The prevalence and impact of overweight and obesity in an Australian obstetric population

Objective: To assess the prevalence and impact of overweight and obesity in an Australian obstetric population. Design, setting and participants: The Mater Mother's Hospital (MMH), South Brisbane, is an urban tertiary referral maternity hospital. We reviewed data for the 18401 women who were booked for antenatal care at the MMH, delivered between January 1998 and December 2002, and had a singleton pregnancy. Of those women, 14 230 had an estimated pre-pregnancy body mass index (BMI) noted in their record; 2978 women with BMI 40 kg/m(2)). Main outcome measures: Prevalence of overweight and obesity in an obstetric population; maternal, peripartum and neonatal outcomes associated with raised BMI. Results: Of the 14230 women, 6443 (45%) were of normal weight, and 4809 (34%) were overweight, obese or morbidly obese. Overweight, obese and morbidly obese women were at increased risk of adverse outcomes (figures represent adjusted odds ratio [AOR] [95% Cl]): hypertensive disorders of pregnancy (overweight 1.74 [1.45-2.15], obese 3.00 [2.40-3.74], morbidly obese 4.87 [3.27-7.24]); gestational diabetes (overweight 1.78 [1.25-2.52], obese 2.95 [2.05-4.25], morbidly obese 7.44 [4.42-12.54]); hospital admission longer than 5 days (overweight 1.36 [1.13-1.63], obese 1.49 [1.21-1.86], morbidly obese 3.18 [2.19-4.61]); and caesarean section (overweight 1.50 [1.36-1.66], obese 2.02 [1.79-2.29], morbidly obese 2.54 [1.94-3.321). Neonates born to obese and morbidly obese women had an increased risk of birth defects (obese 1.58 (1.02-2.46], morbidly obese 3.41 [1.67-6.94]); and hypoglycaemia (obese 2.57 [1.39-4.78], morbidly obese 7.14 [3.04-16.74]). Neonates born to morbidly obese women were at increased risk of admission to intensive care (2.77 [1.81-4.25]); premature delivery (< 34 weeks' gestation) (2.13 [1.13-4.01]); and jaundice (1.44 [1.09-1.89]). Conclusions: Overweight and obesity are common in pregnant women. Increasing BMI is associated with maternal and neonatal outcomes that may increase the costs of obstetric care. To assist in planning health service delivery, we believe that BMI should be routinely recorded on perinatal data collection sheets

Sense of place: A tool to assist the meaningful engagement of communities in protected area management

This study used ‘sense of place’ as a research tool to help understand the relationship between a community and their local protected area, Brisbane Forest Park. To establish an indication of the community’s relative degree of sense of place, we considered and measured both the strength (intensity) and orientation (focus) of sense of place. We developed a new method to measure sense of place that considers and measures the elements constituting sense of place, independent of one another, utilising qualitative data collected in in-depth semi-structured interviews. Exploring both the strength and orientation of an individual's sense of place provides a way of exploring the desired nature of community involvement in the management of the Park. It was found that the stronger an individuals’ sense of place, the greater their place dependence and commitment, and the greater their desire to be involved in management. Analysing the strength and orientation of sense of place illustrated that there is a high degree of diversity in how individuals perceive and feel about area, and their desire to be involved in management. The type of information obtained in this study is important and useful to the management agencies if they are to successfully engage the community in meaningful ways.

Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.