Depression symptoms across cultures: an IRT analysis of standard depression symptoms using data from eight countries.
Ontology highlight
ABSTRACT:
SUBMITTER: Haroz EE
PROVIDER: S-EPMC6022281 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
Publications
Depression symptoms across cultures: an IRT analysis of standard depression symptoms using data from eight countries.
Haroz E E EE Bolton P P Gross A A Chan K S KS Michalopoulos L L Bass J J
Social psychiatry and psychiatric epidemiology 20160415 7
<h4>Purpose</h4>Prevalence estimates of depression vary between countries, possibly due to differential functioning of items between settings. This study compared the performance of the widely used Hopkins symptom checklist 15-item depression scale (HSCL-15) across multiple settings using item response theory analyses. Data came from adult populations in the low and middle income countries (LMIC) of Colombia, Indonesia, Kurdistan Iraq, Rwanda, Iraq, Thailand (Burmese refugees), and Uganda (N = 4 ...[more]
Similar Datasets
Project description:Understanding the characteristics of the SARS-Cov-2/Covid-19 pandemic is central to developing control strategies. Here we show how a simple Susceptible-Infective-Recovered (SIR) model applied to data for eight European countries and the United Kingdom (UK) can be used to forecast the descending limb (post-peak) of confirmed cases and deaths as a function of time, and predict the duration of the pandemic once it has peaked, by estimating and fixing parameters using only characteristics of the ascending limb and the magnitude of the first peak. As with all epidemiological analyses, unanticipated behavioral changes will result in deviations between projection and observation. This is abundantly clear for the current pandemic. Nonetheless, accurate short-term projections are possible, and the methodology we present is a useful addition to the epidemiologist's armamentarium. Since our predictions assume that control measures such as lockdown, social distancing, use of masks etc. remain the same post-peak as before peak, deviations from our predictions are a measure of the extent to which loosening of control measures have impacted case-loads and deaths since the first peak and initial decline in daily cases and deaths. The predicted and actual case fatality ratio, or number of deaths per million population from the start of the pandemic to when daily deaths number less than five for the first time, was lowest in Norway (pred: 44 ± 5 deaths/million; actual: 36 deaths/million) and highest for the United Kingdom (pred: 578 +/- 65 deaths/million; actual 621 deaths/million). The inferred pandemic characteristics separated into two distinct groups: those that are largely invariant across countries, and those that are highly variable. Among the former is the infective period, TL(TL¯=16.3±2.7 days) ; the average time between contacts, TR(TR¯=3.8±0.5) days and the average number of contacts while infective, R(R¯=4.4±0.5) . In contrast, there is a highly variable time lag TD between the peak in the daily number of confirmed cases and the peak in the daily number of deaths, ranging from a low of TD = 2,4 days for Denmark and Italy respectively, to highs of TD = 12, 15 for Germany and Norway respectively. The mortality fraction, or ratio of deaths to confirmed cases, was also highly variable, ranging from low values 3%, 5% and 5% for Norway, Denmark and Germany respectively, to high values of 18%, 20% and 21% for Sweden, France, and the UK respectively. The probability of mortality rather than recovery was a significant correlate of the duration of the pandemic, defined as the time from 12/31/2019 to when the number of daily deaths fell below 5. Finally, we observed a small but detectable effect of average temperature on the probability α of infection per contact, with higher temperatures associated with lower infectivity. Policy implications of our findings are also briefly discussed.
| S-EPMC7273298 | biostudies-literature
Project description:BackgroundAs the SARS-Cov-2/Covid-19 pandemic continues to ravage the world, it is important to understanding the characteristics of its spread and possible correlates for control to develop strategies of response.MethodsHere we show how a simple Susceptible-Infective-Recovered (SIR) model applied to data for eight European countries and the United Kingdom (UK) can be used to forecast the descending limb (post-peak) of confirmed cases and deaths as a function of time, and predict the duration of the pandemic once it has peaked, by estimating and fixing parameters using only characteristics of the ascending limb and the magnitude of the first peak.ResultsThe predicted and actual case fatality ratio, or number of deaths per million population from the start of the pandemic to when daily deaths number less than five for the first time, was lowest in Norway (predicted: 44 ± 5 deaths/million; actual: 36 deaths/million) and highest for the United Kingdom (predicted: 578 +/- 65 deaths/million; actual 621 deaths/million). The inferred pandemic characteristics separated into two distinct groups: those that are largely invariant across countries, and those that are highly variable. Among the former is the infective period, TL = 16.3 ± 2.7 days, the average time between contacts, TR = 3.8+/- 0.5 days and the average number of contacts while infective R = 4.4 +/- 0.5. In contrast, there is a highly variable time lag TD between the peak in the daily number of confirmed cases and the peak in the daily number of deaths, ranging from lows of TD = 2,4 days for Denmark and Italy respectively, to highs of TD = 12, 15 for Germany and Norway respectively. The mortality fraction, or ratio of deaths to confirmed cases, was also highly variable, ranging from low values 3%, 5% and 5% for Norway, Denmark and Germany respectively, to high values of 18%, 20% and 21% for Sweden, France, and the UK respectively. The probability of mortality rather than recovery was a significant correlate of the duration of the pandemic, defined as the time from 12/31/2019 to when the number of daily deaths fell below 5. Finally, we observed a small but detectable effect of average temperature on the probability α of infection per contact, with higher temperatures associated with lower infectivity.ConclusionsOur simple model captures the dynamics of the initial stages of the pandemic, from its exponential beginning to the first peak and beyond, with remarkable precision. As with all epidemiological analyses, unanticipated behavioral changes will result in deviations between projection and observation. This is abundantly clear for the current pandemic. Nonetheless, accurate short-term projections are possible, and the methodology we present is a useful addition to the epidemiologist's armamentarium. Our predictions assume that control measures such as lockdown, social distancing, use of masks etc. remain the same post-peak as before peak. Consequently, deviations from our predictions are a measure of the extent to which loosening of control measures have impacted case-loads and deaths since the first peak and initial decline in daily cases and deaths. Our findings suggest that the two key parameters to control and reduce the impact of a developing pandemic are the infective period and the mortality fraction, which are achievable by early case identification, contact tracing and quarantine (which would reduce the former) and improving quality of care for identified cases (which would reduce the latter).
| S-EPMC7605556 | biostudies-literature
Project description:AimsThere is a need of more quantitative standardised data to compare local Mental Health Systems (MHSs) across international jurisdictions. Problems related to terminological variability and commensurability in the evaluation of services hamper like-with-like comparisons and hinder the development of work in this area. This study was aimed to provide standard assessment and comparison of MHS in selected local areas in Europe, contributing to a better understanding of MHS and related allocation of resources at local level and to lessen the scarcity in standard service comparison in Europe. This study is part of the Seventh Framework programme REFINEMENT (Research on Financing Systems' Effect on the Quality of Mental Health Care in Europe) project.MethodsA total of eight study areas from European countries with different systems of care (Austria, England, Finland, France, Italy, Norway, Romania, Spain) were analysed using a standard open-access classification system (Description and Evaluation of Services for Long Term Care in Europe, DESDE-LTC). All publicly funded services universally accessible to adults (≥18 years) with a psychiatric disorder were coded. Care availability, diversity and capacity were compared across these eight local MHS.ResultsThe comparison of MHS revealed more community-oriented delivery systems in the areas of England (Hampshire) and Southern European countries (Verona - Italy and Girona - Spain). Community-oriented systems with a higher proportion of hospital care were identified in Austria (Industrieviertel) and Scandinavian countries (Sør-Trøndelag in Norway and Helsinki-Uusimaa in Finland), while Loiret (France) was considered as a predominantly hospital-based system. The MHS in Suceava (Romania) was still in transition to community care.ConclusionsThere is a significant variation in care availability and capacity across MHS of local areas in Europe. This information is relevant for understanding the process of implementation of community-oriented mental health care in local areas. Standard comparison of care provision in local areas is important for context analysis and policy planning.
| S-EPMC6998926 | biostudies-literature
Project description:BackgroundNovel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates.ObjectiveThe aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America.MethodsA machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information.ResultsOur results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available.ConclusionsWe demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.
| S-EPMC6470460 | biostudies-literature
Project description:Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24-68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = -0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.
| S-EPMC7876288 | biostudies-literature
Project description:Open clinical trial data offer many opportunities for the scientific community to independently verify published results, evaluate new hypotheses and conduct meta-analyses. These data provide valuable opportunities for scientific advances in medical research. Herein we present the comparative meta-analysis of different standard of care treatments from newly available comparator arm data from several prostate cancer clinical trials. Comparison of survival rates following treatment with mitoxantrone or docetaxel in combination with prednisone as well as prednisone alone, validated the previously demonstrated superiority of treatment with docetaxel. Additionally, comparison of four testosterone suppression treatments in hormone-refractory prostate cancer revealed that subjects who had undergone surgical castration had significantly lower survival rates than those treated with LHRH, anti-androgen or LHRH plus anti-androgen, suggesting that this treatment option is less optimal. This study illustrates how the use of patient-level clinical trial data enables meta-analyses that can provide new insights into clinical outcomes of standard of care treatments and thus, once validated, has the potential to help optimize healthcare delivery.
| S-EPMC4862324 | biostudies-literature
Project description:Experiencing depression symptoms, even at mild to moderate levels, is associated with maladaptive outcomes for adolescents. We used network analysis to evaluate which symptoms (and associations between symptoms) are most central to adolescent depression. Participants were part of a large, diverse community sample (N = 1,409) of adolescents between 13 and 19 years of age. Network analysis was used to identify the most central symptoms (nodes) and associations between symptoms (edges) assessed by the Children's Depression Inventory. We also evaluated these centrality indicators for network robustness using stability and accuracy tests, associated symptom centrality with mean levels of symptoms, and examined potential differences between the structure and connectivity of depression networks in boys and girls. The most central symptoms in the network were self-hatred, loneliness, sadness, and pessimism. The strongest associations between symptoms were sadness-crying, anhedonia-school dislike, sadness-loneliness, school work difficulty-school performance decrement, self-hatred-negative body image, sleep disturbance-fatigue, and self-deprecation-self-blame. The network was robust to stability and accuracy tests. Notably, symptom centrality and mean levels of symptoms were not associated. Boys and girls' networks did not differ in levels of connectivity, though the link between body image and self-hatred was stronger in girls than boys. Self-hatred, loneliness, sadness, and pessimism were the most central symptoms in adolescent depression networks, suggesting that these symptoms (and associations between symptoms) should be prioritized in theoretical models of adolescent depression and could also serve as important treatment targets for adolescent depression interventions.
| S-EPMC6535368 | biostudies-literature
Project description:BackgroundResearch suggests there is heterogeneity in treatment response for internet-delivered cognitive behavioral therapy (iCBT) users, but few studies have investigated the trajectory of individual symptom change across iCBT treatment. Large patient data sets using routine outcome measures allows the investigation of treatment effects over time as well as the relationship between outcomes and platform use. Understanding trajectories of symptom change, as well as associated characteristics, may prove important for tailoring interventions or identifying patients who may not benefit from the intervention.ObjectiveWe aimed to identify latent trajectories of symptom change during the iCBT treatment course for depression and anxiety and to investigate the patients' characteristics and platform use for each of these classes.MethodsThis is a secondary analysis of data from a randomized controlled trial designed to examine the effectiveness of guided iCBT for anxiety and depression in the UK Improving Access to Psychological Therapies (IAPT) program. This study included patients from the intervention group (N=256) and followed a longitudinal retrospective design. As part of the IAPT's routine outcome monitoring system, patients were prompted to complete the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) after each supporter review during the treatment period. Latent class growth analysis was used to identify the underlying trajectories of symptom change across the treatment period for both depression and anxiety. Differences in patient characteristics were then evaluated between these trajectory classes, and the presence of a time-varying relationship between platform use and trajectory classes was investigated.ResultsFive-class models were identified as optimal for both PHQ-9 and GAD-7. Around two-thirds (PHQ-9: 155/221, 70.1%; GAD-7: 156/221, 70.6%) of the sample formed various trajectories of improvement classes that differed in baseline score, the pace of symptom change, and final clinical outcome score. The remaining patients were in 2 smaller groups: one that saw minimal to no gains and another with consistently high scores across the treatment journey. Baseline severity, medication status, and program assigned were significantly associated (P<.001) with different trajectories. Although we did not find a time-varying relationship between use and trajectory classes, we found an overall effect of time on platform use, suggesting that all participants used the intervention significantly more in the first 4 weeks (P<.001).ConclusionsMost patients benefit from treatment, and the various patterns of improvement have implications for how the iCBT intervention is delivered. Identifying predictors of nonresponse or early response might inform the level of support and monitoring required for different types of patients. Further work is necessary to explore the differences between these trajectories to understand what works best for whom and to identify early on those patients who are less likely to benefit from treatment.
| S-EPMC10372559 | biostudies-literature
Project description:BACKGROUND: The ability of an intracellular pathogen to establish infection depends on the capacity of the organism to survive and replicate inside the host. Mycobacterium fortuitum is a bacteria that contains genes involved in the detoxification of the oxygen reactive species such as those produced by the host during the infection. In this work, we investigate the effects of hydrogen peroxide on the transcription and expression of these genes by developing a real time quantitative PCR technique (qRT-PCR) using the ribosomal promoter region (rrnA-P1) as reference product for quantification of the mRNA levels. RESULTS: M. fortuitum cultures were treated with different hydrogen peroxide concentrations (0.02 to 20 mM) during several periods of time (30 to 120 minutes). The activity of the enzymes KatGII and SodA, and the transcription of corresponding genes were evaluated. The transcriptional regulator furAII gene was also studied. The ribosomal promoter region rrnA-P1 was validated as referential product under the stress conditions checked by qRT-PCR. Minor changes were observed under the conditions tested except when bacteria were incubated in the presence of 20 mM hydrogen peroxide. Under those conditions, the levels of transcription of the three genes under study increased at 30 minutes of treatment. The viability of the bacteria was not influenced under the conditions tested. CONCLUSION: In this work, we have quantified transcriptional responses to stress suggesting that, the opportunistic pathogen M. fortuitum is more resistant and differs in behaviour in the presence of hydrogen peroxide, when compared to the major pathogen Mycobacterium tuberculosis and the saprophyte Mycobacterium smegmatis. Besides, we demonstrate the mycobacterial non-coding region rrnA-P1 to be a suitable reference product in the analysis of qRT-PCR transcriptional data of M. fortuitum.
| S-EPMC2443159 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic affected the mental health of the general population through multiple pathways. The aim of this study was to examine anxiety, depression, self-confidence, and social connectedness among the general population of eight countries during the COVID-19 pandemic, their underlying factors, and vulnerable groups.MethodsA web-based survey was administered to persons from the general population of China, Greece, Italy, Netherlands, Russia, Sweden, the United Kingdom, and the United States. The survey included the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and items on self-confidence, social connectedness, and socio-demographics. Data were analyzed with descriptive statistics, exploratory factor analysis and regression analysis.ResultsTwenty-three thousand six hundred twenty-two respondents completed the survey. Overall, 42% of the total sample had mild to severe anxiety symptoms and 43% had mild to severe depression symptoms. 14% to 38% reported suboptimal ratings in self-confidence, social participation, contact with family and friends, and feeling connected to others. In the exploratory factor analyses, in most countries, one dominant factor had a high influence on GAD-7, PHQ-9 sum scores and self-confidence with eigenvalue (% variance) above 3.2 (53.9%). One less dominant factor had a high influence on social connectedness scores with eigenvalue (% variance) ranging above 0.8 (12.8%). Being younger, female, having chronic conditions, perceived as risky to COVID-19 infection, and feeling not very well protected against COVID-19 were significantly associated with the two underlying factors.ConclusionsAnxiety, depression, and problems with self-confidence and social connectedness were highly prevalent in the general population of eight countries during the early phase of the COVID-19 pandemic. This highlights the importance of the allocation of additional resources to implement policies to mitigate the impact of the pandemic on mental health.
| S-EPMC9672616 | biostudies-literature