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Epidemiology:
doi: 10.1097/EDE.0b013e3182117615
Letters

Interviewer Identity as Exclusion Restriction in Epidemiology

Bärnighausen, Till; Bor, Jacob; Wandira-Kazibwe, Speciosa; Canning, David

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Department of Global Health and Population, Harvard School of Public Health, Boston, MA, Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa, tbaernig@hsph.harvard.edu (Bärnighausen)

Department of Global Health and Population, Harvard School of Public Health, Boston, MA (Bor)

Concave International, Kampala, Uganda (Wandira-Kazibwe)

Department of Global Health and Population, Harvard School of Public Health, Boston, MA (Canning)

Supported by National Institutes of Health/National Institute of Child Health and Human Development (NIH/NICHD) (Grant 1R01-HD058482-01) and the William F. Milton Fund, Harvard University (to T.B.), and William and Flora Hewlett Foundation (Grant 2008-2302), NIH/National Institute of Aging (NIA) (Grant 5 P30 AG024409), and NIH/NIA (Grant 1R21AG032572-01) (to D.C.).

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To the Editor:

We read with great interest the thoughtful commentary by Geneletti et al1 on 2 recent studies in this journal (one by Chaix et al,2 the other by ourselves3), which correct estimation for selection effects. We agree with the conclusion of Geneletti et al that modeling selection is “problem-specific, as well as dependent on assumptions made and the type of additional data available.” We would like to point out, however, that our problem-specific approach to using Heckman-type selection models should be widely applicable in epidemiology.

The performance of a Heckman-type model depends critically on the use of valid exclusion restrictions,4,5 ie, variables that determine sample selection but do not independently affect the outcome of interest. Our innovation on the approach—to use the interviewer identity as an exclusion restriction—offers an opportunity to examine and control for selection on unobserved factors in many epidemiologic studies for several reasons.

1. Studies where interviewers act as agents of data collection, such as in surveys and surveillances, are a common source of data in epidemiology. Because epidemiologists are often closely involved in the data collection, they should have access to data on interviewer identity even in many of those cases where this information is not included in the routinely available datasets.

2. Interviewers differ in their experience, motivation, and attitudes, and thus have varying success contacting eligible individuals and eliciting consent from individuals they have contacted6,7—ie, interviewer identity determines sample selection. This hypothesis is testable.

3. Interviewer identity does not affect many of the variables of interest in epidemiology. Although this hypothesis is usually not testable,4 an interviewer effect can often be ruled out on theoretical considerations. Interviewer identity cannot influence factors that are neither assessed by an interviewer nor affected in any way by interviewer contact (eg, many factors measured in biologic samples such as HIV status, hemoglobin levels, or the presence of a particular gene). Although matching of interviewers to eligible individuals can introduce associations between interviewer identity and an outcome, as long as the matching criteria are known these associations can be easily controlled for in the analysis.

Heckman-type selection models are well established in economics, sociology, and political science,4,8–10 but are rarely used in epidemiology. The recognition that epidemiologists often have at their disposal a highly plausible exclusion restriction to model the effect of selection on unobserved factors may increase the use of Heckman-type models, potentially leading to new insights into selection effects.

Till Bärnighausen

Department of Global Health and Population

Harvard School of Public Health

Boston, MA

Africa Centre for Health and Population Studies

University of KwaZulu-Natal

Mtubatuba, South Africa

tbaernig@hsph.harvard.edu

Jacob Bor

Department of Global Health and Population

Harvard School of Public Health

Boston, MA

Speciosa Wandira-Kazibwe

Concave International

Kampala, Uganda

David Canning

Department of Global Health and Population

Harvard School of Public Health

Boston, MA

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REFERENCES

1. Geneletti S, Mason A, Best N. Adjusting for selection effects in epidemiologic studies: why sensitivity analysis is the only “solution.” Epidemiology. 2011;22:36–39.

2. Chaix B, Billaudeau N, Thomas F, et al. Neighborhood effects on health: correcting bias from neighborhood effects on participation. Epidemiology. 2011;22:18–26.

3. Bärnighausen T, Bor J, Wandira-Kazibwe S, et al. Correcting HIV prevalence estimates for survey nonparticipation using Heckman-type selection models. Epidemiology. 2011;22:27–35.

4. Vella F. Estimating models with sample selection bias: a survey. J Hum Res. 1998;33:127–169.

5. Heckman JJ. Sample selection bias as a specification error. Economtrica. 1979;47:153–161.

6. Groves RM, Couper MP. Nonresponse in Household Interview Surveys. New York: Wiley; 1998.

7. Blohm M, Hox J, Koch A. The influence of interviewers' contact behavior on the contact and cooperation rate in face-to-face household surveys. Int J Publ Opin Res. 2006;19:97–111.

8. Winship C, Mare RD. Models for sample selection bias. Annu Rev Sociol. 1992;18:327–350.

9. Puhani PA. The Heckman correction for sample selection and its critique. J Econ Surv. 2000;14:53–68.

10. Dubin JA, Rivers D. Selection bias in linear regression, logit and probit models. Sociol Methods Res. 1989;18:360–390.

© 2011 Lippincott Williams & Wilkins, Inc.

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