For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

There're 2 datasets:

1. Life expectancy contains information about life expectancy for men and for women, happiness score and fertility rate.
2. Suicide Rate contains information about the suicide rate and GDP per capita on each country.

Number of suicides and suicide rates by sex and age in England and Wales. Includes information on conclusion type, the proportion of suicides by method, and the median registration delay.

BackgroundIn Europe, men have lower rates of attempted suicide compared to women and at the same time a higher rate of completed suicides, indicating major gender differences in lethality of suicidal behaviour. The aim of this study was to analyse the extent to which these gender differences in lethality can be explained by factors such as choice of more lethal methods or lethality differences within the same suicide method or age. In addition, we explored gender differences in the intentionality of suicide attempts.Methods and FindingsMethods. Design: Epidemiological study using a combination of self-report and official data. Setting: Mental health care services in four European countries: Germany, Hungary, Ireland, and Portugal. Data basis: Completed suicides derived from official statistics for each country (767 acts, 74.4% male) and assessed suicide attempts excluding habitual intentional self-harm (8,175 acts, 43.2% male).Main Outcome Measures and Data Analysis. We collected data on suicidal acts in eight regions of four European countries participating in the EU-funded “OSPI-Europe”-project (www.ospi-europe.com). We calculated method-specific lethality using the number of completed suicides per method * 100 / (number of completed suicides per method + number of attempted suicides per method). We tested gender differences in the distribution of suicidal acts for significance by using the χ2-test for two-by-two tables. We assessed the effect sizes with phi coefficients (φ). We identified predictors of lethality with a binary logistic regression analysis. Poisson regression analysis examined the contribution of choice of methods and method-specific lethality to gender differences in the lethality of suicidal acts.Findings Main ResultsSuicidal acts (fatal and non-fatal) were 3.4 times more lethal in men than in women (lethality 13.91% (regarding 4106 suicidal acts) versus 4.05% (regarding 4836 suicidal acts)), the difference being significant for the methods hanging, jumping, moving objects, sharp objects and poisoning by substances other than drugs. Median age at time of suicidal behaviour (35–44 years) did not differ between males and females. The overall gender difference in lethality of suicidal behaviour was explained by males choosing more lethal suicide methods (odds ratio (OR) = 2.03; 95% CI = 1.65 to 2.50; p < 0.000001) and additionally, but to a lesser degree, by a higher lethality of suicidal acts for males even within the same method (OR = 1.64; 95% CI = 1.32 to 2.02; p = 0.000005). Results of a regression analysis revealed neither age nor country differences were significant predictors for gender differences in the lethality of suicidal acts. The proportion of serious suicide attempts among all non-fatal suicidal acts with known intentionality (NFSAi) was significantly higher in men (57.1%; 1,207 of 2,115 NFSAi) than in women (48.6%; 1,508 of 3,100 NFSAi) (χ2 = 35.74; p < 0.000001).Main limitations of the studyDue to restrictive data security regulations to ensure anonymity in Ireland, specific ages could not be provided because of the relatively low absolute numbers of suicide in the Irish intervention and control region. Therefore, analyses of the interaction between gender and age could only be conducted for three of the four countries. Attempted suicides were assessed for patients presenting to emergency departments or treated in hospitals. An unknown rate of attempted suicides remained undetected. This may have caused an overestimation of the lethality of certain methods. Moreover, the detection of attempted suicides and the registration of completed suicides might have differed across the four countries. Some suicides might be hidden and misclassified as undetermined deaths.ConclusionsMen more often used highly lethal methods in suicidal behaviour, but there was also a higher method-specific lethality which together explained the large gender differences in the lethality of suicidal acts. Gender differences in the lethality of suicidal acts were fairly consistent across all four European countries examined. Males and females did not differ in age at time of suicidal behaviour. Suicide attempts by males were rated as being more serious independent of the method used, with the exceptions of attempted hanging, suggesting gender differences in intentionality associated with suicidal behaviour. These findings contribute to understanding of the spectrum of reasons for gender differences in the lethality of suicidal behaviour and should inform the development of gender specific strategies for suicide prevention.

India IN: Suicide Mortality Rate: Male data was reported at 17.800 NA in 2016. This records a decrease from the previous number of 18.000 NA for 2015. India IN: Suicide Mortality Rate: Male data is updated yearly, averaging 18.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.600 NA in 2000 and a record low of 17.700 NA in 2010. India IN: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Data from Heuer (1979) on suicide rates in West Germany classified by age, sex, and method of suicide.
A data frame with 306 observations and 6 variables.

Linear regression estimation results for suicidal rate among rural men and rural women.

Abstract Objective: to perform spatiotemporal analysis of suicide mortality in the elderly in Brazil. Methods: a mixed ecological study was carried out in which deaths from suicide among the elderly were analyzed using data from the Mortality Information System (MIS) and socio-demographic variables, from 2000 to 2014, with a trend analysis of this period. Univariate and bivariate spatial analysis was performed using the Moran Global and Moran Map index to evaluate the intensity and significance of spatial clusters. Results: there were 19,806 deaths due to suicide among the elderly in Brazil between 2000 and 2014. The ratio of male and female mortality rates was 4:1, with increasing trends for both genders (R2>0.8), but with greater intensity among men (p=0.0293). There was a moderate autocorrelation for men (I>0.40), with clusters forming for both genders in the south of Brazil. Bivariate analysis showed the formation of clusters in the southern region with the Human Development Index and aging variables and in the north and northeast regions based on dependence and illiteracy ratio. Conclusions: mortality due to suicide among the elderly has a tendency to increase and is unequally distributed in Brazil.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_MaxPap Smear), breast cancer (CIMin Mammogram - CI Max Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

Men have a higher rate of completed suicide than women, which suggests that sex chromosome abnormalities may be related to the pathophysiology of suicide. Recent studies have found an aberrant loss of chromosome Y (LOY) in various diseases; however, no study has investigated whether there is an association between LOY and suicide. The purpose of this study was to determine whether LOY occurs in men who completed suicide. Our study consisted of 286 male Japanese subjects comprised of 140 suicide completers without severe physical illness (130 post-mortem samples of peripheral blood and 10 brains) and 146 age-matched control subjects (130 peripheral blood samples from healthy individuals and 16 post-mortem brains). LOY was measured as the chromosome Y/chromosome X ratio of the fluorescent signal of co-amplified short sequences from the Y-X homologous amelogenin genes (AMELY and AMELX). Regression analyses showed that LOY in the blood of suicide completers was significantly more frequent than that found in controls (odds ratio = 3.50, 95% confidence interval = 1.21–10.10), but not in the dorsolateral prefrontal cortex (DLPFC) region of brain. Normal age-dependent LOY in blood was found in healthy controls (r = -0.353, p < 0.001), which was not seen in suicide completers (r = -0.119, p = 0.177). DLPFC tissue had age-dependent LOY (B = -0.002, p = 0.015), which was independent of phenotype. To our knowledge, this is the first study demonstrating that LOY in blood is associated with suicide completion. In addition, our findings are the first to also indicate that age-dependent LOY may occur not only in blood, but also in specific brain regions.

Linear regression estimation results for suicidal rate among urban men and urban women.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Background: Despite most suicides occurring in low-and-middle-income countries (LAMICs), limited reports on suicide rates in older adults among LAMICs are available. In Ecuador, high suicide rates have been reported among adolescents. Little is known about the epidemiology of suicides among older adults in Ecuador.Aim: To examine the sociodemographic characteristics of suicides among older adults living in Ecuador from 1997 to 2019.Methods: An observational study was conducted using Ecuador's National Institute of Census and Statistics database from 1997 to 2019 in Ecuadorians aged 60 and older. International Classification of Diseases 10th Revision (ICD-10) (X60-X84)-reported suicide deaths were included in addition to deaths of events of undetermined intent (Y21-Y33). Sex, age, ethnicity, educational level, and method of suicide were analyzed. Annual suicide rates were calculated per 100,000 by age, sex, and method. To examine the trends in rates of suicide, Joinpoint analysis using Poisson log-linear regression was used.Results: Suicide rates of female older adults remained relatively stable between 1997 and 2019 with an average annual percentage increase of 2.4%, while the male rates increased between 2002 and 2009, 2014 and 2016, and maintained relatively stable within the past 3 years (2017–2019). The annual age-adjusted male suicide rate was 29.8 per 100,000, while the female suicide rate was 5.26 per 100,000 during the study period. When adding deaths of undetermined intent, the annual male rate was 60.5 per 100,000, while the same rate was 14.3 for women. The most common suicide method was hanging (55.7%) followed by self-poisoning (26.0%). The highest suicide numbers were reported in urban districts, men, and those with lower education status.Conclusion: This study contributes to building the baseline for further studies on suicide rates of older adults in Ecuador. Results highlight priority areas of suicide prevention. By examining suicide trends over 23 years, findings can help inform policy and future interventions targeting suicide prevention.

Suicide is among the main challenges that need to be addressed in developed countries. In this paper, we analyse suicides across the 17 Spanish regions over the period 2014–2019. More precisely, our objective is to re-study the determinants of suicides focusing on the latest economic expansion period. We use count panel data models and sex stratification. A range of aggregate socioeconomic regional-level factors have been identified. Our empirical results show that: (1) a socioeconomic urban-rural suicide gaps exist; (2) there are significant gender differences, for the women a Mediterranean suicide pattern appears whereas unemployment levels have a significant importance for men, (3) social isolation factors, when significant, they show an (a priori) surprisingly positive result. We provide new highlights for suicide prevention in Spain. Precisely, it is highlighted that jointly policies by gender and attending to vulnerable groups are both necessary.

BackgroundThe health effects of recent economic crises differ markedly by population group. The objective of this systematic review is to examine evidence from longitudinal studies on factors influencing resilience for any health outcome or health behaviour among the general population living in countries exposed to financial crises.MethodsWe systematically reviewed studies from six electronic databases (EMBASE, Global Health, MEDLINE, PsycINFO, Scopus, Web of Science) which used quantitative longitudinal study designs and included: (i) exposure to an economic crisis; (ii) changes in health outcomes/behaviours over time; (iii) statistical tests of associations of health risk and/or protective factors with health outcomes/behaviours. The quality of the selected studies was appraised using the Quality Assessment Tool for Quantitative Studies. PRISMA reporting guidelines were followed.ResultsFrom 14,584 retrieved records, 22 studies met the eligibility criteria. These studies were conducted across 10 countries in Asia, Europe and North America over the past two decades. Ten socio-demographic factors that increased or protected against health risk were identified: gender, age, education, marital status, household size, employment/occupation, income/ financial constraints, personal beliefs, health status, area of residence, and social relations. These studies addressed physical health, mortality, suicide and suicide attempts, mental health, and health behaviours. Women’s mental health appeared more susceptible to crises than men’s. Lower income levels were associated with greater increases in cardiovascular disease, mortality and worse mental health. Employment status was associated with changes in mental health. Associations with age, marital status, and education were less consistent, although higher education was associated with healthier behaviours.ConclusionsDespite widespread rhetoric about the importance of resilience, there was a dearth of studies which operationalised resilience factors. Future conceptual and empirical research is needed to develop the epidemiology of resilience.

Abstract Mortality indicators for Brazilians aged between 10 and 24 years old were analyzed. Data were obtained from the Global Burden of Disease (GBD) 2019 Study, and absolute numbers, proportion of deaths and specific mortality rates from 1990 to 2019 were analyzed, according to age group (10 to 14, 15 to 19 and 20 to 24 years), sex and causes of death for Brazil, regions and Brazilian states. There was a reduction of 11.8% in the mortality rates of individuals aged between 10 and 24 years in the investigated period. In 2019, there were 13,459 deaths among women, corresponding to a reduction of 30.8% in the period. Among men there were 39,362 deaths, a reduction of only 6.2%. There was an increase in mortality rates in the North and Northeast and a reduction in the Southeast and South states. In 2019, the leading cause of death among women was traffic injuries, followed by interpersonal violence, maternal deaths and suicide. For men, interpersonal violence was the leading cause of death, especially in the Northeast, followed by traffic injuries, suicide and drowning. Police executions moved from 77th to 6th place. This study revealed inequalities in the mortality of adolescents and young adults according to sex, causes of death, regions and Brazilian states.

ABSTRACT Objective To describe the epidemiological profile and analyze the time trend of suicide mortality among adolescents (10-19 years old) from the Brazilian Northeast, from 2001 to 2015. Methods This is an observational study, which took place in the Northeast region, Brazil. The study period was from 2001 to 2015. Deaths from intentional self-harm (X60 to X84). exogenous poisoning of undetermined intent (Y10 to Y19) and intentional self-harm (Y87.0) were considered, according to the 10th Review of the International Classification of Diseases (ICD-10), for adolescents aged 10 to 19 years. The variables analyzed were: sex, age group, race / color, specific ICD, state of residence and suicide mortality rate/100,000 inhabitants. Results There were 3,194 deaths due to suicide in the age group studied, with a male predominance (62.1%; n = 1,984), age group 15 to 19 years (84.8%; n = 2,707), race/brown color (65.4%; n = 2,090); between 4 and 7 years of schooling (31.7%; n = 1,011) and at CID X70 (47.8%; n = 1,528). The time trend of mortality was increasing from 2001 to 2015 (APC: 2.4%; p < 0.01), with higher rates in males. There was an increasing trend in the suicide rate, among men, throughout the period (AAPC: 2.9%; p < 0.01). In women, a decreasing trend was identified as of 2004 (APC: -2.2%; p < 0.01). Conclusion The epidemiological profile was characterized by male gender, age group 15-19 years, color/brown race and average schooling. The trend showed a growth pattern in males and a decline in females. It is recommended that public policies are aimed at the adolescent population.

IntroductionMajor depressive disorder (MDD) is a debilitating psychiatric disorder and is strongly associated with suicidal ideation and acute suicidality. While sex differences are evident across nearly all stages of depression, sex-specific mechanisms in acute suicidality remain not fully understood. This gap is notable given that women are twice as likely as men to develop depression, show earlier onset and greater symptom severity, and account for two-thirds of suicide attempts, whereas men have higher suicide completion rates. At the molecular level, sex differences also influence pharmacological treatment response, yet the biological mechanisms underlying these disparities remain not fully understood.MethodsIn an exploratory approach, we investigated genome-wide gene expression changes in peripheral blood from 14 acutely suicidal patients with MDD (seven females, seven males) without comorbid somatic conditions, compared with sex-matched healthy controls. Gene expression profiles, generated using Affymetrix microarrays, were corrected for multiple testing and further examined through Gene Ontology enrichment, Gene Set Enrichment, Weighted Gene Co-expression Network, and Protein–Protein Interaction analyses.Results/DiscussionWhen analyzed as a combined group, suicidal MDD patients exhibited 87 differentially expressed genes (DEGs). However, stratification by sex revealed 665 DEGs in females, whereas no significant DEGs were detected in males. These findings, validated through pathway- and network-level analyses, suggest that previous studies pooling male and female MDD patients may have overlooked sex-specific effects. Nevertheless, given the small group number of patients, it cannot be excluded that the absence of DEGs in males may be due to a coincidental genetic profile of the group. Larger confirmatory studies, or re-analyses of existing datasets with sex-specific stratification, are therefore essential. In female suicidal MDD patients, both single-gene and pathway-oriented analyses highlighted immune and inflammatory processes, particularly the NF-κB pathway, consistent with prior evidence and pointing to additional targets such as tumor necrosis factor–alpha inducible protein 6.ConclusionCollectively, these findings underscore the critical importance of sex-specific molecular research in acutely suicidal MDD patients and may inform the development of more targeted therapeutic approaches.