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IMPORTANCE: Patients with head and neck squamous cell cancer (HNSCC) are often uninsured or underinsured at the time of their diagnosis. This access to care has been shown to influence treatment decisions and survival outcomes. OBJECTIVE: To examine the association of the Patient Protection and Affordable Care Act (ACA) health care legislation with rates of insurance coverage and access to care among patients with HNSCC. DESIGN, SETTING, AND PARTICIPANTS: Prospectively gathered data from the Surveillance, Epidemiology, and End Results (SEER) database were used to examine rates of insurance coverage and access to care among 89 038 patients with newly diagnosed HNSCC from January 2007 to December 2014. Rates of insurance were compared between states that elected to expand Medicaid coverage in 2014 and states that opted out of the expansion. Statistical analysis was performed from January 1, 2007, to December 31, 2014. MAIN OUTCOMES AND MEASURES: Rates of insurance coverage and disease-specific and overall survival. RESULTS: Among 89 038 patients newly diagnosed with HNSCC (29 384 women and 59 654 men; mean [SD] age, 59.8 [7.6] years), there was an increase after implementation of the ACA in the percentage of patients enrolled in Medicaid (16.2% after vs 14.8% before; difference, 1.4%; 95% CI, 1.1%-1.7%) and private insurance (80.7% after vs 78.9% before; difference, 1.8%; 95% CI, 1.2%-2.4%). In addition, there was a large decrease in the rate of uninsured patients after implementation of the ACA (3.0% after vs 6.2% before; difference, 3.2%; 95% CI, 2.9%-3.5%). This decrease in the rate of uninsured patients and the associated increases in Medicaid and private insurance coverage were only different in the states that adopted the Medicaid expansion in 2014. No survival data are available after implementation of the ACA, but prior to that point, from 2007 to 2013, uninsured patients had reduced 5-year overall survival (48.5% vs 62.5%; difference, 14.0%; 95% CI, 12.8%-15.2%) and 5-year disease-specific survival ...

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2-2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example z> 1. However they generally perform better than template-fitting methods at low redshift (z< 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority. ; Sinergia program of the Swiss National Science Foundation German Research Foundation (DFG) Ts 17/2-1 Istituto Nazionale Astrofisica (INAF) Agenzia Spaziale Italiana (ASI) 2018-23-HH.0 1.05.01.88.04 European Space Agency European Commission Agenzia Spaziale Italiana (ASI) Belgian Federal Science Policy Office Canadian Euclid Consortium Centre National D'etudes Spatiales Danish Space Research Institute Portuguese Foundation for Science and Technology European Commission Spanish Government National Aeronautics & Space Administration (NASA) Netherlandse Onderzoekschool Voor Astronomie Norwegian Space Agency Romanian Space Agency State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space O ffice (SSO) United Kingdom Space Agency