Gaëlle Santin1, Béatrice Geoffroy2, Laetitia Bénézet2, Pauline Delézire2, Juliette Chatelot2, Rémi Sitta3, Jean Bouyer4, Alice Gueguen3. 1. Department of Occupational Health, InVS French Institute for Public Health Surveillance, 12 rue du Val d'Osne, F-94415 Saint-Maurice, France. Electronic address: g.santin@invs.sante.fr. 2. Department of Occupational Health, InVS French Institute for Public Health Surveillance, 12 rue du Val d'Osne, F-94415 Saint-Maurice, France. 3. CESP Center for Research in Epidemiology and Population Health U1018-Population-Based Cohorts' Research Platform, INSERM, Hôpital Paul Brousse, 16 Avenue Paul Vaillant-Couturier, F-94807 Villejuif, France. 4. CESP Center for Research in Epidemiology and Population Health U1018-Reproduction, Child Development Research Platform, INSERM, Hôpital Kremlin Bicêtre, 82 rue du Général Leclerc F-94276 Le Kremlin-Bicêtre, France.
Abstract
OBJECTIVES: To show how reweighting can correct for unit nonresponse bias in an occupational health surveillance survey by using data from administrative databases in addition to classic sociodemographic data. STUDY DESIGN AND SETTING: In 2010, about 10,000 workers covered by a French health insurance fund were randomly selected and were sent a postal questionnaire. Simultaneously, auxiliary data from routine health insurance and occupational databases were collected for all these workers. To model the probability of response to the questionnaire, logistic regressions were performed with these auxiliary data to compute weights for correcting unit nonresponse. Corrected prevalences of questionnaire variables were estimated under several assumptions regarding the missing data process. The impact of reweighting was evaluated by a sensitivity analysis. RESULTS: Respondents had more reimbursement claims for medical services than nonrespondents but fewer reimbursements for medical prescriptions or hospitalizations. Salaried workers, workers in service companies, or who had held their job longer than 6 months were more likely to respond. Corrected prevalences after reweighting were slightly different from crude prevalences for some variables but meaningfully different for others. CONCLUSION: Linking health insurance and occupational data effectively corrects for nonresponse bias using reweighting techniques. Sociodemographic variables may be not sufficient to correct for nonresponse.
OBJECTIVES: To show how reweighting can correct for unit nonresponse bias in an occupational health surveillance survey by using data from administrative databases in addition to classic sociodemographic data. STUDY DESIGN AND SETTING: In 2010, about 10,000 workers covered by a French health insurance fund were randomly selected and were sent a postal questionnaire. Simultaneously, auxiliary data from routine health insurance and occupational databases were collected for all these workers. To model the probability of response to the questionnaire, logistic regressions were performed with these auxiliary data to compute weights for correcting unit nonresponse. Corrected prevalences of questionnaire variables were estimated under several assumptions regarding the missing data process. The impact of reweighting was evaluated by a sensitivity analysis. RESULTS: Respondents had more reimbursement claims for medical services than nonrespondents but fewer reimbursements for medical prescriptions or hospitalizations. Salaried workers, workers in service companies, or who had held their job longer than 6 months were more likely to respond. Corrected prevalences after reweighting were slightly different from crude prevalences for some variables but meaningfully different for others. CONCLUSION: Linking health insurance and occupational data effectively corrects for nonresponse bias using reweighting techniques. Sociodemographic variables may be not sufficient to correct for nonresponse.
Authors: Emma Gorman; Alastair H Leyland; Gerry McCartney; Ian R White; Srinivasa Vittal Katikireddi; Lisa Rutherford; Lesley Graham; Linsay Gray Journal: Am J Epidemiol Date: 2014-09-16 Impact factor: 4.897