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Enregistrement W3003963326 · doi:10.1016/j.eclinm.2020.100279

Fall in US cancer death rates: Time to pop the champagne?

2020· article· en· W3003963326 sur OpenAlexaff
Aakash Desai, Bishal Gyawali

Notice bibliographique

RevueEClinicalMedicine · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésMedicineCancerLung cancerScopusMortality rateDemographyGerontologyOncologyMEDLINEInternal medicineLaw

Résumé

récupéré en direct d'OpenAlex

The fall in mortality rates for cancer in the US between 2016 and 2017, as reported by the American Cancer Society (ACS) in a recent publication, urged a big debate about who or what deserved the credit for such progress [[1]Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30Crossref PubMed Scopus (12753) Google Scholar]. Researchers found that since 1991 the cancer death rate has dropped 29% but the 2.2% decline in mortality rates from 2016 to 2017 was the largest single-year decline in cancer mortality ever reported, compared against the 1.5% decline per year for the decade 2008–2017. Who deserves credit for this success? Since this fall was primarily driven by lung cancer, many experts speculated that this was the success story of treatment advances which has dramatically changed over the decade with the introduction of genomic and immunotherapy-based drugs. Without a doubt, the fall in cancer mortality is a welcome news to everyone in the oncology community. However, before getting too excited, it is important to look at the trends in cancer mortality over many years (Fig 7 of the original publication) [[1]Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30Crossref PubMed Scopus (12753) Google Scholar]. Although this study cannot prove whether the fall in mortality in 2016–2017 is statistically better than previous years, we see that the graphs do not reveal a dramatic drop and are in the general downward trend for both cancer mortality and lung cancer mortality rates for each gender. Thus, in our opinion, this downward trend of mortality graphs serves more as a reassurance than a cause for celebration. What indeed contributed to the fall in lung cancer mortality rates? A combination of all efforts, including continued decline in smoking rates across both the genders, better treatment, improved surgery and radiation techniques, improved supportive care and screening may have had an impact. There is clear relationship of reduced smoking with improvement in cancer mortality rates over last few decades, and this is visible in the mortality graphs. If we consider the reduced incidence and mortality from lung cancer [[2]de Groot P.M. Wu C.C. Carter B.W. Munden R.F. The epidemiology of lung cancer.Transl Lung Cancer Res. 2018; 7: 220Crossref PubMed Scopus (333) Google Scholar] and decades-long latency period between smoking initiation and lung cancer occurrence [[3]Doll R. Evolution of knowledge of the smoking epidemic.Tobacco Sci Policy Public Health. 2010; : 1-13Google Scholar], it seems that the success of tobacco control policies and cancer prevention programs could be a major contributor to the decline in cancer deaths. Furthermore, a decline in the rates of smoking from 20.9% in 2005 to 15.1% in 2015 support the continued role of tobacco control in reduced lung cancer incidence and mortality [[4]Control CfD, Prevention. Smoking and tobacco use fact sheet. Retrieved October. 2016;26:2009.Google Scholar]. While the contribution of screening is probably minimal given the low uptake of lung cancer screening, the improvement in diagnostics could have some positive effect on mortality. The contribution of improved surgery and radiation cannot be discounted as well. Interestingly, all four cancers where mortality rates are dropping (lung, colorectal, prostate, breast) are the cancers where all three modalities of treatment are an important component of care. The improvement in supportive care should have an impact across all tumor types broadly. Finally, the billion-dollar question, how much contribution to this fall in mortality rates is due to advances in cancer drugs? We do not intend to discount the substantial impact advancement in cancer drugs have made to the lives of patients with cancer. However, have these effects been big enough to change mortality rates at the population level? The advances in drugs have exclusively occurred in the advanced setting which accounts for 57% of all lung cancers in the US [[1]Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30Crossref PubMed Scopus (12753) Google Scholar]. Of these, 15% are patients with small cell lung cancer where no therapeutic advances have been made over the years. Furthermore, most therapeutic advances are applicable only to certain subgroup of patients with lung cancer. Important advances in genomic targeted therapy in lung cancer happened in 2004 (erlotinib approval) and 2011 (crizotinib approval) begging the question why fall in mortality rates would be delayed until 2016–2017. Newer targeted drugs such as osimertinib and alectinib were approved first in late 2015 and after 2017 and thus, wouldn't be able to affect mortality rates in 2016–2017. Other genomic directed therapies approved on the basis of smaller single arm trials like BRAF and MEK inhibitors in lung cancer are applicable for fewer patients and are approved in or after 2017. What about immunotherapies? The only immunotherapies in lung cancer approved before 2016 shown to have an effect on mortality rates between 2016 and 2017 are pembrolizumab monotherapy 2nd line, pembrolizumab monotherapy 1st line and nivolumab 2nd line. Other immunotherapy advances in lung cancer such as pembrolizumab combination therapy or durvalumab in stage III occurred after this period. The pembrolizumab approvals were limited to PDL1 positive tumors, further restricting the patient pool eligible for this treatment. Furthermore, FDA approval doesn't immediately translate to real-world adoption since only half of patients in the US eligible for targeted therapies were receiving such treatment [[5]Singal G. Miller P.G. Agarwala V. et al.Association of patient characteristics and tumor genomics with clinical outcomes among patients with Non–Small cell lung cancer using a clinicogenomic database.JAMA. 2019; 321: 1391-1399Crossref PubMed Scopus (250) Google Scholar]. Notwithstanding the beneficial effect genome based and immunotherapy drugs may have provided, the benefits would be too small and cannot explain such a difference in mortality rates at the population level, more so when clinical adoption is an issue. Therefore, based on the timing of drug approvals, as well as the fraction of patients eligible for and ultimately receive these drugs, the contribution of drug innovation to fall in population mortality rates in lung cancer and thereby overall cancer mortality rates is very low. An exception is probably melanoma, where drugs may have contributed to improving mortality rates; however, one fact that is often overlooked in such reports is the effect of overdiagnosis. Indeed, examining Fig. 2 of the ACS report reveals continued increase in incidence of melanoma coinciding with the continued decrease in mortality from melanoma [[1]Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30Crossref PubMed Scopus (12753) Google Scholar]. The ACS report also found that black men were twice as likely to die of cancer as Asian/Pacific Islander men and 20 percent more likely to die than white men. Furthermore, men and women living in certain states are also more likely to develop and succumb to risk reducible cancers such as lung cancer, cervical cancer and melanoma [[1]Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30Crossref PubMed Scopus (12753) Google Scholar]. These did not make much news, because these are stories of failure that do not make us feel as good as the stories of success, but are vital to our continued progress. Indeed, we completely support the conclusions made in a different analysis of the same ACS report, “Public-health policies are not personalized to any individual but can promote longevity for all of us” [[6]Prasad V. Our best weapons against cancer are not magic bullets.Nature. 2020; 577: 451Crossref PubMed Scopus (10) Google Scholar]. We hope that our comment provides complementary analysis to contribute to this debate. Finally, it is important to remember that the ACS report is an ecological analysis and cannot prove any causal relations. The key take-home from this study for us is reassurance that we are moving in the right direction but not necessarily a cause for celebration yet. Unsurprisingly, the low-hanging fruit to achieve better outcomes overall seems to be equitable access to cancer care, which is in line with our argument for cancer groundshot parallel to cancer moonshot [[7]Gyawali B. Sullivan R. Booth C.M. Cancer groundshot: going global before going to the moon.Lancet Oncol. 2018; 19: 288-290Summary Full Text Full Text PDF PubMed Scopus (12) Google Scholar]. Paraphrasing Robert Frost:The woods are lovely, dark and deep,But we have promises to keep,And miles to go before we sleep,And miles to go before we sleep. None None

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,164
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,194
Tête enseignante GPT0,437
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2020
Routes d'admission1
Résumé présentoui

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