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Key messages What is the key question? Are clinical outcomes (improvement in exercise performance and health status) and completion of pulmonary rehabilitation (PR) associated with socioeconomic deprivation in patients with COPD? What is the bottom line? Using data from the national audit of PR clinical outcomes in England and Wales, we demonstrate that patients living in more socially deprived areas are less likely to complete a programme of PR, but clinical outcomes in those who do finish treatment are similar to those referred from less deprived areas. Why read on? Targeted interventions aimed at enhancing referral, uptake and completion of PR among patients with COPD living in deprived areas have the potential to improve health outcomes in this population. Introduction COPD is an important and prevalent non-communicable long-term condition that imposes a significant burden of mortality and morbidity worldwide.1 The prevalence and impact (eg, the risk of hospitalisation) of COPD has been shown to be associated with poor socioeconomic status in many countries including the UK.2 3 National clinical audits of hospital care (2014) in England and Wales (E&W) have similarly demonstrated a gradient of prevalence across socioeconomic strata.4

alence and impact (eg, the risk of hospitalisation) of COPD has been shown to be associated with poor socioeconomic status in many countries including the UK.2 3 National clinical audits of hospital care (2014) in England and Wales (E&W) have similarly demonstrated a gradient of prevalence across socioeconomic strata.4 Clinical trials of pulmonary rehabilitation (PR) have demonstrated unequivocal and substantial improvements in breathlessness, exercise limitation and health status in people with COPD.5 The 2015 E&W national audit of PR comprehensively appraised services and patient outcomes in a cohort of over 7000 patients attending an assessment for PR, providing for the first time a detailed assessment of the effectiveness of PR ‘in the field’.6 The audit has confirmed that the benefits of PR observed in clinical trials are delivered at a comparable magnitude in real-life clinical practice in patients who attend and complete therapy. However, the audit also confirms that many patients do not enrol or complete their course of PR and as expected there is heterogeneity in the response to therapy. Previous studies conducted in single centres or regions have investigated factors predicting PR adherence, completion and clinical response but due to limitations of sample size and geographical scope have not identified clear-cut patient level indicators predicting these outcomes.7–12 In particular, it is unknown whether social deprivation (across the breadth of national socioeconomic strata) is a predictor of these outcomes of therapy. This is an important question because PR is one of the few interventions in COPD that has been shown to reduce subsequent healthcare resource utilisation (and costs) including days spent in hospital.13 14 Given that the risk of hospitalisation is also greater in populations with low socioeconomic status,15 16 an understanding of the relationship between social disadvantage and other disease or demographic factors and the prediction of successful attendance and completion of PR might allow targeted interventions/resource aimed at enhancing both the uptake and completion of PR as well as clinical outcomes in particular groups of patients. This could in turn reduce morbidity and healthcare costs in hard-to-reach populations such as those at social disadvantage.

l attendance and completion of PR might allow targeted interventions/resource aimed at enhancing both the uptake and completion of PR as well as clinical outcomes in particular groups of patients. This could in turn reduce morbidity and healthcare costs in hard-to-reach populations such as those at social disadvantage. In the E&W national PR audit, individual patient postcodes were used to obtain a set of deprivation indices (Index of Multiple Deprivation (IMD)) pertaining to the area in which the patient lived at the time of their assessment for treatment. In this study, we determined whether completion of treatment and clinical outcomes recorded during the audit were associated with IMD and whether any such associations held true when adjusted for other disease and demographic variables recorded at baseline assessment for PR. Methods Audit methodology The National COPD Audit Programme, commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical Audit Programme (NCA), delivers a programme of work that aims to drive improvements in the quality of care and services provided for patients with COPD in E&W. The programme is led by the Royal College of Physicians, working in partnership with the British Thoracic Society, the British Lung Foundation, the Primary Care Respiratory Society UK, the Royal College of General Practitioners and the Health and Social Care Information Centre.

s provided for patients with COPD in E&W. The programme is led by the Royal College of Physicians, working in partnership with the British Thoracic Society, the British Lung Foundation, the Primary Care Respiratory Society UK, the Royal College of General Practitioners and the Health and Social Care Information Centre. The first national audit of clinical outcomes of PR in E&W reported clinical outcomes in 2016.6 A detailed account of the audit methodology is provided in the online supplementary material and reports are also publicly accessible at http://www.rcplondon.ac.uk/projects/national-copd-audit-programme-pulmonary-rehabilitation-workstream. 10.1136/thoraxjnl-2016-209376.supp1supplementary data

The first national audit of clinical outcomes of PR in E&W reported clinical outcomes in 2016.6 A detailed account of the audit methodology is provided in the online supplementary material and reports are also publicly accessible at http://www.rcplondon.ac.uk/projects/national-copd-audit-programme-pulmonary-rehabilitation-workstream. 10.1136/thoraxjnl-2016-209376.supp1supplementary data Prior to undertaking the audit, PR services across E&W were comprehensively surveyed and mapped (see online supplementary material for details) with 230 programmes invited to enrol patients. Programmes were requested to audit all patients with a primary respiratory diagnosis of COPD who attended an initial assessment for PR between 12 January 2015 and 10 April 2015, with a further 3-month period (to 10 July 2015) to allow the patients who had been recruited and consented to complete their PR and for data to be entered onto the online data collection tool. The clinical audit dataset was developed by the PR workstream group, in consultation with COPD experts across E&W. The dataset is provided in the online supplementary material and is also available to download via the weblink above. Section 251 approval was obtained via the National Confidentiality Advisory Group for the collection of patient identifiable data. Caldicott Guardian approval was obtained from each participating unit before access to the online audit web tool was granted and patients enrolled to the audit provided individual written consent before their data were uploaded.

he National Confidentiality Advisory Group for the collection of patient identifiable data. Caldicott Guardian approval was obtained from each participating unit before access to the online audit web tool was granted and patients enrolled to the audit provided individual written consent before their data were uploaded. Index of multiple deprivation Definitions and assessment of deprivation and socioeconomic position are complex.17 Social deprivation arises from the inequity of an individual's socioeconomic position comparative to others, formed from the hierarchical distribution of wealth, welfare and opportunity within society.18 Deprivation covers a broad range of issues and refers to unmet needs caused by a lack of resources of all kinds, not just financial. E&W produce their own separate indices of multiple deprivation (http://www.neighbourhood.statistics.gov.uk/dissemination/MetadataDownloadPDF; http://www.gov.uk/government/uploads/system/uploads/attachment_data/file/6222/1871538.pdf), with scores that are not directly comparable. For this audit, patient postcodes were used to obtain a set of deprivation indices data pertaining to the area in which the patient lived at the time of their enrolment to PR; these data being publicly available.

ploads/system/uploads/attachment_data/file/6222/1871538.pdf), with scores that are not directly comparable. For this audit, patient postcodes were used to obtain a set of deprivation indices data pertaining to the area in which the patient lived at the time of their enrolment to PR; these data being publicly available. Clinical outcomes Three outcomes were chosen to reflect success or otherwise in various aspects of a PR programme. First, patients were considered to have completed PR if they attended a discharge assessment (irrespective of attendance rates during the programme), where other outcomes were recorded. The other two outcomes relate to improvements in health status and in exercise performance, and applied to those with relevant baseline and discharge values. The Incremental Shuttle Walk test (ISWT) and the 6 min walk test (6MWT) were amalgamated by taking the percentage change from initial to discharge assessment. Health status measures comprised the St George's Respiratory Questionnaire (SGRQ), the Chronic Respiratory Questionnaire (CRQ) and the COPD Assessment Test (CAT). Since these ordinal scales were unsuitable for computing percentage change, a combined health status outcome was obtained by amalgamating minimum clinically important differences (MCID). The MCID for the SGRQ was a reduction of 4 points in the total score, for the CRQ an increase of 0.5 points in the average of the four domain scores and for the CAT a reduction of 2 points.19–21 The combined health status outcome measure was then the achievement of any of these MCID. Baseline scores for exercise and health status were categorised into quartiles—best quartile through to worst quartile of scores. Patients having more than one type of assessment (eg, ISWT and 6MW for exercise, or SGRQ and CAT for health status) were placed according to the assessment quartile they performed best on. These and all other baseline patient/clinical characteristics used in the analysis were as categorised in table 2.

rtile of scores. Patients having more than one type of assessment (eg, ISWT and 6MW for exercise, or SGRQ and CAT for health status) were placed according to the assessment quartile they performed best on. These and all other baseline patient/clinical characteristics used in the analysis were as categorised in table 2. Statistical analysis The χ2 test was used to test for association between deprivation quintiles and other baseline characteristics, for association between baseline characteristics and whether or not patients attended a discharge assessment and (for those with relevant data) whether an MCID was achieved in regard to health status. The Mann-Whitney U test (2 groups) or Kruskal-Wallis test (>2 groups) was used to test for association in the percentage change in exercise distance, the distribution of which was notably skewed. Preliminary visual graphical inspection of outcomes by IMD within each stratum of factors in table 1 and baseline assessment quartiles was undertaken to inspect for evidence of interaction. No such evidence was found. Binary regression (STATA binreg procedure, rr link option) was used to assess the association of deprivation quintile with whether or not patients attended a discharge assessment, and with whether or not an MCID in health status was achieved. Median regression (STATA qreg/qreg2 procedures) was used to assess the association of deprivation quintile with percentage change in exercise scores; the object being to estimate the median of the dependent outcome variable, conditional on the values of the independent variables. Risk ratios (binary regression) or coefficients (median regression) were estimated, with adjustments made for possible confounder variables as independent predictors (those described in table 1, together with baseline quartiles for health status and exercise). p Values and CIs were also estimated and adjusted for programme clustering effects; SEs that are robust to intracluster correlation were obtained for both types of regression by using the option ‘cluster’. Unknown data were coded to preserve the full sample size in the regression, notably affecting Medical Research Council (MRC) grade and body mass index. Due to the number of tests performed, statistical significance was regarded as p<0.001. Also, the large sample sizes sometimes resulted in small, clinically non-relevant differences being statistically significant and this is reflected in the overall interpretation.

cal Research Council (MRC) grade and body mass index. Due to the number of tests performed, statistical significance was regarded as p<0.001. Also, the large sample sizes sometimes resulted in small, clinically non-relevant differences being statistically significant and this is reflected in the overall interpretation. Table 1 Prevalence of sample characteristics at baseline across IMD deprivation quintiles results where the association was statistically significant at the 5% level

cal Research Council (MRC) grade and body mass index. Due to the number of tests performed, statistical significance was regarded as p<0.001. Also, the large sample sizes sometimes resulted in small, clinically non-relevant differences being statistically significant and this is reflected in the overall interpretation. Table 1 Prevalence of sample characteristics at baseline across IMD deprivation quintiles results where the association was statistically significant at the 5% level IMD Q1 most deprived Q2 Q3 Q4 Q5 least deprived Total p Value* All patients 26% 1915 22% 1570 20% 1476 17% 1256 14% 1046 7263 <0.001† Gender Male 25% 959 21% 825 20% 785 18% 709 15% 592 3870 0.001 Female 28% 956 22% 745 20% 691 16% 547 13% 454 3393 Age <60 43% 495 23% 266 18% 207 11% 121 5% 61 1150 <0.001 60–64 32% 322 23% 229 20% 198 15% 150 10% 95 994 65–69 25% 392 21% 337 21% 327 18% 290 14% 225 1571 70–74 22% 322 21% 310 20% 303 17% 257 20% 290 1482 75–79 19% 223 21% 246 20% 234 23% 260 16% 184 1147 ≥80 18% 161 20% 182 23% 207 19% 178 21% 191 919 Smoking status Current 38% 600 23% 357 18% 291 13% 206 8% 120 1574 <0.001 <0.001 Ex 23% 1179 22% 1111 21% 1056 18% 923 16% 812 5081 Never 17% 77 18% 78 22% 97 23% 104 20% 88 444 Not known 36% 59 15% 24 20% 32 14% 23 16% 26 164 Mental health disorder comorbidity No 26% 1725 22% 1456 21% 1382 17% 1176 15% 991 6730 <0.001 Yes 36% 190 21% 114 18% 94 15% 80 10% 55 533 Patient’s living arrangements House/flat with other 24% 1076 21% 961 21% 930 18% 827 16% 702 4496 <0.001 <0.001 House/flat alone 32% 651 22% 460 19% 399 15% 311 11% 235 2056 Other 33% 52 25% 40 20% 32 15% 23 6% 10 157 Not known 25% 136 20% 109 21% 115 17% 95 18% 99 554 Patient-reported MRC dyspnoea score at assessment Grades 1/2 22% 254 19% 221 22% 262 18% 206 20% 230 1173 <0.001 <0.001 Grade 3 25% 655 21% 556 21% 539 19% 481 14% 359 2590 Grade 4 28% 644 24% 552 19% 438 16% 366 13% 288 2288 Grade 5 29% 185 22% 140 20% 123 16% 98 13% 84 630 Not known 30% 177 17% 101 20% 114 18% 105 15% 85 582 Note: Percentages for the reported variables total 100% horizontally across the IMD quintiles.

73 <0.001 <0.001 Grade 3 25% 655 21% 556 21% 539 19% 481 14% 359 2590 Grade 4 28% 644 24% 552 19% 438 16% 366 13% 288 2288 Grade 5 29% 185 22% 140 20% 123 16% 98 13% 84 630 Not known 30% 177 17% 101 20% 114 18% 105 15% 85 582 Note: Percentages for the reported variables total 100% horizontally across the IMD quintiles. *χ2 test: first p values exclude not known, second p value includes not known. †Goodness-of-fit test: null hypothesis—all five quintile categories have equal frequencies. IMD, Index of Multiple Deprivation; MRC, Medical Research Council. Additional analyses were performed to consider the potential impact that categorising IMD may have had, particularly in regard to discharge assessment. These were done for England alone (96% of all cases) since the numerical scale for Wales was different. Random effects logistic regression was also used as an alternative to binary regression. Additional results are provided in online supplementary material. Role of the funding source The National COPD Audit Programme is commissioned by the HQIP (http://www.HQIP.org.uk) as part of the NCA. The NCA is funded by NHS England, the Welsh Government and, with some individual audits, also funded by the Health Department of the Scottish Government, Department of Health, Social Services and Public Safety (DHSSPS) Northern Ireland and the Channel Islands. The authors are independent from HQIP and the audit funders who had no influence over the writing of the manuscript.

d, with some individual audits, also funded by the Health Department of the Scottish Government, Department of Health, Social Services and Public Safety (DHSSPS) Northern Ireland and the Channel Islands. The authors are independent from HQIP and the audit funders who had no influence over the writing of the manuscript. Results Two hundred and ten programmes (195 in England and 15 in Wales) enrolled 7413 patients to the audit. Based on individual programme activity data provided as part of the audit of the organisation and resources of PR22 for the same case acquisition time period as the clinical audit (12 January to 15 April 2015), the response rate was estimated as 73% of eligible patients. The reason for non-enrolment was because either the patient was not approached or declined to provide consent for their data to be used. (W)IMD deprivation data was available for 7263 patients, but unknown for 150 (2%) (across 75 programmes). If the COPD PR audit sample living in E&W was comparable to E&W as a whole, then 20% of the sample would live within each national quintile of postcode areas. This was not so (goodness-of-fit test, p<0.001, table 1). The sample was relatively deprived with 48% living in the two most deprived quintiles and only 31% in the two ‘least deprived’ quintiles. Table 1 shows statistically significant associations between deprivation quintile and other baseline variables. Analysis of associations between undertaking of baseline exercise and health status assessments and IMD quintiles is provided in the online supplementary material. Of the initial sample, 6772 (91%) were ‘white British or Irish or from any other white background’, 201 (3%) were ‘other ethnic groups’ and 440 (6%) were ‘not stated’. The number of cases from other ethnicities was too small to allow a meaningful analysis of PR completion or outcome by ethnic group.

lementary material. Of the initial sample, 6772 (91%) were ‘white British or Irish or from any other white background’, 201 (3%) were ‘other ethnic groups’ and 440 (6%) were ‘not stated’. The number of cases from other ethnicities was too small to allow a meaningful analysis of PR completion or outcome by ethnic group. Baseline characteristics of the sample and the univariate association with the three audit outcomes are shown in table 2. Many baseline factors were associated with attendance at a discharge assessment, which was achieved for 4456 (59%) overall. Of these, data for health status and exercise measures was available for 3664 and 3901, respectively. There was a gradient in attendance at a discharge assessment by IMD quintile, with discharge assessment achieved by 70% in the least deprived quintile and by 50% in the most deprived quintile. A number of baseline sample characteristics were associated with rate of attendance at a discharge assessment and changes in walking performance and health status recorded at the assessment (see table 2 and more detailed description in the online supplementary material). Table 2 Univariate associations for the three study outcomes by each of the study baseline variables

Baseline characteristics of the sample and the univariate association with the three audit outcomes are shown in table 2. Many baseline factors were associated with attendance at a discharge assessment, which was achieved for 4456 (59%) overall. Of these, data for health status and exercise measures was available for 3664 and 3901, respectively. There was a gradient in attendance at a discharge assessment by IMD quintile, with discharge assessment achieved by 70% in the least deprived quintile and by 50% in the most deprived quintile. A number of baseline sample characteristics were associated with rate of attendance at a discharge assessment and changes in walking performance and health status recorded at the assessment (see table 2 and more detailed description in the online supplementary material). Table 2 Univariate associations for the three study outcomes by each of the study baseline variables % of those assessed attending a discharge assessment p Value* % achieving MCID in health status† p Value* % change in ISWT/6MWT exercise test distance Median (IQR), N p Value‡ All patients 59% (4353/7413) 61% (2240/3664) 25 (8–54), n=3901 (W)IMD Q1 most deprived 50% (951/1915) <0.001 <0.001 60% (458/760) 0.20 0.30 27 (9, 63), n=843 <0.001 <0.001 Q2 55% (860/1570) 64% (473/736) 29 (11, 59), n=782 Q3 62% (919/1476) 62% (489/786) 23 (7, 50), n=823 Q4 63% (801/1256) 59% (401/685) 23 (7, 49), n=719 Q5 least deprived 70% (736/1046) 60% (373/623) 22 (6, 47), n=658 Not known 57% (86/150) 62% (46/74) 22 (7, 46), n=76 Gender Male 60% (2353/3948) 0.10 62% (1224/1976) 0.28 23 (7, 50), n=2136 <0.001 Female 58% (2000/3465) 60% (1016/1688) 27 (10, 58), n=1765 Age <60 45% (525/1180) <0.001 64% (281/439) 0.15 33 (15,70), n=459 <0.001 60–64 56% (570/1020) 60% (294/491) 23 (7, 54), n=531 65–69 61% (985/1604) 60% (500/839) 24 (9, 50), n=889 70–74 63% (945/1505) 63% (504/802) 22 (7, 50), n=860 75–79 63% (739/1167) 63% (389/616) 23 (7, 50), n=651 ≥80 63% (589/937) 57% (272/477) 26 (6, 64), n=511 Ethnicity White: British/Irish/other 59% (4006/6772) 0.13 0.05 61% (2062/3368) 0.17 0.12 25 (8, 54), n=3582 0.23 0.30 Other 54% (108/201) 68% (65/95) 31 (9, 63), n=100 Not known 54% (239/440) 56% (113/201) 23 (8, 46), n=219 Previous PR completion Yes 62% (734/1175) 0.006 0.01 59% (361/611) 0.24 0.50 22 (7, 47), n=648 0.03 0.08 No 58% (3286/5650) 62% (1700/2758) 25 (8, 54), n=2952 Not known 57% (333/588) 61% (179/295) 27 (8, 62), n=301 Smoking status Current 47% (753/1614) <0.001 <0.001 58% (359/619) 0.15 0.27 25 (8, 55), n=662 0.44 0.22 Ex 62% (3214/5179) 62% (1676/2725) 24 (8, 53), n=2904 Never 66% (296/449) 64% (160/249) 27 (11, 52), n=258 Not known 53% (90/171) 63% (45/71) 33 (8, 91), n=77 No.

t known 57% (333/588) 61% (179/295) 27 (8, 62), n=301 Smoking status Current 47% (753/1614) <0.001 <0.001 58% (359/619) 0.15 0.27 25 (8, 55), n=662 0.44 0.22 Ex 62% (3214/5179) 62% (1676/2725) 24 (8, 53), n=2904 Never 66% (296/449) 64% (160/249) 27 (11, 52), n=258 Not known 53% (90/171) 63% (45/71) 33 (8, 91), n=77 No. of comorbidities (0–23) 0 61% (1074/1771) 0.001 62% (559/902) 0.16 22 (8, 50), n=958 <0.001 1 60% (1446/2397) 59% (714/1210) 24 (7, 50), n=1305 2 59% (978/1662) 61% (517/850) 27 (8, 52), n=891 3 54% (501/931) 66% (276/419) 26 (9, 57), n=443 ≥4 54% (354/652) 61% (174/283) 35 (13, 68), n=304 Cardiovascular comorbidity§ No 58% (2259/3880) 0.36 60% (1156/1915) 0.32 24 (8, 53), n=2029 0.34 Yes 59% (2094/3533) 62% (1084/1749) 26 (8, 54), n=1872 Diabetes comorbidity No 59% (3794/6426) 0.15 61% (1942/3184) 0.65 24 (8, 53), n=3397 0.07 Yes 57% (559/987) 62% (298/480) 27 (10, 60), n=504 Locomotor problems comorbidity No 60% (3912/6557) <0.001 61% (2022/3297) 0.47 25 (8, 53), n=3528 0.20 Yes 52% (441/856) 59% (218/367) 30 (7, 61), n=373 Mental health disorder comorbidity No 60% (4090/6871) <0.001 61% (2101/3441) 0.71 24 (8, 53), n=3659 <0.001 Yes 49% (263/542) 62% (139/223) 32 (14, 72), n=242 No.

n=504 Locomotor problems comorbidity No 60% (3912/6557) <0.001 61% (2022/3297) 0.47 25 (8, 53), n=3528 0.20 Yes 52% (441/856) 59% (218/367) 30 (7, 61), n=373 Mental health disorder comorbidity No 60% (4090/6871) <0.001 61% (2101/3441) 0.71 24 (8, 53), n=3659 <0.001 Yes 49% (263/542) 62% (139/223) 32 (14, 72), n=242 No. of times patient hospitalised for a COPD exacerbation in the past 12 months 0 61% (2692/4382) <0.001 <0.001 60% (1372/2269) 0.53 0.62 24 (8, 50), n=2441 0.001 0.003 1 59% (789/1330) 61% (397/648) 26 (8, 57), n=693 ≥2 47% (286/608) 64% (154/240) 36 (12, 72), n=244 Not known 54% (586/1093) 63% (317/507) 24 (8, 54), n=523 Oxygen therapy at home at the time of assessment Yes 54% (318/590) 0.008 <0.001 58% (153/265) 0.24 0.48 26 (0, 60), n=263 0.72 0.84 No 59% (3971/6674) 61% (2056/3347) 25 (8, 53), n=3583 Not known 43% (64/149) 60% (31/52) 27 (8, 58), n=55 Patient’s living arrangements House/flat with other 60% (2752/4593) 0.003 0.001 62% (1450/2340) 0.001 0.001 25 (8, 54), n=2470 0.94 0.97 House/flat alone 58% (1220/2100) 59% (594/1013) 25 (8, 53), n=1089 Other 47% (75/159) 83% (48/58) 23 (0, 63), n=62 Not known 55% (306/561) 58% (148/253) 24 (7, 50), n=280 FEV1 ≤0.92 59% (662/1128) 0.04 <0.001 59% (340/579) 0.13 0.19 25 (7, 54), n=586 0.25 0.40 0.93–1.27 64% (716/1115) 60% (363/608) 25 (8, 50), n=628 1.28–1.71 62% (690/1108) 64% (375/588) 23 (7, 50), n=636 1.72+ 60% (653/1089) 64% (363/566) 25 (9, 56), n=590 Not known 55% (1632/2973) 60% (799/1323) 25 (8, 56), n=1461 Body mass index <21 55% (376/688) <0.001 <0.001 53% (161/304) 0.01 0.02 25 (7, 62), n=334 0.008 0.01 21–24 62% (712/1140) 62% (373/601) 22 (8, 48), n=638 25–29 63% (979/1544) 63% (520/828) 25 (10, 51), n=877 30–34 62% (540/865) 65% (304/466) 25 (8, 54), n=487 35 or above 58% (381/661) 61% (201/332) 32 (12, 73), n=340 Not known 54% (1365/2515) 60% (681/1133) 25 (7, 50), n=1225 Patient-reported MRC dyspnoea score at assessment Grades 1/2 64% (760/1195) <0.001 <0.001 55% (360/650) 0.003 0.006 18 (6, 35), n=708 <0.001 <0.001 Grade 3 63% (1669/2656) 62% (893/1435) 23 (8, 48), n=1531 Grade 4 56% (1302/2328) 62% (690/1107) 33 (10, 71), n=1139 Grade 5 49% (312/643) 67% (147/218) 46 (15, 100), n=238 Not known 52% (310/591) 59% (150/254) 25 (10, 53), n=285 ISWT/6MWT distance quartiles at initial assessment 1. Worst quartile 54% (898/1676) <0.001 <0.001 62% (465/746) 0.11 0.03 78 (33, 150), n=824 2 61% (1087/1785) 59% (551/931) 33 (13, 59), n=1005 3 68% (1040/1538) 64% (579/900) 20 (5, 36), n=983 <0.001 4.

0), n=238 Not known 52% (310/591) 59% (150/254) 25 (10, 53), n=285 ISWT/6MWT distance quartiles at initial assessment 1. Worst quartile 54% (898/1676) <0.001 <0.001 62% (465/746) 0.11 0.03 78 (33, 150), n=824 2 61% (1087/1785) 59% (551/931) 33 (13, 59), n=1005 3 68% (1040/1538) 64% (579/900) 20 (5, 36), n=983 <0.001 4. Best quartile 69% (1137/1640) 60% (595/988) 14 (3, 25), n=1089 No ISWT/6MWT 25% (191/774) 51% (50/99) – SGRQ total/CRQ average/CAT score quartiles at initial assessment 1. Worst quartile 51% (634/1247) <0.001 <0.001 76% (466/617) 40 (15, 86), n=571 <0.001 <0.001 2 62% (978/1584) 70% (665/952) 30 (11, 65), n=892 3 68% (950/1399) 64% (586/920) <0.001 22 (7, 46), n=864 4. Best quartile 72% (1210/1690) 45% (523/1175) 19 (5, 38), n=1113 No SGRQ/CRQ/CAT 39% (581/1493) – 25 (7, 66), n=461 *χ2 test: first p values exclude not known, 2nd p value includes not known. †Combined outcome with a MCID achieved in any of the following: for the SGRQ the MCID was a reduction of 4 points in the total score, for the CRQ the MCID was an increase of 0.5 points in the average of the four domain scores and for the CAT the MCID was a reduction of 2 points. ‡Mann-Whitney U test (2 groups) or Kruskal-Wallis test (>2 groups): first p values exclude not known, second p value includes not known. §Any of atrial fibrillation, hypertension, heart failure, ischaemic heart disease, stroke, other cardiovascular.

†Combined outcome with a MCID achieved in any of the following: for the SGRQ the MCID was a reduction of 4 points in the total score, for the CRQ the MCID was an increase of 0.5 points in the average of the four domain scores and for the CAT the MCID was a reduction of 2 points. ‡Mann-Whitney U test (2 groups) or Kruskal-Wallis test (>2 groups): first p values exclude not known, second p value includes not known. §Any of atrial fibrillation, hypertension, heart failure, ischaemic heart disease, stroke, other cardiovascular. 6MWT, 6 min walk test; CAT, COPD Assessment Test; CRQ, Chronic Respiratory Questionnaire; IMD, Index of Multiple Deprivation; ISWT, Incremental Shuttle Walk test; MCID, minimum clinically important differences; MRC, Medical Research Council; PR, pulmonary rehabilitation; SGRQ, St George's Respiratory Questionnaire.

§Any of atrial fibrillation, hypertension, heart failure, ischaemic heart disease, stroke, other cardiovascular. 6MWT, 6 min walk test; CAT, COPD Assessment Test; CRQ, Chronic Respiratory Questionnaire; IMD, Index of Multiple Deprivation; ISWT, Incremental Shuttle Walk test; MCID, minimum clinically important differences; MRC, Medical Research Council; PR, pulmonary rehabilitation; SGRQ, St George's Respiratory Questionnaire. Regression analyses to assess the association of IMD deprivation quintile with each outcome both before and after adjusting for other baseline variables that were associated with both IMD and outcome are presented in tables 3–5. Adjustments were made for the variables from table 1, together with baseline exercise and health status as appropriate, and for PR programme clustering effects (for more detail see online supplementary material). In respect of patients having a discharge assessment, adjustments in the binary regression did diminish the IMD quintile gradient (table 3), but a statistically significant association remained; the risk ratio (95% CI) for patients in the most deprived quintile relative to patients in the least deprived quintile was 0.79 (0.73 to 0.85), that is a 21% (15%–27%) less chance of having a discharge assessment. Furthermore, an IMD gradient was observed at each categorisation level of all others variables listed in table 2 (results not shown). In regard to achieving MCIDs for health status, the binary regression adjustment for all table 1 variables was not achieved because of model convergence issues. However, adjustment was possible for the two most predictive variables, namely baseline score quartile and MRC dyspnoea score. After adjustment no statistically significant (p<0.001) risk ratios were seen (table 4). Regarding percentage change in ISWT/6MWT exercise test distance, the better results for patients living in more deprived areas suggested by unadjusted univariate analyses were largely extinguished after adjustment for baseline exercise score quartile and other variables (table 5).

.001) risk ratios were seen (table 4). Regarding percentage change in ISWT/6MWT exercise test distance, the better results for patients living in more deprived areas suggested by unadjusted univariate analyses were largely extinguished after adjustment for baseline exercise score quartile and other variables (table 5). Table 3 Binary regression risk ratio (with 95% CI and p value) of reaching a discharge assessment for IMD deprivation quintile relative to the reference group of least deprived IMD quintile, for 7413 patients IMD quintile Unadjusted risk ratio (95% CI) After adjusting for table 1 variables* and programme clustering effects Q1 most deprived 0.71 (0.66 to 0.75), p<0.001 0.79 (0.73 to 0.85), p<0.001 Q2 0.78 (0.73 to 0.83), p<0.001 0.84 (0.77 to 0.90), p<0.001 Q3 0.88 (0.84 to 0.94), p<0.001 0.92 (0.86 to 0.98), p=0.02 Q4 0.91 (0.86 to 0.96), p=0.001 0.94 (0.88 to 1.00), p=0.05 Q5 least deprived Reference risk ratio=1.00 Reference risk ratio=1.00 Not known 0.81 (0.71 to 0.94), p=0.005 0.88 (0.75 to 1.04), p=0.14 *Gender, age group, smoking status, mental health disorder comorbidity, patients living arrangements and patient-reported MRC dyspnoea score at initial assessment. IMD, Index of Multiple Deprivation; MRC, Medical Research Council. Table 4 Binary regression risk ratio (with 95% CI and p value) of achieving a minimum clinically important improvement in health status for IMD deprivation quintile relative to the reference group of least deprived IMD quintile for 3664 patients

IMD quintile Unadjusted risk ratio (95% CI) After adjusting for table 1 variables* and programme clustering effects Q1 most deprived 0.71 (0.66 to 0.75), p<0.001 0.79 (0.73 to 0.85), p<0.001 Q2 0.78 (0.73 to 0.83), p<0.001 0.84 (0.77 to 0.90), p<0.001 Q3 0.88 (0.84 to 0.94), p<0.001 0.92 (0.86 to 0.98), p=0.02 Q4 0.91 (0.86 to 0.96), p=0.001 0.94 (0.88 to 1.00), p=0.05 Q5 least deprived Reference risk ratio=1.00 Reference risk ratio=1.00 Not known 0.81 (0.71 to 0.94), p=0.005 0.88 (0.75 to 1.04), p=0.14 *Gender, age group, smoking status, mental health disorder comorbidity, patients living arrangements and patient-reported MRC dyspnoea score at initial assessment. IMD, Index of Multiple Deprivation; MRC, Medical Research Council. Table 4 Binary regression risk ratio (with 95% CI and p value) of achieving a minimum clinically important improvement in health status for IMD deprivation quintile relative to the reference group of least deprived IMD quintile for 3664 patients IMD quintile Unadjusted risk ratio (95% CI) After adjusting for baseline health status score quartile and PR programme clustering effects After adjusting for baseline health status score quartile, patient-reported MRC dyspnoea score at initial assessment and PR programme clustering effects Q1 most deprived 1.01 (0.92 to 1.10), p=0.88 0.91 (0.84 to 0.99), p=0.03 0.91 (0.84 to 0.99), p=0.03 Q2 1.07 (0.99 to 1.17), p=0.10 0.99 (0.93 to 1.06), p=0.83 0.99 (0.92 to 1.06), p=0.79 Q3 1.04 (0.96 to 1.13), p=0.37 0.98 (0.91 to 1.06), p=0.58 0.98 (0.91 to 1.05), p=0.56 Q4 0.98 (0.89 to 1.07), p=0.62 0.93 (0.86 to 1.01), p=0.08 0.93 (0.87 to 1.01), p=0.08 Q5 least deprived Reference risk ratio=1.00 Reference risk ratio=1.00 Reference risk ratio=1.00 Not known 1.04 (0.86 to 1.25), p=0.70 0.98 (0.81 to 1.19), p=0.84 0.97 (0.80 to 1.18), p=0.75 IMD, Indices of Multiple Deprivation; MRC, Medical Research Council; PR, Pulmonary Rehabilitation.

.08 0.93 (0.87 to 1.01), p=0.08 Q5 least deprived Reference risk ratio=1.00 Reference risk ratio=1.00 Reference risk ratio=1.00 Not known 1.04 (0.86 to 1.25), p=0.70 0.98 (0.81 to 1.19), p=0.84 0.97 (0.80 to 1.18), p=0.75 IMD, Indices of Multiple Deprivation; MRC, Medical Research Council; PR, Pulmonary Rehabilitation. Table 5 Median regression coefficients (with 95% CI and p value) for IMD quintile relative to the reference group of least deprived quintile, in the percentage change of ISWT/6MWT exercise test distances for 3901 patients

.08 0.93 (0.87 to 1.01), p=0.08 Q5 least deprived Reference risk ratio=1.00 Reference risk ratio=1.00 Reference risk ratio=1.00 Not known 1.04 (0.86 to 1.25), p=0.70 0.98 (0.81 to 1.19), p=0.84 0.97 (0.80 to 1.18), p=0.75 IMD, Indices of Multiple Deprivation; MRC, Medical Research Council; PR, Pulmonary Rehabilitation. Table 5 Median regression coefficients (with 95% CI and p value) for IMD quintile relative to the reference group of least deprived quintile, in the percentage change of ISWT/6MWT exercise test distances for 3901 patients IMD quintile Unadjusted coefficient (95% CI) After adjusting for baseline ISWT/6MWT exercise score quartile and PR programme clustering effects After adjusting for table 1 variables*, baseline ISWT/6MWT exercise score quartile and PR programme clustering effects Q1 most deprived 5.43 (1.56 to 9.30), p=0.006 2.17 (−1.48 to 5.83), p=0.24 −0.03 (−4.10 to 4.04), p=0.99 Q2 6.70 (2.76 to 10.6), p=0.001 4.55 (1.03 to 8.07), p=0.01 3.43 (−0.71 to 7.58), p=0.10 Q3 1.20 (-2.69 to 5.09), p=0.55 0.07 (−3.72 to 3.86), p=0.97 −1.00 (−5.31 to 3.32), p=0.65 Q4 1.20 (-2.81 to 5.21), p=0.56 0.07 (−3.70 to 3.83), p=0.97 −0.26 (−4.20 to 3.67), p=0.90 Q5 least deprived Reference coefficient=0.00 Reference coefficient=0.00 Reference coefficient=0.00 Not known 0.35 (−8.66 to 9.36), p=0.94 2.17 (−3.54 to 7.88), p=0.46 2.62 (−3.89 to 9.13), p=0.43 Note about coefficients: these estimate the difference in the median (percentage change) in the regression equation with respect to IMD quintile. In the last two columns this is after other variables have been fixed (adjusted for) in the regression equation.

.94 2.17 (−3.54 to 7.88), p=0.46 2.62 (−3.89 to 9.13), p=0.43 Note about coefficients: these estimate the difference in the median (percentage change) in the regression equation with respect to IMD quintile. In the last two columns this is after other variables have been fixed (adjusted for) in the regression equation. *Gender, age group, smoking status, mental health disorder comorbidity, patients living arrangements and patient-reported MRC dyspnoea score at initial assessment. 6MWT, 6 min walk test; IMD, Index of Multiple Deprivation; IPR, pulmonary rehabilitation; MRC, Medical Research Council; SWT, Incremental Shuttle Walk test. Discussion Using data from the first national audit of PR clinical outcomes in E&W, we have demonstrated for the first time at a national level that patients living in more socially deprived areas are less likely to complete therapy. Patients from more deprived IMD quintiles who completed PR were less likely to achieve a gain in health status above accepted thresholds for clinically important change but this association disappeared after adjustment for other baseline variables apart from those from the lowest IMD quintile. After adjustment for baseline variables, IMD was not associated with gains in exercise performance in those completing PR.

health status above accepted thresholds for clinically important change but this association disappeared after adjustment for other baseline variables apart from those from the lowest IMD quintile. After adjustment for baseline variables, IMD was not associated with gains in exercise performance in those completing PR. The E&W national audit of PR has provided the largest audit dataset (>7000 patients) of PR outcomes in COPD to date worldwide and has offered an opportunity to investigate, in a large nationwide sample, clinical and sociodemographic associations with rates of PR completion and gains in health status and exercise performance. In addition to IMD quintile, we identified a number of other variables recorded at baseline assessment for PR (presented in table 2) that were associated with PR completion. These included factors that in themselves were associated with IMD and may therefore have confounded the relationship between IMD and completion such as smoking status and living circumstances (shown in table 1). However, in the multivariate regression analysis, the gradient of completion rates across IMD quintiles remained significant after adjusting for these variables. The relationship between IMD quintile and the clinical outcome of treatment (assessed using health status instruments and field walking tests) was less clear cut. This analysis may have also been influenced by differences at baseline in health status and exercise measures across IMD quintiles (although adjustment for baseline values was included in the multivariate analysis) and also by methods used to assess improvement across these groups. For health status, improvement above accepted MCIDs for the three instruments recorded in the audit (the SGRQ, CRQ and CAT) was calculated because the ordinal nature of health status data precludes meaningful calculation of proportionate change. For exercise measures (ISWT and 6MWD), composite proportionate improvements were calculated partly to maximise the information provided from a continuous variable and also because the most recently cited MCIDs for these tests are markedly different (27 m for the 6MWD and 48 m for the ISWT).23 This proportionate change may have been affected by the absolute baseline value which was different across IMD quintiles, potentially explaining why unadjusted gains in exercise performance were greater in more deprived IMD quintiles.

s for these tests are markedly different (27 m for the 6MWD and 48 m for the ISWT).23 This proportionate change may have been affected by the absolute baseline value which was different across IMD quintiles, potentially explaining why unadjusted gains in exercise performance were greater in more deprived IMD quintiles. However, overall when adjusted for these differences, IMD did not predict change in exercise performance resulting from PR. Taken together, the results suggest that socioeconomic deprivation is a key factor determining the likelihood of completion of PR but for those that do complete treatment, other factors are more important in determining treatment response. Our finding of a greater prevalence of patients referred to PR in lower IMD quintiles is in keeping with previous reports linking socioeconomic deprivation to COPD prevalence, outcomes and health resource utilisation.2 3 16 24 Similarly, using an identical methodology to assess deprivation, the national clinical audit of acute hospital care in COPD conducted in 2014 observed a significant gradient of prevalence across IMD quintiles.4 Indeed, the disparity of prevalence across these quintiles was greater in the national hospital audit than seen in the PR audit. We speculate that this is due to differences in rates of the offer and uptake of PR referral in more deprived populations. Although the PR audit did not assess referral practice, it may be therefore that patients from more deprived areas are less likely to complete treatment and are less likely to be referred to PR in the first place.

t this is due to differences in rates of the offer and uptake of PR referral in more deprived populations. Although the PR audit did not assess referral practice, it may be therefore that patients from more deprived areas are less likely to complete treatment and are less likely to be referred to PR in the first place. Previous reports have attempted to identify factors that predict the successful outcome of PR in COPD.9 Some have identified baseline variables such as current smoking, presence of comorbidities, psychological morbidity and access to transport that predict PR completion and outcome.25 26 However, most reports are restricted to studies in single centres or discrete geographical areas with comparatively small sample sizes and variance in outcome explained by these indices was insufficient to allow discrimination in referral criteria for PR.8 10 It is also recognised that the benefit of PR extends beyond measurable outcome variables such as health status and exercise performance. This was highlighted by a recent large-scale single-centre study in the Netherlands where differential responses to PR were characterised using multidimensional modelling of a number of outcomes that included ‘activity of daily living’ scales, depression and anxiety.27 However, this focused on clinical and physiological variables recorded at baseline PR assessment and did not include social or deprivation indices. Factors such as lack of access to transport to PR services or active smoking may be markers of social deprivation, but to our knowledge, none has reported the association of indices of social deprivation with PR outcome at a national scale.

ed at baseline PR assessment and did not include social or deprivation indices. Factors such as lack of access to transport to PR services or active smoking may be markers of social deprivation, but to our knowledge, none has reported the association of indices of social deprivation with PR outcome at a national scale. We recognise limitations to the interpretation of our data. IMD quintiles are derived from individual postcodes and therefore provide a relative measure of deprivation at small area level. As such, IMD relates to areas and not individuals—within each area there will be both individuals who are deprived and individuals who are not. As with any regression analysis, the variance in the independent variable that is explained depends on the breadth and character of the dependent variables recorded. In this case, this was determined by the audit dataset which was in turn governed by the quality standards against which the outcome of the audit was assessed and the need to assess casemix as a means to interpret these outcomes. The assessment of the outcome of treatment was restricted to those completing a discharge assessment and was measured variably among programmes (ISWT or 6MWT for exercise performance and CRQ, SGRQ or CAT for health status). We recognise that combining these outcomes in our analysis may fail to distinguish subtle differences in how these instruments perform in the field and risk artefact due to differences relative responsiveness and thresholds for change between them. However, we believe the benefit of providing a unified analysis at a national level that can be generalised to programmes across E&W outweighs these caveats. Finally, while we believe this to be a comprehensive picture of services in E&W, we cannot rule out the possibility that PR services exist that were not identified and contacted, and therefore did not participate in the audit.

at a national level that can be generalised to programmes across E&W outweighs these caveats. Finally, while we believe this to be a comprehensive picture of services in E&W, we cannot rule out the possibility that PR services exist that were not identified and contacted, and therefore did not participate in the audit. Our findings have implications for the planning and provision of PR services. The PR audit has highlighted the clinical value of exercise therapies for people with COPD but also suggests that too few patients are being referred for PR, take up the offer of referral or complete therapy.6 Given that the available evidence suggests that completion of PR reduces subsequent health resource utilisation (such as days spent in hospital),13 14 increasing patient referral and service provision of PR is a key health policy objective. Given the greater risk of hospital admission and poor health outcomes in patients living in deprived areas, enhancing access to PR among people living in such areas should be a priority. This may require a number of measures some of which may be straightforward such as making transport to PR services available and some more difficult such as raising awareness of the benefits of PR to patients from deprived areas and changing referral practice among healthcare workers who provide care in these areas.

y. This may require a number of measures some of which may be straightforward such as making transport to PR services available and some more difficult such as raising awareness of the benefits of PR to patients from deprived areas and changing referral practice among healthcare workers who provide care in these areas. In summary, using data from the national audit of PR in COPD across E&W, we have shown that rates of successful completion of therapy are lower in patients from more socioeconomically deprived areas, but that in those that complete therapy patients from all strata of deprivation gain similar benefit from PR. Given that PR is the most effective therapy available for people with COPD, targeted interventions aimed at enhancing referral, uptake and completion of PR among patients living in deprived areas could reduce morbidity and healthcare costs in such hard-to-reach populations. The National COPD Audit Programme is commissioned by the Healthcare Quality Improvement Partnership (http://www.HQIP.org.uk) as part of the National Clinical Audit Programme. Twitter: Follow Katy Beckford @KatyBeckford and Louise Sewell at @LouiseSewell_OT Contributors: All authors were members of the Pulmonary Rehabilitation (PR) Audit Group (led by MS), which designed the PR audit clinical and organisational datasets, provided oversight of the conduct of the audit and had access to the data. The manuscript concept was developed by MS, DL and SS. All authors contributed to the interpretation of the data and the writing of the manuscript.

n (PR) Audit Group (led by MS), which designed the PR audit clinical and organisational datasets, provided oversight of the conduct of the audit and had access to the data. The manuscript concept was developed by MS, DL and SS. All authors contributed to the interpretation of the data and the writing of the manuscript. Funding: The National Clinical Audit is funded by NHS England, the Welsh Government and, with some individual audits, also funded by the Health Department of the Scottish Government, DHSSPS Northern Ireland and the Channel Islands. The authors are independent from HQIP and the audit funders who had no influence over the writing of the manuscript. This paper is supported by the National Institute for Health Research Leicester Respiratory Biomedical Research Unit. The views expressed are those of the author(s) and not necessarily those of the National Health Service (NHS), the National Institute for Health Research or the Department of Health. The British Thoracic Society managed the data collection under the auspices of Sally Welham and Laura Searle. We are grateful to all local pulmonary rehabilitation programmes that contributed data. We are also grateful to Pip Divall, Clinical Librarian at University Hospitals of Leicester NHS Trust for her assistance with systematic literature searches.

d the data collection under the auspices of Sally Welham and Laura Searle. We are grateful to all local pulmonary rehabilitation programmes that contributed data. We are also grateful to Pip Divall, Clinical Librarian at University Hospitals of Leicester NHS Trust for her assistance with systematic literature searches. Competing interests: Professor Steiner reports personal fees from Boehringer Ingelheim, non-financial support from Boehringer Ingelheim and GlaxoSmithKline (GSK), personal fees from Boehringer Ingelheim and GSK, grants from Medical Research Council (MRC) and East Midlands Collaboration for Leadership in Applied Health Research and Care (CLAHRC), outside the submitted work. Dr Bolton reports grants from MRC/Association of British Pharmaceutical Industry (ABPI) and GSK, personal fees from Chiesi, other support from GSK, outside the submitted work. Dr Blakey reports personal fees and non-financial support from GSK, personal fees and non-financial support from Napp, personal fees from Novartis and AstraZeneca, grants from Pfizer, outside the submitted work. Dr Man reports grants and other support from Pfizer, Boehringer Ingelheim, grants from National Institute for Health Research and Medical Research Council, outside the submitted work. Professor Singh reports personal fees from GSK, AstraZeneca and Novartis, grants from MRC/ABPI, National Institute for Health Research and East Midlands CLAHRC, outside the submitted work. PW reports personal fees from Chiesi and AstraZeneca, outside the submitted work.

cal Research Council, outside the submitted work. Professor Singh reports personal fees from GSK, AstraZeneca and Novartis, grants from MRC/ABPI, National Institute for Health Research and East Midlands CLAHRC, outside the submitted work. PW reports personal fees from Chiesi and AstraZeneca, outside the submitted work. Provenance and peer review: Not commissioned; externally peer reviewed