Longitudinal Weight Gain in Women Identified With Polycystic Ovary Syndrome: Results of an Observational Study in Young Women

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1 Longitudinal Weight Gain in Women Identified With Polycystic Ovary Syndrome: Results of an Observational Study in Young Women Helena J. Teede 1,2, Anju E. Joham 1,2, Eldho Paul 1, Lisa J. Moran 1, Deborah Loxton 3, Damien Jolley 1 and Catherine Lombard 1 Objective: Polycystic ovary syndrome (PCOS) affects 6-18% of women. The natural history of weight gain in women with PCOS has not been well described. Here we aimed to examine longitudinal weight gain in women with and without PCOS and to assess the association between obesity and PCOS prevalence. Design and Methods: The observational study was set in the general community. Participants were women randomly selected from the national health insurance scheme (Medicare) database. Mailed survey data were collected by the Australian Longitudinal Study on Women s Health. Data from respondents to survey 4, aged years (2006, n ¼ 9,145) were analyzed. The main outcome measures were PCOS prevalence and body mass index (BMI). Results: Self-reported PCOS prevalence was 5.8% (95% CI: 5.3%-6.4%). Women reporting PCOS had higher weight, mean BMI [2.5 kg/m 2 (95% CI: )], and greater 10-year weight gain [2.6 kg (95% CI: )]. BMI was the strongest correlate of PCOS status with every BMI increment increasing the risk of reporting PCOS by 9.2% (95% CI: 6%-12%). Conclusions: This community based observational study with longitudinal reporting of weight shows that weight, BMI, and 10-year weight gain were higher in PCOS. We report the novel finding that obesity and greater weight gain are significantly associated with PCOS status. Considering the prevalence, major health and economic burden of PCOS, the increasing weight gain in young women, and established benefits of weight loss, these results have major public health implications. (2013) 21, doi: /oby Introduction Polycystic ovary syndrome (PCOS) is the commonest endocrinopathy affecting reproductive aged women with a reported prevalence of 6-18%, depending on the diagnostic criteria and population studied (1-4). Diagnosis is based on the presence of anovulation, hyperandrogenism (clinical or biochemical), and polycystic ovaries on ultrasound (5,6). PCOS has reproductive (hyperandrogenism, anovulation, and infertility), metabolic (dyslipidemia, type 2 diabetes, and potential cardiovascular disease (7)), and psychological features (depression, anxiety, and poor self-esteem) (5,8,9) representing a major health and economic burden. PCOS aetiology is underpinned by both insulin resistance (IR) and hyperandrogenism, with IR increasing hyperandrogenism (10,11). IR in PCOS is both intrinsic (genetic) and extrinsic (lifestyle and 1 Women s Public Health Research, Monash Applied Research Stream, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria, Australia. Correspondence: Helena J. Teede (helena.teede@monash.edu) 2 Diabetes Unit, Southern Health, Clayton, Victoria, Australia 3 Research Centre for Gender, Health and Ageing, University of Newcastle, Callaghan, New South Wales, Australia Disclosure: The authors declared no conflict of interest. Funding agencies: This research was supported by the Australian Government Department of Health and Ageing. Author Contributions: Helena J. Teede: Study concept and design, acquisition, analysis, and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, obtaining funding, and supervision. Anju E. Joham: Analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and obtaining funding. Eldho Paul: Study concept and design, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, and statistical analysis. Lisa J. Moran: Study concept and design, analysis and interpretation of data, and critical revision of the manuscript for important intellectual content. Deborah Loxton: Study concept and design, acquisition, analysis, and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and obtaining funding. Damien Jolley: Study concept and design, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, statistical analysis, and figures. Catherine Lombard: Study concept and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and supervision. Received: 19 December 2011 Accepted: 15 November 2012 Published online 29 November doi: /oby VOLUME 21 NUMBER 8 AUGUST

2 obesity related) (8,11). independently increases both IR and hyperandrogenism, which exacerbates PCOS, whereas weight loss and exercise decrease IR and hyperandrogenism, alleviating severity of PCOS (12-14). Although obesity exacerbates and weight loss ameliorates the features of PCOS, the natural history of body mass index (BMI) in young women with PCOS remains unclear. Irregular cycles, a diagnostic feature of PCOS, increase with obesity (15). However, only limited cross-sectional studies in selected populations have examined relationships between BMI and PCOS prevalence with conflicting results (16,17). No significant difference in PCOS prevalence in university employees was noted across the BMI range (18.9 to >40) (16). Conversely, 28% of overweight women referred for weight loss had PCOS, compared with a previously reported population prevalence of 6.5% in Spain (3,17). Longitudinal studies on weight have to date evaluated self-reported PCOS symptoms such as menstrual irregularity and not diagnosed PCOS (15). The important relationship between longitudinal weight gain, BMI, and subsequently, identified PCOS status thus remains uncertain. Given the dramatically escalating obesity rates in young women (18), if an observational and longitudinal relationship between BMI and PCOS status is confirmed, an increasing PCOS prevalence would be expected. This would lead to greater associated health and economic burdens and would create a greater imperative for prevention of excess weight gain in young women (19). In this setting, we accessed the large, longitudinal, population-based study (surveys 1-4 from ) from the Australian Longitudinal Study on Women s Health (ALSWH), aiming to examine longitudinal weight gain and relationships between BMI, weight and self reported PCOS status. Methods The ALSWH first collected mailed survey data from three age cohorts of Australian women in The current study used data collected from the cohort born (n ¼ 14,779 at survey 1). The study s aims are to examine the relationships between biological, psychological, social, lifestyle factors, women s physical and mental health, and their use of and satisfaction with health care services (20,21). Women were randomly selected from the national health insurance scheme (Medicare) database which includes almost all people who are permanent residents of Australia (21). Women were recruited nationally with intentional over-sampling from rural and remote areas (21). Further details of the methods used by the ALSWH and characteristics of the sample have been reported elsewhere and are available on the ALSWH website ( (20,22,23). A comparison of women who participated in the baseline survey with data from women in the same age range from the Australian census of 1996 showed that the ALSWH participants were reasonably representative of the general population, although these women were slightly more likely to be Australian born and to have a post school qualification when first recruited in 1996 (21,22). The Human Research Ethics Committees of the University of Newcastle and the University of Queensland approved the study methods. With intensive tracking procedures and follow-up, retention of baseline participants was 66% of baseline for survey 2, 61% for survey 3, and 62% for survey 4. We analyzed data from all 8,612 young women who completed survey 4 and responded to the question on PCOS (see Figure 1). It should be noted that not all women completing the survey and responding to the PCOS questions, completed all other questions on the survey; hence, some data are missing (see results). However, the impact of attrition on associations between variables has been found to be minimal (23). Measures Outcome variable: PCOS. At survey 4, women were asked: In the last 3 years, have you been diagnosed or treated for PCOS? Women who gave a yes response were classified as PCOS. All other women who responded to this question with a no were considered as not having PCOS. Nonresponders were excluded from the analysis. Explanatory variables: As the outcome variable was measured only at survey 4, risk factors measured in the previous three surveys were considered as explanatory variables. The explanatory variables for the analyses included: i. BMI: BMI data were collected longitudinally in all surveys as was height and weight based on self-report. We also computed the average BMI for each woman incorporating all available data from the preceding surveys 1-3. ii. Weight change: Self-reported weight data were collected in all surveys. We also computed weight change as the difference between survey 1 and survey 3 weights. iii. Education: Although the educational status was measured in all surveys, only educational status at survey 3 was included in this analysis as this was considered to best reflect the highest level of education attained before the collection of data on the outcome variable, PCOS diagnosis. iv. Occupation: Main occupation was assessed in all surveys. As above, we included occupation data from survey 3 in this analysis. v. Income: Income data were requested in all surveys except survey 1. As above, the income recorded at survey 3 was used in the analysis. vi. Area of residence: At survey 1, women from rural and remote areas were sampled in twice the proportions of the Australian population living in these areas. Women from capital cities and other metropolitan areas made up the balance of the samples. Area of residence was included in the analysis to adjust for the deliberate over sampling in rural and remote areas. vii. Childhood weight: At survey 1, self-reported perception of childhood weight (age 10) was requested based on categories ranging from very underweight to very overweight. Statistical analysis Data were analyzed by experienced biostatisticians (EP and DJ) using Stata software version 10.0 (StataCorp, TX). Cross tabulation was used to examine the relation between categorical explanatory variables and self-reported PCOS, specifically between PCOS status and cycle irregularity (in women not pregnant or on the oral contraceptive pill). As the primary outcome PCOS is binomial, logistic regression analysis was performed to assess the relationship with average (surveys 1-3) BMI based on longitudinal data collected over the three preceding surveys, with PCOS status at survey 4, after adjusting for demographic factors such as education, occupation, VOLUME 21 NUMBER 8 AUGUST

3 Weight Gain and BMI in PCOS Teede et al. FIGURE 1 Flow diagram of data availability for analysis from the Australian Longitudinal Study of Women s Health from surveys 1to4. income, and area of residence and potential confounding variables such as weight change. Crude and adjusted odds ratios were estimated and reported with 95% confidence intervals. Findings Prevalence of self-reported PCOS and relationship to PCOS symptoms from survey 4. At survey 4, the estimated prevalence of self-reported PCOS was 5.8% (95% CI: %). The accuracy of self-reported PCOS is supported by the close correlation with the key PCOS diagnostic feature of irregular menstrual cycles on cross-sectional data analysis at survey 4 (Figure 2) in the subgroup of women who were not pregnant and not on the oral contraceptive pill. higher than non-pcos women at survey 1, and longitudinal data on BMI showed that this difference increased to 3.0 kg/m 2 ( ) at survey 4. Greater longitudinal adult weight gain also occurred over 10 years in women reporting PCOS, with a difference of 2.6 kg ( ) greater than non-pcos women. Reported perception of childhood weight was higher in PCOS women (Table 2). The prevalence Demographic data across the surveys. Key variables of the study population at baseline and survey 4 are shown in Table 1. The main demographic variables are shown in Table 2 with all results reported as odds ratios (95% confidence intervals). Minor demographic differences were noted between self-reported PCOS and non-pcos women, but these were not significant. Presented results were not affected when corrected for key demographic variables (Table 2). Relationship between weight, BMI, and PCOS prevalence. PCOS women had higher weight compared with non-pcos women at all time points including at survey 1 [67.4 kg ( ) vs kg ( )] and survey 4 [76.2 kg ( ) vs kg [ )]. BMI was also higher in PCOS women at all time points including survey 1 [24.5 kg/m 2 ( ) vs kg/m 2 ( ) and survey 4 [27.8 kg/m 2 ( ) vs kg/m 2 ( )]. PCOS women on average reported a BMI of 2.0 kg/m 2 ( ) FIGURE 2 Relationship between menstrual cycle regularity and self-reported PCOS status at survey 4. The number of women who were not pregnant and not on the oral contraceptive pill in each category of menstrual cycle irregularity (none, rarely, sometimes, and often) were 2,041, 493, 504, and 486, respectively. The prevalence of PCOS was 2.7%, 2.3%, 8.7%, and 25.3%, respectively VOLUME 21 NUMBER 8 AUGUST

4 TABLE 1 Characteristics of women with PCOS and without PCOS at surveys 1 and 4 a Survey 1 Survey 4 PCOS (n ¼ 478) Non-PCOS (n ¼ 8,134) PCOS (n ¼ 478) Non-PCOS (n ¼ 8,134) Age, y Weight, kg b BMI, kg/m 2c Type 2 diabetes, % d Hypertension, % e Current smokers, % f OCP use, % g All estimates are adjusted for area of residence to account for over sampling of women from rural and remote areas. a Values are mean 6 standard error or proportions. b Weight unavailable for n ¼ 55 and n ¼ 760 at survey 1 and n ¼ 15 and n ¼ 203 at survey 4. c BMI unavailable for n ¼ 70 and n ¼ 991 at survey 1 and n ¼ 29 and n ¼ 471 at survey 4. d Data missing for n ¼ 1 and n ¼ 25 at survey 1. e Data missing for n ¼ 2 and n ¼ 37 at survey 1. f Data missing for n ¼ 21 and n ¼ 316 at survey 1 and n ¼ 9 in non-pcos women at survey 4. g Data missing for n ¼ 5 and n ¼ 57 at survey 1 and n ¼ 5 and n ¼ 50 at survey 4. of self-reported PCOS increased progressively with increasing BMI (4.3% in group with BMI < 25, 8.1% in group with BMI and 14.0% in group with BMI > 30) (Figure 3). Relationship between BMI and irregular menstrual cycles as a marker of PCOS status. In addition to the strong relationship between self-reported PCOS and both BMI over time (surveys 1-3) and cross-sectional BMI status (survey 4), the key PCOS diagnostic feature of irregular menstrual cycles was also associated strongly with BMI. Cross-sectional survey 4 data (excluding pregnant women and contraceptive pill users) showed that irregular cycles were reported as occurring often in 11.5% of normal weight (BMI kg/m 2 ), 14% of overweight (BMI kg/m 2 ), and 20.7% of obese women (BMI > 30 kg/m 2 ). The risk of often having irregular menstrual cycles increased progressively with increasing BMI in overweight [OR (95% CI): 1.1 ( )] and obese groups [1.9 ( )], compared with women in the healthy weight range. Multivariable analyses of longitudinal predictive factors for PCOS status. Multivariable logistic regression analyses of factors derived from surveys 1-3 that predicted PCOS status at survey 4 showed that average BMI had the strongest association with PCOS status. After adjusting for weight change and other relevant demographic factors (Table 2), the risk of reporting PCOS increased by 9.2% (95% CI: 6-12%) for each BMI unit (kg/m 2 ). Discussion The current study is the first large community based observational study with longitudinal measurements of weight to explore a range of characteristics including weight gain and BMI status in relation to diagnosed PCOS, which was identified at the 10-year time point. The prevalence of self-reported PCOS was 5.8% and was validated against menstrual cycle irregularity as a key diagnostic feature of PCOS. BMI and weight at all surveys, as well as adult weight gain over 10 years, were all higher in PCOS compared with non-pcos women. Weight and BMI were strongly associated with PCOS status, again supported by a clear relationship between BMI and menstrual cycle irregularity. Importantly, we have for the first time demonstrated a clear relationship between longitudinal BMI over 10 years and PCOS status, with mean BMI the strongest correlate of PCOS status. Reported PCOS prevalence varies dramatically based on diagnostic criteria, ethnicities, sample sizes, and population studied (1-4). Evolving diagnostic criteria remain controversial with inconsistent application in clinical and research settings (6,24). National Institute of Health (NIH) criteria comprising hyperandrogenism and anovulation have been superseded by expanded Rotterdam criteria which require two features of PCO on ultrasound, hyperandrogenism, or anovulation (5). The first representative community prevalence study in primarily Caucasian populations reported 8.7% (NIH) and 11.9% (Rotterdam) PCOS prevalence in Australian women aged years (n ¼ 728) (1). Rotterdam diagnosed PCOS increased to 17.8% on imputation for those not consenting to ultrasounds and 70% of PCOS cases were undiagnosed. The current self-reported PCOS prevalence of 5.8% is consistent with the low PCOS diagnosis rates, and therefore, is likely to have underestimated PCOS prevalence. Given that the Rotterdam criteria were published in 2004, it is also most likely that the majority of women reporting diagnosed PCOS in survey 4, conducted in 2006, would have been diagnosed based on NIH criteria. These findings highlight important messages on the need for more community prevalence studies of clinically diagnosed PCOS, along with greater PCOS awareness among both consumers and health professionals to optimize accurate and consistent diagnosis and early treatment of PCOS. In the current large observational study, although self-reported PCOS status was used, this was validated against menstrual cycle irregularity as a key diagnostic feature of PCOS. This was strongly related to self-reported PCOS status. Of the women not reporting PCOS who were not pregnant and not on the oral contraceptive pill, 11.0% reported often having irregular periods and may have had undiagnosed PCOS. However, overall the strong correlation between PCOS and irregular cycles confirmed that self-report was likely to be reasonably sensitive. VOLUME 21 NUMBER 8 AUGUST

5 Weight Gain and BMI in PCOS Teede et al. TABLE 2 Unadjusted and adjusted odds ratios (95% confidence interval) for variables related to PCOS in young women Variable Women with PCOS a (n ¼ 478) Women without PCOS a (n ¼ 8,134) Unadjusted a odds ratio (95% CI) P value Adjusted a odds ratio (95% CI) P value Average BMI, kg/m 2b : ( ) < ( ) <0.001 Mean 6 SE Change in weight, kg c : ( ) < ( ) 0.58 Mean 6 SE Childhood weight perception, % d Underweight e Average ( ) ( ) 0.84 Overweight ( ) < ( ) 0.53 Highest qualification, % f Year 12 or less e Trade/certificate ( ) ( ) 0.94 University/higher university degree ( ) ( ) 0.93 Main occupation No paid job e Clerical/sales/service/ ( ) ( ) 0.38 production/transport worker/laborer Associate professional/tradesperson ( ) ( ) 0.07 /advanced clerical/ service worker Professional/manager/administrator ( ) ( ) 0.14 Annual income, % f Less than $15,999 e $16,000 to $51, ( ) ( ) 0.28 $52,000 or more ( ) ( ) 0.13 BMI, body mass index; SE. standard error. a Adjusted only for area of residence to account for over sampling of women from rural and remote areas. b Average body mass index from surveys 1 to 3. c Change in weight between surveys 1 and 3. d Childhood weight perception at survey 1. e Reference category. f Highest qualification, main occupation, and income (self) at survey 3. Despite the high prevalence of obesity in PCOS and clear hormonal and clinical interactions between obesity and PCOS, literature exploring the relationships between PCOS prevalence and BMI is limited. Observational data includes small cross-sectional studies that do not support relationships between PCOS status and BMI (25). In 113 obese women referred for weight loss, PCOS prevalence was 28%, compared with 6.5% in a previous lean cohort in Spain (3,17), and yet, PCOS status was not related to the degree of obesity (17). In a mixed method study using combined data sets, no relationship between PCOS (64 estimated PCOS cases) and BMI was noted, however weaknesses included incomplete data sets, a selected population of university employees, and a small sample size (16). In a cross-sectional study of 192 Greek women, PCOS prevalence was 6.8% with similar BMI for PCOS and non-pcos women in this selected population (4). Limited prior studies are thus significantly underpowered to address key questions on the relationship between BMI and PCOS. Cross-sectional analysis in this current large, community-based study of 8,612 women has identified 478 cases of PCOS at the 10-year time point and has found that PCOS prevalence is significantly different over the BMI range with 4.3% (BMI < 25 kg/m 2 ), 8.1% (BMI kg/m 2 ), and 14.0% (BMI > 30 kg/m 2 ) PCOS prevalence (Figure 3). Also weight and BMI were significantly higher in those with PCOS, and PCOS status was strongly associated with BMI. Prior longitudinal studies have suggested a relationship between BMI and PCOS symptoms (15), with limitations including case selection based on symptoms only and exclusion of those on oral contraceptives which comprise potentially the most severe PCOS cases. Elevated BMI in 18-year-old women has also been shown to predict subsequent anovulatory infertility where PCOS is the primarily contributor (26). This is the first community-based observational study of weight and BMI in women identifying diagnosed PCOS and we have demonstrated a strong relationship between mean BMI over time and PCOS status. We have significantly advanced knowledge in this area demonstrating that BMI over time is the key longitudinal correlate of subsequently identified PCOS. We have also explored longitudinal weight gain and change in BMI in women who subsequently reported PCOS compared with those not reporting PCOS. In a prior selected PCOS clinic sample, longitudinal weight gain appeared similar to general community weight 1530 VOLUME 21 NUMBER 8 AUGUST

6 position statement (12,14). We have also previously shown that lowintensity community-based strategies are effective in prevention of weight gain in young women (19). This has important clinical and public health implications for the increased proportion of young women with PCOS and enhanced reproductive and metabolic risks associated with obesity. FIGURE 3 Prevalence of self-reported PCOS at survey 4 (2006) across the mean BMI range over surveys 1-3 (from 1996, 2000, 2003). Surveys 1-3 data was used to study the key correlates of PCOS status. PCOS was reported at survey 4. The number of women in the normal, overweight, and obese categories were 5,846, 1,574, and 780, respectively. gain (16), with the authors concluding that obesity in PCOS primarily reflects environmental factors. In the current study, community based, unselected women reporting PCOS had had significantly higher rates of weight gain compared with those not reporting PCOS. Given the current findings and the pathophysiological sequelae of obesity, including IR and hyperandrogenism, we suggest that obesity has a bidirectional relationship with PCOS and both contributes to and is contributed to by PCOS (9,27). This is consistent with previous data showing greater caloric restriction requirements for weight loss in more insulin resistant women and in those with PCOS compared with non-pcos women (28). Further research is needed to explore the important pathophysiological interactions between PCOS and obesity. The results of the current study are of both clinical and public health importance with every single BMI unit higher being associated with a 9.2% higher risk of reporting PCOS. Women with PCOS are inherently at high risk of diabetes and have increased cardiovascular risk factors (29). Increasing BMI is known to exacerbate these metabolic risks in PCOS and non-pcos women. The Nurses Health Study reported each kilogram gained from age 18 years increased the risk of cardiovascular disease by 3.1% and the risk of diabetes increases linearly from a BMI of 22 kg/m 2 (30). In this Australian longitudinal women s health cohort, young women of reproductive age have the highest rate of weight gain (mean 650 g/year), poor diet, low levels of physical activity, and greater barriers to participation in obesity protective behaviors compared with women at other life stages (18,19). The current study highlights that recognition of the health impact of increasing BMI including a higher prevalence of PCOS is important. This recognition can then facilitate targeted screening to optimize PCOS diagnosis and treatment and provides an opportunity to attenuate subsequent complications including infertility, diabetes, and cardiovascular disease through lifestyle intervention and prevention of excess weight gain. The importance of lifestyle interventions and weight loss is paramount as first line therapy for PCOS, improving biochemical and clinical features, as detailed in our systematic review and recent international PCOS The strengths of the current study include the very large, unselected community cohort with high baseline and ongoing participation rates, the use of longitudinal weight and BMI data with limited information bias and a focus on diagnosed PCOS as well as reported isolated PCOS symptoms. Previous longitudinal studies have only focused on relationships between BMI and self-reported symptoms (not diagnosed PCOS) including irregular cycles (15). Limitations of the current study include reliance on self-reported PCOS, although this is generic in large prospective community based observational studies with thousands of women over multiple time points. In the current study, PCOS status was captured at the 10-year time point; hence, timing of PCOS diagnosis was not ascertained, and therefore, we could not capture incident cases. This will be addressed prospectively in the analysis of future ALSWH surveys. Self-reported PCOS prevalence rates correlated with key symptoms (irregular menstrual cycles) and were consistent with current under diagnosed, Australian community-based PCOS prevalence rates (1). Furthermore, recent analyses from this observational study have contributed to the growing body of evidence suggesting such biases are insufficient to preclude meaningful longitudinal analyses (23). Finally, there may be a diagnosis bias, with women who are obese more likely to be screened and diagnosed with PCOS. This needs to be explored further in community prevalence studies. This study is the first observational study with longitudinal measurements of weight and BMI in women subsequently identified with diagnosed PCOS in a representative, community-based, unselected population of primarily Caucasian women and is the first to explore the relationship between body size and diagnosed PCOS rather than PCOS symptom status. The prevalence of self-reported PCOS was 5.8% with PCOS status strongly correlated to menstrual cycle irregularity, as a key diagnostic feature of PCOS. Diagnosis rates of PCOS are low, which is consistent with under diagnosis of PCOS in the community. This emphasizes that a greater awareness of PCOS is needed to facilitate earlier, accurate and consistent diagnosis, thereby enabling early treatment of PCOS. PCOS prevalence was significantly higher with increasing BMI and BMI, weight, and longitudinal increase in weight were all higher in self-reported PCOS compared with non-pcos women. Average BMI over the longitudinal follow-up had the strongest correlation with self-reported PCOS status, with the prevalence of reported PCOS increased 9.2% for every single unit increment in BMI. Given the high rates of weight gain in young women generally, these results highlight a major current and emerging public health issue. Combined with the established benefits of weight loss, this research supports aggressive prevention and management of excess weight in young women.o Acknowledgments The research on which this article is based was conducted as part of the Australian Longitudinal Study on Women s Health undertaken by The University of Newcastle and The University of Queensland. We are grateful to the Australian Government Department of Health and Ageing for funding and to the women who provided the survey data. VC 2012 The Society VOLUME 21 NUMBER 8 AUGUST

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