Eileen O Dareng1, Yinka Olaniyan2, Sally N Adebamowo3, Olabimpe R Eseyin4, Michael K Odutola4, Elonna M Obiefuna4, Richard A Offiong5, Paul P Pharoah6, Clement A Adebamowo7. 1. Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Office of Strategic Information, Research and Training, Institute of Human Virology, Abuja, Nigeria. 2. Department of Obstetrics and Gynecology, National Hospital, Abuja, Nigeria. 3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA; Marlene and Stewart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA. 4. Office of Strategic Information, Research and Training, Institute of Human Virology, Abuja, Nigeria. 5. Department of Obstetrics and Gynecology, University of Abuja Teaching Hospital, Abuja, Nigeria. 6. Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. 7. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA; Marlene and Stewart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA; Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address: cadebamowo@som.umaryland.edu.
Abstract
OBJECTIVES: We explored determinants of attrition in a longitudinal cohort study in Nigeria. STUDY DESIGN AND SETTING: We enrolled 1,020 women into a prospective study. Of these, 973 were eligible to return for follow-up. We investigated the determinants of attrition among eligible women using a sequential mixed methods design. We used logistic regression models to compare the baseline characteristics of responders and nonresponders. At the end of the parent study, we conducted four focus group discussions and eight key informant interviews with nonresponders. RESULTS: Of the 973 women included in the quantitative analysis, 26% were nonresponders. From quantitative analysis, older women were less likely to drop out than younger women (reference: women ≤30 years; OR 0.46; 95% confidence interval [CI] 0.30-0.70, P < 0.001 women 31-44 years; and OR 0.31; 95% CI 0.17-0.56, P < 0.001 women ≥45 years). HIV-positive women were also less likely to drop out of the study (OR 0.45; 95% CI 0.33-0.63, P < 0.001). From qualitative analysis, contextual factors that influenced attrition were high cost of participation, therapeutic misconceptions, inaccurate expectations, spousal disapproval, unpleasant side effects, challenges in maintaining contact with participants, and participant difficulties in locating the study clinic. CONCLUSION: Several participant-, research-, and environment-related factors influence attrition. Retention strategies that address these barriers are important to minimize attrition.
OBJECTIVES: We explored determinants of attrition in a longitudinal cohort study in Nigeria. STUDY DESIGN AND SETTING: We enrolled 1,020 women into a prospective study. Of these, 973 were eligible to return for follow-up. We investigated the determinants of attrition among eligible women using a sequential mixed methods design. We used logistic regression models to compare the baseline characteristics of responders and nonresponders. At the end of the parent study, we conducted four focus group discussions and eight key informant interviews with nonresponders. RESULTS: Of the 973 women included in the quantitative analysis, 26% were nonresponders. From quantitative analysis, older women were less likely to drop out than younger women (reference: women ≤30 years; OR 0.46; 95% confidence interval [CI] 0.30-0.70, P < 0.001 women 31-44 years; and OR 0.31; 95% CI 0.17-0.56, P < 0.001 women ≥45 years). HIV-positive women were also less likely to drop out of the study (OR 0.45; 95% CI 0.33-0.63, P < 0.001). From qualitative analysis, contextual factors that influenced attrition were high cost of participation, therapeutic misconceptions, inaccurate expectations, spousal disapproval, unpleasant side effects, challenges in maintaining contact with participants, and participant difficulties in locating the study clinic. CONCLUSION: Several participant-, research-, and environment-related factors influence attrition. Retention strategies that address these barriers are important to minimize attrition.
Authors: Eileen O Dareng; Sally N Adebamowo; Ayotunde Famooto; Oluwatoyosi Olawande; Michael K Odutola; Yinka Olaniyan; Richard A Offiong; Paul P Pharoah; Clement A Adebamowo Journal: BMC Infect Dis Date: 2019-01-07 Impact factor: 3.090
Authors: N F Bell-Mandla; R Sloot; G Maarman; S Griffith; A Moore; S Floyd; R Hayes; S Fidler; H Ayles; P Bock Journal: BMC Med Res Methodol Date: 2021-11-08 Impact factor: 4.612
Authors: Eileen O Dareng; Bing Ma; Sally N Adebamowo; Ayotunde Famooto; Jacques Ravel; Paul P Pharoah; Clement A Adebamowo Journal: Sci Rep Date: 2020-11-05 Impact factor: 4.379