Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India
Local cervical cancer epidemiological data essential to project the context-specific impact of cervical cancer preventive measures are often missing. We developed a framework, hereafter named Footprinting, to approximate missing data on sexual behaviour, human papillomavirus (HPV) prevalence, or cer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-05-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/81752 |
| _version_ | 1797820341049884672 |
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| author | Irene Man Damien Georges Maxime Bonjour Iacopo Baussano |
| author_facet | Irene Man Damien Georges Maxime Bonjour Iacopo Baussano |
| author_sort | Irene Man |
| collection | DOAJ |
| description | Local cervical cancer epidemiological data essential to project the context-specific impact of cervical cancer preventive measures are often missing. We developed a framework, hereafter named Footprinting, to approximate missing data on sexual behaviour, human papillomavirus (HPV) prevalence, or cervical cancer incidence, and applied it to an Indian case study. With our framework, we (1) identified clusters of Indian states with similar cervical cancer incidence patterns, (2) classified states without incidence data to the identified clusters based on similarity in sexual behaviour, (3) approximated missing cervical cancer incidence and HPV prevalence data based on available data within each cluster. Two main patterns of cervical cancer incidence, characterized by high and low incidence, were identified. Based on the patterns in the sexual behaviour data, all Indian states with missing data on cervical cancer incidence were classified to the low-incidence cluster. Finally, missing data on cervical cancer incidence and HPV prevalence were approximated based on the mean of the available data within each cluster. With the Footprinting framework, we approximated missing cervical cancer epidemiological data and made context-specific impact projections for cervical cancer preventive measures, to assist public health decisions on cervical cancer prevention in India and other countries. |
| first_indexed | 2024-03-13T09:36:58Z |
| format | Article |
| id | doaj.art-fc1ee01e0ffa495ea61e3d4b2ba72f76 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-13T09:36:58Z |
| publishDate | 2023-05-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-fc1ee01e0ffa495ea61e3d4b2ba72f762023-05-25T13:21:15ZengeLife Sciences Publications LtdeLife2050-084X2023-05-011210.7554/eLife.81752Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in IndiaIrene Man0https://orcid.org/0000-0003-3177-6904Damien Georges1Maxime Bonjour2Iacopo Baussano3https://orcid.org/0000-0002-7322-1862Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, FranceEarly Detection, Prevention and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, FranceEarly Detection, Prevention and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, FranceEarly Detection, Prevention and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, FranceLocal cervical cancer epidemiological data essential to project the context-specific impact of cervical cancer preventive measures are often missing. We developed a framework, hereafter named Footprinting, to approximate missing data on sexual behaviour, human papillomavirus (HPV) prevalence, or cervical cancer incidence, and applied it to an Indian case study. With our framework, we (1) identified clusters of Indian states with similar cervical cancer incidence patterns, (2) classified states without incidence data to the identified clusters based on similarity in sexual behaviour, (3) approximated missing cervical cancer incidence and HPV prevalence data based on available data within each cluster. Two main patterns of cervical cancer incidence, characterized by high and low incidence, were identified. Based on the patterns in the sexual behaviour data, all Indian states with missing data on cervical cancer incidence were classified to the low-incidence cluster. Finally, missing data on cervical cancer incidence and HPV prevalence were approximated based on the mean of the available data within each cluster. With the Footprinting framework, we approximated missing cervical cancer epidemiological data and made context-specific impact projections for cervical cancer preventive measures, to assist public health decisions on cervical cancer prevention in India and other countries.https://elifesciences.org/articles/81752cervical cancer incidenceHPV prevalencesexual behaviourimpact projectionclusteringclassification |
| spellingShingle | Irene Man Damien Georges Maxime Bonjour Iacopo Baussano Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India eLife cervical cancer incidence HPV prevalence sexual behaviour impact projection clustering classification |
| title | Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India |
| title_full | Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India |
| title_fullStr | Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India |
| title_full_unstemmed | Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India |
| title_short | Approximating missing epidemiological data for cervical cancer through Footprinting: A case study in India |
| title_sort | approximating missing epidemiological data for cervical cancer through footprinting a case study in india |
| topic | cervical cancer incidence HPV prevalence sexual behaviour impact projection clustering classification |
| url | https://elifesciences.org/articles/81752 |
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