Rural barriers to early lung cancer detection: Exploring access to lung cancer screening programs in New Hampshire and Vermont

BackgroundRural populations face many health disadvantages compared to urban areas. There is a critical need to better understand the current lung cancer screening landscape in these communities to identify targeted areas to improve the impact of this proven tool.MethodsData from the County Health Rankings of New Hampshire and Vermont was reviewed for population density, distribution of adult smokers, and level of education compared to the distribution of Lung Cancer Screening Facilities throughout these two states.ResultsScreening programs in southern counties of Vermont with lower levels of education have decreased access. In New Hampshire, there are no programs within 30 miles of the areas with the largest distribution of smokers, and decreased access in some areas with the lowest levels of education.ConclusionsImproving equitable access to high-quality screening services in rural regions and the creation of targeted interventions to address decreased access in areas of high tobacco use and low education is vital to decreasing the incidence of latestage presentations of lung cancer within these populations.     

For better or worse? Investigating the validity of best–worst discrete choice experiments in health

Discrete choice experiments (DCEs) are frequently used in health economics to measure preferences for nonmarket goods. Best–worst discrete choice experiment (BWDCE) has been proposed as a variant of the traditional “pick the best” approach. BWDCE, where participants choose the best and worst options, is argued to generate more precise preference estimates because of the additional information collected. However, the validity of the approach relies on two necessary conditions: (a) best and worst decisions provide similar information about preferences and (b) asking individuals to answer more than one choice question per task does not reduce data quality. Whether these conditions hold in empirical applications remains under researched. This is the first study to compare participants' choices across three experimental conditions: (a) BEST choices only, (b) WORST choices only, and (c) BEST and WORST choices (BWDCE). We find responses to worst choices are noisier. Implied preferences from the best only and worst only choices are qualitatively different, leading to different WTP values. Responses to BWDCE tasks have lower consistency, and respondents are more likely to use simplifying decision heuristics. We urge caution in using BWDCE as an alternative to the traditional “pick the best” DCE.     

Magnetic resonance elastography with guided pressure waves

Magnetic resonance elastography aims to non-invasively and remotely characterize the mechanical properties of living tissues. To quantitatively and regionally map the shear viscoelastic moduli in vivo, the technique must achieve proper mechanical excitation throughout the targeted tissues. Although it is straightforward, ante manibus, in close organs such as the liver or the breast, which practitioners clinically palpate already, it is somewhat fortunately highly challenging to trick the natural protective barriers of remote organs such as the brain. So far, mechanical waves have been induced in the latter by shaking the surrounding cranial bones. Here, the skull was circumvented by guiding pressure waves inside the subject's buccal cavity so mechanical waves could propagate from within through the brainstem up to the brain. Repeatable, reproducible and robust displacement fields were recorded in phantoms and in vivo by magnetic resonance elastography with guided pressure waves such that quantitative mechanical outcomes were extracted in the human brain.     

Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.