International Journal of Public Health

, Volume 62, Issue 8, pp 949–957 | Cite as

Sample substitution can be an acceptable data-collection strategy: the case of the Belgian Health Interview Survey

  • Stefaan Demarest
  • Geert Molenberghs
  • Johan Van der Heyden
  • Lydia Gisle
  • Herman Van Oyen
  • Sandrine de Waleffe
  • Guido Van Hal
Original Article



Substitution of non-participating households is used in the Belgian Health Interview Survey (BHIS) as a method to obtain the predefined net sample size. Yet, possible effects of applying substitution on response rates and health estimates remain uncertain. In this article, the process of substitution with its impact on response rates and health estimates is assessed.


The response rates (RR)—both at household and individual level—according to the sampling criteria were calculated for each stage of the substitution process, together with the individual accrual rate (AR). Unweighted and weighted health estimates were calculated before and after applying substitution.


Of the 10,468 members of 4878 initial households, 5904 members (RRind: 56.4%) of 2707 households (RRhh: 55.5%) participated. For the three successive (matched) substitutes, the RR dropped to 45%. The composition of the net sample resembles the one of the initial samples. Applying substitution did not produce any important distorting effects on the estimates.


Applying substitution leads to an increase in non-participation, but does not impact the estimations.


Health survey Non-response Sampling Matched substitution 



The BHIS is a project conducted in collaboration with Statistics Belgium, responsible for drawing the sample and the fieldwork management.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no competing interest.


  1. Baldissera S, Ferrante G, Quarchioni E, Minardi V, Possenti V, Carrozzi G, Masocco M, Salmaso S, The PASSI Coordinating group (2014) Field substitution of nonresponders can maintain sample size and structure without altering survey estimates. Τηε experience of the Italian behavioral risk factors surveillance system (PASSI). Ann Epidemiol 24:241–245CrossRefPubMedGoogle Scholar
  2. Chapman DW (1983) The impact of substitution on survey estimates. In: Madow WG, Olkin I, Rubin DB (eds) Incomplete data in sample surveys, 2nd edn. Academic, New York, pp 45–61Google Scholar
  3. Chapman DW (2003) To substitute or not to substitute that is the question. Surv Stat 48:32–34Google Scholar
  4. Chapman DW, Roman AM (1985) An investigation of substitution for an RDD survey. In: JSM Proceedings, survey research methods section. American Statistical Association, Alexandria, VA, pp 269–274. Accessed 29 Apr 2017
  5. Chiu WF, Yucel RM, Zanutto E, Zaslavsky AM (2005) Using matched substitutes to improve imputations for geographically linked databases. Surv Methodol 31:65–72Google Scholar
  6. David MC, Ware RS, Alati R, Dower J, Donald M (2014) Assessing bias in a prospective study of diabetes that implemented substitution sampling as a recruitment strategy. J Clin Epidemiol 67:715–721CrossRefPubMedGoogle Scholar
  7. Demarest S, Gisle L, Van der Heyden J (2007) Playing hard to get: field substitutions in health surveys. Int J Public Health 52:188–189CrossRefPubMedGoogle Scholar
  8. Demarest S, Van der Heyden J, Charafeddine R, Drieskens S, Gisle L, Tafforeau J (2013a) Methodological basics and evolution of the Belgian Health Interview Survey 1997–2008. Arch Public Health 71:24CrossRefPubMedPubMedCentralGoogle Scholar
  9. Demarest S, Van der Heyden J, Charafeddine R, Tafforeau J, Van Oyen H, Van Hal G (2013b) Socio-economic differences in participation of households in a Belgian national health survey. Eur J Public Health 23:981–985CrossRefPubMedGoogle Scholar
  10. Dorsett R (2010) Adjusting for nonignorable sample attrition using survey substitutes identified by propensity score matching: an empirical investigation using labour market data. J Off Stat 26:105Google Scholar
  11. Kish L, Hess I (2004) A “replacement” procedure for reducing the bias of nonresponse. Am Stat 58:295–297CrossRefGoogle Scholar
  12. Li L, Krenzke T, Mohadjer L (2014) Considerations for selection and release of reserve samples for in-person surveys. Surv Methodol 40:105–123Google Scholar
  13. Lorant V, Demarest S, Miermans PJ, Van Oyen H (2007) Survey error in measuring socio-economic risk factors of health status: a comparison of a survey and a census. Int J Epidemiol 36:1292–1299CrossRefPubMedGoogle Scholar
  14. Lynn P (2004) The use of substitution in surveys. Surv Stat 49:14–16Google Scholar
  15. Molenberghs G, Kenward M (2007) Missing data in clinical studies. Wiley, Hoboken, NJ, p 61CrossRefGoogle Scholar
  16. Nishimura R (2015) Substitution of nonresponding units in probability sampling. Diss. University of MarylandGoogle Scholar
  17. Pickery J, Carton A (2008) Oversampling in relation to differential regional response rates. Surv Res Methods 2:83–92Google Scholar
  18. Rubin DB, Zanutto E (2002) Using matched substitutes to adjust for nonignorable nonresponse through multiple imputations. In: Groves RM, Dillman DA, Little RJA, Eltinge J (eds) Survey nonresponse. Wiley, New York, pp 389–402Google Scholar
  19. Smith (2007) Notes on the use of substitutions in surveys. Unpublished NORC report, AugustGoogle Scholar
  20. Van der Heyden J, Demarest S, Van Herck K, De Bacquer D, Tafforeau J, Van Oyen H (2014) Association between variables used in the field substitution and post-stratification adjustment in the Belgian health interview survey and non-response. Int J Public Health 59:197–206CrossRefPubMedGoogle Scholar
  21. Van Oyen H, Tafforeau J, Hermans H, Quataert P, Schiettecatte E, Lebrun L, Bellamammer L (1997) The Belgian health interview survey. Arch Public Health 55:1–13Google Scholar
  22. Vehovar V (1994) Field substitutions-a neglected option. In: Proceedings of the survey research methods section, American Statistical Association, pp 589–594Google Scholar
  23. Vehovar V (1995) Field substitutions in slovene public opinion survey. In: Ferligoj A, Kramberger A (eds) Contributions to methodology and statistics. Proceedings of the international conference on statistics and methodology, Fakulteta za družbene vede, vol 10. Bled, Slovenia, 13–15 September 1993, pp 39–66Google Scholar
  24. Vehovar V (1999) Field substitution and unit nonresponse. J Off Stat 15:335–350Google Scholar
  25. Vehovar V (2003) Field substitutions redefined. Surv Stat 48:32–34Google Scholar

Copyright information

© Swiss School of Public Health (SSPH+) 2017

Authors and Affiliations

  • Stefaan Demarest
    • 1
  • Geert Molenberghs
    • 2
  • Johan Van der Heyden
    • 1
  • Lydia Gisle
    • 1
  • Herman Van Oyen
    • 1
  • Sandrine de Waleffe
    • 3
  • Guido Van Hal
    • 4
  1. 1.Scientific Institute of Public HealthBrusselsBelgium
  2. 2.Hasselt UniversityKU Leuven LeuvenHasselt, LeuvenBelgium
  3. 3.Statistics BelgiumBrusselsBelgium
  4. 4.University of AntwerpAntwerpBelgium

Personalised recommendations