“Footprinting” missing epidemiological data for cervical cancer: a case study in India

Abstract

Background Context-specific cervical cancer epidemiological data are essential to derive local impact projections of cervical cancer preventive measures. However, these are not always available, in particular in low- and middle-income countries (LMICs), where impact projections are essential to plan cervical cancer control programs. Methods and Findings We developed a framework, hereafter named Footprinting, to approximate the sexual behavior, human papillomavirus (HPV) prevalence, and/or cervical cancer incidence data needed for impact projections. The framework was applied to a case study in India, the country with the highest expected cervical cancer burden but still limited access to cervical cancer prevention. With our Footprinting 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 behavior data, 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 for 6 and 8 Indian states, respectively. States in the low-incidence cluster were characterized by less sexual activity with non-regular partners in men and earlier sexual debut in women. Based on these patterns, all 11 Indian states with missing cervical cancer incidence data 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. Conclusions With the Footprinting framework, we enabled approximation of missing cervical cancer epidemiological data and derivation of context-specific impact projection of cervical cancer prevention measures, assisting public health decisions on cervical cancer prevention in India and other LMICs.

Building resilient cervical cancer prevention through gender-neutral HPV vaccination

“…Finally, we estimated the impacts for Tamil Nadu and West Bengal and extrapolated these to other states within each cluster. More details on model calibration can be found in previous publications (Man, Georges, Bonjour, & Baussano, 2022; Man, Georges, de Carvalho, et al, 2022) and in Appendices A.1-3. This study adheres to HPV-FRAME, a quality framework for modelled evaluations of HPV-related cancer control (Appendix A.4) (Canfell et al, 2019).…”

Health and economic effects of introducing single-dose or two-dose human papillomavirus vaccination in India

“…Since not all Indian states had high-quality data on cervical cancer incidence, we derived state-level projections in three steps: (1) we identified clusters of states with high or low cancer incidence; (2) we calibrated EpiMetHeos to type-specific HPV prevalence and sexual behaviour data from a representative high (Tamil Nadu) and low (West Bengal) cancer incidence state (online supplemental figure A.2); (3) we used the resulting projections to extrapolate cancer incidence to other states within the same cluster. Details about the calibration and clustering are provided elsewhere 16 22…”

Health and economic effects of introducing single-dose human papillomavirus vaccination in India

“…Details about the calibration and clustering are provided elsewhere. 22,23 Furthermore, we adjusted the cancer detection rates to match the observed cervical cancer stage distribution in India, and we used key Indian demographic and epidemiological data, including background mortality rate, age distribution and hysterectomy rate. Finally, we extracted 5-year Indian cancer survival based on a literature review (Online Supplement Tables A.2-A.4).…”