Air pollution and lung cancer in India: an escalating public health crisis
Notice bibliographique
Résumé
Dear Editor, Lung cancer has long been considered a smoker’s disease. Yet in India, a strikingly different narrative is emerging: one driven not by tobacco, but by toxic air.1 With some of the world’s highest concentrations of fine particulate matter (PM₂.₅), India faces a silent but escalating cases of lung cancer due to air pollution.2 With ambient fine PM₂.₅ levels routinely exceeding 100 µg/m³ in many urban centres, the risk posed by air pollution as a carcinogen deserves urgent attention.1,2 Ambient air pollution is now classified by the International Agency for Research on Cancer as a Group 1 human carcinogen.3 Exposure to major ambient air pollutants shows a significant association with both the incidence and mortality of cancer, particularly lung cancer.4 While tobacco remains the leading cause of lung cancer, there is an emerging and troubling trend of increasing incidence among never-smokers, particularly in India, where exposure to ambient and household air pollution is pervasive. Noronha et al. reported that, in India, 40–50% of lung cancer cases occur in never-smokers, with the proportion rising to as high as 83% among Indian women, highlighting the growing contribution of air pollution and biomass fuel exposure to the country’s lung cancer burden.5 Estimates from the India State-Level Disease Burden Initiative Air Pollution Collaborators (GBD 2019) indicate that lung cancer accounted for 1.3% of all air pollution-attributable disability-adjusted life-years nationally, a seemingly modest fraction that nevertheless translates into a considerable absolute burden given India’s large population and high levels of both ambient and household particulate exposure.6 The economic burden attributable to air pollution in India was estimated at US$36.8 billion in 2019, of which lung cancer accounted for 1.2%, reflecting substantial productivity loss and treatment expenditure. Although this share is smaller than that of other respiratory conditions, its growth reflects India’s ongoing epidemiological transition and increasing exposure to ambient PM₂.₅.6 One striking finding is that, from 1990 to 2019, deaths attributable to household air pollution declined markedly (64.2%), whereas those linked to ambient PM₂.₅ pollution rose by 115.3%.6 So, the above studies highlight that air pollution is a key non-tobacco driver of India’s lung cancer epidemic. For clinicians and policymakers, the implications of this epidemiological shift are profound. Current low-dose CT screening guidelines (largely derived in high-income settings and focused on heavy smokers) fail to account for environmental exposures as primary risk factors.7 Consequently, the non-smoking population—particularly in highly polluted urban areas—is often excluded from screening, leading to the devastating diagnosis of advanced-stage lung cancer, where curative options are limited. In India, incorporating airborne-pollution exposure history into risk-stratification algorithms may help to identify high-risk individuals among never-smokers and prompt earlier detection. From a public health perspective, targeting ambient pollution is itself an onco-preventive strategy. Reductions in PM₂.₅ levels translate not only to fewer respiratory and cardiovascular deaths, but may also prevent cancers. The Indian Government’s National Clean Air Programme aims for a 20–30% reduction in PM₂.₅ by 2024–2025, but achieving this target remains a challenge given the scale of industrial, vehicular and agricultural emissions.8 So, surveillance programmes and cancer registries should increasingly incorporate geospatial pollution data. Collaborative research is needed to define dose–response curves, histological subtype associations (e.g. adenocarcinoma in never-smokers) and to assess whether pollution modifies treatment outcomes or prognosis. Mitigation of this crisis demands multisectoral action, that is, stricter emission standards, investment in clean energy and integration of air-quality health metrics into cancer-control programmes. Reducing air pollution is not only an environmental imperative, but also an onco-preventive intervention. In conclusion, lung cancer in India must no longer be viewed solely through the lens of tobacco. We are witnessing a hidden epidemic of lung cancer driven by air pollution, and overlooking it means ignoring a preventable cause of cancer. Each delay in clean-air action adds to the cancer registry of tomorrow. Tarun Kumar Suvvari (Conceptualization, Resources, Writing—original draft, Writing—review & editing), Nithya Arigapudi (Conceptualization, Resources, Writing—original draft, Writing—review & editing, Project Administration), and Shreya Veggalam (Resources, Writing—original draft, Writing—review & editing, Supervision). No funding was received. The authors declare no conflicts of interests. Not applicable. Not applicable. No AI or AI tools were used while drafting the manuscript. However, Grammarly tool was used to correct the grammar in the manuscript.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».