Linking population-based cohorts with cancer registries in LMIC: a case study and lessons learnt in India
Year: 2023
Type: article
Abstract: OBJECTIVES: In resource-constrained settings, cancer epidemiology research typically relies on self-reported diagnoses. To test a more systematic alternative approach, we assessed the feasibility of linking a cohort with a cancer registry. SETTING: Data linkage was performed between a population-based cohort in Chennai, India, with a local population-based cancer registry. PARTICIPANTS: Data set of Centre for Cardiometabolic Risk Reduction in South-Asia (CARRS) cohort participants (N=11 772) from Chennai was linked with the cancer registry data set for the period 1982-2015 (N=140 986). METHODS AND OUTCOME MEASURES: Match*Pro, a probabilistic record linkage software, was used for computerised linkages followed by manual review of high scoring records. The variables used for linkage included participant name, gender, age, address, Postal Index Number and father's and spouse's name. Registry records between 2010 and 2015 and between 1982 and 2015, respectively, represented incident and all (both incident and prevalent) cases. The extent of agreement between self-reports and registry-based ascertainment was expressed as the proportion of cases found in both data sets among cases identified independently in each source. RESULTS: There were 52 self-reported cancer cases among 11 772 cohort participants, but 5 cases were misreported. Of the remaining 47 eligible self-reported cases (incident and prevalent), 37 (79%) were confirmed by registry linkage. Among 29 self-reported incident cancers, 25 (86%) were found in the registry. Registry linkage also identified 24 previously not reported cancers; 12 of those were incident cases. The likelihood of linkage was higher in more recent years (2014-2015). CONCLUSIONS: Although linkage variables in this study had limited discriminatory power in the absence of a unique identifier, an appreciable proportion of self-reported cases were confirmed in the registry via linkages. More importantly, the linkages also identified many previously unreported cases. These findings offer new insights that can inform future cancer surveillance and research in low-income and middle-income countries.
Source: BMJ Open
Authors: Aastha Aggarwal, Ranganathan Rama, Preet K Dhillon, Mohan Deepa, Dimple Kondal, Naveen Kaushik, Dipika Bumb, Ravi Mehrotra, Betsy A Kohler, Viswanathan Mohan, Theresa W Gillespie, Alpa V Patel, Swaminathan Rajaraman, Dorairaj Prabhakaran, Kevin C Ward, Michael Goodman
Institutions: Centre for Chronic Disease Control, Public Health Foundation of India, Cancer Institute (WIA), Genentech, Madras Diabetes Research Foundation, North American Association of Central Cancer Registries, Dr. Mohan's Diabetes Specialities Centre, Emory University, Winship Cancer Institute, American Cancer Society
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,839
Award: G5893987255
OpenAlex ID: W4323348268
