Historique
Article de Périodique
A simulative comparison of respondent driven sampling with incentivized snowball sampling - The "strudel effect" (2014)
Auteur(s) :
GYARMATHY, V. A. ;
JOHNSTON, L. G. ;
CAPLINSKIENE, I. ;
CAPLINSKAS, S. ;
LATKIN, C. A.
Année
2014
Page(s) :
71-77
Langue(s) :
Anglais
Domaine :
Drogues illicites / Illicit drugs
Discipline :
MAL (Maladies infectieuses / Infectious diseases)
Thésaurus géographique
LITUANIE
Thésaurus mots-clés
COMPARAISON
;
BOULE DE NEIGE
;
METHODE
;
PREVALENCE
;
USAGER
;
VIH
;
HEPATITE
;
COHORTE
;
PARTAGE DE SERINGUE
Autres mots-clés
Résumé :
BACKGROUND: Respondent driven sampling (RDS) and incentivized snowball sampling (ISS) are two sampling methods that are commonly used to reach people who inject drugs (PWID).
METHODS: We generated a set of simulated RDS samples on an actual sociometric ISS sample of PWID in Vilnius, Lithuania ("original sample") to assess if the simulated RDS estimates were statistically significantly different from the original ISS sample prevalences for HIV (9.8%), Hepatitis A (43.6%), Hepatitis B (Anti-HBc 43.9% and HBsAg 3.4%), Hepatitis C (87.5%), syphilis (6.8%) and Chlamydia (8.8%) infections and for selected behavioral risk characteristics.
RESULTS: The original sample consisted of a large component of 249 people (83% of the sample) and 13 smaller components with 1-12 individuals. Generally, as long as all seeds were recruited from the large component of the original sample, the simulation samples simply recreated the large component. There were no significant differences between the large component and the entire original sample for the characteristics of interest. Altogether 99.2% of 360 simulation sample point estimates were within the confidence interval of the original prevalence values for the characteristics of interest.
CONCLUSIONS: When population characteristics are reflected in large network components that dominate the population, RDS and ISS may produce samples that have statistically non-different prevalence values, even though some isolated network components may be under-sampled and/or statistically significantly different from the main groups. This so-called "strudel effect" is discussed in the paper.
METHODS: We generated a set of simulated RDS samples on an actual sociometric ISS sample of PWID in Vilnius, Lithuania ("original sample") to assess if the simulated RDS estimates were statistically significantly different from the original ISS sample prevalences for HIV (9.8%), Hepatitis A (43.6%), Hepatitis B (Anti-HBc 43.9% and HBsAg 3.4%), Hepatitis C (87.5%), syphilis (6.8%) and Chlamydia (8.8%) infections and for selected behavioral risk characteristics.
RESULTS: The original sample consisted of a large component of 249 people (83% of the sample) and 13 smaller components with 1-12 individuals. Generally, as long as all seeds were recruited from the large component of the original sample, the simulation samples simply recreated the large component. There were no significant differences between the large component and the entire original sample for the characteristics of interest. Altogether 99.2% of 360 simulation sample point estimates were within the confidence interval of the original prevalence values for the characteristics of interest.
CONCLUSIONS: When population characteristics are reflected in large network components that dominate the population, RDS and ISS may produce samples that have statistically non-different prevalence values, even though some isolated network components may be under-sampled and/or statistically significantly different from the main groups. This so-called "strudel effect" is discussed in the paper.
Affiliation :
Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA