Abstract: health care utilization and the economic condition

The relationship between economic conditions and health care utilization is extremely important. Understanding how a recession or expansion affects a nation’s health would enable government legislators and budget officials—as major payers of health care—to better anticipate health care utilization and model the demand for health care in order to slow the upward spiral of health costs. Using quarterly panel data for Florida hospitals from 1997 to 2009, I will use a fixed effects model to estimate how the economic condition affects health care utilization. I will use the county unemployment rate to proxy for the economic condition and the number of inpatients with preventable diagnoses who are admitted through the emergency room department to approximate health care utilization. I found that even when controlling for population, hospital, and time effects, a one percent increase in the unemployment rate results in a .18 percent increase in the number of patients with preventable diagnoses who were admitted through the ED. Also in line with the literature, I found that men are also more affected by the economic condition than women and blacks are more affected than whites. The economic condition has a negative effect on the number of elective procedures ED inpatients have.

My Own Project: ER and preventable care admits

So I finally finished my own project. I used the same data I’ve been working with (quarterly inpatient data from Florida), but now only focusing on two types of patients: ER admits and patients with preventable conditions. Preventable conditions are diagnoses for which timely and effective ambulatory care reduces the risks of hospitalization by either preventing the onset of or managing an illness or condition. Examples include pneumonia, congestive heart failure, asthma, and immunizable conditions. As a result, preventable conditions are frequently used as a measure of health care access and so I hypothesized that during a recession there would be more of these types of conditions. ER admits are an important subgroup because hospitals must provide everyone with a minimum degree of service even if the patient has no insurance and cannot pay. The ER is the only part of a hospital that is required to treat everyone, so I hypothesized that during a recession there would be more ER admits. My dependent variables of interest are the number of ER admits, preventable condition admits, and PC through ER admits for each hospital. My explanatory variables were the county unemployment rate and county population controls and hospital and time controls. When I only looked at the effect of unemployment on ER or PC admits, the effect was not significantly different from zero. However, when I looked at patients that met both criteria, the effect was positive and significant. In other words, during recessions there are more ER admits with diseases that could have been prevented with proper health care.

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Finally the data came!

Yeah! The data finally came. After getting lost in the post office for over a week, the Florida inpatient and outpatient data for 2009 and 2010 finally came. Today I created all the counts and collapsed it on individual doctors (instead of each patient). Once I append it to the other years and merge it with all the controls, Prof He and Mello’s data will be finished and I can start working on my project fully.  The final panels will have the top five counties, hospitals, diagnosis, prognosis, and procedures for each attending doctor, operating doctor, and hospital for the inpatient and outpatient data.  I can’t wait until it is time to do regressions!

Control Variables

After compiling the panel datasets, the only thing left to do is updating the control variables. Controls are things like average personal income, number of elderly persons, number of persons of a given race or gender in a county and the number of beds of a hospital. Control variables are extremely important! Remember, we want to find the effect of unemployment on the number of Medicare patient procedures. So, let’s say we find a positive relationship between them (when we’re not using controls). It could be because counties with more minorities have higher unemployment and minorities are more likely to have Medicare and go to the hospital than whites. If this is the case, unemployment actually has no effect on procedures and is just picking up the effect of race. So last week I searched through the US census and Florida websites in search of the needed control variables and now we have as many control variables for as many years as we can. All that’s left is for the 2009/2010 data to come and be cleaned, and then we’re ready for business. I can’t wait!!!


Last week we made one awesome breakthrough with the Florida inpatient data. Unfortunately, the data collectors in Florida changed how they recorded doctor ID’s in 2006, which means that we can’t can’t follow a doctor’s behavior throughout the whole time period even though they have worked for the whole period. We broke the Florida data collector’s code, and I’ve been busy this past week running checks to make sure it actually works. I ran a test on the coded vs original doctor ID to see if they are working in the same hospital and preforming the same procedure. So far it looks good!