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Record W1544284103 · doi:10.4103/0973-1482.92216

Effective strategies for oral cancer control in India

2012· editorial· en· W1544284103 on OpenAlexaboutno aff
Pankaj Chaturvedi

Bibliographic record

VenueJournal of Cancer Research and Therapeutics · 2012
Typeeditorial
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerOncologyTraditional medicineInternal medicine

Abstract

fetched live from OpenAlex

Let me first explain why we decided to have a special issue on Oral Cancer. India has the dubious distinction of harboring world's highest number (nearly 20%) of oral cancers. It is aptly labeled the oral cancer capital of the world with an estimated 1% of the population having oral premalignant lesions. Each year approximately one million people in India are diagnosed with oral cancers and half of them die a painful death within 12 months of diagnosis due to late presentation. A small fraction of newly diagnosed oral cancer patients enjoy long term survival. However they live with chronic pain, functional impairment and disfigurement resulting in varying degree of social isolation. Second primary cancers due the ubiquitous "Field cancerization" phenomenon pose an additional challenge in long term survivors. Oral Cancer is ideal for screening but biologically the worst among Head and Neck Squamous cell cancers especially when diagnosed in advanced stages. Ironically, the etiology of this leading cause of cancer related death in men is associated with well established risk habit such as tobacco, areca nut and alcohol. Genetic susceptibility and dietary factors may enhance the effect of these carcinogens. Undoubtedly, oral cancer control is a serious issue which needs a multi-pronged approach to curb this largely preventable disease burden. In this editorial, I intend to share evidence-based and practical approaches to reduce the burden of oral cancer in India Reduce tobacco consumption: Smokeless tobacco is the single most important cause of high incidence of oral cancer in India. According to the Global Adult Tobacco Survey of Ministry of Health and Family Welfare released in November 2010, nearly 160 million Indians are using smokeless Tobacco. In India, an alarming number of school children and young adults are picking up this habit. A study from Mumbai Cancer Registry reported the life time risk of developing oral cancer in chronic tobacco users as one in thirty. No amount of screening /early detection will make significant impact if tobacco consumption rates remain high. Apart from oral cancer, tobacco use is also strongly associated with a plethora of health problems. Tobacco is responsible for 1 in 5 of all male deaths in middle age. Men, who smoke, lose ten years of their lives, as a result of tuberculosis, respiratory and heart diseases, stroke and cancer. We need to channelize our efforts for impeccable implementation of Cigarette and Other Tobacco Product Act 2003 and several subsequent amendments. Increasing taxes on all tobacco products: Tobacco consumption being price sensitive, tax increase leads to significant decrease in consumption. Certain states have very low tax levels on bidis which is a bigger problem in rural area and amongst the poor. The extremely high prevalence of smokeless tobacco usage amongst youth is the result of low tax rate and price of the product. As the age of initiation of tobacco habit is eight to twelve years, incorporation of tobacco control in school curriculum will go a long way in reducing the tobacco consumption. Opportunistic Counseling: As per the Cochrane analysis, two minutes of Physician's advice is highly effective in motivating people to quit. All physicians, regardless of specialties, must counsel high risk subjects such as those with poor oral hygiene and those using tobacco, areca nut, alcohol etc. The U.S. Preventive Services Task Force and Canadian authority have recommended counseling patients against the use of tobacco in any form, particularly with heavy alcohol consumption. Ban tobacco containing food substances under Food Safety and Standard Act of India, 2011: Gutka, Pan Masala, tobacco tooth paste are amongst the biggest public health menace in current society. Promoting Oral Health in Indian population: Poor oral hygiene and specific bacterial microflora in the oral cavity have been linked with development of oral cancers. Many bacteria in the oral cavity have been linked with oral squamous carcinoma. Apart from bacteria, other agents such as viruses (Human Papilloma Virus and Epstein Barr virus), fungus like Candida Albicans have been implicated in carcinogenesis. A powerful public health campaigns to promote good oral hygiene would have beneficial effects not only in reducing oral cancer incidence but several other disorders of the oral cavity. It is anticipated that promotion of healthy life style will influence knowledge, aptitude and hygiene at all levels of society. Health Education is the basic but most crucial component of oral health promotion. National Alcohol Control Policy - Alcohol is one of the leading risk factors for disease burden in low developing countries and the third largest risk factor in developed countries. It is responsible for the loss of 58.3 million (four per cent of total) of Disability-Adjusted Life Years mainly on account of the neuropsychiatric conditions. Alcohol consumption is a well known risk factor for the development of squamous cell carcinoma of the head and neck. It has been found that cancer of tongue and floor of mouth have a stronger association with alcohol consumption than other oral cavity sites. Alcohol by-products and polcyclic aromatic hydrocarbons are shown to form DNA adducts. Acetaldehyde, a metabolite of alcohol is a known human carcinogen. Alcohol's solvent effect is shown to increase the permeability of oral mucosa to harmful tobacco carcinogens. Akin to tobacco, the country desperately needs a National Alcohol control policy to reduce production, sale, consumption, advertisement of alcohol products. Areca Nut control - A lesser known reason of epidemic proportion of oral cancer in India is the rampant use of areca nut chewing. Areca nut or betel nut is a psycho-stimulant and an addictive substance. The cancer causing properties of areca nut have been well reported in animal, human and epidemiologic studies. The World Health Organization and International Agency for Research on Cancer has classified Areca nut as a Group 1 human carcinogens with sufficient evidence of increased risk of submucus fibrosis, premalignant oral lesion and cancers of the oral cavity. Cancers caused by Betel nut chewing shows a strong dose-response relationship for frequency and duration of chewing. National Oral cancer Screening Program - A well structured national oral cancer screening program may lead to early detection and prevention of oral cancers. A visual examination by health workers is evidence-based cost-effective screening tool for oral cancer in a population with high prevalence. India has huge workforce of health workers in their existing government aided health care setup that can be exploited for it. Opportunistic Screening - It is prudent for all clinicians (all dentists and all doctors) to perform a careful examinations of the oral cavity in those with high risk habits and those with symptoms and signs. Oral cancer examination should be made compulsory as a part of systemic examination of all patients/subjects especially those with high risk habits. Dentists are the best at oral screening and regular visit may lead to detection at earlier stage. A periodic visit to the dentist as a part of routine health check up, similar to the western world, will have a positive impact. Oral self-examination, though lacking support from literature, is a simple tool to make individuals more conscious about oral health. Let me conclude by what Mahatma Gandhi said - " If you want to find a solution, go and live where the problem is." Oral cancer is our unique problem and it is for us to find a solution rather than look westward.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.342
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.483
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations28
Published2012
Admission routes1
Has abstractyes

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