This statistic depicts the percentage of the global population with select mental health and substance use disorders as of 2017, by gender. According to the data, a total of **** percent of males and **** percent of females suffered from mental health or substance use disorders globally.

BackgroundAnxiety disorders, depression and schizophrenia are the focus of global mental health attention, resulting in a significant number of disability-adjusted life years and a considerable social and economic burden. It’s can affect the socioeconomic landscape as a result of experiencing a global epidemic. And rarely, different Socio-demographic Index (SDI) levels and Age-Period-Cohort (APC) have been used to evaluate the prevalence of mental disorders worldwide.MethodsUsing data from the Global Burden of Disease 2021 (GBD) database, this study assessed trends in the incidence and prevalence of anxiety disorders, depression, and schizophrenia in countries with different SDI levels from 1990 to 2021. Joinpoint and periodic cohort (APC) models were used to sort out the effects of age, period and cohort on incidence. Data were categorized into 5-year age groups and 95% uncertainty intervals (UI) were calculated to account for data variability.ResultsIn countries with different SDI levels, the age-standardized average annual percentage change (AAPC) in the incidence of anxiety were all shown to be increasing, and there were large gender differences between the different SDI levels, with a maximum of 0.97 (0.76–1.18) for females in countries with a high SDI level, Age-standardized more rates per 100,000 people in high SDI countries, from 658.87 in 1990 to 841.56 in 2021, and the largest gender differences in countries with a low to moderate SDI level, with AAPCs for males and females of 0.04 (0.04–0.05), 0.86 (0.63–1.09); for depression, only the countries with medium-high SDI levels were statistically significant compared to the countries with medium-low SDI levels, with AAPCs of 0.05 (0.04–0.07), 0.04 (0.04–0.05); for schizophrenia in addition to the AAPCs of the countries with medium-high SDI levels showed an increase of 0.16 (0.13–0.18); the rest decreased.ConclusionThis study highlights the current status of global incidence and prevalence of mental disorders and examines the complex interactions between the period of onset and cohort of onset of mental disorders using APC modeling, with differences in gender differences in mental disorders in countries with different SDIs, and significant differences in countries with low to medium SDI levels, requiring further exploration of the mechanisms by which socio-economic development influences gender-specific mental health. Countries with different SDI levels have responded to unique trends within their specific socioeconomic, cultural, and historical contexts, suggesting the need for contextualized public health strategies to effectively respond to and manage the incidence and prevalence of mental disorders in these different settings. Prevalence of mental disorders. This points the way to more in-depth future research on treatments and interventions for mental disorders.

Antidepressant Use in Scandinavia

A Study of Population Characteristics and Drug Utilization Rates

By [source]

About this dataset

This fascinating dataset examines the use of antidepressant medications among children and adolescents in Denmark, Norway, and Sweden from 2007 until 2017. Through a comprehensive exploration of drug usage along with population characteristics, we can uncover deeper insights into the prevalence of antidepressant use in this demographic and its potential causes. By carefully inspecting this data set which contains details about drug use, census data and associated drug names by code, we can shed light on an important issue with far reaching implications for public health worldwide

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How to use the dataset

This dataset offers an opportunity to analyze antidepressant use among children and adolescents in Denmark, Norway and Sweden from 2007 to 2017. To get started with your analysis, you'll need to familiarize yourself with the dataset. Below are some simple steps for getting acquainted with the available resources:

• Familiarize yourself with the column descriptions and data types. Each column contains meaningful information about drug use and population characteristics in the three countries during this window of time.
• Review the drug_names file contained in this dataset for a detailed list of drugs associated with each code represented in the main table. This is particularly important because ATC (Anatomical Therapeutic Chemical) codes provide an easy shorthand way of referring to individual medications without being too long-winded or cluttering up columns not relevant to your particular question or hypothesis
• Explore correlations between different parameters using crosstabs, scatterplots, or other common visualizations as necessary
• Use census data contained in census_data file as a reference when discussing population makeup within any given country during this period

With this approach, you will have all that's necessary to derive meaningful results out of this dataset! Good luck on your exploration!

Research Ideas

• Comparing the sex, age and population weights of those using different types of antidepressants in each country
• Tracking consumption trends across countries and between genders over time
• Correlating antidepressant use with national income indicators such as GDP per capita or overall Mental Health Index scores

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: census.csv | Column name | Description | |:--------------|:------------------------------------------| | year | Year of the data (Integer) | | sex | Gender of the population (String) | | age | Age group of the population (Integer) | | cnt | Number of people using the drug (Integer) | | country | Country of the population (String) |

File: drug_names.csv | Column name | Description | |:---------------|:------------------------------------------------------------------| | atc | Anatomical Therapeutic Chemical (ATC) code for the drug. (String) | | formalname | Formal name of the drug. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The World Health Organization estimates that almost 300 million people suffer from depression worldwide. Depression is the most common mental health disorder and shows racial disparities in disease prevalence, age of onset, severity of symptoms, frequency of diagnosis, and treatment utilization across the United States. Since depression has both social and genetic risk factors, we propose a conceptual model wherein social stressors are primary risk factors for depression, but genetic variants increase or decrease individual susceptibility to the effects of the social stressors. Our research strategy incorporates both social and genetic data to investigate variation in symptoms of depression (CES-D scores). We collected data on financial strain (difficulty paying bills) and personal social networks (a model of an individual’s social environment), and we genotyped genetic variants in five genes involved in stress reactivity (HTR1a, BDNF, GNB3, SLC6A4, and FKBP5) in 135 African Americans residing in Tallahassee, Florida. We found that high financial strain and a high percentage of people in one’s social network who are a source of stress or worry were significantly associated with higher CES-D scores and explained more variation in CES-D scores than did genetic factors. Only one genetic variant (rs1360780 in FKBP5) was significantly associated with CES-D scores and only when the social stressors were included in the model. Interestingly, the effect of FKPB5 appeared to be strongest in individuals with high financial strain such that participants with a T allele at rs1360780 in FKBP5 and high financial strain had the highest mean CES-D scores in our study population. These results suggest that material disadvantage and a stressful social environment increases the risk of depression, but that individual-level genetic variation may increase susceptibility to the adverse health consequences of social stressors.

BackgroundPoststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge.AimThis study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims.MethodsA cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression.ResultsAccording to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04).ConclusionMore than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue.

Depression is a complex and heterogeneous mental health disorder affecting an estimated 280 million individuals worldwide. Although various antidepressant medications are available, a significant proportion of patients experience medication-resistant depression. This clinical case report highlights the critical importance of integrating pharmacogenomics into clinical practice, which is still not routinely done in many countries, through the detailed examination of a mid-20s male patient diagnosed with medication-resistant depression. Genetic analysis revealed specific variations in the cytochrome P450 genes, namely CYP2D6, CYP2C19, and CYP1A2, which are crucial for drug metabolism. By investigating the impact of these genetic variations on the patient’s treatment response, we provide evidence-based recommendations for adjusting antidepressant medications based on the individual’s unique pharmacogenomic profile. As demonstrated in the case report, this ultimately results in a positive clinical outcome and would have been advantageous to implement earlier as part of the patient’s management.