[Association between gestational diabetes mellitus and preterm birth subtypes]

Objective: To investigate the association between gestational diabetes mellitus (GDM) and preterm birth subtypes. Methods: Based on the cohort of pregnant women in Anqing Prefectural Hospital, the pregnant women who received prenatal screening in the first or second trimesters were recruited into baseline cohorts; and followed up for them was conducted until delivery, and the information about their pregnancy status and outcomes were obtained through electronic medical record system and questionnaire surveys. The log-binomial regression model was used to explore the association between GDM and preterm birth [iatrogenic preterm birth, spontaneous preterm birth (preterm premature rupture of membranes and preterm labor)]. For multiple confounding factors, the propensity score correction model was used to compute the adjusted association. Results: Among the 2 031 pregnant women with a singleton delivery, the incidence of GDM and preterm birth were 10.0% (204 cases) and 4.4% (90 cases) respectively. The proportions of iatrogenic preterm birth and spontaneous preterm birth in the GDM group (n=204) were 1.5% and 5.9% respectively, while the proportions in non-GDM group (n=1 827) were 0.9% and 3.2% respectively, and the difference in the proportion of spontaneous preterm birth between the two groups was significant (P=0.048). Subtypes of spontaneous preterm were further analyzed, and the results showed that the proportions of preterm premature rupture of membranes and preterm labor in the GDM group were 4.9% and 1.0% respectively, while the proportions in the non-GDM group were 2.1% and 1.1% respectively. It showed that the risk of preterm premature rupture of membranes in GDM pregnant women was 2.34 times (aRR=2.34, 95%CI: 1.16-4.69) higher than that in non-GDM pregnant women. Conclusions: Our results showed that GDM might increase the risk of preterm premature rupture of membranes. No significant increase in the proportion of preterm labor in pregnant women with GDM was found.

[Proportion and rate: connotation and understanding route]

Proportion and rate have multiple and overlapping meanings, which blur their concepts. Based on the existence of the states and the occurrence of the events and their measuring process, we first put forward the concept of "cumulative number of states in point time". Considering the general meaning of "rate" in mathematics and the units of the elements in indexes, this paper puts forward the concept of "the change of cumulative number of states in point time", which is equal to the commonly acknowledged concept "number of incident event within observation period" or "absolute rate", and further constructs relative rate and proportion. Proportions can be classified into three types: time-point (or rate-type) constitutional proportion, time-period incidence proportion and their synthesis, time-period constitutional proportion. The essential difference between relative rate and time-period proportions is whether the observation period is regarded as a one-unit-length fixed period which would be further moved to the description of the indexes. Furthermore, the sources populations of relative rate and proportions are exclusively those at the beginning of the observation period. Thus, we established a unified identification route about ratios, proportions, and rates, the basic indicators of categorical data in populations. These are applicable to both fixed and dynamic populations. The paper aims to clarify the connotation of the indexes and the feasible understanding route and provide some reference for the population researchers.

[May cross-sectional studies provide causal inferences?]

Due to the flaws inherited in synchronicity, statistical association and survivor bias on variables under measurement, a common 'consensus' has been reached on "cross-sectiional studies (CSS) can lead to failure on causal inference". In this paper, under both causal thinking and diagram, the real and measured cross-sections are clearly defined that these two concepts only exist theoretically. In real CSS research, the temporal orders of measured variables are all non-synchronic, equivalent to the assumption that measurement variables are independent to each other, or there is no differentiated classification bias. Similar to cumulative case-control or historical cohort studies, both exposure and outcome that exist or occur before their measurements in cross-sectional studies, are actions of historical reconstruction or doing 'Archaeology'. One of the common preconditions for causal inference in such studies is that: there must be a causal relation between the measured variables and their historical counterparts. The measured variables are all agents of their corresponding real counterparts, and the temporal orders are not that important in causal inference. It is necessary to better understand the analytic role of the CSS.