1 Department of Population Health Sciences, University of Wisconsin Medical School, Madison, WI.
2 Department of Biostatistics and Medical Informatics, University of Wisconsin Medical School, Madison, WI.
Received for publication November 5, 2002; accepted for publication August 28, 2003.
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ABSTRACT |
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adolescent; child; diabetes mellitus, type I; health; longitudinal studies; quality of life; risk factors
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INTRODUCTION |
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People with type 1 diabetes have been shown to have a lower quality of life than the general population (3, 4). In the literature, quality of life has also been found to be better among persons with better glycemic control (57), male gender (2, 8), younger age (5, 8), higher socioeconomic status (5, 9, 10), and fewer late complications (5, 9, 11). Findings have been mixed regarding the relations between quality of life in people with type 1 diabetes and duration of diabetes (either no association (5, 11) or a better quality of life with a shorter duration (2, 9)) and treatment regimen (either no association (11, 12) or a better quality of life with more intensive treatment (3, 8)). In these studies, three types of measures were implemented to conceptualize and quantify quality of life: generic quality of life, which measured various domains of functioning and well-being that were applicable to different diseases (2, 4, 5, 8, 9, 11); diabetes-specific quality of life, which focused on the specific problems posed by diabetes (6, 7, 11, 12); and overall quality of life, which provided a global assessment of quality of life and could be a score for multidimensional generic quality of life or a single-question measurement (10, 13).
Despite the rich research on quality of life in persons with type 1 diabetes, results have been limited or inconclusive, for the following three reasons. First, although type 1 diabetes is most often diagnosed in childhood, most studies have been conducted in adults (2, 4, 5, 8, 9, 11). Less has been done in studying quality of life among young people (6, 7, 12). Second, many studies have had rather small sample sizes (approximately 69108 (5, 7, 9, 11, 12)) and therefore may not have had enough power to address some questions. Third, there have been few longitudinal studies (4, 14), which permit assessment of change in quality of life over time and the effects of risk factors within a given person.
The Wisconsin Diabetes Registry Study is a population-based cohort study that follows participants from the diagnosis of type 1 diabetes. We have longitudinally collected data on these persons self-rated global health status and risk factors. The data provided us with a unique opportunity to examine one measure of quality of life among youths and young adults with type 1 diabetes. Therefore, we aimed to use self-rated global health to evaluate quality of life in a population-based cohort of children, adolescents, and young adults followed continuously from diabetes diagnosis and to examine its association with longitudinally measured clinical and sociodemographic factors.
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MATERIALS AND METHODS |
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Data collection
Details on data collection and specimen handling and testing are available elsewhere (15, 16). In brief, demographic information, including birth date, parental educational level, parental occupation, race, and sex, was collected by telephone interview 23 months after diagnosis. Although parental educational level and occupation can change over time, only the baseline values were recorded. Starting 34 months after diagnosis, subjects were asked to submit a blood specimen at each routine visit to their local physician or clinic, or every 4 months if no visit was scheduled. The blood was delivered in plastic foam containers to the studys central laboratory, where it was analyzed for total glycosylated hemoglobin. Among 569 subjects in the present analysis, 79 percent returned at least one sample per year across the duration of their study participation.
We mailed questionnaires every 6 months to obtain self-reports on diabetes management, perception of health, and diabetes-related hospitalizations, as well as information on the patients physician and health insurance. At least one questionnaire was returned each year by 95 percent of persons in the analysis.
Participants were examined for eye retinopathy and kidney microalbuminuria complications by means of standard protocols (15, 17) during the first year after diagnosis and at 4, 7, and 9 years duration. Retinopathy status was determined using a severity scale developed previously (17, 18), ranging from no retinopathy in either eye to treated or proliferative retinopathy in both eyes. Subjects were classified as having retinopathy if they had at least one eye with retinopathy. Urinary albumin excretion rates were quantified from 24-hour urine specimens (obtained at the initial and 4-year examinations) or from timed overnight urine specimens (obtained at the 7- and 9-year examinations). Microalbuminuria was defined by a urinary albumin excretion rate 70 µg/minute in 24-hour samples and
20 µg/minute in timed overnight samples. In the present analysis, 80 percent of participants had three or more valid measures of retinopathy status, and 77 percent had three or more valid measures of microalbuminuria status.
Self-rated health status and risk factors
Information on participants self-rated health was collected through a question on the mailed questionnaire: "Compared with other people your age, do you feel that right now your health is excellent, good, fair, or poor?".
We hypothesized the following risk factors to be potentially related to self-rated health. These factors can be divided into three categories. Sociodemographic factors included participants age at questionnaire completion, sex, race, mothers total number of years of education, and parental socioeconomic level, defined using the scheme of Stevens and Cho (19), which assigned a score between 14 and 90 to rank occupations from the lowest to the highest. Diabetic factors included participants age at diagnosis of diabetes, duration of type 1 diabetes, any hypoglycemic episodes in the previous 6 months, any hospitalization in the previous 6 months, number of insulin injections per day, insulin dose per day, glycosylated hemoglobin level, and presence of retinopathy or microalbuminuria. We also examined questionnaire-management factors, including information on who completed the questionnaire (the participant or a proxy respondent) and the effect of noncompliance, measured by the average number of questionnaires submitted per year. Among these risk factors, sex, race, mothers years of education, parental socioeconomic level, age at diabetes diagnosis, and the compliance measure were time-independent variables, and others varied across time points.
From each questionnaire submission, we obtained data on the subjects self-rated health and risk factors, except for glycosylated hemoglobin and complication measurements, which were assessed at different time points. The glycosylated hemoglobin level chosen to correspond to each time point of self-rated health was the average of all glycosylated hemoglobin measurements made between the current questionnaire submission and the previous questionnaire submission. For complication measurements, each self-rated health point was related to the closest complication status measured prior to the questionnaire submission.
Statistical methods
Means or percentages for identified risk factors in the four categories of self-rated health were used to describe the characteristics of four health perception groups. We performed significance testing to compare the four health groups using the generalized estimating equations approach with the exchangeable correlation structure (20), which took into account correlation among measurements from the same person. Generalized estimating equations analyses were performed using the GENMOD procedure in SAS (21).
To display the longitudinal pattern of self-rated health, we plotted the probability of reporting health better than or equal to "good" against duration of type 1 diabetes for different age-at-diabetes-diagnosis groups. We obtained the plot by smoothing the scatterplot of the indicator of reporting health that was better than or equal to good versus diabetes duration. This was done for each age-at-diagnosis group. We used a plot that converted the probability scale to the log odds scale to empirically check whether a more complex trend for the log odds of self-rated health and diabetes duration across age-at-diagnosis groups was needed in the regression model described below.
We examined the relation between self-rated health and multiple risk factors using a random-effects model for ordinal response data (2225). This model can describe the dependence of longitudinally measured "ordinal" self-rated health responses (with alternatives: excellent, good, fair, and poor) on multiple risk factors. The random-effects model for ordinal response data uses the proportional odds model (26) to characterize the relation of ordinal-scaled self-rated health to risk factors. It assumes that the regression coefficients in the proportional odds model vary from person to person, thus reflecting the natural heterogeneity of self-rated health caused by unmeasured factors. Since self-rated health reflects peoples own perceptions, and everyones definition of, for example, excellent health is different even with the same true health, the heterogeneity assumption is suitable for our data. The model also assumes that the variability in regression coefficients can be represented by a probability distribution and, therefore, correlation of repeated self-ratings of health from the same person arises from their sharing a probability distribution (27).
Because participants in the Wisconsin Diabetes Registry Study had repeated measurements taken across time, we can estimate the change in self-rated health between two levels of a risk factor within a given person, in addition to the change averaged across different persons. The within- and between-individual changes can be quite different; therefore, it is necessary to consider these two changes jointly (28). Here, we adopted a modeling technique that can distinguish between-individual changes from within-individual changes (28).
More specifically, suppose Yij is the level of health reported by participant i at questionnaire submission j and the possible values of Yij are 1, 2, 3, and 4 (1 = poor, 2 = fair, 3 = good, and 4 = excellent); then the random-effects model used is
where c = 1, 2, or 3 represents different health levels; zi represents all of the time-independent risk factors identified for participant i (the boldface type denotes multiple factors); xij represents time-dependent risk factors for participant i at questionnaire submission j; denotes the average time-dependent risk factor value of all questionnaire submissions for participant i;
c,
, ßb, and ßw are the fixed values (the "fixed" effects); and ai is the intercept of participant i and is assumed to follow a normal distribution with mean zero and variance
2 (the "random" effect).
For time-independent variables, exp() is the odds ratio for reporting better health in a comparison of, for example, females with males. For time-dependent variables, the model decomposes risk factors into components from the average value of all submissions and differences between each submission and the average value. Exp(ßb) is interpreted as the odds ratio for reporting better health when comparing participants who, for example, were hospitalized in the previous 6 months versus those who were not. Exp(ßw) is interpreted as the odds ratio for reporting better health for a participant who was hospitalized in the previous 6 months versus self-reporting by the same participant if he/she had not been hospitalized. Exp(ßb) estimates the population average, and exp(ßw) estimates the change within the same person. Through the random effect ai, equation 1 allows each participant to have his/her own probability of reporting better health. The variance
2 represents the degree of heterogeneity across participants in reporting of self-rated health that is not attributable to identified risk factors (27).
The random-effects model for ordinal response data in equation 1 can be fitted using PROC NLMIXED in SAS, version 7, or later versions. An example program is shown in the Appendix. Details on the program description can be found in the paper by Agresti et al. (24).
To investigate potential effects of noncompliance, we included the return rate for the questionnaires in equation 1 as a main effect and as interactions with other important risk factors. Risk factor coefficients were then readjusted to the mean compliance of all study participants. This approach can be viewed as a "pattern-mixture" model (29). The idea is to first estimate the relation between risk factors and self-rated health under given compliance patterns and then to obtain an average effect over the mixture of compliance patterns. A difference between the mixture effect and the effect without the compliance adjustment indicates the need to adjust for noncompliance.
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RESULTS |
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When comparing within- and between-individual effects for time-dependent risk factors, we found that the within-individual effect had less variation (a narrower confidence interval) than the between-individual effect, and the significance of the effect could change. This indicates that the relation of longitudinally measured self-reported health to risk factors within the same person was more reliable and consistent than the relation based on the cross-sectional average among different people.
Parents or other family members often answered questionnaires in the early years for participants who were diagnosed with diabetes at a young age. Therefore, age at diagnosis was related to having questionnaires answered by others. Results from table 3 show that participants were less likely to report good health when they themselves answered the questionnaire than when the questionnaire was answered by others. After adjustment for age at diagnosis and other diabetic factors, this effect was still significant but became less apparent. Furthermore, the interaction between age at diagnosis and the indicator variable for who answered the questionnaire was not significant. The tendency for parents to report better health remained the same among children diagnosed at different ages.
The compliance index was added to each model for the unadjusted diabetic factor effect of table 3. Results showed little change in diabetic factor coefficients, comparing the mixture effects with the unadjusted effects in table 3 (not shown).
Variability in self-rated health
The estimate of variance 2 in the random-effects model (equation 1) with all identified risk factors was 3.51 (95 percent confidence interval: 2.84, 4.18), which indicated that participants varied in their reported health even after adjustment for all identified risk factors. This variability reflected the possibility that unmeasured factors and individual differences in health perception could lead to heterogeneity.
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DISCUSSION |
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In our study, males reported better health than females, and people at higher socioeconomic levels generally reported better health than those at lower levels. This is consistent with the findings of other studies (2, 5, 810). We also found that questionnaires answered by the subjects themselves were less likely to show good health ratings than those answered by others. This is an important consideration for studies, such as ours, in which surrogate responses are used for children. Parents who respond on behalf of their children may have difficulty separating their own feelings from their childs and may be affected by feelings of guilt, or may respond favorably as a method of coping with their childs chronic illness. Since we included very young children, we did not exclude parents responses, but we adjusted the results of the final models for type of respondent.
Our findings demonstrate that people who are younger at diagnosis of diabetes rate their health more highly than do people who are older at diagnosis, even after adjustment for respondent, duration, and possible risk factors. This may be due to "unmeasured" exposures and/or to better adaptation in coping with diabetes among persons with younger ages at diagnosis. We also find that self-rated health for people with type 1 diabetes decreases gradually as duration of diabetes increases, even when diabetes management and complications are taken into account. The duration effect may be absent in the diagnosis group >20 years (figure 1). Several studies reported no significant association between quality of life and duration of diabetes (5, 11). In most of these studies, older diagnosis groups were mixed with younger diagnosis groups, which might partially explain this absence of significance, especially if there was a wide range in ages at diagnosis in the study population.
Another important finding is that better glycemic control was associated with better health perception, even after adjustment for additional risk factors. This is important to know for children and adolescents, because achieving near-normal glycemia can be especially challenging and stressful for this age group over time. We also noted a significant inverse relation between number of insulin injections and self-rated health. However, this might be explained by treatment changes made in response to poor perceived health, and the relation was attenuated after adjustment for diabetes duration, glycemic control, and hospitalization. Maintaining good glycemic control during the first decade of diabetes may outweigh the increased burden involved, even in children and adolescents, and long-term factors or hospitalization may have a greater importance in overall perception of health than daily-care aspects. Two other studies also reported a significant correlation between glycemic control and quality of life in young people (6, 7).
The presence of retinopathy was associated with reporting worse health, but this effect was marginal after adjustment for other risk factors. Microalbuminuria was not associated with health perception. Studies of adults with diabetes have consistently found that complications are associated with worsened quality of life (5, 9, 11). Our findings might be due to the low complication rate in these particular children and adolescents, or the presence of microalbuminuria and early measures of retinopathy might have no immediate impact on a persons perception of health. Unlike adults, most of our study population has not developed overt kidney disease or vision problems, so they may have little or no change in their health perceptions due to complications. An additional reason for little correspondence between complications and quality of life is the long intervals between assessments of complications relative to the questionnaire frequency. Children and their parents might have known about complications from their nonstudy care long before complications were measured in the study; thus, the estimated effects based on the study measurements may not be as significant as they would have been otherwise.
Self-rated health was used as a measure of quality of life in our study. This measurement reflects peoples overall perceptions of health, is easy to obtain, and has been shown to be a powerful predictor of morbidity and mortality (31, 32). However, unlike other, more complex quality-of-life measurements (e.g., the generic Medical Outcomes Study 36-Item Short Form (SF-36) (3) and the diabetes-specific Diabetes Quality of Life questionnaire (11)), it does not allow examination of the impact of risk factors on different aspects of quality of life (e.g., physical, emotional, and social well-being). Nevertheless, our results provide useful information for selecting potential risk factors, understanding longitudinal patterns, and creating appropriate statistical methods for more detailed studies of quality of life among persons with type 1 diabetes.
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ACKNOWLEDGMENTS |
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APPENDIX |
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To keep the illustration simple, we have included only the time-independent risk factor sex and the time-dependent variable hospitalization.
data healthdat;
/* read the data set, where hospm is the mean value of all hospitalization statuses for each participant */
set healthinput (keep = id health sex hospm hosp);
/* indicators of self-rated health */
if health = 1 then y1 = 1; else y1 = 0;
if health = 2 then y2 = 1; else y2 = 0;
if health = 3 then y3 = 1; else y3 = 0;
if health = 4 then y4 = 1; else y4 = 0;
/* within-hosp change */
hospwithin = hosp hospm;
run;
proc nlmixed data = healthdat;
bounds a2del a3del > 0;
/* initial values */
parms a1 = 8.01 a2del = 2.28 a3del = 3.16 r = 0.59 bb = 1.26 bw = 0.75 = 1;
/* logit of cumulative probabilities */
1 = a1 + ai + (r*sex) + (bb*hospm) + (bw*hospwithin);
2 = (a1 a2del) + ai + (r*sex) + (bb*hospm) + (bw*hospwithin);
3 = (a1 a2del a3del) + ai + (r*sex) + (bb*hospm) + (bw*hospwithin);
/* probability of each health level */
p1 = 1 (exp(1)/(1 + exp(
1)));
p2 = 1 (exp(2)/(1 + exp(
2))) p1;
p3 = 1 (exp(3)/(1 + exp(
3))) p1 p2;
p4 = 1 p1 p2 p3;
/* likelihood of the data */
jp = (p1**y1)*(p2**y2)*(p3**y3)*(p4**y4);
ll = log(jp);
model y1 ~ general(ll);
random ai ~ normal(0, *
) subject = id;
run;
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NOTES |
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REFERENCES |
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