Health-related quality of life associated with daytime and nocturnal hypoglycaemic events: a time trade-off survey in five countries

Utility values are obtained by asking respondents to ‘trade off’ a portion of their remaining lifespan for an improved health state [19, 22‐29]. For example, respondents might choose between two options: A – to live in full health for 27 years and 0 months; or B – to have diabetes, experience non-severe daytime hypoglycaemia once a month, and live for 30 years and 0 months. If the respondent chooses option A, they are willing to exchange remaining lifetime (i.e., 3 years) to avoid living with diabetes and non-severe hypoglycaemia once a month, thereby indicating their utility in this health state is less than 0.9 (=27 years/30 years). Conversely, if they choose B, they indicate that their utility in this health state is more than 0.9. To make the trade-offs as realistic as possible, the time horizons used were based on each respondent’s projected life expectancy, obtained using the country, age and sex of the respondent at the time of the study, and the most recent World Health Organization life tables [30]. In order to test respondents’ understanding of the TTO concept, a test question was included offering a choice of full health and a longer remaining lifetime, or less than full health and a reduced lifetime. Respondents choosing the second option were excluded from analysis.

To identify the point of indifference (where both options are equally acceptable), respondents were asked the question repeatedly, varying only the number of years living in full health each time. This procedure followed standard bisection methodology, using a starting point of utility = 0.6 to reduce the utility value to an interval of 0.05.

Particular attention was given to the distribution tails. Respondents who either chose not to trade lifetime at a utility value of 0.95, or who were willing to trade a very high proportion of their remaining lifetime (0.875) to be restored to full health, were both carefully screened. Responses were excluded if the respondents refused to trade on ethical or religious grounds, or if they did not understand the question. However, those who believed the health state manageable or who stated a desire to live as long as possible due to obligations (e.g., caregivers) were retained.

The descriptions of hypoglycaemia health states were derived from a survey of 247 UK patients with diabetes. The survey was initially developed based on expert opinion garnered through an advisory board (which included all authors). The patients validated symptom descriptions for a non-severe daytime hypoglycaemic event, a non-severe nocturnal hypoglycaemic event and a severe hypoglycaemic event. In addition, they stated the level to which having hypoglycaemic events affected the frequency of certain diabetes-related and hypoglycaemia-related actions and worries in their daily lives. Together, this led to the definition of the 13 health states describing diabetes alone or diabetes combined with hypoglycaemia of differing event types and frequencies (Additional file 1: Table S1 and Additional file 2: Table S2).

Data were collected through an internet-based survey using an existing panel of prospective participants, an approach previously used successfully by other groups [31‐33]. The panel covered a representative sample of the general populations in Canada, Germany, Sweden, the United States (US) and the UK. There remains a debate in the literature as to whether patient or public preferences carry the most influence when determining the value attached to a particular health state [34‐36]. Therefore, to determine whether responses differed between the general population and people who may have personal experience of hypoglycaemia, a second population with type 1 diabetes and a third population with type 2 diabetes were also identified from the available panel. The study was carried out in accordance with the European Pharmaceutical Market Research Association code of conduct. Ethical approval was obtained from the University of Western Ontario Health Sciences Research Ethics Board in Canada (review number 181676) and Regionala Etikprövningsnåmnden, Lund, Sweden, where appropriate. All those contacted had previously agreed to participate in internet-based surveys. Various channels including web banners, telephone interviews and personal interviews were used for recruitment to ensure it remained representative. Only people aged 18 years or older were approached and took part at their discretion, and respondent anonymity was preserved throughout. Depending on the country, respondents were offered ‘points’ for online shopping or entry in a draw (nominal value 1–2 euros) as incentives to participate. The questionnaire was programmed in a commercial survey software package. To improve the answer quality and prevent unconsidered responses, a 10-second delay was introduced to pages containing a large amount of text. The survey was carried out in Swedish (Sweden), German (Germany), English (Canada, UK, and US) and French (Canada). Canadian respondents could choose between a French- or an English-language version. The questionnaire was translated into the respective language and back to English to ensure the veracity of each translation.

The functionality of the questionnaire was validated in a pilot study of 200 respondents. Following this, a few minor adjustments were made, including the addition of extra background questions and a question for screening respondents trading a very high proportion of their remaining life expectancy, and slight wording alterations to increase clarity.

Respondents were excluded based on the following criteria:

In order to avoid respondent fatigue, individuals did not evaluate all 13 health states but were randomly assigned to:

In addition, respondents were required to provide their age and sex; to answer socioeconomic questions regarding their employment, household and region; and to complete an EQ-5D™ survey. Respondents with diabetes were asked to provide further information regarding the duration of diabetes, current medication, prevalence of hypoglycaemic events, awareness of hypoglycaemic events and current HbA_1c value or 7-day average blood sugar level. In the general population group, 1.8% and 8.7% of respondents had type 1 diabetes and type 2 diabetes, respectively.

All statistical analyses were performed using SAS® version 9.2 statistical software. A utility value was assigned to each health state based on each individual response, derived from the midpoint of the indifference interval derived as described above. The average utility value was calculated for each health state, and a disutility per hypoglycaemic event derived by dividing the difference between the average utility and the baseline diabetes state utility by the number of annual events, to ensure that the resulting value reflected the effect of hypoglycaemia alone.

For each hypoglycaemic event class, up to four different event frequencies (health states) were evaluated (Additional file 2: Table S2). Unit values per hypoglycaemic incident type were calculated from the average TTO value for each frequency, weighted according to the distribution of those specific hypoglycaemic event frequencies among the participants with diabetes.

Statistical results for the type 1 and type 2 diabetes populations contain combined data from any general population respondents with type 1 or type 2 diabetes and data from the respective type 1 or type 2 diabetes populations.

As the response distribution was unknown but suspected to be non-normal, non-parametric bootstrapping was used to simulate standard errors and confidence intervals (CIs) for the mean TTO values. This method estimates the parameter’s distribution by repeatedly resampling the original data set with replacement [37‐39]. For the present study, 10,000 iterations were performed.

Because it is rare for a patient with diabetes to experience severe hypoglycaemic events without non-severe events, some of the worries and limits to daily activities may already be accounted for by the disutility associated with non-severe events. Therefore, the initial disutility value (determined as the intercept in a regression model) for non-severe hypoglycaemic events was subtracted from the value obtained for severe hypoglycaemic events.

Of the original 11,196 general population respondents who started the questionnaire, 10,087 (90%) completed it. A further 1178 respondents (10.5%) were excluded for failing the test question and 623 (6%) were excluded due to inconsistencies. The final general population sample was based on 8286 respondents, constituting 82% of the initial total, spread across five countries (1696 from Canada, 1607 from Germany, 1635 from Sweden, 1675 from the UK and 1673 from the US). The diabetes populations comprised 551 people with type 1 and 1603 with type 2. Table 1 summarizes background characteristics for each population.

For the baseline diabetes health state, 22% of the general population respondents (19–21% for the other health states) chose either not to trade or to trade all, placing them at the distribution extremes. Of these, 8% (8–11% for the other health states) were excluded from analysis according to the predefined criteria. Similar magnitudes of exclusion were seen in the diabetes populations.

The average TTO utility values for each health state are shown in Table 2 and Additional file 3: Figure S1. The utility value for well-controlled baseline diabetes was 0.844. Respondents considered living in a health state with severe or non-severe nocturnal hypoglycaemia worse than living in a health state with severe or non-severe daytime hypoglycaemia, irrespective of event frequency (Table 2). Similarly, they considered a health state with severe hypoglycaemia worse than a health state with an equivalent frequency of non-severe hypoglycaemia (for ‘one quarterly’, daytime, the values were 0.739 and 0.812 respectively).

In general, as the frequency of hypoglycaemia increased, the utility value decreased, with the exception of non-severe daytime hypoglycaemia, where a frequency of one event quarterly resulted in the same utility as one event monthly (Table 2).

Table 3 shows the estimated average disutility associated with an annual hypoglycaemic event for the general population overall or stratified by country, and for the populations with either type 1 or type 2 diabetes.

Looking at the total population, a non-severe nocturnal hypoglycaemic event was associated with a disutility that was decreased by 0.003 (that is, from 0.007 to 0.004 [95% CI 0.002 to 0.003]) compared with non-severe daytime hypoglycaemia, equivalent to a significant 63.4% increase in negative impact (Figure 1). Severe nocturnal hypoglycaemia was associated with a disutility decrease of 0.006 (from 0.062 to 0.057 [95% CI −0.0001 to 0.0114]) compared with severe daytime hypoglycaemia, which was not statistically significant (Additional file 4: Figure S2).

When country-specific disutility values were compared with the overall values (Table 3), participants from Germany reported significantly lower disutilities (0.002 [95% CI 0.001 to 0.003] compared with 0.004 [95% CI 0.004 to 0.005] overall) for non-severe daytime hypoglycaemia (p = 0.00098) and non-severe nocturnal hypoglycaemia (0.004 [95% CI 0.003 to 0.006] compared with 0.007 [95% CI 0.006 to 0.007]; p = 0.0027). Similarly, Swedish participants also produced significantly lower disutilities for non-severe daytime hypoglycaemia (0.003 [95% CI 0.001 to 0.004] compared with 0.004 [95% CI 0.004 to 0.005] overall; p = 0.049) and severe daytime hypoglycaemia (0.047 [95% CI 0.038 to 0.055] compared with 0.057 [95% CI 0.053 to 0.061]; p = 0.032). In contrast, Canadian participants reported a significantly higher disutility for non-severe daytime hypoglycaemia (0.006 [95% CI 0.004 to 0.007] compared with 0.004 [95% CI 0.004 to 0.005] overall; p = 0.047). No country-specific significant differences in severe nocturnal hypoglycaemia disutilities were reported.

Overall, the disutilities obtained from the populations with type 1 or type 2 diabetes were similar to those of the general population. However, the type 2 diabetes population reported a significantly higher disutility for severe nocturnal hypoglycaemia (p = 0.008) compared with the general population (Additional file 5: Figure S3 and Additional file 6: Figure S4).

Correction of the initial disutility value for severe hypoglycaemic events resulted in an average reduction in the unit value for severe disutilities of 0.02 (ranging between 0.01 for Germany and 0.03 for UK; data not shown). However, this correction did not significantly affect the main findings.

Ethical approval was granted by the University of Western Ontario Health Sciences Research Ethics Board in Canada (review number 181676) and Regionala Etikprövningsnåmnden, Lund, Sweden. No ethical approval was required for the other participating countries.