1. Journal Club Notes

2. Learning Objectives
Explain the criteria used in judging whether an association is causal
Explain what is meant by a case-crossover study design
Identify the advantages and limitations of a case-crossover study design
Distinguish between traditional logistic regression and conditional logistic regression

3. Guidelines for Judging whether an association is causal
Temporal relationship – Required
If a factor is believed to be a cause of a disease, exposure must come before the disease
Short interval must come before the outcome (e.g., LBW, SGA, Preterm Birth)
What about gestational diabetes, obese at beginning of pregnancy, preeclampsia?

4. Guidelines for Judging whether an association is causal
Strength of the association
Measured by the relative risk of the odds Ratio
The stronger the association, the more likely it is that the relationship is causal
Dose-response relationship
As the dose of exposure increases, the risk of the disease increases
In our case the exposure is a short time interval, so “increasing the exposure” means reducing the time interval
Absence of this does not rule out causality – e.g., no disease may develop up to a certain level of exposure; above this level disease develops

5. Guidelines for Judging whether an association is causal
Replication of findings
Expect to find the relationship in different studies and in different populations
Expect relationship within subgroups of the population, unless there is a reason for different results
Biologic plausibility
Consistency with biologic knowledge
Note that sometimes epidemiologic observations preceded biologic knowledge
What about the association between short pregnancy interval and increased risk of gestational diabetes?
Short pregnancy interval And Increased risk of beginning a subsequent pregnancy obese?

6. Guidelines for Judging whether an association is causal
Consideration of alternate explanations
Could the finding be the result of confounding
What have the authors controlled for and what has been left out?
Could the association between short interval and preterm birth be the result of failing to adequately control for other factors associated with both the interval and preterm birth?
Cessation of exposure
Expect the risk of disease to decline when the exposure is removed
Need to consider possibility that pathogenic process cannot be reversed
Reduced risk of poor outcome when interval becomes longer

7. Guidelines for Judging whether an association is causal
Consistency with other knowledge
Findings consistent with other results
If different results, then why? Different methodology? Better study design?
Results from this study are contrary to a large body of research
Specificity of the association
Certain exposure associated with only one disease
Weakest of the criteria – especially as medical knowledge as increased over time
Absence of specificity does not negate causality

8. Hanley GE, et al., “interpregnancy Internal and Adverse pregnancy outcomes: An Analysis of successive pregnancies”, obstetrics and gynecology, vol. 129, no. 3, March 2017.

9. Are Interpregnancy Interval and Outcomes causal or not?
Large body of literature that looked at this association used cross-sectional data and found an association between short intervals and poor outcomes
A few studies with longitudinal data suggest that short interval may not be causal for poor outcomes, e.g., lbw
This study found no increased risk of preterm birth, SGA, LBW or NICu admission after a short pregnancy interval
New study design – case-crossover study
Two other studies have come out since this is
One of a high risk population; one of a general population – finds an association between short interval and poor outcomes but the magnitude of the effect is much smaller than in the previous literature

10. Case-crossover study design
Women had three singleton pregnancies and two interpregnancy intervals
Include only women who an adverse outcome in either her second or third pregnancy (but not both) AND different intervals
each woman is her own control with differences in exposures and outcomes
Exclude women who had normal outcomes in both their 2nd and 3rd pregnancies
Exclude women who had abnormal outcomes in both their 2nd and 3rd pregnancies
Exclude women with the same intervals for both periods
Need very large sample sizes to get adequate power

11. Are Interpregnancy Interval and Outcomes causal or not?
This study answers the question of: whether the risk of a poor outcome after a short or very long interval is greater than the risk of a poor outcome after the same woman experiences a more typical interval?
Many advantages to this methodology
Controls for so many factors that we may not have data on – e.g., genetics, socioeconomic status, education
Disadvantages to this methodology
Cannot control for factors that we cannot measure that change between pregnancies
Practice styles, technological advances – can try to control for these; using a year or year range

12. Are Interpregnancy Interval and Outcomes causal or not?
Two other studies have come out since this study, also using a case-crossover design
One of a high risk population
one of a general population – finds an association between short interval and poor outcomes but the magnitude of the effect is much smaller than in the previous literature
Negative effect of very short interval is less than previously thought
More research needed to get consistency in the results

13. Types of logistic regression
Traditional logistic regression
Dependent variable is often a yes/no type of outcome (e.g., preterm birth, SGA, etc.)
Independent variables include the exposure of interest (interval length), plus variables that control for confounding factors (e.g., age, year, diabetes, hypertension, smoking, hx of perinatal death)
Conditional logistic regression
Dependent variable is a yes/no type of outcome given a matched case/control [in this study a woman matched to her successive pregnancies]
Independent variables include the exposure of interest, plus same control variables as in the traditional regression

14. Conditional Logistic Regression
Traditional Logistic Regression