Corrigendum

June 2014 | Corrigendum

Abraham JM. How Might The Affordable Care Act’s Coverage Expansion Provisions Influence Demand for Medical Care? Milbank Q.2014;92(1):63-87.

DOI: 10.1111/1468–0009.12041

An error occurred when calculating the values for the rows “Quasi-experimental literature (lower-bound point estimate from literature),” “Percentage change relative to overall demand,” “Quasi-experimental literature (upper-bound point estimate from literature),” and “Percentage change relative to overall demand” in Table 4, and as a result the values and percentages in the text on page 81 are erroneous. This was noticed following publication in the March 2014 issue of The Milbank Quarterly.

Table 4 is reproduced below with the correct data in bold.

Table 4. Potential Impact of Coverage Expansion on Overall Demand for Medical Care
Inpatient Stays ED Visits Office-Based Visits Prescribed Medicines
Annual utilization estimates for noninstitutionalized civilian US population 29,249,278 55,060,952 1,539,093,888 3,203,007,232
Adjusted MEPS estimate: uninsured respond in same way as privately insured −229,000 −1,213,000 33,085,000 36,650,000
Percentage change relative to overall demand −.78 −2.2 2.15 1.14
Adjusted MEPS estimate: uninsured respond in same way as publicly insured 1,096,000 2,012,000 51,085,000 205,150,000
Percentage change relative to overall demand 3.75 3.65 3.32 6.40
Quasi-experimental literature (lower-bound point estimate from literature) 014 −235,65018 3,806,60019a 014
Percentage change relative to overall demand 014,17 −0.43 0.25 0
Quasi-experimental literature (upper-bound point estimate from literature) 1,207,19013b 1,885,20013b 52,340,75016 75,147,00017
Percentage change relative to overall demand 4.13 3.42 3.40 2.35
The first 2 sets of estimated changes in medical care utilization are based on results from the descriptive analysis in Table 3. The final 2 sets of estimates are calculated from the quasi-experimental literature using the lower- and upper- bound point estimates for the specific service categories, measured as percentage changes from baseline. Each percentage change is then applied to the baseline uninsured utilization rates from the MEPS (weighted across the 2 subgroups based on their relative size) and multiplied by 25,000,000, which is the current CBO estimate for the expected number of uninsured who will gain coverage.aMiller estimates the change in probability of visit rather than the number of visits.
bAnderson, Dobkin, and Gross estimate the change in probability rather than number.

 

On page 80, the text in the last paragraph should read:

Table 4 summarizes these results. With respect to inpatient stays, 3 of the 4 scenarios suggest expected increases in demand, ranging from 0 to 1.2 million stays per year (0 to 4.13% of aggregate utilization). But if the newly insured respond in the same way as the privately insured do, inpatient stays are predicted to fall slightly. ED visits also show considerable variation. Two scenarios suggest a drop in ED visits, and 2 scenarios suggest an increase. All 4 scenarios indicate a positive demand response with respect to office-based visits and suggest an increase of between 3.8 million and 52.3 million additional visits per year (a 0.25% to 3.4% increase from current baseline levels overall). Finally, with respect to prescribed medicines, the smallest and largest predicted effects suggest the possibility of no demand response to a 6.4% increase in overall utilization.

The author regrets any inconvenience caused by these errors.

Read on Wiley Online Library

Volume 92, Issue 2 (pages 404–405)
DOI: 10.1111/1468-0009.12063
Published in 2014