Survival disparities in Indigenous and non-Indigenous New Zealanders with colon cancer: the role of patient comorbidity, treatment and health service factors

Background Ethnic disparities in cancer survival have been documented in many populations and cancer types. The causes of these inequalities are not well understood but may include disease and patient characteristics, treatment differences and health service factors. Survival was compared in a cohort of Maori ( Indigenous) and non-Maori New Zealanders with colon cancer, and the contribution of demographics, disease characteristics, patient comorbidity, treatment and healthcare factors to survival disparities was assessed. Methods Maori patients diagnosed as having colon cancer between 1996 and 2003 were identified from the New Zealand Cancer Registry and compared with a randomly selected sample of non-Maori patients. Clinical and outcome data were obtained from medical records, pathology reports and the national mortality database. Cancer-specific survival was examined using Kaplane-Meier survival curves and Cox hazards modelling with multivariable adjustment. Results 301 Maori and 328 non-Maori patients with colon cancer were compared. Maori had a significantly poorer cancer survival than non-Maori ( hazard ratio (HR) 1.33, 95% CI 1.03 to 1.71) that was not explained by demographic or disease characteristics. The most important factors contributing to poorer survival in Maori were patient comorbidity and markers of healthcare access, each of which accounted for around a third of the survival disparity. The final model accounted for almost all the survival disparity between Maori and non-Maori patients ( HR 1.07, 95% CI 0.77 to 1.47). Conclusion Higher patient comorbidity and poorer access and quality of cancer care are both important explanations for worse survival in Maori compared with non-Maori New Zealanders with colon cancer

An ongoing case-control study to evaluate the NHS Bowel Cancer Screening Programme

© 2014 Massat et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA

Insurance impacts survival for children, adolescents, and young adults with bone and soft tissue sarcomas.

BackgroundWhile racial/ethnic survival disparities have been described in pediatric oncology, the impact of income has not been extensively explored. We analyzed how public insurance influences 5-year overall survival (OS) in young patients with sarcomas.MethodsThe University of California San Francisco Cancer Registry was used to identify patients aged 0-39 diagnosed with bone or soft tissue sarcomas between 2000 and 2015. Low-income patients were defined as those with no insurance or Medicaid, a means-tested form of public insurance. Survival curves were computed using the Kaplan-Meier method and compared using log-rank tests and Cox models. Causal mediation was used to assess whether the association between public insurance and mortality is mediated by metastatic disease.ResultsOf 1106 patients, 39% patients were classified as low-income. Low-income patients were more likely to be racial/ethnic minorities and to present with metastatic disease (OR 1.96, 95% CI 1.35-2.86). Low-income patients had significantly worse OS (61% vs 71%). Age at diagnosis and extent of disease at diagnosis were also independent predictors of OS. When stratified by extent of disease, low-income patients consistently had significantly worse OS (localized: 78% vs 84%, regional: 64% vs 73%, metastatic: 23% vs 30%, respectively). Mediation analysis indicated that metastatic disease at diagnosis mediated 15% of the effect of public insurance on OS.ConclusionsLow-income patients with bone and soft tissue sarcomas had decreased OS regardless of disease stage at presentation. The mechanism by which insurance status impacts survival requires additional investigation, but may be through reduced access to care

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

Field-portable pixel super-resolution colour microscope.

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm(2). This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate 'rainbow' like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings