Severe maternal morbidity following stillbirth in Western Australia 2000–2015: a population-based study

Purpose There is scant literature about the management of stillbirth and the subsequent risk of severe maternal morbidity (SMM). We aimed to assess the risk of SMM associated with stillbirths compared with live births and whether this differed by the presence of maternal comorbidities. Methods In this retrospective cohort study, we used a population-based dataset of all stillbirths and live births ≥ 20 weeks’ gestation in Western Australia between 2000 and 2015. SMM was identified using a published Australian composite for use with routinely collected hospital morbidity data. Maternal comorbidities were identified in the Hospital Morbidity Data Collection or the Midwives Notification System using a modified Australian chronic disease composite. Multivariable Poisson regression was used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for factors associated with SMM in analyses stratified by the presence of maternal comorbidities. Singleton and multiple pregnancies were examined separately. Results This study included 458,639 singleton births (2319 stillbirths and 456,320 live births). The adjusted RRs for SMM among stillbirths were 2.30 (95% CI 1.77, 3.00) for those without comorbidities and 4.80 (95% CI 4.11, 5.59) (Interaction P value < 0.0001) for those with comorbidities compared to live births without and with comorbidities, respectively. Conclusion In Western Australia between 2000 and 2015, mothers of stillbirths both with and without any maternal comorbidities had an increased risk of SMM compared with live births. Further investigation into why women who have had a stillbirth without any existing conditions or pregnancy complications develop SMM is warranted

Trends and burden of diabetes in pregnancy among Aboriginal and non-Aboriginal mothers in Western Australia, 1998–2015

Background Diabetes in pregnancy (DIP), which includes pre-gestational and gestational diabetes, is more prevalent among Aboriginal women. DIP and its adverse neonatal outcomes are associated with diabetes and cardiovascular disease in the offspring. This study investigated the impact of DIP on trends of large for gestational age (LGA) in Aboriginal and non-Aboriginal populations, and added to the limited evidence on temporal trends of DIP burden in these populations. Methods We conducted a retrospective cohort study that included all births in Western Australia between 1998 and 2015 using linked population health datasets. Time trends of age-standardised and crude rates of pre-gestational and gestational diabetes were estimated in Aboriginal and non-Aboriginal mothers. Mixed-effects multivariable logistic regression was used to estimate the association between DIP and population LGA trends over time. Results Over the study period, there were 526,319 births in Western Australia, of which 6.4% were to Aboriginal mothers. The age-standardised annual rates of pre-gestational diabetes among Aboriginal mothers rose from 4.3% in 1998 to 5.4% in 2015 and remained below 1% in non-Aboriginal women. The comparable rates for gestational diabetes increased from 6.7 to 11.5% over the study period in Aboriginal women, and from 3.5 to 10.2% among non-Aboriginal mothers. LGA rates in Aboriginal babies remained high with inconsistent and no improvement in pregnancies complicated by gestational diabetes and pre-gestational diabetes, respectively. Regression analyses showed that DIP explained a large part of the increasing LGA rates over time in Aboriginal babies. Conclusions There has been a substantial increase in the burden of pre-gestational diabetes (Aboriginal women) and gestational diabetes (Aboriginal and non-Aboriginal) in recent decades. DIP appears to substantially contribute to increasing trends in LGA among Aboriginal babies

Parental occupational exposure to pesticides and risk of childhood cancer in Switzerland: a census-based cohort study.

Pesticide exposure is a suspected risk factor for childhood cancer. We investigated the risk of developing childhood cancer in relation to parental occupational exposure to pesticides in Switzerland for the period 1990-2015. From a nationwide census-based cohort study in Switzerland, we included children aged &lt; 16 years at national censuses of 1990 and 2000 and followed them until 2015. We extracted parental occupations reported at the census closest to the birth year of the child and estimated exposure to pesticides using a job exposure matrix. Cox proportional hazards models, adjusted for potential confounders, were fitted for the following outcomes: any cancer, leukaemia, central nervous system tumours (CNST), lymphoma, non-CNS solid tumours. Analyses of maternal (paternal) exposure were based on approximately 15.9 (15.1) million-person years at risk and included 1891 (1808) cases of cancer, of which 532 (503) were leukaemia, 348 (337) lymphomas, 423 (399) CNST, and 588 (569) non-CNS solid tumours. The prevalence of high likelihood of exposure was 2.9% for mothers and 6.7% for fathers. No evidence of an association was found with maternal or paternal exposure for any of the outcomes, except for "non-CNS solid tumours" (High versus None; Father: adjusted HR [95%CI] =1.84 [1.31-2.58]; Mother: 1.79 [1.13-2.84]). No evidence of an association was found for main subtypes of leukaemia and lymphoma. A post-hoc analysis on frequent subtypes of "non-CNS solid tumours" showed positive associations with wide CIs for some cancers. Our study suggests an increased risk for solid tumours other than in the CNS among children whose parents were occupationally exposed to pesticides; however, the small numbers of cases limited a closer investigation of cancer subtypes. Better exposure assessment and pooled studies are needed to further explore a possible link between specific childhood cancers types and parental occupational exposure to pesticides

Family history of cancer and the risk of childhood brain tumors: a pooled analysis of the ESCALE and ESTELLE studies (SFCE)

PURPOSE: Although some specific genetic syndromes such as neurofibromatosis (NF) have been identified as risk factor of childhood brain tumors (CBT), the potential role of inherited susceptibility in CBT has yet to be elucidated. METHODS: To further investigate this, we conducted a pooled analysis of two nationwide case-control studies ESCALE and ESTELLE. The mothers of 509 CBT cases and 3,102 controls aged under 15 years who resided in France at diagnosis/interview, frequency-matched by age and gender, responded to a telephone interview conducted by trained interviewers. Pooled odds ratio (OR) and 95% confidence intervals (95% CI) were estimated using unconditional logistic regression. RESULTS: CBT was significantly associated with the family history of cancer in relatives (OR 1.2, 95% CI 1.0-1.5). The OR was slightly higher for maternal relatives than for paternal relatives, and when at least two relatives had a history of cancer. CBT was significantly associated with a family history of brain tumor (OR 2.1, 95% CI 1.3-3.7). This association seemed stronger for first-degree relatives (mother, father, and siblings), for whom, by contrast, no association was seen for cancers other than CBT. No specificity by CBT subtypes or by age of the children were found for any of these findings. CONCLUSION: Our findings support the hypothesis of a familial susceptibility of CBT, not due to being a known NF carrier

Early mortality among Aboriginal and non-Aboriginal women who had a preterm birth in Western Australia: A population-based cohort study

Background: Having a preterm (<37 weeks' gestation) birth may increase a woman's risk of early mortality. Aboriginal and Torres Strait Islander (hereafter Aboriginal) women have higher preterm birth and mortality rates compared with other Australian women. Objectives: We investigated whether a history of having a preterm birth was associated with early mortality in women and whether these associations differed by Aboriginal status. Methods: This retrospective cohort study used population-based perinatal records of women who had a singleton birth between 1980 and 2015 in Western Australia linked to Death Registry data until June 2018. The primary and secondary outcomes were all-cause and cause-specific mortality respectively. After stratification by Aboriginal status, rate differences were calculated, and Cox proportional hazard regression was used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for allcause and cause-specific mortality. Results: There were 20,244 Aboriginal mothers (1349 deaths) and 457,357 nonAboriginal mothers (7646 deaths) with 8.6 million person-years of follow-up. The all-cause mortality rates for Aboriginal mothers who had preterm births and term births were 529.5 and 344.0 (rate difference 185.5, 95% CI 135.5, 238.5) per 100,000 person-years respectively. Among non-Aboriginal mothers, the corresponding figures were 125.5 and 88.6 (rate difference 37.0, 95% CI 29.4, 44.9) per 100,000 personyears. The HR for all-cause mortality for Aboriginal and non-Aboriginal mothers associated with preterm birth were 1.48 (95% CI 1.32, 1.66) and 1.35 (95% CI 1.26, 1.44), respectively, compared with term birth. Compared with mothers who had term births, mothers of preterm births had higher relative risks of mortality from diabetes, cardiovascular, digestive and external causes. Conclusions: Both Aboriginal and non-Aboriginal women who had a preterm birth had a moderately increased risk of mortality up to 38 years after the birth, reinforcing the importance of primary prevention and ongoing screening.Helen D. Bailey, Caitlin Gray, Akilew A. Adane, Natalie A. Strobel, Scott W. White, Rhonda Marriott, Gizachew A. Tessema, Carrington C. J. Shepherd, Mary Shar

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Development and potential role of type-2 sodium-glucose transporter inhibitors for management of type 2 diabetes

There is a recognized need for new treatment options for type 2 diabetes mellitus (T2DM). Recovery of glucose from the glomerular filtrate represents an important mechanism in maintaining glucose homeostasis and represents a novel target for the management of T2DM. Recovery of glucose from the glomerular filtrate is executed principally by the type 2 sodium-glucose cotransporter (SGLT2). Inhibition of SGLT2 promotes glucose excretion and normalizes glycemia in animal models. First reports of specifically designed SGLT2 inhibitors began to appear in the second half of the 1990s. Several candidate SGLT2 inhibitors are currently under development, with four in the later stages of clinical testing. The safety profile of SGLT2 inhibitors is expected to be good, as their target is a highly specific membrane transporter expressed almost exclusively within the renal tubules. One safety concern is that of glycosuria, which could predispose patients to increased urinary tract infections. So far the reported safety profile of SGLT2 inhibitors in clinical studies appears to confirm that the class is well tolerated. Where SGLT2 inhibitors will fit in the current cascade of treatments for T2DM has yet to be established. The expected favorable safety profile and insulin-independent mechanism of action appear to support their use in combination with other antidiabetic drugs. Promotion of glucose excretion introduces the opportunity to clear calories (80–90 g [300–400 calories] of glucose per day) in patients that are generally overweight, and is expected to work synergistically with weight reduction programs. Experience will most likely lead to better understanding of which patients are likely to respond best to SGLT2 inhibitors, and under what circumstances

Primordial Nucleosynthesis for the New Cosmology: Determining Uncertainties and Examining Concordance

Big bang nucleosynthesis (BBN) and the cosmic microwave background (CMB) have a long history together in the standard cosmology. The general concordance between the predicted and observed light element abundances provides a direct probe of the universal baryon density. Recent CMB anisotropy measurements, particularly the observations performed by the WMAP satellite, examine this concordance by independently measuring the cosmic baryon density. Key to this test of concordance is a quantitative understanding of the uncertainties in the BBN light element abundance predictions. These uncertainties are dominated by systematic errors in nuclear cross sections. We critically analyze the cross section data, producing representations that describe this data and its uncertainties, taking into account the correlations among data, and explicitly treating the systematic errors between data sets. Using these updated nuclear inputs, we compute the new BBN abundance predictions, and quantitatively examine their concordance with observations. Depending on what deuterium observations are adopted, one gets the following constraints on the baryon density: OmegaBh^2=0.0229\pm0.0013 or OmegaBh^2 = 0.0216^{+0.0020}_{-0.0021} at 68% confidence, fixing N_{\nu,eff}=3.0. Concerns over systematics in helium and lithium observations limit the confidence constraints based on this data provide. With new nuclear cross section data, light element abundance observations and the ever increasing resolution of the CMB anisotropy, tighter constraints can be placed on nuclear and particle astrophysics. ABRIDGEDComment: 54 pages, 20 figures, 5 tables v2: reflects PRD version minor changes to text and reference