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Published in: Emerging Themes in Epidemiology 1/2018

Open Access 01-12-2018 | Research article

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

Authors: Laura A. Schieve, Shericka Harris, Matthew J. Maenner, Aimee Alexander, Nicole F. Dowling

Published in: Emerging Themes in Epidemiology | Issue 1/2018

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Abstract

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.
Literature
1.
go back to reference Morton LM, Cahill J, Hartge P. Reporting participation in epidemiologic studies: a survey of practice. Am J Epidemiol. 2006;163(3):197–203.CrossRefPubMed Morton LM, Cahill J, Hartge P. Reporting participation in epidemiologic studies: a survey of practice. Am J Epidemiol. 2006;163(3):197–203.CrossRefPubMed
2.
go back to reference Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17(9):643–53.CrossRefPubMed Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17(9):643–53.CrossRefPubMed
3.
go back to reference Xu M, Richardson L, Campbell S, Pintos J, Siemiatycki J. Response rates in case-control studies of cancer by era of fieldwork and by characteristics of study design. Ann Epidemiol. 2018;28(6):385–91.CrossRefPubMed Xu M, Richardson L, Campbell S, Pintos J, Siemiatycki J. Response rates in case-control studies of cancer by era of fieldwork and by characteristics of study design. Ann Epidemiol. 2018;28(6):385–91.CrossRefPubMed
4.
go back to reference Bartlett JW, Harel O, Carpenter JR. Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression. Am J Epidemiol. 2015;182(8):730–6.CrossRefPubMedPubMedCentral Bartlett JW, Harel O, Carpenter JR. Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression. Am J Epidemiol. 2015;182(8):730–6.CrossRefPubMedPubMedCentral
5.
go back to reference van den Akker M, Buntinx F, Metsemakers JF, Knottnerus JA. Morbidity in responders and non-responders in a register-based population survey. Fam Pract. 1998;15(3):261–3.CrossRefPubMed van den Akker M, Buntinx F, Metsemakers JF, Knottnerus JA. Morbidity in responders and non-responders in a register-based population survey. Fam Pract. 1998;15(3):261–3.CrossRefPubMed
6.
go back to reference Keeter S, Miller C, Kohut A, Groves RM, Presser S. Consequences of reducing nonresponse in a national telephone survey. Public Opin Q. 2000;64(2):125–48.CrossRefPubMed Keeter S, Miller C, Kohut A, Groves RM, Presser S. Consequences of reducing nonresponse in a national telephone survey. Public Opin Q. 2000;64(2):125–48.CrossRefPubMed
8.
go back to reference Kreiger N, Nishri ED. The effect of nonresponse on estimation of relative risk in a case-control study. Ann Epidemiol. 1997;7(3):194–9.CrossRefPubMed Kreiger N, Nishri ED. The effect of nonresponse on estimation of relative risk in a case-control study. Ann Epidemiol. 1997;7(3):194–9.CrossRefPubMed
9.
go back to reference Page WF. Using longitudinal data to estimate nonresponse bias. Soc Psychiatry Psychiatr Epidemiol. 1991;26(3):127–31.CrossRefPubMed Page WF. Using longitudinal data to estimate nonresponse bias. Soc Psychiatry Psychiatr Epidemiol. 1991;26(3):127–31.CrossRefPubMed
10.
go back to reference Madigan MP, Troisi R, Potischman N, Brogan D, Gammon MD, Malone KE, Brinton LA. Characteristics of respondents and non-respondents from a case-control study of breast cancer in younger women. Int J Epidemiol. 2000;29(5):793–8.CrossRefPubMed Madigan MP, Troisi R, Potischman N, Brogan D, Gammon MD, Malone KE, Brinton LA. Characteristics of respondents and non-respondents from a case-control study of breast cancer in younger women. Int J Epidemiol. 2000;29(5):793–8.CrossRefPubMed
11.
go back to reference Hatch EE, Hahn KA, Wise LA, Mikkelsen EM, Kumar R, Fox MP, Brooks DR, Riis AH, Sorensen HT, Rothman KJ. Evaluation of selection bias in an internet-based study of pregnancy planners. Epidemiology. 2016;27(1):98–104.CrossRefPubMedPubMedCentral Hatch EE, Hahn KA, Wise LA, Mikkelsen EM, Kumar R, Fox MP, Brooks DR, Riis AH, Sorensen HT, Rothman KJ. Evaluation of selection bias in an internet-based study of pregnancy planners. Epidemiology. 2016;27(1):98–104.CrossRefPubMedPubMedCentral
13.
go back to reference Brugha TS, Spiers N, Bankart J, Cooper SA, McManus S, Scott FJ, Smith J, Tyrer F. Epidemiology of autism in adults across age groups and ability levels. Br J Psychiatry. 2016;209(6):498–503.CrossRefPubMed Brugha TS, Spiers N, Bankart J, Cooper SA, McManus S, Scott FJ, Smith J, Tyrer F. Epidemiology of autism in adults across age groups and ability levels. Br J Psychiatry. 2016;209(6):498–503.CrossRefPubMed
14.
go back to reference Schneider KL, Clark MA, Rakowski W, Lapane KL. Evaluating the impact of non-response bias in the Behavioral Risk Factor Surveillance System (BRFSS). J Epidemiol Community Health. 2012;66(4):290–5.CrossRefPubMed Schneider KL, Clark MA, Rakowski W, Lapane KL. Evaluating the impact of non-response bias in the Behavioral Risk Factor Surveillance System (BRFSS). J Epidemiol Community Health. 2012;66(4):290–5.CrossRefPubMed
15.
go back to reference Gorman E, Leyland AH, McCartney G, White IR, Katikireddi SV, Rutherford L, Graham L, Gray L. Assessing the representativeness of population-sampled health surveys through linkage to administrative data on alcohol-related outcomes. Am J Epidemiol. 2014;180(9):941–8.CrossRefPubMedPubMedCentral Gorman E, Leyland AH, McCartney G, White IR, Katikireddi SV, Rutherford L, Graham L, Gray L. Assessing the representativeness of population-sampled health surveys through linkage to administrative data on alcohol-related outcomes. Am J Epidemiol. 2014;180(9):941–8.CrossRefPubMedPubMedCentral
17.
go back to reference Geronimus AT, Bound J, Ro A. Residential mobility across local areas in the United States and the geographic distribution of the healthy population. Demography. 2014;51(3):777–809.CrossRefPubMedPubMedCentral Geronimus AT, Bound J, Ro A. Residential mobility across local areas in the United States and the geographic distribution of the healthy population. Demography. 2014;51(3):777–809.CrossRefPubMedPubMedCentral
18.
go back to reference Hurley SE, Reynolds P, Goldberg DE, Hertz A, Anton-Culver H, Bernstein L, Deapen D, Peel D, Pinder R, Ross RK, West D, Wright WE, Ziogas A, Horn-Ross PL. Residential mobility in the California Teachers Study: implications for geographic differences in disease rates. Soc Sci Med. 2005;60(7):1547–55.CrossRefPubMed Hurley SE, Reynolds P, Goldberg DE, Hertz A, Anton-Culver H, Bernstein L, Deapen D, Peel D, Pinder R, Ross RK, West D, Wright WE, Ziogas A, Horn-Ross PL. Residential mobility in the California Teachers Study: implications for geographic differences in disease rates. Soc Sci Med. 2005;60(7):1547–55.CrossRefPubMed
19.
go back to reference Blumberg SJ, Ganesh N, Luke JV, Gonzales G. Wireless substitution: state-level estimates from the National Health Interview Survey, 2012. Natl Health Stat Rep. 2013;70:1–16. Blumberg SJ, Ganesh N, Luke JV, Gonzales G. Wireless substitution: state-level estimates from the National Health Interview Survey, 2012. Natl Health Stat Rep. 2013;70:1–16.
20.
go back to reference Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M. The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network. J Autism Dev Disord. 2012;42(10):2121–40.CrossRefPubMedPubMedCentral Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M. The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network. J Autism Dev Disord. 2012;42(10):2121–40.CrossRefPubMedPubMedCentral
22.
go back to reference Hertz-Picciotto I, Croen LA, Hansen R, Jones CR, van de Water J, Pessah IN. The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environ Health Perspect. 2006;114(7):1119–25.CrossRefPubMedPubMedCentral Hertz-Picciotto I, Croen LA, Hansen R, Jones CR, van de Water J, Pessah IN. The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environ Health Perspect. 2006;114(7):1119–25.CrossRefPubMedPubMedCentral
Metadata
Title
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
Authors
Laura A. Schieve
Shericka Harris
Matthew J. Maenner
Aimee Alexander
Nicole F. Dowling
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Emerging Themes in Epidemiology / Issue 1/2018
Electronic ISSN: 1742-7622
DOI
https://doi.org/10.1186/s12982-018-0081-y

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