Interpretation
Our finding that C. trachomatis and N. gonorrhoeaescreening and treatment reduced preterm birth or low birthweight among nulliparous women is similar to the AuTop trial,(22) which found that Bacterial vaginosis (BV) screening and treatment reduced preterm birth among nulliparous, but not multiparous low-risk pregnant women in France. Authors of the AuTop trial hypothesized that the differential effect may be due to nulliparous women having an unknown risk for preterm birth, whereas multiparous participants had a low risk as women with a history preterm birth or late miscarriage were excluded from enrollment. However, the Maduo study did not exclude participants based on risk factors and 13% of multiparous women had a documented history of preterm birth or low birth weight outcomes. AuTop authors also speculated that the results could be due to higher treatment uptake among nulliparous compared to multiparous women (85% vs 79%). In Maduo, the prevalence of C. trachomatis and N. gonorrhoeaeinfections and treatment uptake were similar by parity. Another reason for the differential impact in our study could be that nulliparous women were less likely to be living with HIV compared to multiparous women (5% vs. 24%) suggesting a lower risk of previous exposure to C. trachomatis and N. gonorrhoeae infections. Some limited research supports the idea that a degree of protective immunity could develop as a result of prior chlamydial infection, which would make the screening and treatment intervention less effective.(23)
Our finding that STI screening and treatment could reduce adverse birth outcomes compared to syndromic management adds some support to previous systematic reviews and meta-analyses. Adachi et al., found eight studies that evaluated the effect of screening on adverse pregnancy outcomes. (8) Five studies found that treatment with erythromycin reduced adverse birth outcomes.(24-28) Two systematic reviews and meta-analyses assessed the relationship between antenatal infection and adverse birth outcomes. One review found 12 relevant observational studies, (20) and the meta-analysis results concluded that C. trachomatis was associated with preterm labor (unadjusted OR =1·29; 95% CI 1·11–1·50) and low birth weight (unadjusted OR = 1·80; 95% CI 1·20–2·71). However, that review found substantial heterogeneity across studies and only two studies reported whether women had received treatment. Another systematic review and meta-analysis assessing N. gonorrhoeaefound that women with N. gonorrhoeae were more likely to experience preterm birth (OR 1·55, 95% CI 1·21 to 1·99, n=18 studies) and low birth weight (OR 1·66, 95% CI 1·12 to 2·48, n=8). (7) Included studies were all observational, most did not control for confounding, and most women with N. gonorrhoeae had received treatment.
A recent study investigated the relationship between C. trachomatis, N. gonorrhoeae , and Trichomonas (T.) vaginalisscreening and treatment among pregnant women living with HIV in South Africa(29) and found no association between screening and preterm birth or low birth weight. However, this study was limited to women living with HIV, did not exclude symptomatic participants, found the testing group was less likely to be on anti-retroviral therapy, and treated participants for T. vaginalis . Further, participants in that study had high levels of persistent infections as 27% were positive forC. trachomatis at repeat testing.(30) No participants in the current study were positive at test-of-cure.(31)
Given the equipoise around the impact of STI screening on adverse pregnancy outcomes, future research may consider individual randomization or other methods for addressing confounding, measurement bias, and lack of power. As discussed by Low in a recent commentary,(32) observational studies face major threats to internal validity as many factors are associated with both STIs and pregnancy outcomes. Case-control and cohort studies are subject to measurement bias when receipt of treatment is not recorded because treatment likely reduces the risk of the outcome.(32) Cluster trials, such as the current study and the upcoming WANTAIM Trial(33) seek to reduce confounding and measurement bias; however, confounding can still occur through unmeasured baseline imbalance and clustering has a negative impact on the power to detect an effect and thus cluster designs typically require larger sample sizes.(34)