Of Competitive Pressures and Care Quality: Infertility Treatment - PowerPoint PPT Presentation

Of Competitive Pressures and Care Quality: Infertility Treatment Case Helen Schneider Department of Economics, University of Texas at Austin

Importance of the Study About 11 percent of American women 15–44 years of age have difficulty getting  pregnant or carrying a pregnancy to term (CDC). Today, over 1 percent of all infants born in the United States every year are  conceived using assisted reproductive technologies (ART). To meet this increased demand for ART, the number of infertility clinics in the  United States has increased from 263 in 1995 when CDC started collecting the data to 456 in 2012. Today, in vitro fertilization (ivf) is the most successful infertility treatment but it  is costly. On average, nationally, a “fresh” IVF cycle costs $12,000, before medications, which typically add  $3,000 to $5,000. (Forbes 2014) PGD and other tests add extra cost. To address costs, many states passed insurance mandates that require  employers to cover - or offer to cover- infertility treatments.

Previous Literature Competition and Quality of Health Outcomes  Hospital competition decreases mortality rates (Sari 2002, Kessler & McClellan 2000, Schneider  2008, Gaynor et al. 2011) while Propper et al. 2008 find higher mortality rates in competitive markets Mergers increase readmission rates but do not affect mortality rates (Ho & Hamilton 2000)  Mutter et al. 2008 look at 12 different dimensions of inpatient quality. They find that the effect  of competition is not “unidirectional”. Infertility Treatment Markets: The Effect of Competition  With more ivf clinics entering the market, many fear that under competitive pressures doctors  will pursue aggressive treatments so that the clinics can advertise high success rates. Limits on competition have been proposed. (Kolata 2002, Bergh et al. 1999, Wells 1999) Steiner 2005 measured competition as number of clinics in the area and found that competition  did not affect pregnancy rates but decreased high order multiples (triplets and higher). Hamilton & McManus 2005 measure competition with a simple dummy variable (1=monopoly,  0-otherwise). They find that competition does not increase multiple birth rate.

Literature Review (continues) Infertility Treatment Markets: The Effect of Insurance Mandates   Universal insurance mandates are associated with greater utilization of ART and other infertility treatments (ovulation- inducing drugs and artificial insemination (Henne & Bundorf 2008; Hamilton & McManus 2011; Bitler & Schmidt 2012)  Universal insurance mandates decrease multiple births per ART birth (Henne & Bundorf 2008; Hamilton & McManus 2011)  Universal insurance mandates increase triplet and higher- order births by 26% (Buckles 2012)

Contribution to Previous Research Recent data allows for a better measure of competitive  pressures than what we see from previous research This study calculates competition index (HHI) to measure  competitive pressures that ivf clinics face The study examines the relationship between competition and  infertility mandates

Empirical Model: Competition Variable We use Herfindahl–Hirschman Index (HHI) to measure market  competition. The index is constructed based on total ivf cycles performed for each clinic. Increases in the Herfindahl index generally indicate a decrease in competition and an increase of market power, whereas decreases indicate the opposite. The index can vary from zero (perfect competition) to 10,000 (monopoly). We use metropolitan statistical area (MSA) as the relevant  market for infertility clinics in our sample.

Empirical Model: Infertility Mandates By 2012 fifteen states passed infertility mandates of which six  states (Hawaii, Illinois, New Jersey, Massachusetts, Maryland, and Rhode Island) require all insurance plans to cover ivf. In addition, Arkansas, Montana and Ohio require some plans (all  HMO’s or all non-HMO’s) to cover the costs ivf treatments. We use both definitions of the universal mandate to test the  sensitivity of our results. In our definition of mandated infertility benefits, we do not  include states like Texas that only require health insurance plants to offer infertility insurance since employers have the right to refuse such coverage. We also exclude states like California that require coverage of all infertility treatments except ivf.

Empirical Model The following empirical model is used in this study:               Quality Mandate HHI ( Mandate HHI ) Market ais 0 1 s 2 i 3 s i 4 s ai where the dependent variable measures quality of health outcomes for age  cohort a in state s for clinic i . Coefficient β 1 captures the effect of state infertility mandates,  coefficient β 2 captures the effect of market competition and  β 3 captures the interaction of competition and state mandates.  Variable Market s is a vector of controls for variables that vary across states  that might also affect the market. These include: median family income (at MSA level), population, female labor force participation rate, and percentage of women educated (women with at least Bachelor degree at state level).

Data We use 2012 ART Fertility Clinic Success Rates Report. The data is  publicly available by Center of Disease Control and Prevention (CDC). The unit of analysis is a clinic performing ART (no patient level data is available). Clinic-level data was collected on 176,247 ART cycles at 456 reporting  clinics in the United States during 2012, resulting in 51,267 live births (deliveries of one or more living infants) and 65,160 live born infants (CDC). Market area characteristics came from publicly available state and  MSA-level data. Female labor force participation for year 2012 was collected by the Bureau of Labor Statistics (BLS) at the state level. Percentage of educated women variable is based on National Center for Education Statistics report. This data is collected at the state level and captures percent of women with at least a bachelor’s degree. MSA- level income per variable data came from the US Census Bureau. Data on state infertility mandates came from the American Society for Reproductive Medicine.

Descriptive statistics for selected variables Mean Min Max (st. deviation) Ivf cycles for women aged under 92.01 0 1463 35 (123.27) Ivf cycles for women aged 35-39 89.21 0 1694 (145.63) Ivf cycles for women aged 40 and 38.35 0 884 above (78.22) Average embryos transferred for 1.97 1 4.3 women aged under 35 (0.30) Average embryos transferred for 2.30 1 3.8 women aged under 35-39 (0.36) Average embryos transferred for 2.74 1 6 women aged 40 and above (0.76) Mandate 0.15 0 1 (0.36) HHI 4138.93 576.52 10000 (3289.28) Multiples rate for women aged 31.2 0 1 under 35 (17.2) Multiples rate for women aged 26.9 0 1 35-39 (20.5)

Descriptive statistics continued Mean Min Max (st. deviation) 29.2 0 1 Multiples rate for (20.1) women aged 40+ 70.17 0 100 I CSI (19.48) 5.904 0 100 PGD (11.26) 28.245 17.4 48.6 Women’s (4.66) education 47,698.83 22,400 81,068 I ncome per (8975.783) capita

Preliminary Results Number of ivf cycles  HHI does not increase ivf cycles  Health insurance mandates have a positive and significant effect on ivf cycles (p< 0.01  for women under 35 and p<0.05 for women between 35 and 39) Other significant variables include women’s education and SART membership. Both  significantly increase average number of cycles for all women. Average embryos transferred  For women younger than 35 none of the policy variables are significant  For women over 35 years of age competition decreases number of embryos  transferred (P<0.1); mandates decrease number of embryos transferred although the effect is not statistically significant; interaction variable is positive and significant (p<0.1 for women 35-40 and P<0.05 for women >40) Other significant variables are women’s education, pgd rate (negative and statistically  significant effect), SART membership (significant for women under 35)

Preliminary Results: Multiple gestations women 35-39 women 35-39 women > 40 women> 40 -2.383 -1.057 -8.62 -16.107* Mandate (3.28) (2.537) (12.18) (8.84) 0.000565** 0.000459* 0.00297* 0.00973* HHI (0.000314) (0.000250) (0.00178) (0.00567) 0.00572 -0.00495 0.0270 0.0310 Volume (0.00515) (0.00520) (0.0448) (0.0457) 0.000286** 0.000314** 0.00108 0.00119* I ncome per (0.000132) (0.000131) (0.000678) (.000650) capita -0.533** -0.487* -2.39* -1.20 Women’s (0.266) (0.253) (1.43) (1.39) education -0.0608 -0.0640 -0.448 -0.208 I CSI (0.0531) (0.0523) (0.275) (-.259) -0.0502 -0.0483 -1.029** -1.096*** PGD (0.0666) (0.0683) (0.444) (0.391) -11.874*** -11.854*** 0.207 -2.633 SART (3.378) (3.389) (16.92) (15.338) membership 0.00158* 0.0105** I nteraction (0.0009) (0.00511) 406 406 404 404 N 2.53** 2.56*** 2.33** 2.34** F 0.0748 0.0848 0.0994 0.0997 R-squared

Future research  Estimate the effect of changes in HHI on multiple gestations  Re-estimate the model for high order multiples  Sensitivity analyses: use a different definition of market