Triangulating differential nonresponse by race in a telephone survey

Jessica T DeFrank, J Michael Bowling, Barbara K Rimer, Jennifer M Gierisch, Celette Sugg Skinner, Jessica T DeFrank, J Michael Bowling, Barbara K Rimer, Jennifer M Gierisch, Celette Sugg Skinner

Abstract

Introduction: In 1994, the U.S. Department of Health and Human Services mandated sufficient inclusion of racial and ethnic minorities in all federally funded research. This mandate requires researchers to monitor study samples for research participation and differential survey nonresponse. This study illustrates methods to assess differential survey nonresponse when population race data are incomplete, which is often the case when studies are conducted among members of health plans.

Methods: We collected data as part of the PRISM (Personally Relevant Information about Screening Mammography) study, a trial funded by the National Institutes of Health to increase rates of annual mammography adherence. We used two methods to estimate racial distribution of the PRISM study population. The first method, called E-Tech, estimated race of the sample frame by using individuals' names and zip codes. In the second method, we conducted interviews with a subsample of PRISM study refusals. We validated both estimation methods through comparisons with self-reported race. We used race information generated by E-Tech, interviewer estimates, and self-report to assess differential nonresponse in the PRISM study.

Results: The E-Tech method had moderate sensitivity (48%) in estimating race of black participants but higher specificity (97%) and positive predictive value (71%). The interviewer-estimation method had high sensitivity (100%), high specificity (95%), and moderate positive predictive value (80%). Black women were less likely than white women to be reached for study participation.

Conclusion: There was slight differential nonresponse by race in the PRISM study. Techniques described here may be useful for assessing differential nonresponse in samples with incomplete data on race.

Figures

Figure
Figure
PRISM (Personally Relevant Information about Screening Mammography) participant recruitment for baseline and refusal interviews.

References

    1. Lessler JT, Kalsbeek WD. Nonsampling error in surveys. New York (NY): John Wiley & Sons; 1992.
    1. Jones J. The effects of non-response on statistical inference. J Health Soc Policy. 1996;8(1):49–62.
    1. Mishra SI, Dooley D, Catalano R, Serxner S. Telephone health surveys: potential bias from noncompletion. Am J Public Health. 1993;83(1):94–99.
    1. Partin MR, Malone M, Winnett M, Slater J, Bar-Cohen A, Caplan L. The impact of survey nonresponse bias on conclusions drawn from a mammography intervention trial. J Clin Epidemiol. 2003 Sep;56(9):867–873.
    1. Sheikh K, Mattingly S. Investigating non-response bias in mail surveys. J Epidemiol Community Health. 1981;35(4):293–296.
    1. Link MW, Mokdad AH, Stackhouse HF, Flowers NT. Race, ethnicity, and linguistic isolation as determinants of participation in public health surveillance surveys. Prev Chronic Dis. 2006 Jan;3(1):A09. Available from: .
    1. Des Jarlais G, Kaplan CP, Haas JS, Gregorich SE, Perez-Stable EJ, Kerlikowske K. Factors affecting participation in a breast cancer risk reduction telephone survey among women from four racial/ethnic groups. Prev Med. 2005;41(3-4):720–727.
    1. Moorman PG, Newman B, Millikan RC, Tse CK, Sandler DP. Participation rates in a case-control study: The impact of age, race, and race of interviewer. Ann Epidemiol. 1999;9(3):188–195.
    1. Corbie-Smith G, Moody-Ayers S, Thrasher AD. Closing the circle between minority inclusion in research and health disparities. Arch Intern Med. 2004;164(13):1362–1364.
    1. Shavers VL, Lynch CF, Burmeister LF. Factors that influence African-Americans' willingness to participate in medical research studies. Cancer. 2001;91(1 Suppl):233–236. [Published erratum in: Cancer 2001;91(6):1187]
    1. Corbie-Smith G, Thomas SB, St George DM. Distrust, race, and research. Arch Intern Med. 2002;162(21):2458–2463.
    1. Giuliano AR, Mokuau N, Hughes C, Tortolero-Luna G, Risendal B, Ho RCS, et al. Participation of minorities in cancer research: the influence of structural, cultural, and linguistic factors. Ann Epidemiol. 2000;10(8 Suppl):S22–S34.
    1. NIH guidelines on the inclusion of women and minorities as subjects in clinical research [Internet] Washington (DC): U.S. Department of Health and Human Services; 1994. Available from:
    1. Satia JA, Galanko JA, Rimer BK. Methods and strategies to recruit African Americans into cancer prevention surveillance studies. Cancer Epidemiol Biomarkers Prev. 2005;14(3):718–721.
    1. Reed PS, Foley KL, Hatch J, Mutran EJ. Recruitment of older African Americans for survey research: a process evaluation of the community and church-based strategy in the Durham Elders Project. Gerontologist. 2003;43(1):52–61.
    1. Morris MC, Colditz GA, Evans DA. Response to a mail nutritional survey in an older bi-racial community population. Ann Epidemiol. 1998;8(5):342–346.
    1. Rimer BK, Conaway MR, Lyna PR, Rakowski W, Woods-Powell CT, Tessaro I, et al. Cancer screening practices among women in a community health center population. Am J Prev Med. 1996;12(5):351–357.
    1. Paskett ED, DeGraffinreid C, Tatum CM, Margitic SE. The recruitment of African-Americans to cancer prevention and control studies. Prev Med. 1996;25(5):547–553.
    1. SPA employees by race/ethnicity [Internet] Raleigh: North Carolina Office of State Personnel; [cited 2006 Jul 31]. Available from: .
    1. Rimer BK, Halabi S, Sugg Skinner C, Lipkus IM, Strigo TS, Kaplan EB, et al. Effects of a mammography decision-making intervention at 12 and 24 months. Am J Prev Med. 2002;22(4):247–257.
    1. Health Maintenance Consortium Resource Center [Homepage on the Internet] College Station: Texas A&M University School of Rural Public Health; Available from: .
    1. Standard definitions: final dispositions of case codes and outcome rates for surveys. 4th ed. Lenexa (KS): The American Association for Public Opinion Research; 2006.
    1. The E-tech process [Internet] South Hackensack (NJ): Ethnic Technologies; [cited 2006 Jul 31]. Available from: .
    1. Hennekens CH, Buring JE, Mayrent SL, editors. Epidemiology in medicine. Hagerstown (MD): Lippincott Williams & Wilkins; 1987. pp. 327–347.Screening;
    1. Stokes ME, Davis CS, Koch GG. Categorical data analysis using the SAS system. 2nd ed. Cary (NC): SAS Institute, Inc; 2000.
    1. Kwok RK, Yankaskas BC. The use of census data for determining race and education as SES indicators: a validation study. Ann Epidemiol. 2001 Apr;11(3):171–177.
    1. Buescher PA, Gizlice Z, Jones-Vessey KA. Discrepancies between published data on racial classification and self-reported race: evidence from the 2002 North Carolina live birth records. Public Health Rep. 2005;120(4):393–398.
    1. Bailey G, Thomas E. Some aspects of African-American vernacular English phonology. In: Mufwene S, Rickford J, Bailey G, Baugh J, editors. African American English: structure, history, and use. Routledge; London (UK): 1988. pp. 85–109.
    1. Curtin R, Presser S, Singer E. Changes in telephone survey nonresponse over the past quarter century. Public Opinion Quarterly 2005;69(1):87–98.

Source: PubMed

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