We enrolled 120,621 people in our trial (60,352 intervention and 60,269 control). Most participants were ages 25–34 (57.9%) and lived in urban zip codes (78.3%) (Table 1). Half (49.7%) of the participants resided in zip codes with median household incomes of less than $57,000. Sample characteristics were balanced across the control and intervention groups.
Table 1
Demographic Baseline Characteristics
Participant Characteristics | Control | Intervention |
n = 60,269 | n = 60,352 |
n (%) | n (%) |
Age | | |
18–24 | 18,444 (30.6) | 18,341 (30.4) |
25–34 | 34,792 (57.8) | 34,995 (58.0) |
35+ | 7,033 (11.7) | 7,016 (11.6) |
Region a | | |
Midwest | 12,588 (20.9) | 12,662 (21.0) |
Northeast | 9,422 (15.6) | 9,652 (16.0) |
South | 24,741 (41.0) | 24,338 (40.4) |
West | 13,601 (22.5) | 13,617 (22.6) |
Median household income in zip code b | | |
< $25,000 | 1,051 (2.5) | 1,089 (2.6) |
$25,000 - $49,999 | 13,535 (32.6) | 13,301 (32.1) |
$50,000 - $74,999 | 16,465 (39.6) | 16,316 (39.4) |
$75,000 - $99,999 | 6,896 (16.6) | 7,033 (17.0) |
> $100,000 | 3,587 (8.6) | 3,656 (8.8) |
Proportion with Bachelor's degree in zip code c | | |
< 20% | 11,375 (27.3) | 11,149 (26.9) |
20% - <30% | 10,730 (25.8) | 10,527 (25.4) |
30% - <50% | 12,849 (30.9) | 13,137 (31.7) |
≥ 50% | 6,637 (16.0) | 6,628 (16.0) |
Rural or urban county d | | |
Urban | 43,321 (78.5) | 43,097 (78.1) |
Rural | 11,898 (21.5) | 12,084 (21.9) |
Notes: |
(a) Regions as listed in https://www.nationalgeographic.org/maps/united-states-regions/. |
(b) Source: U.S. Census American Community Survey, 2015–2019 5-year estimates from https://data.census.gov/cedsci. Median income in past 12 months by ZCTA, Table S1903. Data missing for 31.2% of participants due to low zip code population or incorrect zip code entry. |
(c) Source: U.S. Census American Community Survey, 2015–2019 5-year estimates from https://data.census.gov/cedsci. Educational attainment by ZCTA, Table S1501. Data missing for 31.1% of participants due to low zip code population or incorrect zip code entry. |
(d) Source: U.S. Census 2010 Urban Area to ZCTA Relationship File. Data missing for 8.4% of participants due to incorrect zip code entry. |
Compared to the national population of people giving birth, our study sample was slightly younger, more likely to reside in lower-income and less-educated areas, and more likely to reside in a rural area (Table S2).
Choice of hospital during pregnancy
Among people enrolled in the trial, 10.2% (9.8% intervention, 10.5% control) reported their choice of hospital during pregnancy and the average gestational age was 12 weeks at the time they provided this information. Compared to those who did not report this outcome, participants who reported this outcome lived in higher income communities (27.0% vs. 25.5% live in zip codes with median incomes higher than $75,000) and were more likely to live in an urban area (84.1% vs. 77.6% in an urban area) (Table S3).
The average star rating of the hospitals selected by people in the intervention population was significantly higher than in controls (average star rating 2.61 (SD 1.60) intervention vs. 2.24 (SD 1.44), p < 0.001) (Fig. 2A). Of the participants who reported their hospital, 19.0% of the intervention group selected 5-star hospitals while 10.8% of the control group selected 5-star hospitals.
Choice of hospital reported after delivery
Among participants, 8,035 (6.7%; 6.6% intervention, 6.7% control) reported the hospital in which they gave birth after the delivery.
The average star rating of the hospitals selected by the intervention population was significantly higher than in controls (average star rating 2.30 (SD 1.42) intervention vs. 2.19 (SD 1.42) control, p = 0.001) (Fig. 2B). Of the participants who reported their hospital, 11.5% of the intervention group selected 5-star hospitals while 9.2% of the control group selected 5-star hospitals.
Among the 1,681 (1.4%) of participants who reported their hospital choice both during pregnancy and after delivery, 60.5% and 54.5% reported the same hospital at both instances for the control and intervention groups, respectively (Table S4).
Secondary outcomes
People in the intervention group were more likely to believe that the choice of hospital impacts the likelihood of having a cesarean delivery (38.5% vs. 33.1%, p < 0.001, response rates of 6.9% and 6.7%). However, there were no differences in respondents’ beliefs that hospitals in their communities had different care quality levels (91.0% vs. 90.0%, p = 0.10, response rates of 7.3% and 7.2%) or that cesarean delivery rates are important to consider when choosing a hospital (76.2% vs. 74.3%, p = 0.050, response rates of 7.3% and 7.2%) (Table 2)
Table 2
Participant survey responses regarding the importance of cesarean rates in selection of hospital
Survey Question | Response | Control | Intervention | p-value |
Hospital Impact on Delivery (Do you think the facility where you plan to deliver will impact your chance of having a C-section?) | | n = 4,064 | n = 4,136 | |
Very or somewhat likely | 1,345 (33.1) | 1,591 (38.5) | < 0.001 |
Use in Hospital Selection (How much does the C-section rate of a facility matter to you when deciding where you'll deliver?) | | n = 4,340 | n = 4,390 | |
High or medium priority | 3,226 (74.3) | 3,343 (76.2) | 0.05 |
Knowledge of Variation (How different are healthcare facilities when it comes to quality of care?) | | n = 4,338 | n = 4,390 | |
Very or somewhat different | 3,903 (90.0) | 3,995 (91.0) | 0.10 |
After delivery, 18,066 (29.9%) people in the intervention group and 18,139 (30.0%) people in the control group reported how their babies were delivered. There was no difference in the fraction of participants reporting they had cesarean deliveries (31.4% intervention, 31.4% control, p = 0.98).
Sub-group analysis
Among the participants randomized to the intervention, 50,611 (83.9%) did not open any educational modules offered as part of the intervention and 50,241 (83.4%) did not use the hospital-look up tool. Among those in the intervention group who reported a delivery hospital, 3,419 (57.8%) did not open any educational modules. There were no clear differences in the mean star ratings of hospitals chosen during pregnancy stratified by the number of educational modules opened (0 modules 2.62, 1 module 2.62, 2 modules 2.65, 3 + modules 2.54) (Table S5).
We conducted exploratory analyses on the differential impact of the intervention across several subgroups (Fig. 3). We hypothesized that participants with more resources and higher education would be more likely to respond to the intervention. Using median income of the zip code of residence as a proxy for resources and education, we did not observe any substantive difference in the impact of the intervention between participants who lived in higher and lower education zip codes (p = 0.85).
Many hospitals did not report their cesarean rates, and therefore some participants had limited choice of hospitals with a star rating when they used the hospital look-up tool. This was less of an issue in the five states where we used state government data because hospitals were mandated to respond. The star rating differences between intervention and control groups was 0.47 stars for participants living in these five states vs. 0.36 stars for the rest of the nation (test of interaction, p = 0.18).
To further understand the influence of hospital choice, we examined the relationship between the number of hospitals with a 3-star or higher rating nearby and the impact of the intervention (0.31 difference in star ratings between intervention and controls among participants with no hospitals with a 3 star or higher rating within 10 miles, 0.36 for one hospital, 0.44 for more than one hospital, test of interaction p = 0.03). Finally, we compared participants who had a choice of hospitals with disparate star ratings (0.42 difference in star ratings between intervention and controls among participants where the maximum star rating is at least two stars greater than the minimum star rating among hospitals within 10 miles of the participant, 0.34 if this is not true, p = 0.25).