Modifying and validating the Composite International Diagnostic Interview (CIDI) for use in Nepal

Background : Efforts to develop and validate fully‐structured diagnostic interviews of mental disorders in non‐Western countries have been largely unsuccessful. However, the principled methods of translation, harmonization, and calibration that have been developed by cross‐national survey methodologists have never before been used to guide such development efforts. The current report presents the results of a rigorous program of research using these methods designed to modify and validate the Composite International Diagnostic Interview (CIDI) for an epidemiological survey in Nepal. Methods : A five‐step process of translation, harmonization, and calibration was used to modify the instrument. A blinded clinical reappraisal design was used to validate the instrument. Results : Preliminary interviews with local mental health expert led to a focus on major depressive episode, mania/hypomania, panic disorder, post‐traumatic stress disorder, generalized anxiety disorder, and intermittent explosive disorder. After an iterative process of multiple translations‐revisions guided by the principles developed by cross‐national survey methodologists, lifetime DSM‐IV diagnoses based on the final Nepali CIDI had excellent concordance with diagnoses based on blinded Structured Clinical Interview for DSM‐IV (SCID) clinical reappraisal interviews. Conclusions : Valid assessment of mental disorders can be achieved with fully‐structured diagnostic interviews even in low‐income non‐Western settings with rigorous implementation of replicable developmental strategies. Copyright © 2013 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97206/1/mpr1375.pd

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use

The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123 and EAHC 20081308). The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health.Aims: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as ‘Super Learner’. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.publishersversionepub_ahead_of_prin

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use

The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123 and EAHC 20081308). The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health.Aims: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as ‘Super Learner’. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.publishersversionepub_ahead_of_prin

Proof-of-concept of a data-driven approach to estimate the associations of comorbid mental and physical disorders with global health-related disability

Objective: The standard method of generating disorder-specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods: We propose an alternative, data-driven, method of generating disorder-specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self-reports and uses Generalized Random Forests(GRF) to predict global (rather than disorder-specific) disability assessed by clinician ratings or by survey respondent self-reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder-specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys(n=53,645).Results: Adjustments for comorbidity decreased estimates of disorder-specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions: The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder-specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity

Age of onset and cumulative risk of mental disorders:a cross-national analysis of population surveys from 29 countries

Background: Information on the frequency and timing of mental disorder onsets across the lifespan is of fundamental importance for public health planning. Broad, cross-national estimates of this information from coordinated general population surveys were last updated in 2007. We aimed to provide updated and improved estimates of age-of-onset distributions, lifetime prevalence, and morbid risk. Methods: In this cross-national analysis, we analysed data from respondents aged 18 years or older to the World Mental Health surveys, a coordinated series of cross-sectional, face-to-face community epidemiological surveys administered between 2001 and 2022. In the surveys, the WHO Composite International Diagnostic Interview, a fully structured psychiatric diagnostic interview, was used to assess age of onset, lifetime prevalence, and morbid risk of 13 DSM-IV mental disorders until age 75 years across surveys by sex. We did not assess ethnicity. The surveys were geographically clustered and weighted to adjust for selection probability, and standard errors of incidence rates and cumulative incidence curves were calculated using the jackknife repeated replications simulation method, taking weighting and geographical clustering of data into account. Findings: We included 156 331 respondents from 32 surveys in 29 countries, including 12 low-income and middle-income countries and 17 high-income countries, and including 85 308 (54·5%) female respondents and 71 023 (45·4%) male respondents. The lifetime prevalence of any mental disorder was 28·6% (95% CI 27·9–29·2) for male respondents and 29·8% (29·2–30·3) for female respondents. Morbid risk of any mental disorder by age 75 years was 46·4% (44·9–47·8) for male respondents and 53·1% (51·9–54·3) for female respondents. Conditional probabilities of first onset peaked at approximately age 15 years, with a median age of onset of 19 years (IQR 14–32) for male respondents and 20 years (12–36) for female respondents. The two most prevalent disorders were alcohol use disorder and major depressive disorder for male respondents and major depressive disorder and specific phobia for female respondents. Interpretation: By age 75 years, approximately half the population can expect to develop one or more of the 13 mental disorders considered in this Article. These disorders typically first emerge in childhood, adolescence, or young adulthood. Services should have the capacity to detect and treat common mental disorders promptly and to optimise care that suits people at these crucial parts of the life course. Funding: None.</p

Estimating treatment coverage for people with substance use disorders:an analysis of data from the World Mental Health Surveys

Substance use is a major cause of disability globally. This has been recognized in the recent United Nations Sustainable Development Goals (SDGs), in which treatment coverage for substance use disorders is identified as one of the indicators. There have been no estimates of this treatment coverage cross-nationally, making it difficult to know what is the baseline for that SDG target. Here we report data from the World Health Organization (WHO)'s World Mental Health Surveys (WMHS), based on representative community household surveys in 26 countries. We assessed the 12-month prevalence of substance use disorders (alcohol or drug abuse/dependence); the proportion of people with these disorders who were aware that they needed treatment and who wished to receive care; the proportion of those seeking care who received it; and the proportion of such treatment that met minimal standards for treatment quality (“minimally adequate treatment”). Among the 70,880 participants, 2.6% met 12-month criteria for substance use disorders; the prevalence was higher in upper-middle income (3.3%) than in high-income (2.6%) and low/lower-middle income (2.0%) countries. Overall, 39.1% of those with 12-month substance use disorders recognized a treatment need; this recognition was more common in high-income (43.1%) than in upper-middle (35.6%) and low/lower-middle income (31.5%) countries. Among those who recognized treatment need, 61.3% made at least one visit to a service provider, and 29.5% of the latter received minimally adequate treatment exposure (35.3% in high, 20.3% in upper-middle, and 8.6% in low/lower-middle income countries). Overall, only 7.1% of those with past-year substance use disorders received minimally adequate treatment: 10.3% in high income, 4.3% in upper-middle income and 1.0% in low/lower-middle income countries. These data suggest that only a small minority of people with substance use disorders receive even minimally adequate treatment. At least three barriers are involved: awareness/perceived treatment need, accessing treatment once a need is recognized, and compliance (on the part of both provider and client) to obtain adequate treatment. Various factors are likely to be involved in each of these three barriers, all of which need to be addressed to improve treatment coverage of substance use disorders. These data provide a baseline for the global monitoring of progress of treatment coverage for these disorders as an indicator within the SDGs

The descriptive epidemiology of DSM-IV Adult ADHD in the World Health Organization World Mental Health Surveys

We previously reported on the cross-national epidemiology of ADHD from the first 10 countries in the WHO World Mental Health (WMH) Surveys. The current report expands those previous findings to the 20 nationally or regionally representative WMH surveys that have now collected data on adult ADHD. The Composite International Diagnostic Interview (CIDI) was administered to 26,744 respondents in these surveys in high-, upper-middle-, and low-/lower-middle-income countries (68.5% mean response rate). Current DSM-IV/CIDI adult ADHD prevalence averaged 2.8% across surveys and was higher in high (3.6%)- and upper-middle (3.0%)- than low-/lower-middle (1.4%)-income countries. Conditional prevalence of current ADHD averaged 57.0% among childhood cases and 41.1% among childhood subthreshold cases. Adult ADHD was significantly related to being male, previously married, and low education. Adult ADHD was highly comorbid with DSM-IV/CIDI anxiety, mood, behavior, and substance disorders and significantly associated with role impairments (days out of role, impaired cognition, and social interactions) when controlling for comorbidities. Treatment seeking was low in all countries and targeted largely to comorbid conditions rather than to ADHD. These results show that adult ADHD is prevalent, seriously impairing, and highly comorbid but vastly under-recognized and undertreated across countries and cultures

Improving reports of health risks: Life history calendars and measurement of potentially traumatic experiences

ObjectivesRecall error biases reporting of earlier life experiences, even potentially traumatic experiences (PTEs). Better tools for accurate retrospective reporting of PTEs and other health risk factors have the potential for broad scientific and health intervention benefits.MethodsWe designed a life history calendar (LHC) to support this task and randomized more than 1000 individuals to each arm of a retrospective diagnostic interview, including detailed measures of PTEs, with and without the LHC. This is one of the largest experiments ever done to assess the benefit of an LHC approach and the only large‐scale experiment done in a poor, agrarian, non‐Western setting (rural Nepal).ResultsResults demonstrate use of an LHC in retrospective measurement can significantly increase lifetime reports of PTEs, especially reports of two or more PTEs. The LHC increases PTE reporting more for men and those with less education.ConclusionsThe LHC approach is practical for many uses ranging from large surveys of the general population to clinical intake of new patients. It significantly increases reporting of health risk factors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167102/1/mpr1853.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167102/2/mpr1853_am.pd