Frequency and management of maternal infection in health facilities in 52 countries (GLOSS): a 1-week inception cohort study

Background: Maternal infections are an important cause of maternal mortality and severe maternal morbidity. We report the main findings of the WHO Global Maternal Sepsis Study, which aimed to assess the frequency of maternal infections in health facilities, according to maternal characteristics and outcomes, and coverage of core practices for early identification and management. Methods: We did a facility-based, prospective, 1-week inception cohort study in 713 health facilities providing obstetric, midwifery, or abortion care, or where women could be admitted because of complications of pregnancy, childbirth, post-partum, or post-abortion, in 52 low-income and middle-income countries (LMICs) and high-income countries (HICs). We obtained data from hospital records for all pregnant or recently pregnant women hospitalised with suspected or confirmed infection. We calculated ratios of infection and infection-related severe maternal outcomes (ie, death or near-miss) per 1000 livebirths and the proportion of intrahospital fatalities across country income groups, as well as the distribution of demographic, obstetric, clinical characteristics and outcomes, and coverage of a set of core practices for identification and management across infection severity groups. Findings: Between Nov 28, 2017, and Dec 4, 2017, of 2965 women assessed for eligibility, 2850 pregnant or recently pregnant women with suspected or confirmed infection were included. 70·4 (95% CI 67·7–73·1) hospitalised women per 1000 livebirths had a maternal infection, and 10·9 (9·8–12·0) women per 1000 livebirths presented with infection-related (underlying or contributing cause) severe maternal outcomes. Highest ratios were observed in LMICs and the lowest in HICs. The proportion of intrahospital fatalities was 6·8% among women with severe maternal outcomes, with the highest proportion in low-income countries. Infection-related maternal deaths represented more than half of the intrahospital deaths. Around two-thirds (63·9%, n=1821) of the women had a complete set of vital signs recorded, or received antimicrobials the day of suspicion or diagnosis of the infection (70·2%, n=1875), without marked differences across severity groups. Interpretation: The frequency of maternal infections requiring management in health facilities is high. Our results suggest that contribution of direct (obstetric) and indirect (non-obstetric) infections to overall maternal deaths is greater than previously thought. Improvement of early identification is urgently needed, as well as prompt management of women with infections in health facilities by implementing effective evidence-based practices.Fil: Althabe, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Espinoza, Marisa Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Pasquale, Julia. No especifíca;Fil: Hernández Muñoz, Rosalinda. No especifíca;Fil: Carvajal, Javier. No especifíca;Fil: Escobar, María Fernanda. No especifíca;Fil: Cecatti, José Guilherme. No especifíca;Fil: Ribeiro Do Valle, Carolina C.. No especifíca;Fil: Mereci, Wilson. No especifíca;Fil: Vélez, Paola. No especifíca;Fil: Pérez, Aquilino M.. No especifíca;Fil: Vitureira, Gerardo. No especifíca;Fil: Leroy, Charlotte. No especifíca;Fil: Roelens, Kristien. No especifíca;Fil: Vandenberghe, Griet. No especifíca;Fil: Aguemon, Christiane Tshabu. No especifíca;Fil: Cisse, Kadari. No especifíca;Fil: Ouedraogo, Henri Gautier. No especifíca;Fil: Kannitha, Cheang. No especifíca;Fil: Rathavy, Tung. No especifíca;Fil: Tebeu, Pierre Marie. No especifíca;Fil: Bustillo, Carolina. No especifíca;Fil: Bredy, Lara. No especifíca;Fil: Herrera Maldonado, Nazarea. No especifíca;Fil: Abdosh, Abdulfetah Abdulkadir. No especifíca;Fil: Teklu, Alula M.. No especifíca;Fil: Kassa, Dawit Worku. No especifíca;Fil: Kumar, Vijay. No especifíca;Fil: Suri, Vanita. No especifíca;Fil: Trikha, Sonia. No especifíca

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Quality of medicines for life-threatening pregnancy complications in low- and middle-income countries: A systematic review.

BackgroundThere are concerns about the quality of medicines available in low- and middle-income countries (LMIC) to manage hemorrhage, pre-eclampsia/eclampsia and sepsis. We aimed to identify, critically appraise, and synthesize the findings of studies on the quality of these three types of medicines available in LMIC.MethodsThis systematic review searched Medline, EMBASE and LILACS (from inception to 25 May 2020) for studies on the quality of selected medicines available in LMIC that provided at least the amount of active pharmaceutical ingredient. We contacted study authors for additional information. We excluded simulation studies. We used the MEDQUARG tool to assess study quality. The main outcome was the prevalence of failed samples.FindingsWe identified 9699 unique citations and included 34 studies (3159 samples from 40 countries) in the review. Most studies (65%) had low quality (scores ConclusionsThere is a widespread problem with the quality of medicines used to manage life-threatening maternal conditions in LMIC. This can be a contributing factor to high maternal mortality rates in these regions

Call to action for a South American network to fight COVID-19 in pregnancy

A call to action for joint efforts by South American centers to tackle COVID-19 in pregnancy150226026

Aetiology and use of antibiotics in pregnancy-related infections: results of the WHO Global Maternal Sepsis Study (GLOSS), 1-week inception cohort.

BackgroundPregnancy-related infections are important contributors to maternal sepsis and mortality. We aimed to describe clinical, microbiological characteristics and use of antibiotics by source of infection and country income, among hospitalized women with suspected or confirmed pregnancy-related infections.MethodsWe used data from WHO Global Maternal Sepsis Study (GLOSS) on maternal infections in hospitalized women, in 52 low-middle- and high-income countries conducted between November 28th and December 4th, 2017, to describe the frequencies and medians of maternal demographic, obstetric, and clinical characteristics and outcomes, methods of infection diagnosis and causative pathogens, of single source pregnancy-related infection, other than breast, and initial use of therapeutic antibiotics. We included 1456 women.ResultsWe found infections of the genital (n = 745/1456, 51.2%) and the urinary tracts (UTI) (n = 531/1456, 36.5%) to be the most frequent. UTI (n = 339/531, 63.8%) and post-caesarean skin and soft tissue infections (SSTI) (n = 99/180, 55.0%) were the sources with more culture samples taken and microbiological confirmations. Escherichia coli was the major uropathogen (n = 103/118, 87.3%) and Staphylococcus aureus (n = 21/44, 47.7%) was the commonest pathogen in SSTI. For 13.1% (n = 191) of women, antibiotics were not prescribed on the same day of infection suspicion. Cephalosporins (n = 283/531, 53.3%) were the commonest antibiotic class prescribed for UTI, while metronidazole (n = 303/925, 32.8%) was the most prescribed for all other sources. Ceftriaxone with metronidazole was the commonest combination for the genital tract (n = 98/745, 13.2%) and SSTI (n = 22/180, 12.2%). Metronidazole (n = 137/235, 58.3%) was the most prescribed antibiotic in low-income countries while cephalosporins and co-amoxiclav (n = 129/186, 69.4%) were more commonly prescribed in high-income countries.ConclusionsDifferences in antibiotics used across countries could be due to availability, local guidelines, prescribing culture, cost, and access to microbiology laboratory, despite having found similar sources and pathogens as previous studies. Better dissemination of recommendations in line with antimicrobial stewardship programmes might improve antibiotic prescription

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies