Alcohol Use reports an estimated average percent of people who consumed alcohol by type of use and by age range. For the purpose of these data, binge use of alcohol was defined as drinking five or more drinks on the same occasion; i.e. at the same time or within a couple of hours. Dependence is defined consistent with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) definition as:Spending a lot of time engaging in activities related to substance useUsing a substance in greater quantities or for a longer time than intendedDeveloping tolerance (i.e., needing to use the substance more than before to get desired effects or noticing that the same amount of substance use had less effect than before)Making unsuccessful attempts to cut down on useContinuing substance use despite physical health or emotional problems associated with substance useReducing or eliminating participation in other activities because of substance useExperiencing withdrawal symptoms.Similarly, Abuse is also defined consistent with the DSM-IV definition as the following lifestyle symptoms due to the use of illicit drugs in the past 12 months:Experiencing problems at work, home, and schoolDoing something physically dangerousExperiencing Repeated trouble with the lawExperiencing Problems with family or friends

The alcohol consumption per capita ranking is led by Romania with ***** liters, while Georgia is following with ***** liters. In contrast, Bangladesh is at the bottom of the ranking with **** liters, showing a difference of ***** liters to Romania. Depicted is the estimated alcohol consumption in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This layer represents the Percent of Adults who Binge Drink calculated from the 2014-2017 Colorado Behavioral Risk Factor Surveillance System (County or Regional Estimates) data set. These data represent the estimated prevalence of Binge Drinking among adults (Age 18+) for each county in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Regional estimates were used if there was not enough sample size to calculate a single county estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).

Table of INEBase Average consumption of alcoholic beverages (in grams of pure alcohol) by sex and age group. Average and standard deviation. Population aged 15 years old and over that consumes alcoholic beverages one or more days per week. National. European Health Survey

The prevalence of the metabolic syndrome is rising worldwide. Its association with alcohol intake, a major lifestyle factor, is unclear, particularly with respect to the influence of drinking with as opposed to outside of meals. We investigated the associations of different aspects of alcohol consumption with the metabolic syndrome and its components. In cross-sectional analyses of 14,375 active or retired civil servants (aged 35–74 years) participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we fitted logistic regression models to investigate interactions between the quantity of alcohol, the timing of its consumption with respect to meals, and the predominant beverage type in the association of alcohol consumption with the metabolic syndrome. In analyses adjusted for age, sex, educational level, income, socioeconomic status, ethnicity, smoking, body mass index, and physical activity, light consumption of alcoholic beverages with meals was inversely associated with the metabolic syndrome (≤4 drinks/week: OR = 0.85, 95%CI 0.74–0.97; 4 to 7 drinks/week: OR = 0.75, 95%CI 0.61–0.92), compared to abstention/occasional drinking. On the other hand, greater consumption of alcohol consumed outside of meals was significantly associated with the metabolic syndrome (7 to 14 drinks/week: OR = 1.32, 95%CI 1.11–1.57; ≥14 drinks/week: OR = 1.60, 95%CI 1.29–1.98). Drinking predominantly wine, which occurred mostly with meals, was significantly related to a lower syndrome prevalence; drinking predominantly beer, most notably when outside of meals and in larger quantity, was frequently associated with a greater prevalence. In conclusion, the alcohol—metabolic syndrome association differs markedly depending on the relationship of intake to meals. Beverage preference—wine or beer—appears to underlie at least part of this difference. Notably, most alcohol was consumed in metabolically unfavorable type and timing. If further investigations extend these findings to clinically relevant endpoints, public policies should recommend that alcohol, when taken, should be preferably consumed with meals.

Deaths from alcohol-related conditions, all ages, directly age-standardised rate per 100,000 population (standardised to the European standard population).

Rationale Alcohol consumption is a contributing factor to hospital admissions and deaths from a diverse range of conditions. Alcohol misuse is estimated to cost the NHS about £3.5 billion per year and society as a whole £21 billion annually.

The Government has said that everyone has a role to play in reducing the harmful use of alcohol - this indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related deaths can be reduced through local interventions to reduce alcohol misuse and harm.

The proportion of disease attributable to alcohol (alcohol attributable fraction) is calculated using a relative risk (a fraction between 0 and 1) specific to each disease, age group, and sex combined with the prevalence of alcohol consumption in the population. All mortality records are extracted that contain an attributable disease and the age and sex-specific fraction applied. The results are summed into quinary age bands for the numerator and a directly standardised rate calculated using the European Standard Population. This revised indicator uses updated alcohol attributable fractions, based on new relative risks from ‘Alcohol-attributable fractions for England: an update’ (1) published by PHE in 2020. A detailed comparison between the 2013 and 2020 alcohol attributable fractions is available in Appendix 3 of the PHE report (2). A consultation was also undertaken with stakeholders where the impact of the new methodology on the LAPE indicators was quantified and explored (3).

The calculation that underlies all alcohol-related indicators has been updated to take account of the latest academic evidence and more recent alcohol-consumption figures. The result has been that the newly published mortality and admission rates are lower than those previously published. This is due to a change in methodology, mainly because alcohol consumption across the population has reduced since 2010. Therefore, the number of deaths and hospital admissions that we attribute to alcohol has reduced because in general people are drinking less today than they were when the original calculation was made.

Figures published previously did not misrepresent the burden of alcohol based on the previous evidence – the methodology used in this update is as close as sources and data allow to the original method. Though the number of deaths and admissions attributed to alcohol each year has reduced, the direction of trend and the key inequalities due to alcohol harm remain the same. Alcohol remains a significant burden on the health of the population and the harm alcohol causes to individuals remains unchanged.

References:

PHE (2020) Alcohol-attributable fractions for England: an update PHE (2020) Alcohol-attributable fractions for England: an update: Appendix 3 PHE (2021) Proposed changes for calculating alcohol-related mortality

Definition of numerator Deaths from alcohol-related conditions based on underlying cause of death, registered in the calendar year for all ages. Each alcohol-related death is assigned an alcohol attributable fraction based on underlying cause of death (and all cause of deaths fields for the conditions: ethanol poisoning, methanol poisoning, toxic effect of alcohol). Alcohol-attributable fractions were not available for children.

Mortality data includes all deaths registered in the calendar year where the local authority of usual residence of the deceased is one of the English geographies and an alcohol attributable diagnosis is given as the underlying cause of death. Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: MUSE implementation guidance.

Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: IRIS implementation guidance.

Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change, and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change, and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at: 2011 implementation guidance.

Definition of denominator ONS mid-year population estimates aggregated into quinary age bands.

Caveats There is the potential for the underlying cause of death to be incorrectly attributed on the death certificate and the cause of death misclassified. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator.

The confidence intervals do not take into account the uncertainty involved in the calculation of the AAFs – that is, the proportion of deaths that are caused by alcohol and the alcohol consumption prevalence that are included in the AAF formula are only an estimate and so include uncertainty. The confidence intervals published here are based only on the observed number of deaths and do not account for this uncertainty in the calculation of attributable fraction - as such the intervals may be too narrow.

These data represent the predicted (modeled) prevalence of Heavy Drinking among adults (Age 18+) for each census tract in Colorado. Heavy Drinking is defined for males as having 15 or more drinks per week and for females as having 8 or more drinks per week. Heavy Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Annual data for Great Britain on teetotalism, drinking in the week before survey interview, frequent drinking and units drunk, including analysis by sex, age and socioeconomic status.

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

This dataset presents the footprint of the crude percentage of adults who consume more than two standard drinks per day on average. Lifetime risky alcohol consumption is defined as those adults who consume more than two standard drinks per day on average, thereby increasing their lifetime risk. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data.

Alcohol consumption in India amounted to about *****billion liters in 2020 and was estimated to reach about **** billion liters by 2024. The increase in the consumption of these beverages can be attributed to multiple factors, including the rising levels of disposable income and a growing urban population, among others.   Alcohol market in India India’s alcohol market consisted of two main kinds of liquor – Indian made Indian liquor or IMIL, and Indian made foreign liquor or IMFL. This was in addition to beer, wine, and other imported alcohol. Country liquor accounted for the highest market share, while spirits took up the majority of the consumption market. Young consumers Although the average per-adult intake of alcohol was considerably lower in India when compared to other countries such as the United States, heavy drinkers among young Indians were more prevalent. Men were more likely to drink than women by a large margin and were also more prone to episodic drinking. According to a study, over ** percent of Indians aged under 25 purchase or consume alcoholic beverages even though it is illegal. This was despite bans on alcohol in some states across the country and limitations on sales in some others.

The Food Consumption Survey maps out what the Netherlands eats and drinks. The food consumption data provide insight into the consumption of foodstuffs, the intake of macro- and micronutrients, the intake of potentially harmful chemical substances and the development thereof (trends). The figures in this table come from the Food Consumption Survey (VCP) 2012-2016 of the National Institute for Public Health and the Environment (RIVM). This table contains data on the intake and consumption of foodstuffs in grams per day of people aged 1 to 80 years. The data are broken down by sex and age and also for different population groups. Not all breakdowns are available, then blank lines are shown. The available breakdowns are: - Total population - Age in 2 groups - Sex in 2 groups - Age and sex in 4 groups - Age and sex in 16 groups - Education by age and sex in 4 groups - Region - Region by sex - Region by age in 2 groups - Region by age and sex in 4 groups - Urbanization - Urbanization by sex - Urbanization by age in 2 groups - Urbanization by age and sex in 4 groups In addition, the age groups for alcohol intake are based on 14 to 80 year olds. The nutritional data were collected from approximately 4,000 children and adults with two 24-hour nutritional surveys. More information about the study can be found on the website www.wateetnederland.nl (see also the publications on this website). Statline contains a number of tables with information. [English] 1. EXPLANATION The Food Consumption Survey charts the food consumption in the Netherlands. The food consumption data provide insight into consumption, the intake of macro and micronutrients, the intake of potential chemical substances and its changes in time. The figures in this table are from the Food Consumption Survey (VCP) 2012-2016 of the National Institute for Public Health and the Environment (RIVM). This table contains data on the name of food intake in grams per day of people aged 1 to 80 years. The data are broken down by gender and age and different population groups. Not all the breakdowns are available, if not, then empty lines are shown in the table. The available breakdowns are: - Total population - Age in 2 groups - Sex in 2 groups - Age and Sex in 4 groups - Age and Sex in 16 groups - Education broken down by age and sex in 4 groups - Region - Region broken down by sex - Region broken down by age in 2 groups - Region broken down age and sex in 4 groups - Urbanization - Urbanization broken down by sex - Urbanization broken down by age in 2 groups - Urbanization broken down by age and sex in 4 groups Another matter is that the age groups for intake of alcohol are based on age 14-80 years. The data have been collected from around 4,000 children and adults with two 24-hour dietary recalls. More information absout the study can be found on the website www.wateetnederland.nl (see also publications on this website) or in the other tables on RIVM Statline. Data available: 2012-2016 [Nederlands] Data available from: 2012-2016 Status of the figures: Version 2019-06 [Nederlands] Status of the data: Version 2019-06 When will new figures be released? In 2022. The next survey will run from 2019 to 2021. [Nederlands] When are the data updated? In 2022. The next survey will be performed from 2019 till 2021.

This layer represents the Percent of Adults who Drink Heavily calculated from the 2014-2017 Colorado Behavioral Risk Factor Surveillance System (County or Regional Estimates) data set. These data represent the estimated prevalence of Heavy Drinking among adults (Age 18+) for each county in Colorado. Heavy Drinking is defined for males as having 15 or more drinks per week and for females as having 8 or more drinks per week. Heavy Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Regional estimates were used if there was not enough sample size to calculate a single county estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).

This synthetic Kidney Disease Dataset is designed for educational and research purposes in the fields of data science, healthcare, and public health. The dataset contains essential features such as age, gender, medical history, lifestyle factors, and various health metrics to study and predict the onset and progression of kidney disease.

Dataset Features

• patient_id: Unique identifier for each individual in the dataset.
• age: Age of the individual.
• gender: Gender of the individual (e.g., Male, Female).
• ethnicity: Ethnic background of the individual (e.g., Caucasian, Asian).
• socioeconomic_status: Socioeconomic status of the individual (e.g., High, Middle, Low).
• education_level: Highest level of education attained by the individual (e.g., High School, Bachelor's).
• bmi: Body Mass Index (BMI) of the individual.
• smoking: Whether the individual is a smoker ("Yes" or "No").
• alcohol_consumption: Frequency of alcohol consumption (e.g., daily, weekly).
• physical_activity: Level of physical activity (numeric value).
• diet_quality: Quality of the individual's diet (numeric value).
• sleep_quality: Quality of the individual's sleep (numeric value).
• family_history_kidney_disease: Whether the individual has a family history of kidney disease.
• family_history_hypertension: Whether the individual has a family history of hypertension.
• family_history_diabetes: Whether the individual has a family history of diabetes.
• previous_acute_kidney_injury: Whether the individual has a history of acute kidney injury.
• urinary_tract_infections: Whether the individual has a history of urinary tract infections.
• systolic_bp: Systolic blood pressure value.
• diastolic_bp: Diastolic blood pressure value.
• fasting_blood_sugar: Fasting blood sugar level.
• hb_a1c: Hemoglobin A1c level.
• serum_creatinine: Serum creatinine level, an indicator of kidney function.
• bunlevels: Blood urea nitrogen (BUN) levels.
• gfr: Glomerular filtration rate, an indicator of kidney function.
• protein_in_urine: Amount of protein present in the urine.
• acr: Albumin-to-creatinine ratio in urine.
• serum_electrolytes_sodium: Sodium levels in serum electrolytes.
• serum_electrolytes_potassium: Potassium levels in serum electrolytes.
• serum_electrolytes_calcium: Calcium levels in serum electrolytes.
• serum_electrolytes_phosphorus: Phosphorus levels in serum electrolytes.
• hemoglobin_levels: Hemoglobin levels.
• cholesterol_total: Total cholesterol level.
• cholesterol_ldl: Low-density lipoprotein (LDL) cholesterol level.
• cholesterol_hdl: High-density lipoprotein (HDL) cholesterol level.
• cholesterol_triglycerides: Triglycerides level in the blood.
• aceinhibitors: Whether the individual is on ACE inhibitors.
• diuretics: Whether the individual is on diuretics.
• nsaids_use: Whether the individual uses NSAIDs.
• statins: Whether the individual is on statins.
• antidiabetic_medications: Whether the individual is on antidiabetic medications.
• edema: Whether the individual experiences edema (fluid retention).
• fatigue_levels: Level of fatigue experienced by the individual.
• nausea_vomiting: Whether the individual experiences nausea or vomiting.
• muscle_cramps: Whether the individual experiences muscle cramps.
• itching: Whether the individual experiences itching.
• quality_of_life_score: A score indicating the individual's overall quality of life.
• heavy_metals_exposure: Whether the individual has been exposed to heavy metals.
• occupational_exposure_chemicals: Whether the individual has been exposed to chemicals at work.
• water_quality: Quality of the water consumed by the individual.
• medical_checkups_frequency: Frequency of medical checkups attended by the individual.
• medication_adherence: Adherence to prescribed medications.
• health_literacy: The individual’s understanding of health information.
• diagnosis: The final diagnosis of the individual, indicating whether they have kidney disease or not.

Distribution

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Usage

This dataset is ideal for a range of applications:

• Kidney Disease Prediction: Build machine learning models to predict the likelihood of kidney disease based on contributing factors.
• Risk Factor Analysis: Analyze which factors (e.g., medical conditions, lifestyle choices) are associated with higher risks of developing kidney disease.
• Kidney Function Analysis: Study the relationship between kidney function indicators (e.g., serum creatinine, GFR) and other healt

This dataset contains medical and demographic data for a cohort of 53,477 patients, which is designed to help predict the risk of developing End-Stage Renal Disease (ESRD) based on various features. The dataset includes both training and testing subsets (70% training and 30% testing). Each row corresponds to an individual patient’s medical history, demographics, comorbidities, laboratory values, medications, and ESRD risk outcome.

Columns: Patient ID: Unique identifier for each patient. Dataset Split: Indicates whether the patient data is part of the training or testing set for the model. Age: The age of the patient (in years), generated using a normal distribution with a mean of 67.05 and standard deviation of 10. Gender: The patient's gender (either "Male" or "Female"). Smoking: Indicates whether the patient smokes ("Yes" or "No"). Alcohol: Indicates whether the patient consumes alcohol ("Yes" or "No"). Hypertension: Indicates whether the patient has hypertension ("Yes" or "No"). Coronary Artery Disease: Indicates whether the patient has coronary artery disease ("Yes" or "No"). Cancer: Indicates whether the patient has a history of cancer ("Yes" or "No"). Chronic Liver Disease: Indicates whether the patient has chronic liver disease ("Yes" or "No"). Diabetic Retinopathy: Indicates whether the patient has diabetic retinopathy ("Yes" or "No"). Baseline Serum Creatinine (mg/dL): The baseline serum creatinine level, a marker of kidney function, measured in mg/dL. Mean Serum Creatinine (mg/dL): The mean serum creatinine level, also a marker of kidney function, measured in mg/dL. Cholesterol (mg/dL): The cholesterol level, measured in mg/dL. Triglyceride (mg/dL): The triglyceride level, measured in mg/dL. LDL-C (mg/dL): The low-density lipoprotein cholesterol (LDL-C) level, measured in mg/dL. HDL-C (mg/dL): The high-density lipoprotein cholesterol (HDL-C) level, measured in mg/dL. Uric Acid (mg/dL): The uric acid level, measured in mg/dL. Calcium (mg/dL): The calcium level, measured in mg/dL. Phosphate (mg/dL): The phosphate level, measured in mg/dL. Hemoglobin (g/dL): The hemoglobin level, measured in grams per deciliter. Albumin (g/dL): The albumin level, a protein in blood, measured in grams per deciliter. HS-CRP (mg/dL): High-sensitivity C-reactive protein level, a marker of inflammation, measured in mg/dL. HbA1c (%): The hemoglobin A1c percentage, an indicator of long-term blood sugar levels. Glucose (mg/dL): The glucose level in the blood, measured in mg/dL. NSAID: Whether the patient is using nonsteroidal anti-inflammatory drugs (NSAIDs) ("Yes" or "No"). Statin: Whether the patient is using statin medications ("Yes" or "No"). Metformin: Whether the patient is using metformin, typically for diabetes treatment ("Yes" or "No"). Insulin: Whether the patient is using insulin ("Yes" or "No"). Dipeptidyl Peptidase-4 Inhibitor: Whether the patient is using DPP-4 inhibitors, another class of medication for diabetes ("Yes" or "No"). ESRD Risk: The target variable indicating whether the patient is at risk of developing End-Stage Renal Disease ("Yes" or "No"). This is the outcome variable for predictive modeling.

The National Institute of Mental Health (NIMH) Research Volunteer (RV) Data Set

A comprehensive dataset characterizing healthy research volunteers in terms of clinical assessments, mood-related psychometrics, cognitive function neuropsychological tests, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unprecedented in its depth of characterization of a healthy population and will allow a wide array of investigations into normal cognition and mood regulation.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Release Notes

Release v2.0.0

This release includes data collected between 2020-06-03 (cut-off date for v1.0.0) and 2024-04-01. Notable changes in this release:

1. 769 new participants have been added along with re-evaluation data for 15 participants. Total unique participants count is now 1859.
2. visit and age_at_visit columns added to phenotype files to distinguish between visits and intervals between them.
3. Follow-up online survey data included.
4. Replaced Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) with General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively.
5. Discontinued the Perceived Health rating survey.
6. Added Brief Trauma Questionnaire (BTQ) and Big Five personality survey to online screening questionnaires.
7. MRI:
   • Replaced ADNI-3 resting state sequence with a multi-echo sequence with higher spatial resolution.
   • Replaced field map scans with a shorter reversed-blipped EPI scan.
8. MEG:
   • Some participants have 6-minute empty room data instead of the shorter duration empty room acquisition.

See the CHANGES file for complete version-wise changelog.

Participant Eligibility

To be eligible for the study, participants need to be medically healthy adults over 18 years of age with the ability to read, speak and understand English. All participants provided electronic informed consent for online pre-screening, and written informed consent for all other procedures. Participants with a history of mental illness or suicidal or self-injury thoughts or behavior are excluded. Additional exclusion criteria include current illicit drug use, abnormal medical exam, and less than an 8th grade education or IQ below 70. Current NIMH employees, or first degree relatives of NIMH employees are prohibited from participating. Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers visit the study website, check a box indicating consent, and fill out preliminary screening questionnaires. The questionnaires include basic demographics, the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure, the DSM-5 Level 2 Cross-Cutting Symptom Measure - Substance Use, the Alcohol Use Disorders Identification Test (AUDIT), the Edinburgh Handedness Inventory, and a brief clinical history checklist. The WHODAS 2.0 is a 15 item questionnaire that assesses overall general health and disability, with 14 items distributed over 6 domains: cognition, mobility, self-care, “getting along”, life activities, and participation. The DSM-5 Level 1 cross-cutting measure uses 23 items to assess symptoms across diagnoses, although an item regarding self-injurious behavior was removed from the online self-report version. The DSM-5 Level 2 cross-cutting measure is adapted from the NIDA ASSIST measure, and contains 15 items to assess use of both illicit drugs and prescription drugs without a doctor’s prescription. The AUDIT is a 10 item screening assessment used to detect harmful levels of alcohol consumption, and the Edinburgh Handedness Inventory is a systematic assessment of handedness. These online results do not contain any personally identifiable information (PII). At the conclusion of the questionnaires, participants are prompted to send an email to the study team. These results are reviewed by the study team, who determines if the participant is appropriate for an in-person interview.

Participants who meet all inclusion criteria are scheduled for an in-person screening visit to determine if there are any further exclusions to participation. At this visit, participants receive a History and Physical exam, Structured Clinical Interview for DSM-5 Disorders (SCID-5), the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The purpose of these cognitive and psychometric tests is two-fold. First, these measures are designed to provide a sensitive test of psychopathology. Second, they provide a comprehensive picture of cognitive functioning, including mood regulation. The SCID-5 is a structured interview, administered by a clinician, that establishes the absence of any DSM-5 axis I disorder. The KBIT-2 is a brief (20 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Biological and physiological measures

Biological and physiological measures are acquired, including blood pressure, pulse, weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), c-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, three additional tubes of blood samples are collected and banked for future analysis, including genetic testing.

Imaging Studies

Participants were given the option to enroll in optional magnetic resonance imaging (MRI) and magnetoencephalography (MEG) studies.

MRI

On the same visit as the MRI scan, participants are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks asses attention and executive functioning (Flanker Inhibitory Control and Attention Task), executive functioning (Dimensional Change Card Sort Task), episodic memory (Picture Sequence Memory Task), and working memory (List Sorting Working Memory Task). The MRI protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed, and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional, and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

MEG

The optional MEG studies were added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system. The position of the head was localized at the beginning and end of the recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For some participants, photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants, a BrainSight neuro-navigation unit was used to coregister the MRI, anatomical fiducials, and localizer coils directly prior to MEG data acquisition.

Specific Survey and Test Data within Data Set

NOTE: In the release 2.0 of the dataset, two measures Brief Trauma Questionnaire (BTQ) and Big Five personality survey were added to the online screening questionnaires. Also, for the in-person screening visit, the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) were replaced with the General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. The Perceived Health rating survey was discontinued.

1. Preliminary Online Screening Questionnaires

Survey or Test	BIDS TSV Name
Alcohol Use Disorders Identification Test (AUDIT)	audit.tsv
Brief Trauma Questionnaire (BTQ)	btq.tsv
Big-Five Personality	big_five_personality.tsv
Demographics	demographics.tsv
Drug Use Questionnaire

Colorado County BRFSS Binge Drinking Prevalence represents the Percent of Adults who Binge Drink calculated from the 2018-2022 Colorado Behavioral Risk Factor Surveillance System (County Estimates) data set. These data represent the estimated prevalence of Binge Drinking among adults (Age 18+) for each county in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Data is suppressed if there was not enough data to calculate a reliable estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2018-2022). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. COUNTY (County Name)FULL (Full County Name)LABEL (Proper County Name)County FIPS (County FIPS Code as String)NUM FIPS (County FIPS Code as Number)CENT LAT (County Centroid Latitude)CENT LONG (County Centroid Longitude)US FIPS (Full FIPS Code)Binge Percent (County estimate for prevalence of Binge Drinking among adults Age 18+)Lower Confidence Limit (Lower 95% Confidence Interval for Binge Percent Value)Upper Confidence Limit (Upper 95% Confidence Interval for Binge Percent Value)Years (2018-2022)

aExposure categories were: Abstainer, a person not having had a drink containing alcohol within the last year; DI 0–19.99 g of pure alcohol daily (females) and 0–39.99 g (males); DII, 20–39.99 g (females) and 40–59.99 g (males); and DIII, >40 g (females) and >60 g (males). Binge drinking was defined as having at least one occasion of five or more drinks in the last month. For IHD, the categories refer to non-binge drinkers.bFor these risk factor–disease pairs, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk from smoking and the same disease to estimate RRs for each age category.cThis category includes ICD-9 codes 210–239.dThese odds ratios were used to estimate PAF as described in the Methods section.eUsed to estimated PAF for having drunk alcohol in the last 6 h before injury.

aExposure categories were: Abstainer, a person not having had a drink containing alcohol within the last year; DI 0–19.99 g of pure alcohol daily (females) and 0–39.99 g (males); DII, 20–39.99 g (females) and 40–59.99 g (males); and DIII, >40 g (females) and >60 g (males). Binge drinking was defined as having at least one occasion of five or more drinks in the last month. For IHD, the categories refer to non-binge drinkers.bFor these risk factor–disease pairs, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk from smoking and the same disease to estimate RRs for each age category.cThis category includes ICD-9 codes 210–239.dThese odds ratios were used to estimate PAF as described in the Methods section.eUsed to estimated PAF for having drunk alcohol in the last 6 h before injury.

Variables: id number, age, sex, level of school education, profession, marital status, weight, height, systolic blood pressure, diastolic blood pressure, low physical activity practice (yes = 1, no = 2), Low fruit and vegetable intake (yes = 1, no = 2), smoking (yes = 1, no = 2), alcohol consumption (yes = 1, no = 2), history of high total blood cholesterol (yes = 1, no = 2), history of diabetes (yes = 1, no = 2). (XLSX)