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British Journal of Cancer logoLink to British Journal of Cancer
. 2014 Jun 10;111(3):608–618. doi: 10.1038/bjc.2014.310

The role of patient, tumour and system factors in socioeconomic inequalities in lung cancer treatment: population-based study

L F Forrest 1,2,*, M White 1,2, G Rubin 1,3, J Adams 1,2
PMCID: PMC4119983  PMID: 24918815

Abstract

Background:

Reducing socioeconomic inequalities in lung cancer treatment may reduce survival inequalities. However, the reasons for treatment variation are unclear.

Methods:

Northern and Yorkshire cancer registry, Hospital Episode Statistics and lung cancer audit data sets were linked. Logistic regression was used to explore the role of stage, histology, performance status and comorbidity in socioeconomic inequalities in lung cancer treatment, for 28 733 lung cancer patients diagnosed in 2006–2010, and in a subgroup with stage recorded (n=7769, 27%).

Results:

Likelihood of receiving surgery was significantly lower in the most deprived group (odds ratio (OR)=0.75, 95% confidence interval (CI) 0.65–0.86); however, the OR was attenuated when including histological subtype (OR=0.82, 95% CI 0.71–0.96). Patients in the most deprived group were significantly less likely to receive chemotherapy in the fully adjusted full cohort model including performance status (OR=0.64, 95% CI 0.58–0.72) but not in the staged subgroup model when performance status was included (OR=0.88, 95% CI 0.72–1.08). Socioeconomic inequalities in radiotherapy were not found.

Interpretation:

Socioeconomic inequalities in performance status statistically explained socioeconomic inequalities in receipt of chemotherapy in the selective staged subgroup, but not in the full cohort. Socioeconomic variation in histological subtype may account for some of the socioeconomic inequalities in surgery.

Keywords: lung cancer, treatment, surgery, chemotherapy, radiotherapy, socioeconomic position

In England, less than 10% of those diagnosed with lung cancer survive for 5 years (Coleman et al, 2011). Lung cancer patients of lower socioeconomic position (SEP) have poorer survival (Rachet et al, 2010). It has been suggested that socioeconomic differences in receipt of cancer treatment might at least partially contribute to survival inequalities (Woods et al, 2006). There is some evidence that socioeconomic inequalities in lung cancer survival can be statistically explained by inequalities in treatment (Jack et al, 2006; Forrest et al, 2013b).

Socioeconomic inequalities in receipt of lung cancer surgery and chemotherapy, but not radiotherapy, were found in both universal (UHCS) and non-universal health-care systems, in a recent systematic review and meta-analysis (Forrest et al, 2013a). These findings could not be explained by the type of health-care system or by socioeconomic inequalities in stage at diagnosis. However, not all of the studies included in the review reported details of stage and histology, both of which influence treatment type (Forrest et al, 2013a), and very few took comorbidity into account. The review authors recommended that the reasons for socioeconomic inequalities in treatment should be more thoroughly investigated in studies including statistical control for comorbidity, stage and histology (Forrest et al, 2013a).

Performance status (PS), a global measure of functional status and an important consideration for clinicians treating lung cancer (NICE, 2005), is a factor that has not previously been well explored in studies examining socioeconomic inequalities in treatment. Although comorbidity and PS measure different things (the number of concurrent health conditions over a period of time before cancer diagnosis and general health status at the time of lung cancer diagnosis, respectively), both variables may be used as surrogate measures of suitability for treatment (Ludbrook et al, 2003). It is unclear how well comorbidity and PS capture fitness for treatment but, as the number of comorbidities varies by SEP for cancer patients (Louwman et al, 2010), this may help to explain inequalities in treatment.

Lung cancers are broadly classified into small cell (SCLC) and non-small cell (NSCLC) cancers, with NSCLC accounting for ∼80% of lung cancers. Non-small cell lung cancer can be further divided into squamous cell carcinoma, adenocarcinoma and large cell carcinoma subtypes (NICE, 2005). Squamous cell carcinoma is strongly associated with smoking, as is SCLC (Hirsch et al, 2008). Adenocarcinomas are a morphologically heterogeneous group and, although they are associated with smoking (Sharpe et al, 2012), they are also found in those who have never smoked, particularly in women (Hirsch et al, 2008). As SEP is associated with smoking and with histological subtype (Sharpe et al, 2012), histological subtype may confound the relationship between SEP and treatment for lung cancer.

In this study, we linked Northern and Yorkshire Cancer Registry and Information Centre (NYCRIS), Hospital Episode Statistics (HES) and National Lung Cancer Audit (LUCADA) data sets in order to examine the factors that may help to explain socioeconomic inequalities in lung cancer treatment (surgery, chemotherapy and radiotherapy). The role of stage, histology (and, within this, histological subtype), PS and comorbidity in statistically explaining socioeconomic inequalities in lung cancer treatment was specifically explored.

Materials and Methods

Data sources

The Northern and Yorkshire Cancer Registry is one of eight English regional cancer registries that collect a common minimum cancer data set (NYCRIS, 2012). Data on SEP, age, sex, histology, tumour, year of diagnosis, GP referral and details of receipt of treatment (surgery, chemotherapy and radiotherapy) were obtained from registry data. Comorbidity data were obtained from HES.

Incomplete recording of stage data is a major limitation of UK cancer registry data. However, stage data are collected by the LUCADA, a non-mandatory register of clinical information on patients diagnosed with lung cancer in the United Kingdom. The audit initially included only a subset of registry patients (66% nationally in 2006, increasing to 93% in 2010; NHS Information Centre, 2012).

Records were allocated a unique, randomly generated, key number, derived from the NHS number by NYCRIS. Data from the three data sources (NYCRIS cancer registry, HES and LUCADA data) were anonymised and supplied by NYCRIS. The HES and LUCADA data were then linked to the regional registry data using key numbers.

Variables of interest

Socioeconomic position was measured using the agreed methodology for all English cancer registries, the rank of the income domain of the Index of Multiple Deprivation (IMD). This is an area-based measure of SEP (HM Government, 2013), grouped into quintiles, where Q5 is the most deprived and Q1 the least deprived. The England-wide distribution of IMD was used. This is periodically updated to allow inclusion of the most recent data. The income domain of IMD2010 was used for patients diagnosed between 2007 and 2010. For those diagnosed in 2006 the income domain of IMD2007 was used.

Age at diagnosis was categorised into four groups: age <60, 60–69, 70–79 and 80+ years. Year of diagnosis was included to take into account changes in rates of treatment over time. GP referral was categorised as yes or no.

Lung cancer was categorised into the following eight histological subtypes: adenocarcinoma, large cell carcinoma, non-small cell carcinoma, squamous cell carcinoma, small cell carcinoma, other specified carcinoma, unspecified carcinoma (Riaz et al, 2012) and neoplasm. Lung cancer histology was classified as NSCLC, including adenocarcinoma, large cell carcinoma, non-small cell carcinoma and squamous cell carcinoma subtypes; SCLC; and other histology (including unspecified carcinoma, neoplasm and other specified carcinomas (including carcinoid tumours)), using ICD-0-3 morphology codes to categorise histological subtypes (WHO (World Health Organisation), 2000). When examining NSCLC and SCLC separately, the unspecified carcinoma and neoplasm subtypes were excluded and the other specified carcinoma subtype was included as probable NSCLC (Riaz et al, 2012).

A weighted comorbidity score was calculated by NYCRIS using the Charlson comorbidity index (CCI; Charlson et al, 1987) using the number of in-patient HES admissions for 16 specified conditions (excluding metastatic cancer) in the 3–18 months before diagnosis. HES-linked comorbidity data were not available for patients diagnosed in 2009–2010 as, because of national problems in calculating the comorbidity score, there was a time lag in data availability. Comorbidity score was categorised as 0, 1–2, 3+, missing or unavailable.

Stage and PS data were obtained from LUCADA. Stage was assigned using the TNM staging system (Sobin and Wittekind, 1997) and categorised as I, II, III, IV or missing. Performance status at the time of lung cancer diagnosis was recorded on a scale of 0 (asymptomatic) to 4 (bedridden) using the Eastern Cooperative Group PS scale (NICE, 2005) and categorised as 0, 1–2, 3–4 or missing.

Analysis

Data for 29 385 patients with a primary diagnosis of lung cancer (ICD10 C33 and C34), diagnosed between 1 January 2006 and 31 December 2010, were obtained. Of these, 652 had tumour registration based on death-certification only and were excluded from analyses, leaving an eligible cohort of 28 733.

The distribution of stage, histological subtype, PS and comorbidity by SEP was examined using χ2 tests. The distribution of each variable in the subgroup that had stage recorded (n=7769) was compared with that in the full cohort using χ2 tests to determine the representativeness of the subgroup.

Univariable and multivariable logistic regressions were used to examine the likelihood of receipt of each of three treatments – surgery, chemotherapy and radiotherapy, at any time after diagnosis – by SEP, in the full cohort and the staged subgroup. Receipt of surgery was also examined for probable NSCLC-only patients (n=16 278). Recipients of chemotherapy and radiotherapy were examined separately in probable NSCLC (n=16 278) and SCLC (n=3495) populations. Age, sex, histology (or histological subtype), year of diagnosis, GP referral, comorbidity, PS and stage (where available) were controlled for in fully adjusted models. A forward stepwise approach was used to explore which variables were important in explaining socioeconomic inequalities in treatment. The R2 statistic was examined as a measure of model fit, to determine the amount (%) of variance in receipt of treatment explained by each model. Odds ratios (ORs) with 95% confidence intervals (CIs) for the likelihood of receipt of treatment in each SEP quintile compared with the least deprived were reported. A likelihood ratio test was performed to determine the overall significance of each categorical variable. Analysis was carried out in Stata v12.0 (StataCorp, College Station, TX, USA).

Results

Table 1 shows the demographic and clinical characteristics of the cohort. Of the 28 733 patients included in the full cohort analysis, 7769 (27%) had stage and 8885 (31%) had a PS score recorded in LUCADA, and 8475 (29%) had a comorbidity score ascertained from HES. There were significant differences between the full cohort and the staged subgroup in the distribution of age group, histology, comorbidity and receipt of treatment, but not SEP or sex. The staged subgroups were younger, had a higher proportion of NSCLC patients and a higher proportion receiving treatment. Significant differences in distribution of PS, number of comorbidities and histology, but not stage at diagnosis, were seen by SEP. A higher proportion of more deprived patients had poor PS, more comorbidity and a squamous cell histological subtype (Supplementary Table 1).

Table 1. Demographic and clinical characteristics of full cohort and staged subgroup.

 
 Full cohort
 Staged subgroup
  
Variable N % N % χ2 P
Deprivation quintile
 28 733
 100
 7769
 100
 0.86
 0.930
1 (Least deprived) 3389 11.8 931 12.0    
2 4178 14.5 1118 14.4    
3 4848 16.9 1300 16.7    
4 6710 23.4 1831 23.6    
5 (Most deprived)
 608
 33.4
 2589
 33.3
  
  
Sex
 28 733
 100
 7769
 100
 0.44
 0.511
Female 13 254 46.1 3559 45.8    
Male
 15 479
 53.9
 4210
 54.2
  
  
Age group
 28 733
 100
 7769
 100
 62.65
 <0.001
<60 3682 12.8 1041 13.4    
60–69 7595 26.4 2189 28.2    
70–79 10 248 35.7 2843 36.6    
80+
 7208
 25.1
 1696
 21.8
  
  
Year of diagnosis
 28 733
 100
 7769
 100
 2.1e+0.3
 <0.001
2006 5533 19.3 671 8.6    
2007 5712 19.9 866 11.2    
2008 5851 20.4 1556 20.0    
2009 5871 20.4 2140 27.6    
2010
 5766
 20.1
 2536
 32.6
  
  
Comorbidity score
 28 733
 100
 7769
 100
 39.02
 <0.001
0 4010 14.0 995 12.8    
1–2 3531 12.3 857 11.0    
3+ 934 3.3 226 2.9    
Missing 10 175 35.4 1977 25.5    
Unavailable
 10 083
 35.1
 3714
 47.8
  
  
Stage
 28 733
 100
 7769
 100
 2.9e+0.4
 <0.001
I 1186 4.1 1186 15.3    
II 552 1.9 552 7.1    
III 2273 7.9 2273 29.3    
IV 3758 13.1 3758 48.4    
Missing
 20 964
 73.0
 —
 —
  
  
Performance status
 28 733
 100
 7769
 100
 1.8e+0.4
 <0.001
0 1842 6.4 1493 19.2    
1–2 4865 16.9 3870 49.8    
3–4 2178 7.6 1763 22.7    
Missing
 19 848
 69.1
 643
 8.3
  
  
GP referral
 28 733
 100
 7769
 100
 976.52
 <0.001
Yes 15 452 53.8 5351 68.9    
No
 13 281
 46.2
 2418
 31.1
  
  
Histology
 28 733
 100
 7769
 100
 765.53
 <0.001
NSCLC 15 123 52.6 5116 65.9    
SCLC 3495 12.2 582 7.5    
Other
 10 115
 35.2
 2071
 26.7
  
  
Histological subtype
 28 733
 100
 7769
 100
 849.92
 <0.001
Adenocarcinoma 4462 15.5 1473 19.0    
Squamous cell 5229 18.2 1850 23.8    
Large cell 768 2.7 169 2.2    
Non-small cell 4664 16.2 1624 20.9    
Small cell 3495 12.2 582 7.5    
Other specified 1155 4.0 298 3.8    
Unspecified carcinoma 520 1.8 93 1.2    
Neoplasm
 8440
 29.4
 1680
 21.6
  
  
Receipt of surgery
 28 733
 100
 7769
 100
 92.99
 <0.001
Yes 2894 10.1 1001 12.9    
No
 25 839
 89.9
 6768
 87.1
  
  
Receipt of chemotherapy
 28 733
 100
 7769
 100
 192.96
 <0.001
Yes 8348 29.1 2732 35.2    
No
 20 385
 70.9
 5037
 64.8
  
  
Receipt of radiotherapy
 28 733
 100
 7769
 100
 175.30
 <0.001
Yes 9611 33.4 3069 39.5    
No 19 122 66.6 4700 60.5    
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Abbreviations: CCI score=Charlson comorbidity score; CI=confidence interval; IMD=Index of Multiple Deprivation; NSCLC=non-small cell lung cancer; OR=odds ratio; SCLC=small cell lung cancer.

χ2=χ2 for difference in variable distribution between full cohort and staged subgroup.

Surgery

In the full cohort, the odds of receipt of surgery were significantly lower in the most compared to the least deprived group in the unadjusted analysis (OR=0.78, 95% CI 0.69–0.89) and in the fully adjusted multivariable analysis (OR=0.75, 95% CI 0.65–0.86). When histology was further broken down into histological subtypes then the SEP OR was attenuated (OR=0.82, 95% CI 0.71–0.96) (Table 2) and the amount of outcome variance explained by the model greatly increased (from R2=24.32% to 35.29%).

Table 2. Likelihood of receipt of lung cancer surgery, by SEP, adjusted for selected patient, tumour and system factors, for full cohort.

 
 Receipt of surgery (2894/28 733)
 Unadjusted (n=28 733)
 Adjusted – sex, age, year, CCI, GP referral, stage, PS, histological subtype (n=28 733, R2=35.29%)
Variable N % OR (95% CI) P OR (95% CI) P
Deprivation quintile
 2894
 10.1
  
  
  
 <0.001
  
  
  
 0.01
1 (Least deprived) 400 11.8         1.00      
2 458 11.0 0.92 0.80 1.06   0.91 0.77 1.08  
3 512 10.6 0.88 0.77 1.01   0.97 0.82 1.15  
4 613 9.1 0.75 0.66 0.86   0.80 0.68 0.94  
5 (Most deprived)
 911
 9.5
 0.78
 0.69
 0.89
  
 0.82
 0.71
 0.96
  
Sex
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
Female 1405 10.6         1.00      
Male
 1489
 9.6
  
  
  
  
 0.78
 0.71
 0.85
  
Age group
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
<60 560 15.2         1.00      
60–69 1118 14.7         1.04 0.92 1.19  
70–79 1048 10.2         0.79 0.69 0.90  
80+
 168
 2.3
  
  
  
  
 0.22
 0.18
 0.26
  
Year of diagnosis
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
2006 505 9.1         1.00      
2007 547 9.6         1.10 0.95 1.27  
2008 502 8.6         0.96 0.83 1.12  
2009 682 11.6         1.50 1.31 1.88  
2010
 658
 11.4
  
  
  
  
 1.58
 1.29
 1.92
  
CCI score
 2894
 10.1
  
  
  
  
  
  
  
 0.0002
0 482 12.0         1.00      
1–2 326 9.2         0.90 0.75 1.07  
3+ 83 9.0         1.01 0.75 1.36  
Missing 1021 10.0         0.91 0.79 1.06  
Unavailable
 982
 9.7
  
  
  
  
 0.69
 0.58
 0.81
  
GP referral
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
No 979 7.4         1.00      
Yes
 1915
 12.4
  
  
  
  
 1.27
 1.15
 1.40
  
Stage
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
I 607 51.2         1.00      
II 195 35.3         0.43 0.33 0.56  
III 144 6.3         0.04 0.04 0.06  
IV 55 1.5         0.01 0.01 0.02  
Missing
 1893
 9.0
  
  
  
  
 0.11
 0.09
 0.14
  
Performance status
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
0 583 31.7         1.00      
1–2 480 9.9         0.33 0.27 0.40  
3–4 15 0.7         0.05 0.03 0.09  
Missing
 1816
 9.2
  
  
  
  
 0.41
 0.34
 0.50
  
Histological subtype
 2894
 10.1
  
  
  
  
  
  
  
 <0.001
Adenocarcinoma 998 22.4         1.00      
Squamous cell 1011 19.3         0.86 0.77 0.97  
Large cell 124 16.2         0.64 0.51 0.80  
Non-small cell 215 4.6         0.19 0.16 0.22  
Other (specified) 470 40.7         2.40 2.06 2.80  
Small cell 54 1.6         0.06 0.04 0.07  
Unspecified carcinoma 14 2.7         0.14 0.08 0.25  
Neoplasm 8 0.1         0.01 0.00 0.01  
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Abbreviations: CCI score=Charlson comorbidity score; CI=confidence interval; IMD=Index of Multiple Deprivation; NSCLC=non-small cell lung cancer; OR=odds ratio; PS=performance status; SCLC=small cell lung cancer; SEP=socioeconomic position.

A similar result was seen for receipt of surgery in the most, compared with the least, deprived group in patients with probable NSCLC (OR=0.84, 95% CI 0.72–0.98; Supplementary Table 2), and in the subgroup that had stage recorded (OR=0.61, 95% CI 0.44–0.83; Supplementary Table 3).

Chemotherapy

Socioeconomic position was associated with receipt of chemotherapy in the fully adjusted full cohort model (OR=0.61, 95% CI 0.55–0.68). The inclusion of histological subtype rather than histology only marginally attenuated the odds ratio (OR=0.64, 95% CI 0.58–0.72; Table 3).

Table 3. Likelihood of receipt of lung cancer chemotherapy, by SEP, adjusted for selected patient, tumour and system factors, for full cohort.

 
 Receiving chemotherapy (8348/28 733)
 Unadjusted (n=28 733)
 Adjusted – sex, age, year, comorbidity score, GP referral, stage, PS, histological subtype (n=28 733, R2=34.97%)
Variable N % OR (95% CI) P OR (95% CI) P
Deprivation quintile
 8348
 29.1
  
  
  
 <0.001
  
  
  
 <0.001
1 (least deprived) 1133 33.4 1.00       1.00      
2 1275 30.5 0.87 0.79 0.96   0.85 0.75 0.96  
3 1425 29.4 0.83 0.75 0.91   0.82 0.73 0.93  
4 1898 28.3 0.79 0.72 0.86   0.70 0.63 0.79  
5 (Most deprived)
 2617
 27.2
 0.75
 0.69
 0.81
  
 0.64
 0.58
 0.72
  
Sex
 8348
 29.1
  
  
  
  
  
  
  
 0.43
Female 3810 28.8         1.00      
Male
 4538
 29.3
  
  
  
  
 1.03
 0.96
 1.10
  
Age group
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
<60 2221 60.3         1.00      
60–69 3332 43.9         0.54 0.49 0.60  
70–79 2452 23.9         0.24 0.22 0.27  
80+
 343
 4.8
  
  
  
  
 0.05
 0.05
 0.06
  
Year of diagnosis
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
2006 1506 27.2         1.00      
2007 1667 29.2         1.19 1.07 1.31  
2008 1681 28.7         1.09 0.98 1.20  
2009 1773 30.2         1.47 1.28 1.68  
2010
 1721
 29.9
  
  
  
  
 1.49
 1.29
 1.72
  
Comorbidity score
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
0 1224 30.5         1.00      
1–2 669 19.0         0.67 0.59 0.77  
3+ 122 13.1         0.50 0.39 0.63  
Missing 3503 34.4         1.12 1.00 1.24  
Unavailable
 2830
 28.1
  
  
  
  
 0.76
 0.67
 0.85
  
GP referral
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
No 2566 19.3         1.00      
Yes
 5782
 37.4
  
  
  
  
 2.04
 1.91
 2.19
  
Stage
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
I 223 18.8         1.00      
II 163 29.5         2.17 1.65 2.85  
III 1032 45.4         5.77 4.71 7.06  
IV 1314 35.0         4.49 3.69 5.46  
Missing
 5616
 26.8
  
  
  
  
 3.55
 2.90
 4.34
  
Performance status
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
0 1151 62.5         1.00      
1–2 2067 42.5         0.49 0.43 0.56  
3–4 113 5.2         0.04 0.03 0.05  
Missing
 5017
 25.3
  
  
  
  
 0.32
 0.27
 0.37
  
Histological subtype
 8348
 29.1
  
  
  
  
  
  
  
 <0.001
Adenocarcinoma 1658 37.2         1.00      
Squamous cell 1767 33.8         0.89 0.81 0.98  
Large cell 271 35.3         0.94 0.79 1.12  
Non-small cell 1670 35.8         0.97 0.88 1.07  
Other (specified) 377 32.6         0.84 0.72 0.98  
Small cell 2381 68.1         5.25 4.70 5.86  
Unspecified carcinoma 66 12.7         0.36 0.27 0.49  
Neoplasm 158 1.9         0.08 0.06 0.09  
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Abbreviations: CCI score=Charlson comorbidity score; CI=confidence interval; IMD=Index of Multiple Deprivation; NSCLC=non-small cell lung cancer; OR=odds ratio; PS=performance status; SCLC=small cell lung cancer; SEP=socioeconomic position.

In the subgroup of patients who had stage recorded, no statistically significant association between SEP and receipt of chemotherapy was found in the unadjusted analysis (OR=0.86, 95% CI 0.73–1; Table 4) but was seen in a multivariable analysis including age, sex, histological subtype, year of diagnosis, GP referral, CCI score and stage (OR=0.73, 95% CI 0.60 to 0.88). However, on the addition of PS to the model the OR was attenuated and this association was no longer significant (OR=0.88, 95% CI 0.72–1.08; Table 4). Including PS also increased the model fit (R2=28.04% without PS, 35.86% with).

Table 4. Likelihood of receipt of lung cancer chemotherapy, by SEP, adjusted for selected patient, tumour and system factors, for staged subgroup.

 
 Receiving chemotherapy (2732/7769)
 Unadjusted (n=7769)
 Adjusted – sex, age, year, CCI, GP referral, stage, PS, histological subtype (n=7769, R2=35.86%)
Variable N % OR (95% CI) P OR (95% CI) P
Deprivation quintile
 2732
 35.2
  
  
  
 0.19
  
  
  
 0.34
1 (Least deprived) 351 37.7 1.00       1.00      
2 414 37.0 0.97 0.81 1.16   1.00 0.80 1.26  
3 445 34.2 0.86 0.72 1.02   1.04 0.83 1.31  
4 638 34.8 0.88 0.75 1.04   0.92 0.74 1.14  
5 (Most deprived)
 884
 34.1
 0.86
 0.73
 1.00
  
 0.88
 0.72
 1.08
  
Sex
 2732
 35.2
  
  
  
  
  
  
  
 0.78
Female 1255 35.3         1.00      
Male
 1477
 35.1
  
  
  
  
 1.02
 0.90
 1.15
  
Age group
 2732
 35.2
  
  
  
  
  
  
  
 <0.001
<60 690 66.3         1.00      
60–69 1103 50.4         0.57 0.48 0.69  
70–79 821 28.9         0.27 0.22 0.32  
80+
 118
 7.0
  
  
  
  
 0.06
 0.05
 0.08
  
Year of diagnosis
 2732
 35.2
  
  
  
  
  
  
  
 0.03
2006 211 31.5         1.00      
2007 323 37.3         1.48 1.13 1.93  
2008 556 35.7         1.39 1.09 1.77  
2009 758 35.4         1.46 1.09 1.95  
2010
 884
 34.9
  
  
  
  
 1.46
 1.08
 1.97
  
CCI score
 2732
 35.2
  
  
  
  
  
  
  
 0.03
0 353 35.5         1.00      
1–2 203 23.7         0.74 0.57 0.95  
3+ 40 17.7         0.68 0.43 1.07  
Missing 800 40.5         1.05 0.84 1.33  
Unavailable
 1336
 36.0
  
  
  
  
 0.92
 0.74
 1.15
  
GP referral
 2732
 35.2
  
  
  
  
  
  
  
 <0.001
No 603 24.9         1.00      
Yes
 2129
 39.8
  
  
  
  
 1.98
 1.72
 2.28
  
Stage
 2732
 35.2
  
  
  
  
  
  
  
 <0.001
I 223 18.8         1.00      
II 163 29.5         2.26 1.71 2.98  
III 1032 45.4         6.06 4.92 7.46  
IV
 1314
 35.0
  
  
  
  
 4.70
 3.84
 5.75
  
Performance status
 2732
 35.2
  
  
  
  
  
  
  
 <0.001
0 933 62.5         1.00      
1–2 1561 40.3         0.41 0.35 0.48  
3–4 78 4.4         0.03 0.02 0.04  
Missing
 160
 24.9
  
  
  
  
 0.27
 0.21
 0.34
  
Histological subtype
 2732
 35.2
  
  
  
  
  
  
  
 <0.001
Adenocarcinoma 652 44.3         1.00      
Squamous cell 732 39.6         0.81 0.68 0.96  
Large cell 73 43.2         0.96 0.65 1.41  
Non-small cell 683 42.1         0.91 0.77 1.09  
Other (specified) 110 36.9         0.85 0.63 1.16  
Small cell 411 70.6         5.42 4.15 7.07  
Unspecified carcinoma 19 20.4         0.45 0.24 0.84  
Neoplasm 52 3.1         0.10 0.08 0.14  
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Abbreviations: CCI score=Charlson comorbidity score; CI=confidence interval; IMD=Index of Multiple Deprivation; NSCLC=non-small cell lung cancer; OR=odds ratio; PS=performance status; SCLC=small cell lung cancer; SEP=socioeconomic position.

When chemotherapy was examined separately in probable NSCLC and SCLC populations, socioeconomic inequalities in receipt of chemotherapy were found for NSCLC (OR=0.67, 95% CI 0.59–0.76; Supplementary Table 4) and SCLC (OR=0.57, 95% CI 0.43–0.75; Supplementary Table 5). For NSCLC patients, the likelihood of chemotherapy increased over time but this was not seen for SCLC.

Radiotherapy

No association between SEP and receipt of radiotherapy was found in the full cohort in the fully adjusted model including histology (OR=1.03, CI 0.95–1.13) or histological subtype (OR=1.02, CI 0.93–1.11; Table 5). Similar results were found in the subgroup of patients who had stage recorded (OR=1.01, 95% CI 0.86–1.19). Different patterns of results were seen when receipt of radiotherapy was examined separately in probable NSCLC (OR=1.11, 95% CI 1–1.24) (Supplementary Table 6) and SCLC (OR=0.84, 95% CI 0.66–1.07; Supplementary Table 7) populations, but again were nonsignificant.

Table 5. Likelihood of receipt of lung cancer radiotherapy, by SEP, adjusted for selected patient, tumour and system factors, for full cohort.

 
 Receiving radiotherapy (9611/28 733)
 Unadjusted (n=28 733)
 Adjusted – sex, age, year, CCI, GP referral, stage, PS, histological subtype (n=28 733, R2=12.20%)
Variable N % OR (95% CI) P OR (95% CI) P
Deprivation quintile
 9611
 33.5
  
  
  
 0.80
  
  
  
 0.70
1 (Least deprived) 1126 33.2 1.00       1.00      
2 1401 33.5 1.01 0.92 1.12   1.04 0.94 1.15  
3 1612 33.3 1.00 0.91 1.10   1.02 0.92 1.13  
4 2215 33.0 0.99 0.91 1.08   0.98 0.89 1.08  
5 (Most deprived)
 3257
 33.9
 1.03
 0.95
 1.12
  
 1.02
 0.93
 1.11
  
Sex
 9611
 33.5
  
  
  
  
  
  
  
 0.38
Female 4258 32.1         1.00      
Male
 5353
 34.6
  
  
  
  
 1.02
 0.97
 1.08
  
Age group
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
<60 1707 46.4         1.00      
60–69 3140 41.3         0.85 0.78 0.92  
70–79 3330 32.5         0.66 0.61 0.72  
80+
 1434
 19.9
  
  
  
  
 0.50
 0.45
 0.55
  
Year of diagnosis
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
2006 1857 33.6         1.00      
2007 1865 32.7         0.95 0.88 1.04  
2008 1865 31.9         0.89 0.81 0.97  
2009 2060 35.1         1.11 1.00 1.23  
2010
 1964
 34.1
  
  
  
  
 1.11
 1.00
 1.25
  
Comorbidity score
 9611
 33.5
  
  
  
  
  
  
  
 0.004
0 1422 35.5         1.00      
1–2 1042 29.5         0.92 0.83 1.02  
3+ 228 24.4         0.79 0.66 0.94  
Missing 3657 35.9         0.96 0.88 1.05  
Unavailable
 3262
 32.4
  
  
  
  
 0.85
 0.77
 0.93
  
GP referral
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
No 3262 24.6         1.00      
Yes
 6349
 41.1
  
  
  
  
 1.66
 1.57
 1.75
  
Stage
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
I 370 31.2         1.00      
II 234 42.4         1.44 1.16 1.80  
III 1206 53.1         2.24 1.91 2.63  
IV 1259 33.5         1.17 1.00 1.36  
missing
 6542
 31.2
  
  
  
  
 1.45
 1.24
 1.69
  
Performance status
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
0 832 45.2         1.00      
1–2 2375 48.8         1.39 1.24 1.56  
3–4 387 17.8         0.52 0.44 0.61  
Missing
 6017
 30.3
  
  
  
  
 0.88
 0.77
 1.00
  
Histological subtype
 9611
 33.5
  
  
  
  
  
  
  
 <0.001
Adenocarcinoma 1508 33.8         1.00      
Squamous cell 2509 48.0         1.73 1.59 1.89  
Large cell 326 42.5         1.45 1.24 1.70  
Non-small cell 2269 48.7         1.88 1.72 2.05  
Other (specified) 267 23.1         0.59 0.51 0.69  
Small cell 1562 44.7         1.59 1.44 1.75  
Unspecified carcinoma 163 31.4         1.14 0.93 1.40  
Neoplasm 1007 11.9         0.39 0.35 0.43  
Open in a new tab

Abbreviations: CCI score=Charlson comorbidity score; CI=confidence interval; IMD=Index of Multiple Deprivation; NSCLC=non-small cell lung cancer; OR=odds ratio; PS=performance status; SCLC=small cell lung cancer; SEP=socioeconomic position.

Discussion

Principal findings

This is one of the first UK registry-based studies to include a wide range of confounders and potentially important explanatory factors including stage, histology, comorbidity and PS, in order to determine their influence on socioeconomic inequalities in lung cancer treatment. In this study, we found socioeconomic inequalities in the receipt of surgery and chemotherapy, but not radiotherapy, for lung cancer, in the full cohort analyses. Having taken all the above factors into account, socioeconomic inequalities in receipt of surgery persisted. However, socioeconomic inequalities in receipt of chemotherapy were not found in the staged subgroup on addition of PS to the stepwise model.

Socioeconomic differences in PS statistically accounted for much of the socioeconomic inequality in receipt of chemotherapy in the staged subgroup. Socioeconomic differences in histological subtype may partially account for some of the observed socioeconomic differences in receipt of surgery observed.

Strengths and limitations

The use of multiple data set linkage (NYCRIS cancer registrations, HES and LUCADA) allowed us to include a broader range of potential confounders than previous UK registry studies (Jack et al, 2006; Berglund et al, 2012). Only two other UK studies (using early-year LUCADA audit data) have included PS in a multivariable analysis of receipt of lung cancer treatment (Rich et al, 2011a, 2011b). We were able to include later years of LUCADA data (2009–2010), which are more complete.

The population-based approach and the completeness and validity of the cancer registry data are the strengths of this study, although there may be some under-reporting of chemotherapy and radiotherapy treatments within registry data sets (Riaz et al, 2010). We used data from the north of England that may limit the generalisability of the findings to other locations. The high level of missing data for some variables is also a major limitation. Multiple imputation was considered but is not recommended, where over 50% of values for a variable are missing (White et al, 2011). To address the problem of missing data, we analysed complete-case data for the subset of patients who had stage recorded (the majority of whom also had PS recorded). As results from complete-case analyses can be biased (Sterne et al, 2009) we also analysed the full cohort and included missing categories for stage, PS and comorbidity, although this too can result in bias.

The validity of PS and CCI score as proxy measures of patients well-being is unclear. Performance status is a measure of patients acute functional status and need for care, assigned on a scale of 0–4 by the care team. Only moderate agreement in allocating PS score was found in an interobserver reliability study (Sorensen et al, 1993). However, there was good agreement when allocating good (PS 0–2) compared with poor PS (PS 3–4), which were similar to the groupings we employed.

The Charlson comorbidity index is a validated instrument for measuring comorbidity (Charlson et al, 1987) over a longer period of time. However, it may underestimate comorbidity as patients who suffer from a relevant condition but are treated entirely in primary care score zero. It has also been suggested that it is a crude measure of comorbidity, as patients with mild and severe forms of a disease receive the same score (Berglund et al, 2012). This could be a problem for conditions such as chronic pulmonary obstructive disease, where the severity of the disease is likely to influence the likelihood of receiving surgery for lung cancer. However, the index only contains details of conditions that are serious enough to require in-patient care.

Interpretation of results and comparison with other studies

Socioeconomic inequalities in receipt of surgery may be partially explained by socioeconomic differences in histological subtype. We found a significant association between SEP and histological subtype, with a lower proportion of squamous cell and higher proportion of adenocarcinoma subtype (and higher rates of treatment in this latter subtype) in the least deprived compared with most deprived group. A previous UK lung cancer study found that adenocarcinoma was less clearly associated with deprivation than other histological subtypes, possibly as it is less strongly associated with smoking (Bennett et al, 2008), and smoking is strongly socioeconomically patterned. It may be that health factors relating to smoking, rather than histological subtype, help to determine receipt of surgery, and we cannot rule out uncontrolled confounding related to smoking status. It is likely that smokers have generally poorer health and, although we were able to include PS and CCI score in the analysis, these measures may not fully capture this.

In agreement with the results from our systematic review of socioeconomic inequalities in lung cancer treatment (Forrest et al, 2013a), we found socioeconomic inequalities in receipt of surgery, and these remained after inclusion of stage, PS and comorbidity. In contrast, the only two other UK studies that included PS in a multivariable analysis of receipt of treatment, using national LUCADA data, found no association between SEP and receipt of surgery but did find an association with receipt of chemotherapy (Rich et al, 2011a, 2011b). We also found that, when including PS, SEP remained associated with a lower likelihood of receipt of chemotherapy in the full cohort but that SEP was no longer associated with receipt of chemotherapy in the staged subgroup.

The first few years of LUCADA data included only a small subset of registry patients and there were significant differences in stage at diagnosis, histology and PS when comparing patients from hospital Trusts with high levels of missing data with those who had low levels (Rich et al, 2011b). The validity of the pre-2007 LUCADA data has also been queried due to the poor entry of staging data (Murdoch et al, 2010). It may be that patients included in LUCADA in the early years of the audit are not representative of the full spectrum of patients diagnosed with lung cancer in England and this may explain the different pattern of results found using early audit data compared with studies using registry data. Concordance of recording of data on receipt of chemotherapy in LUCADA compared with registry data is reportedly poor, with 48% of patients with chemotherapy recorded in national registry data having no record of chemotherapy in LUCADA (Riaz et al, 2010); therefore, again this might account for some of the differences found.

Socioeconomic inequalities in receipt of radiotherapy were not found, although different patterns were seen for NSCLC compared with SCLC, when examined separately. It was not possible to distinguish between palliative and radical radiotherapy. Low-dose palliative radiotherapy is most commonly given, whereas fewer than 10% of patients receive high-dose radiotherapy with potentially curative intent. It is possible that differential effects by SEP might be seen if treatment-intent was examined, with more deprived SEP patients more likely to get palliative radiotherapy, and less deprived patients are likely to get curative radiotherapy. Potentially, these differential effects could effectively cancel each other out in statistical analyses and might help to explain why no overall association was found.

Implications for policy and practice

In this study, a higher percentage of more deprived patients had a squamous cell subtype that is strongly associated with smoking, although we were unable to measure smoking status in this cohort. Surgery rates were also lower for this histological subtype. Non-smokers are less likely to develop lung cancer and if they do then it may be that they are more likely to have a histological subtype that is more amenable to surgery. This is a further reason, if any other were required, to continue to promote aggressive antismoking and smoking-cessation campaigns.

The guidelines indicate that chemotherapy should be offered to stage III NSCLC patients and to stage IV patients with good PS (NICE, 2005). Socioeconomic differences in PS may determine whether a patient receives chemotherapy. Although there is a long chain of causality from health behaviours earlier in life to health status in later life, healthy behaviours should be encouraged, as patients who are in better health are likely to have a greater chance of receiving chemotherapy. It is unclear whether making lifestyle changes once diagnosed with cancer is likely to do much to improve PS, although a recent systematic review and meta-analysis produced preliminary evidence for improved survival for early-stage lung cancer patients who quit smoking after diagnosis (Parsons et al, 2010).

We were unable to take patient choice into account. Poorer health literacy may influence patient choice and understanding of risk, and this may vary by SEP (Protheroe et al, 2013), as might more fatalistic attitudes and health beliefs. If patients have poor capacity to process and understand basic health information, then they are less able to make appropriate health and treatment decisions (Nutbeam, 2008). It is important that clinicians take this into account when discussing treatment options.

Differences in communication patterns between health professionals and patients by SEP have been described that may influence the treatment prescribed (Murphy et al, 2010). Doctors may make treatment decisions based on which patients they consider likely to do well, using factors such as age, weight and comorbidity (Dixon-Woods et al, 2006) and these judgements may disadvantage more deprived lung cancer patients (Forrest, 2013). Treatment decisions should be clearly documented and should be based on the clinical guidelines.

Further research

The results from this study suggest that socioeconomic inequalities in PS statistically explain socioeconomic inequalities in receipt of chemotherapy in the subgroup of patients whose cancer was staged. However, this staged subgroup may not be representative of the full regional cohort as patients within this were more likely to be younger and to receive treatment. A previous study has shown a socioeconomic gradient in completeness of data on stage and grade of cancer, which could be interpreted as inequality in investigative intensiveness (Adams et al, 2004). It may be that younger patients receive more intensive investigation and so are more likely to be staged (Adams et al, 2004) and so, although PS may explain inequalities in chemotherapy in this group, they are a selective cohort. This is a relationship that needs to be clarified in other data sets, ideally with lower levels of missing data for stage and PS.

The observed relationship between histological subtype and receipt of surgery has not been consistently reported (Lüchtenborg et al, 2012) and further studies are also needed to confirm this association.

It would be useful to look at receipt of radiotherapy by curative or palliative intent to determine whether there are different patterns in likelihood of treatment by SEP.

Conclusions

Socioeconomic inequalities in lung cancer surgery and chemotherapy, but not in radiotherapy, were found. We have been able to investigate a number of factors that may be important in the relationship between SEP and receipt of treatment that have previously not been well explored; however, the high levels of missing data limit the conclusions that can be drawn.

Although histological subtype may account for some of the socioeconomic gradients in surgery, it does not explain it all. Socioeconomic inequalities in PS did not explain inequalities in chemotherapy in the full cohort analyses (although it did within the staged subgroup), and nor did stage or number of comorbidities, suggesting that other factors are responsible.

Further research is required to investigate the unexplained variance in treatment rates, exploring factors such as patient choice, doctor–patient communication of risk and benefit, and possible system variation by region, hospital and individual clinician.

Acknowledgments

LFF was funded by the ESRC (PhD studentship ES/I020926/1) as a member of Fuse, the Centre for Translational Research in Public Health (www.fuse.ac.uk). MW is director, GR a senior investigator and JA is funded as a staff member of Fuse. Fuse is a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. We acknowledge the funding for Fuse from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, under the auspices of the UKCRC. We would like to thank staff at NYCRIS for extracting and supplying the data for linkage.

Author Contributions

LFF designed the study, obtained the data, conducted the analysis and wrote the first draft of the manuscript. JA, MW and GR had the initial idea for the study, obtained the funding, and were involved in data interpretation and critical revision of the manuscript. The final manuscript was approved by all authors before submission. All authors will act as guarantor.

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc)

Disclaimer

The views expressed in this paper do not necessarily represent those of the funders or UKCRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethical Approval

Ethical Approval was applied for through the Integrated Research Application System (IRAS) for NHS Research Ethics Committee (REC) approval. A favourable ethical opinion was obtained from the Proportionate Review subcommittee of the NRES Committee East of England REC on the 13 December 2011 (REC reference 11/EE/0537).

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License.

Supplementary Material

Supplementary Tables
Click here for additional data file. (457KB, doc)

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