Predictors of early postpartum mental distress in mothers with midwifery home care - results from a nested case-control study

PRINCIPLES: The prevalence of early postpartum mental health conditions is high. Midwives and other health professionals visiting women at home may identify mothers at risk. This seems crucial given decreasing trends of length of hospital stay after childbirth. This study aimed to identify predictors of maternal mental distress in a midwifery home care setting. METHODS: Using the statistical database of independent midwives' services in Switzerland in 2007, we conducted a matched nested case-control study. Out of a source population of 34,295 mothers with midwifery home care in the first ten days after childbirth, 935 mothers with maternal distress and 3,645 controls, matched by midwife, were included. We analysed whether socio-demographic, maternal and neonatal factors predict maternal mental distress by multivariable conditional logistic regression analysis. RESULTS: Infant crying problems and not living with a partner were the strongest predictors for maternal distress, whereas higher parity was the most protective factor. Significantly elevated risks were also found for older age, lower educational levels, breast/breastfeeding problems, infant weight gain concerns, neonatal pathologies and use of midwifery care during pregnancy. A lower likelihood for maternal distress was seen for non-Swiss nationality, full-time employment before birth, intention to return to work after birth and midwife-led birth. CONCLUSION: The study informs on predictors of maternal mental distress identified in a home care setting in the early postpartum period. Midwives and other health care professionals should pay particular attention to mothers of excessively crying infants, single mothers and primipara, and assess the need for support of these mothers

Volatile Organic Compounds in the Po Basin. Part A: Anthropogenic VOCs

Measurements of volatile organic compounds (VOCs) were performed in the Po Basin, northern Italy in early summer 1998 within the PIPAPO project as well as in summer 2002 and autumn 2003 within the FORMAT project. During the three campaigns, trace gases and meteorological parameters were measured at a semi-rural station, around 35 km north of the city center of Milan. Low toluene and benzene concentrations and lower toluene to benzene ratios on weekends, on Sundays, and in August enabled the identification of a ‘weekend' and a ‘vacation' effect when anthropogenic emissions were lower due to less traffic and reduced industrial activities, respectively. Recurrent nighttime cyclohexane peaks suggested a periodical short-term release of cyclohexane close to the semi-rural sampling site. A multivariate receptor model analysis resulted in the distinction of different characteristic concentration profiles attributed to natural gas, biogenic impact, vehicle exhaust, industrial activities, and a single cyclohexane sourc

Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.Comment: 22 pages, 14 figure

Volatile Organic Compounds in the Po Basin. Part B: Biogenic VOCs

Measurements of volatile organic compounds (VOCs) were performed in the Po Basin, northern Italy in early summer 1998, summer 2002, and autumn 2003. During the three campaigns, trace gases and meteorological parameters were measured at a semi-rural station, around 35 km north of the city center of Milan. Bimodal diurnal cycles of isoprene with highest concentrations in the morning and evening were found and could be explained by the interaction of emissions, chemical reactions, and vertical mixing. The diurnal cycle could be qualitatively reproduced by a three-dimensional Eulerian model. The nighttime decay of isoprene could be attributed mostly to reactions with NO3, while the decay of the isoprene oxidation products could not be explained with the considered chemical reactions. Methanol reached very high mixing ratios, up to 150 ppb. High concentrations with considerable variability occurred during nights with high relative humidities and low wind speeds. The origin of these nighttime methanol concentrations is most likely local and biogenic but the specific source could not be identifie

The relative risk of second primary cancers in Switzerland: a population-based retrospective cohort study.

More people than ever before are currently living with a diagnosis of cancer and the number of people concerned is likely to continue to rise. Cancer survivors are at risk of developing a second primary cancer (SPC). This study aims to investigate the risk of SPC in Switzerland. The study cohort included all patients with a first primary cancer recorded in 9 Swiss population-based cancer registries 1981-2009 who had a minimum survival of 6 months, and a potential follow-up until the end of 2014. We calculated standardized incidence ratios (SIR) to estimate relative risks (RR) of SPC in cancer survivors compared with the cancer risk of the general population. SIR were stratified by type of first cancer, sex, age and period of first diagnosis, survival period and site of SPC. A total of 33,793 SPC were observed in 310,113 cancer patients. Both male (SIR 1.18, 95%CI 1.16-1.19) and female (SIR 1.20, 95%CI 1.18-1.22) cancer survivors had an elevated risk of developing a SPC. Risk estimates varied substantially according to type of first cancer and were highest in patients initially diagnosed with cancer of the oral cavity and pharynx, Hodgkin lymphoma, laryngeal, oesophageal, or lung cancer. Age-stratified analyses revealed a tendency towards higher RR in patients first diagnosed at younger ages. Stratified by survival period, risk estimates showed a rising trend with increasing time from the initial diagnosis. We observed strong associations between particular types of first and SPC, i.e. cancer types sharing common risk factors such as smoking or alcohol consumption (e.g. repeated cancer of the oral cavity and pharynx (SIR <sub>males</sub> 20.12, 95%CI 17.91-22.33; SIR <sub>females</sub> 37.87, 95%CI 30.27-45.48). Swiss cancer survivors have an increased risk of developing a SPC compared to the general population, particularly patients first diagnosed before age 50 and those surviving more than 10 years. Cancer patients should remain under continued surveillance not only for recurrent cancers but also for new cancers. Some first and SPCs share lifestyle associated risk factors making it important to promote healthier lifestyles in both the general population and cancer survivors

On the relationship between total ozone and atmospheric dynamics and chemistry at mid-latitudes – Part 1: Statistical models and spatial fingerprints of atmospheric dynamics and chemistry

We use statistical models for mean and extreme values of total column ozone to analyze "fingerprints" of atmospheric dynamics and chemistry on long-term ozone changes at northern and southern mid-latitudes on grid cell basis. At each grid cell, the r-largest order statistics method is used for the analysis of extreme events in low and high total ozone (termed ELOs and EHOs, respectively), and an autoregressive moving average (ARMA) model is used for the corresponding mean value analysis. In order to describe the dynamical and chemical state of the atmosphere, the statistical models include important atmospheric covariates: the solar cycle, the Quasi-Biennial Oscillation (QBO), ozone depleting substances (ODS) in terms of equivalent effective stratospheric chlorine (EESC), the North Atlantic Oscillation (NAO), the Antarctic Oscillation (AAO), the El Niño/Southern Oscillation (ENSO), and aerosol load after the volcanic eruptions of El Chichón and Mt. Pinatubo. The influence of the individual covariates on mean and extreme levels in total column ozone is derived on a grid cell basis. The results show that "fingerprints", i.e., significant influence, of dynamical and chemical features are captured in both the "bulk" and the tails of the statistical distribution of ozone, respectively described by mean values and EHOs/ELOs. While results for the solar cycle, QBO, and EESC are in good agreement with findings of earlier studies, unprecedented spatial fingerprints are retrieved for the dynamical covariates. Column ozone is enhanced over Labrador/Greenland, the North Atlantic sector and over the Norwegian Sea, but is reduced over Europe, Russia and the Eastern United States during the positive NAO phase, and vice-versa during the negative phase. The NAO's southern counterpart, the AAO, strongly influences column ozone at lower southern mid-latitudes, including the southern parts of South America and the Antarctic Peninsula, and the central southern mid-latitudes. Results for both NAO and AAO confirm the importance of atmospheric dynamics for ozone variability and changes from local/regional to global scales

Extreme events in total ozone over Arosa – Part 1: Application of extreme value theory

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs