Related Experiment Video
Updated: May 20, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Using administrative register data for adjusting non-response bias in the finnish gambling harms survey
Jukka Kontto1, Hanna Tolonen2,3, Anne H Salonen3,4
1Finnish Institute for Health and Welfare, Department of Public Health, P.O. Box 30, Helsinki, FI-00271, Finland. jukka.kontto@thl.fi.
Background:
Low response rates are an increasing problem in population-based gambling surveys. Selective non-response may cause biased findings. Supporting information from administrative registers, whenever available for non-respondents can be utilized to estimate the effect of non-response to the gambling-related outcomes. The aim of this study is to evaluate the effect of non-response to the prevalences of two gambling measures: gambling participation and problem gambling.
Methods:
Population-based Finnish Gambling Harms mixed-mode (online and postal) Survey 2016 was conducted among 18-year-olds or older in three geographical regions in Finland (response rate 36.2%). Weighted prevalences of gambling measures were calculated exploiting the respondents' data (n = 7,153). The study sample (N = 19,741) was individually linked to socio-demographic data from Statistics Finland to obtain information on both respondents and non-respondents. Multiple imputation was utilized to calculate the adjusted prevalences of gambling measures by register-based variables: sex, age, residential area, family structure, household equivalised disposable income, highest education degree, employment status, and native language. Crude prevalences were compared against weighted and non-response adjusted prevalences.
Results:
For gambling participation, there was no difference between the crude (81.9% [95% CI 81.0-82.8%]) and the weighted (83.2% [95% CI 82.3-84.0%]) prevalences (p-value 0.09), or between the crude and the non-response adjusted (82.3% [95% CI 81.6-83.0%]) prevalences (p-value 0.49). However, the non-response adjusted (2.8% [95% CI 2.4-3.3%]) prevalence of problem gambling was higher compared to the crude (1.9% [95% CI 1.6-2.3%]) prevalence (p-value 0.002), while there was no difference between the crude and the weighted (2.2% [95% CI 1.9-2.7%]) prevalences (p-value 0.26).
Conclusions:
Non-response had an effect of problem gambling prevalence in a Finnish Gambling Harms Survey 2016. The presence of non-response bias should be checked when analysing population surveys. Using administrative register data enables unique opportunities to increase the reliability of the results and to adjust the estimates for non-response.
Trial Registration:
Clinical trial number: not applicable.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
08:53Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
Published on: May 31, 2019
Related Concept Videos
Archival Research
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Surveys
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Censoring Survival Data
Bias in Epidemiological Studies