Corrigendum: Fetoplacental transmission and placental response to SARS-CoV-2: evidence from the literature.

[This corrects the article DOI: 10.3389/fmed.2022.962937.].

Variation in hospital morbidities in an Australian neonatal intensive care unit network.

OBJECTIVE: There is an expectation among the public and within the profession that the performance and outcome of neonatal intensive care units (NICUs) should be comparable between centres with a similar setting. This study aims to benchmark and audit performance variation in a regional Australian network of eight NICUs. DESIGN: Cohort study using prospectively collected data. SETTING: All eight perinatal centres in New South Wales and the Australian Capital Territory, Australia. PATIENTS: All live-born infants born between 23+0 and 31+6 weeks gestation admitted to one of the tertiary perinatal centres from 2007 to 2020 (n=12 608). MAIN OUTCOME MEASURES: Early and late confirmed sepsis, intraventricular haemorrhage, medically and surgically treated patent ductus arteriosus, chronic lung disease (CLD), postnatal steroid for CLD, necrotising enterocolitis, retinopathy of prematurity (ROP), surgery for ROP, hospital mortality and home oxygen. RESULTS: NICUs showed variations in maternal and neonatal characteristics and resources. The unadjusted funnel plots for neonatal outcomes showed apparent variation with multiple centres outside the 99.8% control limits of the network values. The hierarchical model-based risk-adjustment accounting for differences in patient characteristics showed that discharged home with oxygen is the only outcome above the 99.8% control limits. CONCLUSIONS: Hierarchical model-based risk-adjusted estimates of morbidity rates plotted on funnel plots provide a robust and straightforward visual graphical tool for presenting variations in outcome performance to detect aberrations in healthcare delivery and guide timely intervention. We propose using hierarchical model-based risk adjustment and funnel plots in real or near real-time to detect aberrations and start timely intervention.

Fetoplacental transmission and placental response to SARS-CoV-2: Evidence from the literature.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a dreadful novel coronavirus with global health concerns among pregnant women. To date, the vertical transmission of SARS-CoV-2 during pregnancy remains controversial. We briefly report recent findings of placental response to SARS-CoV-2 infection and updates on vertical transmission. We systematically searched PubMed and Google Scholar databases according to PRISMA guidelines for studies reporting the effects of SARS-CoV-2 infection on the placenta and possibility of vertical transmission. We identified 45 studies reporting 1,280 human placentas that were analyzed by molecular pathology methods and 11,112 placenta-derived cells from a publicly available database that was analyzed using bioinformatics tools. The main finding of this study is that the SARS-CoV-2 canonical entry receptors (ACE2 and TMPRSS2) are abundantly expressed on the placenta during the first trimester, and this expression diminishes across gestational age. Out of 45 eligible studies identified, 24 (53.34%) showed no evidence of vertical transmission, 15 (33.33%) supported the hypothesis of very rare, low possibility of vertical transmission and 6 (13.33%) were indecisive and had no comment on vertical transmission. Furthermore, 433 placentas from 12 studies were also identified for placental pathology investigation. There was evidence of at least one form of maternal vascular malperfusion (MVM), 57/433 (13.1%), fetal vascular malperfusion (FVM), 81/433 (18.7%) and placental inflammation with excessive infiltration of CD3+ CD8+ lymphocytes, CD68+ macrophages and CD20+ lymphocytes in most of the eligible studies. Decidual vasculopathy (3.2%), infarction (3.2%), chronic histiocytic intervillositis (6.0%), thrombi vasculopathy (5.1%) were also observed in most of the MVM and FVM reported cases. The results indicated that SARS-CoV-2 induces placenta inflammation, and placenta susceptibility to SARS-CoV-2 decreases across the pregnancy window. Thus, SARS-CoV-2 infection in early pregnancy may adversely affect the developing fetus.

Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach.

Ebola virus (EBV) disease is a globally acknowledged public health emergency, endemic in the west and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in the Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impact of reported violence on the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation was based on the integrated nested Laplace approximation (INLA). Our findings revealed a positive association between violent events in the affected areas and the reported EBV cases (posterior mean = 0.024, 95% CI: 0.005, 0.045) and deaths (posterior mean = 0.022, 95% CI: 0.005, 0.041). Translating to an increase of 2.4% and 2.2% in the relative risks of EBV cases and deaths associated with a unit increase in violent events (one additional Ebola case is associated with an average of 45 violent events). We also observed clusters of EBV cases and deaths spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hot spot identification, location-specific disease surveillance and intervention.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study.

BACKGROUND: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. METHODS: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. RESULTS: We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. CONCLUSION: We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

Severe dehydration among cholera patients in Yemen: A cohort profile.

INTRODUCTION: Introduction Cholera, an acute diarrheal illness caused by ingestion of food or water contaminated with Vibrio cholerae, is one of the major causes of morbidity and mortality globally. The occurrence of outbreaks of cholera are difficult to prevent in low and middle-income countries, especially those under armed conflicts. METHODS: This study aimed to describe the characteristics of a cohort of inpatients with cholera in two main hospitals in Taiz and Sana'a, Yemen, between 3rd February 2017 and 8th December 2017. Patient data were entered into an excel database and analyzed using STATA 16.1. Descriptive summaries of patient's data were presented as frequencies and percentages. Patients' demographic and clinical characteristics were compared using the Chi-square test. RESULTS: Preliminary findings from 172 hospitalizations for cholera during the study period include 163 that were severely dehydrated (94.8%). Age, education, hand hygiene, sanitation, water source, stool content and malnutrition were significantly associated with severe dehydration. CONCLUSIONS: This data contributes to a greater understanding of the associated risk factors for the occurrence of the infectious disease in the study region. Future study will analyze the risks for severe dehydration and diarrhea, and the associated healthcare costs.

Effects of time-lagged meteorological variables on attributable risk of leishmaniasis in central region of Afghanistan.

BACKGROUND: Leishmaniasis remains one of the world's most neglected vector-borne diseases, affecting predominantly poor communities mainly in developing countries. Previous studies have shown that the distribution and dynamics of leishmaniasis infections are sensitive to environmental factors, however, there are no studies on the burden of leishmaniasis attributable to time-varying meteorological variables. METHODS: This study used data from 3 major leishmaniosis afflicted provinces of Afghanistan, between 2003 and 2009, to provide empirical analysis of change in heat/cold-leishmaniosis association. Non-linear and delayed exposure-lag-response relationship between meteorological variables and leishmaniasis were fitted with a distributed lag non-linear model applying a spline function which describes the dependency along the range of values with a lag of up to 12 months. We estimated the risk of leishmaniasis attributable to high and low temperature. RESULTS: The median monthly mean temperature and rainfall were 16.1 °C and 0.6 in., respectively. Seasonal variations of leishmaniasis were consistent between males and females, however significant differences were observed among different age groups. Temperature effects were immediate and persistent (lag 0-12 months). The cumulative risks were highest at cold temperatures. The cumulative relative risks (logRR) for leishmaniasis were 6.16 (95% CI: 5.74-6.58) and 1.15 (95% CI: 1.32-1.31) associated with the 10th percentile temperature (2.16 °C) and the 90th percentile temperature (28.46 °C). The subgroup analysis showed increased risk for males as well as young and middle aged people at cold temperatures, however, higher risk was observed for the elderly in heat. The overall leishmaniasis-temperature attributable fractions was estimated to be 7.6% (95% CI: 7.5%-7.7%) and mostly due to cold. CONCLUSION: Findings in this study highlight the non-linearity, delay of effects and magnitude of leishmaniasis risk associated with temperature. The disparity of risk between different subgroups can hopefully advise policy makers and assist in leishmaniasis control program.

Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus.

BACKGROUND: During outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly. METHODS: Network centrality metrics were used to characterize hospital-acquired Middle East Respiratory Syndrome Coronavirus (HA-MERS) outbreaks in the Kingdom of Saudi Arabia between 2012 and 2016. Covariate-adjusted multivariable logistic regression models were applied to assess the effect of individual level risk factors and network level metrics associated with increase in length of hospital stay and risk of deaths from MERS. RESULTS: About 27% of MERS cases were hospital acquired during the study period. The median age of healthcare workers and hospitalized patients were 35 years and 63 years, respectively, Although HA-MERS were more connected, we found no significant difference in degree centrality metrics between HA-MERS and non-HA-MERS cases. Pre-existing medical conditions (adjusted Odds ratio (aOR)=2.43, 95% confidence interval: (CI) [1.11-5.33]) and hospitalized patients (aOR=29.99, 95% CI [1.80-48.65]) were the strongest risk predictors of death from MERS. The risk of death associated with 1-day increased length of stay was significantly higher for patients with comorbidities. CONCLUSION: Our investigation also revealed that patients with an HA-MERS infection experienced a significantly longer hospital stay and were more likely to die from the disease. Healthcare worker should be reminded of their potential role as hubs for pathogens because of their proximity to and regular interaction with infected patients. On the other hand, this study has shown that while healthcare workers acted as epidemic attenuators, hospitalized patients played the role of an epidemic amplifier.

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016.

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5-17). The eigenvector centrality was significantly (p < 0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.