N Praveen Birur1, Keerthi Gurushanth2, Sanjana Patrick3, Sumsum P Sunny4, Shubhasini A Raghavan2, Shubha Gurudath2, Usha Hegde5, Vidya Tiwari6, Vipin Jain7, Mohammed Imran3, Pratima Rao3, Moni Abraham Kuriakose8. 1. KLE Society's Institute of Dental Sciences; Oral Cancer Screening Program, Biocon Foundation, Bengaluru, India. 2. KLE Society's Institute of Dental Sciences, Bengaluru, India. 3. Oral Cancer Screening Program, Biocon Foundation, Bengaluru, India. 4. Head and Neck Oncology, Mazumdar Shaw Cancer Center, Bengaluru, India. 5. JSS Dental College, Mysore, Karnataka, India. 6. Oral Medicine Specialist, KLE Society's Institute of Dental Sciences, Bengaluru, India. 7. Department of Public Health Dentistry, KLE Society's Institute of Dental Sciences, Bengaluru, India. 8. Cochin Cancer Research Center, Kerala, India.
Abstract
BACKGROUND: The global incidence of oral cancer occurs in low-resource settings. Community-based oral screening is a strategic step toward downstaging oral cancer by early diagnosis. The mobile health (mHealth) program is a technology-based platform, steered with the aim to assess the use of mHealth by community health workers (CHWs) in the identification of oral mucosal lesions. MATERIALS AND METHODS: mHealth is a mobile phone-based oral cancer-screening program in a workplace setting. The participants were screened by two CHWs, followed by an assessment by an oral medicine specialist. A mobile phone-based questionnaire that included the risk assessment was distributed among participants. On specialist recommendation an oral surgeon performed biopsy on participants. The diagnosis by onsite specialist that was confirmed by histopathology was considered as gold standard. All individuals received the standard treatment protocol. A remote oral medicine specialist reviewed the uploaded data in Open Medical Record System. Sensitivity, specificity, positive and negative predictive values were calculated. Inter-rater agreement was analyzed with Cohen's kappa coefficient (κ) test, and the diagnostic ability of CHWs, onsite specialist, and remote specialist was illustrated using receiver operating characteristic curve. RESULTS: CHWs identified oral lesions in 405 (11.8%) individuals; the onsite specialist identified oral lesions in 394 (11.4%) individuals; and the remote specialist diagnosed oral lesions in 444 (13%). The inter-rater agreement between the CHW and the onsite specialist showed almost perfect agreement with the κ score of 0.92, and a substantial agreement between CHW and remote specialist showed a score of 0.62. The sensitivity, specificity, positive and negative predictive values of CHWs in the identification of oral lesion were 84.7, 97.6, 84.8, and 97.7%, respectively. CONCLUSION: The trained CHWs can aid in identifying oral potentially malignant disorders and they can be utilized in oral cancer-screening program mHealth effectively.
BACKGROUND: The global incidence of oral cancer occurs in low-resource settings. Community-based oral screening is a strategic step toward downstaging oral cancer by early diagnosis. The mobile health (mHealth) program is a technology-based platform, steered with the aim to assess the use of mHealth by community health workers (CHWs) in the identification of oral mucosal lesions. MATERIALS AND METHODS: mHealth is a mobile phone-based oral cancer-screening program in a workplace setting. The participants were screened by two CHWs, followed by an assessment by an oral medicine specialist. A mobile phone-based questionnaire that included the risk assessment was distributed among participants. On specialist recommendation an oral surgeon performed biopsy on participants. The diagnosis by onsite specialist that was confirmed by histopathology was considered as gold standard. All individuals received the standard treatment protocol. A remote oral medicine specialist reviewed the uploaded data in Open Medical Record System. Sensitivity, specificity, positive and negative predictive values were calculated. Inter-rater agreement was analyzed with Cohen's kappa coefficient (κ) test, and the diagnostic ability of CHWs, onsite specialist, and remote specialist was illustrated using receiver operating characteristic curve. RESULTS: CHWs identified oral lesions in 405 (11.8%) individuals; the onsite specialist identified oral lesions in 394 (11.4%) individuals; and the remote specialist diagnosed oral lesions in 444 (13%). The inter-rater agreement between the CHW and the onsite specialist showed almost perfect agreement with the κ score of 0.92, and a substantial agreement between CHW and remote specialist showed a score of 0.62. The sensitivity, specificity, positive and negative predictive values of CHWs in the identification of oral lesion were 84.7, 97.6, 84.8, and 97.7%, respectively. CONCLUSION: The trained CHWs can aid in identifying oral potentially malignant disorders and they can be utilized in oral cancer-screening program mHealth effectively.
Entities:
Keywords:
Early detection; oral cancer screening; oral medicine specialist; oral potentially malignant disorders; telemedicine
Authors: Tanya Walsh; Saman Warnakulasuriya; Mark W Lingen; Alexander R Kerr; Graham R Ogden; Anne-Marie Glenny; Richard Macey Journal: Cochrane Database Syst Rev Date: 2021-12-10
Authors: Mary E Northridge; Kera F Weiserbs; Shabnam Seyedzadeh Sabounchi; Andrea Torroni; Nathalie S Mohadjeri-Franck; Steven Gargano; Eliot George; Tina C Littlejohn; Andrea B Troxel; Yinxiang Wu; Paul A Testa; Jennifer Wismer; Kiah Zaremba; Peter Tylawsky; Babak Bina Journal: J Public Health Res Date: 2022-08-30