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To determine stillbirth ratio and its association with maternal, perinatal, and delivery characteristics, as well as geographic differences in Latin American countries (LAC). We analysed data from the Perinatal Information System of the Latin American Center for Perinatology and Human Development (CLAP) between January 2018 and June 2021 in 8 health facilities from five LAC countries (Bolivia, Guatemala, Honduras, Nicaragua, and the Dominican Republic). Maternal, pregnancy, and delivery characteristics, in addition to pregnancy outcomes were reported. Estimates of association were tested using chi-square tests, and P < 0.05 was regarded as significant. Bivariate analysis was conducted to estimate stillbirth risk. Prevalence ratios (PR) with their 95% confidence intervals (CI) for each predictor were reported. In total, 101,852 childbirths comprised the SIP database. For this analysis, we included 99,712 childbirths. There were 762 stillbirths during the study period; the Stillbirth ratio of 7.7/1,000 live births (ranged from 3.8 to 18.2/1,000 live births across the different maternities); 586 (76.9%) were antepartum stillbirths, 150 (19.7%) were intrapartum stillbirths and 26 (3.4%) with an ignored time of death. Stillbirth was significantly associated with women with diabetes (PRadj 2.36; 95%CI [1.25-4.46]), preeclampsia (PRadj 2.01; 95%CI [1.26-3.19]), maternal age (PRadj 1.04; 95%CI [1.02-1.05]), any medical condition (PRadj 1.48; 95%CI [1.24-1.76, and severe maternal outcome (PRadj 3.27; 95%CI [3.27-11.66]). Pregnancy complications and maternal morbidity were significantly associated with stillbirths. The stillbirth ratios varied across the maternity hospitals, which highlights the importance for individual surveillance. Specialized antenatal and intrapartum care remains a priority, particularly for women who are at a higher risk of stillbirth.

IntroductionCOVID-19 is associated with higher morbimortality in pregnant people compared with non-pregnant people. At present, the benefits of maternal immunisation are considered to outweigh the risks, and therefore, vaccination is recommended during pregnancy. However, additional information is needed on the safety of the vaccines in this population.Methods and analysisThis a retrospective cohort nested case–control study in pregnant people who attended maternity hospitals from eight Latin American and Caribbean countries. A perinatal electronic clinical history database with neonatal and obstetric information will be used. The proportion of pregnant people immunised with COVID-19 vaccines of the following maternal and neonatal events will be described: preterm infant, small for gestational age, low birth weight, stillbirth, neonatal death, congenital malformations, maternal near miss and maternal death. Moreover, the risk of prematurity, small for gestational age and low birth weight associated with exposure to COVID-19 vaccines will be estimated. Each case will be matched with two groups of three randomly selected controls. Controls will be matched by hospital and mother’s age (±3 years) with an additional matching by delivery date and conception time in the first and second control groups, respectively. The estimated required sample size for the main analysis (exposure to any vaccine) concerning ‘non-use’ is at least 1009 cases (3027 controls) to detect an increased probability of vaccine-associated event risk of 30% and at least 650 cases (1950 controls) to detect 30% protection. Sensitivity and secondary analyses considering country, type of vaccine, exposure windows and completeness of immunisation will be reported.EthicsThe study protocol was reviewed by the Ethical Review Committee on Research of the Pan American Health Organization. Patient informed consent was waived due to the retrospective design and the utilisation of anonymised data (Ref. No: PAHOERC.0546.01). Results will be disseminated in open access journals.

Background Latin America has the highest Cesarean Section Rates (CSR) in the world. Robson’s Ten Group Classification System (RTGCS) was developed to enable understanding the CSR in different groups of women, classified according to obstetric characteristics into one of ten groups. The size of each CS group may provide helpful data on quality of care in a determined region or setting. Data can potentially be used to compare the impact of conditions such as maternal morbidity on CSR. The objective of this study is to understand the impact of Severe Maternal Morbidity (SMM) on CSR in ten different groups of RTGCS. Methods Secondary analysis of childbirth information from 2018 to 2021, including 8 health facilities from 5 Latin American and Caribbean countries (Bolivia, Guatemala, Honduras, Nicaragua, and the Dominican Republic), using a surveillance database (SIP-Perinatal Information System, in Spanish) implemented in different settings across Latin America. Women were classified into one of RTGCS. The frequency of each group and its respective CSR were described. Furthermore, the sample was divided into two groups, according to maternal outcomes: women without SMM and those who experienced SMM, considering Potentially Life-threatening Conditions, Maternal Near Miss and Maternal Death as the continuum of morbidity. Results Available data were obtained from 92,688 deliveries using the Robson Classification. Overall CSR was around 38%. Group 5 was responsible for almost one-third of cesarean sections. SMM occurred in 6.7% of cases. Among these cases, the overall CSR was almost 70% in this group. Group 10 had a major role (preterm deliveries). Group 5 (previous Cesarean section) had a very high CSR within the group, regardless of the occurrence of maternal morbidity (over 80%). Conclusion Cesarean section rate was higher in women experiencing SMM than in those without SMM in Latin America. SMM was associated with higher Cesarean section rates, especially in groups 1 and 3. Nevertheless, group 5 was the major contributor to the overall CSR.

The burden of maternal morbidity in neonatal outcomes can vary with the adequacy of healthcare provision and tool implementation to improve monitoring. Such information is lacking in Latin American countries, where the decrease in severe maternal morbidity and maternal death remains challenging. To determine neonatal outcomes according to maternal characteristics, including different degrees of maternal morbidity in Latin American health facilities. This is a secondary cross-sectional analysis of the Perinatal Information System (SIP) database from eight health facilities in five Latin American and Caribbean countries. Participants were all women delivering from August 2018 to June 2021, excluding cases of abortion, multiple pregnancies and missing information on perinatal outcomes. As primary and secondary outcome measures, neonatal near miss and neonatal death were measured according to maternal/pregnancy characteristics and degrees of maternal morbidity. Estimated adjusted prevalence ratios (PRadj) with their respective 95% CIs were reported. In total 85,863 live births were included, with 1,250 neonatal near miss (NNM) cases and 695 identified neonatal deaths. NNM and neonatal mortality ratios were 14.6 and 8.1 per 1,000 live births, respectively. Conditions independently associated with a NNM or neonatal death were the need for neonatal resuscitation (PR 16.73, 95% CI [13.29-21.05]), being single (PR 1.45, 95% CI [1.32-1.59]), maternal near miss or death (PR 1.64, 95% CI [1.14-2.37]), preeclampsia (PR 3.02, 95% CI [1.70-5.35]), eclampsia/HELPP (PR 1.50, 95% CI [1.16-1.94]), maternal age (years) (PR 1.01, 95% CI [<1.01-1.02]), major congenital anomalies (PR 3.21, 95% CI [1.43-7.23]), diabetes (PR 1.49, 95% CI [1.11-1.98]) and cardiac disease (PR 1.65, 95% CI [1.14-2.37]). Maternal morbidity leads to worse neonatal outcomes, especially in women suffering maternal near miss or death. Based on SIP/PAHO database all these indicators may be helpful for routine situation monitoring in Latin America with the purpose of policy changes and improvement of maternal and neonatal health.

Objectives. To assess a birth registry to explore maternal mortality and morbidity and their association with other factors. Study Design. Exploratory multicentre cross-sectional analysis with over 700 thousand childbirths from twelve Latin American and Caribbean countries between 2009 and 2012. The WHO criteria for maternal morbidity were employed to split women, following a gradient of severity of conditions, into (1) maternal death (MD); (2) maternal near miss (MNM); (3) potentially life-threatening conditions (PLTC); (4) less severe maternal morbidity (LSMM); (5) any maternal morbidity; and (6) women with no maternal morbidity. Their prevalence and estimated risks of adverse maternal outcomes were assessed. Results. 712,081 childbirths had a prevalence of MD and MNM of 0.14% and 3.1%, respectively, while 38% of women had experienced morbidity. Previous maternal morbidity was associated with higher risk of adverse maternal outcomes and also the extremes of reproductive ages, nonwhite ethnicity, no stable partner, no prenatal care, smoking, drug and alcohol use, elective C-section, or induction of labour. Poorer perinatal outcomes were proportional to the severity of maternal outcomes. Conclusions. The findings corroborate WHO concept regarding continuum of maternal morbidity, reinforcing its importance in preventing adverse maternal outcomes and improving maternal healthcare in different settings.

Recent published evidence indicates a negative correlation between density of populations and the distance of their environments to a suitably defined ‘niche centroid’. This empirical observation lacks theoretical grounds. We provide a theoretical underpinning for the empirical relationship between population density and position in niche space, and use this framework to understand the circumstances under which the relationship will fail. We propose a metapopulation model for the area of distribution, as a system of ordinary differential equations coupled with a dispersal kernel. We present an analytical approximation to the solution of the system as well as R code to solve the full model numerically. We use this tool to analyze various scenarios and assumptions. General and realistic demographic assumptions imply a good correlation between position in niche space and population abundance. Factors that modify this correlation are: transitory states, a heterogeneous spatial structure of suitability, and Allee effects. We also explain why the raw output of the niche modeling algorithm MaxEnt is not a good predictor of environmental suitability. Our results elucidate the empirical results for spatial patterns of population size in niche terms, and provide a theoretical basis for a structured theory of the niche.

Robust methods by which to generate virtual species are needed urgently in the emerging field of distributional ecology to evaluate performance of techniques for modeling ecological niches and species distributions and to generate new questions in biogeography. Virtual species provide the opportunity to test hypotheses and methods based on known and unbiased distributions. We present Niche Analyst (NicheA), a toolkit developed to generate virtual species following the Hutchinsonian approach of an n-multidimensional space occupied by the species. Ecological niche models are generated, analyzed, and visualized in an environmental space, and then projected to the geographic space in the form of continuous or binary species distribution models. NicheA is implemented in a stable and user-friendly Java platform. The software, online manual, and user support are freely available at < http://nichea.sourceforge.net >.

Blonder et al. (2014, Global Ecology and Biogeography, 23, 595–609) introduced a new multivariate kernel density estimation (KDE) method to infer Hutchinsonian hypervolumes in the modelling of ecological niches. The authors argued that their KDE method matches or outperforms several methods for estimating hypervolume geometries and for conducting species distribution modelling. Further clarification, however, is appropriate with respect to the assumptions and limitations of KDE as a method for species distribution modelling. Using virtual species and controlled environmental scenarios, we show that KDE both under- and overestimates niche volumes depending on the dimensionality of the dataset and the number of occurrence records considered. We suggest that KDE may be a viable approach when dealing with large sample sizes, limited sampling bias and only a few environmental dimensions.

Recent studies have used occupancy models (OM) and ecological niche models (ENM) to provide a better understanding of species' distributions at different scales. One of the main ideas underlying the theoretical foundations of both OM and ENM is that they are positively related to abundance: higher occupancy implies higher density and more suitable areas are likely to have more abundant populations. Here, we analyze the relationship between habitat use measured in terms of occupancy probabilities from OM and environmental suitability derived from ENM in three different Neotropical mammal species: Leopardus wiedii, Cuniculus paca, and Dasypus novemcinctus. For ENM, we used climatic and vegetation cover variables and implemented a model calibration and selection protocol to select the most competitive models. For OM, we used a single-species, single-season model with site covariates for camera-trap data from six different sites throughout the Neotropical realm. Covariates included vegetation percentage, normalized difference vegetation index, normalized difference water index, and elevation. For each site, we fit OM using all possible combinations of variables and selected the most competitive (ΔAICc < 2) to build an average OM. We explored relationships between estimated suitability and occupancy values using Spearman correlation analysis. Relationships between ENM and OM tended to be positive for the three Neotropical mammals, but the strength varied among sites, which could be explained by local factors such as site characteristics and conservation status of areas. We conjecture that ENM are suitable to understand spatial patterns at coarser geographic scales because the concept of the niche is about the species as a whole, whereas OM are more relevant to explain the distribution locally, likely reflecting transient dynamics of populations resulting from many local factors such as community composition and biotic processes.

A study published recently argued against a relationship between population density and position in geographic and environmental spaces. We found a number of methodological problems underlying the analysis. We discuss the main issues and conclude that these problems hinder a robust conclusion about the original question.