Risk factors for adverse outcomes in women with high-risk pregnancy and their neonates, Haiti

Objectives. To determine the prevalence of maternal death, stillbirth and low birthweight in women with (pre-) eclampsia and complicated pregnancies or deliveries in Centre de Références des Urgences Obstétricales, an obstetric emergency hospital in Port-au-Prince, Haiti, and to identify the main risk factors for these adverse pregnancy outcomes. Methods. We conducted a retrospective cohort study of pregnant women admitted to Centre de Référence des Urgences Obstétricales between 2013 and 2018 using hospital records. Risk factors investigated were age group, type of pregnancy (singleton, multiple), type of delivery and use of antenatal care services. Results. A total of 31 509 women and 24 983 deliveries were included in the analysis. Among these, 204 (0.6%) maternal deaths (648 per 100 000 women giving birth), 1962 (7.9%) stillbirths and 11 008 (44.1%) low birthweight neonates were identified. Of all admissions, 10 991 (34.9%) were women with (pre-)eclampsia. Caesarean section significantly increased the risk of maternal death in the women with a complicated pregnancy and women with (pre-)eclampsia, but reduced the risk of stillbirth in such women. Not attending antenatal care was associated with a significantly higher risk of stillbirth (odds ratio (OR) 4.82; 95% confidence interval (CI) 3.55–6.55) and low birthweight (OR 1.40; 95% CI 1.05–1.86) for women with complicated pregnancies. Conclusion. To prevent and treat pregnancy complications as early as possible, antenatal care attendance is crucial. Improving the quality of and access to antenatal care services and providing it free to all pregnant women in Haiti is recommended

Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014–2018

In low-resource settings, detection of healthcare-acquired outbreaks in neonatal units relies on astute clinical staff to observe unusual morbidity or mortality from sepsis as microbiological diagnostics are often absent. We aimed to generate reliable (and automated) early warnings for potential clusters of neonatal late onset sepsis using retrospective data that could signal the start of an outbreak in an NCU in Port au Prince, Haiti, using routinely collected data on neonatal admissions. We constructed smoothed time series for late onset sepsis cases, late onset sepsis rates, neonatal care unit (NCU) mortality, maternal admissions, neonatal admissions and neonatal antibiotic consumption. An outbreak was defined as a statistical increase in any of these time series indicators. We created three outbreak alarm classes: 1) thresholds: weeks in which the late onset sepsis cases exceeded four, the late onset sepsis rates exceeded 10% of total NCU admissions and the NCU mortality exceeded 15%; 2) differential: late onset sepsis rates and NCU mortality were double the previous week; and 3) aberration: using the improved Farrington model for late onset sepsis rates and NCU mortality. We validated pairs of alarms by calculating the sensitivity and specificity of the weeks in which each alarm was launched and comparing each alarm to the weeks in which a single GNB positive blood culture was reported from a neonate. The threshold and aberration alarms were the strongest predictors for current and future NCU mortality and current LOS rates (p<0.0002). The aberration alarms were also those with the highest sensitivity, specificity, negative predictive value, and positive predictive value. Without microbiological diagnostics in NCUs in low-resource settings, applying these simple algorithms to routinely collected data show great potential to facilitate early warning for possible healthcare-acquired outbreaks of LOS in neonates. The methods used in this study require validation across other low-resource settings

Correction: Early warning for healthcare acquired infections in neonatal care units in a low-resource setting using routinely collected hospital data: The experience from Haiti, 2014-2018.

[This corrects the article DOI: 10.1371/journal.pone.0269385.]