High rates of placental inflammation among samples collected by the Multi-Omics for Mothers and Infants consortium.

BACKGROUND: The Multi-Omics for Mothers and Infants consortium aims to improve birth outcomes. Preterm birth is a major obstetrical complication globally and causes significant infant and childhood morbidity and mortality. OBJECTIVE: We analyzed placental samples (basal plate, placenta or chorionic villi, and the chorionic plate) collected by the 5 Multi-Omics for Mothers and Infants sites, namely The Alliance for Maternal and Newborn Health Improvement Bangladesh, The Alliance for Maternal and Newborn Health Improvement Pakistan, The Alliance for Maternal and Newborn Health Improvement Tanzania, The Global Alliance to Prevent Prematurity and Stillbirth Bangladesh, and The Global Alliance to Prevent Prematurity and Stillbirth Zambia. The goal was to analyze the morphology and gene expression of samples collected from preterm and uncomplicated term births. STUDY DESIGN: The teams provided biopsies from 166 singleton preterm (<37 weeks' gestation) and 175 term (≥37 weeks' gestation) deliveries. The samples were fixed in formalin and paraffin embedded. Tissue sections from these samples were stained with hematoxylin and eosin and subjected to morphologic analyses. Other placental biopsies (n=35 preterm, 21 term) were flash frozen, which enabled RNA purification for bulk transcriptomics. RESULTS: The morphologic analyses revealed a surprisingly high rate of inflammation that involved the basal plate, placenta or chorionic villi, and the chorionic plate. The rate of inflammation in chorionic villus samples, likely attributable to chronic villitis, ranged from 25% (Pakistan site) to 60% (Zambia site) of cases. Leukocyte infiltration in this location vs in the basal plate or chorionic plate correlated with preterm birth. Our transcriptomic analyses identified 267 genes that were differentially expressed between placentas from preterm vs those from term births (123 upregulated, 144 downregulated). Mapping the differentially expressed genes onto single-cell RNA sequencing data from human placentas suggested that all the component cell types, either singly or in subsets, contributed to the observed dysregulation. Consistent with the histopathologic findings, gene ontology analyses highlighted the presence of leukocyte infiltration or activation and inflammatory responses in both the fetal and maternal compartments. CONCLUSION: The relationship between placental inflammation and preterm birth is appreciated in developed countries. In this study, we showed that this link also exists in developing geographies. In addition, among the participating sites, we found geographic- and population-based differences in placental inflammation and preterm birth, suggesting the importance of local factors.

Acceptability of a Randomized Trial of Anti-depressant Medication or Interpersonal Therapy for Treatment of Perinatal Depression in Women with HIV.

Postpartum depression (PPD) affects nearly 20% of postpartum women in Sub-Saharan Africa (SSA), where HIV prevalence is high. Depression is associated with worse HIV outcomes in non-pregnant adults and mental health disorders may worsen HIV outcomes for postpartum women and their infants. PPD is effectively treated with psychosocial or pharmacologic interventions; however, few studies have evaluated the acceptability of treatment modalities in SSA. We analyzed interviews with 23 postpartum women with HIV to assess the acceptability of two depression treatments provided in the context of a randomized trial. Most participants expressed acceptability of treatment randomization and study visit procedures. Participants shared perceptions of high treatment efficacy of their assigned intervention. They reported ongoing HIV and mental health stigma in their communities and emphasized the importance of social support from clinic staff. Our findings suggest a full-scale trial of PPD treatment will be acceptable among women with HIV in Zambia.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Association of maternal prenatal copper concentration with gestational duration and preterm birth: a multicountry meta-analysis.

BACKGROUND: Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). OBJECTIVES: This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. METHODS: Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. RESULTS: The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 µg/mL and standard deviation of 0.43 µg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 µg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. CONCLUSIONS: Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.

Comparison of Masimo Total Hemoglobin SpHb® continuous non-invasive hemoglobin monitoring device with laboratory complete blood count measurement using venous sample: Protocol for an observational substudy of the Pregnancy Risk and Infant Surveillance and Measurement Alliance Maternal and Newborn Health (PRISMA MNH) study.

Background: The Masimo Total Hemoglobin SpHb® is a continuous and non-invasive handheld device to measure hemoglobin levels. Previous research has found that SpHb is able to accurately detect hemoglobin levels in adult patients with a similar degree of bias and standard deviation to point-of-care invasive method measurements. Generally, limited clinical evidence, lack of validation of Masimo at higher than and lower than hemoglobin threshold values, and scientific consensus supporting the use of Masimo for accurate hemoglobin testing for the diagnosis of anemia during pregnancy calls for further research. Methods and analysis: The proposed prospective cohort will be nested within the ongoing Pregnancy Risk and Infant Surveillance and Measurement Alliance (PRISMA) Maternal and Newborn Health (MNH) study. Three study sites (located in Zambia, Kenya, and Pakistan) will participate and collect hemoglobin data at five time points (<20 weeks, 20 weeks, 28 weeks, 36 weeks' gestation, and six weeks postpartum). We will measure hemoglobin using a venous blood sample via hematology auto-analyzer complete blood count (gold standard) and the non-invasive device. The primary objective is to assess agreement between Masimo total hemoglobin and complete blood count and on a continuous scale using Intraclass Correlation Coefficient and Bland-Altman Analysis. The second objective is to assess agreement between the two measures on a binary scale using Positive Percentage Agreement and Negative Percentage Agreement, Cohen's Kappa, and McNemar Test. On an ordinal scale, agreement will be measured using Weighted Cohen's Kappa and Harrel's Concordance Index. Lastly, we will assess factors that might affect the accuracy of Masimo total hemoglobin using linear mixed models. Conclusions: The primary aim of this study is to assess the validity of the non-invasive Masimo device compared to the gold standard method of invasive hemoglobin measurements during pregnancy and postpartum periods for the diagnosis of anemia.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

BACKGROUND: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data. METHODS: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. RESULTS: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh). CONCLUSIONS: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.

Maternal COVID-19 infection and associated factors: A cross-sectional study.

BACKGROUND: Since the declaration of COVID-19 as a global pandemic, several studies have been conducted to examine associated factors. However, few studies have focused on pregnant women infected with COVID-19 in sub-Saharan Africa. Therefore, this study investigated the prevalence and factors associated with COVID-19 infection among pregnant women at the Levy Mwanawasa University Teaching Hospital and Women and Newborn Hospital of the University Teaching Hospitals in Lusaka, Zambia. METHODS: A cross-sectional study was conducted between March and July 2021. Women were recruited as they presented for antenatal care. Data was collected using a structured questionnaire to capture variables of interest (socio-demographic, clinical and obstetric). COVID-19 diagnosis was made using a nasopharyngeal swab by PCR test. Multivariable logistic regression was used to control for confounding and calculate the odds ratios for each explanatory variable and respective 95% confidence intervals. RESULTS: The study enrolled 352 participants with a mean (standard deviation [SD]) age of 30.1 years (5.6). One hundred thirty of 352 (36.9%; 95% CI: 31.9 to 42.2) participants had a confirmed positive SARS-CoV-2 test result. At univariable analysis, factors associated with COVID-19 were increased gestational age, education status and maternal HIV serostatus. Women with a secondary level of education were less likely to have COVID-19 infection than those with a primary level of education (AOR = 0.23, 95% CI: 0.09-0.63). On the other hand, a one-week increase in gestational age was associated with higher odds of COVID-19 infection (AOR = 1.03, 95% CI: 1.01-1.06). CONCLUSION: The results showed that the prevalence of COVID-19 infection among pregnant women was 36.9% and was associated with increased gestational age and a lower level of education. To mitigate adverse maternal outcomes, there is a need to screen for COVID-19 strictly and broadly monitor prenatal women presenting for healthcare.

Progesterone and prolactin levels in pregnant women living with HIV who delivered preterm and low birthweight infants: A nested case-control study.

BACKGROUND: Antiretroviral therapy (ART) is associated with high rates of adverse birth outcomes, including preterm birth and low birthweight. Studies suggest that progesterone and prolactin may play important intermediary roles. METHODS: We analyzed data from the Antenatal Component of the PROMISE trial, a multi-center study of pregnant women taking antiretroviral regimens (lopinavir/ritonavir-containing ART or zidovudine alone) to prevent mother-to-child HIV transmission. In a nested case-control study, we compared data from women who gave birth to preterm (<37 weeks gestation) and/or low birthweight (<2500 g) infants to matched individuals who did not. We measured serum progesterone and prolactin at 24-34 weeks gestation. We used conditional logistic regression to describe relationships between hormone levels, birth outcomes, and antiretroviral regimens. RESULTS: 299 women and their newborns were included (146 cases, 153 controls). When compared to women receiving zidovudine alone, those on ART had higher odds of progesterone levels under the 10th percentile (adjusted odds ratio [AOR]:2.34, 95%CI:1.41-3.89) and 25th percentile (AOR:2.07, 95%CI:1.46-2.94). However, higher levels of progesterone-rather than lower levels-were associated with our composite case outcome at the 10th percentile (AOR:1.88, 95%CI:0.77-4.59) and 25th percentile (AOR:1.96, 95%CI:1.06-3.61). Associations were not observed between prolactin, antiretroviral regimen, and birth outcomes. CONCLUSION: We observed lower progesterone levels among women allocated to ART regimens; however, higher progesterone levels were associated with preterm birth and/or low birthweight. While features of the study design may have contributed to these findings, they nevertheless highlight the potentially complex mechanisms underpinning adverse birth outcomes and HIV.

Maternal mid-upper arm circumference to predict small for gestational age: Findings in a Zambian cohort.

OBJECTIVE: To compare the performance of mid upper arm circumference (MUAC) and body mass index (BMI) for prediction of small for gestational age (SGA) in Zambia. METHODS: This is a secondary analysis of an ongoing clinical cohort that included women with a single gestation and MUAC measured before 24 weeks of pregnancy. We assessed relationships between maternal MUAC and birth weight centile using regression. The performance of MUAC and BMI to predict SGA was compared using receiver operating characteristic curves and the effect of maternal HIV was investigated in sub-group analyses. RESULTS: Of 1117 participants, 847 (75%) were HIV-negative (HIV-) and 270 (24%) were HIV-positive (HIV+). Seventy-four (7%) delivered severe SGA infants (<3rd centile), of whom 56 (76%) were HIV- and 18 (24%) were HIV+ (odds ratio [OR] 1.01, 95% confidence interval [CI] 0.58-1.75). MUAC was associated with higher birth weight centile (+1.2 centile points, 95% CI 0.7-1.6; P < 0.001); this relationship was stronger among HIV+ women (+1.7 centile points, 95% CI 0.8-2.6; P < 0.001) than HIV- women (+0.9 centile points, 95% CI 0.4-1.4; P = 0.001). The discriminatory power was similar, albeit poor (area under the curve [AUC] < 0.7), between MUAC and BMI for the prediction of SGA. In stratified analysis, MUAC and BMI showed excellent discrimination predicting severe SGA among HIV+ (AUC 0.83 and 0.81, respectively) but not among HIV- women (AUC 0.64 and 0.63, respectively). CONCLUSION: Maternal HIV infection increased the discrimination of both early pregnancy MUAC and BMI for prediction of severe SGA in Zambia. CLINICAL TRIAL NUMBER: ClinicalTrials.gov (NCT02738892).

Missing Outcome Data in Epidemiologic Studies.

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.

Different effects for different questions: An illustration using short cervix and the risk of preterm birth.

OBJECTIVE: To illustrate the difference between exposure effects and population attributable effects. METHODS: We examined the effect of mid-pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation. RESULTS: Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth. CONCLUSION: Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment.

Background: Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings. Methods: Blind sweep ultrasounds, consisting of six freehand ultrasound sweeps, were collected by sonographers in the USA and Zambia, and novice operators in Zambia. We developed artificial intelligence (AI) models that used blind sweeps to predict gestational age (GA) and fetal malpresentation. AI GA estimates and standard fetal biometry estimates were compared to a previously established ground truth, and evaluated for difference in absolute error. Fetal malpresentation (non-cephalic vs cephalic) was compared to sonographer assessment. On-device AI model run-times were benchmarked on Android mobile phones. Results: Here we show that GA estimation accuracy of the AI model is non-inferior to standard fetal biometry estimates (error difference -1.4 ± 4.5 days, 95% CI -1.8, -0.9, n = 406). Non-inferiority is maintained when blind sweeps are acquired by novice operators performing only two of six sweep motion types. Fetal malpresentation AUC-ROC is 0.977 (95% CI, 0.949, 1.00, n = 613), sonographers and novices have similar AUC-ROC. Software run-times on mobile phones for both diagnostic models are less than 3 s after completion of a sweep. Conclusions: The gestational age model is non-inferior to the clinical standard and the fetal malpresentation model has high AUC-ROCs across operators and devices. Our AI models are able to run on-device, without internet connectivity, and provide feedback scores to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.

Effect of weekly 17-hydroxyprogesterone caproate on small for gestational age among pregnant women with HIV in Zambia.

The IPOP trial demonstrated a reduced risk of severe small for gestational age among infants born to women with HIV who received weekly intramuscular 17 alpha-hydroxyprogesterone caproate. This secondary analysis examined the 17P treatment effect in subgroups of maternal BMI, parity, timing of antiretroviral therapy (ART) initiation, and ART regimen. We found that 17P was more effective among nulliparous women, women who started ART before pregnancy, and those taking protease inhibitors.

Interpersonal therapy versus antidepressant medication for treatment of postpartum depression and anxiety among women with HIV in Zambia: a randomized feasibility trial.

INTRODUCTION: Postpartum depression (PPD) is a prevalent and debilitating disease that may affect medication adherence and thus maternal health and vertical transmission among women with HIV. We assessed the feasibility of a trial of interpersonal psychotherapy (IPT) versus antidepressant medication (ADM) to treat PPD and/or anxiety among postpartum women with HIV in Lusaka, Zambia. METHODS: Between 29 October 2019 and 8 September 2020, we pre-screened women 6-8 weeks after delivery with the Edinburgh Postnatal Depression Scale (EPDS) and diagnosed PPD or anxiety with the Mini International Neuropsychiatric Interview. Consenting participants were randomized 1:1 to up to 11 sessions of IPT or daily self-administered sertraline and followed for 24 weeks. We assessed EPDS score, Clinical Global Impression-Severity of Illness (CGI-S) and medication side effects at each visit and measured maternal HIV viral load at baseline and final study visit. Retention, visit adherence, change in EPDS, CGI-S and log viral load were compared between groups with t-tests and Wilcoxon signed rank tests; we report mean differences, relative risks and 95% confidence intervals. A participant satisfaction survey assessed trial acceptability. RESULTS: 78/80 (98%) participants were retained at the final study visit. In the context of the COVID-19 pandemic, visit adherence was greater among women allocated to ADM (9.9 visits, SD 2.2) versus IPT (8.9 visits, SD 2.4; p = 0.06). EPDS scores decreased from baseline to final visit overall, though mean change was greater in the IPT group (-13.8 points, SD 4.7) compared to the ADM group (-11.4 points, SD 5.5; p = 0.04). Both groups showed similar changes in mean log viral load from baseline to final study visit (mean difference -0.43, 95% CI -0.32, 1.18; p = 0.48). In the IPT group, viral load decreased significantly from baseline (0.9 log copies/ml, SD 1.7) to final visit (0.2 log copies/ml, SD 0.9; p = 0.01). CONCLUSIONS: This pilot study demonstrates that a trial of two forms of PPD treatment is feasible and acceptable among women with HIV in Zambia. IPT and ADM both improved measures of depression severity; however, a full-scale trial is required to determine whether treatment of PPD and anxiety improves maternal-infant HIV outcomes.

Psychosocial issues and coping mechanisms of pregnant and postnatal women diagnosed with COVID-19: A qualitative study.

OBJECTIVE: There is a paucity of data on the psychosocial issues and coping mechanisms among pregnant and postnatal women with COVID-19 infection. We, therefore, aimed to explore the psychosocial issues and coping mechanisms of pregnant and postnatal women diagnosed with COVID-19 at tertiary-level hospitals. METHODS: This was a qualitative phenomenological study conducted in 2021 with a sample size of 16 women admitted at two referral hospitals serving as COVID-19 admission facilities for pregnant and postnatal women in Lusaka, Zambia. In-depth interviews were conducted via telephone to understand what these women experienced when diagnosed with COVID-19. All the interviews were audio-recorded and transcribed verbatim. Thematic analysis was conducted using the six steps approach to develop emerging themes. RESULTS: Two major themes emerged: psychosocial issues and coping mechanisms. The primary psychosocial issues were worry and stigma. Women worried about infecting their unborn baby or neonate, being separated from the baby, the general safety of the baby, and the health of other family members. Women also worried about the attitude of health care providers and faced discrimination or stigma because of their infection. Thus, some coping mechanisms were developed that helped them, such as a positive attitude, keeping the disease secret, reliance on family members for support and using positive information from social media. CONCLUSION: This study provides unique insights into the psychosocial experiences of pregnant and postnatal women diagnosed with COVID-19. Women were particularly concerned about the unborn baby's well-being and discrimination.This study suggests the need for policy and clinical practice to consider the integration of effective mental health services into the provision of maternal health and COVID-19 services.

Prediction of gestational age using urinary metabolites in term and preterm pregnancies.

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.