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Objective: Traumatic brain injury (TBI) and stroke are well-known causes of acquired epilepsy. TBI is also a risk factor for stroke, and injury-induced stroke may indirectly convey a proportion of the epilepsy risk following TBI. We studied the extent to which the effect of TBI on epilepsy operated through intermediary stroke. Methods: We analyzed a nationwide, matched, register-based cohort of adults (Formula presented.) 40 years of age whose first TBI at Danish hospitals was recorded between 2004 and 2016. A matched reference population was sampled for comparison. During follow-up, we recorded all acute strokes. Cox proportional hazard models and the difference method were used to estimate the total and controlled direct effect hazard ratios (HRs) of TBI on epilepsy and the indirect effect HRs of TBI on epilepsy operating through stroke, and to calculate the proportion eliminated. Analyses were stratified by severity of, age at, and time since TBI. Results: We followed 57 900 persons with TBI (48.6% males) from median age 61 years (interquartile range = 51–75), and 561 977 age- and sex-matched references. The total effect of TBI on epilepsy was higher for persons aged 40–59 years (HR = 5.15, 95% confidence interval [CI] = 4.65–5.72) than for persons aged (Formula presented.) 60 years (HR = 4.55, 95% CI = 4.19–4.95). In contrast, the indirect effect of TBI mediated by stroke was lower for persons aged 40–59 years (HR = 1.02, 95% CI = 1.02–1.03) than for persons aged (Formula presented.) 60 years (HR = 1.05, 95% CI = 1.04–1.06). We estimated 2.3% and 5.6% of the risk of epilepsy after TBI to operate through stroke for these age groups, respectively. Significance: Less than 6% of the risk of epilepsy following TBI operated through intermediary stroke. However, this mechanism seems to play an increasing role with age and for late onset epilepsies. This warrants further investigation.

Social theories posit that peers affect students’ academic self-concept (ASC). Most prominently, Big-Fish-Little-Pond, invidious comparison, and relative deprivation theories predict that exposure to academically stronger peers decreases students’ ASC, and exposure to academically weaker peers increases students’ ASC. These propositions have not yet been tested experimentally. We executed a large and pre-registered field experiment that randomized students to deskmates within 195 classrooms of 41 schools (N = 3,022). Our primary experimental analysis found no evidence of an effect of peer achievement on ASC in either direction. Exploratory analyses hinted at a subject-specific deskmate effect on ASC in verbal skills, and that sitting next to a lower-achieving boy increased girls’ ASC (but not that sitting next to a higher-achieving boy decreased girls’ ASC). Critically, however, none of these group-specific results held up to even modest corrections for multiple hypothesis testing. Contrary to theory, our randomized field experiment thus provides no evidence for an effect of peer achievement on students’ ASC.

Rates of ADHD diagnosis vary across regions in many countries. However, no prior study has investigated how much within-country geographic variation in ADHD diagnoses is explained by variation in ADHD symptom levels. We examine whether ADHD symptom levels explain variation in ADHD diagnoses among children and adolescents using nationwide survey and register data in Norway. Geographical variation in incidence of ADHD diagnosis was measured using Norwegian registry data from the child and adolescent mental health services for 2011–2016. Geographical variation in ADHD symptom levels in clinics’ catchment areas was measured using data from the Norwegian mother, father and child cohort study for 2011–2016 (n = 39,850). Cross-sectional associations between ADHD symptom levels and the incidence of ADHD diagnoses were assessed with fractional response models. Geographical variation in ADHD diagnosis rates is much larger than what can be explained by geographical variation in ADHD symptoms levels. Treatment in the Norwegian child and adolescent mental health services is free, universally available upon referral, and practically without competition from the private sector. Factors beyond health care access and unequal symptom levels seem responsible for the geographical variation in ADHD diagnosis.

A common causal identification strategy in political science is selection on observables. This strategy assumes one observes a set of covariates that is, after statistical adjustment, sufficient to make treatment status as-if random. Under adjustment methods such as matching or inverse probability weighting, coefficients for control variables are treated as nuisance parameters and are not directly estimated. This is in direct contrast to regression approaches where estimated parameters are obtained for all covariates. Analysts often find it tempting to give a causal interpretation to all the parameters in such regression models—indeed, such interpretations are often central to the proposed research design. In this paper, we ask when we can justify interpreting two or more coefficients in a regression model as causal parameters. We demonstrate that analysts must appeal to causal identification assumptions to give estimates causal interpretations. Under selection on observables, this task is complicated by the fact that more than one causal effect might be identified. We show how causal graphs provide a framework for clearly delineating which effects are presumed to be identified and thus merit a causal interpretation, and which are not. We conclude with a set of recommendations for how researchers should interpret estimates from regression models when causal inference is the goal.

PURPOSE: A growing area of research in epidemiology is the identification of health-related sibling spillover effects, or the effect of one individual’s exposure on their sibling’s outcome. The health within families may be inextricably confounded by unobserved factors, rendering identification of sibling spillovers challenging. METHODS: We demonstrate a gain-score regression method for identifying exposure-to-outcome spillover effects within sibling pairs in linear models. The method can identify the exposure-to-outcome spillover effect if only one sibling’s exposure affects the other’s outcome; and it identifies the difference between the spillover effects if both siblings’ exposures affect the others’ outcomes. The method fails with outcome-to-exposure spillover or outcome-to-outcome spillover. Analytic results, Monte Carlo simulations, and a brief application demonstrate the method and its limitations. RESULTS: We estimate the spillover effect of a child’s preterm birth on an older sibling’s literacy skills, measured by the Phonological Awareness Literacy Screening-Kindergarten test. We analyze 20,010 sibling pairs from a population-wide, Wisconsin-based (United States) birth cohort. Without covariate adjustment, we estimate that preterm birth modestly decreases an older sibling’s test score. CONCLUSION: Gain-scores are a promising strategy for identifying exposure-to-outcome spillover effects in sibling pairs while controlling for sibling-invariant unobserved confounding.

IntroductionAttention-deficit/hyperactivity disorder (ADHD) is among the most common mental disorders in children and adolescents, and it is a strong risk factor for several adverse psychosocial outcomes over the lifespan. There are large between-country and within-country variations in diagnosis and medication rates. Due to ethical and practical considerations, a few studies have examined the effects of receiving a diagnosis, and there is a lack of research on effects of medication on long-term outcomes.Our project has four aims organised in four work packages: (WP1) To examine the prognosis of ADHD (with and without medication) compared with patients with other psychiatric diagnoses, patients in contact with public sector child and adolescent psychiatric outpatient clinics (without diagnosis) and the general population; (WP2) Examine within-country variation in ADHD diagnoses and medication rates by clinics’ catchment area; and(WP3) Identify causal effects of being diagnosed with ADHD and (WP4) ADHD medication on long-term outcomes.Method and analysisOur project links several nationwide Norwegian registries. The patient sample is all persons aged 5–18 years that were in contact with public sector child and adolescent psychiatric outpatient clinics in 2009–2011. Our comparative analysis of prognosis will be based on survival analysis and mixed-effects models. Our analysis of variation will apply mixed-effects models and generalised linear models. We have two identification strategies for the effect of being diagnosed with ADHD and of receiving medication on long-term outcomes. Both strategies rely on using preference-based instrumental variables, which in our project are based on provider preferences for ADHD diagnosis and medication.Ethics and disseminationThe project is approved by the Regional Ethics Committee, Norway (REC number 2017/2150/REC south-east D). All papers will be published in open-access journals and results will be presented in national and international conferences.Trial registration numbersISRCTN11573246 and ISRCTN11891971.

High school track choice determines college access in many countries. We hypothesize that some qualified students avoid the college-bound track in high school simply because they overestimate admission requirements. To test this hypothesis, we conducted a randomized field experiment that communicated the admission standards of local secondary schools on the academic track to students in Hungary before the application deadline. We targeted the subset of students (“seeds”) who occupied the most central position in the classroom-social networks, aiming to detect both direct effects on the track choice of targeted seeds and spillover effects on their untreated peers. We found neither a direct effect nor a spillover effect on students’ applications or admissions on average. Further analyses, however, revealed theoretically plausible heterogeneity in the direct causal effect of the intervention on the track choice of targeted seeds. Providing information about admission standards increased applications and admissions to secondary schools on the academic track among seeds who had a pre-existing interest in the academic track but were unsure of their chances of admission. This demonstrates that publicizing admissions standards can set students on a more ambitious educational trajectory. We discuss the implications for theory and policy.

Objective - Preference-based instrumental variables (PP IV) designs can identify causal effects when patients receive treatment due to variation in providers’ treatment preference. We offer a systematic review and methodological assessment of PP IV applications in health research. Study Design and Setting - We included studies that applied PP IV for evaluation of any treatment in any population in health research (PROSPERO: CRD42020165014). We searched within four databases (Medline, Web of Science, ScienceDirect, SpringerLink) and four journals (including full-text and title and abstract sources) between January 1, 1998, and March 5, 2020. We extracted data on areas of applications and methodology, including assumptions using Swanson and Hernan's (2013) guideline. Results - We included 185 of 1087 identified studies. The use of PP IV has increased, being predominantly used for treatment effects in cancer, cardiovascular disease, and mental health. The most common PP IV was treatment variation at the facility-level, followed by physician- and regional-level. Only 12 percent of applications report the four main assumptions for PP IV. Selection on treatment may be a potential issue in 46 percent of studies. Conclusion - The assumptions of PP IV are not sufficiently reported in existing work. PP IV-studies should use reporting guidelines.

In this pre-registered study, we analyze the effects of need-based financial aid grant offers on the educational outcomes of low-income college students based on a large-scale randomized experiment (n=48,804). We find evidence that the grant offers increase two-year persistence by 1.7 percentage points among four-year college students. The estimated effect on six-year bachelor’s degree completion is of similar size—1.5 percentage points—but is not statistically significant. Among two-year students, we find positive—but not statistically significant—effects on persistence and bachelor’s degree completion (1.2 and 0.5 percentage points, respectively). We find little evidence that effects vary by cohort, race, gender or the prior receipt of food stamps. However, further exploratory results do suggest that the offers reduce associate’s degree completion rates for two-year community college students by around 3 percentage points, with no statistically significant evidence of effects on technical college students. We also estimate that the effects of actually receiving grant money are very similar, though slightly greater than the effects of merely receiving a grant offer. Overall, our results show only very small effects of the need-based grant offers on college students’ trajectories towards degree completion. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Background Behavioral interventions produce clinically significant weight reduction, with many participants regaining weight subsequently. Most interventions focus on an individual, but dietary and physical activity behaviors occur with, or are influenced by, domestic partners. According to interdependence theory, couples who approach behavior change as a problem to be tackled together versus independently are more likely to utilize communal coping processes to promote behavior change. We utilized interdependence theory to develop a partner-assisted intervention to increase long-term weight loss. Methods Community-dwelling individuals (index participants) cohabitating with a partner with 1) overweight and at least one obesity-related comorbidity or 2) obesity are randomized to participate in a standard weight management program alone or with their partner. The weight management program involves biweekly, in-person, group sessions focusing on weight loss for six months, followed by three group sessions and nine telephone calls focusing on weight loss maintenance for twelve months. In the partner-assisted arm, partners participate in half of the group sessions and telephone calls. Couples receive training in principles of cognitive behavioral therapy for couples, including sharing thoughts and feelings and joint problem solving, to increase communal coping. The primary outcome is participant weight loss at 24 months, with caloric intake and moderate-intensity physical activity as secondary outcomes. Partner weight and caloric intake will also be analyzed. Mediation analyses will examine the role of interdependence variables and social support. Discussion This trial will provide knowledge about effective ways to promote long-term weight loss and the role of interdependence constructs in weight loss. Clinical trials identifier: NCT 03801174.

Endogenous selection bias is a central problem for causal inference. Recognizing the problem, however, can be difficult in practice. This article introduces a purely graphical way of characterizing endogenous selection bias and of understanding its consequences ( Hernán et al. 2004 ). We use causal graphs (direct acyclic graphs, or DAGs) to highlight that endogenous selection bias stems from conditioning (e.g., controlling, stratifying, or selecting) on a so-called collider variable, i.e., a variable that is itself caused by two other variables, one that is (or is associated with) the treatment and another that is (or is associated with) the outcome. Endogenous selection bias can result from direct conditioning on the outcome variable, a post-outcome variable, a post-treatment variable, and even a pre-treatment variable. We highlight the difference between endogenous selection bias, common-cause confounding, and overcontrol bias and discuss numerous examples from social stratification, cultural sociology, social network analysis, political sociology, social demography, and the sociology of education.

The identification of causal peer effects (also known as social contagion or induction) from observational data in social networks is challenged by two distinct sources of bias: latent homophily and unobserved confounding. In this paper, we investigate how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental variables (IV) in a large set of data generating models with homophily and confounding. We use directed acyclic graphs to represent these models and employ multiple IV strategies and report three main identification results. First, using a single fixed gene (or allele) as an IV will generally fail to identify peer effects if the gene affects past values of the treatment. Second, multiple fixed genes/alleles, or, more promisingly, time-varying gene expression, can identify peer effects if we instrument exclusion violations as well as the focal treatment. Third, we show that IV identification of peer effects remains possible even under multiple complications often regarded as lethal for IV identification of intra-individual effects, such as pleiotropy on observables and unobservables, homophily on past phenotype, past and ongoing homophily on genotype, inter-phenotype peer effects, population stratification, gene expression that is endogenous to past phenotype and past gene expression, and others. We apply our identification results to estimating peer effects of body mass index (BMI) among friends and spouses in the Framingham Heart Study. Results suggest a positive causal peer effect of BMI between friends.

© 2016 by The University of Chicago. All rights reserved. Effects of disadvantaged neighborhoods on child educational outcomes likely depend on a family’s economic resources and the timing of neighborhood exposures during the course of child development. This study investigates how timing of exposure to disadvantaged neighborhoods during childhood versus adolescence affects high school graduation and whether these effects vary across families with different income levels. It follows 6,137 children in the PSID from childhood through adolescence and overcomes methodological problems associated with the joint endogeneity of neighborhood context and family income by adapting novel counterfactual methods—a structural nested meanmodel estimated via two-stage regression with residuals—for timevarying treatments and time-varying effect moderators. Results indicate that exposure to disadvantaged neighborhoods, particularly during adolescence, has a strong negative effect on high school graduation and that this negative effect is more severe for children from poor families.

Mortality crossovers are often understood to be the result of differential mortality selection. Models of mortality selection commonly assume a single dimension of heterogeneity, which stratifies populations into homogenous frail and robust subpopulations with proportional hazards. We propose a more realistic mortality selection model in which black and white populations are stratified by multiple crosscutting dimensions of heterogeneity, resulting in heterogeneous subpopulations. In the multidimensional model, in contrast to the conventional unidimensional model, the rank order of subpopulation mortalities is dynamic over age. As a result, a crossover can arise in either of two ways: from a change in the share of subpopulations in the black and white populations (analogous to the crossover in the standard, unidimensional mortality selection model), or alternatively, from a change in the rank order of subpopulation mortalities, regardless of subpopulation shares. The latter possibility has no analogue in the standard, unidimensional model. Our results therefore identify a new mechanism by which mortality selection can create mortality crossovers.

Theory suggests that neighborhood effects depend not only on where individuals live today, but also on where they lived in the past. Previous research, however, usually measures neighborhood context only once and does not account for length of residence, thereby understating the detrimental effects of long-term neighborhood disadvantage. This study investigates effects of duration of exposure to disadvantaged neighborhoods on high school graduation. It follows 4,154 children in the Panel Study of Income Dynamics, measuring neighborhood context once per year from age 1 to 17. The analysis overcomes the problem of dynamic neighborhood selection by adapting novel methods of causal inference for time-varying treatments. In contrast to previous analyses, these methods do not “control away” the effect of neighborhood context operating indirectly through time-varying characteristics of the family; thus, they capture the full impact of a lifetime of neighborhood disadvantage. We find that sustained exposure to disadvantaged neighborhoods has a severe impact on high school graduation that is considerably larger than effects reported in prior research. We estimate that growing up in the most (compared to the least) disadvantaged quintile of neighborhoods reduces the probability of graduation from 96 to 76 percent for black children, and from 95 to 87 percent for nonblack children.

This study examines how the neighborhood environments experienced over multiple generations of a family influence children’s cognitive ability. Building on recent research showing strong continuity in neighborhood environments across generations of family members, we argue for a revised perspective on “neighborhood effects” that considers the ways in which the neighborhood environment in one generation may have a lingering impact on the next generation. To specify such multigenerational effects is not simply a theoretical problem, but poses considerable methodological challenges. Instead of traditional regression techniques that may obscure multigenerational effects of neighborhood disadvantage, we utilize newly developed methods designed to generate unbiased treatment effects when treatments and confounders vary over time. The results confirm a powerful link between neighborhoods and cognitive ability that extends across generations. Being raised in a high-poverty neighborhood in one generation has a substantial negative effect on child cognitive ability in the next generation. A family’s exposure to neighborhood poverty across two consecutive generations reduces child cognitive ability by more than half a standard deviation. A formal sensitivity analysis suggests that results are robust to unobserved selection bias.

The effect of death of a spouse on the mortality of the survivor (the "widowhood effect") is well-established. We investigated how the effect of widowhood on mortality depends on the neighborhood concentration of widowed individuals in the United States. We developed a large, nationally representative, and longitudinal dataset from Medicare claims and other data sources characterizing 200,000 elderly couples, with nine years of follow-up (1993-2002), and estimated multilevel grouped discrete-time hazard models. In neighborhoods with a low concentration of widowed individuals, widowhood increased the odds of death for men by 22% and for women by 17%, compared to 17% for men, and 15% for women in neighborhoods with a high concentration of widowed individuals. Our findings suggest that neighborhood structural contexts - that provide opportunities for interacting with others and favoring new social engagements - could be potential modifiers of the widowhood effects and as such requires more systematic consideration in future research of widowhood effects on well-being and mortality.

Complete Center for Disease Control death certificate records and Centers for Medicare and Medicaid Services 100% Standard Analytic File for hospice claims for 2002 were used to describe the whole population of hospice users and nonusers in the United States. The overall hospice utilization rate for persons 65 years and older was 28.6%. Hospice utilization varied by cause of death, and was highest for individuals with malignancies (65%), kidney disease and nephritis (55%), and Alzheimer's disease (41%). Hospice utilization was lowest for conditions leading to rapid or unexpected death, such as accidents and suicide (0%), influenza and pneumonia (3%), and sepsis (6%). Considerable geographic differences in hospice utilization existed, with hospice use higher in the South and the Southwest and lower in the Midwest and the Northeast. State-specific usage rates ranged from 8% in Alaska to 49% in Arizona. Our findings highlight opportunities for the hospice industry to provide more care, opportunities defined by diagnostic and geographic axes.

The health effects of marital status are frequently cited in the current debate on marriage promotion, but little is known about how marital health effects vary across groups. This article assembles the largest properly longitudinal and nationally representative dataset of elderly married couples in the United States (N = 410,272 couples) and provides strong evidence that the “widowhood effect”—how the death of a spouse increases the mortality of the survivor-varies substantially by race. The authors find that whites married to whites suffer a large and enduring widowhood effect. By contrast, blacks married to blacks do not suffer a detectable widowhood effect, possibly because they manage to extend the survival advantage of marriage into widowhood. For racially intermarried men, wife's race appears to dominate the size and presence of the widowhood effect entirely, regardless of husband's own race. These results likely arise from differences in the marital cultures and marital contexts of black and white couples. More generally, these results demonstrate that the health effects of social ties depend on the individual attributes of the actors they connect.

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, d-separation, confounding, endogenous selection, and overcontrol. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than an associational concept, that conventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, time-point treatments (including the famous backdoor criterion), as well identification criteria for multiple, time-varying treatments.

In human populations, variation in mate availability has been linked to various biological and social outcomes, but the possible effect of mate availability on health or survival has not been studied. Unbalanced sex ratios are a concern in many parts of the world, and their implications for the health and survival of the constituent individuals warrant careful investigation. We indexed mate availability with contextual sex ratios and investigated the hypothesis that the sex ratio at sexual maturity might be associated with long-term survival for men. Using two unique data sets of 7,683,462 and 4,183 men who were followed for more than 50 years, we found that men who reached their sexual maturity in an environment with higher sex ratios (i. e., higher proportions ofreproductively ready men) appeared to suffer higher long-term mortality risks than those in an environment with lower sex ratios. Mate availability at sexual maturity may be linked via several biological and social mechanisms to long-term survival in men.

Increased mortality following the death of a spouse (the “widowhood effect”) may be due to (1) causation, (2) bias from spousal similarity (homogamy), or (3) bias from shared environmental exposures. This article proposes new tests for bias in the widowhood effect by examining husbands, wives, and ex-wives in a longitudinal sample of over 1 million elderly Americans. If the death of an ex-wife has no causal effect on the mortality of her husband, then an observed association between the mortality of an ex-wife and her husband may indicate bias, while the absence of an effect of an ex-wife’s death on her husband’s mortality would discount the possibility of homogamy bias (and also of one type of shared-exposure bias). Results from three empirical tests provide strong evidence for an effect of a current wife’s death on her husband’s mortality yet no statistically signifi cant evidence for an effect of an ex-wife’s death on her husband’s mortality. These results strengthen the causal interpretation of the widowhood effect by suggesting that the widowhood effect is not due to homogamy bias to any substantial degree.

Objectives. We investigated the effect of spousal bereavement on mortality to document cause-specific bereavement effects by the causes of death of both the predecedent spouse and the bereaved partner. Methods. We obtained data from a nationally representative cohort of 373 189 elderly married couples in the United States who were followed from 1993 to 2002. We used competing risk and Cox models in our analyses. Results. For both men and women, the death of a predecedent spouse from almost all causes, including various cancers, infections, and cardiovascular diseases, increased the all-cause mortality of the bereaved partner to varying degrees. Moreover, the death of a predecedent spouse from any cause increased the survivor's cause-specific mortality for almost all causes, including cancers, infections, and cardiovascular diseases, to varying degrees. Conclusions. The effect of widowhood on mortality varies substantially by the causes of death of both spouses, suggesting that the widowhood effect is not restricted to one aspect of human biology. Future research should examine the specific mechanisms of the widowhood effect and identify opportunities for health interventions.

We used complete Centers for Disease Control death certificate records and the Centers for Medicare and Medicaid Services 100% Standard Analytic File for hospice claims for 2002 to examine differences in hospice utilization between African-American and white decedents living in the United States. White decedents were more likely to use hospice in the year before their death than African-American decedents (29% vs 22%). Cause-specific hospice utilization rates among women were consistently higher than among men within a given race. African-American decedents were consistently less likely to use hospice than white decedents for almost all conditions. Hospice utilization was lower among African-American than among white decedents in 31 of 40 states. The higher the overall hospice utilization in a state, the less the positive difference between white and African-American usage rates; that is, the more accepted hospice is, as measured by ‘market share’, the lower the racial disparity in its use.