Version v1

Companion Files for Big Data for Epidemiology: Applied Data Analysis Using National Health Surveys

Kindratt, Tiffany

Description

This repository includes all companion files (syntax, data files, etc.) for the open textbook, Big Data for Epidemiology: Applied Data Analysis Using National Health Surveys. This textbook was developed based on a need to develop open education resources to train future public health professionals how to conduct applied data analysis using secondary national health surveys. A recent study of local health departments demonstrates that MPH graduates may not be prepared for analytical/assessment competencies, including a lack of knowledge, skills and abilities for data collection, database management, data cleaning, quantitative data analysis/statistics, and data analysis using SAS statistical software. A lack of MPH program course offerings and structured curriculum materials available for developing applied data analysis courses may contribute to the limited analytical/assessment competencies among recent graduates.

The open textbook includes details on how to analyze public-use data from five common national health surveys, including the National Health Interview Survey (NHIS), Medical Expenditure Panel Survey (MEPS), Health Information National Trends Survey (HINTS), Behavior Risk Factor Surveillance Survey (BRFSS) and National Health and Nutrition and Examination Survey (NHANES).

This data repository includes accompanying SAS syntax and data files for:
Chapter 4 Basic Data AnalysisChapter 5 Complex Survey Design FeaturesChapter 6 National Health Interview Survey (NHIS)Chapter 7 Medical Expenditure Panel Survey (MEPS)Chapter 8 Health Information National Trends Survey (HINTS)Chapter 9 Behavioral Risk Factor Surveillance System (BRFSS)Chapter 10 National Health and Nutrition Examination Survey (NHANES)

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.5

FAIR Score

69%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

ICPSR - Interuniversity Consortium for Political and Social Research

Assigned Domain

Subfield

Artificial Intelligence

Field

Computer Science

Domain

Physical Sciences

Confidence Score

54%

Source

Scholar Data Model

Normalization Factors

FT

43.27

CTw

1.00

MTw

1.00