PHQ-8 scores and estimation of depression prevalence - Author's reply

We thank Brooke Levis and colleaguesfor their interest in our work and for suggesting that we might have overestimated the prevalence of depression by using the eight-item Patient Health Questionnaire (PHQ-8) in our study. Although we acknowledged the limitations associated with the use of the PHQ-8, we believe that further discussion is required.It should be noted that a study of this size, with a representative sample of 27 countries and 258 888 participants, would not be feasible using clinical interviews, and the use of an instrument such as the PHQ-8 is considered more appropriate

Mood and anxiety disorders across the adult lifespan: a European perspective

BACKGROUND: The World Mental Health Survey Initiative (WMHSI) has advanced our understanding of mental disorders by providing data suitable for analysis across many countries. However, these data have not yet been fully explored from a cross-national lifespan perspective. In particular, there is a shortage of research on the relationship between mood and anxiety disorders and age across countries. In this study we used multigroup methods to model the distribution of 12-month DSM-IV/CIDI mood and anxiety disorders across the adult lifespan in relation to determinants of mental health in 10 European Union (EU) countries. METHOD: Logistic regression was used to model the odds of any mood or any anxiety disorder as a function of age, gender, marital status, urbanicity and employment using a multigroup approach (n = 35500). This allowed for the testing of specific lifespan hypotheses across participating countries. RESULTS: No simple geographical pattern exists with which to describe the relationship between 12-month prevalence of mood and anxiety disorders and age. Of the adults sampled, very few aged ≥ 80 years met DSM-IV diagnostic criteria for these disorders. The associations between these disorders and key sociodemographic variables were relatively homogeneous across countries after adjusting for age. CONCLUSIONS: Further research is required to confirm that there are indeed stages in the lifespan where the reported prevalence of mental disorders is low, such as among younger adults in the East and older adults in the West. This project illustrates the difficulties in conducting research among different age groups simultaneously

The role impairment associated with mental disorder risk profiles in the WHO World Mental Health International College Student Initiative

OBJECTIVE: The objective of this study is to assess the contribution of mental comorbidity to role impairment among college students. METHODS: Web-based self-report surveys from 14,348 first-year college students (Response Rate [RR] = 45.5%): 19 universities, eight countries of the World Mental Health International College Student Initiative. We assessed impairment (Sheehan Disability Scales and number of days out of role [DOR] in the past 30 days) and seven 12-month DSM-IV disorders. We defined six multivariate mental disorder classes using latent class analysis (LCA). We simulated population attributable risk proportions (PARPs) of impairment. RESULTS: Highest prevalence of role impairment was highest among the 1.9% of students in the LCA class with very high comorbidity and bipolar disorder (C1): 78.3% of them had severe role impairment (vs. 20.8%, total sample). Impairment was lower in two other comorbid classes (C2 and C3) and successively lower in the rest. A similar monotonic pattern was found for DOR. Both LCA classes and some mental disorders (major depression and panic, in particular) were significant predictors of role impairment. PARP analyses suggest that eliminating all mental disorders might reduce severe role impairment by 64.6% and DOR by 44.3%. CONCLUSIONS: Comorbid mental disorders account for a substantial part of role impairment in college students. © 2018 John Wiley & Sons, Ltd

Estudi de l'estat de Salut autopercebut: Modelització de l'índex d'utilitat EQ-5D mitjançant un model tobit

Diploma d'Estudis Avançats - Programa de doctorat en Estadística, Anàlisi de dades i bioestadística. 2008. Tutors: Josep Fortiana i Jordi AlonsoObjective: Health status measures usually have an asymmetric distribution and present a high percentage of respondents with the best possible score (ceiling effect), specially when they are assessed in the overall population. Different methods to model this type of variables have been proposed that take into account the ceiling effect: the tobit models, the Censored Least Absolute Deviations (CLAD) models or the two-part models, among others. The objective of this work was to describe the tobit model, and compare it with the Ordinary Least Squares (OLS) model, that ignores the ceiling effect. Methods: Two different data sets have been used in order to compare both models: a) real data comming from the European Study of Mental Disorders (ESEMeD), in order to model the EQ5D index, one of the measures of utilities most commonly used for the evaluation of health status; and b) data obtained from simulation. Cross-validation was used to compare the predicted values of the tobit model and the OLS models. The following estimators were compared: the percentage of absolute error (R1), the percentage of squared error (R2), the Mean Squared Error (MSE) and the Mean Absolute Prediction Error (MAPE). Different datasets were created for different values of the error variance and different percentages of individuals with ceiling effect. The estimations of the coefficients, the percentage of explained variance and the plots of residuals versus predicted values obtained under each model were compared. Results: With regard to the results of the ESEMeD study, the predicted values obtained with the OLS model and those obtained with the tobit models were very similar. The regression coefficients of the linear model were consistently smaller than those from the tobit model. In the simulation study, we observed that when the error variance was small (s=1), the tobit model presented unbiased estimations of the coefficients and accurate predicted values, specially when the percentage of individuals wiht the highest possible score was small. However, when the errror variance was greater (s=10 or s=20), the percentage of explained variance for the tobit model and the predicted values were more similar to those obtained with an OLS model. Conclusions: The proportion of variability accounted for the models and the percentage of individuals with the highest possible score have an important effect in the performance of the tobit model in comparison with the linear model

Assessment of depression in the adult general population using self-reported measures : Psychometric approaches for screening and severity appraisal

This thesis provides evidence on the validity and diagnostic accuracy of generic and specific self-reported measures, developed from different psychometric approaches, to assess depression in the general population. First, we compare the reliability and diagnostic accuracy of the 12-item Short Form Health Survey (SF-12) traditional scoring with Multidimensional Item Response Theory (MIRT) scoring. Secondly, we conduct systematic literature review and meta-analysis of the diagnostic accuracy of the Center for Epidemiologic Studies Depression (CES-D) as a screener for major depression. Finally, we assess the psychometric properties of IRT-based Patient Reported Outcomes Measurement Information System (PROMIS) Depression measures. Our results indicate that: a) the MIRT SF-12 model is more reliable and has comparable diagnostic accuracy than other scoring methods; b) general and specific measures herein included yield good diagnostic accuracy as depression screeners; c) PROMIS Depression meets IRT assumptions, its measures are highly reliable and show good construct validity and responsiveness to change; and shows measurement invariance according to country (Spain and US). We conclude that self-reported measures are adequate for assessing depression in the general population, and provide additional information beyond detection of pathological individuals. The IRT psychometric approach provide higher flexibility and precision in administering and scoring questionnaires in survey studies, also allowing direct comparisons between populations.Aquesta tesi proporciona evidencia sobre la validesa i la capacitat diagnòstica de mesures genèriques i específiques auto-reportades, construïdes des de diferents aproximacions psicomètriques per avaluar depressió en la població general. Primerament, es compara la fiabilitat i capacitat diagnòstica del qüestionari Short Form Health Survey (SF-12) utilitzant el càlcul de puntuació habitual, amb la puntuació obtinguda segons Teoria de Resposta a l’Ítem Multidimensional (TRIM). Posteriorment, s’avalua la capacitat diagnòstica del Center for Epidemiologic Studies Depression (CES-D) per al cribratge de depressió major mitjançant una revisió sistemàtica amb meta-anàlisi. Finalment, s’avaluen les propietats psicomètriques de les mesures de depressió del Patient Reported Outcomes Measurement Information System (PROMIS), basades en Teoria de Resposta a l’Ítem (TRI). Els resultats indiquen que: a) la puntuació basada en el model TRIM del SF-12 és més fiable però presenta capacitat diagnòstica similar als altres mètodes de puntuació; b) les mesures genèriques i especifiques avaluades proporcionen una bona capacitat diagnòstica per al cribratge de depressió en població general; c) el qüestionari PROMIS de Depressió compleix totes les assumpcions de TRI, elevada fiabilitat i bona validesa de constructe i sensibilitat al canvi, i els resultats donen suport a la invariància de les mesures pel que fa al país (Espanya i US). Es conclou que les mesures auto-reportades estudiades són adequades per avaluar depressió en la població general, i proporcionen una informació valuosa que va més enllà la detecció dicotòmica d’individus amb patologia o sense. L’aproximació psicomètrica basada en TRI proporciona major flexibilitat en l’administració i puntuació dels qüestionaris i precisió més elevada, i facilita comparacions directes entre poblacions

Anàlisi factorial confirmatòria per a variables categòriques: Aplicació al qüestionari de discapacitat WHODAS-II

Diploma d'Estudis Avançats - Programa de doctorat en Estadística, Anàlisi de dades i bioestadística. 2008. Tutors: Josep Fortiana i Jordi AlonsoObjective: To describe the methodology of Confirmatory Factor Analyis for categorical items and to apply this methodology to evaluate the factor structure and invariance of the WHO-Disability Assessment Schedule (WHODAS-II) questionnaire, developed by the World Health Organization. Methods: Data used for the analysis come from the European Study of Mental Disorders (ESEMeD), a cross-sectional interview to a representative sample of the general population of 6 european countries (n=8796). Respondents were administered a modified version of the WHODAS-II, that measures functional disability in the previous 30 days in 6 different dimensions: Understanding and Communicating; Self-Care, Getting Around, Getting Along with Others, Life Activities and Participation. The questionnaire includes two types of items: 22 severity items (5 points likert) and 8 frequency items (continuous). An Exploratory factor analysis (EFA) with promax rotation was conducted on a random 50% of the sample. The remaining half of the sample was used to perform a Confirmatory Factor Analysis (CFA) in order to compare three different models: (a) the model suggested by the results obtained in the EFA; (b) the theoretical model suggested by the WHO with 6 dimensions; (c) a reduced model equivalent to model b where 4 of the frequency items are excluded. Moreover, a second order factor was also evaluated. Finally, a CFA with covariates was estimated in order to evaluate measurement invariance of the items between Mediterranean and non-mediterranean countries. Results: The solution that provided better results in the EFA was that containing 7 factors. Two of the frequency items presented high factor loadings in the same factor, and one of them presented factor loadings smaller than 0.3 with all the factors. With regard to the CFA, the reduced model (model c) presented the best goodness of fit results (CFI=0.992,TLI=0.996, RMSEA=0.024). The second order factor structure presented adequate goodness of fit (CFI=0.987, TLI=0.991, RMSEA=0.036). Measurement non-invariance was detected for one of the items of the questionnaire (FD20 ¿ Embarrassment due to health problems). Conclusions: AFC confirmed the initial hypothesis about the factorial structure of the WHODAS-II in 6 factors. The second order factor supports the existence of a global dimension of disability. The use of 4 of the frequency items is not recommended in the scoring of the corresponding dimensions

Anàlisi factorial confirmatòria per a variables categòriques: Aplicació al qüestionari de discapacitat WHODAS-II

Diploma d'Estudis Avançats - Programa de doctorat en Estadística, Anàlisi de dades i bioestadística. 2008. Tutors: Josep Fortiana i Jordi AlonsoObjective: To describe the methodology of Confirmatory Factor Analyis for categorical items and to apply this methodology to evaluate the factor structure and invariance of the WHO-Disability Assessment Schedule (WHODAS-II) questionnaire, developed by the World Health Organization. Methods: Data used for the analysis come from the European Study of Mental Disorders (ESEMeD), a cross-sectional interview to a representative sample of the general population of 6 european countries (n=8796). Respondents were administered a modified version of the WHODAS-II, that measures functional disability in the previous 30 days in 6 different dimensions: Understanding and Communicating; Self-Care, Getting Around, Getting Along with Others, Life Activities and Participation. The questionnaire includes two types of items: 22 severity items (5 points likert) and 8 frequency items (continuous). An Exploratory factor analysis (EFA) with promax rotation was conducted on a random 50% of the sample. The remaining half of the sample was used to perform a Confirmatory Factor Analysis (CFA) in order to compare three different models: (a) the model suggested by the results obtained in the EFA; (b) the theoretical model suggested by the WHO with 6 dimensions; (c) a reduced model equivalent to model b where 4 of the frequency items are excluded. Moreover, a second order factor was also evaluated. Finally, a CFA with covariates was estimated in order to evaluate measurement invariance of the items between Mediterranean and non-mediterranean countries. Results: The solution that provided better results in the EFA was that containing 7 factors. Two of the frequency items presented high factor loadings in the same factor, and one of them presented factor loadings smaller than 0.3 with all the factors. With regard to the CFA, the reduced model (model c) presented the best goodness of fit results (CFI=0.992,TLI=0.996, RMSEA=0.024). The second order factor structure presented adequate goodness of fit (CFI=0.987, TLI=0.991, RMSEA=0.036). Measurement non-invariance was detected for one of the items of the questionnaire (FD20 ¿ Embarrassment due to health problems). Conclusions: AFC confirmed the initial hypothesis about the factorial structure of the WHODAS-II in 6 factors. The second order factor supports the existence of a global dimension of disability. The use of 4 of the frequency items is not recommended in the scoring of the corresponding dimensions

Estudi de l'estat de Salut autopercebut: Modelització de l'índex d'utilitat EQ-5D mitjançant un model tobit

Diploma d'Estudis Avançats - Programa de doctorat en Estadística, Anàlisi de dades i bioestadística. 2008. Tutors: Josep Fortiana i Jordi AlonsoObjective: Health status measures usually have an asymmetric distribution and present a high percentage of respondents with the best possible score (ceiling effect), specially when they are assessed in the overall population. Different methods to model this type of variables have been proposed that take into account the ceiling effect: the tobit models, the Censored Least Absolute Deviations (CLAD) models or the two-part models, among others. The objective of this work was to describe the tobit model, and compare it with the Ordinary Least Squares (OLS) model, that ignores the ceiling effect. Methods: Two different data sets have been used in order to compare both models: a) real data comming from the European Study of Mental Disorders (ESEMeD), in order to model the EQ5D index, one of the measures of utilities most commonly used for the evaluation of health status; and b) data obtained from simulation. Cross-validation was used to compare the predicted values of the tobit model and the OLS models. The following estimators were compared: the percentage of absolute error (R1), the percentage of squared error (R2), the Mean Squared Error (MSE) and the Mean Absolute Prediction Error (MAPE). Different datasets were created for different values of the error variance and different percentages of individuals with ceiling effect. The estimations of the coefficients, the percentage of explained variance and the plots of residuals versus predicted values obtained under each model were compared. Results: With regard to the results of the ESEMeD study, the predicted values obtained with the OLS model and those obtained with the tobit models were very similar. The regression coefficients of the linear model were consistently smaller than those from the tobit model. In the simulation study, we observed that when the error variance was small (s=1), the tobit model presented unbiased estimations of the coefficients and accurate predicted values, specially when the percentage of individuals wiht the highest possible score was small. However, when the errror variance was greater (s=10 or s=20), the percentage of explained variance for the tobit model and the predicted values were more similar to those obtained with an OLS model. Conclusions: The proportion of variability accounted for the models and the percentage of individuals with the highest possible score have an important effect in the performance of the tobit model in comparison with the linear model

Data analysis in surveys with complex sampling design : an aplication to the ESEMed Projet

1.1 Català Aquest Projecte Final de Carrera de la Llicenciatura en Ciències i Tècniques estadístiques tracta dues situacions que poden estar presents quan es dissenyen grans enquestes de salut com són les enquestes amb disseny complexe i/o amb disseny en dues fases. Amb disseny complexe fem referència a dissenys estratificats i/o multietàpics amb diferents probabilitats de selecció. Estudis en dues fases són aquells que no avaluen el resultat d’interès en tota la mostra sinó en una submostra seleccionada en base a la informació obtinguda durant la primera fase de l’estudi. Quan la recollida de dades fa servir qualsevol d’aquestes metodologies és necessari tenir en compte certes consideracions per tal d’evitar estimacions esbiaixades i inferències errònees, donat que, en totes dues situacions, el disseny mostral té efecte tant en l’estimació puntual com en la seva variança. Al llarg d’aquest document exposem i apliquem els mètodes estadístics més freqüents per analizar les dades en cada situació: la linearització en sèrie de Taylor i les tècniques de replicació, i els estimadors d’imputació i re-ponderació, respectivament. 1.2 English . This final thesis for the Degree in Statistical Sciences and Techniques addresses two common situations that can be present when large health surveys are designed, i.e. the complex design surveys and studies with a two-phase design. By complex design we mean a stratified and/or multi-stage design with different probabilities of selection. Twophase studies are those that do not assess the outcome of interest in the whole sample but in a selected subsample based on information obtained in a first phase of the study. When any of these methodologies are applied in data collection, special considerations need to be taken into account in the analysis in order to avoid biased estimations and false inferences, since in the two situations, the sample design affects both the point estimation as well as the variance. Along this document we expose and apply some of the most commonly used statistical methods to analyze data in each of these situations: the Taylor series linearization and the replication techniques, and the imputation and reweighting estimators, respectively

Data analysis in surveys with complex sampling design : an aplication to the ESEMed Projet

1.1 Català Aquest Projecte Final de Carrera de la Llicenciatura en Ciències i Tècniques estadístiques tracta dues situacions que poden estar presents quan es dissenyen grans enquestes de salut com són les enquestes amb disseny complexe i/o amb disseny en dues fases. Amb disseny complexe fem referència a dissenys estratificats i/o multietàpics amb diferents probabilitats de selecció. Estudis en dues fases són aquells que no avaluen el resultat d’interès en tota la mostra sinó en una submostra seleccionada en base a la informació obtinguda durant la primera fase de l’estudi. Quan la recollida de dades fa servir qualsevol d’aquestes metodologies és necessari tenir en compte certes consideracions per tal d’evitar estimacions esbiaixades i inferències errònees, donat que, en totes dues situacions, el disseny mostral té efecte tant en l’estimació puntual com en la seva variança. Al llarg d’aquest document exposem i apliquem els mètodes estadístics més freqüents per analizar les dades en cada situació: la linearització en sèrie de Taylor i les tècniques de replicació, i els estimadors d’imputació i re-ponderació, respectivament. 1.2 English . This final thesis for the Degree in Statistical Sciences and Techniques addresses two common situations that can be present when large health surveys are designed, i.e. the complex design surveys and studies with a two-phase design. By complex design we mean a stratified and/or multi-stage design with different probabilities of selection. Twophase studies are those that do not assess the outcome of interest in the whole sample but in a selected subsample based on information obtained in a first phase of the study. When any of these methodologies are applied in data collection, special considerations need to be taken into account in the analysis in order to avoid biased estimations and false inferences, since in the two situations, the sample design affects both the point estimation as well as the variance. Along this document we expose and apply some of the most commonly used statistical methods to analyze data in each of these situations: the Taylor series linearization and the replication techniques, and the imputation and reweighting estimators, respectively