data

MEPS Tutorial - Some of my helpful notes

There are a lot of lessons that I’ve learned from using the Medical Expenditure Panel Survey (MEPS) data from the Agency for Healthcare Research and Quality (AHRQ). Some of these I learned after I made some mistakes and some I learned from other people. Overall, it’s a short but evolving note of the things that I’ve learned about MEPS and its nuances. I plan on updating this in the future as I expect to learn more new things. But for those who are interested in learning what I’ve learned, you can read my notes on my RPubs page, which is here.

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MEPS Tutorial - Part 3: Applying survey weights using R

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In this tutorial, we will review how survey weights from the Medical Expenditure Panel Survey (MEPS) are applied using R.

The tutorial is available on my GitHub site and RPubs.

The R Markdown code I used to generate this tutorial is available on my GitHub site.

Medical Expenditure Panel Survey (MEPS) Guide - Part 1

INTRODUCTION

Medical Expenditure Panel Survey (MEPS) is a publicly available dataset on healthcare expenditures that is representative of the US population.

The MEPS homepage contains vital information about the methods used to validate household responses and guides on how to properly use these data for research or exploration. You can learn about MEPS in its background section.  

MEPS data files are available for download here. The most important file is the Full Year Consolidated Data Files, which contains the data for unique household responses on their characteristics and expenditures. These data are great practice for those interested in learning more about MEPS. Each of the Full Year Consolidated Data Files contain information about the data in the form of Documentation and Code Books. For example, the 2017 Full Year Consolidated Data Files Documentation and Code Book are located here.

If you area a Stata user, there are Stata programming statements available to copy and paste into a Stata *.do file. These programming statements are used to transform the MEPS data into a *.dta file that is usable by Stata. Follow the instructions in the programming statement to properly transform the MEPS data. This is similar to an extract-transform-load (ETL) process.

MEPS has a library of reports that uses its data. You can search for topics using their search engine. For example, Report #43 describes the annual opioid usage among adults treated for conditions associated with pain versus other conditions from 2013 to 2015.

Other examples of MEPS data being used in research include the following:

  1. Hamad R, Niedzwiecki MJ. The short-term effects of the earned income tax credit on health care expenditures among US adults. Health Serv Res. 2019 Dec;54(6):1295-1304. doi: 10.1111/1475-6773.13204. Epub 2019 Sep 30.

  2.  Watanabe JH. Examining the Pharmacist Labor Supply in the United States: Increasing Medication Use, Aging Society, and Evolution of Pharmacy Practice. Pharmacy (Basel). 2019 Sep 19;7(3). pii: E137. doi: 10.3390/pharmacy7030137.

  3.  Bounthavong M, Li M, Watanabe JH. An evaluation of health care expenditures in Crohn's disease using the United States Medical Expenditure Panel Survey from 2003 to 2013. Res Social Adm Pharm. 2017 May - Jun;13(3):530-538. doi: 10.1016/j.sapharm.2016.05.042. Epub 2016 May 20.