Abstract
Background: Socio-economic status has been associated with diabetes in cross-sectional studies. This study aimed to evaluate associations of household income and wealth with both prevalent and incident diabetes among older adults in the UK. It also evaluated the association between obesity and socio-economic status. Methods: A cohort of people aged ≥50 years was selected from the English Longitudinal Study of Ageing. The relation of prevalent and incident self-reported physician diagnosed diabetes to household income and wealth was evaluated in logistic regression models adjusting for education, social class, housing tenure, age, ethnicity, marital status, body mass index (BMI), smoking, alcohol use and physical activity stratified by sex. The relation of prevalent obesity to household income and wealth was also evaluated using logistic regression models. Results: There were 9053 participants (4021 men and 5032 women) including 721 (8.0%) with diabetes at baseline. Among 8332 participants initially free from diabetes, 246 (3.0%) were diagnosed with diabetes during ∼4 years follow-up. The adjusted odds ratio for prevalent diabetes in the lowest quintile of wealth compared with the highest was 1.56 for men and 2.08 for women. Incident diabetes was associated with lower wealth (P for trend 0.05 for men and 0.004 for women) after adjusting for socio-economic and demographic factors, but attenuated after further adjustment for lifestyle and BMI. Prevalent obesity was significantly associated with lower wealth in women but not in men. Conclusion: Lower wealth, but not income, may be associated with prevalent and incident diabetes among older adults in UK.
Introduction
Unequal distribution of health across the population has been recognized since the 19th century.1 In UK, there is a persistent gradient according to the level of deprivation or socio-economic position in all-cause mortality and in cause-specific mortality as well as morbidity.2,3
Inequality is also observed in diabetes. Lower socio-economic position has been associated with higher risk of diabetes in developed countries4 in addition to traditional risk factors including sex, age, ethnicity, high body weight, low quality dietary intakes, insufficient physical activity and cigarette smoking5–7 by affecting health behaviours, access to care and process of care.8 As an index of socio-economic status including relative deprivation of residential area, education or income have been used in cross-sectional studies evaluating the association with prevalent diabetes.9–16
Evidence of socio-economic inequalities in the incidence of diabetes is on the other hand, scarce. Such inequalities have been suggested by some longitudinal studies, but socio-economic measures were limited to residential deprivation score17,18 or employment grades.19 Education and financial resources were proposed, but the association with incident diabetes were inconsistent.20–23
The relevance of socio-economic measures varies over the life course, although several indicators including education, social class, home ownership, income and wealth have been used. Educational attainment and income may be relevant for younger or middle-aged people, but less applicable for older age populations.24–26 Retirement may be associated with reducing incomes, and consequently current income may not adequately quantify the socio-economic position of older people. Therefore, accumulated effects due to their previous economic situation may be important.24 Occupation and social class are also problematic at older ages as many people are not currently employed and may have engaged in various careers throughout their life.26 Alternatively, financial assets, especially liquid assets, were observed to be associated with health throughout middle and older ages, and more informative among people of ≥60 years.26
Because this information is scarce and difficult to collect, there is little evidence on the association of socio-economic circumstances, particularly financial wealth with diabetes especially at older ages when diabetes is most frequent. This study therefore aimed to explore the association between socio-economic position and both prevalent and incident diabetes in older adults. In addition, the association between socio-economic status and obesity was investigated. The hypothesis that financial wealth is associated with lower risk of diabetes among them was specifically evaluated.
Methods
Data source
The data for this study derive from the English Longitudinal Study of Ageing (ELSA). ELSA is an on-going study that collects information on the personal, economic and social circumstances of the older age population resident in UK. The sample consists of people aged ≥50 years, drawn from private households that participated in the Health Survey for England (HSE) in 1998, 1999 or 2001. Respondents’ partners were also included in the ELSA. Individuals consenting to participate received the baseline ELSA questionnaire interview conducted in 2002–03 (Wave 1) and they were followed up every 2 years with the latest contact at Wave 4 in 2008–09. Physical examination and blood samples are taken every 4 years from Wave 2 (2004–05) onwards through nurse visits. Technical details and the primary analyses of ELSA data are described in detail elsewhere.27–29
For this study, data for participants at Wave 2 were selected because data concerning self-perceived health, lifestyle, socio-economic status and physical examination were available for analysis. Participants were included if they were ≥50 years, but those whose questionnaires were answered by proxies were excluded.
Diabetes
The determination of diabetes status was based on self-report. Cases of prevalent diabetes were those that reported injecting insulin, taking medication for diabetes or being diagnosed with diabetes by a doctor up to Wave 2. Cases of incident diabetes included participants who reported having diabetes for the first time at Wave 3 or 4.
Socio-economic status
Income, financial wealth, educational qualifications, social class and housing tenure were considered as socio-economic position. Income was estimated from the sum of all income including wages, state pensions, private pensions, state benefits and other income obtained over the past 12 months. Equivalized household total income was derived from the total income of a household defined as a single person or a couple and any dependent children with adjustment for the household size using the modified Organisation for Economic Co-operation and Development (OECD) equivalence scale,29,30 and quintiles were used for analyses. Financial wealth was considered as quintile of household net financial wealth, which reflected the liquid assets including cash, savings and investments and debt that a household had.27,29 The quintiles for income and wealth were numbered first to fifth from the lowest to the highest. Educational qualifications was categorized as degree or equivalent, A-level or higher education below degree, O-level or other, CSE or other and no qualifications. Five category version of the National Statistics Socio-Economic Classification was applied in the analyses: managerial and professional, intermediate, small employers and own account workers, lower supervisory and technical, semi-routine and other. Housing tenure was categorized as owner, buying, renting and rent free.
Characteristics of participants
Sex, age, ethnicity and marital status of the respondents were recorded. For the analyses of this study, participants were grouped into five age groups: 50–54, 55–64, 65–74, 75–84 and ≥85 years according to their age at Wave 2. Ethnicity was grouped into white or non-white. Marital status was categorized into single, married, divorced and widowed.
Lifestyle and obesity
Body mass index (BMI), smoking, alcohol consumption and physical activity were considered as components of lifestyle and obesity. BMI was obtained through the nurse visit, and categorized according to the WHO’s definition of obesity: BMI of 25 kg m−2 and more as overweight and 30 kg m−2 and more as obese. Data on smoking were collected in the interview and subsequently smoking status was grouped as never smoker, ex-smoker and current smoker. The frequency of taking alcohol was regrouped as none, occasional, up to twice a week and daily. Strength and frequency of physical activities engaged in daily life were asked, and people having vigorous sports or activities for once a week or more were deemed as highly active and other people were assigned as low in the present analyses.
Statistical analyses
Logistic regression was used to analyse the cross-sectional association between prevalent diabetes and socio-economic variables among Wave 2 respondents. There was appreciable non-response at Waves 3 and 4, with 85% of non-response being accounted for by respondent deaths.31 Dates of diagnosis of diabetes, and dates of death, were not available for a time-to-event analysis. Logistic regression was, therefore, used to analyse the occurrence of incident diabetes among those respondents at Wave 2 who were newly diagnosed with diabetes at Wave 3 or 4. Additionally, data for respondent at Wave 4 who also responded at Wave 2 as not having diabetes were analysed. Logistic models with non-response at Wave 4 as the outcome were also fitted, in order to evaluate socio-economic inequality in non-response.
Analyses were performed for men and women separately, adjusting for confounding variables in three stages. In Model 1, the association of diabetes with income or wealth was adjusted for age group only; in Model 2, all socio-economic status was included as explanatory variables and adjusted along with age group, ethnicity and marital status; in Model 3, BMI category, smoking, alcohol consumption and physical activity were additionally included into the regression model. The quintile of wealth was treated as categorical variable in the multiple logistic regressions to evaluate the odds ratio (OR) of each quintile, and it was also treated as continuous variable for trend analysis.
The prevalence of obesity at Wave 2 and the association with socio-economic status was also assessed. The outcome binary variable obesity was defined by being obese (BMI of 30 kg m−2 and above) with non-obese including overweight (BMI <30 kg m−2) as reference. Multiple logistic regression analyses were conducted stratified by sex with adjustment for age and income or wealth (Model 1), additionally other socio-economic factors, ethnicity and marital status (Model 2), and with further adjustment for lifestyle (Model 3).
All analyses were performed using the statistical software package Stata SE version 10.
Results
Prevalent diabetes and socio-economic status
There were 9432 respondents who participated at Wave 2 and 9053 (96.0%) were eligible for analysis, being ≥50 years and giving their own questionnaire responses. Among the eligible respondents, 721 (8.0%) had prevalent diabetes at Wave 2. Prevalent diabetes was more frequent in men than in women; the proportion of prevalent diabetes at Wave 2 was 9.6% in men and 6.6% in women (table 1). Diabetes increased in frequency with age with the highest prevalence among the 75–84 years old group. People in the second (11.0%) and third (10.7%) quintile of income, who had less wealth (11.7% in the first and 10.1% in the second quintile), no educational qualifications, social class V and renting housing tenure also had higher frequency of diabetes (table 1).
Table 1.
Characteristics of participants
| Prevalence |
Incidence |
|||||||
|---|---|---|---|---|---|---|---|---|
| Eligible participants (n = 9053) | Prevalent diabetes (n = 721) | Column % | P value | No diabetes (n = 8332) | Incident diabetes (n = 246) | Column % | P value | |
| Sex | <0.001 | 0.021 | ||||||
| Men | 4021 | 387 | 9.62 | 3634 | 125 | 3.44 | ||
| Women | 5032 | 334 | 6.64 | 4698 | 121 | 2.58 | ||
| Age group (years) | <0.001 | 0.002 | ||||||
| 50–54 | 1026 | 40 | 3.90 | 986 | 19 | 1.93 | ||
| 55–64 | 3361 | 201 | 5.98 | 3160 | 94 | 2.97 | ||
| 65–74 | 2615 | 262 | 10.02 | 2353 | 93 | 3.95 | ||
| 75–84 | 1657 | 189 | 11.41 | 1468 | 35 | 2.38 | ||
| ≤85 | 394 | 29 | 7.36 | 365 | 5 | 1.37 | ||
| Income quintiles | <0.001 | 0.035 | ||||||
| 5 (highest) | 1730 | 94 | 5.43 | 1636 | 38 | 2.32 | ||
| 4 | 1714 | 103 | 6.01 | 1611 | 49 | 3.04 | ||
| 3 | 1715 | 183 | 10.67 | 1532 | 41 | 2.68 | ||
| 2 | 1716 | 188 | 10.96 | 1508 | 42 | 2.79 | ||
| 1 (lowest) | 1722 | 130 | 7.55 | 1592 | 49 | 3.08 | ||
| Not known | 456 | 23 | 5.04 | 433 | 7 | 1.62 | ||
| Financial wealth quintiles | <0.001 | <0.001 | ||||||
| 5 (highest) | 1727 | 93 | 5.39 | 1634 | 32 | 1.96 | ||
| 4 | 1723 | 101 | 5.86 | 1622 | 34 | 2.10 | ||
| 3 | 1723 | 131 | 7.60 | 1592 | 45 | 2.83 | ||
| 2 | 1705 | 172 | 10.09 | 1533 | 57 | 3.72 | ||
| 1 (lowest) | 1721 | 201 | 11.68 | 1520 | 71 | 4.67 | ||
| Not known | 454 | 23 | 5.07 | 431 | 7 | 1.62 | ||
| Educational qualifications | <0.001 | 0.009 | ||||||
| Degree or equivalent | 1131 | 74 | 6.54 | 1057 | 17 | 1.61 | ||
| A-level or higher education below degree | 1691 | 115 | 6.80 | 1576 | 42 | 2.66 | ||
| O-level or other | 1563 | 98 | 6.27 | 1465 | 35 | 2.39 | ||
| CSE or other | 1222 | 93 | 7.61 | 1129 | 39 | 3.45 | ||
| No qualifications | 3434 | 340 | 9.90 | 3094 | 113 | 3.65 | ||
| Not known | 12 | 1 | 8.33 | 11 | 0 | 0.00 | ||
| Social class | <0.001 | 0.036 | ||||||
| I | 2774 | 197 | 7.10 | 2577 | 61 | 2.37 | ||
| II | 1254 | 78 | 6.22 | 1176 | 30 | 2.55 | ||
| III | 951 | 56 | 5.89 | 895 | 23 | 2.57 | ||
| IV | 965 | 82 | 8.50 | 883 | 32 | 3.62 | ||
| V | 2937 | 282 | 9.60 | 2655 | 98 | 3.69 | ||
| Not known | 172 | 26 | 15.12 | 146 | 2 | 1.37 | ||
| Housing tenure | <0.001 | <0.001 | ||||||
| Owner | 5535 | 422 | 7.62 | 5113 | 136 | 2.66 | ||
| Buying | 1896 | 113 | 5.96 | 1783 | 37 | 2.08 | ||
| Renting | 1485 | 175 | 11.78 | 1310 | 69 | 5.27 | ||
| Rent free | 121 | 10 | 8.26 | 111 | 4 | 3.60 | ||
| Not known | 16 | 1 | 6.25 | 15 | 0 | 0.00 | ||
| Ethnicity | <0.001 | 0.243 | ||||||
| White | 8838 | 679 | 7.68 | 8159 | 239 | 2.93 | ||
| Non-white | 206 | 41 | 19.90 | 165 | 6 | 3.64 | ||
| Not known | 9 | 1 | 11.11 | 8 | 1 | 12.50 | ||
| Marital status | 0.016 | 0.997 | ||||||
| Single | 464 | 29 | 6.25 | 435 | 12 | 2.76 | ||
| Married | 5976 | 460 | 7.70 | 5516 | 163 | 2.96 | ||
| Divorced | 971 | 69 | 7.11 | 902 | 26 | 2.88 | ||
| Widowed | 1641 | 163 | 9.93 | 1478 | 45 | 3.04 | ||
| Not known | 1 | 0 | 0.00 | 1 | 0 | 0.00 | ||
| BMI | <0.001 | <0.001 | ||||||
| <25 | 2008 | 76 | 3.78 | 1932 | 16 | 0.83 | ||
| 25≤ and <30 | 3132 | 209 | 6.67 | 2923 | 79 | 2.70 | ||
| 30≤ | 2085 | 254 | 12.18 | 1831 | 118 | 6.44 | ||
| Not known | 1828 | 182 | 9.96 | 1646 | 33 | 2.00 | ||
| Smoking | 0.002 | 0.247 | ||||||
| Never | 3312 | 225 | 6.79 | 3087 | 80 | 2.59 | ||
| Ex-smoker | 4315 | 392 | 9.08 | 3923 | 117 | 2.98 | ||
| Current | 1424 | 104 | 7.30 | 1320 | 49 | 3.71 | ||
| Not known | 2 | 0 | 0.00 | 2 | 0 | 0.00 | ||
| Alcohol consumption | <0.001 | 0.064 | ||||||
| None | 861 | 113 | 13.12 | 748 | 29 | 3.88 | ||
| Occasionally | 1253 | 126 | 10.06 | 1127 | 41 | 3.64 | ||
| Up to twice a week | 2991 | 220 | 7.36 | 2771 | 76 | 2.74 | ||
| Daily | 2816 | 135 | 4.79 | 2681 | 64 | 2.39 | ||
| Not known | 1132 | 127 | 11.22 | 1005 | 36 | 3.58 | ||
| Physical activity level | <0.001 | 0.014 | ||||||
| High | 2476 | 110 | 4.44 | 2366 | 50 | 2.11 | ||
| Low | 6567 | 611 | 9.30 | 5956 | 196 | 3.29 | ||
| Not known | 10 | 0 | 0.00 | 10 | 0 | 0.00 | ||
P-values are for χ2-test between prevalent diabetes and no diabetes at Wave 2, and incident diabetes and no incident diabetes at follow-up. Figures are frequencies except where stated
The association of socio-economic status and prevalent diabetes was analysed for men and women separately (table 2). After adjusting for age group (Model 1), household income in the second and third quintile was associated with prevalent diabetes in men (OR 1.62, 95% CI 1.14–2.31 and OR 1.65, 95% CI 1.17–2.33) and women (OR 2.05, 95% CI 1.35–3.11 and OR 2.11, 95% CI 1.39–3.19). Being in the two lowest quintiles of wealth were also associated with prevalent diabetes in men (OR 1.87, 95% CI 1.33–2.61; OR 1.53, 95% CI 1.09–2.16), while for women, being in the three lowest wealth quintiles was associated with prevalent diabetes (OR 4.00, 95% CI 2.57–6.21; OR 2.85, 95% CI 1.82–4.48; OR 2.11, 95% CI 1.32–3.38) compared with the highest quintile.
Table 2.
Association of prevalent diabetes and income and wealth
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| Total | Diabetes | Row % | OR (95% CI), P value | OR (95% CI), P value | OR (95% CI), P value | |
| A Men | ||||||
| Income quintiles | ||||||
| 5 (highest) | 888 | 60 | 6.8 | 1.00 | 1.00 | 1.00 |
| 4 | 829 | 65 | 7.8 | 1.11 (0.77–1.60), 0.58 | 1.01 (0.69–1.48), 0.97 | 1.00 (0.68–1.48), 1.00 |
| 3 | 775 | 94 | 12.1 | 1.65 (1.17–2.33), 0.01 | 1.46 (1.00–2.12), 0.05 | 1.41 (0.96–2.06), 0.08 |
| 2 | 745 | 94 | 12.6 | 1.62 (1.14–2.31), 0.01 | 1.31 (0.88–1.94), 0.19 | 1.28 (0.86–1.91), 0.23 |
| 1 (lowest) | 640 | 57 | 8.9 | 1.20 (0.82–1.76), 0.35 | 0.88 (0.57–1.36), 0.57 | 0.86 (0.55–1.33), 0.49 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 853 | 67 | 7.9 | 1.00 | 1.00 | 1.00 |
| 4 | 828 | 57 | 6.9 | 0.88 (0.61–1.27), 0.48 | 0.91 (0.62–1.33), 0.63 | 0.85 (0.58–1.24), 0.40 |
| 3 | 778 | 71 | 9.1 | 1.16 (0.82–1.65), 0.40 | 1.20 (0.82–1.75), 0.34 | 1.03 (0.70–1.51), 0.87 |
| 2 | 685 | 81 | 11.8 | 1.53 (1.09–2.16), 0.02 | 1.58 (1.07–2.34), 0.02 | 1.30 (0.88–1.94), 0.19 |
| 1 (lowest) | 733 | 94 | 12.8 | 1.87 (1.33–2.61), <0.001 | 1.93 (1.29–2.90), 0.002 | 1.56 (1.03–2.37), 0.04 |
| P for trend | <0.001 | <0.001 | 0.008 | |||
| B Women | ||||||
| Income quintiles | ||||||
| 5 (highest) | 842 | 34 | 4.0 | 1.00 | 1.00 | 1.00 |
| 4 | 885 | 38 | 4.3 | 0.99 (0.62–1.59), 0.97 | 0.75 (0.46–1.23), 0.26 | 0.68 (0.41–1.12), 0.13 |
| 3 | 940 | 89 | 9.5 | 2.11 (1.39–3.19), <0.001 | 1.38 (0.88–2.16), 0.16 | 1.22 (0.78–1.92), 0.39 |
| 2 | 971 | 94 | 9.7 | 2.05 (1.35–3.11), 0.001 | 1.18 (0.75–1.87), 0.48 | 1.01 (0.63–1.60), 0.98 |
| 1 (lowest) | 1082 | 73 | 6.7 | 1.36 (0.88–2.09), 0.16 | 0.69 (0.42–1.11), 0.13 | 0.64 (0.39–1.04), 0.07 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 874 | 26 | 3.0 | 1.00 | 1.00 | 1.00 |
| 4 | 895 | 44 | 4.9 | 1.63 (0.99–2.68), 0.05 | 1.56 (0.94–2.58), 0.08 | 1.39 (0.83–2.31), 0.21 |
| 3 | 945 | 60 | 6.3 | 2.11 (1.32–3.38), 0.002 | 1.89 (1.16–3.09), 0.01 | 1.59 (0.96–2.61), 0.07 |
| 2 | 1020 | 91 | 8.9 | 2.85 (1.82–4.48), <0.001 | 2.35 (1.44–3.85), 0.001 | 1.81 (1.10–2.97), 0.02 |
| 1 (lowest) | 988 | 107 | 10.8 | 4.00 (2.57–6.21), <0.001 | 3.15 (1.90–5.21), <0.001 | 2.08 (1.24–3.48), 0.005 |
| P for trend | <0.001 | <0.001 | 0.003 |
Model 1: adjusted for age group; Model 2: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity and marital status; Model 3: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity, marital status, BMI, smoking, alcohol consumption and physical activity. Figures are frequencies except where stated
In Model 2 with income, wealth, education, social class, housing tenure, age group, ethnicity and marital status as explanatory variables, only men in the third quintile remained significant as for income (OR 1.46, 95% CI 1.00–2.15). However, lower wealth was consistently associated with elevated odds of prevalent diabetes for men (OR 1.93, 95% CI 1.29–2.90 in the first and OR 1.58, 95% CI 1.07–2.34 in the second quintile) and for women (OR 3.15, 95% CI 1.90–5.21 in the first, OR 2.35, 95% CI 1.44–3.85 in the second and OR 1.89, 95% CI 1.16–3.09 in the third quintile) compared with the highest. There was strong evidence of an increasing trend in diabetes prevalence with decreasing wealth in both men and women (P for trend <0.001).
After additional adjustment for BMI, smoking, alcohol consumption and physical activity level (Model 3), income was no longer associated with prevalent diabetes but lower wealth was. The lowest quintile in men (1.56, 1.03–2.37) and the two lowest quintiles in women (OR 2.08, 95% CI 1.24–3.48; OR 1.81, 95% CI 1.10–2.97) were significantly associated with prevalent diabetes. Trend tests were significant in both men (P = 0.008) and women (P = 0.003).
Incident diabetes and socio-economic status
Among the 8332 participants without doctor diagnosed diabetes at Wave 2, 6841 (82.1%) participated at Wave 3 and 5998 (72.0%) participated at Wave 4. There were 129 (1.9%) at Wave 3 and 117 (2.0%) at Wave 4, a total of 246 respondents reported developing diabetes at Wave 3 or 4, while 8086 did not. Incident diabetes was observed more frequently in men, especially those in higher age groups with the peak among the 65–74 years old group (table 1). People in the lowest quintile of income, having smaller wealth, no educational qualifications, social class V and renting housing tenure had higher incident diabetes.
After adjustment for age group (table 3, Model 1), women in the bottom two income quintiles (OR 2.26, 95% CI 1.11–4.63; OR 2.99, 95% CI 1.49–6.02), and men in the lowest (OR 1.90, 95% CI 1.09–3.30) and women in the bottom two quintiles (OR 3.95, 95% CI 1.95–8.00; OR 3.19, 95% CI 1.55–6.55) of wealth had higher risk of incident diabetes compared with the highest. The trend analyses for wealth were significant in men (P = 0.004) and women (P < 0.001).
Table 3.
Association of incident diabetes and income and wealth
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| Total | Diabetes | Row % | OR (95% CI), P value | OR (95% CI), P value | OR (95% CI), P value | |
| A Men | ||||||
| Income quintiles | ||||||
| 5 (highest) | 828 | 27 | 3.3 | 1.00 | 1.00 | 1.00 |
| 4 | 764 | 31 | 4.1 | 1.26 (0.75–2.14), 0.38 | 0.98 (0.56–1.72), 0.95 | 1.00 (0.57–1.77), 0.99 |
| 3 | 681 | 21 | 3.1 | 0.96 (0.53–1.72), 0.88 | 0.61 (0.32–1.15), 0.13 | 0.64 (0.33–1.21), 0.17 |
| 2 | 651 | 26 | 4.0 | 1.34 (0.76–2.36), 0.31 | 0.74 (0.39–1.40), 0.36 | 0.76 (0.40–1.45), 0.40 |
| 1 (lowest) | 583 | 19 | 3.3 | 1.06 (0.58–1.95), 0.84 | 0.58 (0.29–1.16), 0.12 | 0.62 (0.31–1.24), 0.18 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 786 | 22 | 2.8 | 1.00 | 1.00 | 1.00 |
| 4 | 771 | 19 | 2.5 | 0.87 (0.47–1.63), 0.67 | 0.87 (0.46–1.65), 0.66 | 0.81 (0.42–1.55), 0.52 |
| 3 | 707 | 26 | 3.7 | 1.34 (0.75–2.40), 0.32 | 1.30 (0.70–2.43), 0.41 | 1.12 (0.60–2.11), 0.72 |
| 2 | 604 | 24 | 4.0 | 1.54 (0.85–2.78), 0.15 | 1.50 (0.77–2.93), 0.24 | 1.26 (0.64–2.50), 0.50 |
| 1 (lowest) | 639 | 33 | 5.2 | 1.90 (1.09–3.30), 0.02 | 1.76 (0.89–3.48), 0.11 | 1.51 (0.75–3.06), 0.25 |
| P for trend | 0.004 | 0.047 | 0.146 | |||
| B Women | ||||||
| Income quintiles | ||||||
| 5 (highest) | 808 | 11 | 1.4 | 1.00 | 1.00 | 1.00 |
| 4 | 847 | 18 | 2.1 | 1.52 (0.71–3.25), 0.28 | 1.11 (0.51–2.42), 0.79 | 1.00 (0.46–2.20), 1.00 |
| 3 | 851 | 20 | 2.4 | 1.72 (0.81–3.64), 0.16 | 1.08 (0.49–2.39), 0.85 | 1.01 (0.46–2.25), 0.98 |
| 2 | 877 | 36 | 4.1 | 2.99 (1.49–6.02), 0.002 | 1.63 (0.76–3.49), 0.21 | 1.50 (0.70–3.24), 0.30 |
| 1 (lowest) | 1009 | 30 | 3.0 | 2.26 (1.11–4.63), 0.03 | 1.16 (0.53–2.55), 0.71 | 1.13 (0.51–2.50), 0.76 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 848 | 10 | 1.2 | 1.00 | 1.00 | 1.00 |
| 4 | 851 | 15 | 1.8 | 1.51 (0.67–3.37), 0.32 | 1.37 (0.61–3.11), 0.45 | 1.27 (0.55–2.89), 0.57 |
| 3 | 885 | 19 | 2.1 | 1.86 (0.86–4.03), 0.12 | 1.64 (0.73–3.68), 0.23 | 1.36 (0.61–3.08), 0.45 |
| 2 | 929 | 33 | 3.6 | 3.19 (1.55–6.55), 0.002 | 2.48 (1.12–5.45), 0.03 | 1.99 (0.90–4.42), 0.09 |
| 1 (lowest) | 881 | 38 | 4.3 | 3.95 (1.95–8.00), <0.001 | 2.80 (1.23–6.33), 0.01 | 1.93 (0.85–4.42), 0.12 |
| P for trend | <0.001 | 0.004 | 0.061 |
Model 1: adjusted for age group; Model 2: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity and marital status; Model 3: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity, marital status, BMI, smoking, alcohol consumption and physical activity. Figures are frequencies except where stated
When income, wealth, education, social class, housing tenure, age group, ethnicity and marital status were included as explanatory variables in the logistic regression analysis (Model 2), there were higher odds of incident diabetes for women in the bottom two wealth quintiles (OR 2.80, 95% CI 1.23–6.33; OR 2.48, 95% CI 1.12–5.45) but no such association was found to be significant in men. However, a significant trend of higher risk for incident diabetes with lower wealth was observed in both genders (P = 0.047 in men and 0.004 in women).
Further adjustment for BMI, smoking, alcohol consumption and physical activity (Model 3) reduced the risk for incident diabetes. Wealth was not found to be significant in both genders and P for trend in wealth quintiles was marginally insignificant among women (P = 0.061) and 0.146 in men.
As a sensitivity analysis, the effect of non-response at follow-up was evaluated. There were 8332 participants without diagnosed diabetes at Wave 2, but 2334 (28.0%) responses were not available at Wave 4. Among the 5998, Wave 2 non-diabetic participants who successfully responded at Wave 4, 112 (1.9%) at Wave 3 and 117 (2.0%) at Wave 4, totalling 229 (3.8%) developed diabetes since Wave 2. Multiple logistic regression, adjusting for socio-economic status, age group, ethnicity and marital status (Model 2) showed elevated OR of incident diabetes among men and women with lower wealth (OR 2.12, 95% CI 1.01–4.48 for men in the first quintile; OR 3.21, 95% CI 1.40–7.36, OR 2.66, 95% CI 1.19–5.92 for women in the bottom two quintiles) compared with the highest. Further adjustment (Model 3) attenuated their significance, and P for trend was 0.074 for men and 0.053 for women.
The association between non-response at Wave 4 and wealth at Wave 2 was analysed with multiple logistic regression. After adjusting for socio-economic status, age group, ethnicity and marital status (Model 2), lower wealth was significantly associated with higher OR of non-responding in both genders (OR 1.34, 95% CI 1.01–1.78 for men in the first quintile: OR 1.54, 95% CI 1.18–2.00 and OR 1.28, 95% CI 1.00–1.64 for women in the bottom two quintiles), and with further adjustment (Model 3), only women with the lowest wealth had significantly elevated OR compared with the highest (OR 1.32, 95% CI 1.00–1.73).
Obesity and socio-economic status
Obesity is one of the most important risk factors for diabetes.4 In this analysis, the OR associating prevalent diabetes with being obese was OR 2.41 (95% CI 1.64–3.54) among men and OR 3.65 (95% CI 2.45–5.43) among women; the incident diabetes by obesity was OR 6.62 (95% CI 3.08–14.22) among men and OR 8.09 (95% CI 3.74–17.50) among women compared with normal (BMI under 25 kg m−2) after adjusting for socio-economic status, age group, ethnicity, marital status, smoking, alcohol use and physical activity.
In addition to obesity being an independent risk factor for prevalent and incident diabetes, obesity itself was also associated with socio-economic status (table 4). With the adjustment for age group (Model 1), the ORs for either men and women with lower wealth were significantly higher compared with those with the highest wealth, the association being stronger in women.
Table 4.
Association of prevalent obesity and income and wealth
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| Total | Obesity | Row % | OR (95% CI), P value | OR (95% CI), P value | OR (95% CI), P value | |
| A Men | ||||||
| Income quintiles | ||||||
| 5 (highest) | 768 | 194 | 25.3 | 1.00 | 1.00 | 1.00 |
| 4 | 721 | 186 | 25.8 | 1.08 (0.85–1.36), 0.53 | 0.95 (0.74–1.22), 0.69 | 0.95 (0.74–1.22), 0.66 |
| 3 | 643 | 179 | 27.8 | 1.25 (0.98–1.59), 0.07 | 1.00 (0.77–1.30), 0.98 | 0.95 (0.73–1.24), 0.70 |
| 2 | 597 | 165 | 27.6 | 1.33 (1.04–1.71), 0.03 | 0.97 (0.73–1.29), 0.85 | 0.95 (0.72–1.27), 0.75 |
| 1 (lowest) | 489 | 133 | 27.2 | 1.26 (0.97–1.64), 0.09 | 0.94 (0.70–1.26), 0.67 | 0.93 (0.69–1.25), 0.62 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 747 | 160 | 21.4 | 1.00 | 1.00 | 1.00 |
| 4 | 718 | 168 | 23.4 | 1.11 (0.87–1.42), 0.39 | 1.07 (0.83–1.38), 0.62 | 1.04 (0.81–1.35), 0.75 |
| 3 | 647 | 185 | 28.6 | 1.50 (1.17-1.91), 0.001 | 1.32 (1.01–1.72), 0.04 | 1.27 (0.97–1.66), 0.08 |
| 2 | 553 | 168 | 30.4 | 1.69 (1.31–2.18), <0.001 | 1.40 (1.05–1.86), 0.02 | 1.31 (0.98–1.76), 0.07 |
| 1 (lowest) | 553 | 176 | 31.8 | 1.67 (1.30–2.15), <0.001 | 1.35 (1.00–1.83), 0.05 | 1.27 (0.93–1.73), 0.13 |
| P for trend | <0.001 | 0.019 | 0.065 | |||
| B Women | ||||||
| Income quintiles | ||||||
| 5 (highest) | 732 | 171 | 23.4 | 1.00 | 1.00 | 1.00 |
| 4 | 772 | 243 | 31.5 | 1.52 (1.20–1.92), <0.001 | 1.23 (0.97–1.57), 0.09 | 1.23 (0.96–1.57), 0.10 |
| 3 | 770 | 260 | 33.8 | 1.79 (1.42–2.25), <0.001 | 1.28 (1.00–1.65), 0.05 | 1.22 (0.95–1.58), 0.12 |
| 2 | 777 | 271 | 34.9 | 1.88 (1.49–2.37), <0.001 | 1.23 (0.95–1.60), 0.11 | 1.13 (0.87–1.47), 0.34 |
| 1 (lowest) | 850 | 255 | 30.0 | 1.59 (1.25–2.01), <0.001 | 1.02 (0.78–1.33), 0.90 | 0.96 (0.74–1.26), 0.79 |
| Financial wealth quintiles | ||||||
| 5 (highest) | 773 | 148 | 19.1 | 1.00 | 1.00 | 1.00 |
| 4 | 781 | 208 | 26.6 | 1.55 (1.22–1.98), <0.001 | 1.48 (1.16–1.89), 0.002 | 1.46 (1.14–1.87), 0.003 |
| 3 | 790 | 255 | 32.3 | 2.07 (1.63–2.61), <0.001 | 1.93 (1.51–2.48), <0.001 | 1.91 (1.49–2.46), <0.001 |
| 2 | 807 | 264 | 32.7 | 2.21 (1.74–2.79), <0.001 | 1.96 (1.51–2.55), <0.001 | 1.91 (1.47–2.49), <0.001 |
| 1 (lowest) | 752 | 326 | 43.4 | 3.31 (2.63–4.17), <0.001 | 2.98 (2.27–3.93), <0.001 | 2.89 (2.18–3.82), <0.001 |
| P for trend | <0.001 | <0.001 | <0.001 |
Model 1: adjusted for age group; Model 2: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity and marital status; Model 3: adjusted for financial wealth (or income), education, social class, housing tenure, age group, ethnicity, marital status, smoking, alcohol consumption and physical activity. Figures are frequencies except where stated
With further adjustment (Model 3), the significant risk due to wealth was attenuated in men, but for women the risk was maintained with the OR for being obese in the first quintile OR 2.89 (95% CI 2.18–3.82), the second OR 1.91 (95% CI 1.47–2.49), the third OR 1.91 (95% CI 1.49–2.46) and the fourth OR 1.46 (95% CI 1.14–1.87) compared with the highest.
Discussion
Lower financial wealth was associated with higher risk of prevalent and incident diabetes among older adults in UK. The association with prevalent diabetes persisted even after adjustment for other socio-economic and diabetes-related risk factors. In addition, lower wealth was associated with prevalent obesity in both men and women. Conversely, the association between lower wealth and incident diabetes persisted after adjustment for other socio-economic and demographic factors, but attenuated with further adjustment for lifestyle and obesity.
Socio-economic factors and diabetes
Previous epidemiological studies have shown the existence of socio-economic inequalities in diabetes. Living in deprived areas,9–12 having lower education14–16 and lower income14 increased the risk of prevalent diabetes. Deprived residential area17,18,32 and employment grades19 were also associated with incident diabetes.
Financial wealth was found to be a significant risk factor for diabetes in the present study. This finding about the importance of wealth vs. education, social class or income at older ages has not been reported previously on diabetes; however, it is consistent with the observations in other studies indicating financial assets being associated with health and disability throughout adulthood and older age.26,33
Lower socio-economic status is considered to influence health through health behaviours, access to care and process of care.8 Wealth reflects the accumulated assets through life time and is more unequally distributed among people than income or other socio-economic indicators,34 and therefore affect more strongly on people’s health choices and obesity which leads to increased diabetes.
Obesity and diabetes
Higher BMI showed a strong relationship with prevalent diabetes and incident diabetes in the present study, especially among women. Obesity was also associated with lower wealth among women, indicating that wealth is directly and indirectly affecting women’s diabetes. This gender difference is consistent with previous studies that obesity was negatively associated with socio-economic status in women but not necessarily in men in developed countries.35,36
Limitations
There are several limitations in this study. Most of the data were acquired through self-report, which could lead to bias. Especially, diabetes was determined through self-report of doctor-diagnosed diabetes. Pierce et al.37 reported that 18.5% of the diabetic patients ascertained by considering fasting plasma glucose were undiagnosed in the ELSA population. The authors concluded that socio-economic status was not significantly related to having undiagnosed diabetes. Another study that surveyed undiagnosed diabetes among British women aged 60–79 years did not find an association between socio-economic status and being correctly diagnosed.38 However, the effect of under diagnosis and under reporting to the present study is still unpredictable.
Another limitation is that the duration of follow-up differed by subjects. Incident diabetes was detected at Waves 3 and 4, but the interviews were conducted along 2 years at each wave. In addition, there were people who did not participate in Wave 4 or both Waves 3 and 4 that led to be regarded as non-diabetic cases. This resulted in reduced statistical power, and may have changed the characteristics of the people identified as incident diabetes. Women with low wealth showed higher risk of non-responding; however, sensitivity analysis considering only Wave 2 participants without known diabetes that responded at Wave 4 similarly showed increased OR of incident diabetes by lower wealth in men and women.
Lack of data about family history of diabetes may be another limitation. As having a family history is a significant risk factor in diabetes,6 adjusting for the presence of it may alter the results of the analyses.
In conclusion, lower financial wealth was significantly associated with higher prevalence and incidence of diabetes in both genders, whereas income was not. Obesity is an important risk factor for incident and prevalent diabetes, but itself was also associated with socio-economic status showing that financial wealth is associated with diabetes directly and indirectly via obesity.
Acknowledgements
The data were made available through the UK Data Archive (UKDA). ELSA was developed by a team of researchers based at the National Centre for Social Research, University College London and the Institute for Fiscal Studies. The data were collected by the National Centre for Social Research. The funding is provided by the National Institute of Aging in the USA, and a consortium of UK government departments co-ordinated by the Office for National Statistics. The developers and funders of ELSA and the Archive do not bear any responsibility for the analyses or interpretations presented here.
Conflicts of interest: None declared.
Key points.
Inequality exists in the prevalence of diabetes as well as other health problems, and previous studies have shown that some socio-economic status relate with prevalent diabetes.
The association between socio-economic status and incident diabetes is less evident.
Lower financial wealth was significantly associated with prevalent and incident diabetes in both genders.
Obesity, which is one of the most important risk factors of diabetes, was also significantly associated with lower financial wealth predominantly in women.
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