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Background: Depression and anxiety are both prevalent among university students. They frequently co-occur and share risk factors. Yet few studies have focused on identifying students at highest risk of first-onset and persistence of either of these conditions. Methods: Multicenter cohort study among Spanish first-year university students. At baseline, students were assessed for lifetime and 12-month Major Depressive Episode and/or Generalized Anxiety Disorder (MDE-GAD), other mental disorders, childhood-adolescent adversities, stressful life events, social support, socio-demographics, and psychological factors using web-based surveys; 12-month MDE-GAD was again assessed at 12-month follow-up. Results: A total of 1253 students participated in both surveys (59.2% of baseline respondents; mean age = 18.7 (SD = 1.3); 56.0% female). First-onset of MDE-GAD at follow-up was 13.3%. Also 46.7% of those with baseline MDE-GAD showed persistence at follow-up. Childhood/Adolescence emotional abuse or neglect (OR= 4.33), prior bipolar spectrum disorder (OR= 4.34), prior suicidal ideation (OR=4.85) and prior lifetime symptoms of MDE (ORs=2.33-3.63) and GAD (ORs=2.15-3.75) were strongest predictors of first-onset MDE-GAD. Prior suicidal ideation (OR=3.17) and prior lifetime GAD symptoms (ORs=2.38-4.02) were strongest predictors of MDE-GAD persistence. Multivariable predictions from baseline showed AUCs of 0.76 for first-onset and 0.81 for persistence. 74.9% of first-onset MDE-GAD cases occurred among 30% students with highest predicted risk at baseline. Limitations: Self-report data were used; external validation of the multivariable prediction models is needed. Conclusion: MDE-GAD among university students is frequent, suggesting the need to implement web-based screening at university entrance that identify those students with highest risk.

Introduction: The Cognitive Distortions Questionnaire (CD-Quest) is a self-report questionnaire that assesses common cognitive distortions. Although the CD-Quest has excellent psychometric properties, its length may limit its use. Methods: We attempted to develop short-forms of the CD-Quest using RiskSLIM - a machine learning method to build short-form scales that can be scored by hand. Each short-form was fit to maximize concordance with the total CD-Quest score for a specified number of items based on an objective function, in this case R2, by selecting an optimal subset of items and an optimal set of small integer weights. The models were trained in a sample of US undergraduate students (N = 906). We then validated each short-form on five independent samples: two samples of undergraduate students in Brazil (Ns = 182, 183); patients with depression in Brazil (N = 62); patients with social anxiety disorder in the US (N = 198); and psychiatric outpatients in Turkey (N = 269). Results: A 9-item short-form with integer scoring was created that reproduced the total 15-item CD-Quest score in all validation samples with excellent accuracy (R2 = 90.4-93.6%). A 5-item ultra-short-form had good accuracy (R2 = 78.2-85.5%). Discussion: A 9-item short-form and a 5-item ultra-short-form of the CD-Quest both reproduced full CD-Quest scores with excellent to good accuracy. These shorter versions of the full CD-Quest could facilitate measure-ment of cognitive distortions for users with limited time and resources.

Background Self-report data on mental distress indicate a deterioration of population mental health in many countries during the COVID-19 pandemic. A Norwegian epidemiological diagnostic psychiatric interview survey was conducted from January to September 2020, allowing for comparison of mental disorder and suicidal ideation prevalence from before through different pandemic periods. Prevalence of suicide deaths were compared between 2020 and 2014–2018. Methods Participants from the Trondelag Health Study (HUNT) in Trondheim were recruited through repeated probability sampling. Using the Composite International Diagnostic Interview (CIDI 5.0) (n = 2154), current prevalence of mental disorders and suicidal ideation was examined in repeated cross-sectional analyzes. Data on suicide deaths was retrieved from the Norwegian Cause of Death Registry and compared for the months March to May in 2014–2018 and 2020. Findings Prevalence of current mental disorders decreased significantly from the pre-pandemic period (January 28th to March 11th 2020; 15•3% (95% CI 12•4–18•8)) to the first pandemic period (March 12th – May 31st; 8•7% (6•8–11•0)). Prevalences were similar between the pre-pandemic period and the interim (June 1st July 31st; 14•2% (11•4–17•5)) and second periods (August 1st-September 18th; 11•9% (9•0–15•6)). No significant differences were observed in suicidal ideation or in suicide deaths. Interpretation Except for a decrease in mental disorders in the first pandemic period, the findings suggest stable levels of mental disorders, suicidal ideation and suicide deaths during the first six months of the COVID-19 pandemic compared to pre-pandemic levels. Potential methodological and contextual explanations of these findings compared with findings from other studies are discussed. Funding None.

BACKGROUND AND OBJECTIVES: Associations between asthma and anxiety and mood disorders are well established, but little is known about their temporal sequence. We examined associations between a wide range of DSM-IV mental disorders with adult onset of asthma and whether observed associations remain after mental comorbidity adjustments. METHODS: During face-to-face household surveys in community-dwelling adults (n = 52,095) of 19 countries, the WHO Composite International Diagnostic Interview retrospectively assessed lifetime prevalence and age at onset of 16 DSM-IV mental disorders. Asthma was assessed by self-report of physician's diagnosis together with age of onset. Survival analyses estimated associations between first onset of mental disorders and subsequent adult onset asthma, without and with comorbidity adjustment. RESULTS: 1860 adult onset (21 years+) asthma cases were identified, representing a total of 2,096,486 person-years of follow up. After adjustment for comorbid mental disorders several mental disorders were associated with subsequent adult asthma onset: bipolar (OR = 1.8; 95%CI 1.3-2.5), panic (OR = 1.4; 95%CI 1.0-2.0), generalized anxiety (OR = 1.3; 95%CI 1.1-1.7), specific phobia (OR = 1.3; 95%CI 1.1-1.6); post-traumatic stress (OR = 1.5; 95%CI 1.1-1.9); binge eating (OR = 1.8; 95%CI 1.2-2.9) and alcohol abuse (OR = 1.5; 95%CI 1.1-2.0). Mental comorbidity linearly increased the association with adult asthma. The association with subsequent asthma was stronger for mental disorders with an early onset (before age 21). CONCLUSIONS: A wide range of temporally prior mental disorders are significantly associated with subsequent onset of asthma in adulthood. The extent to which asthma can be avoided or improved among those with early mental disorders deserves study. The World Health Organization World Mental Health (WMH) Survey Initiative is supported by the National Institute of Mental Health (NIMH; R01 MH070884); the John D. and Catherine T. MacArthur Foundation; the Pfizer Foundation; the US Public Health Service (R13-MH066849, R01-MH069864, R01 DA016558); the Fogarty International Center (FIRCA R03-TW006481).The European surveys were funded by the European Commission (Contracts QLG5-1999-01042, SANCO 2004123, EAHC 20081308); Spain (FIS 00/0028); Ministerio de Ciencia y Tecnología, Spain (SAF 2000-158-CE); Departament de Salut, Generalitat de Catalunya, Spain; Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP).The World Mental Health Japan (WMHJ) Survey was supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health, Labour and Welfare. The Mexican National Comorbidity Survey (MNCS) was supported by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (CONACyT-G30544- H). The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health(NIMH; U01-MH60220) and the Robert Wood Johnson Foundation (RWJF; Grant 044708)

Mood disorders are psychiatric disorders in which mood disturbance is the predominant clinical feature. These are among the most commonly occurring and seriously impairing diseases that affect women. Major depression, the most prevalent of the mood disorders, is arguably the number one disease among women in the world in terms of total disease burden. Ascertainment of mood disorders is typically based on patient self-reports, although information obtained from clinical observations and from informants should be included in a thorough diagnostic evaluation. No definitive medical tests are available to diagnose mood disorders. Semi-structured research diagnostic interviews have been developed for use by clinicians to standardize psychiatric diagnoses. Diagnoses based on these clinical interviews represent the gold standard in the field. However, more typically, general population epidemiologic studies base diagnoses on less expensive fully structured interviews administered by trained interviewers who are not clinicians. Methodological research has documented good concordance between diagnoses of depression based on these fully structured interviews and follow-up semi-structured clinical validation interviews, although fully structured diagnostic interviews appear to over-diagnose mania.

Publisher Summary This chapter presents the study that directly measured local cerebral glucose use by positron emission tomography (PET) with simultaneous recording of EEG from 16 electrodes spaced over the left hemisphere to maximize spatial resolution. Simultaneous cerebral glucography with positron emission tomography and topographic quantitative electroencephalography is carried out in six normal volunteers. The presence of resting occipital alpha activity is associated with relatively low glucose use consistent with the phenomenon of alpha blocking with visual input. Higher EEG amplitude is associated with higher glucose use in some posterior and central regions. The harmlessness, repeatability, and low cost of EEG topography give it some advantages over the high cost of PET or isotopic regional blood flow techniques. Extended studies are further necessary to define regional relationships; simultaneous PET and EEG studies are able to better characterize metabolic information available in the scalp electrical activity.

Cross-lagged panel correlations (CLPC) is a technique developed some time ago by Campbell (1963) to assess the relative importance of the variables X and y in a causal analysis by comparing the lagged zero-order correlations rXly2 and rYlX2. Though subjected to withering criticism (Duncan, 1969a, 1972; Heise, 1970; Rogosa, 1979, 1980), the technique has been widely used in psychology and occasionally in sociology (a score of applications are cited in Sims and Wilkerson, 1977 and Rogosa, 1980; and the bibliographies in these sources are by no means complete). More recently, the technique has been proposed as a test for spuriousness. We examine both applications of the method.

This chapter discusses the separation of the effects of position and change in model containing different static scores. There is difficulty in estimating the model of such sort. As the change score is a linear function of the two static scores, it is impossible to obtain separate estimates for these three effects as long as they operate linearly. Mathematically, it is impossible to separate these effects without making some additional assumptions. This impossibility conforms to the intuitive impossibility of an effect changing by six units when it is known that the two static scores differ by five units. Change is simply not something independent of the initial and final scores. The need to constrain the static score parameters can be avoided, at times, if other variables are brought into consideration. There must be good theoretical reason for choosing to take this approach, though for the parameter estimates are highly sensitive to the particular specification used. In general, the models of this sort require some empirical determinant, effect, or indicator of one latent construct to be measured and assumed not to have a parallel relationship to the other construct. Whenever theory specifies a nonparallel relationship of this sort, it will be possible to separate the influences of status and change.

The preceding chapters have largely been concerned with methods for analyzing and interpreting panel data that have already been collected. Here we turn our attention to considerations that arise in the data collection phase of research. We will discuss such questions as: Should one perform a panel study, or will some simpler research design suffice? How much time should separate the waves of a panel study? How can one minimize sample attrition, and adjust for attrition that has occurred? Only to a limited degree are these statistical questions. To a great extent they concern pragmatic questions, such as how to locate the subjects of a panel study in follow-ups. Our emphasis will be on strategies researchers have developed for dealing with such questions in their own work.

The problems that error in the measurement of variables poses for identification and parameter estimation in regression analysis are reasonably well understood.1 Our concern here is with the special problems due to measurement error in panel models. But to establish notation and remind the reader of results we will need in our treatment, we begin by reviewing the problem of measurement error in regression and correlation analysis.

This chapter discusses a problem that arises in the course of deciding the structural equations to estimate, in initial model specification. The focus is on methods for determining the length of the lagged relationship between two variables in a model. This is obviously true for deciding whether a causal relationship between X and Y should be estimated at all. However, it is equally true for the issue of specifying the lag over which a presumed effect will be estimated. If one variable responds very rapidly to change in another variable, a model that assumes influences occur only after a long time may fail to detect them. On the other hand, if change occurs only very slowly, a model in which lagged effects are assumed to make their impact within a very short time span may fail to detect any significant change. The decision to include the causal influence of one variable on another can usually be made on theoretical ground; less often is it possible to decide on an appropriate causal lag on this basis because almost never does social science theory makes concrete predictions about the duration of causal effects. The attraction of this strategy is that it requires no a priori information about the structure of the causal lags in the model outside of some general notions about the range of plausible time intervals over which a causal effect is likely to occur. Its drawback is that if multicollinearity among predictor variables is present , then effects with different lags will be estimated with low precision when they are all included in the model.

This chapter Discusses the results of a three-wave panel study of a population in a rural region of Illinois to illustrate the sorts of algebraic manipulations. . In the analysis of the data, the authors of the study convincingly demonstrated the value of using complex measurement models to describe the relationships between true and measured indicators of alienation and SES. Comparing the impacts of earlier alienation over the two intervals on the change components of subsequent alienation, it was found that A 1 has less impact on ΔA 2-1 than A 2 does on ΔA 3-2. Even though initial alienation does not exhibit a strong impact on change in alienation over an interval of one year, the impact of time 2 alienation is quite strong over the longer interval of four years. In all, the decomposition of alienation into change and lack of change components has uncovered a number of patterns missed when the static score equations were interpreted directly. In particular, the negative impacts of initial alienation on subsequent changes, the greater impact of initial alienation on change over the longer rather than the shorter time period, and the consistency in the relative effects of alienation and SES on change are all missed when the decomposition is not carried out.

This chapter discusses the casual analysis of change. In experimental research, spuriousness and direction of causality are evaluated by randomization and manipulation, respectively. The random assignment of subjects to a treatment or control group guarantees that there are no differences between the two groups on any variable apart from those because of statistical fluctuation, and probability theory can tell the likelihood of a fluctuation of given magnitude. The experimental design is frequently impractical, unethical, or too divorced from reality to shed light on the issues of concern. Under these circumstances, a statistical analysis of cross-sectional data based on the assumption that causal influences are at work in one direction only would run a serious risk of bias in estimating the causal influence assumed to be present. But the more sophisticated analyses that abandon the assumption of unidirectional causality cannot be carried out. Though not a complete substitute for the experimental design, panel analysis goes farther toward resolving the ambiguities in causal inference than other forms of analysis.

Publisher Summary This chapter discusses the estimation of sensitivity analysis. Sensitivity analyses can be carried out in several ways. One is to re-estimate the parameters of interest using different assumptions than those employed in the original analysis. When the additional over-identifying information required to re-estimate models is not available, it will almost always be possible to focus on a range of plausible values for a parameter assumed in the original model to have a given value. When this can be done, it is possible to re-estimate the model by varying the assumed value of this critical parameter within the range considered plausible. In analyzing a given set of panel data, the researcher may discover on the basis of cross-correlogram analysis that the causal lag appropriate for the research problem can be constrained only within a relatively wide range. If the causal model estimated embodies all the correct causal relationships but is estimated with an incorrect causal lag, parameter estimates can be biased. If the true lag lies within a range that is longer than the interval between observations but shorter than the total time interval covered in the multiwave panel, no special difficulty arises.

This chapter discusses the serial correlation of errors that leads to a correlation between lagged endogenous variables and the prediction errors for the unlagged endogenous variable. When this occurs, none of the structural coefficients can be estimated without bias. However, if one is willing to make a specific assumption about the form of the serial correlation, it may be possible to transform the structural equation so that the correlation between the prediction error and the lagged endogenous variable disappears. This type of correlated error is a common phenomenon in the analysis of time series data and is known as serial correlation. Standard approaches for detecting its presence and for correcting parameter estimates for the bias have been developed for time series problems. In practice, serially correlated errors can arise because of the misspecification of the functional form of the relationship between X t and X t + U and because of an omitted exogenous variable. If a linear form is being used when a curvilinear relationship actually exists, then the patterns of systematic under and over-prediction will arise along different parts of the range. As values in this range will contain a certain degree of stability across time, the error terms will be serially correlated. When omitted variables exist, serial correlation becomes one consequence. Assuming that the omitted variable or variables have some consistency through time, the prediction errors at successive times will be related through their common dependence on the omitted influences.

Thus far we have considered change and its determinants from an explicit causal modeling perspective. At times, though, it is of interest to have simple descriptive statistics to describe change in the sample as a whole, irrespective of how that change was caused. Here one is concerned with such questions as how much change has occurred, whether all members of the sample have experienced change uniformly, and how much of the change in individual positions is governed by their initial position. We will review several different approaches to these problems in this chapter.

Background: Better information on the human capital costs of early-onset mental disorders could increase sensitivity of policy makers to the value of expanding initiatives for early detection and treatment. Data are presented on one important aspect of these costs: the associations of early-onset mental disorders with adult household income. Methods: Data come from the World Health Organization (WHO) World Mental Health Surveys in 11 high-income, five upper-middle income, and six low/lower-middle income countries. Information about 15 lifetime DSM-IV mental disorders as of age of completing education, retrospectively assessed with the WHO Composite International Diagnostic Interview, was used to predict current household income among respondents aged 18 to 64 (n = 37,741) controlling for level of education. Gross associations were decomposed to evaluate mediating effects through major components of household income. Results: Early-onset mental disorders are associated with significantly reduced household income in high and upper-middle income countries but not low/lower-middle income countries, with associations consistently stronger among women than men. Total associations are largely due to low personal earnings (increased unemployment, decreased earnings among the employed) and spouse earnings (decreased probabilities of marriage and, if married, spouse employment and low earnings of employed spouses). Individual-level effect sizes are equivalent to 16% to 33% of median within-country household income, and population-level effect sizes are in the range 1.0% to 1.4% of gross household income. Conclusions: Early mental disorders are associated with substantial decrements in income net of education at both individual and societal levels. Policy makers should take these associations into consideration in making health care research and treatment resource allocation decisions. The European Study of the Epidemiology of Mental Disorders project is funded by the European Commission (Contract Nos. QLG5-1999-01042; SANCO 2004123; and EAHC 20081308), the Piedmont Region (Italy), Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain (Grant No. FIS 00/0028), Ministerio de Ciencia y Tecnología, Spain (Grant No. SAF 2000 158-CE), Departament de Salut, Generalitat de Catalunya, Spain, Instituto de Salud Carlos III (Grant Nos. CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP) and other local agencies, and by an unrestricted educational grant from GlaxoSmithKline.