Analysis of Non-Response Bias in a Mailed Health Survey
ABSTRACT The objective of this study was to identify characteristics of non-respondents and late respondents to a mailed health survey. Persons who returned and those who did not return the questionnaire were compared using health insurance data, which indicated their age, sex, and health care expenditures in the previous year. Insurance and questionnaire data were used to compare early and late survey respondents and to compare categories of non-respondents. Questions covered use of health services, health status, and sociodemographic characteristics. Participants were members of health insurance plans in Geneva, Switzerland, 19–45 years old (n = 1822). Respondents (n = 1424) and non-respondents (n = 398) were of similar age and sex. The proportion of persons who had health care expenditures greater than zero Swiss francs (SFr) was higher among respondents (75%) than among non-respondents (69%, p = 0.03). Among non-respondents, expenditures of persons who explicitly refused to participate (2378 SFr) were higher than expenditures of persons who moved out of Geneva (1085 SFr) or who failed to return the questionnaire (1592 SFr, p = 02). Among respondents, being born in a Switzerland, having completed elementary school, having generated health care expenditures, and reporting good physical health were independent predictors of early response. In conclusion, low response rates to mailed health surveys may result in overestimating the utilization of health services. However, non-respondents did not constitute a homogenous group, and the strength and even direction of non-response bias depended on the mechanisms of non-response.
- SourceAvailable from: Jørgen Lous
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- "The literature reveals different trends regarding non-response in relation to sex and age. Some reports have found, as we did, overrepresentation of young men among non-responders, while other studies have found no differences with respect to sex or age [14, 19–22]. Low mental health tended to predict non-response in this study, although there was no statistically significant correlation. "
ABSTRACT: A randomized intervention study, “Preventive consultations for 20- to 40-year-old young adults”, investigated whether preventive consultations with a general practitioner could help young adults with multiple psychosocial and lifestyle problems to change health behavior. To optimize the response rate of questionnaires at 1 year post-intervention, the non-responders were reminded by telephone. The aim of this study was to examine potential selection bias induced by non-response by comparing responder and non-responder populations at baseline, and to examine the impact on outcomes by comparing initial respondents to respondents after telephone reminding. Non-responders were compared with primary responders using logistic regression models that included socio-demographic factors, health-related factors, and variables related to the intervention study. In order to describe the impact of including responders after telephone reminding on the intervention’s effect on different health, resource, and lifestyle outcomes, we compared results in models including and excluding responders after telephone reminding. Telephone contact raised the response by 10% from 316 (64%) to 364 (74%) among young adults with multiple problems. Being male was the only factor that significantly predicted non-response in the model after adjustment for other variables. The responders after telephone reminding tended to improve health and lifestyle more than the primary responders, but not significantly so. Although the additional responses did not change the estimates of the 1-year effect on health and lifestyle changes, it contributed to increased precision of the results. Even though the population of primary non-responders had to some degree a different composition than the primary responders, inclusion of responders after telephone reminding did not significantly change the estimates for effect at the 1-year follow-up; however, the additional responses increased the precision of the estimates. Trial registration ClinicalTrials.gov: NCT01231256BMC Research Notes 09/2014; 7(1):632. DOI:10.1186/1756-0500-7-632
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- "Acock, 2005). Large surveys have pointed that, although respondents and nonrespondents in patient satisfaction surveys may differ according to several demographic and clinical characteristics, differences in satisfaction between them tend to be relatively small (Lasek, Barkley, Harper, & Rosenthal, 1997) and nonrespondents do not constitute a homogenous group (Etter & Perneger, 1997). Many highly sophisticated statistical methods to handle the problem of missing responses are now available (e.g., Little & Rubin, 2002), and the use of them is becoming a standard. "
ABSTRACT: To some extent, results always depend on the methods used, and the complete picture of the phenomenon of interest can be drawn only by combining results of different data processing techniques. This emphasizes the use of a wide arsenal of methods for processing and analyzing patient satisfaction surveys.The purpose of this study was to introduce the self-organizing map (SOM) to nursing science and to illustrate the use of the SOM with patient satisfaction data. The SOM is a widely used artificial neural network suitable for clustering and exploring all kind of data sets. The study was partly a secondary analysis of data collected for the Attractive and Safe Hospital Study from four Finnish hospitals in 2008 and 2010 using the Revised Humane Caring Scale. The sample consisted of 5,283 adult patients. The SOM was used to cluster the data set according to (a) respondents and (b) questionnaire items. The SOM was also used as a preprocessor for multinomial logistic regression. An analysis of missing data was carried out to improve the data interpretation. Combining results of the two SOMs and the logistic regression revealed associations between the level of satisfaction, different components of satisfaction, and item nonresponse. The common conception that the relationship between patient satisfaction and age is positive may partly be due to positive association between the tendency of item nonresponse and age. The SOM proved to be a useful method for clustering a questionnaire data set even when the data set was low dimensional per se. Inclusion of empty responses in analyses may help to detect possible misleading noncausative relationships.Nursing Research 09/2014; 63(5):333-345. DOI:10.1097/NNR.0000000000000054 · 1.50 Impact Factor
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- "By definition a regretted situation is unpleasant to recall, and revisiting it in detail and sharing these details with others may be too much to ask for, whether confidentiality is guaranteed or not. As evidence of an unusual and possibly emotional response pattern, we have not previously seen explicit refusals approaching the proportion of 21.4% observed in this study; in our experience less than one tenth of the eligible sample will check the “I do not wish to participate” box in a typical survey – e.g., about 5% in a survey of health insurance plan members . Limited evidence suggests that people who explicitly refuse to participate in a survey may differ from those who fail to respond . "
ABSTRACT: Tracing mail survey responses is useful for the management of reminders but may cause concerns about anonymity among prospective participants. We examined the impact of numbering return envelopes on the participation and the results of a survey on a sensitive topic among hospital staff. In a survey about regrets associated with providing healthcare conducted among hospital-based doctors and nurses, two randomly drawn subsamples were provided numbered (N = 1100) and non-numbered (N = 500) envelopes for the return of completed questionnaires. Participation, explicit refusals, and item responses were compared. We also conducted a meta-analysis of the effect of questionnaire/envelope numbering on participation in health surveys. The participation rate was lower in the "numbered" group than in the "non-numbered" group (30.3% vs. 35.0%, p = 0.073), the proportion of explicit refusals was higher in the "numbered" group (23.1% vs 17.5%, p = 0.016), and the proportion of those who never returned the questionnaire was similar (46.6% vs 47.5%, p = 0.78). The means of responses differed significantly for 12 of 105 items (11.4%), which did not differ significantly from the expected frequency of type 1 errors, i.e., 5% (permutation test, p = 0.078). The meta-analysis of 7 experimental surveys (including this one) indicated that numbering is associated with a 2.4% decrease in the survey response rate (95% confidence interval 0.3% to 4.4%). Numbered return envelopes may reduce the response rate and increase explicit refusals to participate in a sensitive survey. Reduced participation was confirmed by a meta-analysis of randomized health surveys. There was no strong evidence of bias.BMC Medical Research Methodology 01/2014; 14(1):6. DOI:10.1186/1471-2288-14-6 · 2.17 Impact Factor