Introduction. The analysis looks at mental and physical health data from 2000-2019 from various sources the main one being the World Health Organization (WHO).

Task: Analyze health data to gain insights into current consumers health patterns globally and in Kenya to be utilized to make data driven decisions.

Stakeholders: -Company founders and C-suite teams. -Human Resource and Mental Health Professionals. -Government policy makers.

Analysis Objectives: -What is the trend in global and local consumer mental and physical health? -How can these trends influence public and corporate strategies?

ROCCC of Data: A good data source is ROCCC which stands for Reliable, Original, Comprehensive, Current, and Cited.

-Reliablity — High — The data comes from global population sample data sources.

-Originality — LOW — Third party provider (WHO).

-Comprehensive — HIGH — There are several variables summarized into between 1,700-10,980 observations for a period of over 15 years which was fairly comprehensive.

-Current — MID — Data is 3 years old and may not be as relevant as there is no covid data updated to it.

-Cited — HIGH — Data collected from a reliable third party that comprehensively reports its data collection process publicly.

Overall, the dataset is good quality data however its recommended that an updated analysis be done on the health trends during and post-covid.

Key Insights

-There is a higher average suicide rate in men than women both globally and also in Kenya.

-Kenya has a higher average suicide rate for both genders compared to the global average as at 2019.

-The average probability of death between the age of 30 to 70 from from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been decreasing since 2008 however an increase has been observed since 2016.

-There has been a significant increase in the prevalence of alcohol and substance use disorder in Kenya, moreover, the prevalence in the country increases as the prevalence of anxiety disorders, eating disorders and schizophrenia increases according to the Kenyan correlation heat map.

-As evident on the correlation heat map the prevalence various mental health issues have an impact on each other.

-The global probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease has been falling significantly since the 2000s, however, its only been steadily decreasing in Kenya. Men are also at a higher risk of death from these diseases compared to women both globally and locally in Kenya.

-The probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been observed to be significantly inversely proportional to the prevalence of alcohol, substance use anxiety and eating disorders.

-Suicide rates have been observed to not have a significant direct relationship with any mental health disorders both globally and locally however the most significant correlation is the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in the global analysis.

-Globally a significant inverse relationship between road traffic death rate and eating disorders has been observed however there is a slightly significant relationship between depressive disorders and road traffic death which should be an indicator for further research.

-In Kenya, its been observed that road traffic deaths are inversely proportional to the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease but directly proportional to eating, anxiety, alcohol and substance use disorders.

-Depressive disorders is the most significant variable that has an impact on suicide rates in Kenya therefore further study can look into the impact of depression on attempted and reported suicide cases and other factors that may influence suicide as it has been on the rise in Kenya.

-Road traffic accidents have a significant impact of the mental health of several Kenyans.

Recommendations.

-There should be more education regarding suicide prevention for NGOs.

-Corporate firms should look into providing observed health insurance and mental health days off in addition to more sick days for the affected.

-The government can implement policies and programs that provide more efficient facilities for the handling of observed health issues.

-Insurance companies can restructure their products around the knowledge that mental health issues in Kenya have a significant direct relationship to each other and also that the prevalence of alcohol and substance use critically impacts the road traffic death rate in Kenya.

-The government should critically look at the increase in the prevalence of alcohol...

Contains covariate data for "The association between alcohol consumption per capita and suicide mortality across 30 European countries" which were extracted from the Pew Research Center (pewresearch.org), World Bank Group (worldbank.org), and Eurostat (ec.europa.eu/eurostat). Also contains dummy variables to represent: the 2008 global economic recession, changes from ICD-9 to ICD-10, and the COVID-19 pandemic. All covariates which were initially considered are included in this dataset. However, data were further cleaned according to methods described in the associated publication prior to analysis. Within the dataset: edu = Educational attainment (completion of post-secondary or equivalent) lit = Literacy, adult total (% of people ages 15 and above) unemp = Unemployment, total (% of total labor force) (modeled ILO estimate) divorce = Divorce rate migration = Net migration rate relig.muslim = Proportion of the population who identified as Muslim relig.buddhist = Proportion of the population who identified as Buddhist lff = Female labour force participation (% of total labor force) gdp = Gross domestic product based on purchasing power parity (GDP (PPP)) gini = Gini index density = Population density urban = Proportion of the population living in urban areas recession, covid, icd: Dummy variables detailed above.

This dataset consists of data from INEGI, a government statistical institution in Mexico. I wanted to know how the normal trend was coming and how much the covid pandemic in Mexico had an impact on the data that I present in this dataset.

The columns in this dataset are:

• Year: It is the year of the population census

• Total_Population: Total amount of population in the country

• Men_pop: The number of men in the national population

• Women_Pop: The number of women in the national population

• Total_Births: Total number of births in the country

• Men_births: Number of men born in the country

• Women_births: Number of women born in the country

• Death_rate: Annual death rate in the country

• Men_DeathRate: Number of deaths of men annually

• Women_DeathRate: Number of deaths of women annually

• Marriage index: Is the number of marriages per year

• Divorce_rate: Is the number of divorces per year

• suicide_rate: Is the number of suicides per year

• men_ suicide _rate: Is the number of male suicides per year

• women_ suicide _rate: Is the number of female suicides per year

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.

Adjusted estimated differences in country-level monthly suicide rates per 100,000 population during COVID-19 pandemic compared to same months in the pre-pandemic periods.

The information presented here is compiled from the Cook County Medical Examiner’s Office.The data sets include information from deaths starting in August 2014 to the present, with information updated daily.It contains information about deaths that occurred in Cook County that were under the Medical Examiner’s jurisdiction. Not all deaths that occur in Cook County are reported to the Medical Examiner or fall under the jurisdiction of the Medical Examiner.Effective April 1, 2022, the Cook County Medical Examiner’s Office no longer takes jurisdiction over hospital, nursing home or hospice COVID-19 deaths unless there is another factor that falls within the Office’s jurisdiction. Data continues to be collected for COVID-19 deaths in Cook County on the Illinois Dept. of Public Health COVID-19 dashboard (https://dph.illinois.gov/covid19/data.html).The Medical Examiner’s Office determines cause and manner of death for those cases that fall under its jurisdiction.Cause of death describes the reason the person died.Manner of death falls under one of five categories:· Homicide· Suicide· Natural· Accident· UndeterminedThe information posted here may be graphic in nature and may not be appropriate for all users.Published 11/21/17 and updated daily.