Demographics

Overall, 1,440 adults with OI responded to the survey. Respondents were mostly female (70%) and from Europe (63%) with a median age of 43 years (range 18–85). As previously reported [23], most adults rated their OI as moderate (47%), while the smallest proportion rated their OI as severe (14%). Similarly, the majority of adults reported clinical OI type 1 (38%), 3 (16%) and 4 (11%) (Table 1; [23]). Examining the relationship between clinical OI type and self-reported OI severity revealed a broad alignment (Appendix Tables 4 and 5; [23]). Further details, including a breakdown by geographic region and employment status, are reported in Appendix Tables 4 and 5.

Healthcare resource use

Within a 12-month period, adults with OI reported visiting a wide range of HCPs (a mean total of 40.5 visits). These included visits to generalists, such as family doctors and nurse practitioners (mean total 7.7 visits); specialists, such as rheumatologists and neurologists (mean total 10.7 visits); and therapists, such as occupational and rehabilitation therapists (mean total 22.2 visits). Among these, the most frequently visited generalists were general practitioners/family doctors (mean 5.0 visits); the most frequently visited specialists were dentists/orthodontists (mean 2.3 visits); and the most frequently visited therapists were physiotherapists (mean 13.6 visits; Table 2 and Fig. 1A–C).

Table 2 Healthcare resource use, productivity loss and out-of-pocket spending related to OI during a given timeframeFig. 1

Visits to A generalist, B specialist and C therapist HCPs in the past 12 months a. Abbreviations: HCP, healthcare professional. Footnotes: a Box plot elements: Minimum: The lower end of the whisker represents the minimum value in the dataset, excluding outliers; First Quartile (Q1): The bottom edge of the box represents the first quartile, which is the value below which 25% of the data falls; Median (Q2): The horizontal line within the box represents the median, which is the middle value in the dataset when sorted in ascending order. It divides the data into two equal halves; Third Quartile (Q3): The top edge of the box represents the third quartile, which is the value below which 75% of the data falls; Maximum: The upper end of the whisker represents the maximum value in the dataset, excluding outliers; Interquartile Range (IQR): The length of the box, defined by the distance between the first quartile (Q1) and the third quartile (Q3), represents the interquartile range. It measures the spread of the central 50% of the data; Whiskers: The vertical lines extending from the box represent the range of values that fall within a certain distance from the quartiles. The specific range is often defined as 1.5 times the IQR. Data points beyond the whiskers are considered outliers; Mean: The ‘x’ represents the mean, which is the average value of the dataset and is a measure of the central tendency

Within a 12-month period, two-thirds of adults (66%) visited a hospital (mean 3.7 visits), and one-third (33%) visited the emergency department (mean 0.8 visits). A considerable proportion reported spending at least one night in hospital (14%) or rehabilitation (17%; mean 1.2 visits for both; Table 2 and Fig. 2).

Fig. 2

Frequency of hospital and in-patient care use in the past 12 months a,b,c,d,e. Footnotes: a Box plot elements: Minimum: The lower end of the whisker represents the minimum value in the dataset, excluding outliers; First Quartile (Q1): The bottom edge of the box represents the first quartile, which is the value below which 25% of the data falls; Median (Q2): The horizontal line within the box represents the median, which is the middle value in the dataset when sorted in ascending order. It divides the data into two equal halves; Third Quartile (Q3): The top edge of the box represents the third quartile, which is the value below which 75% of the data falls; Maximum: The upper end of the whisker represents the maximum value in the dataset, excluding outliers; Interquartile Range (IQR): The length of the box, defined by the distance between the first quartile (Q1) and the third quartile (Q3), represents the interquartile range. It measures the spread of the central 50% of the data; Whiskers: The vertical lines extending from the box represent the range of values that fall within a certain distance from the quartiles. The specific range is often defined as 1.5 times the IQR. Data points beyond the whiskers are considered outliers; Mean: The ‘x’ represents the mean, which is the average value of the dataset and is a measure of the central tendency; b All respondents were asked about their hospital visits in the past 12 months; c Emergency department visits were only asked to respondents who reported visits to the hospital in the past 12 months; d The number of nights spent in hospital was only asked of respondents who reported visiting the emergency department in the past 12 months; e All respondents were asked about how many nights they spent in hospital in the past 12 months

Within a 12-month period, adults underwent a mean total of 8.0 diagnostic tests, with blood tests (mean 2.6 tests), X-rays (mean 1.9 tests), and urine tests (mean 1.1 tests) being the most frequent (Table 2 and Fig. 3).

Fig. 3

Frequency of diagnostic tests in the past 12 months a. Abbreviations: CT, computerised tomography scan; MRI, magnetic resonance imaging. Footnotes: a Box plot elements: Minimum: The lower end of the whisker represents the minimum value in the dataset, excluding outliers; First Quartile (Q1): The bottom edge of the box represents the first quartile, which is the value below which 25% of the data falls; Median (Q2): The horizontal line within the box represents the median, which is the middle value in the dataset when sorted in ascending order. It divides the data into two equal halves; Third Quartile (Q3): The top edge of the box represents the third quartile, which is the value below which 75% of the data falls; Maximum: The upper end of the whisker represents the maximum value in the dataset, excluding outliers; Interquartile Range (IQR): The length of the box, defined by the distance between the first quartile (Q1) and the third quartile (Q3), represents the interquartile range. It measures the spread of the central 50% of the data; Whiskers: The vertical lines extending from the box represent the range of values that fall within a certain distance from the quartiles. The specific range is often defined as 1.5 times the IQR. Data points beyond the whiskers are considered outliers; Mean: The ‘x’ represents the mean, which is the average value of the dataset and is a measure of the central tendency

Up until the time of the survey, adults with OI had undergone a mean total of 11.8 surgeries. The most common were fracture repairs (mean 5.6 surgeries) and rodding surgeries (mean 3.2 surgeries) while surgeries related to basilar invagination (mean 0.1 surgeries) and the heart (mean 0 surgeries) were the least common (Table 2 and Fig. 4).

Fig. 4

Frequency of surgeries in an individual’s lifetime a. Footnotes: a Box plot elements: Minimum: The lower end of the whisker represents the minimum value in the dataset, excluding outliers; First Quartile (Q1): The bottom edge of the box represents the first quartile, which is the value below which 25% of the data falls; Median (Q2): The horizontal line within the box represents the median, which is the middle value in the dataset when sorted in ascending order. It divides the data into two equal halves; Third Quartile (Q3): The top edge of the box represents the third quartile, which is the value below which 75% of the data falls; Maximum: The upper end of the whisker represents the maximum value in the dataset, excluding outliers; Interquartile Range (IQR): The length of the box, defined by the distance between the first quartile (Q1) and the third quartile (Q3), represents the interquartile range. It measures the spread of the central 50% of the data; Whiskers: The vertical lines extending from the box represent the range of values that fall within a certain distance from the quartiles. The specific range is often defined as 1.5 times the IQR. Data points beyond the whiskers are considered outliers; Mean: The ‘x’ represents the mean, which is the average value of the dataset and is a measure of the central tendency

Within a 12-month period, the proportion of adults using queried consumables or services ranged from 18–82%, depending on the type of consumable or service. Dental work was the service used by the highest proportion of adults (82%); manual wheelchairs, walking aids and home modifications were used by equal proportions of individuals (45% for each; Table 2 and Fig. 5).

Fig. 5

Proportion of respondents reporting consumables or services use in the past 12 months

Drivers of healthcare resource use

Adults with self-reported moderate or severe OI reported higher resource use when compared with adults with mild OI. For instance, adults with moderate (IRR 1.7, P < 0.01) and severe (IRR 2.8, P < 0.01) OI were more likely to visit a physiotherapist within a 12-month period than those with mild OI. Exceptions were observed in visits to orthopaedic surgeons, neurologists, hospitals and ERs, where individuals with mild OI reported higher resource use (Appendix Tables 6 and 7, Appendix Figure 1A–E).

Various clinical signs, symptoms and events were associated with higher resource use. For example, individuals who experienced pain (IRR 2.6, P < 0.01) or leg fractures (IRR 4.7, P < 0.01) were more likely to spend a night in the hospital within a 12-month period compared with those without (Appendix Tables 6 and 7, Appendix Figure 2A–G).

Female respondents more frequently reported higher resource use when compared with male respondents. For example, within a 12-month period, female respondents were 2.0 (IRR, P < 0.01) times more likely to visit a neurologist (Appendix Tables 6 and 7, Appendix Figure 3A–E).

No consistent relationships in resource use were noted across age groups. For instance, while respondents aged 41–50 years were 6.0 (IRR, P < 0.01) times more likely to visit a nutritionist within a 12-month period when compared with 18- to 30-year-olds, they were 0.4 (IRR, P < 0.01) times as likely to visit a dentist (Appendix Tables 6 and 7, Appendix Figure 4A–E).

Productivity loss

Most adults with OI were in paid employment (58%; 34% employed full-time, 16% part-time, 7% self-employed and 1% in paid full-time internships or on sick leave from their paid positions). A substantial proportion (15%) were in early retirement due to their disability, and some (2%) faced challenges securing employment.

Within a 4-week period, nearly one-third (29%) of adults in paid employment reported missing workdays (mean 1.7 days missed; Table 2). Notably, one-third (33%) of adults expressed concerns about potential job loss.

Drivers of productivity loss

Respondents with self-reported moderate (IRR 2.3, P < 0.01) and severe (IRR 1.8, P < 0.01) OI were more likely to miss a day of work than individuals with mild OI (Appendix Tables 6 and 7, Appendix Figure 1F).

Various clinical signs, symptoms and events, such as fractures (excluding vertebral fractures), were associated with increased productivity loss. For instance, respondents who had experienced at least one arm fracture were 1.9 (IRR, P < 0.01) times more likely to miss a workday compared with those who had not (Appendix Tables 6 and 7, Appendix Figure 2H).

Female participants were 1.4 (IRR, P < 0.01) times more likely to miss a workday compared with male participants (Appendix Tables 6 and 7, Appendix Figure 3F).

Individuals aged 18–30 years missed fewer workdays than other age groups. For instance, adults aged 51–60 years were 1.7 (IRR, P < 0.01) times more likely to miss a workday compared with those aged 18–30 years (Appendix Tables 6 and 7, Appendix Figure 4F).

Out-of-pocket spending

Of the queried expenses, adults with OI spent a mean total of €191 (range €0–€42,292) out-of-pocket over 4 weeks (Table 2 and Fig. 6). Personal care or support assistance emerged as the category on which respondents spent the most.

Fig. 6

Out-of-pocket spending in the past 4 weeks a,b,c. Footnotes: a Out-of-pocket costs were converted into Euros (€) using the conversion rate in effect on July 1, 2021; b Respondents who indicated their use of Chilean peso (CLF) were excluded from the analysis due to complexities arising from the unusual currency conversion rate; c Box plot elements: Minimum: The lower end of the whisker represents the minimum value in the dataset, excluding outliers; First Quartile (Q1): The bottom edge of the box represents the first quartile, which is the value below which 25% of the data falls; Median (Q2): The horizontal line within the box represents the median, which is the middle value in the dataset when sorted in ascending order. It divides the data into two equal halves; Third Quartile (Q3): The top edge of the box represents the third quartile, which is the value below which 75% of the data falls; Maximum: The upper end of the whisker represents the maximum value in the dataset, excluding outliers; Interquartile Range (IQR): The length of the box, defined by the distance between the first quartile (Q1) and the third quartile (Q3), represents the interquartile range. It measures the spread of the central 50% of the data; Whiskers: The vertical lines extending from the box represent the range of values that fall within a certain distance from the quartiles. The specific range is often defined as 1.5 times the IQR. Data points beyond the whiskers are considered outliers; Mean: The ‘x’ represents the mean, which is the average value of the dataset and is a measure of the central tendency

Notably, almost two-thirds (64%) of adults with OI expressed concerns about their future financial circumstances.

Drivers of out-of-pocket spending

Adults with self-reported moderate or severe OI consistently spent more out-of-pocket when compared with adults with mild OI. For instance, individuals with self-reported moderate (IRR 27.9, P < 0.01) and severe (IRR 7.5, P < 0.01) OI were notably more likely to incur personal care or support assistance expenses compared with those with mild OI (Appendix Tables 6 and 7, Appendix Figure 1G).

Various clinical signs, symptoms and events were associated with higher out-of-pocket expenses. Remarkably, experiencing a fracture was the factor most strongly associated with a higher likelihood of spending on personal care or support assistance. Individuals who had fractured were 8.0 (IRR, P < 0.01) times more likely to incur these costs compared with those who had not (Appendix Tables 6 and 7, Appendix Figure 2I).

Female participants consistently reported higher out-of-pocket expenses than male participants. For example, female participants were 4.3 (IRR, P < 0.01) times more likely to incur personal care or support assistance expenses (Appendix Tables 6 and 7, Appendix Figure 3F).

No consistent relationships in out-of-pocket spending were noted across age groups. For instance, while individuals aged 51–60 years were 41.1 (IRR, P < 0.01) times more likely to incur personal care expenses compared with 18- to 30-year-olds, they were 0.8 (IRR, P < 0.01) times as likely to spend money on travel to medical appointments (Appendix Tables 6 and 7, Appendix Figure 4G).

Pairwise analyses supplementing analysis of drivers

The pairwise analyses revealed multiple factors associated with resource use (Appendix Tables 8–14), productivity loss (Appendix Table 15) and out-of-pocket spending (Appendix Table 16), most of which were consistent with the results of regression analysis. Inconsistencies are highlighted in Appendix Figures 1–4.

Impact of coronavirus disease (COVID-19)

The COVID-19 pandemic had a significant impact on the healthcare-seeking behaviour of adults with OI and may have resulted in a notable decrease in healthcare resource utilisation reported in the IMPACT survey. In the 12 months prior to survey fielding (1 July–30 September 2021), a substantial proportion of adults with OI reported not only visiting fewer healthcare providers (59%) but also receiving fewer medical diagnostic tests (54%) compared with their usual patterns. Moreover, half of the respondents (50%) reported a shift to predominantly online appointments, and a notable proportion (41%) reported actively avoiding seeking medical care during this period.