area under the receiver operating characteristic curve (AUC), we quantified the discriminatory accuracy (DA) of the intersectional strata for discerning those who received opioid prescriptions from those who did not. The AR of opioid prescription receipt ranged from 2.77% (95% CI 2.69-2.86) among low-income men aged 18-34, living alone, without psychological distress, to 28.25% (95% CI 27.95-28.56) among medium-income women aged 65 and older, living alone, with psychological distress. In a model that conflated both additive and interaction effects, the intersectional strata had a fair DA for discerning opioid users from non-users (VPC = 13.2%, AUC = 0.68). However, in the model that decomposed total effects into additive and interaction effects, the VPC was very low (0.42%) indicating the existence of small interaction effects for a number of the intersectional strata.

CONCLUSIONS

The intersectional MAIHDA approach aligns with the aims of precision public health, through improving the evidence base for health policy by increasing understanding of both health inequalities and individual heterogeneity. This approach is particularly relevant for socioeconomically conditioned outcomes such as opioid prescription receipt. We have identified intersections of social position within the Swedish population at greater risk for opioid prescription receipt.

Hospital differences in mortality rates after hip fracture surgery in Denmark

Background

Thirty-day mortality after hip fracture is widely used when ranking hospital performance, but the reliability of such hospital ranking is seldom calculated. We aimed to quantify the variation in 30-day mortality across hospitals and to determine the hospital general contextual effect for understanding patient differences in 30-day mortality risk.

Methods

Patients aged ≥65 years with an incident hip fracture registered in the Danish Multidisciplinary Fracture Registry between 2007 and 2016 were identified (n=60,004). We estimated unadjusted and patient-mix adjusted risk of 30-day mortality in 32 hospitals. We performed a multilevel analysis of individual heterogeneity and discriminatory accuracy with patients nested within hospitals. We expressed the hospital general contextual effect by the median odds ratio (MOR), the area under the receiver operating characteristics curve and the variance partition coefficient (VPC).

Results

The overall 30-day mortality rate was 10%. Patient characteristics including high sociodemographic risk score, underweight, comorbidity, a subtrochanteric fracture, and living at a nursing home were strong predictors of 30-day mortality (area under the curve=0.728). The adjusted differences between hospital averages in 30-day mortality varied from 5% to 9% across the 32 hospitals, which correspond to a MOR of 1.18 (95% CI: 1.12-1.25). However, the hospital general context effect was low, as the VPC was below 1% and adding the hospital level to a single-level model with adjustment for patient-mix increased the area under the receiver operating characteristics curve by only 0.004 units.

Conclusions

Only minor hospital differences were found in 30-day mortality after hip fracture. Mortality after hip fracture needs to be lowered in Denmark but possible interventions should be patient oriented and universal rather than focused on specific hospitals.

Genetic and Environmental Risk Factors Associated With Trajectories of Depression Symptoms From Adolescence to Young Adulthood

Importance

Less favorable trajectories of depressive mood from adolescence to early adulthood are associated with current and later psychopathology, impaired educational attainment, and social dysfunction, yet the genetic and environmental risk factors associated with these trajectories are not fully established. Examining what risk factors are associated with different trajectories of depressive mood could help identify the nature of depression symptoms and improve preventive interventions for those at most risk.

Objective

To examine the differential associations of genetic and environmental risk factors with trajectories of depression symptoms among individuals observed from ages 10 to 24 years.

Design, Setting, and Participants

In a longitudinal cohort study established in 1990 and currently ongoing (the Avon Longitudinal Study of Parents and Children [ALSPAC]), growth mixture modeling was used to identify trajectories of depression symptoms in 9394 individuals in the United Kingdom. Associations of different risk factors with these trajectories were then examined. Analysis was conducted between August 2018 and January 2019.

Main Outcomes and Measures

Trajectories were composed from depression symptoms measured using the Short Mood and Feelings Questionnaire at 9 occasions from ages 10 to 24 years. Risk factors included sex, a polygenic risk score taken from a recent genome-wide association study of depression symptoms, maternal postnatal depression, partner cruelty to the offspring's mother when the child was aged 2 to 4 years, childhood anxiety at age 8 years, and being bullied at age 10 years.

Results

Data on all risk factors, confounders, and the outcome were available for 3525 individuals, including 1771 (50.2%) who were female. Trajectories were assessed between the mean (SD) age of 10.7 (0.3) years and mean (SD) age of 23.8 (0.5) years. Overall, 5 distinct trajectories of depression symptoms were identified: (1) stable low (2506 individuals [71.1%]), (2) adolescent limited (325 individuals [9.2%]), (3) childhood limited (203 individuals [5.8%]), (4) early-adult onset (393 individuals [11.1%]), and (5) childhood persistent (98 individuals [2.8%]). Of all the associations of risk factors with trajectories, sex (odds ratio [OR], 6.45; 95% CI, 2.89-14.38), the polygenic risk score for depression symptoms (OR, 1.47; 95% CI, 1.10-1.96), and childhood anxiety (OR, 1.30; 95% CI, 1.16-1.45) showed the strongest association with the childhood-persistent trajectory of depression symptoms compared with the stable-low trajectory. Maternal postnatal depression (OR, 2.39; 95% CI, 1.41-4.07) had the strongest association with the early-adult-onset trajectory, while partner cruelty to mother (OR, 2.30; 95% CI, 1.36-3.90) had the strongest association with the adolescent-limited trajectory. Bullying (OR, 8.08; 95% CI, 4.92-13.26) showed the strongest association with the childhood-limited trajectory.

Conclusions and Relevance

The least favorable trajectories of depression symptoms (childhood persistent and early-adult onset) were associated with both genetic and environmental risk factors, but the 2 trajectories of limited duration that had resolved by early adulthood (childhood limited and adolescent limited) were not associated with the polygenic risk score or maternal postnatal depression. Bullying was strongly associated with both the childhood-persistent and childhood-limited trajectories, suggesting that this risk factor may have a time-specific effect. These findings suggest that examining genetic and multiple time-specific environmental antecedents could help identify trajectories of varying onset and chronicity.

Identifying Critical Points of Trajectories of Depressive Symptoms from Childhood to Young Adulthood

Depression is a common mental illness and research has focused on late childhood and adolescence in an attempt to prevent or reduce later psychopathology and/or social impairments. It is important to establish and study population-averaged trajectories of depressive symptoms across adolescence as this could characterise specific changes in populations and help identify critical points to intervene with treatment. Multilevel growth-curve models were used to explore adolescent trajectories of depressive symptoms in 9301 individuals (57% female) from the Avon Longitudinal Study of Parents and Children, a UK based pregnancy cohort. Trajectories of depressive symptoms were constructed for males and females using the short mood and feelings questionnaire over 8 occasions, between 10 and 22 years old. Critical points of development such as age of peak velocity for depressive symptoms (the age at which depressive symptoms increase most rapidly) and the age of maximum depressive symptoms were also derived. The results suggested that from similar initial levels of depressive symptoms at age 11, females on average experienced steeper increases in depressive symptoms than males over their teenage and adolescent years until around the age of 20 when levels of depressive symptoms plateaued and started to decrease for both sexes. Females on average also had an earlier age of peak velocity of depressive symptoms that occurred at 13.5 years, compared to males who on average had an age of peak velocity at 16 years old. Evidence was less clear for a difference between the ages of maximum depressive symptoms which were on average 19.6 years for females and 20.4 for males. Identifying critical periods for different population subgroups may provide useful knowledge for treating and preventing depression and could be tailored to be time specific for certain groups. Possible explanations and recommendations are discussed.

Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.

General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A conceptual tutorial

To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs).

Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but “statistically significant” SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.

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The intra-context correlation is a measure of the general contextual effect (GCE).

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Contextual measures of association inform on specific contextual effects (SCEs).

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Many multilevel regression analyses only report SCEs.

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Paradoxically, the lower the GCE the easier it is to detect “statistically significant” SCEs.

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Multilevel regression analysis need to consider both GCEs and SCEs.

Child and contextual effects in the emergence of differential maternal sensitivity across siblings

The present study examined the effects of socioeconomic status (SES) and sibling differences in birth weight on sibling differences in the receipt of maternal sensitivity (i.e., differential parenting). It was hypothesized that sibling differences in birth weight would predict absolute differential parenting across the sibship (i.e., the more different siblings' birth weight, the more different the level of sensitivity in the family, overall) and child-specific differential parenting (i.e., relatively heavier siblings receiving more sensitivity, compared to his or her counterpart within the family). It was also hypothesized that there would be greater sibling differences in birth weight in lower SES settings. Multiparous mothers were recruited within two weeks of childbirth and filmed interacting with each of their children when younger siblings were 1.60 years (SD = .16, N = 396 younger siblings) and next-older siblings were 4.05 (SD = .75; N = 396 older siblings). Videotapes were coded for maternal sensitivity. Multilevel path-analysis revealed that lower-SES families exhibited greater sibling differences in birth weight, which corresponded to greater absolute differential parenting. Also, heavier siblings received relatively higher levels of sensitivity within the family. This study demonstrates that child and contextual factors operate together in predicting differential parenting. (PsycINFO Database Record

Chronic Obstructive Pulmonary Disease in Sweden: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Socioeconomic, ethnic and gender disparities in Chronic Obstructive Pulmonary Disease (COPD) risk are well established but no studies have applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework to study this outcome. We study individuals at the first level of analysis and combinations of multiple social and demographic categorizations (i.e., intersectional strata) at the second level of analysis. Here we used MAIHDA to assess to what extent individual differences in the propensity of developing COPD are at the intersectional strata level. We also used MAIHDA to determine the degree of similarity in COPD incidence of individuals in the same intersectional stratum. This leads to an improved understanding of risk heterogeneity and of the social dynamics driving socioeconomic and demographic disparities in COPD incidence. Using data from 2,445,501 residents in Sweden aged 45–65, we constructed 96 intersectional strata combining categories of age, gender, income, education, civil- and migration status. The incidences of COPD ranged from 0.02% for young, native males with high income and high education who cohabited to 0.98% for older native females with low income and low education who lived alone. We calculated the intra-class correlation coefficient (ICC) that informs on the discriminatory accuracy of the categorizations. In a model that conflated additive and interaction effects, the ICC was good (20.0%). In contrast, in a model that measured only interaction effects, the ICC was poor (1.1%) suggesting that most of the observed differences in COPD incidence across strata are due to the main effects of the categories used to construct the intersectional matrix while only a minor share of the differences are attributable to intersectional interactions. We found conclusive interaction effects. The intersectional MAIHDA approach offers improved information to guide public health policies in COPD prevention, and such policies should adopt an intersectional perspective.

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We use multilevel analysis of individual heterogeneity and discriminatory accuracy.

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There is a clear difference in COPD incidence between intersectional strata.

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Intersectionality improves mapping of socioeconomic differences in COPD incidence.

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Preventive measures should be based on intersectional rather than classic analyses.

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.

Protecting energy intakes against income shocks

Whether and how changes in economic circumstances or household income affect individuals' diet and nutritional intakes is of substantial interest for policy purposes. This paper exploits a period of substantial income volatility in Russia to examine the extent to which, as well as how individuals protect their energy intakes in the face of unanticipated shocks to household income. Using rich data from the Russia Longitudinal Monitoring Survey, our results suggest that households use substitution, disproportionally cutting back spending on non-foods to protect spending on foods, change the composition of the consumption basket, and increase the consumption of 'cheaper' calories. Taken together, however, we find that total energy intakes as well as the nutritional composition of the diet are almost fully protected against income shocks. Specifically, we find that 12-16% of the effect of permanent income shocks on food expenditures is transmitted to energy intakes, with 84-88% protected through insurance mechanisms.

Observed sensitivity during family interactions and cumulative risk: A study of multiple dyads per family

The present study sought to investigate the family, individual, and dyad-specific contributions to observed cognitive sensitivity during family interactions. Moreover, the influence of cumulative risk on sensitivity at the aforementioned levels of the family was examined. Mothers and 2 children per family were observed interacting in a round robin design (i.e., mother-older sibling, mother younger-sibling and sibling-dyad, N = 385 families). Data were dyadic, in that there were 2 directional scores per interaction, and were analyzed using a multilevel formulation of the Social Relations Model. Variance partitioning revealed that cognitive sensitivity is simultaneously a function of families, individuals and dyads, though the importance of these components varies across family roles. Cognitive sensitivity for mothers was primarily attributable to individual differences, whereas cognitive sensitivity for children was predominantly attributable to family and dyadic differences, especially for youngest children. Cumulative risk explained family and individual variance in cognitive sensitivity, particularly when actors were older or in a position of relative competence or authority (i.e., mother to children, older to younger siblings). Overall, this study demonstrates that cognitive sensitivity operates across levels of family organization, and is negatively impacted by psychosocial risk. (PsycINFO Database Record

Emotional problems among recent immigrants and parenting status: Findings from a national longitudinal study of immigrants in Canada

The present study examined predictors of emotional problems amongst a nationally representative cohort of recent immigrants in Canada. Specifically, the effects of parenting status were examined given the association between parenting stress and mental health. Data came from the Longitudinal Survey of Immigrants to Canada (N = 7055). Participants were recruited 6-months post landing (2001–2002) and followed up at 2 and 4 years. Self-reported emotional problems over time were considered as a function of parenting status (Two Parent, Lone Parent, Divorced Non-Parent, Non-Divorced Non-Parent) and sociodemographic characteristics. Odds of emotional problems were higher among Two Parent, OR = 1.12 (1.01, 1.24), Lone Parent, OR = 2.24 (1.75, 2.88), and Divorced Non-Parent, OR = 1.30 (1.01, 1.66) immigrants compared to Non-Divorced Non-Parents. Visible minority status, female gender, low income, and refugee status were associated with elevated risk. Findings reveal that immigrant parents are at risk for emotional health problems during the post-migration period. Such challenges may be compounded by other sociodemographic risk.

Randomized controlled trial of an online mother-daughter body image and well-being intervention

OBJECTIVE

Poor body image is a public health issue. Mothers are a key influence on adolescent girls' body image. This study evaluated an accessible, scalable, low-intensity internet-based intervention delivered to mothers (Dove Self Esteem Project Website for Parents) on mothers' and their adolescent daughters' body image and psychosocial well-being.

METHOD

British mother-daughter dyads (N = 235) participated in a cluster randomized controlled trial (assessment-only control; mothers viewed the website without structured guidance [website-unstructured]; mothers viewed the website via a tailored pathway [website-tailored]). Dyads completed standardized self-report measures of body image, related risk factors, and psychosocial outcomes at baseline, 2 weeks post-exposure, 6-week, and 12-month follow-up.

RESULTS

Dyadic models showed that relative to the control, mothers who viewed the website reported significantly higher self-esteem at post-exposure (website-tailored), higher weight esteem at 6-week follow-up (website-tailored), lower negative affect at 12-month follow-up (website-tailored), engaged in more self-reported conversations with their daughters about body image at post-exposure and 6-week follow-up, and were 3-4.66 times more likely to report seeking additional support for body image issues at post-exposure (website-tailored), 6-week, and 12-month (website-tailored) follow-up. Daughters whose mothers viewed the website had higher self-esteem and reduced negative affect at 6-week follow-up. There were no differences on daughters' body image, and risk factors among mothers or daughters, at post-exposure or follow-up. Tailoring website content appeared beneficial.

CONCLUSIONS

This intervention offers a promising 'first-step' toward improving psychosocial well-being among mothers and daughters. In order to further optimize the intervention, future research to improve body image-related outcomes and to understand mechanisms for change would be beneficial. (PsycINFO Database Record

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health

BACKGROUND AND AIM

Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between "specific" (measures of association) and "general" (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health.

METHODS

We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics.

RESULTS

For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%.

CONCLUSION

Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood "effects" are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.

Capturing the temporal sequence of interaction in young siblings

We explored whether young children exhibit subtypes of behavioral sequences during sibling interaction. Ten-minute, free-play observations of over 300 sibling dyads were coded for positivity, negativity and disengagement. The data were analyzed using growth mixture modeling (GMM). Younger (18-month-old) children's temporal behavioral sequences showed a harmonious (53%) and a casual (47%) class. Older (approximately four-year-old) children's behavior was more differentiated revealing a harmonious (25%), a deteriorating (31%), a recovery (22%) and a casual (22%) class. A more positive maternal affective climate was associated with more positive patterns. Siblings' sequential behavioral patterns tended to be complementary rather than reciprocal in nature. The study illustrates a novel use of GMM and makes a theoretical contribution by showing that young children exhibit distinct types of temporal behavioral sequences that are related to parenting processes.

Individual- and area-level influence on suicide risk: a multilevel longitudinal study of Swedish schoolchildren

BACKGROUND

Characteristics related to the areas where people live have been associated with suicide risk, although these might reflect aggregation into these communities of individuals with mental health or social problems. No studies have examined whether area characteristics during childhood are associated with subsequent suicide, or whether risk associated with individual characteristics varies according to childhood neighbourhood context.

METHOD

We conducted a longitudinal study of 204,323 individuals born in Sweden in 1972 and 1977 with childhood data linked to suicide (n = 314; 0.15%) up to age 26-31 years. Multilevel modelling was used to examine: (i) whether school-, municipality- or county-level characteristics during childhood are associated with later suicide, independently of individual effects, and (ii) whether associations between individual characteristics and suicide vary according to school context (reflecting both peer group and neighbourhood effects).

RESULTS

Associations between suicide and most contextual measures, except for school-level gender composition, were explained by individual characteristics. There was some evidence of cross-level effects of individual- and school-level markers of ethnicity and deprivation on suicide risk, with qualitative interaction patterns. For example, having foreign-born parents increased the risk for individuals raised in areas where they were in a relative minority, but protected against suicide in areas where larger proportions of the population had foreign-born parents.

CONCLUSIONS

Characteristics that define individuals as being different from most people in their local environment as they grow up may increase suicide risk. If robustly replicated, these findings have potentially important implications for understanding the aetiology of suicide and informing social policy.

The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family

BACKGROUND

  Although many children grow up with more than one sibling, we do not yet know if sibling dyads within families show similarities to one another on sibling affection and hostility. In the present study the hypotheses were tested that (a) there will be significant between family variation in change in sibling affection and hostility and (b) this between family variation will be explained by maternal affective climate, operationalized as positive and negative ambient parenting, differential parenting and maternal malaise.

METHODS

  A general population sample of families with single and multiple sibling dyads were visited twice, 2 years apart. Up to 2 children in a family acted as informants; 253 relationships were rated in 118 families. A cross-classified, multilevel model was fit to separate between-family and within-family variance in sibling relationships while simultaneously controlling for informant and partner influences.

RESULTS

  Thirty-seven percent of the variance in change in sibling affection and 32% of the variance in change in sibling hostility was between family variance. The measured maternal affective climate including, maternal malaise and maternal ambient and differential hostility and affection explained between family differences.

CONCLUSIONS

  Sibling relationship quality clusters in families and is partly explained by maternal affective climate.