The Incontinence Quality of Life Instrument in a survey of primary care patients

The following candidate variables were chosen for inclusion in an initial regression model for the I-QOL scores: age, type of incontinence (stress, urge, mixed), incontinence is perceived as a “problem” (yes/no), log-transformed number of daytime and nighttime incontinence episodes, amount of wetting (wets outer clothing or runs down leg/wets pad or underpants only), self-reported health status (excellent to poor), and education. In this initial model, there was no significant association between I-QOL scores and age, type of incontinence, or education. These variables thus were not retained in the final model, which is shown in Table 3. The model provided a good fit to the data (F_8,190 = 43.1; P < .0001; R² = .64). In addition to presenting the results of the model that excluded subjects with incomplete I-QOL scores, Table 3 shows the results of the model that included these subjects by using imputed values. There were only minor differences between the models. The I-QOL score decreased as the number of daytime and nighttime incontinence episodes increased and as the amount of urine loss increased. After controlling for other variables, the I-QOL averaged 12 points fewer among those women who considered their incontinence problematic. Further, there was a strong relation between the I-QOL and self-reported general health status.

DISCUSSION

Urinary incontinence is common among women in the community, but loss of bladder control is perceived quite differently by various respondents. In a survey of 36,000 Americans with incontinence, Jeter and Wagner reported that 17% described their incontinence as a major problem with important social implications,¹ but the rest described it as a relatively minor problem with limited impact on their respective lifestyles. Self-administered quality of life instruments are valuable in measuring the impact of incontinence on the lives of subjects, of identifying subjects among whom interventions might have a beneficial impact on quality of life, and in following the natural history of incontinence and its treatment.

One of these instruments, the I-QOL, has been used in the clinical trial setting. We were impressed with reports of its performance and selected it for use in a postal survey of community dwelling patients of 2 family medicine clinics. It was immediately apparent that the 22-item length of the instrument posed problems in a postal survey because, even though the missing rate for individual items was no higher than 13%, only 80.7% of I-QOL scores were complete. We found that older, less educated subjects were more likely to return incomplete questionnaires. We did not contact subjects to obtain responses to the omitted questions. Rather, we chose to make use of responses to other questions to impute the missing I-QOL scores. Although the differential response rates suggested the possibility of selection bias, there was in fact little difference in the coefficients of regression models when using complete versus complete plus imputed data.

As reported by Patrick and colleagues in the clinical trial context,⁶ we found that the I-QOL score correlates strongly with physical measures of the extent of incontinence, including the number of incontinence episodes and the amount of wetting. Similar to those researchers, we found no relation between the I-QOL and age, education, or the type of incontinence. These are desirable properties for a condition-specific quality of life instrument; it is responsive to the impact of incontinence on the quality of life and not to “nuisance” variables. Another desirable characteristic was that the I-QOL correlates strongly with the statement, “wetting is a problem.” Figure 2 shows the relations between the I-QOL score and the probability that a respondent would state that incontinence is a problem. To understand Figure 2, consider that each subject will agree or disagree with the statement, “wetting is a problem.” Each subject thus appears on the graph at a probability level of 0 or 1. Because there is considerable overlap of the data points on the display, the point for each subject has been “jittered,” ie, given a random amount of vertical displacement to better display the density of data points in relation to the I-QOL. The curve on the plot is the fit of a “loess” smoother to the data points.16 This smoother is, in essence, a running weighted average of the proportion of subjects who reported that wetting is a problem. When the I-QOL score was low, there was a high probability that incontinence would be seen as a problem. The I-QOL score at which 50% of women found incontinence to be a problem was about 77, and the probability declined rapidly after that.

There were significant correlations between the I-QOL score and the subscales of the SF-12 generic quality of life instrument (r = .49 for the mental component of the SF-12 and r = .33 for the physical component). We found, however, that the I-QOL was more sensitive than the generic instruments in identifying subjects who considered incontinence to be a problem. This is shown in the receiver operating characteristic curves displayed in Figure 3. The area under the I-QOL receiver operating characteristic curve was 0.84, compared with 0.63 for the medical component scale and 0.58 for the physical component scale of the SF-12. It is reassuring that a condition-specific instrument performs better than a generic one.