Journal of the New Zealand Medical Association, 15-February-2008, Vol 121 No 1269
Comparison of different markers of socioeconomic status with cardiovascular disease and diabetes risk factors in the Diabetes, Heart and Health Survey
Patricia A Metcalf, Robert R K Scragg, David Schaaf, Lorna Dyall, Peter N Black, Rod T Jackson
The New Zealand Ministry of Health (MOH) has reported on socioeconomic (SES) inequalities for various measures of health,1,2 including cardiovascular disease (CVD) risk factors using data from MOH funded studies.2–4 In general, a more adverse pattern of CVD risk factors were found in the lower SES groups, but they did not simultaneously adjust for the other SES measures.2–4 Furthermore, mortality from CVD has been reported to be higher in lower SES groups, particularly with the area-based deprivation indicator NZDep91.5
We have previously reported a trend to a more adverse pattern of CVD risk factor levels in the lower SES groups.6 The strongest associations were related to income and education rather than New Zealand Socioeconomic Index (NZSEI).7 However, raised blood pressure was associated with low education, and prevalence of diabetes mellitus with income.6
A study carried out in Sweden reported that education, income, and occupational class could not be used interchangeably, as although they are correlated, they each measure a different phenomena and tap into different causal mechanisms.8 The latter study found that education was the strongest predictor of diabetes, income was the strongest predictor of all cause mortality, but myocardial infarction morbidity and mortality showed a more mixed picture.8
Occupational class is a measure of the physical work environment and how workplace itself is organised.8 Income provides material or immaterial resources for health, such as better housing, clothing, food and resources for dealing with stressful and demanding situations.8 On the other hand, educational attainment relates to the ability to turn information into practical measures and behaviours.8 There appear to have been few previous reports where the NZSEI, income, education, and NZDep2001 have been adjusted for simultaneously.
The aim of this study was to compare CVD and diabetes risk factors across New Zealand Socioeconomic Index (NZSEI) classes, income, levels of tertiary education groups, and NZDep2001 classes to determine whether there were important independent gradient differentials after adjusting for other measures of socioeconomic status.
The Auckland Diabetes, Heart and Health Survey was carried out between December 2001 and November 2003. Adults aged 35 to 74 years were recruited from two sampling frames: one was a cluster sample where random starting point addresses were obtained from Statistics New Zealand and the probability of selection was proportional to the number of people living in that mesh block (response rate 61.3%); and the other was a random sample taken from the November 2000 Auckland electoral rolls stratified into 5-year age bands and included all people living in the Auckland area, but excluding Franklin and Rodney (response rate 60%).
Out of the 4049 participants interviewed, 1408 were from the cluster sample, and 2641 were from the electoral roll. Twenty-nine people were excluded as they were outside the age range thus leaving 4020. These participants comprised 47.8% males and 52.2% females, 50.3% Europeans and Others, 25.0% Maori, and 24.7% Pacific people (mostly of Samoan, Tongan, Niuean, or Cook Islands origin). Ethical Committee approval was obtained from the Auckland Ethics Committees.
Interviews were carried out in halls or clinics close to participant’s homes. Personnel were trained in the administration of the questionnaires and in taking blood pressure and other measurements. Participants filled in a questionnaire which included questions on ethnicity, education level attained, smoking history, occupation, gross combined household income, and past medical history. Ethnicity was defined according to the current NZ census.9
Occupations were first coded using the New Zealand classification of occupations.10 Occupational class was then assigned as the highest of the participant or their spouse, or for retired people using their main lifetime occupation for the New Zealand Socioeconomic Index (NZSEI).7 NZSEI was then transformed into discrete ‘occupational classes’ as proposed by Davis et al.7
These classes are comprised of: Class 1 – legislators and administrators; Class 2 – various professionals; Class 3 – corporate managers, associate professionals, and the armed forces; Class 4 – trade workers, plant operators and office clerks; Class 5 – other trade workers, machine operators and labourers; and Class 6 – market-orientated agricultural and fishery workers. Classes 1 and 2 and Classes 5 and 6 were combined due to their small numbers.
Education was classified as no tertiary education, Certificate (e.g. Trade or Technicians, apprenticeship or typing), Diploma (e.g. Teacher, Nurse, or Business Management), or Degree (e.g MA, PhD, BA, BSc, or Medicine). Combined yearly household income categories were “missing” and <$30,000, $30,001 to <$50,000, $50,001 to $70,000, and >$70,000. After geocoding the address of each participant, the 10-category NZDep2001 was assigned according to Salmond and Crampton.11
Participants fasted from 10pm the evening before the interview and collected a first morning urine sample which they brought along. A 75-gram oral glucose tolerance test was carried out in participants who had not been previously diagnosed with diabetes, and a fasting and 2-hour post Glucaid drink blood samples were collected for glucose measurement. Plasma glucose was measured using an enzymatic method [Roche Products (NZ)]. Participants were classified as having newly diagnosed diabetes mellitus using 1998 WHO criteria using fasting glucose ≥7.0 mmol/L or 2-hour post glucose load of ≥11.1 mmol/L for diabetes.12
Serum cholesterol was measured using an enzymatic method13 and HDL-cholesterol was measured using a combination of a polyion and a divalent cation (Roche). Serum triglycerides were measured enzymatically. Urinary albumin was measured using an immunoturbidimetric method. Haemoglobin A1c was measured by High Performance Liquid Chromatography on a Biorad Variant II instrument.
An Omron-Hem-706 oscillometric blood pressure pulse monitor was used to measure blood pressure two times after the participant had been seated for at least 5 minutes. A person was classified as having raised blood pressure if the mean of the two measured blood pressures was ≥140 mmHg systolic or ≥90 mmHg diastolic, or if they reported taking medication for raised blood pressure.
Weight and height were measured to the nearest 0.1 kg and 0.5 cm, respectively. Body mass index (BMI) was calculated as weight (in kg) divided by the square of height (in m). Obesity was defined as a body mass index >30 kg/m2, and overweight as a body mass index between >25 and 30 kg/m2. Waist and hips were measured to the nearest 0.5 cm. The 5-year cardiovascular risk was calculated using the Framingham equation.14 Moderate and vigorous leisure exercise was assessed using a 3-month physical activity recall questionnaire that has been validated.15
Participant data were weighted according to the sampling frame that they were obtained from and means, standard errors, prevalences and odds ratios calculated using dual frame sampling methodology16-18 using SAS survey procedures.19 Odds ratios and means were first estimated after adjusting for age, ethnicity and gender; and in the second step, NZSEI, income, education, and NZDep2001 were entered to estimate their independent effects.
Because of the positively skewed frequency distribution of urinary albumin and exercise times, these were converted to loge values for calculations; the results are presented as geometric means (the exponential of the mean of the logged data) and associated 95% tolerance factor. The strength of the associations of CVD and diabetes risk factors with SES measures were assessed using partial correlation coefficients adjusted for age, gender, and ethnicity.
Income was similarly correlated with NZSEI and NZDep2001 (both 0.37). The correlation between the NZSEI and NZDep2001 was -0.34 (NZSEI has 10 = low and 90 = high, whereas NZDep has 1 = least deprived and 10 = most deprived), and between NZSEI and education was 0.24. The correlations between income and education (0.16) and NZDep2001 and education (-0.18) were lowest.
Means and odds ratios for CVD and diabetes risk factors are shown in Table 1 by NZSEI occupational classes after adjusting for age, gender and ethnicity. Compared to NZSEI class 1, 2, & 3, the prevalence of total and previously diagnosed diabetes and current smoking were significantly higher in NZSEI class 5 & 6, and mean HbA1c, 5-year CVD risk, BMI, and waist-to-hip ratios. Mean exercise levels, HDL-cholesterol concentrations, and stature were significantly lower. However, after further adjusting for income, education and NZDep2001, the only significant difference between NZSEI class 1, 2, & 3 and NZSEI class 5 & 6 was the higher waist-to-hip ratio in the latter group.
The higher prevalence of total diabetes mellitus and previously diagnosed diabetes in the lowest NZSEI group were no longer significant after further adjusting for income, and the higher BMI level was no longer significant after further adjusting for education. The lower HDL-cholesterol levels were no longer significant after further adjusting for income or NZDep2001, the higher 5-year CVD risk was no longer significant after adjusting for income, education or NZDep2001, the higher HbA1c levels by income and education, the lower stature was no longer significant after further adjusting for income and NZDep2001, the lower exercise levels were explained by education and NZDep2001 or income and education, and the higher smoking levels by education or NZDep2001.
Table 2 shows means and odds ratios for CVD and diabetes risk factors by combined household income groups, adjusted for age, gender, and ethnicity. Compared to people with incomes ≥$70,001, people on incomes <$30,000 had significantly lower HDL-cholesterol concentrations, lower stature, and lower time spent exercising per week, and significantly higher fasting and 2-hour glucose concentrations, HbA1c levels, 5-year CVD risk, urinary albumin concentrations and waist-to-hip ratios—plus higher prevalence of total, newly and previously diagnosed diabetes mellitus, and current cigarette smoking levels.
After further adjusting for the other SES measures, only fasting glucose and newly diagnosed cases of diabetes mellitus were no longer significant and were explained by NZDep2001.
The last column in Table 2 shows mean levels of CVD and diabetes risk factors in those who either did not know their combined household income or refused to record it. Based on the risk factor levels, it would appear that many belonged to the lowest income group, with the exception of exercise times, urinary albumin concentrations and 5 year CVD risk, that were intermediate between the 2 lowest income groups.
Means and odds ratios for CVD and diabetes risk factors by level of education are shown in Table 3. After adjusting for age, gender, and ethnicity, people with no tertiary education had higher fasting glucose levels, HbA1c levels, BMI, and waist-to-hip ratios and a higher prevalence of current cigarette smoking, and lower exercise times compared to those with a university degree. After further adjusting for the other SES measures, only the higher prevalence of smoking and higher BMI levels remained significant.
The higher waist-to-hip ratio was no longer significant after NZSEI was included in the model, the lower exercise time was no longer significant after NZSEI or income were included in the model, HbA1c was no longer significant after NZDep2001 was included in the model, and the higher fasting glucose concentrations were no longer significant after income and NZSEI were included in the model.
Table 4 shows means and odds ratios for CVD and diabetes risk factor levels by NZDep2001 classes. Compared to NZDep2001 class 1 & 2 (least deprived), there were trends towards higher diastolic blood pressure, fasting and 2-hour glucose concentrations, HbA1c, 5-year CVD risk, BMI, waist-to-hip ratios, urinary albumin and higher smoking, total and newly diagnosed diabetes, and raised blood pressure prevalence, and lower exercise, stature, and HDL-cholesterol levels in the more deprived NZDep2001 classes. However, the initially significant higher raised blood pressure, total and newly diagnosed diabetes mellitus prevalence and lower stature in the most deprived NZDep2001 class were explained by household income.
Further adjustment for the number of adults and number of children in the household tended to attenuate the associations slightly for both income and NZDep2001.
After adjusting for age, gender, and ethnicity, partial correlation coefficients showed stronger associations between income and 2-hour glucose concentrations, height, total, and previously diagnosed diabetes. Similarly adjusted partial correlations were stronger with NZDep2001 for diastolic blood pressure, HDL-cholesterol, 5-year CVD risk, BMI, waist-to-hip ratio, exercise time, urinary albumin, raised blood pressure, and smoking. The partial correlations with HbA1c were similar for both NZDep2001 and income.
Main findings—The current study has shown independent associations for low household income and more deprivation with 2-hour glucose concentrations, HbA1c levels, 5-year CVD risk, waist-to-hip ratios, urinary albumin concentrations, and cigarette smoking—and lower HDL-cholesterol levels and exercise time compared to the highest SES stratum.
Income also showed independent adverse associations with total and previously diagnosed diabetes, and height. More deprived NZDep2001 classes showed adverse independent associations with fasting glucose concentrations, diastolic blood pressure, and BMI. The occupation-based NZSEI only showed an independent association with waist-to-hip ratio, and education only showed independent associations with BMI and smoking habit.
Income showed stronger associations with 2-hour glucose, total, and previously diagnosed diabetes mellitus, whereas NZDep2001 showed stronger associations with diastolic blood pressure, raised blood pressure, HDL-cholesterol, fasting glucose, 5-year CVD risk, BMI, waist-to-hip ratio, urinary albumin concentrations, exercise levels, and prevalence of smoking. The strength of the association with HbA1c was similar for NZDep2001 and income.
Blood pressure—There was a trend towards a higher prevalence of raised blood pressure across the more deprived NZDep2001 groups, and diastolic blood pressure levels were significantly higher in the most deprived NZDep2001 class compared to the least deprived (Table 4). But raised blood pressure was not associated with any of the other SES measures. In contrast, the 1996–1997 Health Survey reported an inverse association between self-reported high blood pressure and income, level of education and NZDep96,4 as did the 2002–2003 NZ Health Survey using NZDep2001,2 but they did not adjust for the other SES measures simultaneously.
The 1988–1990 Workforce Diabetes Survey reported higher systolic blood pressure levels and higher prevalence of raised blood pressure in the lower education groups compared to those with a University education in a working population.6 These results, taken together, indicate a consistent SES determinant for blood pressure.
Lipids—Both low income and more deprived NZDep2001 classes showed independent associations for HDL-cholesterol with lower levels, but no significant differences for total cholesterol or triglycerides. HDL-cholesterol levels showed a similar association with the Elley-Irving SES measure and education in females in the 1989-90 LINZ survey.20 HDL-cholesterol concentrations were inversely associated with income and NZDep96 in the 1996-1997 NZ Health Survey,4 and with NZDep2001 in the 2002-2003 NZ Health Survey,2 but not with self-reported cholesterol lowering medications. The 1997 NNS also reported an inverse trend between HDL-cholesterol and NZDep96, and an inverse trend between cholesterol and NZDep96 in males.21
Diabetes—The current study showed that both income and NZDep2001 had adverse associations with measures of glucose tolerance and prevalence of diabetes mellitus (Tables 2 and 4). However, NZDep2001 explained the initially elevated fasting glucose concentrations and higher prevalence of newly diagnosed diabetes associated with income.
On the other hand, the initially significant associations between NZDep2001 and total and newly diagnosed diabetes were explained by income. This suggests that both income and level of deprivation are associated with newly diagnosed diabetes. However, only income was associated with previously diagnosed cases of diabetes, suggesting that the presence of diabetes may have an adverse impact on an individual’s earning power. The 1996–1997 NZ Health Survey reported a higher prevalence of self-reported diabetes in lower income and NZDep96 groups, but not education,4 and the 2002–2003 also reported a higher prevalence of self-reported diabetes in the lower NZDep2001 groups.2
The 1988–1990 Workforce Diabetes Survey also found inverse associations between diabetes prevalence, 2 hour glucose levels and income.6,22
CVD risk—The 5-year risk of CVD was higher in the lowest income and more deprived NZDep2001 groups (Tables 2 and 4), and was stronger for NZDep2001. Although this finding does not appear to have been reported previously, the New Zealand census-mortality study found a strong gradient between death from CVD and NZDep2001.5
Urinary albumin—The finding of increasing urinary albumin concentrations with more deprived NZDep2001 classes also does not appear to have been previously reported. We have noted that increased urinary albumin concentrations may be a marker of CVD risk.23–25
BMI and waist-to-hip ratio—BMI showed an inverse relationship with NZDep2001, but not income (Tables 2 and 4), and the waist-to-hip ratio showed an inverse relationship with both NZDep2001 and income, that was stronger for NZDep2001. The 1989-1990 LINZ survey also reported an inverse relationship for BMI with the Elley-Irving SES measure, and a trend in females with education.20 Similarly, the Elley-Irving SES and education showed inverse trends with waist-to-hip ratio.20 The 1997 NNS also showed an inverse trend between BMI and NZDep96 and an inverse trend in females between NZDep96 and waist-to-hip ratio.21
Height—Both income and NZDep2001 showed an inverse association with height, that was stronger for NZDep2001. Similar associations were observed with the Elley-Irving SES measure in the 1989-1990 LINZ survey,20 and the 1996–1997 NZ Health Survey.4 In females, there was an inverse trend between height and education in the LINZ survey,20 and an inverse trend in both males and females with NZDep2001 in the 1997 NNS.21
Leisure-time exercise—Exercise times were lower in the lowest income groups and there was an inverse trend across NZDep2001 groups, which were stronger for NZDep2001 than for income (Tables 1 and 4). The 1989–1990 LINZ survey reported a similar trend in females with the Elley-Irving SES measure, but not with education.26 Whereas, the 1996–1997 NZ Health survey reported an inverse association between exercise levels and education, but not with income or NZDep96.4 However, the 2002–2003 NZ Health survey reported an inverse trend between exercise and NZDep2001 in females only.2
Smoking—The finding of an increased prevalence of smoking in the lower SES strata, but particularly for NZDep2001 in the current study, has been consistently reported with all measures of SES.2,4,20,21
Study limitations—NZSEI is an occupation-based measure that can be difficult to assign to a housewife or a person who has retired or is unemployed. This can be partly overcome by using a past occupation, or the occupation of an employed spouse. In the current survey we have assigned the NZSEI to the higher of the participant or spouse, or for those who had retired to their main life-time occupation. Another disadvantage, compared to income or education, is that the occupation(s) of an individual have to be coded and then mapped onto the NZSEI scale. It can also be difficult to code an occupation if insufficient information is given, such as ‘Engineer’.
A major disadvantage of income is that some people refuse to divulge the information and others do not know (Table 2 missing column), however it is easy to measure and code, as is education. In addition, poor health may actually lead to a drop in income.
A disadvantage of NZDep2001, aside from being an area-based rather than individual-based, is that the address of the participant must be first geocoded using a computer that requires matching addresses. In the current study, many people who lived on the borders of suburbs chose the next suburb as their domicile. Furthermore, NZDep is likely to have a higher misclassification error than the other SES measures as not all deprived people live in deprived small areas, and vice-versa. Despite these limitations, both household income and the area-based NZDep2001 have shown important associations with cardiovascular disease risk factor levels.
We note that when collecting data in surveys that it still important to collect information on income, education and occupation (and ethnicity) as they measure different aspects of the construct of SES, and may have varying associations with different risk factors due to different causal pathways. Including both the area-based NZDep and individual SES information in any model is required to fully adjust for confounding in analyses of the association of other exposures (e.g. diet) with CVD.27
Study strengths—The major strengths of the current study are its size, and its community-based sample. Limitations to the current study include the collection of a single measure for lipids, glucose tolerance, and urinary albumin, the measurement of blood pressure on a single occasion, and that cigarette smoking and exercise information was based on self-report.
Cardiovascular disease and diabetes risk factor levels showed a more adverse pattern in the lower SES groups compared to the highest SES groups. In general, stronger associations were observed for NZDep2001 than for the other measures of SES. These findings endorse the use of NZDep as a tool for informing health-related policy development in New Zealand, where other measures of SES cannot be obtained. It will be important to continue to update this readily accessible tool in order to maintain its predictive validity.
Competing interests: None known.
Author information: Patricia A Metcalf, Senior Lecturer and Senior Research Fellow; Robert K R Scragg, Associate Professor; Rod Jackson, Professor, Division of Epidemiology and Biostatistics, David Schaaf, Research Fellow; Division of Pacific Health; Lorna Dyall, Senior Lecturer, Division of Maori Health; School of Population Health, Tamaki Campus, University of Auckland; Peter Black, Associate Professor, Department of Medicine, School of Medicine, University of Auckland.
Acknowledgments: This survey was funded by the Health Research Council of New Zealand. We thank the technical and clerical staff who conducted the study so capably and efficiently; the people of Auckland for participating; and North Shore and Waitakere Hospitals, Te Pai Netball Centre, Takapuna District Cricket Club, Belmont Rose Centre, Glen Eden Ceramco Park Centre, Nga Tapuwhai Community Centre, Trust Health Care, Manurewa Nathan Homestead, Otara Leisure Centre (Te Puke O Tara Community Centre), and the Mangere Town Centre for providing examination rooms.
Correspondence: Dr Patricia Metcalf, Division of Epidemiology and Biostatistics, School of Population Health, Tamaki Campus, University of Auckland, Private Bag 92019, Auckland, New Zealand. Fax: +64 (0)9 3737000; email: email@example.com
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