Probability of Major Depression Classification Based on the SCID, CIDI and MINI Diagnostic Interviews : A Synthesis of Three Individual Participant Data Meta-Analyses

Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results.To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women : systematic review and meta-analysis of individual participant data

AbstractObjective To evaluate the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression in pregnant and postpartum women. Design Individual participant data meta-analysis. Data sources Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, and Web of Science (from inception to 3 October 2018). Eligibility criteria for selecting studies Eligible datasets included EPDS scores and major depression classification based on validated diagnostic interviews. Bivariate random effects meta-analysis was used to estimate EPDS sensitivity and specificity compared with semi-structured, fully structured (Mini International Neuropsychiatric Interview (MINI) excluded), and MINI diagnostic interviews separately using individual participant data. One stage meta-regression was used to examine accuracy by reference standard categories and participant characteristics. Results Individual participant data were obtained from 58 of 83 eligible studies (70%; 15 557 of 22 788 eligible participants (68%), 2069 with major depression). Combined sensitivity and specificity was maximised at a cut-off value of 11 or higher across reference standards. Among studies with a semi-structured interview (36 studies, 9066 participants, 1330 with major depression), sensitivity and specificity were 0.85 (95% confidence interval 0.79 to 0.90) and 0.84 (0.79 to 0.88) for a cut-off value of 10 or higher, 0.81 (0.75 to 0.87) and 0.88 (0.85 to 0.91) for a cut-off value of 11 or higher, and 0.66 (0.58 to 0.74) and 0.95 (0.92 to 0.96) for a cut-off value of 13 or higher, respectively. Accuracy was similar across reference standards and subgroups, including for pregnant and postpartum women. Conclusions An EPDS cut-off value of 11 or higher maximised combined sensitivity and specificity; a cut-off value of 13 or higher was less sensitive but more specific. To identify pregnant and postpartum women with higher symptom levels, a cut-off of 13 or higher could be used. Lower cut-off values could be used if the intention is to avoid false negatives and identify most patients who meet diagnostic criteria. Registration PROSPERO (CRD42015024785)

Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data

Objective: To evaluate the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression in pregnancy and postpartum. Design: Individual participant data meta-analysis. Data Sources: Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science were searched (inception – October 3, 2018). Eligibility criteria for selecting studies: Eligible datasets included EPDS scores and major depression classification via validated diagnostic interviews. Bivariate random-effects meta-analysis was used to estimate EPDS sensitivity and specificity compared to semi-structured, fully structured (Mini International Neuropsychiatric Interview [MINI] excluded), and MINI diagnostic interviews, separately, using individual participant data. One-stage meta-regression was used to examine accuracy by reference standard categories and participant characteristics. Results: Individual participant data were obtained from 58 of 83 eligible studies (70%; 15,557 of 22,788 eligible participants [68%], 2,069 cases). Combined sensitivity and specificity was maximized at cutoff 11 across reference standards. Among studies with a semi-structured interview (36 studies, 9,066 participants, 1,330 cases), sensitivity and specificity (95% CI) were 0.85 (0.79 to 0.90) and 0.84 (0.79 to 0.88) for cutoff 10, 0.81 (0.75 to 0.87) and 0.88 (0.85 to 0.91) for cutoff 11, and 0.66 (0.58 to 0.74) and 0.95 (0.92 to 0.96) for cutoff 13. Accuracy was similar across reference standards and subgroups, including for women in pregnancy and postpartum. Conclusions: An EPDS cutoff of 11 maximized combined sensitivity and specificity; a cutoff of ≥ 13 was less sensitive but more specific. To identify women in pregnancy and postpartum with higher symptom levels, a cutoff of 13 or greater could be used. Lower cutoffs could be used if the intention is to avoid false negatives and identify most patients who meet diagnostic criteria.</p

Evaluating the integration of tuberculosis screening and contact investigation in tuberculosis clinics in Ethiopia: A mixed method study

Background Aligned with global childhood tuberculosis (TB) road map, Ethiopia developed its own in 2015. The key strategies outlined in the Ethiopian roadmap are incorporating TB screening in Integrated Maternal, Neonatal and Child Illnesses (IMNCI) clinic for children under five years (U5) and intensifying contact investigations at TB clinic. However, these strategies have never been evaluated. Objective To evaluate the integration of tuberculosis (TB) screening and contact investigation into Integrated Maternal, Neonatal and Child Illnesses (IMNCI) and TB clinics in Addis Ababa, Ethiopia. Methods The study used mixed methods with stepped-wedge design where 30 randomly selected health care facilities were randomized into three groups of 10 during August 2016-Novem-ber 2017. The integration of TB screening into IMNCI clinic and contact investigation in TB clinic were introduced by a three-day childhood TB training for health providers. An in-depth interview was used to explore the challenges of the interventions and supplemented data on TB screening and contact investigation. Results Overall, 180896 children attended 30 IMNCI clinics and145444 (80.4%) were screened for TB. A total of 688 (0.4%) children had presumptive TB and 47(0.03%) had TB. During the pre-intervention period, 51873 of the 85278 children (60.8%) were screened for TB as compared to 93570 of the 95618 children (97.9%) in the intervention (p<0.001). This had resulted in 149 (0.30%) and 539 (0.6%) presumptive TB cases in pre-intervention and intervention periods (p<0.001), respectively. Also, nine TB cases (6.0%) in pre-intervention and 38 (7.1%) after intervention were identified (p = 0.72). In TB clinics, 559 under-five (U5) contacts were identified and 419 (80.1%) were screened. In all, 51(9.1%) presumed TB cases and 12 (2.1%) active TB cases were identified from the traced contacts. TB screening was done for 182 of the 275 traced contacts (66.2%) before intervention and for 237 of the 284 of the traced (83.5%) under intervention (p<0.001). Isoniazid prevention therapy (IPT) was initiated for 69 of 163 eligible contacts (42.3%) before intervention and for 159 of 194 eligible children (82.0%) under intervention (p<0.001). Over 95% of health providers indicated that the integration of TB screening into IMNCI and contact investigation in TB clinic is acceptable and practical. Gastric aspiration to collect sputum using nasogastric tube was reported to be difficult. Conclusions Integrating TB screening into IMNCI clinics and intensifying contact investigation in TB clinics is feasible improving TB screening, presumed TB cases, TB cases, contact screening and IPT coverage during the intervention period. Stool specimen could be non-invasive to address the challenge of sputum collection

A homolog of human ski-interacting protein in rice positively regulates cell viability and stress tolerance

Abiotic stresses are major limiting factors for growth, development, and productivity of crop plants. Here, we report on OsSKIPa, a rice homolog of human Ski-interacting protein (SKIP) that can complement the lethal defect of the knockout mutant of SKIP homolog in yeast and positively modulate cell viability and stress tolerance of rice. Suppression of OsSKIPa in rice resulted in growth arrest and reduced cell viability. The expression OsSKIPa is induced by various abiotic stresses and phytohormone treatments. Transgenic rice overexpressing OsSKIPa exhibited significantly improved growth performance in the medium containing stress agents (abscisic acid, salt, or mannitol) and drought resistance at both the seedling and reproductive stages. The OsSKIPa-overexpressing rice showed significantly increased reactive oxygen species-scavenging ability and transcript levels of many stress-related genes, including SNAC1 and rice homologs of CBF2, PP2C, and RD22, under drought stress conditions. More than 30 OsSKIPa-interacting proteins were identified, but most of these proteins have no matches with the reported SKIP-interacting proteins in animals and yeast. Together, these data suggest that OsSKIPa has evolved a specific function in positive modulation of stress resistance through transcriptional regulation of diverse stress-related genes in rice

Factors associated with fears due to COVID-19: A Scleroderma Patient-centered Intervention Network (SPIN) COVID-19 cohort study.

INTRODUCTION: No studies have examined factors associated with fear in any group of people vulnerable during COVID-19 due to pre-existing medical conditions. OBJECTIVE: To investigate factors associated with fear of consequences of COVID-19 among people living with a pre-existing medical condition, the autoimmune disease systemic sclerosis (SSc; scleroderma), including country. METHODS: Pre-COVID-19 data from the Scleroderma Patient-centered Intervention Network (SPIN) Cohort were linked to COVID-19 data collected in April 2020. Multivariable linear regression was used to assess factors associated with continuous scores of the 10-item COVID-19 Fears Questionnaire for Chronic Medical Conditions, controlling for pre-COVID-19 anxiety symptoms. RESULTS: Compared to France (N = 156), COVID-19 Fear scores among participants from the United Kingdom (N = 50) were 0.12 SD (95% CI 0.03 to 0.21) higher; scores for Canada (N = 97) and the United States (N = 128) were higher, but not statistically significant. Greater interference of breathing problems was associated with higher fears due to COVID-19 (Standardized regression coefficient = 0.12, 95% CI 0.01 to 0.23). Participants with higher financial resources adequacy scores had lower COVID-19 Fear scores (Standardized coefficient = -0.18, 95% CI -0.28 to -0.09). CONCLUSIONS: Fears due to COVID-19 were associated with clinical and functional vulnerabilities in this chronically ill population. This suggests that interventions may benefit from addressing specific clinical issues that apply to specific populations. Financial resources, health policies and political influences may also be important. The needs of people living with chronic illness during a pandemic may differ depending on the social and political context in which they live

Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis.

OBJECTIVES:Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores ≥10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 ≥10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. STUDY DESIGN AND SETTING:Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. RESULTS:A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 ≥10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 ≥10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 ≥14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 ≥14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). CONCLUSION:PHQ-9 ≥10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies