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1.
Chinese Journal of Epidemiology ; (12): 112-118, 2019.
Article in Chinese | WPRIM | ID: wpr-738225

ABSTRACT

Cohort study is an irreplaceable method for studies related to maternal and child health. Compared with other countries, China's maternal and child cohort studies started relatively later but has its unique developing track. This paper summarizes the basic information and characteristics of the maternal and child cohorts with wide and great influence in China in the past 25 years.


Subject(s)
Child , Humans , Child Health , Child Health Services/organization & administration , China , Cohort Studies , Maternal Health , Maternal Health Services/organization & administration
2.
Article in Chinese | WPRIM | ID: wpr-737923

ABSTRACT

The overall details of causality frames in the objective world remain obscure, which poses difficulty for causality research. Based on the temporality of cause and effect, the objective world is divided into three time zones and two time points, in which the causal relationships of the variables are parsed by using Directed Acyclic Graphs (DAGs). Causal DAGs of the world (or causal web) is composed of two parts. One is basic or core to the whole DAGs, formed by the combination of any one variable originating from each time unit mentioned above. Cause effect is affected by the confounding only. The other is an internal DAGs within each time unit representing a parent-child or ancestor-descendant relationship, which exhibits a structure similar to the confounding. This paper summarizes the construction of causality frames for objective world research (causal DAGs), and clarify a structural basis for the control of the confounding in effect estimate.


Subject(s)
Humans , Causality , Computer Graphics , Confounding Factors, Epidemiologic , Data Interpretation, Statistical , Epidemiologic Methods
3.
Chinese Journal of Epidemiology ; (12): 770-775, 2018.
Article in Chinese | WPRIM | ID: wpr-738044

ABSTRACT

Objective: To investigate the association between maternal pre-pregnant body mass index and gestational weight gain, as well as their interaction on neonatal birthweight. Methods: We built a cohort in Anqing Municipal Hospital from January 2014 to March 2015, enrolling pregnant women who decided to give birth in this hospital. All women were asked to fill a questionnaire for basic information collection. Medical information of both pregnant women and their newborns were obtained through electronic medical record. Chi-square analysis, multinomial logistic regression, multiplicative and additive interaction methods were used to analyze the association between pre-pregnant body mass index and gestational weight gain as well as their interactions on birth weight of the neonates. Results: A total of 2 881 pregnant women were included in this study. Of the 2 881 newborns, 359 (12.46%) were small for gestational age (SGA) and 273 (9.48%) were large for gestational age (LGA). After adjusting the possible confounding factors, results from the multinomial logistic regression showed that pre-pregnancy underweight women were more possible to deliver SGA (aRR=1.33, 95%CI: 1.02-1.73). If the gestational weight gain was below the recommended criteria, the risk of SGA (aRR=1.64, 95%CI: 1.23-2.19) might increase. Pre-pregnancy overweight/obese could increase the risk of being LGA (aRR=1.86, 95%CI: 1.33-2.60). Maternal gestational weight gain above the recommendation level was associated with higher rates of LGA (aRR=2.03, 95%CI: 1.49-2.78). Results from the interaction analysis showed that there appeared no significant interaction between pre-pregnancy BMI and gestational weight on birthweight. Conclusion: Pre-pregnancy body mass index and gestational weight gain were independently associated with neonatal birthweight while pre-pregnancy BMI and gestational weight gain did not present interaction on birthweight.


Subject(s)
Female , Humans , Infant, Newborn , Pregnancy , Birth Weight , Body Mass Index , Body Weight , China/epidemiology , Cohort Studies , Gestational Weight Gain , Infant, Small for Gestational Age , Logistic Models , Obesity/epidemiology , Overweight/epidemiology , Pregnancy Complications , Pregnant Women , Risk Factors , Thinness/epidemiology , Weight Gain
4.
Chinese Journal of Epidemiology ; (12): 858-861, 2018.
Article in Chinese | WPRIM | ID: wpr-738060

ABSTRACT

One of the commonly accepted merits of cohort studies (CSs) refers to the exposure precedes outcome superior to other observational designs. We use Directed Acyclic Graphs to construct a causal graph among research populations under CSs. We notice that the substitution of research population in place of a susceptible one can be used for effect estimation. Its correctness depends on the outcome-free status of the substituted population and the performance of both screening and diagnosis regarding the outcomes under study at baseline. The temporal precedence of exposure over outcome occurs theoretically, despite the opposite happens in realities. Correct effect estimate is affected by both the suitability of population substitution and the validities of outcome identification and exclusion.


Subject(s)
Causality , Cohort Studies , Confounding Factors, Epidemiologic , Epidemiologic Methods , Mass Screening , Research Design
5.
Chinese Journal of Epidemiology ; (12): 999-1002, 2018.
Article in Chinese | WPRIM | ID: wpr-738086

ABSTRACT

Confounding affects the causal relation among the population. Depending on whether the confounders are known, measurable or measured, they can be divided into four categories. Based on Directed Acyclic Graphs, the strategies for confounding control can be classified as (1) the broken-confounding-path method, which can be further divided into single and dual broken paths, corresponding to exposure complete intervention, restriction and stratification, (2) and the reserved-confounding-path method, which can be further divided into incomplete exposure intervention (in instrumental variable design and non-perfect random control test), mediator method and matching method. Among them, random control test, instrumental variable design or Mendelian randomized design, mediator method can meet the requirements for controlling all four types of confounders, while the restriction, stratification and matching methods are only applicable to known, measurable and measured confounders. Identifying the mechanisms of confounding control is a prerequisite for obtaining correct causal effect estimates, which will be helpful in research design.


Subject(s)
Humans , Causality , Confounding Factors, Epidemiologic , Models, Statistical , Random Allocation , Randomized Controlled Trials as Topic , Research Design
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