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Vaginal dysbiosis has been shown to increase the risk of some adverse birth outcomes. HIV infection may be associated with shifts in the vaginal microbiome. We characterized microbial communities in vaginal swabs collected between 16–20 gestational weeks in the Zambian Preterm Birth Prevention Study to investigate whether HIV and its treatment alter the microbiome in pregnancy. We quantified relative abundance and diversity of bacterial taxa by whole-genome shotgun sequencing and identified community state types (CST) by hierarchical clustering. Associations between exposures—HIV serostatus (HIV+ vs HIV-) and preconceptional ART (ART+ vs ART-)—and microbiome characteristics were tested with rank-sum, and by linear and logistic regression, accounting for sampling by inverse-probability weighting. Of 261 vaginal swabs, 256 (98%) had evaluable sequences; 98 (38%) were from HIV+ participants, 55 (56%) of whom had preconceptional ART exposure. Major CSTs were dominated by: L. crispatus (CST 1; 17%), L.] iners (CST 3; 32%), Gardnerella vaginalis (CST 4-I; 37%), G. vaginalis & Atopobium vaginae (CST 4-II; 5%), and other mixed anaerobes (CST 4-III; 9%). G. vaginalis was present in 95%; mean relative abundance was higher in HIV+ (0.46±0.29) compared to HIV- participants (0.35±0.33; rank-sum p = .01). Shannon diversity was higher in HIV+/ART+ (coeff 0.17; 95%CI (0.01,0.33), p = .04) and HIV+/ART- (coeff 0.37; 95%CI (0.19,0.55), p < .001) participants compared to HIV-. Anaerobe-dominant CSTs were more prevalent in HIV+/ART+ (63%, AOR 3.11; 95%CI: 1.48,6.55, p = .003) and HIV+/ART- (85%, AOR 7.59; 95%CI (2.80,20.6), p < .001) compared to HIV- (45%). Restricting the comparison to 111 women in either CST 3 (L. iners dominance) or CST 1 (L. crispatus dominance), CST 3 frequency was similar in HIV- (63%) and HIV+/ART- participants (67%, AOR 1.31; 95%CI: (0.25,6.90), p = .7), but higher in HIV+/ART+ (89%, AOR 6.44; 95%CI: (1.12,37.0), p = .04). Pregnant women in Zambia, particularly those with HIV, had diverse anaerobe-dominant vaginal microbiota.

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Background Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. Methods and findings This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3–40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4–40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. Conclusions We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess ...

Sub-Saharan Africa bears a disproportionate burden of preterm Background: birth and other adverse outcomes. Not only is the background rate of preterm birth higher than in North America and Europe, but many facilities lack essential equipment and personnel resources to care for preterm neonates. A better understanding of the demographic, clinical, and biologic underpinnings of preterm birth is urgently needed to plan interventions and inform new discovery.The Zambian Preterm Birth Prevention Study (ZAPPS) is a Methods: prospective antenatal cohort established at the Women and Newborn Hospital of the University Teaching Hospital (UTH) in Lusaka, Zambia. We recruit pregnant women from the antenatal clinics of district health centers and the UTH for study participation. Women undergo ultrasound examination to determine eligibility by gestational age criteria. Enrolled participants receive routine antenatal and postnatal care, lab testing, midtrimester cervical length measurement, serial fetal growth monitoring and careful assessment of birth outcomes.Between August 2015 and September 2017, we screened 1784 Results: women, of whom 1450 (81.2%) met inclusion criteria and were enrolled. The median age at enrollment of study participants is 27 years (IQR 23-32). Participants are enrolled at a median gestational age of 16 weeks (IQR 13-18). Among all parous participants (N=866; 64%), 21% (N=182) reported a prior miscarriage, 49% (N=424) reported a prior preterm birth, and 13% (N=116) reported a prior stillbirth. The HIV seroprevalence in our cohort is 24%.We have established a large antenatal cohort to characterize the Discussion: Gates Open Research epidemiological and biological determinants of adverse birth outcomes in Lusaka, Zambia. Findings from this cohort will help guide future studies, clinical care, and policy in the prevention and treatment of adverse birth outcomes.