Journal Club Notes

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

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?

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

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?

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

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

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

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

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

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

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

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

Conditional Logistic Regression Traditional Logistic Regression