Respondents in an epidemiologic survey had fewer psychotropic prescriptions than nonrespondents: an insight into health-related selection bias using routine health insurance data

Vercambre, Marie-Noël; Gilbert, Fabien

doi:10.1016/j.jclinepi.2012.05.002

Cited by 27 publications

(23 citation statements)

References 39 publications

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“…Non-participants are reported to use more alcohol than participants [9,10] and to have higher smoking and alcohol related mortality [11]. Non-participants of health surveys also more often use out-patient health care and have higher hospitalization rates than participants [4,6,7,12,13], have more psychotropic prescriptions [14], and have a higher mortality rate during the follow-up [15][16][17].…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Selection bias was reduced by recontacting nonparticipants

Karvanen

Tolonen

Härkänen

et al. 2016

Journal of Clinical Epidemiology

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Objective: One of the main goals of health examination surveys (HES) is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some non-participants are collected. Study Design and Setting: In the FINRISK 2007 study, a populationbased health study conducted in Finland, a random sample of 10 000 men and women aged 25-74 were invited to participate. The study included a questionnaire data collection and a health examination. 6255 individuals participated in the study. Out of 3745 non-participants, 473 returned a simplified questionnaire after a recontact. Both the participants and the nonparticipants were followed up for death and hospitalizations. The follow-up data allowed to check the assumptions on the missing data mechanism and tailored multiple imputation methods were used to handle the missing data. Results: Non-participation is a strong predictor for mortality in the fiveyear follow-up. However, the recontact response does not predict mortality or morbidity among the non-participants when adjusted for age and sex. The M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 result suggests that the recontact respondents can be used as proxy for all non-participants. A comparison of raw estimates and estimates adjusted for selection bias reveals clear differences in the estimated population prevalences of smoking and heavy alcohol usage. Conclusion: All efforts to collect data on non-participants are likely to be useful even if the response rate for the recontact remains low. Statistical analysis of the recontact respondents provides an indication of the extent of the selection bias, even in studies where follow-up data are not available to check the assumptions.Keywords: survey; non-response; bias; missing data; multiple imputation What is new?:• Register data on cause specific mortality and morbidity can be used to check the assumptions on the missing data mechanism.• In the FINRISK 2007 study, we found that while the participants and the non-participants clearly differ, the non-participants with and without a recontact response have similar mortality and morbidity when adjusted for age and sex.• We propose a multiple imputation approach to handle data missing not at random. The approach is applicable when data on a non-response questionnaire are available.

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Selection bias was reduced by recontacting nonparticipants

Karvanen

Tolonen

Härkänen

et al. 2016

Journal of Clinical Epidemiology

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“…Many non-participation analyses on health variables are based on supplementary surveys of 'participating non-participants' willing to complete a shortened version of the survey, with the inherent risk of partly reproducing the same non-participation bias. 32 Second, the use of registries reduced common methodological problems such as recall bias and missing responses. 15 Third, since the registry data are based on financial reimbursement from the SIA, they are considered to be accurate and reliable.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…In concert with this interpretation, an epidemiological survey on mental health found participants to have fewer psychotropic prescriptions than non-participants, although using more medical services for somatic disorders. 32 The assurance of confidentiality in the invitation letter in the HAP study, hereunder that the questionnaire was not related to the employer or SIA, probably partly counteracted nonparticipation due to fear of "exposure", 26 but how much is not easily quantifiable. Diagnoses may also have yielded differences in personal relevance motivation, as the survey overall was directed more towards mental than physical aspects of work, health and sickness absence.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Registry-based analysis of participator representativeness: a source of concern for sickness absence research?

et al. 2016

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ObjectivesSelective participation can bias results in epidemiological surveys. The importance of health status is often suggested as a possible explanation for non-participation but few empirical studies exist. In a population-based study, explicitly focused on sickness absence, health and work, we examined whether a history of high levels of sickness absence was associated with non-participation.DesignThe study is based on data from official sickness absence registers from participants, non-participants and the total target population of the baseline survey of the Health Assets Project (HAP).SettingHAP is a population-based cohort study in the Västra Götaland region in South Western Sweden.ParticipantsHAP included a random population cohort (n=7984) and 2 cohorts with recent sickness absence (employees (n=6140) and non-employees (n=990)), extracted from the same overall general working-age population.Primary outcome measuresWe examined differences in participation rates between cohorts (2008), and differences in previous sickness absence (2001–2008) between participants (individual-level data) and non-participants or the target population (group-level data) within cohorts.ResultsParticipants had statistically significant less registered sickness absence in the past than non-participants and the target population for some, but not all, of the years analysed. Yet these differences were not of substantial size. Other factors than sickness absence were more important in explaining differences in participation, whereby participants were more likely to be women, older, born in Nordic countries, married and have higher incomes than non-participants.ConclusionsAlthough specifically addressing sickness absence, having such experience did not add any substantial layer to selective participation in the present survey. Detailed measures are needed to gain a better understanding for health selection in health-related surveys such as those addressing sickness absence, for instance in order to discriminate between selection due to ability or motivation for participation.

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“…Sensitivity analyses were carried 3 out using a cut-off of at least six prescriptions and categorising ever versus never use yielding similar 4 results. Previous studies have relied on self-report measures of mental health which are subject to a 5 variety of biases, most importantly responder bias, as individuals with poor mental health are less 6 likely to respond to questionnaires (Vercambre and Gilbert, 2012). Utilising administrative data 7 overcomes this, and although it is subject to potential misclassification bias, this will affect such a 8 small proportion of individuals in the large population wide prescribing dataset it is likely to have 9 little effect on the results.…”

Section: Area Characteristics 40mentioning

confidence: 99%