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Abortion Underreporting in Add Health: Findings and Implications

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Abstract

Well-documented, large-scale abortion underreporting on U.S. surveys raises questions about the use of abortion self-reports for statistical inference. This paper is the first to evaluate the completeness of the abortion data in the National Longitudinal Study of Adolescent to Adult Health (Add Health). Comparisons of Add Health’s estimated abortion rates to external sources show that the Add Health data capture 35% of expected abortions. Thus, Add Health performed no better than other surveys in collecting abortion data. Further, no differences in underreporting by race/ethnicity or age at abortion were found. We suggest that the current U.S. social environment generates high levels of abortion stigma, which yields abortion underreporting. We conclude that due to underreporting, survey self-reports of abortion need to be evaluated, contextualized, and used with caution.

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Notes

  1. Specifically, Udry et al. (1996) found 35% women reported more abortions than observed in the insurance data (the source of the external estimates). This unexpected finding, amidst trends of underreporting, may be due to how women pay for abortions. For instance, Jerman et al. (2016) show women tend to self-pay for abortions, which would prevent these abortions from being recorded in the insurer’s data.

  2. This age range varies slightly. For example, Jones and Forrest (1992) find underreporting among younger ages, likely reflecting differences in normative childbearing years since the authors analyzed data from 1976 to 1988. Additionally, Tennekoon (2017) found underreporting among older women, but the author used age at survey rather than age at abortion.

  3. Differential attrition and non-response bias analyses have been conducted at each wave and have concluded that patterns of non-response do not bias point estimates of measured health and health behavior indicators (Brownstein et al. 2010; Chantala et al. 2005; Kalsbeek et al. 2001, 2002).

  4. Because the CDC data are provided in age ranges, the Add Health data were also restricted to best match these ranges (see Appendix 1 in Supplementary Material). Spline and linear interpolated age estimates from the Guttmacher Institute’s survey of abortion patients were compared with the CDC-matched estimates used for these analyses to test robustness (see Appendix 2 in Supplementary Material).

  5. Recently, Tennekoon (2017) proposed a modeling approach to analyzing abortion reporting, which could help address this methodological limitation. Unfortunately, these models are sensitive to parameterization. For instance, in Tennekoon’s (2017) analyses of the National Survey of Family Growth, their robustness tests yielded reporting rates ranging from 25.82% to 67.85%, depending on the model’s specifications. Thus, this model carries limitations of its own.

References

  • Allison, P. D. (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods & Research, 28(3), 301–309.

    Article  Google Scholar 

  • Astbury-Ward, E., Parry, O., & Carnwell, R. (2012). Stigma, abortion, and disclosure—Findings from a qualitative study. The Journal of Sexual Medicine, 9(12), 3137–3147.

    Article  Google Scholar 

  • Brownstein, N., Kalsbeek, W. D., Tabor, J., Entzel, P., Daza, E., & Harris, K. M. (2010). Non-response in wave IV of the National Longitudinal Study of Adolescent Health. Chapel Hill, NC: Carolina Population Center.

    Google Scholar 

  • Chantala, K., Kalsbeek, W. D., & Andraca, E. (2005). Non-response in wave II of the Add Health study. Chapel Hill, NC: Carolina Population Center.

    Google Scholar 

  • Cockrill, K., Upadhyay, U. D., Turan, J., & Foster, D. G. (2013). The stigma of having an abortion: Development of a scale and characteristics of women experiencing abortion stigma. Perspectives on Sexual and Reproductive Health, 45(2), 79–88.

    Article  Google Scholar 

  • Coutts, E., & Jann, B. (2011). Sensitive questions in online surveys: Experimental results for the randomized response technique (RRT) and the unmatched count technique (UCT). Sociological Methods & Research, 40(1), 169–193.

    Article  Google Scholar 

  • Fu, H., Darroch, J. E., Henshaw, S. K., & Kolb, E. (1998). Measuring the extent of abortion underreporting in the 1995 national survey of family growth. Family Planning Perspectives, 30(3), 128–138.

    Article  Google Scholar 

  • Goffman, E. (1963). Stigma: Notes on the management of spoiled identity. New York: Simon and Schuster.

    Google Scholar 

  • Grossman, D. (2017). Sexual and reproductive health under the trump presidency: Policy change threatens women in the USA and worldwide. Journal of Family Planning and Reproductive Health Care, 43(2), 89–91.

    Article  Google Scholar 

  • Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.

    Article  Google Scholar 

  • Guttmacher Institute. (2015). Abortion patient survey 2008 (APS). Guttmacher center for population research and innovation and dissemination. https://www.guttmacher.org/population-center/dataset/2008-abortion-patient-survey-aps.

  • Hanschmidt, F., Linde, K., Hilbert, A., Riedel-Heller, S. G., & Kersting, A. (2016). Abortion stigma: A systematic review. Perspectives on Sexual and Reproductive Health, 48, 169–177.

    Article  Google Scholar 

  • Harris, K. M. (2009). The National Longitudinal Study of Adolescent to Adult Health (Add Health), waves I & II, 1994–1996; wave III, 2001–2002; wave IV, 2007-2009 [machine readable data file and documentation]. Chapel Hill: Carolina Population Center, University of North Carolina.

    Google Scholar 

  • Harris, K. M. (2010). An integrative approach to health. Demography, 47(1), 1–22.

    Article  Google Scholar 

  • Harris, K. M. (2013). The Add Health study: Design and accomplishments. Page. Chapel Hill, NC: Carolina Population Center.

    Google Scholar 

  • Henshaw, S. K. (1995). 2000-01 national survey of contraception use among women having abortions (data set R3, McKean, E.A., J.J.). [machine-readable data file and documentation]. New York: The Alan Guttmacher Institute (Producer). Los Altos, CA: Sociometrics Corporation, Data Archive on Adolescent Pregnancy and Pregnancy Prevention (Producer & Distributer).

  • Henshaw, S. K., & Kost, K. (2008). Trends in the characteristics of women obtaining abortions, 1974 to 2004. New York: Guttmacher Institute.

    Google Scholar 

  • Jagannathan, R. (2001). Relying on surveys to understand abortion behavior: Some cautionary evidence. American Journal of Public Health, 91(11), 1825–1831.

    Article  Google Scholar 

  • Jerman, J., & Jones, R. K. (2014). Secondary measures of access to abortion services in the United States, 2011 and 2012: gestational age limits, cost, and harassment. Women’s Health Issues, 24(4), e419–e424.

    Article  Google Scholar 

  • Jerman, J., Jones, R. K., & Onda, T. (2016). Characteristics of U.S. abortion patients in 2014 and changes since 2008. New York: Guttmacher Institute.

    Google Scholar 

  • Jones, R. K. (2008). 2008 survey of abortion patients. [Machine-readable data file and documentation]. New York: The Alan Guttmacher Institute.

    Google Scholar 

  • Jones, R. K. (2011). How commonly do US abortion patients report attempts to self-induce? American Journal of Obstetrics and Gynecology, 204(1), 23.e1–23.e4.

    Article  Google Scholar 

  • Jones, R., Darroch, J. E., & Henshaw, S. K. (2004). 2000-01 national survey of contraception use among women having abortions (data set R3, McKean, E.A., J.J.) [machine-readable data file and documentation]. New York: The Alan Guttmacher Institute (Producer). Los Altos, CA: Sociometrics Corporation, Data Archive on Adolescent Pregnancy and Pregnancy Prevention (Producer & Distributer).

  • Jones, E. F., & Forrest, J. D. (1992). Underreporting of abortion in surveys of U.S. women: 1976 to 1988. Demography, 29(1), 113–126.

    Article  Google Scholar 

  • Jones, R. K., & Finer, L. B. (2012). Who has second-trimester abortions in the United States? Contraception, 85(6), 544–551.

    Article  Google Scholar 

  • Jones, R. K., & Kavanaugh, M. L. (2011). Changes in abortion rates between 2000 and 2008 and lifetime incidence of abortion. Obstetrics and Gynecology, 117(6), 1358–1366.

    Article  Google Scholar 

  • Jones, R. K., & Kost, K. (2007). Underreporting of induced and spontaneous abortion in the United States: An analysis of the 2002 national survey of family growth. Studies in Family Planning, 38(3), 187–197.

    Article  Google Scholar 

  • Kalsbeek, W. D., Morris, C. B., & Vaughn, B. J. (2001). Effects of nonresponse on the mean squared error of estimates from a longitudinal study. In Annual meeting of the American statistical association.

  • Kalsbeek, W. D., Yang, J., & Agans, R. P. (2002). Predictors of nonresponse in a longitudinal survey of adoelscents. In Survey research methods section of the American Statistical Association (pp. 1740–45). Alexandria, VA: American Statistical Association.

  • Kumar, A. (2013). Everything is not abortion stigma. Women’s Health Issues, 23(6), e329–e331.

    Article  Google Scholar 

  • Kumar, A., Hessini, L., & Mitchell, Ellen. M. H. (2009). Conceptualising abortion stigma. Culture, Health & Sexuality, 11(6), 625–639.

    Article  Google Scholar 

  • Lara, D., Strickler, J., Olavarrieta, C. D., & Ellertson, C. (2004). Measuring induced abortion in Mexico a comparison of four methodologies. Sociological Methods & Research, 32(4), 529–558.

    Article  Google Scholar 

  • Lindberg, L., & Scott, R. H. (2018). Effect of ACASI on reporting of abortion and other pregnancy outcomes in the US national survey of family growth. Studies in Family Planning. https://doi.org/10.1111/sifp.12068.

    Google Scholar 

  • Little, Roderick. J. A., & Rubin, D. B. (2014). Statistical analysis with missing data. New York: Wiley.

    Google Scholar 

  • Mercer, A. W., Kreuter, F., Keeter, S., & Stuart, E. A. (2017). Theory and practice in nonprobability surveys: parallels between causal inference and survey inference. Public Opinion Quarterly, 81(S1), 250–271.

    Article  Google Scholar 

  • Nash, E., Gold, R. B., Rathbun, G., & Vierboom, Y. (2015). Laws affecting reproductive health and rights: 2014 state policy review. Guttmacher Institute. Retrieved January 22, 2017, from https://www.guttmacher.org/laws-affecting-reproductive-health-and-rights-2014-state-policy-review.

  • Norris, A., Bessett, D., Steinberg, J. R., Kavanaugh, M. L., De Zordo, S., & Becker, D. (2011). Abortion stigma: A reconceptualization of constituents, causes, and consequences. Women’s Health Issues, 21(3), S49–S54.

    Article  Google Scholar 

  • Rasch, V., Muhammad, H., Urassa, E., & Bergström, S. (2000). Self-reports of induced abortion: An empathetic setting can improve the quality of data. American Journal of Public Health, 90(7), 1141–1144.

    Article  Google Scholar 

  • Shellenberg, K. M., Moore, A. M., Bankole, A., Juarez, F., Omideyi, A. K., Palomino, N., et al. (2011). Social stigma and disclosure about induced abortion: Results from an exploratory study. Global Public Health, 6(sup1), S111–S125.

    Article  Google Scholar 

  • Tennekoon, V. (2017). Counting unreported abortions: A binomial-thinned zero-inflated Poisson model. Demographic Research, 36(2), 41–72.

    Article  Google Scholar 

  • Udry, R. J., Gaughan, M., Schwingl, P. J., & van den Berg, B. J. (1996). A medical record linkage analysis of abortion underreporting. Family Planning Perspectives, 28(5), 228–231.

    Article  Google Scholar 

  • United States Census Bureau. (2016). International data base: Mid-year population by single year age groups—Custom region—United States. Retrieved January 2, 2017, from https://www.census.gov/data-tools/demo/idb/informationGateway.php.

  • United States Department of Health and Human Services, Centers for Disease Control and Prevention, and National Center for Health Statistics. (2018). Bridged-race population estimates, United States July 1st resident population by state, county, age, sex, bridged-race, and hispanic origin. compiled from 1990–1999 bridged-race intercensal population estimates (released by NCHS on 7/26/2004); revised bridged-race 2000–2009 intercensal population estimates (released by NCHS on 10/26/2012); and bridged-race vintage 2017 (2010–2017) postcensal population estimates (released by NCHS on 6/27/2018).

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Acknowledgements

The author wishes to thank Kathleen Mullan Harris and S. Philip Morgan for their helpful and thoughtful feedback and comments on this paper. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1,650,116. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. Note: Use of this acknowledgment requires no further permission from the persons named. This paper uses data from NCHS. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NCHS, which is responsible only for the initial data.

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  1. Department of Sociology, University of North Carolina at Chapel Hill, 155 Hamilton Hall, CB 3210, Chapel Hill, NC, 27599, USA

    Katherine I. Tierney

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Tierney, K.I. Abortion Underreporting in Add Health: Findings and Implications. Popul Res Policy Rev 38, 417–428 (2019). https://doi.org/10.1007/s11113-019-09511-8

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