Obesity Research & Clinical Practice (2008) 2, 203—214
ORIGINAL ARTICLE
Are baby boomers booming too much? An epidemiological description of overweight and obese baby boomers Graeme Hugo a,∗, Anne W. Taylor b, Eleonora Dal Grande b a
National Centre for Social Application of GIS, University of Adelaide, South Australia, Australia Population Research and Outcome Studies Unit, Department of Health, PO Box 287, Rundle Mall, Adelaide, South Australia 5000, Australia
b
Received 13 March 2008 ; received in revised form 8 May 2008; accepted 8 May 2008
KEYWORDS Obesity; Overweight; Baby boomers; Surveillance; Risk factors; Chronic disease
Summary Objective: To provide a social, demographic, and health-related description of overweight and obese baby boomers (born between 1946 and 1964). Method: Data were collected using a monthly chronic disease and risk factor surveillance system in which a representative random sample of South Australians are selected from the Electronic White Pages each month and interviewed using computer assisted telephone interviewing (CATI). Results: In 2006—2007, 65% of baby boomers in South Australia were overweight or obese, and 26% were obese. There were statistically significant increases in both categories between 2002 and 2007. In 2006—2007, the overweight or obese groups were significantly different on a wide range of social, demographic and health-related variables when compared to their non-overweight peers at the univariate level. In the multivariate analysis the obese group was more likely to have risk factors (high blood pressure, insufficient exercise) and chronic disease (diabetes, asthma, arthritis). They were also more likely to be in lower socio-economic areas, to be of Aboriginal or Torres Strait Islander origin and have lower levels of education.
Abbreviations: ARIA, accessibility/Remoteness Index of Australia; ATSI, Aboriginal and Torres Strait Islander; BMI, body mass index; CATI, computer assisted telephone interviewing; CI, confidence interval; EWP, Electronic White Pages; HBP, high blood pressure; IRSD, index of relative social disadvantage; K10, Kessler 10; OECD, Organisation for Economic Co-Operation and Development; SA, South Australia; SAMSS, South Australian Monitoring and Surveillance System; SEIFA, socio-economic index for areas; SPSS, statistical package for social sciences. ∗ Tel.: +61 8 83035646. E-mail address: [email protected] (G. Hugo). 1871-403X/$ — see front matter © 2008 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2008.05.001
204
G. Hugo et al. Conclusions: Addressing the high rates of overweight and obesity within the baby boomers generation should be a policy priority. As this generation moves towards old age the significant associations between body mass index and chronic disease and disability promise to increase demand upon an already pressurized health system. © 2008 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Background The obesity epidemic in Organisation for Economic Co-operation and Development (OECD) countries, including Australia, has been well documented [1—4]. The epidemic affects most subgroups in the population regardless of age, gender, ethnicity and socio-economic status [5,6]. It is the argument of this paper that there is a group for which research and policy attention are pressing and urgent—–the post World War Baby Boomer Generation who were born between 1946 and 1964 [7,8]. This group is of particular significance because they represent a large proportion of the Australian population (29.5% in 2006) [9] and previous research has shown high rates of obesity and alarming predictions into the future [4]. International research has shown that baby boomers are more obese than their predecessors were at the same age and obtained this additional weight at a younger age than the cohort before them [10]. In addition, overweight and obese baby boomers are more likely to die earlier [11] and use an increased number of hospital services [12]. Within Australia, effort and focus in recent years has centred on child and adolescent obesity [13—15]. There is little published literature on obesity within the baby boomer generation per se, although the Australian Longitudinal Study on Women’s Heath has produced results highlighting the effect of weight change on the well-being of middle-aged women [16] and the effect of obesity on diabetes [17]. The AusDiab study has also reported a range of obesity-related results [18,19]. There are clear associations between obesity and the range of conditions, including chronic disease, disability and dementia that typically effect an ageing population [20—25]. As such, there is an urgency for intervention among baby boomers since this is a final window of opportunity before they enter the older age groups. Australia is faced with a doubling of its population aged 65 and over within the next two decades [26]. If people reach older ages with higher levels of chronic illness, disability and mental illness, it will place an even greater pressure on
the health system in terms of delivery of services, costs and future funding. The introduction of effective interventions to address obesity among baby boomers is dependent on having a sound understanding of the determinants of being overweight in this generation and identifying the subgroups with higher prevalence of obesity and overweight. This paper contributes to the field by drawing on a sample of baby boomers in the Australian state of South Australia (SA) to demonstrate the prevalence and impact of obesity and overweight according to a range of social and demographic indicators.
Methods Survey design and sample section The primary data for this study were collected using the South Australian Monitoring and Surveillance System (SAMSS) from January 2006 to December 2007. SAMSS is a telephone monitoring system designed to systematically monitor the trends of diseases, health-related problems, risk factors and other health services issues over time for the South Australian health system [27]. Interviews are conducted on approximately 600 randomly selected people of all ages each month. All households in SA with a telephone connected and the telephone number listed in the Electronic White Pages (EWP) are eligible for selection in the sample. SAMSS began data collection in 2002. A letter introducing the survey is sent to the selected household. Within each of these households, the person with the last birthday is chosen for interview. There are no replacements for non-respondents. Up to 10 call backs are made to the household to interview the selected persons. Interviews are conducted by trained health interviewers. SAMSS utilises a Computer Assisted Telephone Interviewing (CATI) system to conduct the interviews. The data are weighted by area (metropolitan/rural), age, gender and probability of selection in the household to the most recent SA
Are baby boomers booming too much? population data so that the results are representative of the SA population [28]. In the period January 2006 to December 2007, a total of n = 14,203 interviews were conducted (69.8% response rate). Analyses were limited to people who were born between 1946 and 1964 who provided their height and weight (n = 3490).
Data items Height (m) and weight (kg) questions are included in SAMSS every month. Body mass index (BMI) was determined by the calculation: kg/m2 , as defined by the World Health Organization [5]. The criteria for classifying BMI are underweight < 18.5; normal: 18.5—24.9; overweight: 25.0—29.9; obese ≥30.0 kg/m2 . Demographic variables included in the analyses were sex, area of residence defined by the Accessibility/Remoteness Index of Australia (ARIA)1 [29], number of people aged 16 years and over in the household, any children living in the household (aged less than 16 years), family structure, country of birth, language other than English spoken at home, Aboriginal or Torres Strait Islander (ATSI) status, marital status, work status, highest educational attainment, family’s money situation, dwelling status (e.g. own or rent), and gross annual household income. The socio-economic indexes for areas (SEIFA) index of relative social disadvantage (IRSD), developed by the Australian Bureau of Statistics, identifies geographical areas, such as postcodes, of relative disadvantage [30]. The SEIFA IRSD is a composite measure based on selected census variables, such as income, educational attainment and employment status. The SEIFA IRSD scores were grouped into quintiles for analysis where the highest quintile comprised postcodes with the highest SEIFA IRSD scores (most advantaged areas). General health was determined by the respondents rating their health on a scale from excellent to poor. Co-morbid conditions included medically confirmed diabetes, current asthma, cardiovascular disease (heart attack, angina, heart disease and/or stroke), arthritis, osteoporosis, current mental health condition and level of psychological distress of respondents as determined using the Kessler Psychological Distress 10 item scale (K10) [31]. Self-reported health risk factor data included current high blood pressure (HBP) and cholesterol, physical activity (derived from the amount of time 1 ARIA is an indicator of remoteness/accessibility derived from measures of road distance between populated localities and service centres.
205 spent walking, and doing moderate and vigorous activity in a 1-week period) [32], smoking status, household smoking environment, short term and long term alcohol risk (derived from the number of alcoholic drinks per day and the number of times per week alcohol was consumed) [33], and the number of fruit and vegetables consumed daily [34]. Health or health-related services used in the last four weeks were asked of respondents.
Data analyses Data were analysed using SPSS Version 14.0 and Stata Version 9.0. The conventional 5% level was used to determine statistical significance and 95% confidence intervals were provided for estimates. All univariate (2 tests) and multivariate analyses (logistic regression) were conducted using weighted data. Significant changes in the prevalence of overweight and obesity (BMI ≥ 25.0), and obesity (BMI ≥ 30.0) over time for people aged 18 years and over were examined using the 2 for trend test for the period 2002—2007. Separate univariate 2 tests using the 2006 and 2007 data combined were undertaken to determine the socio-demographic and health-related variables associated with being classified as overweight/obese (BMI ≥ 25.0) or obese (BMI ≥ 30.0). All independent variables that were statistically significant at the 0.25 level in each of the univariate analyses were entered into the logistic regression analyses [35]. Variables to be entered into the logistic regression were also refitted in Stata to check for multi-collinearity.
Results Fig. 1 shows the prevalence of both obesity and overweight/obese increased significantly among baby boomers between 2002 and 2007. The overall proportion of baby boomers in SA in 2006—2007 who were classified as overweight/obese was 65.0% (95% confidence intervals 63.4—66.5, n = 2267) and obese was 26.0% (95% confidence intervals 24.5—27.4, n = 906). Table 1 shows the univariate analyses for the demographic and socio-economic variables associated with overweight/obese and obese categories. At the univariate level those living in accessible and moderately accessible areas (as measured by ARIA), those in the lowest educational attainment categories, those of ATSI origin, those indicating they have ‘just enough money to get through’, and those in the lowest quintiles of the SEIFA IRSD were statistically significantly more likely to be in both the
206 Table 1 Socio-demographic univariate analyses of baby boomers who were overweight and obese (compared to normal weight and underweight) and obese (compared to overweight, normal weight, and underweight), South Australia, 2006—2007 Overweight and obese
Obese
n
%
OR (95% OR)
Sex of respondent Female Male
967/1705 1300/1785
56.7 72.8
1.00 2.05 (1.78—2.36)
ARIA—–Accessibility Remoteness Index of Australia Highly accessible Accessible and moderately accessible Remote and very remote
1805/2838 363/509 99/142
63.6 71.3 69.4
Number of people in household, 16 years and over 1 2 3 4 or more people
225/372 1184/1804 541/794 316/519
Children aged less than 16 years in household No Yes
p-Value
%
OR (95% OR)
p-Value
ORIGINAL ARTICLE
Are baby boomers booming too much? An epidemiological description of overweight and obese baby boomers Graeme Hugo a,∗, Anne W. Taylor b, Eleonora Dal Grande b a
National Centre for Social Application of GIS, University of Adelaide, South Australia, Australia Population Research and Outcome Studies Unit, Department of Health, PO Box 287, Rundle Mall, Adelaide, South Australia 5000, Australia
b
Received 13 March 2008 ; received in revised form 8 May 2008; accepted 8 May 2008
KEYWORDS Obesity; Overweight; Baby boomers; Surveillance; Risk factors; Chronic disease
Summary Objective: To provide a social, demographic, and health-related description of overweight and obese baby boomers (born between 1946 and 1964). Method: Data were collected using a monthly chronic disease and risk factor surveillance system in which a representative random sample of South Australians are selected from the Electronic White Pages each month and interviewed using computer assisted telephone interviewing (CATI). Results: In 2006—2007, 65% of baby boomers in South Australia were overweight or obese, and 26% were obese. There were statistically significant increases in both categories between 2002 and 2007. In 2006—2007, the overweight or obese groups were significantly different on a wide range of social, demographic and health-related variables when compared to their non-overweight peers at the univariate level. In the multivariate analysis the obese group was more likely to have risk factors (high blood pressure, insufficient exercise) and chronic disease (diabetes, asthma, arthritis). They were also more likely to be in lower socio-economic areas, to be of Aboriginal or Torres Strait Islander origin and have lower levels of education.
Abbreviations: ARIA, accessibility/Remoteness Index of Australia; ATSI, Aboriginal and Torres Strait Islander; BMI, body mass index; CATI, computer assisted telephone interviewing; CI, confidence interval; EWP, Electronic White Pages; HBP, high blood pressure; IRSD, index of relative social disadvantage; K10, Kessler 10; OECD, Organisation for Economic Co-Operation and Development; SA, South Australia; SAMSS, South Australian Monitoring and Surveillance System; SEIFA, socio-economic index for areas; SPSS, statistical package for social sciences. ∗ Tel.: +61 8 83035646. E-mail address: [email protected] (G. Hugo). 1871-403X/$ — see front matter © 2008 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2008.05.001
204
G. Hugo et al. Conclusions: Addressing the high rates of overweight and obesity within the baby boomers generation should be a policy priority. As this generation moves towards old age the significant associations between body mass index and chronic disease and disability promise to increase demand upon an already pressurized health system. © 2008 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Background The obesity epidemic in Organisation for Economic Co-operation and Development (OECD) countries, including Australia, has been well documented [1—4]. The epidemic affects most subgroups in the population regardless of age, gender, ethnicity and socio-economic status [5,6]. It is the argument of this paper that there is a group for which research and policy attention are pressing and urgent—–the post World War Baby Boomer Generation who were born between 1946 and 1964 [7,8]. This group is of particular significance because they represent a large proportion of the Australian population (29.5% in 2006) [9] and previous research has shown high rates of obesity and alarming predictions into the future [4]. International research has shown that baby boomers are more obese than their predecessors were at the same age and obtained this additional weight at a younger age than the cohort before them [10]. In addition, overweight and obese baby boomers are more likely to die earlier [11] and use an increased number of hospital services [12]. Within Australia, effort and focus in recent years has centred on child and adolescent obesity [13—15]. There is little published literature on obesity within the baby boomer generation per se, although the Australian Longitudinal Study on Women’s Heath has produced results highlighting the effect of weight change on the well-being of middle-aged women [16] and the effect of obesity on diabetes [17]. The AusDiab study has also reported a range of obesity-related results [18,19]. There are clear associations between obesity and the range of conditions, including chronic disease, disability and dementia that typically effect an ageing population [20—25]. As such, there is an urgency for intervention among baby boomers since this is a final window of opportunity before they enter the older age groups. Australia is faced with a doubling of its population aged 65 and over within the next two decades [26]. If people reach older ages with higher levels of chronic illness, disability and mental illness, it will place an even greater pressure on
the health system in terms of delivery of services, costs and future funding. The introduction of effective interventions to address obesity among baby boomers is dependent on having a sound understanding of the determinants of being overweight in this generation and identifying the subgroups with higher prevalence of obesity and overweight. This paper contributes to the field by drawing on a sample of baby boomers in the Australian state of South Australia (SA) to demonstrate the prevalence and impact of obesity and overweight according to a range of social and demographic indicators.
Methods Survey design and sample section The primary data for this study were collected using the South Australian Monitoring and Surveillance System (SAMSS) from January 2006 to December 2007. SAMSS is a telephone monitoring system designed to systematically monitor the trends of diseases, health-related problems, risk factors and other health services issues over time for the South Australian health system [27]. Interviews are conducted on approximately 600 randomly selected people of all ages each month. All households in SA with a telephone connected and the telephone number listed in the Electronic White Pages (EWP) are eligible for selection in the sample. SAMSS began data collection in 2002. A letter introducing the survey is sent to the selected household. Within each of these households, the person with the last birthday is chosen for interview. There are no replacements for non-respondents. Up to 10 call backs are made to the household to interview the selected persons. Interviews are conducted by trained health interviewers. SAMSS utilises a Computer Assisted Telephone Interviewing (CATI) system to conduct the interviews. The data are weighted by area (metropolitan/rural), age, gender and probability of selection in the household to the most recent SA
Are baby boomers booming too much? population data so that the results are representative of the SA population [28]. In the period January 2006 to December 2007, a total of n = 14,203 interviews were conducted (69.8% response rate). Analyses were limited to people who were born between 1946 and 1964 who provided their height and weight (n = 3490).
Data items Height (m) and weight (kg) questions are included in SAMSS every month. Body mass index (BMI) was determined by the calculation: kg/m2 , as defined by the World Health Organization [5]. The criteria for classifying BMI are underweight < 18.5; normal: 18.5—24.9; overweight: 25.0—29.9; obese ≥30.0 kg/m2 . Demographic variables included in the analyses were sex, area of residence defined by the Accessibility/Remoteness Index of Australia (ARIA)1 [29], number of people aged 16 years and over in the household, any children living in the household (aged less than 16 years), family structure, country of birth, language other than English spoken at home, Aboriginal or Torres Strait Islander (ATSI) status, marital status, work status, highest educational attainment, family’s money situation, dwelling status (e.g. own or rent), and gross annual household income. The socio-economic indexes for areas (SEIFA) index of relative social disadvantage (IRSD), developed by the Australian Bureau of Statistics, identifies geographical areas, such as postcodes, of relative disadvantage [30]. The SEIFA IRSD is a composite measure based on selected census variables, such as income, educational attainment and employment status. The SEIFA IRSD scores were grouped into quintiles for analysis where the highest quintile comprised postcodes with the highest SEIFA IRSD scores (most advantaged areas). General health was determined by the respondents rating their health on a scale from excellent to poor. Co-morbid conditions included medically confirmed diabetes, current asthma, cardiovascular disease (heart attack, angina, heart disease and/or stroke), arthritis, osteoporosis, current mental health condition and level of psychological distress of respondents as determined using the Kessler Psychological Distress 10 item scale (K10) [31]. Self-reported health risk factor data included current high blood pressure (HBP) and cholesterol, physical activity (derived from the amount of time 1 ARIA is an indicator of remoteness/accessibility derived from measures of road distance between populated localities and service centres.
205 spent walking, and doing moderate and vigorous activity in a 1-week period) [32], smoking status, household smoking environment, short term and long term alcohol risk (derived from the number of alcoholic drinks per day and the number of times per week alcohol was consumed) [33], and the number of fruit and vegetables consumed daily [34]. Health or health-related services used in the last four weeks were asked of respondents.
Data analyses Data were analysed using SPSS Version 14.0 and Stata Version 9.0. The conventional 5% level was used to determine statistical significance and 95% confidence intervals were provided for estimates. All univariate (2 tests) and multivariate analyses (logistic regression) were conducted using weighted data. Significant changes in the prevalence of overweight and obesity (BMI ≥ 25.0), and obesity (BMI ≥ 30.0) over time for people aged 18 years and over were examined using the 2 for trend test for the period 2002—2007. Separate univariate 2 tests using the 2006 and 2007 data combined were undertaken to determine the socio-demographic and health-related variables associated with being classified as overweight/obese (BMI ≥ 25.0) or obese (BMI ≥ 30.0). All independent variables that were statistically significant at the 0.25 level in each of the univariate analyses were entered into the logistic regression analyses [35]. Variables to be entered into the logistic regression were also refitted in Stata to check for multi-collinearity.
Results Fig. 1 shows the prevalence of both obesity and overweight/obese increased significantly among baby boomers between 2002 and 2007. The overall proportion of baby boomers in SA in 2006—2007 who were classified as overweight/obese was 65.0% (95% confidence intervals 63.4—66.5, n = 2267) and obese was 26.0% (95% confidence intervals 24.5—27.4, n = 906). Table 1 shows the univariate analyses for the demographic and socio-economic variables associated with overweight/obese and obese categories. At the univariate level those living in accessible and moderately accessible areas (as measured by ARIA), those in the lowest educational attainment categories, those of ATSI origin, those indicating they have ‘just enough money to get through’, and those in the lowest quintiles of the SEIFA IRSD were statistically significantly more likely to be in both the
206 Table 1 Socio-demographic univariate analyses of baby boomers who were overweight and obese (compared to normal weight and underweight) and obese (compared to overweight, normal weight, and underweight), South Australia, 2006—2007 Overweight and obese
Obese
n
%
OR (95% OR)
Sex of respondent Female Male
967/1705 1300/1785
56.7 72.8
1.00 2.05 (1.78—2.36)
ARIA—–Accessibility Remoteness Index of Australia Highly accessible Accessible and moderately accessible Remote and very remote
1805/2838 363/509 99/142
63.6 71.3 69.4
Number of people in household, 16 years and over 1 2 3 4 or more people
225/372 1184/1804 541/794 316/519
Children aged less than 16 years in household No Yes
p-Value
%
OR (95% OR)
p-Value
