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We examined prospective associations between men's common mental disorders in the decades prior to offspring conception and subsequent paternal antenatal mental health problems. Data came from a prospective intergenerational cohort study which assessed common mental disorder nine times from age 14 to 29 years, and in the third trimester of subsequent pregnancies to age 35 years ( N  = 295 pregnancies to 214 men). Men with histories of adolescent and young adult common mental disorders were over four times more likely to experience antenatal mental health problems. Future research identifying modifiable perinatal factors that counteract preconception risk would provide further targets for intervention., Declaration of Interest: None.

Measures of biological age based on blood DNA methylation, referred to as age acceleration (AA), have been developed. We examined whether AA was associated with health risk factors and overall and cause-specific mortality. At baseline (1990-1994), blood samples were drawn from 2,818 participants in the Melbourne Collaborative Cohort Study (Melbourne, Victoria, Australia). DNA methylation was determined using the Infinium HumanMethylation450 BeadChip array (Illumina Inc., San Diego, California). Mixed-effects models were used to examine the association of AA with health risk factors. Cox models were used to assess the association of AA with mortality. A total of 831 deaths were observed during a median 10.7 years of follow-up. Associations of AA were observed with male sex, Greek nationality (country of birth), smoking, obesity, diabetes, lower education, and meat intake. AA measures were associated with increased mortality, and this was only partly accounted for by known determinants of health (hazard ratios were attenuated by 20%-40%). Weak evidence of heterogeneity in the association was observed by sex (P = 0.06) and cause of death (P = 0.07) but not by other factors. DNA-methylation-based AA measures are associated with several major health risk factors, but these do not fully explain the association between AA and mortality. Future research should investigate what genetic and environmental factors determine AA.

Background: In cross-population comparisons of disease occurrence (prevalence, incidence), a common public health question is the extent to which variations in the distribution of risk factors for the disease explain observed differences. Limited work has been done on formalizing this problem, which is conceptually tantamount to quantifying the degree of confounding for the 'population effect' induced by different factors. A common approach is to compare 'unadjusted' and 'adjusted' regression-based estimates of that parameter, but the interpretation of the resulting 'contribution' measures may be hindered by other confounding sources and non-collapsibility issues. Interactions also raise interpretational challenges., Methods: We formalized this problem using directed acyclic graphs and the potential outcomes framework, on the basis of which we defined a series of estimands that address specific questions and are identifiable under certain causal assumptions. We subsequently determined possible estimators. A study of regional differences in egg allergy prevalence in 1-year-olds was used for illustration., Results: The main estimands defined were: the change in the prevalence or incidence difference induced by compositional variations in measured risk factors, all at once and individually, relative to a reference population; and the proportion of the crude difference that remains unexplained by measured factors. Standardization (g-computation), inverse probability weighted (IPW) and doubly robust IPW estimators of these estimands were considered., Conclusions: This work provides a causal theoretical basis for studying disease occurrence differences between populations. The proposed measures can be used to answer the questions that arise in this context under a set of clearly stated assumptions., (© The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association)

Background: Little is known about the long-term psychosocial outcomes associated with self-harm during adolescence. We aimed to determine whether adolescents who self-harm are at increased risk of adverse psychosocial outcomes in the fourth decade of life, using data from the Victorian Adolescent Health Cohort Study., Methods: We recruited a stratified, random sample of 1943 adolescents from 44 schools across the state of Victoria, Australia. The study started on Aug 20, 1992, and finished on March 4, 2014. We obtained data relating to self-harm from questionnaires and telephone interviews at eight waves of follow-up, commencing at mean age 15·9 years (SD 0·5; waves 3-6 during adolescence, 6 months apart) and ending at mean age 35·1 years (SD 0·6; wave 10). The outcome measures at age 35 years were social disadvantage (divorced or separated, not in a relationship, not earning money, receipt of government welfare, and experiencing financial hardship), common mental disorders such as depression and anxiety, and substance use. We assessed the associations between self-harm during adolescence and the outcome measures at 35 years (wave 10) using logistic regression models, with progressive adjustment: (1) adjustment for sex and age; (2) further adjustment for background social factors; (3) additional adjustment for common mental disorder in adolescence; and (4) final additional adjustment for adolescent antisocial behaviour and substance use measures., Findings: From the total cohort of 1943 participants, 1802 participants were assessed for self-harm during adolescence (between waves 3 and 6). Of these, 1671 were included in the analysis sample. 135 (8%) reported having self-harmed at least once during adolescence. At 35 years (wave 10), mental health problems, daily tobacco smoking, illicit drug use, and dependence were all more common in participants who had reported self-harm during the adolescent phase of the study (n=135) than in those who had not (n=1536): for social disadvantage odds ratios [ORs] ranged from 1·34 (95% CI 1·25-1·43) for unemployment to 1·88 (1·78-1·98) for financial hardship; for mental health they ranged from 1·61 (1·51-1·72) for depression to 1·92 (1·79-2·04) for anxiety; for illicit drug use they ranged from 1·36 (1·25-1·49) for any amphetamine use to 3·39 (3·12-3·67) for weekly cannabis use; for dependence syndrome they were 1·72 (1·57-1·87) for nicotine dependence, 2·67 (2·38-2·99) for cannabis dependence, and 1·74 (1·62-1·86) for any dependence; and the OR for daily smoking was 2·00 (1·89-2·12). Adjustment for socio-demographic factors made little difference to these associations but a further adjustment for adolescent common mental disorders substantially attenuated most associations, with the exception of daily tobacco smoking (adjusted OR 1·74, 95% CI 1·08-2·81), any illicit drug use (1·72, 1·07-2·79) and weekly cannabis use (3·18, 1·58-6·42). Further adjustment for adolescent risky substance use and antisocial behaviour attenuated the remaining associations, with the exception of weekly cannabis use at age 35 years, which remained independently associated with self-harm during adolescence (2·27, 1·09-4·69)., Interpretation: Adolescents who self-harm are more likely to experience a wide range of psychosocial problems later in life. With the notable exception of heavy cannabis use, these problems appear to be largely accounted for by concurrent adolescent mental health disorders and substance use. Complex interventions addressing the domains of mental state, behaviour, and substance use are likely to be most successful in helping this susceptible group adjust to adult life., Funding: National Health and Medical Research Council, the Royal Children's Hospital Foundation, and the Murdoch Childrens Research Institute., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another 'distinct' variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time - a commonly encountered scenario in epidemiological studies., Methods: We simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems., Results: The standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one., Conclusion: We recommend the use of FCS or MVNI in a similar longitudinal setting, and when encountering convergence issues due to a large number of time points or variables with missing values, the two-fold FCS with exploration of a suitable time window.

Statistics on mortality related to each disease are usually based on the so-called underlying cause of death, which is selected from the diseases declared on the standardized death certificate using international rules. However, the assumption that each death is caused by exactly one disease is debatable, particularly with an aging population in an era where infectious diseases are replaced by chronic and degenerative diseases. The need to consider multiple causes of death has been acknowledged in epidemiologic research, with a growing body of literature producing statistics based on any mention of a disease on the death certificate. Yet there has not been a formal framework proposed for the statistical modeling of death arising from multiple causes. We propose a model for multiple cause of death data grounded on an empirical approach that assigns weights to each cause on the death certificate. We describe how this model for multiple-cause mortality, which extends the usual competing risks model used to conceptualize single-cause mortality, can serve to study the burden and etiology of mortality related to each disease, particularly using Cox regression methodology. We discuss how the multiple-cause, single-cause, and "any-mention" approaches compare in this regard. A simulation study and an application to a study of socioeconomic inequalities in mortality show the value of the proposed methods for exploiting this precious source of data to gain new insights, especially for certain diseases. See video abstract at, http://links.lww.com/EDE/B84.

Disasters occur frequently in the United States (US) and their impact on acute morbidity, mortality and short-term increased health needs has been well described. However, barring mental health, little is known about the medium or longer-term health impacts of disasters. This study sought to determine if there is an association between community-level disaster exposure and individual-level changes in disability and/or the risk of death for older Americans. Using the US Federal Emergency Management Agency's database of disaster declarations, 602 disasters occurred between August 1998 and December 2010 and were characterized by their presence, intensity, duration and type. Repeated measurements of a disability score (based on activities of daily living) and dates of death were observed between January 2000 and November 2010 for 18,102 American individuals aged 50-89 years, who were participating in the national longitudinal Health and Retirement Study. Longitudinal (disability) and time-to-event (death) data were modelled simultaneously using a 'joint modelling' approach. There was no evidence of an association between community-level disaster exposure and individual-level changes in disability or the risk of death. Our results suggest that future research should focus on individual-level disaster exposures, moderate to severe disaster events, or higher-risk groups of individuals., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objective: To investigate a new approach to calculating cause-related standardized mortality rates that involves assigning weights to each cause of death reported on death certificates., Methods: We derived cause-related standardized mortality rates from death certificate data for France in 2010 using: (i) the classic method, which considered only the underlying cause of death; and (ii) three novel multiple-cause-of-death weighting methods, which assigned weights to multiple causes of death mentioned on death certificates: the first two multiple-cause-of-death methods assigned non-zero weights to all causes mentioned and the third assigned non-zero weights to only the underlying cause and other contributing causes that were not part of the main morbid process. As the sum of the weights for each death certificate was 1, each death had an equal influence on mortality estimates and the total number of deaths was unchanged. Mortality rates derived using the different methods were compared., Findings: On average, 3.4 causes per death were listed on each certificate. The standardized mortality rate calculated using the third multiple-cause-of-death weighting method was more than 20% higher than that calculated using the classic method for five disease categories: skin diseases, mental disorders, endocrine and nutritional diseases, blood diseases and genitourinary diseases. Moreover, this method highlighted the mortality burden associated with certain diseases in specific age groups., Conclusion: A multiple-cause-of-death weighting approach to calculating cause-related standardized mortality rates from death certificate data identified conditions that contributed more to mortality than indicated by the classic method. This new approach holds promise for identifying underrecognized contributors to mortality.

Background: Studies in various countries have shown that homeless people have high mortality levels. The aims of this study concerning the French population were to investigate mortality among the homeless and to study their causes of death in comparison to those of the general population., Methods: A representative sample of 1145 homeless deaths registered by an association was matched to the national database of medical causes of death using common descriptive variables. Log-binomial regression was used to compare mortality among the homeless to that of the general population. Multiple imputation was used to manage missing causes of deaths., Results: Out of the 1145 registered homeless deaths, 693 were matched to the causes of death database. Homeless deaths were young (average age: 49). Overall, homeless deaths were slightly more frequent during winter. Among all deaths, the probability of being homeless was higher when dying from hypothermia (RR = 6.4), alcohol-related deaths (RR = 1.7), mental disorders, diseases of the digestive and circulatory systems, and undetermined causes (RR from 1.5 to 3.7)., Conclusion: The homeless died at 49 years old on average compared with 77 in the general population in 2008-10. The health of homeless people should be considered not only in winter periods or in terms of alcohol- or cold-related conditions. This study also highlights the need for more precise data to estimate the mortality risks of the homeless in France., (© The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

Purpose: To determine whether sulfur hexafluoride (SF6) gas is noninferior to longer-acting gases in macular hole surgery and whether withholding postoperative face-down positioning (FDP) is noninferior to FDP., Design: Registry-style, prospective, nonrandomized, observational cohort study., Participants: Patients with idiopathic macular holes undergoing primary surgery., Methods: Surgeons were invited to submit clinical details of all macular hole cases receiving surgery. Baseline demographic and clinical information were collected, as well as details of surgical intervention and postoperative posturing advice. Primary follow-up data were collected 3 months postoperatively., Main Outcome Measures: Macular hole closure at 3 months. A noninferiority approach was used, with a noninferiority margin set at 5% decreased frequency of success., Results: A total of 2456 eyes of 2367 patients were included in the study. Outcomes were available in 94.9% of cases (2330/2456). The rate of macular hole closure was 95.0% (2214/2330). Sulfur hexafluoride gas was found to be noninferior to longer-acting gases (95% confidence interval [CI] for adjusted effect on success, -1.76 to +2.25), and noninferiority was demonstrated regardless of macular hole size. Although withholding FDP was found to be noninferior to FDP for the study population as a whole (95% CI for adjusted effect on success, -4.21 to +0.64), the result was inconclusive in holes >400 μm in diameter (95% CI, -9.31 to +1.04). Lack of internal limiting membrane (ILM) peel, increasing hole size, hole duration ≥9 months, increasing age, and 20-gauge surgery all were associated with lower odds of success. Vitreous attachment to the hole margin was not associated with outcome when corrected for hole size, and combined phacovitrectomy surgery was not observed to affect the odds of success in phakic eyes., Conclusions: Sulfur hexafluoride gas tamponade was noninferior to longer-acting gases in the surgical management of macular hole. Withholding FDP was noninferior to FDP in holes ≤400 μm in diameter. In holes >400 μm in diameter, noninferiority of withholding FDP could not be concluded. We would advise caution if posturing is withheld in this group on the basis of the results of this study and of others., (Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Background: The relative index of inequality and the slope index of inequality are the two major indices used in epidemiologic studies for the measurement of socioeconomic inequalities in health. Yet the current definitions of these indices are not adapted to their main purpose, which is to provide summary measures of the linear association between socioeconomic status and health in a way that enables valid between-population comparisons. The lack of appropriate definitions has dissuaded the application of suitable regression methods for estimating the slope index of inequality., Methods: We suggest formally defining the relative and slope indices of inequality as so-called least false parameters, or more precisely, as the parameters that provide the best approximation of the relation between socioeconomic status and the health outcome by log-linear and linear models, respectively. From this standpoint, we establish a structured regression framework for inference on these indices. Guidelines for implementation of the methods, including R and SAS codes, are provided., Results: The new definitions yield appropriate summary measures of the linear association across the entire socioeconomic scale, suitable for comparative studies in epidemiology. Our regression-based approach for estimation of the slope index of inequality contributes to an advancement of the current methodology, which mainly consists of a heuristic formula relying on restrictive assumptions. A study of the educational inequalities in all-cause and cause-specific mortality in France is used for illustration., Conclusion: The proposed definitions and methods should guide the use and estimation of these indices in future studies.

Competing risks arise in the analysis of failure times when there is a distinction between different causes of failure. In many studies, it is difficult to obtain complete cause of failure information for all individuals. Thus, several authors have proposed strategies for semi-parametric modeling of competing risks when some causes of failure are missing under the missing at random (MAR) assumption. As many authors have stressed, while semi-parametric models are convenient, fully-parametric regression modeling of the cause-specific hazards (CSH) and cumulative incidence functions (CIF) may be of interest for prediction and is likely to contribute towards a fuller understanding of the time-dynamics of the competing risks mechanism. We propose a so-called "direct likelihood" approach for fitting fully-parametric regression models for these two functionals under MAR. The MAR assumption not being verifiable from the observed data, we propose an approach for performing sensitivity analyses to assess the robustness of inferences to departures from this assumption. The method relies on so-called "pattern-mixture models" from the missing data literature and was evaluated in a simulation study. This sensitivity analysis approach is applicable to various competing risks regression models (fully-parametric or semi-parametric, for the CSH or the CIF). We illustrate the proposed methods with the analysis of a breast cancer clinical trial, including suggestions for ad hoc graphical goodness-of-fit assessments under MAR., (© 2015, The International Biometric Society.)

Competing risks arise when patients may fail from several causes. Strategies for modeling event-specific quantities often assume that the cause of failure is known for all patients, but this is seldom the case. Several authors have addressed the problem of modeling the cause-specific hazard rates with missing causes of failure. In contrast, direct modeling of the cumulative incidence function has received little attention.We provide a general framework for regression modeling of this function in the missing cause setting, encompassing key models such as the Fine and Gray and additive models, by considering two extensions of the Andersen–Klein pseudo-value approach. The first extension is a novel inverse probability weighting method, whereas the second extension is based on a previously proposed multiple imputation procedure.We evaluated the gain in using these approaches with small samples in an extensive simulation study. We analyzed the data from an Eastern Cooperative Oncology Group breast cancer treatment clinical trial to illustrate the practical value and ease of implementation of the proposed methods.