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Effect of sampling frames on response rates in the WHO MONICA risk factor surveys
Author(s) -
Hermann K. Wolf,
Kari Kuulasmaa,
Hanna Tolonen,
Susana Sans,
Anu Molarius,
Brian J. Eastwood
Publication year - 2005
Publication title -
european journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.825
H-Index - 111
eISSN - 1573-7284
pISSN - 0393-2990
DOI - 10.1007/s10654-005-0600-3
Subject(s) - medicine , epidemiology , risk factor , public health , behavioral risk factor surveillance system , environmental health , sampling (signal processing) , population , pathology , computer science , filter (signal processing) , computer vision
Sample surveys are used to investigate occurrence and determinants of diseases in populations. Their reliability is influenced by quality of sampling frame and response rate. We investigated relationship between sampling frame type and response rates and assessed their impact on non-response bias, using data from the WHO MONICA Project, where 37 centres in 20 countries conducted sample surveys, employing the best locally available sampling frame. Sampling frames fell into three categories: Population registers (PR), electoral registers (ER), and health care registers (HR). Response rate (rrs) was factored into components reflecting quality of sampling frame (contact rate cr) and characterizing willingness of sample members to participate (enrolment rate er). The mean quality score for the sampling frames was 92% for PR, 87% for HR and 85% for ER; they contributed on average 23, 20, and 26% to the respective non-response rates. For all frame types and both sexes the lowest quality score occurred in the age group 35 - 44, suggesting a reduced ability to track migration of a highly mobile population group. The patterns in the age/sex distribution of er indicate at least for males in PR and females in HR a potential for non-response bias. Estimation of non-response bias through an abbreviated questionnaire failed because of low item response. We found that contact rate characterizes sampling frame quality. For all frame types it had a major influence on response rate. It is likely that low er and low cr cause different kind of bias, requiring different measures to minimize their effects.

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