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Lung cancer incidence decreases with elevation: evidence for oxygen as an inhaled carcinogen

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@_tweety_vogel_ @EatlovePray11 Net als zuurstof! https://t.co/pLxfV8zx2I
@loomiscroix @GrimmGreen @OhmMyLanta @GavinNewsom https://t.co/wGJ8m4S69J #JustSayin
@ChaunceyGardner @Christy60901406 @YouTube Wait some time, they'll find out that oxygen is cancerous too. https://t.co/wGJ8m4S69J And the terrible dangers of dihydrogenmonoxid .... #sarcasm
1623 days ago
@Christian_Serpa @Paterpau Respirar es cancerígeno.Vea https://t.co/dG2GMNxGnI El problema no es que algo "aumente la probabilidad de incidencia de la muerte" Lo que ud tiene que calcular es el NÚMERO de muertos adicionales causados en zonas en donde se esparció glifosato, VS masacres y víctimas del narco
RT @Vapoon_de: @cult_cloud Sauerstoff erhöht das Risiko, an Krebs zu erkranken. https://t.co/iozgw4lqjr
RT @Vapoon_de: @cult_cloud Sauerstoff erhöht das Risiko, an Krebs zu erkranken. https://t.co/iozgw4lqjr
1917 days ago
RT @Vapoon_de: @cult_cloud Sauerstoff erhöht das Risiko, an Krebs zu erkranken. https://t.co/iozgw4lqjr
1917 days ago
@cult_cloud Sauerstoff erhöht das Risiko, an Krebs zu erkranken. https://t.co/iozgw4lqjr
@thebyrdlab @michaelhoffman @JimJohnsonSci @wkretzsch @lpachter @yk_tani @manuelrivascruz HIGHLY RELEVANT https://t.co/qJuIgfMcOm
2051 days ago
@DrSarahEJackson @Alan_Beard1 "Risk free"? Not so fast
RT @dhimmel: @michaelhoffman @seandavis12 @GreeneScientist Here is Manubot's python module to retrieve CSL JSON for standard identifiers ht…
2479 days ago
RT @dhimmel: @michaelhoffman @seandavis12 @GreeneScientist Here is Manubot's python module to retrieve CSL JSON for standard identifiers ht…
@michaelhoffman @seandavis12 @GreeneScientist Here is Manubot's python module to retrieve CSL JSON for standard identifiers https://t.co/h46ksYVtNz @seandavis12, similar tools exist for getting bibtex. Check out #DOI Content Negotiation: ``` curl --location --header "Accept: application/x-bibtex" https://t.co/qJuIgfuBWO ```
@Newsweek Here is a real article that presents "evidence for oxygen as an inhaled carcinogen" https://t.co/tOKpMbe1aw Everything is trying to kill you...time to accept that and move on.
@Sci_Hub Would DOIs for open-access articles where Sci-Hub redirects to publisher count as resolved & thus be logged? Example https://t.co/8RhJF1rhRz
PeerJ

Main article text

 

Introduction

Methods

Data collection & preparation

Cancer incidence

Demographic & health data

Climatic & environmental data

Population-weighted mean elevation

County filtering

Regression analysis

Best subset regression

Lasso regression

Partial regression plots

County stratifications

Population subgroupings

Elevation substitutions

Software

Data availability

Results

Strong, negative association between elevation & lung cancer incidence

Models accurately assess known cancer associations

Models produced for each cancer by best subset (Fig. 3A) and lasso (Fig. 3B) regression corresponded with the literature. The lasso (and best subset) models explained 67% (70%) of variation in lung cancer incidence, 51% (57%) in breast, 29% (34%) in colorectal, and 9% (19%) in prostate, (Tables 3 and 2) mirroring a previously described trend in fraction of risk attributable to modifiable factors for each of the four cancers (Danaei et al., 2005).

Elevation’s association with lung cancer is robust to stratification & subgrouping

Lung cancer associates with elevation over environmental correlates

Radon and UVB associations with lung cancer confounded by elevation

Discussion

Confounding effect of elevation

Limitations & future directions

Open data

Supplemental Information

Quality control: selecting exclusion thresholds for counties with high Native American and immigration percentages

We suspected misestimated cancer rates for counties with a high Native American percentage and a poor ability of predictors to assess cancer-risk exposure for counties with high immigration rates. To examine whether these counties were problematic, we created a general model of cancer incidence by regressing all-site cancer incidence against eight demographic and health-related covariates (metro, white, black, education, income, obesity, percent male, and smoking). Elevation was not included in the model to prevent opportunistic threshold selection. The regression was fit on Western-US counties with populations of at least 10,000. Absolute residuals are plotted against percent Native American and the 5-year immigration rate for each county (shaded by their population-based regression weight). Loess curves (displayed in blue with 95% confidence bands) indicate that predicted incidence diverged from reported incidence for both native and immigration-rich counties. Exclusion thresholds were selected, above which counties were filtered (red background), corresponding to the values where absolute residuals began trending higher.

DOI: 10.7717/peerj.705/supp-1

Optimal best subset regression models

Coefficient estimates from are displayed in unstandardized (β) and standardized (βz) forms followed by the confidence interval. The two-tailed coefficient p-value is reported.

DOI: 10.7717/peerj.705/supp-2

County-level dataset

Tab delimited data collected for US counties. Missing values are blank. Source-reported 95% confidence intervals have ‘lower’ and ‘upper’ appended to the corresponding variable name.

DOI: 10.7717/peerj.705/supp-3

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Kamen P. Simeonov and Daniel S. Himmelstein conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Funding

The authors declare there was no funding for this work.

 
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The authors provide an excellent analysis of the public data, essentially confirming Van Pelt's findings of 12 years ago, but the question arises as to whether the two authors and six editors and reviewers involved in the genesis of this paper, 8 people in all, were concerned with the statistical details yet overlooked a fundamental fact of lung cancer. People with lung cancer commonly suffer shor...

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I probably overlooked this in the article, but areas of high elevation probably are sparsely populated, and this means less sampling. Less sampling means greater variance. This correlation could actually be due to the variance in the sampling. IE at some later year, we'd find that the regions of high elevation actually have increased rates of cancer. There are several known cases of reported c...

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Would humans exposed to multiple periods of exposure to an ambient and inspired ppO2 of 0.4ATA and higher, have a tendency to present with a higher incidence of lung cancer than the general population? Commercial divers are regularly saturated at a ppO2 of 0.4ATA for period of 720 hours (30 days). While locked out on a dive, the inspired ppO2 is typically increased to 0.5ATA for periods of 8 hou...

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