Background: Routine clinical surveillance involves serial radiographic imaging following radical surgery in localized non-small cell lung cancer (NSCLC). However, such surveillance can detect only macroscopic disease recurrence and is frequently inconclusive. We investigated if detection of ctDNA before and after resection of NSCLC identifies the patients with risk of relapse, and furthermore, informs about response to management. Methods: We recruited a total of 77 NSCLC patients. A high-throughput 127 targetgene capture technology and a high-sensitivity circulating single-molecule amplification and resequencing technology (cSMART) assay were used to detect the somatic mutations in the tumor tissues as well as the plasma of NSCLC patients before and after surgery to monitor for minimal residual disease (MRD). Kaplan-Meier and Cox regression analysis were performed to evaluate the relapse-free survival (RFS) and overall survival (OS) of patients with predictor variables. Results: Patients with a higher stage (III/IV) and preoperative ctDNA-positive status demonstrated a significant 2.8-3.4-fold risk and 3.8-4.0-fold risk for recurrence and death, respectively. Preoperative ctDNA-positive patients associated with a lower RFS (HR = 3.812, p = 0.0005) and OS (HR = 5.004, p = 0.0009). Postoperative ctDNApositive patients also associated with a lower RFS (HR = 3.076, p = 0.0015) and OS (HR = 3.195, p = 0.0053). Disease recurrence occurred among 63.3% (19/30) of postoperative ctDNA-positive patients. Most of these patients 89.5% (17/19) had detectable ctDNA within 2 weeks after surgery and was identified in advance of radiographic findings by a median of 12.6 months. Conclusion: Advanced stage and preoperative ctDNA-positive are strong predictors of RFS and OS in localized NSCLC patients undergoing complete resection. Postoperative detection of ctDNA increases chance to detect early relapse, thus can fulfill an important role in stratifying patients for immediate further treatment with adjuvant and neoadjuvant therapy.
This study was conducted to assess the current impact of natural gas appliances on air quality in California homes. Data were collected via telephone interviews and measurements inside and outside of 352 homes. Passive samplers measured time‐resolved CO and time‐integrated NOX, NO2, formaldehyde, and acetaldehyde over ~6‐day periods in November 2011 – April 2012 and October 2012 – March 2013. The fraction of indoor NOX and NO2 attributable to indoor sources was estimated. NOX, NO2, and highest 1‐h CO were higher in homes that cooked with gas and increased with amount of gas cooking. NOX and NO2 were higher in homes with cooktop pilot burners, relative to gas cooking without pilots. Homes with a pilot burner on a floor or wall furnace had higher kitchen and bedroom NOX and NO2 compared to homes without a furnace pilot. When scaled to account for varying home size and mixing volume, indoor‐attributed bedroom and kitchen NOX and kitchen NO2 were not higher in homes with wall or floor furnace pilot burners, although bedroom NO2 was higher. In homes that cooked 4 h or more with gas, self‐reported use of kitchen exhaust was associated with lower NOX, NO2, and highest 1‐h CO. Gas appliances were not associated with higher concentrations of formaldehyde or acetaldehyde.
BackgroundEstimating health care costs, either in the context of understanding resource utilization in the implementation of a health plan, or in the context of economic evaluation, has become a common activity of health planners, health technology assessment agencies and academic groups. However, data sources for costs outside of direct service delivery are often scarce. WHO-CHOICE produces global price databases and guidance on quantity assumptions to support country level costing exercises. This paper presents updates to the WHO-CHOICE methodology and price databases for programme costs.MethodsWe collated publicly available databases for 14 non-traded cost variables, as well as a set of traded items used within health systems (traded goods are those which can be purchased from anywhere in the world, whereas non-traded goods are those which must be produced locally, such as human resources). Within each of the variables, missing data was present for some proportion of the WHO member states. For each variables statistical or econometric models were used to model prices for each of the 194 WHO member states in 2010 International Dollars. Literature reviews were used to update quantity assumptions associated with each variable to contribute to the support costs of disease control programmes.ResultsA full database of prices for disease control programme support costs is available for country-specific costing purposes. Human resources are the largest driver of disease control programme support costs, followed by supervision costs.ConclusionsDespite major advances in the availability of data since the previous version of this work, there are still some limitations in data availability to respond to the needs of those wishing to develop cost and cost-effectiveness estimates. Greater attention to programme support costs in cost data collection activities would contribute to an understanding of how these costs contribute to quality of health service delivery and should be encouraged.
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