Name: Weight Among Adults
Creator: Kaggle
License: https://creativecommons.org/licenses/by-sa/4.0/

About this Dataset

Weight Among Adults

Normal Weight, Overweight, and Obesity Among Adults Aged 20 and Over by Selected Population Characteristics

Context:

This dataset provides data on the prevalence of normal weight, overweight, and obesity among adults aged 20 and over, segmented by various population characteristics. The data is sourced from the National Health and Nutrition Examination Survey (NHANES) conducted by the National Center for Health Statistics (NCHS). This dataset is invaluable for understanding the distribution and trends of weight-related health metrics across different demographics in the United States.

Source:

National Health and Nutrition Examination Survey (NHANES): Conducted by NCHS.
Supporting Documentation: Refer to the HUS 2019 Data Finder for detailed definitions, measures, and changes over time.
Appendix Entry: Additional information available in the corresponding Appendix entry.

Source URLs:

Dataset Details and Key Features

This dataset includes data collected over multiple time periods, providing insights into the weight distribution among adults aged 20 and over. Key features include segmentation by sex and specific age ranges.

Key Features:

Time Coverage: Data spans several decades, from 1988-2018.
Demographic Breakdown: Includes data by sex and age groups, allowing for detailed analysis.
Percentage Data: Provides percentage estimates of normal weight, overweight, and obesity.
Standard Error: Includes standard error for each estimate, indicating the precision of the estimates.

Usage

Research and Analysis:

Health Trends: Study trends in weight distribution among different demographic groups.
Public Health Initiatives: Inform public health strategies and interventions targeting obesity and overweight issues.
Socioeconomic Analysis: Analyze the impact of socio-economic factors on weight-related health metrics.

Policy Making:

Policy Development: Develop policies aimed at reducing obesity rates and promoting healthy weight.
Resource Allocation: Allocate resources effectively to areas with higher prevalence of overweight and obesity.
Program Evaluation: Evaluate the effectiveness of past and current public health programs.

Healthcare Planning:

Preventive Measures: Design preventive measures and programs based on demographic data.
Community Outreach: Plan community outreach programs targeting high-risk groups.
Nutritional Guidelines: Inform the creation of nutritional guidelines and recommendations.

Data Maintenance:

Maintainer: National Center for Health Statistics
Publisher: Centers for Disease Control and Prevention
Last Updated: August 29, 2023

Quality Assurance:

Data Validation: Ensures data accuracy through rigorous validation processes.
Consistency Checks: Regular consistency checks to maintain data integrity.

Additional Notes:

For detailed definitions and explanations of measures, refer to the PDF or Excel version of this table in the HUS 2019 Data Finder.
Data is collected from the National Health and Nutrition Examination Survey (NHANES), ensuring comprehensive coverage and reliability.

Columns:

Column Name	Description
INDICATOR	Indicator for the data type, e.g., Normal weight
PANEL	Panel identifier for the survey
PANEL_NU	Numerical value representing the panel
UNIT	Unit of measurement, e.g., Percent of population
UNIT_NU	Numerical value representing the unit
STUB_NA	Stub name for category, e.g., Total
STUB_LA	Label for the stub category, e.g., All persons
YEAR	The year or period the data was recorded
YEAR_NUM	Numerical value representing the year or period
AGE	Age group category, e.g., 20 years and over
AGE_NUM	Numerical value representing the age group
ESTIMATE	Estimated percentage
SE	Standard error of the estimate

Tables

Weight

@kaggle.melissamonfared_weight_among_adults.weight

24.07 kB
3,360 rows
15 columns

CREATE TABLE weight (
  "indicator" VARCHAR,
  "panel" VARCHAR,
  "panel_num" BIGINT,
  "unit" VARCHAR,
  "unit_num" BIGINT,
  "stub_name" VARCHAR,
  "stub_name_num" BIGINT,
  "stub_label" VARCHAR,
  "stub_label_num" DOUBLE,
  "year" VARCHAR,
  "year_num" BIGINT,
  "age" VARCHAR,
  "age_num" DOUBLE,
  "estimate" DOUBLE,
  "se" DOUBLE
);