Socioeconomic position and delays in lung cancer diagnosis: should we target the more deprived?

Socio-demographic inequalities in stage at diagnosis of lung cancer: A French population-based study

BACKGROUND

Diagnosing patients at a non-advanced stage has become a mainstay of lung cancer prevention and control strategies. Understanding socio-demographic inequalities in stage at diagnosis may improve the targeting of interventions on patients at higher risk. This study aimed to identify these socio-demographic determinants in a large-scale French population-based cancer registry.

METHODS

All incident lung cancers diagnosed between 2008 and 2019 identified from the Poitou-Charentes Cancer Registry (south-west France) were included. Stage at diagnosis was categorised as advanced/non-advanced (TNM III/IV vs I/II) according to the 8th TNM edition, the objective being to ensure a consistent level of prognosis over time. Socio-demographic variables included age, sex, the French European Deprivation Index (EDI) and patient's place of residence. Their impact on stage at diagnosis was quantified by multivariate logistic regression models with subgroup analyses by histological subtype.

RESULTS

Out of the 15,487 included patients, 75% were diagnosed at an advanced stage (66% to 95% depending on the histological subtype), 17% at a non-advanced stage and 10% at a non-specified stage. Multivariate analysis showed different patterns according to histological subtypes. In patients with adenocarcinoma, a higher risk of advanced stage was found for younger and older patients (u-shape), those most deprived, and those living in rural areas. The same effect of age was reported for squamous cell carcinomas, while no association was found for small-cell lung carcinomas.

CONCLUSIONS

This study highlighted substantial socio-demographic inequalities in stage at diagnosis, specifically for adenocarcinoma patients. Diagnosis strategies could be refined and strengthened in the non-smoker population, in which adenocarcinomas are mainly reported.

Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients

Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented and evaluated using 20% out-of-sample cross validations via the area under the curve (AUC). Optimal model parameters were selected based on 10-fold cross validation. There were 11,602 patients included in the analysis. Evaluated quality indicators included, primarily, overall proportion achieving "time from referral date to diagnosis date ≤ 28 days" and proportion achieving "time from diagnosis date to first treatment date (any intent) ≤ 14 days". Results showed that the support vector machine learning methods performed well, followed by nearest neighbour, based on out-of-sample AUCs of 0.89 (in-sample = 0.99) and 0.85 (in-sample = 0.99) for the first indicator, respectively. These models can be implemented in the registry databases to help healthcare workers identify patients who may not meet these indicators prospectively and enable timely interventions.

Personalised Assessment and Rapid Intervention in Frail Patients with Lung Cancer: The Impact of an Outpatient Occupational Therapy Service

INTRODUCTION

Lung cancer and its treatments cause or accelerate frailty, detrimentally affecting function and quality of life. Occupational therapists (OTs) provide global assessments and interventions, but services are often available for inpatients. The impact of holistic assessment and early intervention in the outpatient setting is unknown.

MATERIALS AND METHODS

A tertiary cancer center in North East England piloted a Lung Cancer Outpatient OT Service for patients with thoracic malignancy and a Clinical Frailty Scale Score (CFS) ≥ 5. Service effectiveness was evaluated through calculation of admission avoidance, hospital length-of-stay reduction, completion/impact of advance care planning and patient/family feedback. Demographics, frailty level, required interventions, and onward referrals were recorded.

RESULTS

A total of 153 patients (median age of 71 (range 46-90) received OT assessment and intervention. 48% were in the lowest socioeconomic quintile. Median CFS score was 5 (mildly frail (range: 4-7)) at initial assessment. Total of 918 interventions were delivered (median: 5, IQR: 3-7, Range 0-22). 48% of patients engaged in future planning (national average: 10%-15% P < .0001) and 78.5% achieved preferred place of death (national average 30%). An estimated 37 hospital admissions were avoided. In admitted patients, average inpatient stay when known to the service was 6.5 days less than other frail lung cancer patients in our unit (95% CI 4-9.1 days P < .0001). Higher CFS was associated with poor survival (P < .05).

CONCLUSION

Outpatient OT services can avoid and shorten hospital admissions through advance care planning, management of functional disruption, onward referral to other allied health professionals and palliative care. A comprehensive multidisciplinary outpatient service may benefit patients further and should be the focus of future research.

Effects of Demographic and Socio-Economic Factors on Investigation Time of Lung Cancer Patients in Denmark: A Retrospective Cohort Study

Background

Lung cancer is one of the most common cancer types worldwide. The significance of the individual socio-economic position on the delay in lung cancer diagnosis has not been properly investigated. The purpose of this nationwide population-based study is to examine the association between position and the length of the primary investigation for lung cancer.

Materials and Methods

This register study was based on all lung cancer patients in Denmark who were diagnosed in 2012 to 2017, in total 28,431 patients. We used a multivariate logistic regression model and multivariate zero-inflated negative binomial model to estimate the effect of education level, family income, difficulty of transport, and cohabitation status on the length of the primary investigation.

Results

We found that the patients' income, difficulty of transport, and cohabitation status were associated with the length of the primary investigation. The chance of carrying out the investigation process within 24 days is higher for patients with a high income (adjusted OR = 0.86 with 95% CI (0.81; 0.91)), lower for patients with troublesome transport (adjusted OR = 0.67 with 95% CI (0.61; 0.72)), and lower for patients living alone (adjusted OR = 0.93 with 95% CI (0.88; 0.99)).

Conclusion

Several socio-economic factors are associated with the length of the primary lung cancer investigation. To ensure that all patients receive the most appropriate health care and to avoid extra investigation time, clinicians may pay extra attention to patients who are less fortunate due to low income, troublesome transport to the hospital, or living alone.

Limitations and perceived delays for diagnosis and staging of lung cancer in Portugal: A nationwide survey analysis

BACKGROUND

We aimed to identify the perception of physicians on the limitations and delays for diagnosing, staging and treatment of lung cancer in Portugal.

METHODS

Portuguese physicians were invited to participate an electronic survey (Feb-Apr-2020). Descriptive statistical analyses were performed, with categorical variables reported as absolute and relative frequencies, and continuous variables with non-normal distribution as median and interquartile range (IQR). The association between categorical variables was assessed through Pearson's chi-square test. Mann-Whitney test was used to compare categorical and continuous variables (Stata v.15.0).

RESULTS

Sixty-one physicians participated in the study (45 pulmonologists, 16 oncologists), with n = 26 exclusively assisting lung cancer patients. Most experts work in public hospitals (90.16%) in Lisbon (36.07%). During the last semester of 2019, responders performed a median of 85 (IQR 55-140) diagnoses of lung cancer. Factors preventing faster referral to the specialty included poor articulation between services (60.0%) and patients low economic/cultural level (44.26%). Obtaining National Drugs Authority authorization was one of the main reasons (75.41%) for delaying the begin of treatment. The cumulative lag-time from patients' admission until treatment ranged from 42-61 days. Experts believe that the time to diagnosis could be optimized in around 11.05 days [IQR 9.61-12.50]. Most physicians (88.52%) started treatment before biomarkers results motivated by performance status deterioration (65.57%) or high tumor burden (52.46%). Clinicians exclusively assisting lung cancer cases reported fewer delays for obtaining authorization for biomarkers analysis (p = 0.023). Higher waiting times for surgery (p = 0.001), radiotherapy (p = 0.004), immunotherapy (p = 0.003) were reported by professionals from public hospitals.

CONCLUSIONS

Physicians believe that is possible to reduce delays in all stages of lung cancer diagnosis with further efforts from multidisciplinary teams and hospital administration.