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

Optimizing Presence–Absence Surveys For Detecting Population Trends

Presence-absence surveys are a commonly used method for monitoring broad-scale changes in wildlife distributions. However, the lack of power of these surveys for detecting population trends is problematic for their application in wildlife management. Options for improving power include increasing the sampling effort or arbitrarily relaxing the type I error rate. We present an alternative, whereby targeted sampling of particular habitats in the landscape using information from a habitat model increases power. The advantage of this approach is that it does not require a trade-off with either cost or the Pr(type I error) to achieve greater power. We use a demographic model of koala (Phascolarctos cinereus) population dynamics and simulations of the monitoring process to estimate the power to detect a trend in occupancy for a range of strategies, thereby demonstrating that targeting particular habitat qualities can improve power substantially. If the objective is to detect a decline in occupancy, the optimal strategy is to sample high-quality habitats. Alternatively, if the objective is to detect an increase in occupancy, the optimal strategy is to sample intermediate-quality habitats. The strategies with the highest power remained the same under a range of parameter assumptions, although observation error had a strong influence on the optimal strategy. Our approach specifically applies to monitoring for detecting long-term trends in occupancy or abundance. This is a common and important monitoring objective for wildlife managers, and we provide guidelines for more effectively achieving it.