Identification and inclusion of gender factors in retrospective cohort studies: the GOING-FWD framework. Issue 4 (9th April 2021)

• Record Type: Journal Article
• Authors: Raparelli, Valeria; Norris, Colleen M.; Bender, Uri; Herrero, Maria Trinidad; Kautzky-Willer, Alexandra; Kublickiene, Karolina; El Emam, Khaled; Pilote, Louise
• Other Names: author non-byline.; Humphries Karin H author non-byline.; Parry Monica author non-byline.; Sapir-Pichhadze Ruth author non-byline.; Abrahamowicz Michal author non-byline.; Bacon Simon author non-byline.; Klimek Peter author non-byline.; Fishman Jennifer author non-byline.; Clair Carole author non-byline.; Dryer Rachel P. author non-byline.; Tadiri Christina P. author non-byline.; Azizi Zahra author non-byline.; Dev Rubee author non-byline.; Alipour Pouria author non-byline.; Jalal Sabeena author non-byline.; Vecchia Alexia Della author non-byline.; Stojanovic Jovana author non-byline.; Hemani Salima author non-byline.; Burnside Heather author non-byline.; Deschinger Carola author non-byline.; Harreiter Juergen author non-byline.; Lindner Simon D. author non-byline.; Gisinger Teresa author non-byline.; Tosti Giulia author non-byline.; Tucci Claudia author non-byline.; Romiti Giulio Francesco author non-byline.; Laučytė-Cibulskiene Agne author non-byline.; Ward Liam author non-byline.; Muñoz Leah author non-byline.; Leon Raquel Gomez De author non-byline.; Lucas Ana Maria author non-byline.; Gayoso Sonia author non-byline.; Nieto Raúl author non-byline.; Sanchez Maria author non-byline.; Amador Sandra author non-byline.; Rochel Cristina author non-byline.; Hart Donna author non-byline.; Hartman/Nickerson Nicole author non-byline.; Fullerton/MacCaul Angie author non-byline.; Smith Jeanette author non-byline.; Lefkowitz Myra author non-byline.; Keir Ann author non-byline.; Warkentin Kyle author non-byline.; Manion Rachael author non-byline.; Regitz-Zagrosek Vera author non-byline.; Schiebinger Londa author non-byline.;
• Is Part Of: BMJ global health. Volume 6:Issue 4(2021)
• Publication Date: 2021-04-09
• Subjects: epidemiology -- public health -- health policies and all other topics -- cohort study; World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2021-005413
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : Gender refers to the socially constructed roles, behaviours, expressions and identities of girls, women, boys, men and gender diverse people. Gender-related factors are seldom assessed as determinants of health outcomes, despite their powerful contribution. The Gender Outcomes INternational Group: to Further Well-being Development (GOING-FWD) project developed a standard five-step methodology applicable to retrospectively identify gender-related factors and assess their relationship to outcomes across selected cohorts of non-communicable chronic diseases from Austria, Canada, Spain, Sweden. Step 1 (identification of gender-related variables): Based on the gender framework of the Women Health Research Network (ie, identity, role, relations and institutionalised gender), and available literature for a certain disease, an optimal 'wish-list' of gender-related variables was created and discussed by experts. Step 2 (definition of outcomes): Data dictionaries were screened for clinical and patient-relevant outcomes, using the International Consortium for Health Outcome Measurement framework. Step 3 (building of feasible final list): a cross-validation between variables per database and the 'wish-list' was performed. Step 4 (retrospective data harmonisation): The harmonisation potential of variables was evaluated. Step 5 (definition of data structure and analysis): The following analytic strategies were identified: (1) local analysis of data not transferable followed by a meta-analysis combining study-level estimates; (2) centrally performed federated analysis of data, with the individual-level participant data remaining on local servers; (3) synthesising the data locally and performing a pooled analysis on the synthetic data and (4) central analysis of pooled transferable data. The application of the GOING-FWD multistep approach can help guide investigators to analyse gender and its impact on outcomes in previously collected data.