The impact of measurement differences on cross-country depression prevalence estimates: A latent transition analysis

Background Depression is currently the second largest contributor to non-fatal disease burden globally. For that reason, economic evaluations are increasingly being conducted using data from depression prevalence estimates to analyze return on investments for services that target mental health. Psychiatric epidemiology studies have reported large cross-national differences in the prevalence of depression. These differences may impact the cost-effectiveness assessments of mental health interventions, thereby affecting decisions regarding government and multi-lateral investment in mental health services. Some portion of the differences in prevalence estimates across countries may be due to true discrepancies in depression prevalence, resulting from differential levels of risk in environmental and demographic factors. However, some portion of those differences may reflect non-invariance in the way standard tools measure depression across countries. This paper attempts to discern the extent to which measurement differences are responsible for reported differences in the prevalence of depression across countries. Methods and findings This analysis uses data from the World Mental Health Surveys, a coordinated series of psychiatric epidemiology studies in 27 countries using multistage household probability samples to assess prevalence and correlates of mental disorders. Data in the current study include responses to the depression module of the World Mental Health Composite International Diagnostic Interview (CIDI) in four countries: Two high-income, western countries—the United States (n = 20, 015) and New Zealand (n = 12,992)—an upper-middle income sub-Saharan African country, South Africa (n = 4,351), and a lower-middle income sub-Saharan African country, Nigeria (n = 6,752). Latent class analysis, a type of finite mixture modeling, was used to categorize respondents into underlying categories based on the variation in their responses to questions in each of three sequential parts of the CIDI depression module: 1) The initial screening items, 2) Additional duration and severity exclusion criteria, and 3) The core symptom questions. After each of these parts, exclusion criteria expel respondents from the remainder of the diagnostic interview, rendering a diagnosis of “not depressed”. Latent class models were fit to each of the three parts in each of the four countries, and model fit was assessed using overall chi-square values and Pearson standardized residuals. Latent transition analysis was then applied in order to model participants’ progression through the CIDI depression module. Proportion of individuals falling into each latent class and probabilities of transitioning into subsequent classes were used to estimate the percentage in each country that ultimately fell into the more symptomatic class, i.e. classified as “depressed”. This latent variable design allows for a non-zero probability that individuals were incorrectly excluded from or retained in the diagnostic interview at any of the three exclusion points and therefore incorrectly diagnosed. Prevalence estimates based on the latent transition model reversed the order of depression prevalence across countries. Based on the latent transition model in this analysis, Nigeria has the highest prevalence (21.6%), followed by New Zealand (17.4%), then South Africa (15.0%), and finally the US (12.5%). That is compared to the estimates in the World Mental Health Surveys that do not allow for measurement differences, in which Nigeria had by far the lowest prevalence (3.1%), followed by South Africa (9.8%), then the United States (13.5%) and finally New Zealand (17.8%). Individuals endorsing the screening questions in Nigeria and South Africa were more likely to endorse more severe depression symptomology later in the module (i.e. they had higher transition probabilities), suggesting that individuals in the two Western countries may be more likely to endorse screening questions even when they don’t have as severe symptoms. These differences narrow the range of depression prevalence between countries 14 percentage points in the original estimates to 6 percentage points in the estimate taking account of measurement differences. Conclusions These data suggest fewer differences in cross-national prevalence of depression than previous estimates. Given that prevalence data are used to support key decisions regarding resource-allocation for mental health services, more critical attention should be paid to differences in the functioning of measurement across contexts and the impact these differences have on prevalence estimates. Future research should include qualitative methods as well as external measures of disease severity, such as impairment, to assess how the latent classes predict these external variables, to better understand the way that standard tools estimate depression prevalence across contexts. Adjustments could then be made to prevalence estimates used in cost-effectiveness analyses

Validation of the “World Health Organization Disability Assessment Schedule for Children, WHODAS-Child” in Rwanda

Overview: The World Health Organization Disability Assessment Schedule for children (WHODAS-Child) is a disability assessment instrument based on the WHO's International Classification of Functioning, Disability and Health for children and youth. It is modified from the original adult version specifically for use with children. The aim of this study was to assess the WHODAS-Child structure and metric properties in a community sample of children with and without reported psychosocial problems in rural Rwanda. Methods: The WHODAS-Child was first translated into Kinyarwanda through a detailed committee translation process and back-translation. Cognitive interviewing was used to assess the comprehension of the translated items. Test-retest reliability was assessed in a group of 64 children. The translated WHODAS-Child was then administered to a final sample of 367 children in southern Kayonza district in rural southeastern Rwanda within a larger psychosocial assessment battery. The latent structure was assessed through confirmatory factor analysis. Reliability was evaluated in terms of internal consistency (Cronbach's alpha) and test-retest reliability (Pearson's correlation coefficient). Construct validity was explored by examining convergence between WHODAS-Child scores and mental disorder status, and divergence of WHODAS-Child scores with protective factors and prosocial behaviors. Concordance between parent and child scores was also assessed. Results: The six-factor structure of the WHODAS-Child was confirmed in a population sample of Rwandan children. Test-retest and inter-rater reliability were high (r = .83 and ICC = .88). WHODAS-Child scores were moderately positively correlated with presence of depression (r = .42, p<.001) and post-traumatic stress disorder (r = .31, p<.001) and moderately negatively correlated with prosocial behaviors (r = .47, p<.001). The Kinyarwanda version of the WHODAS-Child was found to be a reliable and acceptable self-report tool for assessment of functional impairment among children largely referred for psychosocial problems in the study district in rural Rwanda. Further research in low-resource settings and with more general populations is recommended

Women's mental health in Mozambique: is maternity a protective factor?

Abstract Backgroud Globally, women have been shown to have high rates of common mental disorders (CMDs). In low and middle-income countries (LMICs), women face significant challenges related to maternity. However, no study has compared mental health problems among pregnant/post-partum women, childless women of childbearing age, and women with children in a low-income country. We sought to compare the frequency of CMD and suicide risk in a sample of women presenting or accompanying patients in primary care in two Mozambican semi-urban settings. Methods We administered the MINI International Neuropsychiatric Interview to 853 women, of whom 220 (25.8%) were pregnant/post-partum, 177 (20.8%) were non-pregnant and childless, and 456 (53.5%) were non-pregnant and with children more than 1-year-old. Logistic regression models compared the likelihood of a psychiatric disorder across groups, adjusting for sociodemographic and chronic-illness covariates. Results We found a high frequency of CMD and suicide risk among all women in this low-income context sample. In adjusted models, no differences in rates of depression, anxiety, or panic disorder were observed among groups. However, suicide risk was higher in women without children than pregnant/post-partum women. Conclusion The frequency of CMD among women of childbearing age in our study was higher than documented rates in high-income countries and other LMIC. Additionally, we found that motherhood was not protective and that pregnancy and the postpartum period were not stages of increased risk for most disorders. This highlights the need to expand mental health services not only for perinatal women but all women of childbearing age in this and possibly similar settings

Lessons From Rural Peru in Integrating Mental Health Into Primary Care

Peru secured a legislative advance for mental health care with a 2012 law mandating that mental health services be available in primary care. One of the main challenges faced by this reform is implementation in remote regions. This column describes a pilot project in Peru that took place from 2010 to 2014 to develop capacity for including mental health services in primary care in one of the most isolated, high-needs regions of the country. The authors describe use of accompaniment-based training and supervision of clinicians and comprehensive, engaged regional partnerships formed to increase the impact and sustainability of the service expansion

Latent transition probabilities across four countries for part 1 to part 3.

<p>Latent transition probabilities across four countries for part 1 to part 3.</p

Class-specific item endorsement probabilities for LCA model for each part of the module in four countries.

<p><i>Note</i>. Percentages of country samples that are in each class, based on posterior class probabilities, are given in the legends. Entropy (E) is also displayed below the legend.</p