How valid are self-reported height and weight? A comparison between CATI self-report and clinic measurements using a large cohort study

How valid are self-reported height and weight? A comparison between CATI self-report and clinic measurements using a large cohort study Abstract Objective: To examine the relationship between self-reported and clinical measurements for height and weight in adults aged 18 years and over and to determine the bias associated with using household telephone surveys. Method: A representative population sample of adults aged 18 years and over living in the north-west region of Adelaide (n=1,537) were recruited to the biomedical cohort study in 2002/03. A computerassisted telephone interviewing (CATI) system was used to collect self-reported height and weight. Clinical measures were obtained when the cohort study participants attended a clinic for biomedical tests. Result: Adults over-estimated their height (by 1.4 cm) and under-estimated their weight (by 1.7 kg). Using the self-report figures the prevalence of overweight/ obese was 56.0% but this prevalence estimate increased to 65.3% when clinical measurements were used. The discrepancy in self-reported height and weight is partly explained by 1) a rounding effect (rounding height and weight to the nearest 0 or 5) and 2) older persons (65+ years) considerably over-estimating their height. Conclusion: Self-report is important in monitoring overweight and obesity; however, it must be recognised that prevalence estimates obtained are likely to understate the problem. Implications: The public health focus on obesity is warranted, but self-report estimates, commonly used to highlight the obesity epidemic, are likely to be underestimations. Self-report would be a more reliable measure if people did not round their measurements and if older persons more accurately knew their height. (Aust N Z J Public Health 2006; 30: 238-46) Anne W. Taylor, Eleonora Dal Grande, Tiffany K. Gill, Catherine R. Chittleborough Population Research and Outcome Studies, Department of Health, South Australia David H. Wilson, Health Observatory, Department of Medicine, University of Adelaide, South Australia Robert J. Adams Department of Medicine, University of Adelaide, South Australia Janet F. Grant Population Research and Outcome Studies, Department of Health, South Australia Patrick Phillips Department of Endocrine Services, Queen Elizabeth Hospital, South Australia Sarah Appleton Health Observatory, Department of Medicine, University of Adelaide, South Australia Richard E. Ruffin Department of Medicine, University of Adelaide, South Australia, and the North West Adelaide Health Study Team, South Australia In Australia, many States have established surveillance systems, based on self-report data from telephone surveys, as the foundation for the monitoring of priority chronic diseases and associated risk or protective factors. These surveillance systems are commonly used as a means of determining the prevalence of non-registry based chronic diseases. 1,2 In addition, data on chronic disease risk and protective factors, preventive behaviours, attitudes, knowledge, and health care utilisation patterns and other public health activities are commonly collected. 1 Self-reported data from population surveys are, in many instances, the primary source of information for health planners, health promoters and policy determination and the data are used to make health decisions associated with the division of limited health funds. 3 While population surveys are seen as convenient, flexible, timely and inexpensive, 4,5 it is important that self-reported data are of the highest standard to justify the decision making and to lessen erroneous conclusions, policies and practices. The ultimate assumption in the use of health survey data is that the Submitted: August 2005 Revision requested: December 2005 Accepted: February 2006 Correspondence to: Associate Professor Anne Taylor, Population Research and Outcome Studies Unit, Department of Health, PO Box 287, Rundle Mall, South Australia 5000. Fax: (08) 8226 6323; e-mail: anne.taylor@health.sa.gov.au 238 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2006 vol. 30 no. 3

Validity of self-reported height and weight data collected are valid and reliable. Reliability is commonly assessed using test/re-test techniques, and validity (construct, content and criterion) is assessed using various methods. 6 The gold standard criterion concurrent validity studies, where self-report measures are assessed against other forms of measurement, are less common. This paper reports on a criterion validity study that was undertaken to assess the relationship between self-report and clinic measured anthropometry. Many studies have assessed the relationship between self-reported and measured height and weight over many years in many countries using a variety of data collection methods. 7 In relatively recent studies undertaken in Sweden, 8 Wales, 9,10 United Kingdom, 11 United States, 12 France, 13 Brazil 14,15 and Scotland, 16 similar conclusions were reached using self-report results: there is usually an under-estimation of weight and an over-estimation of height. 7 These findings vary by gender, 7,8,12,13,15,16 age, 8,9,12,16,17 socio-economic status (SES), 8 body size, 7,9,17-18,19 and education, 12 and not all findings were significant. 14,15 None of these studies used computer-assisted telephone interviewing (CATI) as the basis of the self-report. In Australia, findings from the personal interview-based 1995 National Health Survey have been reported but no large studies have been undertaken in the past decade. 20,21 The only CATI survey reported was of a small subset of residents in New South Wales (NSW). 22 To overcome this shortfall, this study, using a large randomly selected population, was undertaken to determine the validity of self-reported height and weight (and body mass index (BMI)), in an Australian adult population by comparing self-reported data to clinical examinations. Continued research into the validity, reliability and accuracy of self-report questions are paramount so that self-report data can be used with confidence, or at least with knowledge of the limitations. It is important that major public health indictors such as obesity, with its recent alarming increases in prevalence 23 and its relationship to the major chronic diseases, 24 are monitored. If CATI self-report systems are used it is also important to have updated information on any bias that is present. Methodology Survey sample The data were obtained from the North West Adelaide Health Study (NWAHS), a biomedical cohort study of a representative population sample of adults living in the north and west regions of Adelaide. 25 The NWAHS is a collaboration between the Central Northern Adelaide Health Service (the Queen Elizabeth Hospital and Health Service and the Lyell McEwin Health Service campuses), the South Australian Department of Health, the University of Adelaide, and the University of South Australia. All households in the north-western area of Adelaide with a telephone connected and the telephone number listed in the Electronic White Pages (EWP) were eligible for selection in the study. The sample was stratified into the two health regions: western Adelaide and northern Adelaide. Within each household, the person who had their birthday last and was 18 years or older was selected for interview and invited to attend the clinic. A letter introducing the study and an information brochure was sent to the household of each selected telephone number. Phase 1A of this study, including the recruitment questionnaire, was pilot tested in December 1999 (n=100) and recruitment was conducted between February 2000 and November 2000. Phase 1B of the study was pilot tested in July 2002 (n=75) and recruitment was conducted between August 2002 and July 2003. Data used in these analyses were obtained from Phase 1B. Previous analysis has confirmed that no bias existed between the study participants and the wider NWAHS community for BMI, smoking status, physical activity levels, overall health status and high blood pressure and high cholesterol reading. 26 During the CATI recruitment interview, all respondents were asked a number of questions regarding self-reported physical and mental health conditions and demographic information. Interviews were conducted in English. To ascertain self-report anthropometry, respondents were asked for their height without shoes in centimetres (cm) or feet and inches, and their weight undressed in the morning in kilograms (kg) or stones and pounds. Biomedical assessment of participants at the two clinics (the Queen Elizabeth Hospital and Lyell McEwen Health Service) included measurements of height (measured to the nearest 0.5 cm using a stadiometer) and weight (measured to the nearest 0.1 kg in light clothing and without shoes using standard digital scales) using pre-established clinical protocols. One kilogram was taken from the clinic weight measurement to account for clothing. 20,23 The calculation of BMI for both self-reported and measured data was determined by the calculation kg/m 2, as defined by the World Health Organization (WHO). 27 The criteria for classifying BMI are underweight <18.5; normal 18.5-24.9; overweight 25.0-29.9; obese 30.0-34.9; and severely obese 35 kg/m 2. Statistical analyses The data were weighted by region (western and northern), age groups, sex and probability of selection in the household to the Australian Bureau of Statistic s 2001 Census population. 28 Data were analysed using SPSS version 13.0. The conventional p value of 5% (with 95% confidence intervals) was used as the criteria for statistical significance. Paired samples t-tests were used to compare the means of self-reported and clinical measures of height, weight and BMI and χ 2 tests were performed to compare the prevalence of people who incorrectly self-reported their height by two, five or 10 cm, their weight by two, five or 10 kg, and those who were incorrectly classified according to the WHO BMI criteria by various demographic variables. The data was assessed using the Bland-Altman plots to investigate if the differences between the two are related to the magnitude of the measurement. The plots consist of the differences between self-reported and measured data against the averages of the self-reported and measured data. To measure the degree of misclassification of obesity from self-reported measures, comparisons between self-reported and 2006 vol. 30 no. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 239

Taylor et al. measured results were made in terms of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Scores close to 100% for all these measures indicate high agreement or accuracy between the self-reported and measured height and weight. These comparisons were made for age groups and gender for the overweight and obese BMI categories. Results The overall response rate for Phase 1B of the study was 47.1% (69.4% participation rate), with n=1,537 people participating in both the telephone questionnaire and attending the clinics for biomedical assessments. The average time between self-reported and measured data was 23.5 days (standard deviation=51.6). Anthropometry clinic data collected were missing for 166 (11.8%) participants. In addition, 4.3% (n=66) of participants could not estimate their height and 8.2% (n=125) could not estimate their weight. Analysis was undertaken only on those for which both self-reported and clinic measured details were available (n=1,371; 89.2% of total sample). Table 1 shows the demographic profile of the respondents used in the analyses. The mean age was 44.9 years (SD=17.2) for males and 47.0 years (SD=18.2) for females. Overall, 66.6% of respondents self-reported their height in imperial measurements (feet, inches), while 80.4% of respondents gave their weight in metric measurements (kg). Appropriate realignment of the data were undertaken so that all figures were the metric equivalent. Height, weight and body mass index Table 2 shows the mean values, standard deviation of the mean, and the mean differences for BMI, self-reported and measured height and weight. There were statistically significant differences between overall self-reported and measured height, weight and Table 1: Demographic profile of respondents by gender. Males Females Total n % n % n % 18-24 84 12.2 80 11.9 165 12.0 25-44 292 42.1 250 36.9 542 39.6 45-64 201 28.9 205 30.2 406 29.6 65 years 116 16.8 142 21.0 258 18.8 Country of birth Australia 503 72.5 483 71.3 986 71.9 UK or Ireland 93 13.4 97 14.3 189 13.8 Europe, the USSR and the Baltic States 55 8.0 52 7.6 107 7.7 Other 43 6.2 46 6.8 88 6.4 Marital status Married or living with partner 455 65.6 425 62.7 879 64.1 Separated or divorced 57 8.2 63 9.2 119 8.7 Widowed 17 2.4 57 8.4 74 5.4 Never married 159 22.9 127 18.7 285 20.8 Not stated 6 0.9 6 0.9 12 0.9 Employment status Full-time employed 381 54.9 167 24.7 548 40.0 Part-time/casual employment 84 12.2 183 27.1 268 19.5 Unemployed 22 3.2 20 3.0 42 3.1 Home duties 3 0.5 157 23.1 160 11.7 Retired 137 19.8 105 15.5 242 17.6 Student 39 5.6 23 3.4 61 4.5 Not stated 27 3.9 22 3.3 49 3.6 Educational attainment Secondary 234 33.8 324 47.8 558 40.7 Trade, apprenticeship, certificate, diploma 351 50.6 215 31.7 566 41.2 Bachelor degree or higher 72 10.4 92 13.6 164 12.0 Other/not stated 37 5.3 47 6.9 83 6.1 Gross household annual income 20,000 120 17.3 152 22.5 272 19.9 20,001-40,000 173 25.0 145 21.4 318 23.2 40,001-60,000 158 22.7 134 19.8 292 21.3 60,000+ 206 29.8 197 29.1 404 29.5 Not stated 36 5.2 48 7.1 84 6.2 Total 693 100.0 677 100.0 1,371 100.0 240 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2006 vol. 30 no. 3

Validity of self-reported height and weight Table 2: Mean values and standard deviations of self-reported and clinical measured height, weight and derivation of body mass index. Self-reported Clinically measured Mean difference Height (centimetres) mean (SD) mean (SD) mean (SD) Overall 170.1 (10.23) 168.7 (10.10) 1.4 (3.47) a Male 177.0 (7.54) 175.6 (7.60) 1.4 (3.28) a Female 163.0 (7.41) 161.7 (7.01) 1.3 (3.65) a 18-24 173.4 (9.98) 172.0 (9.70) 1.4 (4.06) a 25-44 171.5 (10.08) 170.7 (9.69) 0.7 (3.00) a 45-64 169.2 (10.15) 168.1 (9.78) 1.1 (3.51) a 65 years 166.5 (9.61) 163.4 (9.48) 3.1 (3.38) a Weight (kilograms) Overall 76.8 (16.45) 78.4 (17.01) -1.7 (3.84) a Male 83.7 (14.72) 85.2 (15.41) -1.5 (4.11) a Female 69.7 (15.08) 71.5 (15.75) -1.8 (3.54) a 18-24 72.4 (13.30) 74.1 (14.30) -1.7 (4.21) a 25-44 77.3 (17.48) 79.0 (18.13) -1.7 (3.82) a 45-64 80.1 (16.77) 81.9 (17.11) -1.8 (4.13) a 65 years 73.2 (14.16) 74.6 (14.56) -1.4 (3.09) a BMI (derived from height and weight) Overall 26.5 (4.93) 27.5 (5.18) -1.0 (1.71) a Male 26.7 (4.34) 27.6 (4.59) -0.9 (1.54) a Female 26.2 (5.46) 27.3 (5.72) -1.1 (1.85) a 18-24 24.0 (3.29) 24.9 (3.64) -1.0 (1.69) a 25-44 26.2 (5.18) 27.0 (5.47) -0.8 (1.54) a 45-64 27.9 (5.18) 28.9 (5.35) -1.0 (1.90) a 65 years 26.3 (4.06) 27.8 (4.28) -1.5 (1.64) a Note: (a) Statistically significant difference, pair-wise t-test, p<0.001. Table 3: Mean difference values (and standard deviations) of self-reported and clinical measured height and weight by gender and age groups. Measured BMI categories Height (cm) Underweight Normal Overweight Obese Male -0.7 (2.73) 1.3 (3.03) 1.3 (3.46) 1.8 (3.34) Female -0.7 (3.80) 0.9 (3.56) 1.6 (3.31) 1.9 (4.05) 18-24 -2.1 (0) 0.9 (3.25) 1.4 (3.64) 3.8 (6.86) 25-44 -0.2 (2.44) 0.6 (3.20) 0.6 (2.70) 1.0 (2.91) 45-64 -3.4 (3.93) 0.6 (2.94) 1.2 (3.54) 1.6 (3.74) 65 years 1.4 (4.79) 3.3 (3.54) 3.1 (3.54) 3.3 (3.33) Overall -0.7 (3.38) 1.1 (3.34) 1.4 (3.39) 1.8 (3.72) Weight (kg) Male 0.5 (1.01) -0.2 (3.51) -1.7 (3.98) -2.7 (4.59) Female 0.02 (2.05) -0.8 (2.99) -2.0 (2.75) -3.3 (4.62) 18-24 -0.5 (2.62) -3.0 (5.08) -3.8 (5.99) 25-44 -0.1 (1.28) -0.9 (3.24) -1.4 (3.16) -3.7 (4.99) 45-64 -1.6 (1.35) -0.04 (3.86) -2.1 (3.53) -2.5 (4.64) 65 years 2.2 (1.87) -0.1 (3.01) -1.7 (2.81) -2.3 (3.10) Overall 0.1 (1.89) -0.5 (3.24) -1.8 (3.50) -3.0 (4.61) 2006 vol. 30 no. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 241

Taylor et al. BMI. When analysed by gender and age groups, significant differences between the mean values for all three measures were found. In general, adults tended to over-estimate their height by 1.4 cm and under-estimate their weight by 1.7 kg. Males and females had similar differences in over-estimating their height, but women had a greater difference in under-estimating their weight (1.8 kg) than males (1.5 kg). People aged 65 years and over over-estimated their height by the greatest amount (3.1 cm) and under-estimated their weight by the smallest amount (1.4 kg). The Bland-Altman plots are shown in Figures 1 to 3 for height, weight and BMI. The plots include the mean difference and the 95% confidence interval of the mean difference. Additional analysis was undertaken to determine the difference in self-reported height and weight by measured BMI category (see Table 3). The overall difference between self-reported height and weight increased as BMI category increased, with people Figure 1: Bland-Altman plot of difference between self-reported and measured height (cm). Difference between reported and measured height (cm) Dif fer 20 en ce bet we 10 en rep ort ed 0 an d (c mem) -10 as ure d hei -20 ght -30-40 140 150 160 170 180 Average between reported and measured height (cm) 190 200 categorised as obese more likely to overstate their height and understate their weight than the other BMI categories. Overall, this was more marked for weight, although young (18-24 yearolds) obese people tended to overstate their height and understate their weight more than other age and sex groups. The 65 years and older normal, overweight and obese groups consistently overstated their height. Underweight people over-stated, rather than under-stated, their weight. Table 4 shows BMI categories according to WHO using selfreported and clinical measures of height and weight. Using selfreported data, it would be estimated that 19.1% of the respondents are obese, compared with 26.3% from the clinical measurements. This means that the population levels of obesity would be underestimated by 7.2% using self-reported data, that is, 27.4% of the respondents should have been classified as obese. Overall, 6.8% of males and 10.6% of females were severely obese (BMI 35) using measured data. Higher-than-expected proportions of people in incorrect categories were found for people who were 65 years and over, widowed, retired or employed in home duties, and in households with low annual income, and lower-than-expected proportions were found for people who were never married or who were in a household with a high annual income. To determine the level of discrepancy, examination of the distribution of absolute differences in height and weight was undertaken (see Table 5). Overall, 49.6% of respondents were incorrect by at least two centimetres in their self-reported height measurement. When the respondents who misreported their height by five centimetres or more (13.5%) were analysed by age, it was the older age groups who were more likely to over- or underestimate. Univariate analysis showed that they were also more likely to be retired or list home duties as their current work status, live in a household where the total yearly income was less than $20,000, be widowed, be born in southern Europe, and be less likely to have a degree. When a similar analysis was undertaken for weight, 51.8% were incorrect in the self-reported measure by Figure 2: Bland-Altman plot of difference between self-reported and measured weight (kg). Figure 3: Bland-Altman plot of difference between self-reported and measured BMI. Difference between reported and measured weight (kg) 30 20 10 0-10 -20-30 40 60 80 100 120 140 Average between reported and measured weight (kg) 160 180 Difference between reported and measured BMI (kg/m2) Dif fer en ce bet we en rep ort ed an(kg d /m me2) as ure d BM I 15 10 5 0-5 -10 10 20 30 40 50 Average between reported and measured BMI (kg/m2) 60 242 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2006 vol. 30 no. 3

Validity of self-reported height and weight Table 4: BMI categories using WHO criteria for self-reported and clinical measured height and weight by gender. Self-reported Measured Percentage Percentage n % n % difference misclassified Overall Underweight (<18.5) 14 1.0 13 0.9 0.1 7.1 Normal (18.5-<25.0) 589 43.0 463 33.8 9.2 21.4 Overweight (25.00-<30.0) 505 36.9 535 39.0-2.1 5.6 Obese (30.0+) 262 19.1 361 26.3-7.2 27.4 Obese 1 (30.0-<35.0) 182 13.3 244 17.8-4.5 25.4 Obese 2 (35.0-<40.0) 58 4.2 83 6.0-1.8 30.1 Obese 3 (40.0+) 22 1.6 35 2.5-0.9 37.1 Total 1,371 100.0 1,371 100.0 Males Underweight (<18.5) 4 0.6 2 0.3 0.3 50.0 Normal (18.5-<25.0) 260 37.6 206 29.7 7.9 20.8 Overweight (25.00-<30.0) 300 43.2 304 43.9-0.7 1.3 Obese (30.0+) 129 18.6 181 26.2-7.6 28.7 Obese 1 (30.0-<35.0) 100 14.4 136 19.6-5.2 26.5 Obese 2 (35.0-<40.0) 25 3.6 37 5.4-1.8 32.4 Obese 3 (40.0+) 5 0.7 8 1.2-0.5 37.5 Total 693 100.0 693 100.0 Females Underweight (<18.5) 10 1.5 10 1.5 0.0 0.0 Normal (18.5-<25.0) 329 48.5 258 38.0 10.5 21.6 Overweight (25.00-<30.0) 205 30.3 230 34.0-3.7 10.9 Obese (30.0+) 133 19.6 179 26.5-6.9 25.7 Obese 1 (30.0-<35.0) 82 12.1 108 15.9-3.8 24.1 Obese 2 (35.0-<40.0) 33 4.9 45 6.7-1.8 26.7 Obese 3 (40.0+) 17 2.6 26 3.9-1.3 34.6 Total 678 100.0 678 100.0 Table 5: Absolute difference in self-reported and clinical measured height (centimetres) and weight (kilograms) by gender. Overall Height Weight n % n % 0 to <2 cm 691 50.4 0 to <2 kg 661 48.2 2 to <5 cm 495 36.1 2 to <5 kg 524 38.2 5 to <10 cm 159 11.6 5 to <10 kg 138 10.1 10 cm or more 26 1.9 10 kg or more 47 3.5 Total 1,371 100.0 Total 1,371 100.0 Males 0 to <2 cm 339 48.9 0 to <2 kg 329 47.4 2 to <5 cm 272 39.2 2 to <5 kg 264 38.1 5 to <10 cm 73 10.5 5 to <10 kg 73 10.5 10 cm or more 10 1.4 10 kg or more 27 3.9 Total 693 100.0 Total 693 100.0 Females 0 to <2 cm 352 51.9 0 to <2 kg 333 49.1 2 to <5 cm 223 33.0 2 to <5 kg 260 38.3 5 to <10 cm 86 12.7 5 to <10 kg 65 9.7 10 cm or more 16 2.4 10 kg or more 20 2.9 Total 678 100.0 Total 678 100.0 2006 vol. 30 no. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 243

Taylor et al. over two kilograms. When assessing the 13.6% who were incorrect by five kilograms or more, they were statistically significantly more likely to be aged between 45 and 64 years, and less likely to be aged between 18 and 24 years. The sensitivity of self-reported overweight and obesity according to the clinical measurements is shown in Table 6. The sensitivity values for obesity were considered good (overall 69.6%) with excellent values for specificity (98.9%), PPV (95.7%) and NPV (90.1%). The values for PPV or false-positive rates (the proportion of people who are truly obese among those whose self-reported height and weight classified them as obese) are very high overall, by gender and by age groups. However, the sensitivity for obesity was lowest for the younger age groups (62.2%) even though the specificity, PPV and NPV was excellent. The other age groups had higher sensitivity, but the NPV decreased as age increased; that is, for the 65 year and over group 87.4% is the proportion of people who are truly not obese among those whose self-reported height and weight classified them as not obese. The sensitivity for overweight is considered to be good (71.8%), however the specificity (85.4%), PPV (75.8%) and NPV (82.6%) varies from good to excellent. These values are lower for overweight compared with these values for obese. The sensitivity for overweight is lower for females and for the younger (18 to 44 years) and older age groups (65 years and over). The specificity, PPV and NPV for overweight decreases as age increases. Discussion Monitoring BMI levels is a major focus of public health surveillance activities because of rising levels of obesity in the population and associated risk of chronic disease. Clinic measurements are ideal but are costly. The majority of data used to report obesity in Australian are based on self-report and, increasingly, telephone-based surveillance systems. This validity study of the bias or systematic error between self-reported and clinical measurements for height, weight and BMI is the largest undertaken in Australia in recent years and the only study comparing estimates from a CATI system. The results indicated that although the self-reported and measured values were strongly correlated, there was a trend for self-reported height to be overestimated (especially by the 65+ year-olds) and self-reported weight to be under-estimated, resulting in an under-estimation of the true population estimate of BMI. This is consistent with other major studies. 9,13,16,20,21 In terms of the individual measurements, there is no difference between males and females for misreporting height (males overreport by 1.4 cm and females by 1.3 cm), but females tend to under-estimate their weight more than males (males 1.5 kg and females 1.8 kg). BMI differences are consistent across gender (males 0.9 and females 1.1) and age groups (between 0.8 and 1.0 kg/m 2 ) except for the 65 and older age group whose BMI is misclassified by 1.5 kg/m 2. Although the mean measurements of height and weight have minor differences, the misclassification of BMI categories changes markedly with more than 9% of people Table 6: Sensitivity, specificity, and positive and negative predictive values for self-reported overweight and obesity according to clinical measurements (derived from height and weight) by age and sex. Obese Prevalence Self-reported Measured Sensitivity Specificity Positive Negative % % % % predictive value predictive value % % Overall 19.1 26.3 69.6 98.9 95.7 90.1 Male 18.6 26.2 68.4 99.0 96.0 89.9 Female 19.6 26.5 70.8 98.8 95.5 90.4 18-24 6.3 10.1 62.2 100.0 100.0 95.9 25-44 18.3 23.5 75.1 99.1 96.2 92.8 45-64 27.7 35.6 75.1 99.1 96.2 92.8 65 years 15.6 28.0 74.4 98.2 95.8 87.4 Overweight Overall 36.8 38.9 71.8 85.4 75.8 82.6 Male 43.2 43.7 76.8 82.8 77.6 82.1 Female 30.3 34.0 65.2 87.7 73.2 83.0 18-24 26.9 35.4 62.9 92.8 82.7 82.0 25-44 34.1 35.7 62.9 92.8 82.7 82.0 45-64 40.1 40.9 73.5 83.1 75.1 81.9 65 years 44.0 44.8 66.7 74.4 67.9 73.4 244 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2006 vol. 30 no. 3

Validity of self-reported height and weight less likely to be in the normal category and the obese category increasing by more than 7%. The strength of this study lies in its representative nature, the large random sample, the relatively high response rate and the standardised clinical operations with stringent measurement standards and regular calibration of scales and height measures. Although the overall stage 1 response rate associated with complete study involvement, including obtaining blood and other bio-medical measurements, was 49.6%, this is high when compared with other recent, comparable Australian studies. The AusDiab study recorded a response rate of 28% and a recent pilot for a national Australian biomedical study reported obtaining blood from 23% of its sample. 29 There is a trend towards lower response rates in all types of population surveys as people maintain their privacy and are increasingly overwhelmed by marketing telephone calls or mail-outs. The additional commitments associated with involvement in a cohort study add to respondent burden. It is apparent that some of the disparity between self-report and clinical measures for height and weight is the rounding effect where, because adults may rarely measure themselves (or be measured), there is a tendency to round the figures end digit preference. 13 Of the respondents who reported their height in metric measurements, 41.2% rounded the measurement (zero or five centimetres as the final digit) while only 18.4% of respondents who gave their height in feet and inches had a zero or six inches as the final digit. Conversely, in terms of weight, most rounding occurred in the imperial measurement (stones and pounds) with more than 80% of weights reported with whole stones or half stones as the final figure. It is acknowledged that some of this rounding effect could be true measurements although histograms of the data highlighted the disproportionate percentage of rounded numbers. Misreporting of actual measurements is compounded by the fact that older people are not accounting for (or are not aware of) loss of height associated with ageing. Over-estimating height in the older population implies that this group is more likely to recall their height as measured at a younger age. 11 Studies have shown that females lose height quicker than men, 30 and most loss of height is age-related bone loss. Thinning of the outer cortical bone leads to vertebral fractures, spinal deformity and loss of height. 31,32 Osteoporosis is a serious public health problem that is increasingly causing concern as our population ages, especially in terms of costly hip fractures. Preventive strategies could include older community members having more frequent height measures to highlight to themselves, and to their doctors, the ramifications of loss of height. Additional benefits to the monitoring of self-report measures would include more realistic BMI measures based on self-report. There are some apparent differences with the findings from this study and previous Australian studies. Compared with the major Australian Bureau of Statistics (ABS) work undertaken in 1995, 21 this population is, when measured, marginally taller (0.8 cm taller for males and 0.3 cm for females) but markedly heavier (by 2.2 kg for males and 3.5 kg for females). The increase in height would be expected on a population basis in line with the ongoing secular increase in height that has not stopped over the past 150 years. 33 The marked increased in weight validates the concern shown in Australia in recent years about obesity. Also of interest in this study is the fact that men and women are over-estimating their height by similar amounts (1.4 cm for males and 1.3 cm for females). This compares with 2.1 cm for males and 1.3 cm for females in the ABS study. For overall BMI there was little difference between the ABS 1995 figures (males 1.2 difference and females 1.3) and the 2002/03 NWAHS figures (males 0.9 and females 1.1). The use of BMI as a continuous variable has shown little effect on analysis and would be recommended for analytical investigations, but the classification into categories has shown significant differences. 8,13 Notwithstanding, self-reported data are still useful for monitoring even if people under- or over-report their height and weight, as long as the question asked is the same and is reliable. The Behavioral Risk Factor Surveillance System (BRFSS) in the US has shown, using self-reported height and weight, the dramatic increase in obesity rates over the past two decades. 34 Similar trends, using the annual South Australia Health Omnibus Survey which has included self-reported height and weight since 1991, have been reported. 23 To enhance the selfreported measures, further work, using data from this cohort, will examine adjustments that can be made to self-reported data to reflect the true estimate. It is also important to note that the two questions asked on the telephone proved to be reliable (BMI Kappa >0.80) as assessed by the CATI Technical Reference Group field testing and have been recommended for use. 35 For surveillance of BMI, a watching brief is required to ensure height and weight do not become increasingly socially desirable responses as health promotion campaigns influence community perceptions and beliefs. Self-reported height and weight remain an integral part of the monitoring and surveillance of overweight and obesity. The limitations associated with self-reported data are still apparent and indicate that the prevalence of obesity in Australia, based on selfreport, is likely to be an under-estimation. Ongoing investigations into alternative and perhaps more reliable measures of obesity, such as waist circumference, are still required. References 1. Rothman KJ, Grennland S, editors. Modern Epidemiology. 2nd ed. Philadelphia (PA): Lippincott Williams & Wilkins; 1998. 2. McQueen D, Puska P, editors. Global Behavioral Risk Factor Surveillance. New York (NY): Kluwer Academic/Plenum Publishers; 2003. 3. Del Boca F, Noll J. Truth or consequences: the validity of self-report data in health services research on addictions. Addictions. 2000;95 Suppl 3:347-60. 4. Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey Methodology. New York (NY): Wiley; 2004. 5. Martin LM, Leff M, Calonge N, Garrett C, Nelson DE. Validation of self reported chronic conditions and health services in a managed care population. Am J Prev Med. 2002;18(3):215-18. 6. Oleske DM, editor. Epidemiology and the Delivery of Health Care Services. 2nd ed. New York (NY): Kluwer Academia/Plenum Publishers; 2001. 7. Engstrom J, Paterson S, Doherty A, Trabulsi M, Speer K. Accuracy of selfreported height and weight in women: an integrative review of the literature. J Midwifery Womens Health. 2003;48:338-45. 8. Bostrom G, Diderichsen F. Socioeconomic differentials in misclassification of height, weight and body mass index based on questionnaire data. Int J Epidemiol. 1997;26:860-6. 2006 vol. 30 no. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 245

Taylor et al. 9. Hill A, Roberts J. Body mass index: a comparison between self-reported and measured height and weight. J Public Health Med. 1998;20:206-10. 10. Roberts R. Can self-reported data accurately describe the prevalence of overweight? Public Health. 1995;109:275-84. 11. Zieland S, Thorogood M, Fuller A, Muir J. Desire for the body normal: body image and discrepancies between self reported and measured height and weight in a British population. J Epidemiol Community Health. 1996;50:105-6. 12. Villanueva E. The validity of self-reported weight in US adults: a population based cross-sectional study. BMC Public Health. 2001;1:11. 13. Niedhammer I, Bugel I, Bonenfant S, Goldberg M, Leclerc A. Validity of self-reported weight and height in the French GAZEL cohort. Int J Obes. 2000;24:1111-8. 14. Chor D, Coutinho ES, Laurenti R. Reliability of self-reported weight and height among State bank employees. Rev Saude Publica. 1999;33(1):16-23. 15. Schmidt MI, Duncan BB, Tavares M, Polanczyk CA, Pellanda L, Zimmer PM. Validation of self-reported weight a study of urban Brazilian adults. Rev Saude Publica. 1993;27(4):271-6. 16. Bolton-Smith C, Woodward M, Tunstall-Pedoe H, Morrison C. Accuracy of the estimated prevalence of obesity from self reported height and weight in an adult Scottish population. J Epidemiol Community Health. 2000;54:143-8. 17. Gunnell D, Berney L, Holland P, Maynard M, Blane D, Frankel S, et al. How accurately are height, weight and leg length reported by the elderly, and how closely are they related to measurements recorded in childhood? Int J Epidemiol. 2000;29:456-64. 18. Lawlor D, Bedford C, Taylor M, Ebrahim S. Agreement between measured and self-reported weight in older women. Results from the British Women s Heart and Health Study. Age Ageing. 2002;31:169-74. 19. Nawaz H, Chan W, Abdulrahman M, Larson D, Katz D. Self-reported weight and height. Implications for obesity research. Am J Prev Med. 2001;20:294-8. 20. Australian Bureau of Statistics. How Australians Measure Up. Canberra (AUST): ABS; 1999. Catalogue No.: 4359.0. 21. Walters AM. Assessment of Self-reported Height and Weight and their use in the Determinants of Body Mass Index. Canberra (AUST): Australian Institute of Health and Welfare; 1995. 22. Flood V, Webb K, Lazarus R, Pang G. Use of self-report to monitor overweight and obesity in populations: some issues for consideration. Aust N Z J Public Health. 2000;24:96-9. 23. Dal Grande E, Gill T, Taylor AW, Chittleborough C, Carter P. Assessing the body mass index of the South Australian population over the past decade: Where will we be in 2013? Aust N Z J Public Health. 2005;29(4):343-8. 24. Australian Institute of Health and Welfare. The Relationship between Obesity, Overweight and Cardiovascular Disease. Canberra (AUST): AIHW; 2004. 25. Taylor A, Dal Grande E, Chittleborough C, Cheek J, Wilson D, Phillips P, et al. The North West Adelaide Health Study Key Biomedical Findings, Policy Implications and Research Recommendations, May 2002. Adelaide (AUST): South Australian Department of Human Services; 2002. 26. Taylor AW, Dal Grande E, Gill T, Chittleborough CR, Wilson DH, Adams R, et al. Do people with risky behaviours participate in biomedical cohort studies. BMC Public Health. 2006;6:11. 27. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO consultation on obesity. Geneva (CHE): World Health Organization; 2000. 28. Australian Bureau of Statistics. Census of Population and Housing Selected Social and Housing Characteristics for Statistical Local Areas, South Australia, 2001. Canberra (AUST): ABS; 2002. Catalogue No.: 2015.4 29. Coyne TJ, Findley MG, Ibiebele TI, Forman DW. Overweight and obesity in Australia: an underestimation of the true prevalence. Med J Aust. 2004;180(2):9304. 30. Perissinotto E, Pisent C, Sergi G, Grigoletto F, Enzi G. Anthropometric measurements in the elderly; age and gender differences. Br J Nutr. 2002;87:177-86. 31. US Department of Health and Human Services. Bone Health and Osteoporosis. A Report of the Surgeon General. Rockville (MD): US DHHS; 2004. 32. Siminoski K, Jiang G, Adachi JD, Hanley DA, Cline G, Ioannidis G, et al. Accuracy of height loss during prospective monitoring for detection of incident vertebral fractures. Osteoporos Int. 2005;16:403-10. 33. Cole T. Secular trends in growth. Proc Nutr Soc. 2000;59:317-24. 34. Behavioral Risk Factor Surveillance System. Overweight and Obesity: Obesity Trends: USA Obesity Trends 1985-2003. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control. Available from: http://www. cdc.gov/nccdphp/dnpa/obesity/trend/maps/ 35. Daly A, Taylor A. Population Health Monitoring and Surveillance: Question Development Field Testing. Field Testing 1 Report Asthma, Demographic Characteristics and Diabetes. Melbourne (AUST): National Public Health Partnership; 2003 May. 246 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2006 vol. 30 no. 3