Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women The Journal of Political Economy, Vol. 104, No.6 (Dec., 1996), Authors: Janet Currie; Jonathan Gruber Presented by (Jason) Chia-cheng Liao April, 2004

The paper try to answer: the key question for health care reform in the U.S. is whether expanded health insurance eligibility will lead to improvements in health outcome. A nine infant death per 1000 births, he U.S. infant mortality rate is among the highest in the industrialized world. The high rate is thought to reflect large numbers of unhealthy newborns. –How can this be improved? Then, where? 1. the poor or 2. more coverage? –Do women use the adequate prenatal care? How much is the Take-out? How much is the “Cost”? Is this cost effectiveness?

In effort to increase the use of prenatal care, the past decade has seen a rapid expansion in the eligibility of pregnant women for Medicaid. The extensions of eligibility groups provide a natural experiment and case study of whether changes in eligibility can actually improve infant health and related topics. The authors use several data sets to investigate the questions. The data sets include the Aid to Families with Dependent Children (AFDC) program data, Current Population Survey (CPS), Vital Statistics data, Medicaid expenditure from the Health Care Financing Administration (HCFA), and National Longitudinal Survey of Youth (NLSY). The backbone of the methodology is a simulation model of each state’s Medicaid eligibility for pregnant women over the period.

The authors divide the eligible people of Medicaid into two groups: –1. “Targeted changes” applied to specific low-income groups; They included: a) changes in eligibility for cash welfare under AFDC program and b) changes that allowed pregnant women with income below AFDC cutoff to receive Medicaid regardless of family structure ( i.e. the single parent). –2. “Broad changes” extended Medicaid coverage to all women with income less than specified level (e.g., 185 percent of the federal poverty level) minus the people in group 1. The women in “broad changes” group have much higher income than group “targeted changes”. For example, in Texas, the cutoff of AFDC for a family of four was only 24 percent of the poverty line in By April 1990, a uniform minimum threshold had established: all states were required to cover pregnant women with incomes up to 133 percent of the property line. States have the matching option to cover up to 185 percent. Some states had even cover beyond that by state- only funds.

15-44-year-old Women’s Medicaid Eligibility By State Over Time

Methodology The effect of the eligibility changes on birth outcomes –Using the Vital Statistics Data, the y (dependant) variables are: 1. the incidence of low birth weight (less than 2,500 grams) and 2. the infant mortality rate in each state and year. Regress these two (state/ year) outcomes on the fraction of year-old women in the event of pregnancy with Medicaid coverage. Problem of this strategy is that the actual fraction eligible depends on the economic and demographic characteristic of the state, which may also be correlated with birth outcomes. For example, a state recession is associated with both increases in eligibility and a higher incidence of low birth weight. Then this situation could induce a spurious effect. (biased estimation, due to omitted variables. )

Solution: simulation sampling The authors instrument the actual fraction eligible of Medicaid in a state and year that depends only on the state’s eligibility rules. To create the instrument, the authors first take 3,000 women from CPS in each year. Then, they calculated the fraction of eligible women for Medicaid in this sample. Then, they estimated the instrumental fraction that depends only on the legislative environment and is independent of other characteristics of states. The final analysis also employed the state fixed effects regression to control the state characteristics and Medicaid policy.

The point estimation of instrument regression suggests that a 30-percentage-point in eligibility increase (rough rate of actually happens over this time period) would lead to a reduction of 1.9 percent of the incidence of low birth weight. (.3x4.347/68.12=.019) The instrument regression of the target group indicates that the 30% point in eligibility rise was associated with an 7.8 percent decline in the incidence of low birth weight. The result for the broad group is only 1.5 percent reduction.

*In column 4 part B, the instrumental variables regression of ‘the target’ indicates that the 30-percentage-point rise was associated with an 8.5 percent decline in the infant mortality rate. *the result for ‘the broad’ is only 2.2 percent decrease of the infant mortality rate in part C. *The column 5 and 6 are the y incidence combined both the low birth weight and the infant mortality rate. Robustness Issue: The author also concerned the robustness of the results. This is caused by those significant results of previous table could be driven by some outlying observations. To reduce the risk, the authors also made the robust regression analysis. (M or R robust?) The results are the following:

The results are similar to those in the table 3, but the coefficients are slightly smaller. The only significant changes is the overall results for low birth weight are no longer statistical significant.

How much is the take-up? The March CPS asks individuals whether they were covered by Medicaid in the previous year. The authors estimated the marginal take-up rate for these Medicaid policy change; that is, for every 100 women made eligible for the coverage of pregnancy, how many women additional report coverage? Of women years old, about 11.4 percent of all women were pregnant at some point during one year of the authors’ sample years (the years of changing policy); this is the baseline rate (i.e. if the full take-up, the rate is )

How much is the take-up? This table was regressed by Linear Probability Models included state and time dummies. The CPS data set consists of 526,830 observations over 14 years. The Medicaid policy makes an extra woman covered will raise the odds that she will be covered by 3.9 percent. Relative to the baseline, this is a take-up rate of 34 percent (34% = 3.9/11.4).

The take-up The overall take-up rate is 34%. The ‘targeted’ take-up rate is 49%, and the ‘broad’ take-up rate is only 16%. The authors give two reasons why the ‘broad’ take-up rate is low: 1st, the ‘broad’ population was less needy. 2nd, the broader policy changes may have been less effective. It may be difficult to bring women who have never received any social assistance into the Medicaid program, either they do not know about it or the stigma effects. Previous research report that many low-income families and their physician are unaware that they can qualify for Medicaid. The authors also ran the cost regression analysis as the following:

The Cost Effectiveness: the authors normalize and deflate health expenditure by Consumer Price Index and use the dollars in For example, the coefficient in the col. 2 refers to the the actual fraction made eligible of all expenditure under the targeted change. (notice that:.301= )

The Cost Effectiveness From the previous table, the majority of the spending comes through inpatient hospital costs. The most striking finding (from the simulated models) is that spending per ‘broad’ eligible is actually higher than spending per ‘target’ eligible. (the richer use more than the poorer) Among targeted eligible, only about half of the spending is on inpatient hospital services, whereas among broad eligible, over 90 percent of spending is on these services.

The Cost Effectiveness The cost of saving a infant’s life: The authors also estimated the increase of actual expenditure is $202 per year per additional eligible women. $224 per targeted eligible per year. This leads the cost of saving a life through targeted eligibility changes was $840,000. (if interested, check with the footnote 22 for details) Is this worthy? According to the other research (Tengs et al, 1995), the child restraint system in cars costs about $5.5 million per child life saved. The similar prenatal care program also costs $1.06 million to save a life according the Institute of Medicine.

Parental Care Utilization analysis by NLSY: Instrumental eligibility is using the actual and simulated fractions by the CPS. Linear probability models is employed in this analysis. OLS in col. 1 says the targeted Medicaid-eligible women are likely to delay prenatal care, but the instrumental models say otherwise. Actually, the targeted group reduced the probability of delay by almost the half when the simulated instrument is used (in col. 3).

Conclusion Overall, the Medicaid is quite efficient especially in the ‘targeted’ group. The efficiency includes the higher take- up rate, more cost-effectiveness, and good utilization of the prenatal care. The authors conclude the targeted expansions were clearly more cost-effective than the broad eligibility changes, these results could be good references for the improvement of future health insurance policies. Simulation Method: J. Cook and L. A. Stefanski. A simulation extrapolation method for parametric measurement error models. Journal of the American Statistical Association, 89: , 1995