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Recombinant human growth hormone (rhGH) therapy is an effective treatment for children with growth disorders. However, poor outcomes are often associated with suboptimal adherence to treatment.
The easypod connected injection device records and transmits injection settings and dose data from patients receiving rhGH. In this study, we evaluated adherence to rhGH treatment, and associated growth outcomes, in Latin American patients.
Adherence and growth data from patients aged 2-18 years from 12 Latin American countries were analyzed. Adherence data were available for 6207 patients with 2,449,879 injections, and growth data were available for 497 patients with 2232 measurements. Adherence was categorized, based on milligrams of rhGH injected versus milligrams of rhGH prescribed, as high (≥85%), intermediate (>56%-<85%), or low (≤56%). Transmission frequency was categorized as high (≥1 per 3 months) or low (<1 per 3 months). Chi-square tests were applied to study the effect of pubertal status at treatment start and sex on high adherence, and to test differences in frequency transmission between the three adherence levels. Multilevel linear regression techniques were applied to study the effect of adherence on observed change in height standard deviation score (∆HSDS).
Overall, 68% (4213/6207), 25% (n=1574), and 7% (n=420) of patients had high, intermediate, and low adherence, respectively. Pubertal status at treatment start and sex did not have a significant effect on high adherence. Significant differences were found in the proportion of patients with high transmission frequency between high (2018/3404, 59%), intermediate (608/1331, 46%), and low (123/351, 35%) adherence groups (
The data extracted from the easypod Connect ecosystem showed high adherence to rhGH treatment in Latin American patients, with positive growth outcomes, indicating the importance of connected device solutions for rhGH treatment in patients with growth disorders.
Adherence to long-term pharmacological treatments, such as growth hormone (GH) therapy for growth disorders, is an area with great potential for improvement [
Adherence to GH treatment in the real-world setting has always been difficult to monitor, given the use of unreliable proxy methods such as patient testimony or records of prescriptions filled/vials counted [
Automatic transmission of injection data provides a more accurate insight into real-world adherence patterns and enables HCPs to potentially eliminate poor adherence as a reason for a suboptimal response to GH treatment [
As different health care systems and variations in clinical practice around the world may affect adherence locally, the deployment of a connected injection device for the treatment of growth disorders across different countries allows the study of behavioral adherence patterns across populations and longitudinally for thousands of patients. This substantial compendium of patient-generated data has also been applied in the context of understanding patterns in diabetes thanks to glucose monitoring technologies [
Global analysis of real-world data obtained from connected injection devices for GH treatment has shown that children with high adherence were most likely to regularly transmit data, and that prepubertal children showed higher adherence than older children and adolescents [
The Latin American region is one of the fastest growing regions in terms of the adoption of digital health [
In this analysis, we included children with growth disorders from 12 Latin American countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Guatemala, Mexico, Nicaragua, Panama, and Peru) and assessed the effects of pubertal status at treatment start, sex, and engagement with treatment on their adherence. An analysis of longitudinal records for a total of 13,553 children in the global database was recently published [
The easypod electromechanical injection device, in combination with the easypod Connect web-based platform, as part of an ADSS [
This was an exploratory descriptive analysis study during which 4 years of adherence data were analyzed from 6207 pediatric patients with 2,449,879 prescribed injections of rhGH delivered via easypod to treat growth disorders, and who were transmitting data to the easypod Connect system between January 2007 and December 2020. These patients resided in 12 Latin American countries (
Eligible patients from each of the participating countries had been enrolled in the database and attended at least one visit, according to local routine clinical practice. Diagnoses and decisions on treatment were made at the discretion of the physician responsible for each patient, following standard endocrinologic practice for each of the participating countries. Prior to enrollment, patients/caregivers reviewed and voluntarily signed an informed consent form materializing their agreement for data collection, storage, and use of their child’s pseudonymized data to create aggregated statistical and general adherence reports.
Participating Latin American countries.
For height, we selected patients with at least two measurements. Adherence (calculated as milligrams of rhGH injected versus milligrams of rhGH prescribed) was categorized as high (≥85%), intermediate (>56%-<85%), or low (≤56%) for patients on either 6 or 7 injections per week, the two possible regimens for treatment with rhGH. The dosage and frequency of rhGH therapy as per easypod settings were defined by HCPs and data transmissions were initiated by the child, parent/caregiver, or HCP. Adherence was assessed overall and monthly, and explored by puberty status at treatment start (nominal cutoffs at 10 years for girls, and 12 years for boys), sex, and transmission frequency (defined as the total number of transmissions divided by the duration of treatment, and categorized as high [≥1 per 3 months] versus low [<1 per 3 months]) in the selected patients with available adherence data for ≥3 months between January 2016 and December 2020. Transmission frequency was calculated in each adherence category as a proxy measure of the patient’s engagement in disease management using easypod. No imputation was made for missing data or withdrawal from the study.
Height data were available for 497 patients with 2232 measurements; this included 355 patients from Argentina, 64 from Brazil, 70 from Guatemala, and 8 from Mexico. Height standard deviation scores (HSDS) were calculated using the World Health Organization references [
Descriptive statistics were used to describe differences over time in adherence (low, intermediate, or high), puberty status (prepubertal or pubertal), and sex. Chi-square tests were applied to test differences in high adherence between girls and boys, and between prepubertal and pubertal girls and boys. In addition, a Chi-square test was applied to test differences in high frequency transmission between the high, intermediate, and low adherence groups. Multilevel linear regression techniques were applied to study the effect of adherence level on ∆HSDS between all observed growth measurements, adjusted for the time intervals between them.
Treatment with rhGH via easypod was conducted according to local practice. This real-world, retrospective analysis of the data set was performed in accordance with the informed consent form, signed by caregivers of children and adult patients materializing their agreement for data collection, storage, and use of their pseudonymized data to create aggregated statistical and general adherence reports.
Any requests for data by qualified scientific and medical researchers for legitimate research purposes will be subject to the healthcare business of Merck KGaA’s Data Sharing Policy. All requests should be submitted in writing to the healthcare business of Merck KGaA’s data sharing portal [
Complete data were available for 6207 patients, where “overall” is defined here (and throughout) as the total number of patients who received rhGH, started treatment at age 2-18 years, and for whom data were available. Transmission data were available for 5086 patients. Patient demographics according to adherence rates are presented for this data set in
Overall patient demographics according to adherence rates.
Characteristic | Adherencea | Total (N=6207) | |||
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High (n=4213) | Intermediate (n=1574) | Low (n=420) |
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Mean age (SD) of boys at start, years | 10.6 (3.4) | 10.9 (3.3) | 10.6 (3.4) | 10.7 (3.3) | |
Boys aged <12 years at start, n (%) | 1424 (68) | 526 (25) | 157 (7) | 2107 (34) | |
Boys aged ≥12 years at start, n (%) | 985 (66) | 394 (26) | 112 (8) | 1491 (24) | |
Mean age (SD) of girls at start, years | 9.7 (2.8) | 10.0 (2.8) | 9.8 (3.2) | 9.8 (2.8) | |
Girls aged <10 years at start, n (%) | 839 (69) | 308 (25) | 72 (6) | 1219 (20) | |
Girls aged ≥10 years at start, n (%) | 965 (69) | 346 (25) | 79 (6) | 1390 (22) |
aAdherence was categorized as high (≥85%), intermediate (>56%-<85%), or low (≤56%).
Proportion of patients who were adherent at each time point. Adherence was recorded for the cross-section of children or their caregivers transmitting data at each time point; no imputation was made for missing data or withdrawal from the study.
Overall, 68% (4213/6207) of patients were in the high adherence category, 25% (n=1574) were in the intermediate adherence category, and 7% (n=420) were in the low adherence category. Furthermore, at each time point, there was a higher proportion of patients in the high adherence category than in the intermediate and low categories combined (
There were no significant differences in high adherence between boys (2409/3598, 67%) and girls (1804/2609, 69%) or between prepubertal and pubertal patients (1424/2107, 68% versus 985/1491, 66% in boys, both 69% [839/1219; 965/1390] in girls, respectively).
Adherence at each time point stratified by nominal pubertal age and sex (A: boys, B: girls). Adherence was recorded for the cross-section of children or their caregivers transmitting data at each time point; no imputation was made for missing data or withdrawal from the study.
In addition, there were significant differences in the proportion of patients with high transmission frequency between the adherence groups; 59% (2018/3404), 46% (608/1331), and 35% (123/351) in the high, intermediate, and low adherence groups, respectively (
The proportion of children in the high adherence category varied by country, ranging from 45% (10/22) in the Dominican Republic to 82% (943/1144) in Brazil (
Adherence rates by age, sex, and country.
Country and adherencea | Boys aged |
Boys aged |
Girls aged |
Girls aged |
Total number of |
Adherence ≥85%, |
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1147 | 759 (66) | |||||||
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High | 322 | 163 | 151 | 123 |
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Intermediate | 113 | 72 | 66 | 41 |
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Low | 46 | 19 | 18 | 13 |
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1144 | 943 (82) | |||||||
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High | 347 | 225 | 169 | 202 |
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Intermediate | 68 | 39 | 35 | 35 |
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Low | 9 | 3 | 6 | 6 |
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1344 | 949 (71) | |||||||
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High | 291 | 242 | 176 | 240 |
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Intermediate | 96 | 84 | 44 | 95 |
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Low | 17 | 33 | 12 | 14 |
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1122 | 625 (56) | |||||||
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High | 170 | 152 | 149 | 154 |
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Intermediate | 125 | 108 | 81 | 86 |
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Low | 39 | 22 | 18 | 18 |
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10 | 8 (80) | |||||||
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High | 4 | 3 | 0 | 1 |
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Intermediate | 0 | 1 | 1 | 0 |
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Low | 0 | 0 | 0 | 0 |
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22 | 10 (45) | |||||||
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High | 1 | 3 | 3 | 3 |
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Intermediate | 2 | 0 | 0 | 4 |
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Low | 2 | 3 | 0 | 1 |
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50 | 27 (54) | |||||||
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High | 7 | 12 | 5 | 3 |
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Intermediate | 8 | 5 | 2 | 3 |
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Low | 1 | 1 | 2 | 1 |
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321 | 221 (69) | |||||||
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High | 71 | 38 | 43 | 69 |
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Intermediate | 26 | 14 | 18 | 16 |
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Low | 8 | 12 | 0 | 6 |
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554 | 355 (64) | |||||||
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High | 113 | 83 | 72 | 87 |
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Intermediate | 48 | 40 | 32 | 33 |
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Low | 23 | 9 | 6 | 8 |
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68 | 33 (49) | |||||||
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High | 13 | 8 | 5 | 7 |
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Intermediate | 8 | 7 | 5 | 4 |
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Low | 6 | 1 | 2 | 2 |
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43 | 23 (53) | |||||||
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High | 6 | 5 | 4 | 8 |
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Intermediate | 5 | 3 | 3 | 6 |
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Low | 2 | 1 | 0 | 0 |
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382 | 260 (68) | |||||||
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High | 79 | 51 | 62 | 68 |
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Intermediate | 27 | 21 | 21 | 23 |
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Low | 4 | 8 | 8 | 10 |
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aAdherence was categorized as high (≥85%), intermediate (>56%-<85%), or low (≤56%).
Catch-up growth between 0-24 months stratified by low/intermediate adherence (<85%) and high adherence (≥85%). ∆HSDS: change in height standard deviation score.
The data extracted from the easypod Connect ecosystem showed that children receiving rhGH therapy demonstrated high adherence over 4 years in a real-world setting in 12 Latin American countries.
Use of easypod Connect, which showed high adherence in patients with high transmission rates, could be regarded as a proxy of usability and utility of the system by patients and HCPs. In such large-scale deployments of digital health systems, usability cannot be easily measured using questionnaires as this would pose a major user/participant burden. Instead, transmissions and data usage can be used toward identifying patterns of usage of the system and adherence to treatment. Indeed, in this regard, a framework has been proposed to guide future implementation and research on the use of digital health tools to support patients with growth disorders who require GH therapy [
We observed that patients who transmitted their data tended to have higher adherence; therefore, a potential hypothesis to further explore is the impact of patients knowing that their adherence data is being observed and is useful to their HCP. Studies in other therapeutic areas have found that factors related to the Theory of Planned Behavior can predict ~50% variance in adherence [
The selected data from this regional study are broadly consistent with recently published data from the global easypod Connect database, which demonstrated that, of 13,553 children, 71% were in the high adherence category [
In both the preliminary Latin American study and other analyses, the proportion of children with high adherence declined over time, and factors such as sex and nominal age at puberty appeared to have only a small effect on adherence rates [
Patient attrition over time may be due to a number of factors. Patients may have been switched to a different rhGH, may have continued taking somatropin rhGH using a pen injector, may have continued taking rhGH using easypod but without performing any further data transmission, or may have stopped treatment completely. Minimizing patient attrition is an important consideration in long-term clinical trials, and determining the predictors of attrition is key to identifying patients at risk of missed visits or dropout; such patients may be excluded from a trial or efforts may need to be made to prevent their subsequent dropout once enrolled [
Indeed, previously reported individual cases of patients receiving rhGH have indicated that direct access to adherence monitoring by HCPs followed by intervention can make a difference to a patient’s management and motivation [
Optimizing adherence might also be achieved through the use of structured and active interventions from HCPs, patient/caregiver support programs [
The strengths of our study include the large data set from a real-world study conducted across 12 countries in one geographic region with substantially diverse and dynamic health care systems, and the fact that the data are derived from a connected injection device, which offers more reliable data compared to data based on the declarations of patients or their parents/caregivers. Comparable insights into patient adherence from other large-scale patient registries for rhGH treatment are not available due to the lack of alternative electronic devices similar to easypod.
Further work is required to assess whether patient/caregiver engagement, as measured by the rate and frequency of data transmission with easypod, is associated with better long-term adherence and clinical outcomes for patients. It would also be interesting to investigate whether or not there is a correlation between the frequency of dose adjustments and the adherence/transmission rate.
Limitations of the study include patient attrition, summarized above, and the fact that the change in adherence rate over time varied from child to child, perhaps due to changes in individual treatment plans or other actions taken by the HCP or child/family. Furthermore, not all data were available for all patients over the same treatment duration, as would be expected in any observational study. Differences between the 12 participating countries in terms of socioeconomic factors, such as reimbursement and/or out-of-pocket expenses for GH therapy or free access to medication, may also have affected individual or local adherence rates. Similarly, differences in prescribing habits (eg, prescription provided for 1, 2, 3, or 6 months), patients’ visits to the clinic where growth response is checked and dose might be adjusted to maximize response to treatment, and refill of prescriptions from country to country may also affect adherence rates. Other potential limitations include the lack of additional information on diagnosis and clinical background, limited data on growth outcomes that do not allow assessment of the full catch-up growth pattern, and lack of patient-reported data such as reasons for discontinuation or interruption of treatment, or predefined actions taken by PSPs [
Finally, usage of the system (ie, adherence and transmission rates) can be a proxy of usability and utility; however, usage has been to a large extent influenced by the way the system was introduced. All of these areas require more research. Our analysis showed less catch-up growth (–0.17 SD over 24 months) in patients who continue to have low/intermediate adherence compared with patients with high adherence, and this difference increased over time. This difference could be translated into centimeters, resulting in 1.1 cm less catch-up growth between 0-24 months for a patient with an average age for the group (10 years). This is in agreement with the literature, which shows that greater adherence leads to improved growth, with poor adherence adversely affecting growth outcomes [
In terms of future research opportunities, analysis of the potential differences between countries with preset additional data collection points would be of interest. The value of this data can be enhanced by complementing it with self-reported height data entered via a patient app. Future research may also allow for automatic self-measurement of height using novel augmented reality technology on mobile phones. Finally, integration with EHRs may facilitate clinical workflows, as has been demonstrated in other therapy areas [
Analysis of the data extracted from easypod Connect showed high adherence to rhGH treatment in Latin American patients, with positive growth outcomes. Thus, our study indicates the potential value of using a connected injection device to monitor and study adherence at an international level. It shows that through our validated method of recording adherence with easypod, we can address an unmet need in rhGH therapy, enabling HCPs to accurately identify patients for whom interventions to improve adherence would be beneficial to improve their growth and other clinical outcomes, particularly as the proportion of children with high adherence declined over time, which is consistent with previous findings. The data also show that children who were most adherent to treatment were more likely to transmit their injection data results regularly and have larger catch-up growth than those who were less adherent. This association between adherence and transmission of data may indicate that sharing data with HCPs has a positive impact on adherence rates, and further studies to confirm this are needed. High adherence and transmission rates may reflect the positive use of the system and could be regarded as indirect indicators of usability and utility of the system by patients and HCPs.
change in height standard deviation score
adherence decision support system
Easypod Connect Observational Study
electronic health record
growth hormone
health care provider
height standard deviation score
patient support program
recombinant human growth hormone
This study was funded by the healthcare business of Merck KGaA, Darmstadt, Germany (CrossRef Funder ID: 10.13039/100009945).
The authors would like to thank the patients and their families and the health care professionals who participated in this study. They would also like to thank Dr Ignacio Bergadá of Hospital de Niños Ricardo Gutiérrez, Buenos Aires, Argentina, for his contribution to the review and interpretation of the data and revision of the manuscript. Medical writing assistance was provided by David Candlish and Amy Evans of inScience Communications, Springer Healthcare Ltd, and funded by the healthcare business of Merck KGaA, Darmstadt, Germany, in accordance with Good Publication Practice (GPP3) guidelines.
AA contributed to review/interpretation of the manuscript. PvD was involved in data analysis, and in data review/interpretation and revision of the manuscript. LA was involved in data collection/analysis and revision of the manuscript. CO contributed to the analysis of the data set and the results of the research project from the perspective of health informatics, evaluation of the evidence for or against the project, and bibliographic review from the perspective of health informatics. LFL was involved in the study design, data interpretation, and revision and review of the manuscript. EK was involved in the concept design, data analysis design, data review/interpretation, and revision of the manuscript. LEC was involved in the design of the study, data review/interpretation, and revision of the manuscript. All authors have approved the final version of the manuscript.
AA is an employee of Merck SA, Buenos Aires, Argentina (an affiliate of Merck KGaA, Darmstadt, Germany). PvD has a consultancy agreement with the healthcare business of Merck KGaA, Darmstadt, Germany. LA is an employee of Ares Trading SA (an affiliate of Merck KGaA, Darmstadt, Germany). CO does not have any conflicts of interest to declare. LFL is Chief Scientific Officer at Adhera Health Inc, which has a commercial relationship with the healthcare business of Merck KGaA, Darmstadt, Germany. EK is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany, and holds shares in the company. LEC has received honoraria as a lecturer from the healthcare business of Merck KGaA, Darmstadt, Germany, NovoNordisk, and Pfizer.