Assessment of demographic and perinatal predictors of non-response and impact of non-response on measures of association in a population-based case control study: Findings from the Georgia Study to Explore Early Development
Open accessAbstract
Background: Participation in epidemiologic studies has declined, raising concerns about selection bias. While estimates derived from epidemiologic studies have been shown to be robust under a wide range of scenarios, additional empiric study is needed. The Georgia Study to Explore Early Development (GA SEED), a population-based case–control study of risk factors for autism spectrum disorder (ASD), provided an opportunity to explore factors associated with non-participation and potential impacts of non-participation on association studies. Methods: GA SEED recruited preschool-aged children residing in metropolitan-Atlanta during 2007–2012. Children with ASD were identified from multiple schools and healthcare providers serving children with disabilities; children from the general population (POP) were randomly sampled from birth records. Recruitment was via mailed invitation letter with follow-up phone calls. Eligibility criteria included birth and current residence in study area and an English-speaking caregiver. Many children identified for potential inclusion could not be contacted. We used data from birth certificates to examine demographic and perinatal factors associated with participation in GA SEED and completion of the data collection protocol. We also compared ASD-risk factor associations for the final sample of children who completed the study with the initial sample of all likely ASD and POP children invited to potentially participate in the study, had they been eligible. Finally, we derived post-stratification sampling weights for participants who completed the study and compared weighted and unweighted associations between ASD and two factors collected via post-enrollment maternal interview: infertility and reproductive stoppage. Results: Maternal age and education were independently associated with participation in the POP group. Maternal education was independently associated with participation in the ASD group. Numerous other demographic and perinatal factors were not associated with participation. Moreover, unadjusted and adjusted odds ratios for associations between ASD and several demographic and perinatal factors were similar between the final sample of study completers and the total invited sample. Odds ratios for associations between ASD and infertility and reproductive stoppage were also similar in unweighted and weighted analyses of the study completion sample. Conclusions: These findings suggest that effect estimates from SEED risk factor analyses, particularly those of non-demographic factors, are likely robust.
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At one site, a responder-non-responder comparison showed that maternal age, education, and race/ethnicity predicted participation, but adjusted risk-factor analyses were largely robust to non-response bias.
“However, an analysis that compared characteristics of responders and non-responders at one site found that, although maternal age, education, and race/ethnicity predicted study participation, risk factor analyses adjusted for these sociodemographic variables were generally robust to the impact of non-response bias (61).”
From Maternal Psychiatric Conditions, Treatment With Selective Serotonin Reuptake Inhibitors, and Neurodevelopmental Disorders (2021) · cites this paper as [61]
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Participation Rates in Epidemiologic Studies
Galea, S., & Tracy, M. (2007) · Annals of Epidemiology · doi:10.1016/j.annepidem.2007.03.013 · Cited by 1,877 · see citing works
Consequences of reducing nonresponse in a national telephone survey.
Keeter, S., Miller, C., Kohut, A., Groves, R., & Presser, S. (2000) · Public Opinion Quarterly · doi:10.1086/317759 · Cited by 1,063 · see citing works
Reporting Participation in Epidemiologic Studies: A Survey of Practice
Morton, L. M., Cahill, J., & Hartge, P. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj036 · Cited by 461 · see citing works
Cheung, K. L., ten Klooster, P. M., Smit, C., de Vries, H., & Pieterse, M. E. (2017) · BMC Public Health · doi:10.1186/s12889-017-4189-8 · Cited by 421 · see citing works
Hertz‐Picciotto, I., Croen, L., Hansen, R. L., Jones, C. R., de Water, J. V., & Pessah, I. N. (2006) · Environmental Health Perspectives · doi:10.1289/ehp.8483 · Cited by 420 · see citing works
Epidemiology of autism in adults across age groups and ability levels
Brugha, T. S., Spiers, N., Bankart, J., Cooper, S.-A., McManus, S., Scott, F. J., Smith, J., & Tyrer, F. (2016) · British Journal of Psychiatry · doi:10.1192/bjp.bp.115.174649 · Cited by 299 · see citing works
Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression
Bartlett, J. W., Harel, O., & Carpenter, J. R. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv114 · Cited by 161 · see citing works
Schendel, D. E., DiGuiseppi, C., Croen, L. A., Fallin, M. D., Reed, P. L., Schieve, L. A., Wiggins, L. D., Daniels, J., Grether, J., Levy, S. E., et al. (2012) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-012-1461-8 · Cited by 154 · see citing works
Self-citationEvaluating the impact of non-response bias in the Behavioral Risk Factor Surveillance System (BRFSS)
Schneider, K. L., Clark, M. A., Rakowski, W., & Lapane, K. L. (2010) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2009.103861 · Cited by 152 · see citing works
Wireless substitution: state-level estimates from the National Health Interview Survey, 2010-2011.
Blumberg, S. J., Luke, J. V., Ganesh, N., Davern, M. E., & Boudreaux, M. H. (2012) · PubMed · Cited by 121 · see citing works
Evaluation of Selection Bias in an Internet-based Study of Pregnancy Planners
Hatch, E. E., Hahn, K. A., Wise, L. A., Mikkelsen, E. M., Kumar, R., Fox, M. P., Brooks, D. R., Riis, A. H., Sørensen, H. T., & Rothman, K. J. (2015) · Epidemiology · doi:10.1097/ede.0000000000000400 · Cited by 94 · see citing works
McGuinn, L. A., Windham, G., Kalkbrenner, A., Bradley, C. B., Di, Q., Croen, L., Fallin, M. D., Hoffman, K., Ladd-Acosta, C., Schwartz, J., et al. (2020) · Epidemiology · doi:10.1097/ede.0000000000001109 · Cited by 72 · see citing works
Self-citationMadigan, M. P., Troisi, R. J., Potischman, N. A., et al. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.5.793 · Cited by 70 · see citing works
Gorman, E., Leyland, A. H., McCartney, G., White, I. R., Katikireddi, S. V., Rutherford, L., Graham, L., & Gray, L. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu207 · Cited by 65 · see citing works
Morbidity in responders and non-responders in a register-based population survey
van den Akker, M. (1998) · Family Practice · doi:10.1093/fampra/15.3.261 · Cited by 48 · see citing works
Geronimus, A. T., Bound, J., & Ro, A. E. (2014) · Demography · doi:10.1007/s13524-014-0299-4 · Cited by 45 · see citing works
Maternal diabetes and hypertensive disorders in association with autism spectrum disorder
Cordero, C., Windham, G. C., Schieve, L. A., Fallin, M. D., Croen, L., Siega‐Riz, A. M., Engel, S. M., Herring, A. H., Stuebe, A. M., Vladutiu, C. J., et al. (2019) · Autism Research · doi:10.1002/aur.2105 · Cited by 42 · see citing works
Self-citationThe impact of selection bias on vaccine effectiveness estimates from test-negative studies
Jackson, M. L., Phillips, C. H., Benoit, J., Kiniry, E. L., Madziwa, L. T., Nelson, J. C., & Jackson, L. A. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.12.022 · Cited by 41 · see citing works
The effect of nonresponse on estimation of relative risk in a case-control study
Kreiger, N., & Nishri, E. D. (1997) · Annals of Epidemiology · doi:10.1016/s1047-2797(97)00013-6 · Cited by 33 · see citing works
Rueegg, C. S., Gianinazzi, M. E., Michel, G., Zwahlen, M., von der Weid, N., Kuehni, C. E., & and the Swiss Paediatric Oncology Group (SPOG) (2017) · PLoS ONE · doi:10.1371/journal.pone.0176442 · Cited by 31 · see citing works
Aigner, A., Grittner, U., & Becher, H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191327 · Cited by 25 · see citing works
Hurley, S. E., Reynolds, P., Goldberg, D. E., et al. (2004) · Social Science & Medicine · doi:10.1016/j.socscimed.2004.07.018 · Cited by 23 · see citing works
Xu, M.-T., Richardson, L., Campbell, S., Pintos, J., & Siemiatycki, J. (2018) · Annals of Epidemiology · doi:10.1016/j.annepidem.2018.04.001 · Cited by 20 · see citing works
Using longitudinal data to estimate nonresponse bias
Page, W. F. (1991) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/bf00782951 · Cited by 10 · see citing works
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- Authors
- Schieve, L. A., Harris, S., Maenner, M. J., Alexander, A., & Dowling, N. F.
- Affiliations
- Centers for Disease Control and Prevention (US)
- Corresponding author
- Schieve, Laura A.
- Venue
- Emerging Themes in Epidemiology
- Published
- 2018-08-16
- Volume
- 15
- Issue
- 1
- Pages
- 12-12
- ISSN
- 1742-7622 (ISSN-L)
- PMC
- PMC6094575
- OpenAlex
- W2885439989
- Cited by
- 12
- References
- 24
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2021: 4 · 2019: 4
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Emerging Themes in Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- selection bias, autism spectrum disorder, risk factors, maternal age, maternal education, infertility, perinatal factors, research participation
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