Epidemiology

, ,

Performing inflation adjustment with price indices in economic analysis

, ,

Introduction

Generally, when I use cost data from the past, I tend to perform some kind of inflation adjustment. That’s because the value of $1 is not the same today as it was a year ago. Therefore, we need an approach to adjust the value of past costs into today’s terms. We think of past costs as nominal (or unadjusted) costs. To express these costs into today’s value, we need to perform an inflation adjustment.

Recently, I was working with my students, and they had pulled cost data from the Agency for Healthcare Research and Quality (AHRQ) Medical Expenditure Panel Survey (MEPS). MEPS provides healthcare expenditure data on a number of categories (e.g., total, outpatient, inpatient, and pharmacy). Once can download and use the MEPS data to study the trends in healthcare costs over time. However, the past healthcare costs are not adjusted for time and will require some application of a price index.

Depending on your needs, MEPS provides recommendations on what kind of price index to use. The most common price index is the Consumer Price Index (CPI). The United States (US) Bureau of Labor and Statistics (BLS) provides data on the CPI, which can be accessed on their website (link).  According to the BLS site, “The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.”

The BLS also has a CPI calculator where you can enter the dollar amount at a specific month and year to estimate its inflation adjusted amount.

However, if you want to use the actual CPI data, you can download these from the BLS website (link).

 

Consumer Price Index Example

Here is a short tutorial on how to download the CPI data and perform an inflation adjustment.

Step 1. Download the CPI data from the BLS website.

Navigate to the BLS website (link). Click on “Tables.” This will bring you to the BLS CPI database page.

You see links to archived CPI releases. There is a lot of data on this site. To make things easier, we’ll focus on the annual CPI numbers.

Step 2. Select the Historical CPI-U, which contains all the annual CPI from its inception in the United States. This will be helpful in converting any dollar year into the inflation-adjusted dollar.

Note: We use the CPI-U because it includes all urban consumers, which represents over 90% of the total US population. There are other types of CPI calculations that you can use based on your needs. I encourage you to explore these.

Once you have downloaded the Historical CPI-U file. Open it and review the columns. The column that we are interested in is the “Annual” column, which contains the annual CPI for that year.  

Step 3: Let’s suppose that we were interested in performing an inflation adjustment for $100 in 2028 to 2025 value. We take the CPI for 2025, which is 321.943 and divide this by the CPI from 2008, which is 215.303.

Step 4: Once we have the inflation adjustment ratio, we can multiply this by $100 from 2008 to get the 2025 inflation-adjusted value.

Therefore, $100 in 2008 is worth $149.53 in 2025.

Personal Consumption Expenditures Example

But what if you wanted to use the Personal Consumption Expenditures (PCE) Price Index instead of CPI? MEPS recommends we use the PCE Price Index when we pool data from multiple years or evaluate trends across multiple years.

The PCE Price Index is available from the US Department of Commerce Bureau of Economic Analysis (BEA)website (link).

According to the Bureau of Economic Analysis, the PCE Price Index measures the “prices that people living in the United States, or those buying on their behalf, pay for goods and services.”

To get the PCE Price Index, you will need to do the following:

Step 1: Go to the BEA’s PCE Price Index website (link). Select Tools > Interactive Data. From there Select “Gross Domestic Product / Personal Income.” This will take you to another page where you can finally select “Interactive Data Tables.”

Step 2: In the interaction data tables page, Select “Section 2 – Personal Income and Outlays.” This will expand the field. Scroll down and Select “Table 2.5.4. Price Indexes for Personal Consumption Expenditures by Function (A).” The (A) indicates “Annual.” This opens the Interaction table where you can Modify the date range. By default, it will give you the most recent year and the last several years.

Step 3: Use the “Modify” option to change the time range.

Change the “First Year” to “2008 A,” This will expand the PCE Price Index time year from 2008 to 2024.

Notice that we don’t have 2025 on this Interactive table. The 2025 annual PCE Price Index is available, but you will have to do a little more digging.

Step 4: Go back to the National Income and Products Account and Select Table 2.8.4. Price Indexes for Personal Consumption Expenditures by Major Type of Product, Monthly (M).

Copy the PCE Price Index for the months of January all the way to December for 2025.

Paste this into Excel and average the PCE Price Index for all twelve months. This will give you the PCE Price Index for 2025.

Step 5: Now that we have the PCE Price Index for 2025, we can return to our previous example and perform an inflation adjustment of $100 from 2008 to its inflation-adjustment 2025 value.
We take the PCE Price Index for 2025, which is 126.919 and divide this by the PCE Price Index from 2008, which is 89.197.

Step 6: Once we have the inflation adjustment ratio, we can multiply this by $100 from 2008 to get the 2025 inflation-adjusted value.

Therefore, $100 in 2008 is worth $142.29 in 2025.

 

Conclusions

Adjusting past costs to current value is an important part of performing economic analyses, particularly when we are looking at the trends. If we don’t make this adjustment, our analysis will be incorrect and will generate the wrong conclusions. In this article, I reviewed two types of price indices, but there are others. MEPS provides a nice guideline on which ones to use on their website (link), and I encourage you to explore these.

 

Disclosures

This is a work in progress and subject to future changes.

This is for educational purposes only.

 

, , , ,

Literary Cafe series: Policy analysis (Part 2) - Interrupted Times Series Analysis with publicly available data

, , , ,

I’m back with some Literary Cafe series updates.

I have regularly informal discussions with my students about interesting papers in the biomedical sciences. Recently, we discussed a great paper by Jurecka and colleagues on the impact of a state-wide law to change the definition of fentanyl possession on opioid-related overdose death rates.

Jurecka and colleagues used publicly available data to perform their research, and I wanted to show my students how this was done using CDC WONDER data. Hence, I started this Literary Care series to document these exercises for others to learn from.

Last month, I wrote an article on how to get data from the CDC WONDER site, which you can read here. I considered this Part 1 (Getting the data).

This is the second part of a two-part series that illustrates how to use publicly available data to replicate the findings from a published study. In Part 2, I use the data from Part 1 to analyze the impact of the statwide fentanyl possession law on opioid-related overdose death rates using an interrupted time series analysis. I posted this on my RPubs site (link) along with part 1 (link).

, ,

Literary Cafe series - Policy Analysis (Part 1): Getting Data From CDC WONDER

, ,

This is Part 1 on a series of articles that I plan to write on how to perform analyses using publicly available data inspired by published studies.

Hence, I wrote an article on how to get death data from CDC WONDER, which I posted on my RPubs site here.

I’m not sure how these articles will evolve, so I’ll start with something simple like this first part, which is to gather the data to perform the analysis (Part 2 is available here).

Meanwhile, I think I’ll call these series of articles, “Literary Cafe series.” (Note: I know that this title needs work.)

, ,

Medication adherence estimations using R - Part 1

, ,

I created a tutorial on how to use the AdhereR package in R to estimate the medication adherence rate for a sample of individuals with prescription claims data. I posted the tutorial on my RPubs page (link).

The two most common medication adherence meaures are the Medication Possession Ratio (MPR) and the Proportion of Days Covered (PDC). This tutorial reviews how to estimate these medication adherence rates using AdhereR in R.

, , , , ,

Propensity score matching in R

, , , , ,

I wrote an introductory tutorial on how to perform propensity score matching using R, which has been posted on my RPubs site (link).

Propensity score matching is a statistical approach to balancing the observed covariates between groups. In observational studies, this method has the potential to mitigate potential confounding and allow us to make causal interpretations. However, there are a lot of approaches and nuances. This intorductory tutorial presents the basics of propensity score methods and how we can use these in our conventional analyses.

, , , ,

Prepost analysis with continuous data using R - Part 1

, , , ,

I wrote a tutorial on how to perform simple prepost analysis using R, which is available on my RPubs page. It covers how to compare two differences (change in value before and after an interention) using independent t test and linear regression approaches. However, it doesn’t cover how to address correlation between two dependent values. Part 2 of prepost analysis will cover those issues.

, , , ,

Mediation analysis using R

, , , ,

It’s not uncommon to see covariates in a regression model that should not be there. For example, measurements that occur after the treatment assignment are included into a regression model as baseline covariates. Rather, one should consider a mediation analysis.

I wrote a tutorial on how to perform mediation analysis using R on my RPubs site (link).

I know that I make this mistake at times. This tutorial helped me to carefully consider which covariates to include in a regression model and which ones to consider for mediation analysis.

, , ,

Survival Analysis - Immortal Time Bias with Stata

, , ,

I wrote a tutorial on how to handle immortal time bias with survival analysis using Stata. In the tutorial, I used a time-varying predictor for the grouping variable and assigned the period before exposure to the control group. This was inspired by the paper Redelmeier and Singh wrote on “Surival in Academy Award-Winner Actors and Actresses.” There was a lot of debate about the rigor of their analyses, and Sylvestre and colleagues re-analyzed the data with immortal time bias in mind. This tutorial uses data from Sylvestre and colleagues to re-create their results.

The tutorial is on my RPubs page. Data used for the tutorial is located on my GitHub page.

To load the data, you can use the Stata import command

import delimited "https://raw.githubusercontent.com/mbounthavong/Survival-analysis-and-immortal-time-bias/main/Data/data1.csv"
, , , ,

MEPS tutorials on linkage files and trend analysis

, , , ,

I create two MEPS tutorials recently. One is on the use of condition-event linkage files to capture the disease-specific costs. I used migraine as a motivating example. In this tutorial, I go through the steps to identify migraine-related costs assocaited with office-based visits and inpatient night stays. In the second tutorial, I review how to perform simple trend analysis with linear regressio models. I pooled MEPS data from 2016 to 2021 and apply the approriate primary sampling units and strata from the pooled file.

The first tutorial is located on my RPubs page (MEPS Tutorial 4 - Using condition-event link (CLNK) file: A case study with migraine). The R Markdown code to create the tutorial is located in my GitHub repository (link).

The second tutorial is also located on my Rpubs page (MEPS Tutorial 5 - Simple Trend Analysis with Linear Models). The R Markdown code to create the tutorial is located in my GitHub repository (link).