Conducting MEPS Data Analysis

From Data to Insights

You've done the heavy lifting. The MEPS data is imported, cleaned, merged, and you understand how to apply the survey design variables. Now comes the rewarding part: turning all that structured data into meaningful insights about healthcare in the United States.

This final step is about conducting the analysis itself. We will cover two major types of analysis: descriptive and regression. These will help you summarize the data and explore relationships between different factors.

Start with Descriptive Analysis

Before you can test a complex hypothesis, you first need to understand the basic characteristics of your population. This is where descriptive analysis comes in. It helps you summarize the data to estimate things like average healthcare costs, the percentage of people with insurance, or the total number of doctor visits nationwide.

Since you're working with MEPS data, you must use Stata's svy command prefix. This ensures your calculations account for the complex survey design, including weights, stratification, and clustering. Without it, your results would not accurately represent the U.S. civilian noninstitutionalized population.

Let's calculate the average total healthcare expenditure for 2021. The variable for this is TOTEXP21. The command is straightforward:

// First, ensure your survey design is set
// This was covered in the previous step, but as a reminder:
svyset VARPSU [pweight=PERWT21F], strata(VARSTR)

// Now, calculate the weighted mean of total expenditures
svy: mean TOTEXP21

The output will give you the estimated national average for total healthcare spending per person. It will also provide a standard error and a 95% confidence interval, which are crucial for understanding the precision of your estimate.

The confidence interval gives you a range in which the true population average likely falls. A narrower interval suggests a more precise estimate.

You can also estimate proportions. For instance, what percentage of the population perceives their health as excellent? Let's assume you've created a binary variable EXCELLENT_HEALTH (where 1 = Excellent, 0 = Not excellent) from the RTHLTH42 variable.

// Calculate the weighted proportion of people in excellent health
svy: prop EXCELLENT_HEALTH

This command will show the estimated proportion of the population reporting excellent health, again with its standard error and confidence interval.

Explore Relationships with Regression

Descriptive statistics are great for summarizing, but they don't explain why something is happening. To explore relationships between variables, you'll need regression analysis. For example, you might want to know if having health insurance is associated with lower out-of-pocket healthcare spending, after accounting for factors like age and health status.

Just like with descriptive analysis, you must use the svy prefix for regression. Let's run a simple linear regression to see how insurance status (INSCOV21) and age (AGE21X) are associated with total healthcare expenditures (TOTEXP21).

// Run a survey-weighted linear regression
svy: regress TOTEXP21 i.INSCOV21 AGE21X

A few things to note in that command. The i. prefix before INSCOV21 tells Stata that this is a categorical variable. Stata will automatically create indicator (or dummy) variables for each category, which is essential for a correct analysis. AGE21X is treated as a continuous variable.

The output will provide coefficients for each variable. The coefficient for AGE21X tells you how much total expenditures are expected to change for each one-year increase in age, holding insurance status constant. For INSCOV21, the coefficients show the difference in average expenditures for each insurance category compared to a baseline or reference category.

Interpret and Report Your Findings

Getting the output is just one step. The real skill is interpreting and communicating what it means. When you look at your regression results, pay close attention to the coefficients, the p-values, and the confidence intervals.

Element	What it Tells You
Coefficient	The size and direction of the relationship. A positive coefficient means the variables move in the same direction; negative means they move in opposite directions.
P-value	Indicates statistical significance. A common threshold is p < 0.05, which suggests there is less than a 5% chance the observed relationship is due to random variation.
Confidence Interval	Provides a range of plausible values for the true coefficient in the population. If the interval does not include zero, the result is typically considered statistically significant.

The MEPS public use files include variables to obtain weighted estimates and to implement a Taylor-series approach to estimate standard errors for weighted survey estimates.

When reporting your findings, be clear and precise. For example, instead of saying "age affects healthcare costs," you would say something like, "After controlling for insurance status, a one-year increase in age was associated with an average increase of $75 in annual healthcare expenditures (95% CI: $60, $90; p < 0.001)."

This statement is powerful because it's specific. It quantifies the relationship, provides the confidence interval for precision, and gives the p-value to show statistical significance. Always remember to also state the year of the data you are using and describe the population your findings represent.

This approach to analysis and reporting allows you to contribute accurate, well-supported insights to the broader conversation about healthcare.

Stata MEPS Data Merging