Abstract：
BACKGROUND: In population-based research, pregnancy may be a repeated event. Despite published guidance on how to address repeated pregnancies to the same individual, a variety of approaches are observed in perinatal epidemiological studies. While some of these approaches are supported by the chosen research question, others are consequences of constraints inherent to a given dataset (eg, missing parity information). These decisions determine how appropriately a given research question can be answered and overall generalizability.
OBJECTIVE: To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable.
METHODS: Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first-observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (ie, we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity (SMM) and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios (RR) for the associations of outcomes with independent variables. Among all-births, we then compared RR from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity.
RESULTS: We observed minor differences in the population characteristics between the all-birth (N=2736,693), random-selection, and first-observed birth cohorts (both N=2284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (eg, SMM 18.9 per 1000 births among primiparous-births vs 16.6 per 1000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. RR differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, that is, crude estimates (eg, lupus-SMM association: 4.01, 95% confidence intervals [CI] 3.54-4.55 vs 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, toward the null for SMM and away from the null for preeclampsia/eclampsia.
CONCLUSIONS: Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented.
OBJECTIVE: To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable.
METHODS: Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first-observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (ie, we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity (SMM) and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios (RR) for the associations of outcomes with independent variables. Among all-births, we then compared RR from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity.
RESULTS: We observed minor differences in the population characteristics between the all-birth (N=2736,693), random-selection, and first-observed birth cohorts (both N=2284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (eg, SMM 18.9 per 1000 births among primiparous-births vs 16.6 per 1000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. RR differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, that is, crude estimates (eg, lupus-SMM association: 4.01, 95% confidence intervals [CI] 3.54-4.55 vs 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, toward the null for SMM and away from the null for preeclampsia/eclampsia.
CONCLUSIONS: Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented.
摘要：
背景：在基于人群的研究中，怀孕可能是一个重复的事件。尽管已经发布了关于如何解决同一个人重复怀孕的指南，在围产期流行病学研究中观察到各种方法。虽然其中一些方法得到了所选研究问题的支持，其他是给定数据集固有约束的结果(例如，缺少奇偶校验信息)。这些决定决定了如何恰当地回答给定的研究问题以及总体的可概括性。
目的：通过评估两种围产期结局的患病率及其与临床和社会独立变量的关联，比较围产期流行病学研究中常用的队列选择和分析方法研究设计：使用与单胎分娩产妇出院记录相关的生命记录，我们创建了4个队列:(1)全产(2)每个人随机选择1个出生(3)每个人首次观察到的出生(4)初产(胎次1).出生抽样不以集群为条件(即，我们没有对给定母亲的所有出生进行采样，而是采样个体出生)。研究结果是严重的产妇发病率和先兆子痫/子痫，自变量为自我报告的种族/民族(作为社会因素)和系统性红斑狼疮.比较四个队列，我们评估了产妇特征的分布，结果的普遍性，总体上按平价分层，以及结果与自变量关联的风险比。在所有出生的人中，然后，我们比较了三种分析策略的风险比：标准推断假设模型中的同一母亲独立抽样出生，使用聚类鲁棒推理，并调整平价。
结果：我们观察到所有出生者之间的人口特征差异很小（N=2,736,693），随机选择，和首次观察到的出生队列（均为N=2,284,660），这些队列与初产出生队列之间的差异更大（N=1,054,684）。结果患病率在所有分娩中始终最低，在初产分娩中最高(例如，初产妇中每1,000例产妇的严重发病率为18.9例。在所有分娩中，每1000个分娩16.6个)。当按平价分层时，在两种结局中,产次2的新生儿结局患病率始终最低,产次1的新生儿结局患病率最高.所有四个队列研究结果的风险比不同，初产出生队列和其他队列之间的差异最明显。在所有出生的人中，稳健推理对估计的置信界限的影响最小，与标准推断相比，即，粗略估计(例如，狼疮重度孕产妇发病率关联：4.01，95%CI3.54-4.55vs.4.01，粗估计值95%CI3.53-4.56），在调整平价时，估计略有偏移，朝向严重孕产妇发病率的无效，远离先兆子痫/子痫的无效。
结论：研究人员应该考虑他们使用的方法之间的一致性，他们的抽样策略，和他们的研究问题。这可能包括完善研究问题，以更好地匹配可用数据的推断，考虑到替代数据源，并适当注意数据限制和由此产生的偏差，以及调查结果的普遍性。如果奇偶校验是一个既定的效果修饰符，应提供分层结果。
目的：通过评估两种围产期结局的患病率及其与临床和社会独立变量的关联，比较围产期流行病学研究中常用的队列选择和分析方法研究设计：使用与单胎分娩产妇出院记录相关的生命记录，我们创建了4个队列:(1)全产(2)每个人随机选择1个出生(3)每个人首次观察到的出生(4)初产(胎次1).出生抽样不以集群为条件(即，我们没有对给定母亲的所有出生进行采样，而是采样个体出生)。研究结果是严重的产妇发病率和先兆子痫/子痫，自变量为自我报告的种族/民族(作为社会因素)和系统性红斑狼疮.比较四个队列，我们评估了产妇特征的分布，结果的普遍性，总体上按平价分层，以及结果与自变量关联的风险比。在所有出生的人中，然后，我们比较了三种分析策略的风险比：标准推断假设模型中的同一母亲独立抽样出生，使用聚类鲁棒推理，并调整平价。
结果：我们观察到所有出生者之间的人口特征差异很小（N=2,736,693），随机选择，和首次观察到的出生队列（均为N=2,284,660），这些队列与初产出生队列之间的差异更大（N=1,054,684）。结果患病率在所有分娩中始终最低，在初产分娩中最高(例如，初产妇中每1,000例产妇的严重发病率为18.9例。在所有分娩中，每1000个分娩16.6个)。当按平价分层时，在两种结局中,产次2的新生儿结局患病率始终最低,产次1的新生儿结局患病率最高.所有四个队列研究结果的风险比不同，初产出生队列和其他队列之间的差异最明显。在所有出生的人中，稳健推理对估计的置信界限的影响最小，与标准推断相比，即，粗略估计(例如，狼疮重度孕产妇发病率关联：4.01，95%CI3.54-4.55vs.4.01，粗估计值95%CI3.53-4.56），在调整平价时，估计略有偏移，朝向严重孕产妇发病率的无效，远离先兆子痫/子痫的无效。
结论：研究人员应该考虑他们使用的方法之间的一致性，他们的抽样策略，和他们的研究问题。这可能包括完善研究问题，以更好地匹配可用数据的推断，考虑到替代数据源，并适当注意数据限制和由此产生的偏差，以及调查结果的普遍性。如果奇偶校验是一个既定的效果修饰符，应提供分层结果。
