Picture two pregnant people walking into the same hospital to give birth.
想象一下,两个孕妇走进同一家医院生孩子。
They have identical medical histories and experienced identical pregnancies. They’re seeing the same obstetrician.
她们有相同的病史,孕期经历也相同。她们看的是同一个产科医生。
The only difference between them is that one is Black and the other is white.
她们之间唯一的区别是一个是黑人,另一个是白人。
According to a study of births in New Jersey hospitals, the Black patient would be about 20 percent more likely to get an unscheduled C-section than the white patient.
根据一项对新泽西州医院分娩情况的研究,黑人孕妇接受计划外剖腹产的可能性比白人孕妇高20%左右。
That number takes into account factors like differences in health status or access to good hospitals and doctors.
这一数据考虑到了健康状况的差异或获得优质医院和医生的机会等因素。
Without controlling for those variables, the number is even higher, with researchers finding that Black pregnant people are almost 25 percent more likely to get an unscheduled C-section than their white peers.
如果不控制这些变量,这个数据甚至更高,研究人员发现,黑人孕妇比白人孕妇接受计划外剖腹产的可能性高出近25%。
C-sections can, of course, save lives.
当然,剖腹产可以挽救生命。
But they also carry all the risks of a serious surgery, so the idea that people might be pressured into having C-sections unnecessarily is troubling enough.
但它们也包含一项重大手术所蕴藏的风险,所以,想到人们可能被迫进行不必要的剖腹产,想到这一点就让人不安。
The fact that this seems to happen disproportionately to Black people is even more disturbing.
这种情况发生在黑人身上的比例似乎更高,这一事实更令人不安。
Joining us today is Adriana Corredor-Waldron, an assistant professor of economics at NC State University and one of the authors of the working paper I mentioned.
今天加入节目的是Adriana Corredor-Waldron,她是北卡罗来纳州立大学的经济学助理教授,也是我提到的那篇工作论文的作者之一。
Thanks so much for taking the time to talk with us today.
非常感谢你今天抽出时间来和我们谈话。
Thank you for having me.
谢谢你们邀请我。
So I’m curious: You know, as somebody focusing on economics, what prompted you to study C-sections?
我很好奇:作为一个研究经济学的人,是什么让你开始研究剖腹产的呢?
In my research in general I am interested in understanding how public policy changes or shape health care provision and also provider behavior.
总的来说,在我的研究中,我感兴趣的是公共政策是如何改变或塑造医疗保健服务以及提供者的行为的。
But specifically to this paper, what we wanted to understand is why Black infants are more likely to be deliver by C-section than white infants—and this is a pattern that you see across the United States.
不过具体到这篇论文,我们想了解的是为什么黑人婴儿比白人婴儿更可能是剖腹产出生的——这种现象在美国各地都很普遍。
We have data on New Jersey, so the first thing that we checked is if we can replicate this part—this pattern in New Jersey.
我们有新泽西州的数据,所以我们关注的第一件事就是能否在新泽西复制这部分模式。
And what we find is that, as well as the aggregates in the U.S., but also previous studies that had suggested in different health care systems, we can see the same pattern emerging for this state.
我们发现,除了美国的总体情况外,之前的研究也表明,在不同的医疗保健体系中,我们发现这个州也有同样的模式。
Mm, and why is an increase in C-sections troubling from, you know, a maternal and fetal health perspective?
从母婴健康的角度来说,为什么剖腹产的增加会带来麻烦?
I want to clarify that this paper’s—what we want to talk about: it’s low-risk mothers getting an unscheduled C-section ... Right.
我想澄清一下,这篇论文——我们想讨论的是:低风险的孕妇进行了计划外剖腹产……是的。
C-sections among high-risk mothers can save the life of the baby and the life of the mother.
高危产妇的剖腹产可以挽救婴儿和母亲的生命。
Absolutely, yeah.
确实如此。
But when we talked about C-sections among low-risk mothers, we are basically saying that mothers are receiving a surgical procedure that carries a higher risk for complications, but also in the case of C-section, that is something that is gonna put the mother in a completely different track for future pregnancies.
但我们说到低风险的母亲接受剖腹产时,其实说的是她们接受的手术会有更高的并发症风险,而且在剖腹产的情况下,这会完全改变母亲未来的怀孕轨道。
So we are taking low-risk mothers and then performing these discretionary C-section, and then from the next birth on they will very likely need another surgical procedure, or another C-section.
我们让低风险的母亲自主选择剖腹产,那下一次生产时,她们很有可能还需要手术,还要再剖一次。
And did you uncover anything that helped explain what’s driving these disparities?
那你有没有发现什么能解释这种现象的原因?
What we have is very rich data from New Jersey from 2008 to 2017.
我们掌握了新泽西州2008年到2017年的大量数据。
And we are able to rule out what people might think is driving this racial disparity: we’re able to rule out, one by one, medical risk factors from the birth certificates that include eclampsia, previous C-section, if it—there is a breech presentation, and we fit all this data from (more than) 900,000 birth to a machine-learning algorithm.
我们可以排除下面这些人们可能认为导致这个差距的原因:我们一个一个地排除了出生证明中的医疗风险因素,包括子痫、剖腹产史、臀位胎位,然后把(超过)90万例的出生数据放到了机器学习算法里。