Research

Inequalities in cancer 2-week-wait referrals: a cross-sectional study in English general practice

Stephanie C Wynne and Mark Ashworth
BJGP Open 2025; 9 (2): BJGPO.2024.0052. DOI: https://doi.org/10.3399/BJGPO.2024.0052

Abstract

Background Practices with higher 2-week-wait (2WW) referral rates demonstrate higher survival for several cancers. Yet, there is little up-to-date evidence exploring factors influencing 2WW referral rates and whether health inequalities exist, particularly after COVID-19.

Aim To establish which patient factors (for example, age, sex, ethnic group, deprivation) and practice factors (for example, remote consultations, frequency of seeing a preferred GP) independently predict 2WW referral rates.

Design & setting A cross-sectional, observational study was performed using data from English general practices for 2021–2022.

Method Multivariable linear regression was used to identify the strongest, independent predictors of 2WW referral rates for all cancers (primary outcome) and for breast, lower-gastrointestinal, lung, and skin cancers separately (secondary outcome).

Results The analysis included 6307 practices. Practices with more females, patients aged ≥75 years, and patients with a greater burden of long-term conditions were associated with higher 2WW referrals for all cancers, as were practices in Northwest England, and those with higher scores for patients feeling involved in care decisions. Conversely, practices with a higher frequency of seeing a preferred GP were predictive of fewer all-cancer 2WW referrals. Practices with a higher proportion of currently smoking patients and Asian and Black ethnicity patients also predicted fewer all-cancer 2WW-referrals, and these associations were strongest for skin cancer, and for breast cancer (except for Black ethnicity). Higher socioeconomic deprivation predicted lower 2WW referrals for lung cancer only.

Conclusion This study analyses factors influencing 2WW referral rates and highlights potential inequalities. This work identifies priority populations, including people who smoke, and Asian and Black ethnic group patients, who may benefit from interventions to increase primary care access. Shared decision making may be an underexplored resource for increasing all-cancer 2WW referral rates.

How this fits in

Inequalities in cancer staging and survival exist in the UK. The COVID-19 pandemic exacerbated many existing health inequalities and impacted primary care delivery, thus an updated understanding of avoidable disparities in 2WW activity is merited. Using data from 2021–2022, when 2WW referral rates had returned to pre-pandemic levels, this study examines patient and practice-level factors that influence 2WW activity. It provides recommendations to improve equitable use of 2WW pathways in future.

Introduction

Early cancer diagnosis is essential for improving survival and access to curative treatment. Currently, only 52% of cancers in the UK are diagnosed at stage I–II,1,2 with a target of 75% by 2028.1 Despite this, inequalities exist in cancer outcomes in the UK. Asian and Black ethnic groups and patients living in more socioeconomically deprived neighbourhoods experience higher odds of late-stage cancer diagnoses compared with White individuals residing in less deprived communities.3–5 Thus, it is necessary to identify inequalities in cancer care, including 2WW pathways, to reduce this disparity.

Also termed the ‘faster diagnosis standard’, 2WW pathways aim to expedite cancer diagnoses by requiring GPs to refer patients with potential cancer symptoms for specialist investigation within 2 weeks.6 Practices sending higher 2WW referrals are associated with improved cancer outcomes (fewer stage III–IV diagnoses and lower mortality rates) for several cancers.2,7 Consequently, the National Institute for Health and Care Excellence updated the 2WW guidelines in 2015 to encourage greater use of this pathway by reducing the risk threshold for referral from 5% to 3%.6

Several studies have identified factors associated with higher 2WW referrals including: older age,8–11 female sex,8,9,12 White ethnic group,8,11,13 lower GP–patient continuity of care,14,15 and practices with registrars in primary care specialty training.8,16 However, most studies precede 2015 changes to 2WW guidelines and pre-date the impact of COVID-19.17,18 Hence, this study aimed to update the existing evidence base by identifying the strongest, independent predictors of 2WW referrals for all cancers during 2021–2022, and then for breast, lower-gastrointestinal, lung and skin cancers separately.

Method

Design

This retrospective cross-sectional study analysed practice-level data from 2021 to 2022. English general practices with a list size <500 (1.3%) were excluded as atypical19 and 1.2% were removed for missing 2WW data, leaving n = 6307 practices remaining.

The primary outcome, number of all-cancer 2WW referrals per 100 000 population, was presented as a rate (number of 2WW referrals × 100 000/list size). Predictor variables (Tables 1 and 2) were selected based on a comprehensive literature review8–16 and to explore pertinent gaps (for example, the relationship between remote consultations and 2WW referrals).20

Data on 2WW referrals was derived from the Office for Health Improvement and Disparities dashboards.21 Descriptive data on population characteristics (for example, age and sex), practice characteristics (for example, list size), and payments to general practice (for example, extended-hours access) were sourced from NHS Digital.22 Data on ethnic group and deprivation were derived from the 2011 National Census23 and 2019 Index of Multiple Deprivation (IMD),24 respectively. Deprivation and ethnic group variables were patient attributed, meaning scores were adjusted for the lower super output area of residence of the practice-registered population and not on the location of the general practice.25

Several measures of patient experience were sourced from the General Practice Patient Survey (GPPS).26 The Quality and Outcomes Framework (QOF) provided data on quality of clinical care (QOF total score),27 rate of people who currently smoke, and enabled the calculation of a composite ‘morbidity index’. QOF indicator ‘SMOK002’ records the number of patients with at ≥1 of a subset of nine long-term conditions (LTCs);28 see Table 1. The denominator of this QOF indicator was used to calculate a ‘morbidity index’ (rate of LTCs per 1000 registered patients) for each practice.

View this table:
Table 1. Descriptive statistics for patient-related factors for all practices submitting 2WW referrals per 100 000 population for all cancers between 2021 and 2022 (n = 6307)

Statistical analysis

A detailed flow diagram of the statistical methods are outlined in Supplementary File S1. Descriptive statistics were provided as mean, standard deviation (SD), and 10th to 90th percentile (continuous variables) or frequency and percentage (categorical variables).

A Pearson correlation or independent-samples t-test assessed univariable relationships between 2WW referral rate and each continuous or binary predictor variable, respectively. Predictor variables with a linear relationship and significant at P<0.1 on univariable analysis were then entered into a multivariable linear regression (MVR) model simultaneously.29 Prior to analysis, the assumptions of MVR were tested, and multicollinearity was assessed, considering a variance inflation factor (VIF) <10 acceptable.30 Reference categories (see Table 3a, b footnote) were selected as the lowest relevance group when considering 2WW referral activity (for example, age <30 years9).

MVR was performed to detect independent associations between 2WW referrals and each predictor variable, rank the strongest predictor variables, and control for confounding factors (variables that may compete with another predictor variable when explaining any observed changes in the outcome variable).31 Three outputs included: (1) an adjusted R 2 value indicating the percentage of variance in 2WW referrals explained by all predictor variables; (2) a standardised beta coefficient ranking the strength of association between 2WW referrals and each predictor variable; and (3) an unstandardised B-coefficient (95% confidence interval [95% CI]) demonstrating the change in 2WW referrals per unit change in each predictor variable.

To simplify the interpretation of the MVR analysis, a stepwise linear regression function was then performed to produce a final, parsimonious model demonstrating the highest adjusted R² value that could be attained from the five strongest predictors of 2WW referrals at P<0.05 significance.

Sensitivity analyses were repeated, as above, for the four separate cancer sites. All analyses were completed using IBM SPSS Statistics (version 28.0).

Results

Descriptive data

The mean (10th to 90th centile) number of 2WW referrals per 100 000 population for all cancers in 2021–2022 was 4307.3 (2442.6–6260.6), equating to around 4% of the population receiving a 2WW referral.

For patient-related variables (Table 1), a third of practice-registered patients were aged <30 years with a smaller proportion (mean 8.4%; SD 3.8%) aged ≥75 years. The majority (mean 79.0%; SD 21.3%) were of White ethnic group with a mean IMD score of 23.3 (SD 11.6), where a larger IMD score reflects higher deprivation.24 The mean morbidity index was 233.4 (SD 5.2) per 1000 practice-registered patients, demonstrating that just under a quarter of patients had at least one LTC. The mean rate of currently smoking patients per 1000 practice-registered patients was 128.0 (SD 41.8).

For practice-related variables (Table 2), the mean practice list size (number of patients per GP full-time equivalent) was 2242.8 (SD 1735.8) and almost half of practices were training practices. QOF total score showed only 120 practices (1.9%) not achieving 100% of the available points. However, a COVID-19 payment-protection scheme may have inflated these scores.27 There were slightly more remote consultations (mean 51.1%; SD 12.9%) than in-person ones (mean 48.9%; SD 12.9%). Frequency of seeing a preferred GP was generally low (mean 39.5%; SD 17.6%), with marked variation across practices (10th to 90th centile: 18.2–63.6%).

View this table:
Table 2. Descriptive statistics for general practice-related factors for all practices submitting 2WW referrals per 100 000 population for all cancers between 2021 and 2022 (n = 6307)

MVR (all cancer 2WW referral rate)

Results from the univariable analysis are provided in Supplementary Tables S2 and S3. There were significant associations between all-cancer 2WW referrals and all predictor variables, except for learning disability prevalence, two locations (Southeast England; Northeast and Yorkshire), and two GPPS variables (Q3: ease of reaching the practice via phone; Q25: satisfaction with appointment times).

All significant variables were entered into a MVR model except for QOF total score (highly skewed) and two GPPS variables (Q50: fully retired; Q86b: felt listened to by the healthcare professional) because of multicollinearity (VIF >10).

The MVR model for all-cancer 2WW referrals containing all eligible predictor variables is provided in Table 3a, b (see Supplementary File S4 for full description). This MVR model had an adjusted R 2 of 48.8%, indicating that approximately half of the variation in 2WW referral rate could be explained by the included predictors.

View this table:
Table 3. (a) Multivariable linear regression (MVR) model showing significant associations between 2WW referrals per 100 000 population for all cancers and patient and practice-related variables 2021–2022. (b) Multivariable linear regression (MVR) model showing 2WW referrals per 100 000 population for all cancers and patient and practice-related variables 2021–2022 (non-significant variables only)a

The parsimonious model (Table 4) identified four strongest positive predictors of all-cancer 2WW referral rate (largest positive beta coefficient): age ≥75 , female sex, patients feeling involved in decisions about care, and practice location in Northwest England. Interpreting the unstandardised B-coefficient (95% CI), a 1% increase in practice-registered patients aged ≥75 years was associated with 160.6 (95% C = 151.4 to 169.9) more all-cancer 2WW referrals per 100 000 population. Conversely, the strongest negative predictor (largest negative beta coefficient) of all-cancer 2WW referrals was a higher frequency of seeing a preferred GP, where a 1% increase in this variable was associated with 17.0 (95% C = 15.2 to 18.8) fewer all-cancer 2WW referrals.

View this table:
Table 4. Parsimonious multivariable linear regression (MVR) model for all-cancer 2WW referrals per 100 000 population for 2021–2022a

Parsimonious models by cancer site (sensitivity analyses)

Sensitivity analyses demonstrated a mean number of 2WW referrals per 100 000 population for suspected breast, lower-gastrointestinal and skin cancer of 817.1 (SD 278.5), 796.2 (SD 351.3) and 886.5 (SD 466.7) respectively. Lung 2WW referrals were substantially lower (86.0, SD 45.1).

MVR models for each cancer site are detailed, in full, in Supplementary Tables S5-S8. However, results of the simplified, parsimonious models are presented in Table 5.

View this table:
Table 5. Parsimonious multivariable linear regression (MVR) models for 2WW referrals per 100 000 population by suspected cancer site (sensitivity analyses) for 2021–2022a

Three variables consistently predicted a higher 2WW referral rate across models: female sex (except lung); higher morbidity index (except skin), and practice location in Northwest England (except breast).

Practices with more patients feeling involved in decisions about care were consistently associated with a higher 2WW referral rate, while higher scores for seeing a preferred GP consistently predicted fewer referrals. These associations were particularly strong for suspected lower-gastrointestinal cancer.

Distinct patterns emerged by cancer site. Age ≥75 years was strongly associated with fewer breast 2WW referrals despite predicting higher all-cancer 2WW referrals. Asian ethnic group predicted fewer 2WW referrals for all cancers, as well as for breast and skin cancer, where this association was especially strong. Similarly, Black ethnic group strongly predicted fewer skin 2WW referrals, and to a lesser extent, fewer all-cancer 2WW referrals. Deprivation exhibited a significant association only with lung 2WW referral rate. Finally, current smoking status predicted a higher lung 2WW referral rate but a lower 2WW referral rate for all cancers and for skin cancer.

Discussion

Summary

This study provides a comprehensive analysis of factors influencing 2WW referrals and updates the evidence base. The strongest predictors of all-cancer 2WW referral rate included female sex , age ≥75 years, patients feeling involved in care decisions, and practice location in Northwest England (higher referral rates), and practices with a higher frequency of patients seeing a preferred GP (lower referral rates). Practices with higher proportions of people who currently smoke and Asian or Black ethnicity patients had fewer 2WW referrals for all cancers, with the strongest association observed for skin cancer, and for breast cancer (except for Black ethnic group). There were slightly more remote consultations compared with in-person appointments in 2021–2022, reflecting sustained COVID-19-related changes to primary care delivery.32 However, there was no association between remote consultations and 2WW referral rate.

Strengths and limitations

This study used data from all English practices (excluding a small proportion with missing or atypical data), reducing sampling bias and increasing generalisability. It included the most comprehensive combination of patient and practice-related predictor variables compared with earlier studies.8–15 The use of MVR permitted adjustment for confounding factors, enabling more accurate interpretation of findings. For instance, people from ethnic minorities often reside in more socioeconomically deprived neighbourhoods,23 and accounting for this helps to discern whether deprivation or ethnic group is more influential on 2WW activity. Additionally, the sensitivity analyses provide a more nuanced understanding about factors that have an impact on 2WW referrals for four prevalent, high-mortality cancers.

Limitations include the use of practice-level data, which prevented the exploration of specific characteristics of patients receiving a 2WW referral and introducing the ecological fallacy.33 For example, higher odds of three or more GP consultations before 2WW referral have been reported for individuals with more comorbidities,34,35 but time to 2WW referral could not be assessed using practice-level data. Furthermore, the composite morbidity index precluded assessment of individual comorbidities on 2WW referrals in our analysis. This analysis also utilised data from the GPPS, which had a low response rate (32.2%), although data is considered representative of the general population.36 Finally, our results leave >50% of the variation in 2WW referrals unaccounted. Remaining variation may be random or unexplained,37 or attributed to the capacity and organisation of local secondary-care diagnostic services.38

Comparison with existing literature

Cancer incidence increases with age,8–10 while individuals from Asian and Black ethnic backgrounds and those residing in more socioeconomically deprived communities have higher odds of stage III–IV diagnoses.3,4 Therefore, to improve equitable access to early diagnosis and advance survival outcomes, efforts are required to provide proportionately higher 2WW referral rates for these populations.

In this analysis, age ≥75 years and female sex predicted higher 2WW referrals, consistent with earlier studies.8–12 These findings align with expected age-related variation8–10 and evidence of higher cancer symptom awareness among women.39,40 Yet, age ≥75 years was associated with fewer breast 2WW referrals. Given that a quarter of invasive breast cancers occur after 75 years41, this indicates potential underutilisation of breast 2WW pathways for older women. It is possible that older women may perceive a lower cancer risk once their breast screening ends at age 70, potentially reducing primary care presentation rates.41

Moreover, we found lower 2WW referral rates for Asian and Black patients, particularly for skin and breast cancer, underlining persistent unmet healthcare needs. This finding is consistent with earlier research,8,10 where factors including lower symptom awareness and barriers to help-seeking (for example, language and embarrassment around speaking with a doctor) may contribute to this disparity.3,42–44

Round and colleagues 11 reported lower 2WW cancer detection in more socioeconomically deprived communities, while the present analysis found no association between deprivation and 2WW referral rates (except for lung cancer). A possible explanation is that our analysis controlled for smoking, a well-documented driver of socioeconomic inequality in cancer incidence45 that is linked with reduced help-seeking behaviours.39,46,47 Indeed, our analysis suggests no additional impact of deprivation on 2WW referral rates beyond the effects of smoking.

Another explanation for the discrepancy in 2WW referral outcomes by deprivation is the use of different primary outcomes. Round and colleagues utilised the 2WW cancer detection rate, which measures the percentage of cancers diagnosed from a 2WW referral (the sensitivity of selecting patients for urgent referral),11 whereas 2WW referral rate does not reflect subsequent cancer diagnoses. Round and colleagues’ finding of lower 2WW cancer detection with higher deprivation may represent higher multimorbidity rates (and subsequently, symptoms) in more deprived communities,48,49 precipitating 2WW referrals for non-cancerous conditions. Our analysis supports this rationale by showing that practices with a higher morbidity index had increased 2WW referral rates .

Conversely, other GP–patient interaction studies have reported lower urgent cancer referrals with increasing comorbidities,10,50,51 yet there is a paucity of English studies investigating this association. We found that higher deprivation predicted fewer lung 2WW referrals. Lung cancer is regarded as ‘difficult to diagnose’ because of overlapping symptoms with comorbidities (for example, chronic obstructive pulmonary disease), which are more prevalent within deprived communities48 and can predispose incidental cancer findings during routine scans.52 Previous research suggests that GPs familiar with their patients may misattribute symptoms to existing LTCs, reducing cancer suspicion.14,15 This corroborates our finding that frequency of seeing a preferred GP, a proxy measure for continuity of care, was strongly associated with fewer 2WW referrals.

Notably, practices with higher scores for patients feeling involved in treatment decisions, a proxy measure for shared decision making (SDM), had higher 2WW referral rates . Some evidence suggests that GPs utilising SDM by discussing the rationale, risks, and benefits of procedures have fewer diagnostic delays for lower-gastrointestinal cancer.53 Further, misalignment between GP and patients’ perceptions of symptoms can discourage re-presentation if symptoms persist.54 Hence, this novel finding suggest a potentially important but underexplored role for SDM in 2WW pathways.

Finally, an unexpected finding was that practices in Northwest England strongly predicted higher 2WW referral rates . North England has the highest cancer incidence because of its distribution of older age and deprived populations,55 thus GPs in Northwest England may have higher cancer suspicion. Furthermore, previous public cancer awareness campaigns56,57 have targeted Northwest England, which could also explain this finding.

Implications for research and practice

Practices with higher 2WW referrals are associated with improved cancer survival, thus initiatives increasing 2WW referral rates are generally considered beneficial.58 Regional variation in 2WW referral rates may highlight learning from best-practice exemplars and inspire quality improvement initiatives to increase 2WW activity.59 Promisingly, we observed higher 2WW referral rates in postgraduate GP training practices, indicating that GP education can increase urgent referrals.

Moreover, public cancer awareness campaigns can increase symptomatic presentation to primary care.60 This analysis highlights priority populations for campaigns including individuals from Asian and Black ethnic backgrounds and people who smoke, who experience lower 2WW referral rates but higher odds of late-stage diagnoses. Utilising ‘community champions’ for culturally tailored messages61 and collaborating with smoking cessation and community pharmacy services may improve outreach to individuals reluctant to access primary care.62 Finally, breast cancer awareness campaigns should encourage women aged ≥70 years to remain vigilant for symptoms once their screening has ended.

However, NHS resource constraints necessitate responsible use of 2WW referrals to prevent system overwhelm.58 Hence, further research on 2WW detection rates (that is, the rate at which GPs select the ‘correct’ patients for 2WW referral), especially for patients with multimorbidity who have ambiguous symptoms, could improve referral efficiency (limiting over- or under-referral). Involving patients in diagnostic decisions through SDM or ‘patient decision aids’63 may enhance 2WW pathways. Specifically, SDM may assist GPs to more accurately identify cancer symptoms and reassure low-risk patients while encouraging timely re-presentation should symptoms persist.

To conclude, these findings and recommendations are salient and timely given the drive to reduce health inequalities exacerbated by COVID-1964 and the ambition to diagnose 75% of cancers in stage I–II by 2028.

Acknowledgements

SW would like to acknowledge the sad passing of co-author Professor Mark Ashworth in October 2024. As a Professor of Primary Care at King’s College London and a Lambeth GP for 30 years, Professor Ashworth was passionate about academic research and education in primary care and believed fervently that all communities, particularly those in deprived areas, should have access to excellence in terms of clinical care. As his Master’s dissertation student, I was one of the many generations of students who he inspired and mentored throughout his career. Without his unfailing enthusiasm, guidance and expertise, this publication would not have been possible. His loss is felt deeply across the academic and primary care community. SW would also like to thank database manager Stevo Durbaba for his help in accessing and compiling the national primary care datasets necessary for this analysis.

Notes

Funding

SW conducted this study while funded to complete a Masters of Public Health (MPH) degree at King’s College London by the Rehabilitation and Therapies department at Guy’s and St Thomas’ NHS Foundation Trust, London. The funder had no involvement in the study design or analysis. MA has been funded through UK Research and Innovation: ‘MELD-B’, NIHR203988; Medical Research Council: ‘Born in South London (eLIXIR)’, MR/X009742 and CANDETECT, CRUK, EDDPGM-May22\100002.

Ethical approval

Ethics committee approval was not required for research based on fully anonymised routine data from publicly accessible sources.

Provenance

Freely submitted; externally peer reviewed.

Data

The dataset relied on in this article is available from the corresponding author on reasonable request.

Competing interests

The authors declare that no competing interests exist.

  • Received February 26, 2024.
  • Revision received July 19, 2024.
  • Accepted October 9, 2024.

This article is Open Access: CC BY license (https://creativecommons.org/licenses/by/4.0/)

References

  1. 1.↵
    1. Cancer Research UK
    Cancer in the UK Overview 2023. accessed. https://www.cancerresearchuk.org/sites/default/files/cancer_in_the_uk_report-overview-03.pdf. 27 Feb 2025.
  2. 2.↵
  3. 3.↵
    1. Lyratzopoulos G,
    2. Abel GA,
    3. Brown CH,
    4. et al.
    (2013) Socio-demographic inequalities in stage of cancer diagnosis: evidence from patients with female breast, lung, colon, rectal, prostate, renal, bladder, melanoma, ovarian and endometrial cancer. Ann Oncol 24 (3):843–850, doi:10.1093/annonc/mds526, pmid:23149571.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Fry A,
    2. White B,
    3. Nagarwalla D,
    4. et al.
    (2023) Relationship between ethnicity and stage at diagnosis in England: a national analysis of six cancer sites. BMJ Open 13 (1), doi:10.1136/bmjopen-2022-062079, pmid:36702581. e062079.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Cancer Research UK
    Cancer in the UK 2020: Socio-economic deprivation. accessed. https://www.cancerresearchuk.org/sites/default/files/cancer_inequalities_in_the_uk.pdf. 27 Feb 2025.
  6. 6.↵
    1. National Institute of Health and Care Excellence
    (2015) Recommendations organised by symptom and findings of primary-care investigations. accessed. https://www.nice.org.uk/guidance/ng12/chapter/Recommendations-organised-by-symptom-and-findings-of-primary-care-investigations. 27 Feb 2025.
  7. 7.↵
    1. McPhail S,
    2. Johnson S,
    3. Greenberg D,
    4. et al.
    (2015) Stage at diagnosis and early mortality from cancer in England. Br J Cancer 112 (Suppl 1):S108–15, doi:10.1038/bjc.2015.49, pmid:25734389.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Mendonca SC,
    2. Abel GA,
    3. Gildea C,
    4. et al.
    (2019) Associations between general practice characteristics with use of urgent referrals for suspected cancer and endoscopies: a cross-sectional ecological study. Fam Pract 36 (5):573–580, doi:10.1093/fampra/cmy118, pmid:30541076.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Zhou Y,
    2. Mendonca SC,
    3. Abel GA,
    4. et al.
    (2018) Variation in “fast-track” referrals for suspected cancer by patient characteristic and cancer diagnosis: evidence from 670 000 patients with cancers of 35 different sites. Br J Cancer 118 (1):24–31, doi:10.1038/bjc.2017.381, pmid:29182609.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Wiering B,
    2. Lyratzopoulos G,
    3. Hamilton W,
    4. et al.
    (2022) Concordance with urgent referral guidelines in patients presenting with any of six “alarm” features of possible cancer: a retrospective cohort study using linked primary care records. BMJ Qual Saf 31 (8):579–589, doi:10.1136/bmjqs-2021-013425, pmid:34607914.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Round T,
    2. Ashworth M,
    3. L’Esperance V,
    4. Møller H
    (2021) Cancer detection via primary care urgent referral and association with practice characteristics: a retrospective cross-sectional study in England from 2009/2010 to 2018/2019. Br J Gen Pract 71 (712):e826–e835, doi:10.3399/BJGP.2020.1030, pmid:34544690.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. National Cancer Registration and Analysis Service
    Routes to Diagnosis Dashboard - Incidence by Demographics, Breakdown by Gender. accessed. https://nhsd-ndrs.shinyapps.io/routes_to_diagnosis/. 5 Jun 2025.
  13. 13.↵
    1. Martins T,
    2. Abel G,
    3. Ukoumunne OC,
    4. Mounce LTA,
    5. et al.
    (2022) Ethnic inequalities in routes to diagnosis of cancer: a population-based UK cohort study. Br J Cancer 127 (5):863–871, doi:10.1038/s41416-022-01847-x, pmid:35661833.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Lyratzopoulos G,
    2. Mendonca SC,
    3. Gildea C,
    4. et al.
    (2018) Associations between diagnostic activity and measures of patient experience in primary care: a cross-sectional ecological study of English general practices. Br J Gen Pract 68 (666):e9–e17, doi:10.3399/bjgp17X694097, pmid:29255108.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Rogers S,
    2. Gildea C,
    3. Meechan D,
    4. Baker R
    (2014) Access, continuity of care and consultation quality: which best predicts urgent cancer referrals from general practice? J Public Health (Oxf) 36 (4):658–666, doi:10.1093/pubmed/fdt127, pmid:24457226.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Weston C,
    2. Ahluwalia S,
    3. Bassett P,
    4. et al.
    (2017) GP training practices in England: a description of their unique features based on national data. Educ Prim Care 28 (6):313–318, doi:10.1080/14739879.2017.1345649, pmid:28709391.
    OpenUrlCrossRefPubMed
  17. 17.↵
  18. 18.↵
    1. NHS England
    COVID-19 Cancer services recovery plan 14 December 2020. accessed. https://www.england.nhs.uk/coronavirus/documents/covid-19-cancer-services-recovery-plan-14-december-2020/. 4 Jun 2025.
  19. 19.↵
    1. Ashworth M,
    2. Schofield P,
    3. Seed P,
    4. et al.
    (2011) Identifying poorly performing general practices in England: a longitudinal study using data from the quality and outcomes framework. J Health Serv Res Policy 16 (1):21–27, doi:10.1258/jhsrp.2010.010006, pmid:21186318.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Archer S,
    2. Calanzani N,
    3. Honey S,
    4. et al.
    (2021) Impact of the COVID-19 pandemic on cancer assessment in primary care: a qualitative study of GP views. BJGP Open 5 (4), doi:10.3399/BJGPO.2021.0056, pmid:34006530. BJGPO.2021.0056.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Office for Health Improvement and Disparities
    Cancer services. accessed. https://fingertips.phe.org.uk/profile/cancerservices. 27 Feb 2025.
  22. 22.↵
    1. NHS England
    NHS England - about us. accessed. https://digital.nhs.uk/about-nhs-digital. 27 Feb 2025.
  23. 23.↵
    1. Office for National Statistics
    UK population by ethnicity. accessed. https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity. 27 Feb 2025.
  24. 24.↵
    1. Ministry of Housing, Communities and Local Government
    The English indices of deprivation 2019 - research report 2019. accessed. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/833947/IoD2019_Research_Report.pdf. 27 Feb 2025.
  25. 25.↵
  26. 26.↵
    1. General Practice Patient Survey
    2022 GP patient survey results released. accessed. https://www.ipsos.com/en-uk/2022-gp-patient-survey-results-released. 27 Feb 2025.
  27. 27.↵
  28. 28.↵
    1. Weston C,
    2. Gilkes A,
    3. Durbaba S,
    4. et al.
    (2016) Long term condition morbidity in English general practice: a cross-sectional study using three composite morbidity measures. BMC Fam Pract 17 (1), doi:10.1186/s12875-016-0563-3, pmid:27894265. 166.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Campbell MJ
    (2006) Statistics at square two: understanding modern statistical applications in medicine (Wiley, Hoboken, NJ).
  30. 30.↵
    1. Field A
    (2009) Discovering statistics using SPSS (SAGE Publications).
  31. 31.↵
    1. Skelly AC,
    2. Dettori JR,
    3. Brodt ED
    (2012) Assessing bias: the importance of considering confounding. Evid Based Spine Care J 3 (1):9–12, doi:10.1055/s-0031-1298595, pmid:23236300.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Vestesson EM,
    2. De Corte KLA,
    3. Crellin E,
    4. et al.
    (2023) Consultation rate and mode by deprivation in English general practice from 2018 to 2022: population-based study. JMIR Public Health Surveill 9 (1), doi:10.2196/44944, pmid:37129943. e44944.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Finney JW,
    2. Humphreys K,
    3. Kivlahan DR,
    4. Harris AHS
    (2011) Why health care process performance measures can have different relationships to outcomes for patients and hospitals: understanding the ecological fallacy. Am J Public Health 101 (9):1635–1642, doi:10.2105/AJPH.2011.300153, pmid:21778493.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Lyratzopoulos G,
    2. Neal RD,
    3. Barbiere JM,
    4. et al.
    (2012) Variation in number of general practitioner consultations before hospital referral for cancer: findings from the 2010 national cancer patient experience survey in england. Lancet Oncol 13 (4):353–365, doi:10.1016/S1470-2045(12)70041-4, pmid:22365494.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Swann R,
    2. Lyratzopoulos G,
    3. Rubin G,
    4. et al.
    (2020) The frequency, nature and impact of GP-assessed avoidable delays in a population-based cohort of cancer patients. Cancer Epidemiol 64 101617, doi:10.1016/j.canep.2019.101617, pmid:31810885.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. General Practice Patient Survey
    GP patient survey 2021 - technical annex. accessed. https://www.gp-patient.co.uk/downloads/2021/GPPS_2021_Technical_Annex_PUBLIC.pdf. 27 Feb 2025.
  37. 37.↵
    1. O’Donnell CA
    (2000) Variation in GP referral rates: what can we learn from the literature? Fam Pract 17 (6):462–471, doi:10.1093/fampra/17.6.462, pmid:11120716.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Burton C,
    2. O’Neill L,
    3. Oliver P,
    4. Murchie P
    (2020) Contribution of primary care organisation and specialist care provider to variation in GP referrals for suspected cancer: ecological analysis of national data. BMJ Qual Saf 29 (4):296–303, doi:10.1136/bmjqs-2019-009469, pmid:31586938.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    1. Crane M,
    2. Scott N,
    3. O’Hara BJ,
    4. et al.
    (2016) Knowledge of the signs and symptoms and risk factors of lung cancer in Australia: mixed methods study. BMC Public Health 16 doi:10.1186/s12889-016-3051-8, pmid:27296668. 508.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Evans REC,
    2. Brotherstone H,
    3. Miles A,
    4. Wardle J
    (2005) Gender differences in early detection of cancer. J Mens Health Gend 2 (2):209–217, doi:10.1016/j.jmhg.2004.12.012.
    OpenUrlCrossRef
  41. 41.↵
  42. 42.↵
    1. Niksic M,
    2. Rachet B,
    3. Warburton FG,
    4. Forbes LJL
    (2016) Ethnic differences in cancer symptom awareness and barriers to seeking medical help in england. Br J Cancer 115 (1):136–144, doi:10.1038/bjc.2016.158, pmid:27280638.
    OpenUrlCrossRefPubMed
  43. 43.
    1. Williams ED,
    2. Whitaker KL,
    3. Piano M,
    4. Marlow LAV
    (2019) Ethnic differences in barriers to symptomatic presentation in primary care: a survey of women in England. Psychooncol 28 (12):2336–2343, doi:10.1002/pon.5225, pmid:31509888.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Forbes LJL,
    2. Atkins L,
    3. Thurnham A,
    4. Layburn J,
    5. et al.
    (2011) Breast cancer awareness and barriers to symptomatic presentation among women from different ethnic groups in east London. Br J Cancer 105 (10):1474–1479, doi:10.1038/bjc.2011.406, pmid:21989188.
    OpenUrlCrossRefPubMed
  45. 45.↵
    1. Payne NWS,
    2. Brown KF,
    3. Delon C,
    4. et al.
    (2022) Socio-economic deprivation and cancer incidence in england: quantifying the role of smoking. PLoS ONE 17 (9), doi:10.1371/journal.pone.0272202, pmid:36129905. e0272202.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. Friedemann Smith C,
    2. Whitaker KL,
    3. Winstanley K,
    4. Wardle J
    (2016) Smokers are less likely than non-smokers to seek help for a lung cancer “alarm” symptom. Thorax 71 (7):659–661, doi:10.1136/thoraxjnl-2015-208063, pmid:26911574.
    OpenUrlAbstract/FREE Full Text
  47. 47.↵
    1. Walabyeki J,
    2. Adamson J,
    3. Buckley HL,
    4. et al.
    (2017) Experience of, awareness of and help-seeking for potential cancer symptoms in smokers and non-smokers: A cross-sectional study. PLoS ONE 12 (8), doi:10.1371/journal.pone.0183647, pmid:28846706. e0183647.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Barnett K,
    2. Mercer SW,
    3. Norbury M,
    4. et al.
    (2012) Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380 (9836):37–43, doi:10.1016/S0140-6736(12)60240-2, pmid:22579043.
    OpenUrlCrossRefPubMed
  49. 49.↵
    1. Skou ST,
    2. Mair FS,
    3. Fortin M,
    4. Guthrie B,
    5. et al.
    (2022) Multimorbidity. Nat Rev Dis Primers 8 (1):48, doi:10.1038/s41572-022-00376-4, pmid:35835758.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Van Hout A,
    2. de Wit NJ,
    3. Rutten FH,
    4. Peeters PHM
    (2011) Determinants of patientʼs and doctorʼs delay in diagnosis and treatment of colorectal cancer. Eur J Gastroenterol Hepatol 23 (11):1056–1063, doi:10.1097/MEG.0b013e32834c4839.
    OpenUrlCrossRefPubMed
  51. 51.↵
    1. Baun M-LL,
    2. Jensen H,
    3. Falborg AZ,
    4. Heje HN,
    5. et al.
    (2019) Ovarian cancer suspicion, urgent referral and time to diagnosis in danish general practice: a population-based study. Fam Pract 36 (6):751–757, doi:10.1093/fampra/cmz013, pmid:31046091.
    OpenUrlCrossRefPubMed
  52. 52.↵
    1. Smith L,
    2. Sansom N,
    3. Hemphill S,
    4. Bradley SH,
    5. et al.
    (2022) Trends and variation in urgent referrals for suspected cancer 2009/2010-2019/2020. Br J Gen Pract 72 (714):34–37, doi:10.3399/bjgp22X718217, pmid:34972804.
    OpenUrlFREE Full Text
  53. 53.↵
    1. Siminoff LA,
    2. Rogers HL,
    3. Thomson MD,
    4. Dumenci L,
    5. et al.
    (2011) Doctor, what’s wrong with me? Factors that delay the diagnosis of colorectal cancer. Pat Educ Couns 84 (3):352–358, doi:10.1016/j.pec.2011.05.002, pmid:21621950.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Amelung D,
    2. Whitaker KL,
    3. Lennard D,
    4. Ogden M,
    5. et al.
    (2020) Influence of doctor-patient conversations on behaviours of patients presenting to primary care with new or persistent symptoms: a video observation study. BMJ Qual Saf 29 (3):198–208, doi:10.1136/bmjqs-2019-009485, pmid:31326946.
    OpenUrlAbstract/FREE Full Text
  55. 55.↵
    1. Arık A,
    2. Dodd E,
    3. Streftaris G
    (2020) Cancer morbidity trends and regional differences in England-A bayesian analysis. PLoS ONE 15 (5), doi:10.1371/journal.pone.0232844, pmid:32433663. e0232844.
    OpenUrlCrossRefPubMed
  56. 56.↵
    1. Cancer Research UK
    Bowel cancer screening campaign: England. accessed. https://www.cancerresearchuk.org/health-professional/awareness-and-prevention/be-clear-on-cancer/bowel-screening-campaigns. 4 Jun 2025.
  57. 57.↵
    1. Northern Cancer Alliance
    Do It For Yourself Lung Cancer Awareness - Northern Cancer Alliance. accessed. https://northerncanceralliance.nhs.uk/campaigns/national-local-tumour-specific-campaigns/. 4 Jun 2025.
  58. 58.↵
    1. Neal RD,
    2. Smith L
    (2021) Urgent cancer referrals: how well are they working and can they be improved? Br J Gen Pract 71 (710):390–391, doi:10.3399/bjgp21X716801, pmid:34446404.
    OpenUrlFREE Full Text
  59. 59.↵
    1. The Behavioural Insights Team
    Improving appropriate urgent GP cancer referrals - a final report by the Behavioural Insights Team 2018. accessed. https://www.greatermanchester-ca.gov.uk/media/1877/gp-referrals.pdf. 27 Feb 2025.
  60. 60.↵
    1. Cancer Research UK.
    Evaluation Findings - Be Clear On Cancer. accessed. Evaluation findings | Cancer Research UK. 6 Jun 2025.
  61. 61.↵
    1. Lofters A,
    2. Prakash V,
    3. Devotta K,
    4. Vahabi M
    (2023) The potential benefits of “community champions” in the healthcare system. Healthc Manage Forum 36 (6):382–387, doi:10.1177/08404704231179911, pmid:37268592.
    OpenUrlCrossRefPubMed
  62. 62.↵
    1. Egbewande OM,
    2. Abdulwasiu MA,
    3. Yusuf RO,
    4. et al.
    (2022) Roles of community pharmacists in cancer management. Innov Pharm 13 (3), doi:10.24926/iip.v13i3.4946, pmid:36627904.
    OpenUrlCrossRefPubMed
  63. 63.↵
    1. National Institute of Health and Care Excellence
    Standards framework for shared-decision-making support tools, including patient decision aids. accessed. https://www.nice.org.uk/corporate/ecd8. 27 Feb 2025.
  64. 64.↵
    1. Marmot M
    Build Back Fairer: The COVID-19 Marmot Review, accessed. https://www.health.org.uk/publications/build-back-fairer-the-covid-19-marmot-review. 27 Feb 2025.
Back to top

In this issue

BJGP Open
Vol. 9, Issue 2
July 2025
Download PDF
Email Article
Citation Tools
Inequalities in cancer 2-week-wait referrals: a cross-sectional study in English general practice
Stephanie C Wynne, Mark Ashworth
BJGP Open 2025; 9 (2): BJGPO.2024.0052. DOI: 10.3399/BJGPO.2024.0052
del.icio.us logo Facebook logo Mendeley logo Bluesky logo

Jump to section

Keywords

Intended for Healthcare Professionals
 
 

British Journal of General Practice