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Inequalities in bariatric surgery in Australia: findings from 49 364 obese participants in a prospective cohort study

Rosemary J Korda, Grace Joshy, Louisa R Jorm, James RG Butler and Emily Banks
Med J Aust 2012; 197 (11): 631-636.
doi:
10.5694/mja12.11035
Abstract

Objectives: To investigate variation, and quantify socioeconomic inequalities, in the uptake of primary bariatric surgery in an obese population.

Design, setting and participants: Prospective population-based cohort study of 49 364 individuals aged 45–74 years with body mass index (BMI) ≥ 30 kg/m2. Data from questionnaires (distributed from 1 January 2006 to 31 December 2008) were linked to hospital and death data to 30 June 2010. The sample was drawn from the 45 and Up Study (approximately 10% of New South Wales population aged 45 included, response rate approximately 18%).17

Main outcome measures: Rates of bariatric surgery and adjusted rate ratios (RRs) in relation to health and sociodemographic characteristics.

Results: Over 111 757 person-years (py) of follow-up, 312 participants had bariatric surgery, a rate of 27.92 per 10 000 py (95% CI, 24.91–31.19). Rates were highest in women, those living in major cities and those with diabetes, and increased significantly with a higher BMI and number of chronic health conditions. Adjusted RRs were 5.27 (95% CI, 3.18–8.73) for those with annual household income ≥ $70 000 versus those with household income < $20 000, and 4.01 (95% CI, 2.41–6.67) for those living in areas in the least disadvantaged quintile versus those in the most disadvantaged quintile. Having versus not having private health insurance (age- and sex-adjusted RR, 9.25; 95% CI, 5.70–15.00) partially explained the observed inequalities.

Conclusions: Bariatric surgery has been shown to be cost-effective in treating severe obesity and associated illnesses. While bariatric surgery rates in Australia are higher in those with health problems, large socioeconomic inequalities are apparent. Our findings suggest these procedures are largely available to those who can afford private health insurance and associated out-of-pocket costs, with poor access in populations who are most in need. Continuing inequalities in access are likely to exacerbate existing inequalities in obesity and related health problems.

Obesity is a major public health challenge for Australia. In the 2007–08 National Health Survey, 24% of Australian adults were reported to be obese and a further 37% overweight.1 Obesity rates are growing and the continuing increase in severe obesity is of particular concern.2,3 It is a major risk factor for type 2 diabetes and a range of other chronic diseases, including cardiovascular, digestive and musculoskeletal disorders,4 as well as overall mortality.5

Clinical guidelines recommend bariatric surgery for those with a body mass index (BMI) over 40 kg/m2, or BMI over 35 kg/m2 and comorbid conditions, after non-surgical options have failed.6,7 This surgery is more effective than non-surgical interventions for the treatment of severe obesity, and it is cost-effective. In addition to substantial weight loss, bariatric surgery can lead to improvements in comorbid conditions including lipid abnormalities, obstructive sleep apnoea and joint disease.8-11 Of particular note is its effectiveness in treating type 2 diabetes,12,13 with one recent trial showing remission rates of 75%–95% within 2 years after surgery.13

Bariatric surgery procedures have been listed on the Medicare Benefits Schedule (MBS) since 1992. Admissions for this surgery rose from 535 to around 17 000 between 1998–1999 and 2007–2008.14 Notably, most of this surgery is carried out in private hospitals and incurs substantial out-of-pocket costs,14 while obesity is concentrated among those of lower socioeconomic status (SES).15 This suggests that groups that are most likely to need surgery are the least likely to have it. However, despite equity concerns,16 there are no published data to date in Australia on the extent of variation in bariatric surgery by health status, SES and other key factors among those potentially eligible for the procedure. In this study, we investigate variation in primary bariatric surgery rates in an obese population, quantify socioeconomic inequalities in rates of surgery and examine the extent to which holding private health insurance (PHI) explains these inequalities.

Methods

We used data from the 45 and Up Study, a cohort study involving 266 848 men and women aged 45 years and over from New South Wales. Study participants were randomly sampled from the Medicare enrolment database. More than 10% of the NSW population aged 45 years and over is included in the cohort (response rate of about 18%).17 Participants received a baseline questionnaire (between 1 January 2006 and 31 December 2008) and gave signed consent for follow-up, including linkage to routine health databases. The study is described in detail elsewhere,17 and questionnaires can be viewed at http://www.45andup.org.au.

Questionnaire data were linked to death data from the NSW Registry of Births, Deaths and Marriages (to 30 June 2010) and to hospital data from the NSW Admitted Patient Data Collection (APDC) from 1 July 2000 to 30 June 2010. The NSW APDC includes records of all hospitalisations in NSW, including reasons for admission (coded using ICD-10-AM) and procedures performed (coded using the Australian Classification of Health Interventions).18 Data were linked probabilistically by the Centre for Health Record Linkage (http://www.cherel.org.au).

The current study included only participants who were obese (BMI ≥ 30 kg/m2), with BMI calculated from weight and height as self-reported on the questionnaire. We excluded anyone who had had previous bariatric surgery recorded in the APDC (ie, between July 2000 and recruitment). The outcome was incident primary bariatric surgery for obesity, defined as the first bariatric surgery procedure recorded after recruitment, identified from the procedure fields in the APDC. Procedures included adjustable gastric banding or gastroplasty (procedure code 30511) or gastric bypass (30512). Partial gastrectomy (30518) may also be used for the treatment of obesity, but we did not include this as it is mostly used for other indications, and our sample included only two such procedures. Participants were followed from the date of recruitment to either the date of admission for bariatric surgery, death, or 30 June 2010, whichever occurred first.

Data on participant characteristics were based on self-reported data from the questionnaire. Variables were categorised as shown in Box 1 and Box 2. Socioeconomic variables included annual pre-tax household income, education and area-level disadvantage. Area-level disadvantage was based on the Socio-Economic Indexes for Areas Index of Relative Socio-Economic Disadvantage (IRSD),19 derived from postcode of residence and categorised into quintiles using cut-off scores from the 2006 Australian census. Other variables included PHI (including holders of a Department of Veterans’ Affairs [DVA] card), BMI, sex, age group, area of residence (based on the Accessibility/Remoteness Index of Australia Plus,20 derived from postcode), marital status, country of birth, self-rated health, diabetes (ever diagnosed by a doctor), number of other doctor-diagnosed chronic conditions, smoking, tertile of physical activity (based on number of weekly sessions of walking and moderate and vigorous activity, weighted for intensity) and alcohol intake.

Negative binomial regression was used to estimate bariatric surgery rates according to baseline characteristics and to model inequality estimates. We used separate multivariable regression models for the two main SES variables of interest — household income and area-level disadvantage. We calculated rate ratios (RRs) for each socioeconomic level using the lowest level as the reference group, adjusting for all other non-SES variables (Model 1). In Model 2, we added PHI. We then quantified the extent to which PHI explained any socioeconomic variation in bariatric surgery rates by testing for equality of the SES coefficients across Models 1 and 2. Stata version 12.1 (StataCorp) was used for all analyses.

Ethics approval for this project was obtained from the NSW Population and Health Services Research Ethics Committee and the Australian National University Human Research Ethics Committee.

Results

Survey and linked hospital and death data were available for 266 724 of the 266 848 current participants in the 45 and Up Study. After excluding those who had BMI data missing (20 262 participants; 7.60%) and those who had had bariatric surgery before recruitment (17 participants), there were 55 038 participants (22.33%) with BMI ≥ 30 kg/m2 who were eligible for this study. As no one over the age of 74 in this sample had bariatric surgery in the follow-up period, we confined our analysis to those aged less than 75 years (49 364 participants).

A total of 312 participants had surgery over 111 757 person-years (py) of follow-up (mean, 2.26; SD, 0.86), giving a rate of 27.92 (95% CI, 24.91–31.19) per 10 000 py. Of these, only one was treated as a public patient and four as DVA patients, with the remainder treated as private patients. The mean BMI (at baseline) of those having surgery was 39.15 kg/m2. The principal diagnosis was recorded as obesity (ICD-10 code E66) in 261 patients (84%) and as diabetes (E10 or E11) in 45 patients (14%). Only six of the 312 procedures were bypass procedures, the remaining 98% being gastric banding or gastroplasty.

Descriptive data showing bariatric surgery rates in relation to participant baseline characteristics are shown in Box 1 and Box 2. Rates of surgery increased with increasing BMI, ranging from 3.72 per 10 000 py (BMI 30–32.49 kg/m2) to 227.77 per 10 000 py (BMI 45–50 kg/m2). Rates varied significantly in relation to all participant characteristics except country of birth and marital status (P > 0.05). Higher rates were associated with being female, younger, a resident in a major city, in poorer health, a non-smoker, a non-drinker and being in the lowest tertile of physical activity.

With regard to SES, unadjusted rates (Box 2 and Box 3) and age–sex- adjusted RRs (Box 2) show that bariatric surgery rates were higher among those who were relatively advantaged. There was a clear socioeconomic gradient with household income; for IRSD, the most notable difference was between the top quintile (low disadvantage) and the other quintiles; for education, rates were highest among those with post-school (non-trade) qualifications and lowest in those with no qualifications; and rates were much higher among those with PHI than among those without.

The degree of socioeconomic in-equality in bariatric surgery rates, after adjusting for all variables except PHI (Model 1), was substantial (Box 4). The adjusted RRs for household income show a clear gradient, with those in the highest bracket (≥ $70 000) five times more likely to have surgery than those in the lowest bracket (< $20 000) (RR, 5.27; 3.18–8.73). After adjusting for PHI (Model 2), the RRs decreased by 35%–62% (P < 0.001 for all income levels), confirming that PHI explained a substantial proportion of income-related inequality. Nevertheless, significant inequality remained, with those in the highest income bracket still being almost twice as likely to have bariatric surgery as those in the lowest bracket (RR, 1.98; 1.15–3.41). When income and education were jointly modelled, this made virtually no difference to the income inequality estimates, while education inequality estimates were not significant in either Model 1 or 2 (results not shown).

Rates of surgery by IRSD quintile show that those living in areas of least disadvantage were four times more likely to have surgery than those living in the most disadvantaged areas (RR, 4.01; 2.41–6.67), after taking into account potential confounding factors (Model 1). After adjusting for PHI, the RR for each quintile of disadvantage decreased by 12%–40% (P < 0.001 for all quintiles). However, significant inequality remained, with those in the least disadvantaged areas still being over twice as likely to have bariatric surgery than those in the most disadvantaged areas (RR, 2.41; 1.48–3.93).

Discussion

There is significant inequality in the uptake of bariatric surgery among obese people in Australia, with the likelihood of surgery increasing with increasing SES. Even when measured using an area-level measure of disadvantage, and adjusting for remoteness and other factors, the magnitude of inequality is substantial. Of particular note is the fivefold higher rate of surgery in those with household incomes of $70 000 or more, compared with that of those with household incomes less than $20 000. PHI accounted for some but not all of the observed SES inequalities. While people with higher education qualifications were twice as likely to have surgery as those with no qualifications, much of this was because of the association between education and income.

Our inequality findings differ from a previous report that showed that bariatric surgery rates in the middle SES quintile of area disadvantage were more than double those of any other SES quintile;14 however, this report was based on the whole population, not the obese population, and hence did not take into account the “need” for surgery. Our findings that bariatric surgery is more common among women, middle-aged rather than older people, and among those living in major cities are consistent with previous reports.14 In addition, the variation in rates we found in relation to health characteristics was in keeping with the indications for surgery7 — the likelihood of surgery increased with increasing BMI, and was greater among those with poor health, diabetes and other chronic conditions. We also found that current smokers were less likely to have surgery than non-smokers.

Strengths of this study include its grounding in a very large population-based cohort, allowing a relatively rare event to be examined; investigation of a large range of factors not recorded in routine data; and use of linked administrative records, allowing virtually complete and objective ascertainment of surgery. A limitation is that BMI was based on self-reported weight and height. However, a validation study involving participants in the 45 and Up Study found that the mean difference between self-reported and measured BMI was not large (on average, 0.74 kg/m2), with sensitivity for classifying obesity of 79%, and importantly, specificity of 99%.21 Although the relatively low response rate and the potential for a “healthy cohort effect” mean that the estimates of surgery rates in our sample may be different to those of the general population, relative comparisons of surgery rates among groups within the cohort remain valid.22,23 Some caution must be applied, however, in generalising the size of the inequality estimates to younger ages, and beyond NSW, which has the highest proportion of private hospital weight loss procedures of all Australian jurisdictions.14

There are many potential barriers to bariatric surgery, apart from cost, that may underlie variations in uptake of surgery. These include patients’ preferences and clinical decisions regarding the suitability of patients for surgery, and possibly views by some that bariatric surgery is largely cosmetic. However, the observed SES-related inequality in rates of surgery is also likely to reflect system-wide issues, including the mix of public and private care, out-of-pocket costs, limited resources and cost-sharing between state and federal governments. Moreover, the current situation is that there is very limited availability of bariatric surgery in public hospitals, while Medicare subsidises bariatric surgery and post-surgical care for private patients, effectively restricting access to people with PHI and those who can afford to pay what are usually large associated out-of-pocket costs.

In 2009 the House of Representatives Standing Committee on Health and Ageing Inquiry into Obesity recommended that equity in access be ensured by publicly funding bariatric surgery.24 Our findings suggest that bariatric surgery, an MBS-listed procedure, is currently largely available only to those who can afford PHI and the associated out-of-pocket costs, with poor access to these cost-effective procedures in the section of the population that is most in need. Continuing inequity in access is likely to exacerbate existing inequalities in obesity and related health problems. However, if bariatric surgery came to be less discretionary over time, particularly for the treatment of type 2 diabetes,25 such inequalities could decline. While resource issues may limit the total number of patients that can have bariatric surgery, there is scope to consider how the distribution of limited supply can be improved, and the potential savings that could be made from increasing supply and improving health outcomes.

1 Primary bariatric surgery rates and rate ratios in relation to demographic and health characteristics at baseline in 49 364 participants with body mass index ≥ 30 kg/m2

Characteristics

No. of participants

No. of primary bariatric procedures/person-years

Surgery rate per 10 000 person-years

Rate ratio* (95% CI)


Total sample

49 364

312/111 757

27.92

Body mass index

30–32.49 kg/m2

22 389

19/51 094

3.72

1.00

32.5–34.99 kg/m2

12 356

51/27 932

18.26

4.78 (2.80–8.16)

35–37.49 kg/m2

6 830

62/15 371

40.34

10.37 (6.11–17.58)

37.5–39.99 kg/m2

3 554

53/7 998

66.27

16.61 (9.58–28.77)

40–42.49 kg/m2

2 119

47/4 695

100.11

25.73 (14.3–46.01)

42.5–44.99 kg/m2

1 133

31/2 517

123.18

31.36 (16.6–59.16)

45–50 kg/m2

983

49/2 152

227.77

64.38 (33.4–123.93)

Male

22 254

71/50 597

14.03

1.00 (

Female

27 110

241/61 160

39.41

2.72 (2.04–3.62)

Age group (years)

45–49

7 390

69/16 701

41.32

1.00 (

50–54

9 493

105/21 653

48.49

1.20 (0.87–1.65)

55–59

10 753

60/24 483

24.51

0.60 (0.42–0.86)

60–64

9 529

56/21 501

26.05

0.66 (0.46–0.95)

65–69

7 503

17/16 847

10.09

0.25 (0.15–0.44)

70–74

4 696

5/10 575

4.73

0.12 (0.05–0.30)

Area of residence

Major city

19 628

157/44 208

35.51

1.00 (

Inner regional

18 639

108/42 122

25.64

0.72 (0.56–0.92)

More remote

11 058

46/25 342

18.15

0.50 (0.36–0.70)

Born in Australia or New Zealand

40 296

263/91 127

28.86

1.00 (

Other country of birth

8 656

48/19 628

24.46

0.95 (0.69–1.30)

Not married

11 444

61/25 899

23.55

1.00

Married or de facto

37 625

251/85 288

29.43

1.33 (0.99–1.78)

Self-rated health

Excellent, very good or good

37 619

205/85 548

23.96

1.00 (

Fair or poor

10 172

101/22 726

44.44

2.08 (1.58–2.74)

Never diagnosed with diabetes

41 603

229/94 277

24.29

1.00 (

Diagnosed with diabetes

7 761

83/17 481

47.48

2.88 (2.04–4.06)

Other chronic conditions

None

14 553

75/33 373

22.47

1.00 (

One

19 583

134/44 518

30.10

1.55 (1.16–2.06)

Two

10 347

69/23 236

29.70

1.66 (1.17–2.36)

Three or more

4 881

34/10 631

31.98

1.91 (1.26–2.91)

Current smoker

3 722

12/8 530

14.07

1.00 (

Past smoker

20 290

135/45 999

29.35

2.82 (1.53–5.20)

Never smoked

25 212

165/56 895

29.00

2.30 (1.25–4.21)

Physical activity

1st tertile (low)

19 077

155/42 837

36.18

1.00 (

2nd tertile

15 988

85/36 426

23.34

0.60 (0.44–0.81)

3rd tertile (high)

13 454

69/30 694

22.48

0.61 (0.44–0.84)

Alcohol consumption (drinks per week)

0

18 469

153/41 612

36.77

1.00 (

1–14

22 688

130/51 303

25.34

0.74 (0.57–0.96)

15 or more

7 308

25/16 653

15.01

0.61 (0.38–0.97)


* Adjusted for age and sex. Reference category. Based on number of weekly sessions of walking and moderate and vigorous activity, weighted for intensity.

2 Primary bariatric surgery rates and rate ratios in relation to socioeconomic characteristics at baseline in 49 364 participants with body mass index ≥ 30 kg/m2

Socioeconomic characteristics

No. of

participants

No. of primary bariatric procedures/person-years

Surgery rate per 10 000 person-years

Rate ratio* (95% CI)


Household income

<$20 000

9 636

27/22 142

12.19

1.00 (

$20 000–$29 999

4 619

18/10 641

16.92

1.31 (0.72–2.39)

$30 000–$39 999 

3 894

19/8 831

21.52

1.58 (0.87–2.85)

$40 000–$49 999 

3 701

25/8 352

29.94

2.09 (1.20–3.62)

$50 000–$69 999 

5 753

55/12 998

42.32

2.86 (1.78–4.58)

≥ $70 000

11 904

123/26 134

47.07

3.25 (2.11–5.03)

Declined to answer or missing data

9 857

45/22 662

19.86

1.37 (0.85–2.22)

Education

No qualifications

6 653

25/15 293

16.35

1.00 (

Intermediate certificate

11 792

58/26 916

21.55

1.14 (0.71–1.82)

Higher school certificate

4 640

28/10 470

26.74

1.42 (0.82–2.44)

Trade or apprenticeship

5 794

22/13 113

16.78

1.41 (0.79–2.53)

Certificate or diploma

10 436

100/23 414

42.71

2.11 (1.36–3.28)

University degree

9 411

79/21 079

37.48

1.89 (1.20–2.97)

Area-level disadvantage by IRSD quintile (Q)

Q1 (high disadvantage)

8 558

35/19 503

17.95

1.00 (

Q2

14 128

72/32 055

22.46

1.28 (0.85–1.94)

Q3

12 163

73/27 704

26.35

1.47 (0.97–2.23)

Q4

5 858

35/13 163

26.59

1.45 (0.89–2.37)

Q5 (low disadvantage)

8 615

95/19 242

49.37

2.88 (1.89–4.39)

No private health insurance

18 590

19/42 528

4.47

1.00 (

Private health insurance

30 774

293/69 229

42.32

9.25 (5.70–15.00)

IRSD = Index of Relative Socioeconomic Disadvantage.

* Adjusted for age and sex. Reference category. Area-level disadvantage was based on the Socio-Economic Indexes for Areas IRSD,19 derived from postcode of residence and categorised into quintiles using cut-off scores from the 2006 Australian census.

3 Rates of bariatric surgery in relation to household income, education level and area-level disadvantage* in 49 364 participants with body mass index ≥ 30 kg/m2

* Area-level disadvantage was based on the Socio-Economic Indexes for Areas Index of Relative Socio-Economic Disadvantage (IRSD),19 derived from postcode of residence and categorised into quintiles using cut-off scores from the 2006 Australian census.

4 Adjusted rate ratios for bariatric surgery in relation to household income and to area-level disadvantage in 49 364 participants with body mass index 30 kg/m2, without and with adjustment for private health insurance*

Rate ratio (95% CI)


Model 1  (no adjustment for private health insurance)

Model 2  (adjusted for private health insurance)


Household income

<$20 000

1.00 

1.00 

$20 000–$29 999

1.72 (0.92–3.25)

1.12 (0.58–2.19)

$30 000–$39 999 

2.42 (1.28–4.60)

1.25 (0.65–2.44)

$40 000–$49 999 

3.06 (1.69–5.55)

1.61 (0.85–3.05)

$50 000–$69 999 

4.49 (2.59–7.79)

1.98 (1.12–3.51)

≥ $70 000

5.27 (3.18–8.73)

1.98 (1.15–3.41)

Private health insurance

No

1.00 

Yes

9.53 (5.08–17.89)

Area-level disadvantage, by IRSD quintile (Q)

Q1 (high disadvantage)

1.00 

1.00 

Q2

1.47 (0.93–2.32)

1.29 (0.82–2.03)

Q3

1.56 (0.98–2.50)

1.22 (0.77–1.93)

Q4

1.60 (0.89–2.86)

1.10 (0.62–1.95)

Q5 (low disadvantage)

4.01 (2.41–6.67)

2.41(1.48–3.93)

Private health insurance

No

1.00 

Yes

13.24 (7.78–22.52)


IRSD = Index of Relative Socio-Economic Disadvantage. * Household income and area-level disadvantage are modelled separately. All models adjusted for body mass index, sex, age, region of residence, country of birth, marital status, self-rated health, diabetes, other chronic conditions, smoking, alcohol consumption and physical activity. Test for heterogeneity between rate ratios in Model 1 and Model 2, P < 0.001 at every level of household income and IRSD. Reference category. Area-level disadvantage was based on the Socio-Economic Indexes for Areas IRSD,19 derived from postcode of residence and categorised into quintiles using cut-off scores from the 2006 Australian census.

Received 
1 Jul 2012
accepted 
28 Oct 2012
Rosemary J Korda, PhD, Research Fellow, National Centre for Epidemiology and Population Health1
Grace Joshy, BSc(Mathematics), MSc(Biostatistics), PhD, Research Fellow, National Centre for Epidemiology and Population Health1
Louisa R Jorm, BVSc, MSc(Epidemiology), PhD, Director, and Foundation Professor of Population Health2
James RG Butler, BEcon, MPolEcon, PhD, Professor and Director, Australian Centre for Economic Research on Health1
Emily Banks, MB BS(Hons), PhD, FAFPHM, Professor, National Centre for Epidemiology and Population Health and Scientific Director,1 and Scientific Director, 45 and Up Study3
1 Australian National University, Canberra, ACT.
2 Centre for Health Research, School of Medicine, University of Western Sydney, Sydney, NSW.
3 Sax Institute, Sydney, NSW.
Acknowledgements: 
We thank the men and women participating in the 45 and Up Study. The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW; and partners National Heart Foundation of Australia, NSW Ministry of Health; beyondblue: the national depression initiative; Ageing, Disability and Home Care, NSW Family and Community Services; UnitingCare Ageing; and the Australian Red Cross Blood Service. We also thank the Centre for Health Record Linkage. This project was supported by National Health and Medical Research Council (NHMRC) project grant 585402. The NHMRC had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Competing Interests: 
Emily Banks is supported by the NHMRC.
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