Marie C.D. Stoner

Maternal and newborn outcomes at a tertiary care hospital in Lusaka, Zambia, 2008–2012

To measure key obstetric and neonatal outcomes recorded at a tertiary hospital in Zambia over a 5‐year period.

Effect of schooling on age-disparate relationships and number of sexual partners among young women in rural South Africa enrolled in HPTN 068

Background: Attending school may have a strong preventative association with sexually transmitted infections among young women, but the mechanism for this relationship is unknown. One hypothesis is that students who attend school practice safer sex with fewer partners, establishing safer sexual networks that make them less exposed to infection. Setting: We used longitudinal data from a randomized controlled trial of young women aged 13–20 years in the Bushbuckridge district, South Africa, to determine whether the percentage of school days attended, school dropout, and grade repetition are associated with having a partner 5 or more years older (age–disparate) and with the number of sexual partners in the previous 12 months. Methods: Risks of having an age-disparate relationship and number of sexual partners were compared using inverse probability of exposure weighted Poisson regression models. Generalized estimating equations were used to account for repeated measures. Results: Young women who attended fewer school days (<80%) and who dropped out of school were more likely to have an age–disparate relationship (risk difference 9.9%, 95% confidence interval [CI]: 3.9% to 16.0%; risk difference (%) dropout 17.2%, 95% CI: 5.4% to 29.0%) and those who dropped out reported having fewer partners (count difference dropout 0.343, 95% CI: 0.192 to 0.495). Grade repetition was not associated with either behavior. Conclusion: Young women who less frequently attend school or who drop out are more likely to have an age-disparate relationship. Young women who drop out have overall more partners. These behaviors may increase the risk of exposure to HIV infection in young women out of school.

The effect of school attendance and school dropout on incident HIV and HSV-2 among young women in rural South Africa enrolled in HPTN 068

Objective: To estimate the association between school attendance, school dropout, and risk of incident HIV and herpes simplex virus type 2 (HSV-2) infection among young women. Design: We used longitudinal data from a randomized controlled trial in rural Mpumalanga province, South Africa, to assess the association between school days attended, school dropout, and incident HIV and HSV-2 in young women aged 13–23 years. Methods: We examined inverse probability of exposure weighted survival curves and used them to calculate 1.5, 2.5, and 3.5-year risk differences and risk ratios for the effect of school attendance on incident HIV and HSV-2. A marginal structural Cox model was used to estimate hazard ratios for the effect of school attendance and school dropout on incident infection. Results: Risk of infection increased over time as young women aged, and was higher in young women with low school attendance (<80% school days) compared with high (≥80% school days). Young women with low attendance were more likely to acquire HIV [hazard ratio (HR): 2.97; 95% confidence interval (CI): 1.62, 5.45] and HSV-2 (HR: 2.47; 95% CI: 1.46, 4.17) over the follow-up period than young women with high attendance. Similarly, young women who dropped out of school had a higher weighted hazard of both HIV (HR 3.25 95% CI: 1.67, 6.32) and HSV-2 (HR 2.70; 95% CI 1.59, 4.59). Conclusion: Young women who attend more school days and stay in school have a lower risk of incident HIV and HSV-2 infection. Interventions to increase frequency of school attendance and prevent dropout should be promoted to reduce risk of infection.

Preconception ART and preterm birth: real effect or selection bias?

University of North Carolina School of Medicine, Chapel Hill, NC, USA (JSAS); University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA (MCS); University of North Carolina School of Medicine, Chapel Hill, NC, USA (MPK); University of Zambia School of Medicine, Lusaka, Zambia (BV); University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA (SRC)

Dosage of Sulfadoxine-Pyrimethamine and Risk of Low Birth Weight in a Cohort of Zambian Pregnant Women in a Low Malaria Prevalence Region.

In Lusaka, Zambia, where malaria prevalence is low, national guidelines continue to recommend that all pregnant women receive sulfadoxine-pyrimethamine (SP) for malaria prophylaxis monthly at every scheduled antenatal care visit after 16 weeks of gestation. Human immunodeficiency virus (HIV)-positive women should receive co-trimoxazole prophylaxis for HIV and not SP, but many still receive SP. We sought to determine whether increased dosage of SP is still associated with a reduced risk of low birth weight (LBW) in an area where malaria transmission is low. Our secondary objective was to determine whether any association between SP and LBW is modified by receipt of antiretroviral therapy (ART). We analyzed data routinely collected from a cohort of HIV-positive pregnant women with singleton births in Lusaka, Zambia, between February 2006 and December 2012. We used a log-Poisson model to estimate the risk of LBW by dosage of SP and to determine whether the association between SP and LBW varied by receipt of ART. Risk of LBW declined as the number of doses increased and appeared lowest among women who received three doses (adjusted risk ratio [ARR] = 0.78; 95% confidence interval [CI] = 0.64-0.95). In addition, women receiving combination ART had a higher risk of delivering an LBW infant compared with women receiving no treatment or prophylaxis (ARR = 1.18; 95% CI = 1.09-1.28), but this risk was attenuated among women who were receiving SP (risk ratio = 1.09; 95% CI = 0.99-1.21). SP was associated with a reduced risk of LBW in HIV-positive women, including those receiving ART, in a low malaria prevalence region.

A retrospective study of HIV, antiretroviral therapy, and pregnancy-associated hypertension among women in Lusaka, Zambia

To investigate the association between HIV, antiretroviral therapy (ART), and pregnancy‐associated hypertension (PAH) in an HIV‐endemic setting.

A population‐based cohort study of stillbirth among twins in Lusaka, Zambia

To determine rates of stillbirth and the associated risk factors for stillbirth among twins delivered in Lusaka, Zambia.

Improving preterm newborn identification in low-resource settings with machine learning

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 = 458), 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 GA.

Quantifying bias between reported last menstrual period and ultrasonography estimates of gestational age in Lusaka, Zambia

To quantify differences in assessing preterm delivery when calculating gestational age from last menstrual period (LMP) versus ultrasonography biometry.

Risk of stillbirth among Zambian women with a prior cesarean delivery

Cesarean delivery (CD) may be associated with stillbirth in future pregnancies. We investigated prior CD as a risk factor for stillbirth in Lusaka, Zambia.