Malaria in the First Trimester of Pregnancy and Fetal Growth: Results from a Beninese Preconceptional Cohort.

BACKGROUND: Malaria in early pregnancy occurs at a time when the placenta is developing, with possible consequences for placental function and fetal growth. We assessed the association between first trimester malaria and fetal growth documented through repeated ultrasound scans. METHODS: The RECIPAL preconceptional cohort included 411 Beninese pregnant women followed from 7 weeks' gestation (wg) until delivery. Among them, 218 had 4 scans for fetal monitoring at 16, 22, 28, and 34 wg. Multivariate seemingly unrelated regression models were used to assess association of microscopic malaria in the first trimester (<15 wg) with abdominal circumference, head circumference, biparietal diameter, and femur length throughout pregnancy. RESULTS: Of 39% (86/218) of women with at least 1 microscopic malarial infection during pregnancy, 52.3% (45/86) were infected in the first trimester. Most women (88.5%) were multiparous. There was no association between adjusted z-scores for fetal growth parameters and first trimester malaria. Parity, newborn sex, socioeconomic level, and maternal body mass index significantly influenced fetal growth. CONCLUSIONS: In a context where malaria infections in pregnancy are well detected and treated, their adverse effect on fetal growth may be limited. Our results argue in favor of preventing and treating infections as early as the first trimester.

Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology.

BACKGROUND: In pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental variable, we proceed by Monte-Carlo simulations to compare the IV-based generalized method of moment (IV-GMM) and the two-stage residual inclusion (2SRI) method in this context. METHODS: We established the formula allowing us to compute the instrument's strength and the confounding level in the context of logistic regression models. We then varied the instrument's strength and the confounding level to cover a large range of scenarios in the simulation study. We also explore two prescription preference-based instruments. RESULTS: We found that the 2SRI is less biased than the other methods and yields satisfactory confidence intervals. The proportion of previous patients of the same physician who were prescribed the treatment of interest displayed a good performance as a proxy of the physician's preference instrument. CONCLUSIONS: This work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding.