The proportion of older adults in Hong Kong is expected to increase from 18% in 2019 to 26% by the year 2029.1 Overcrowding is a serious problem, such that population densities of 57 530 people/km² are present in ageing districts.2 3 Most residents of Hong Kong live in high-rise apartments that require elevators for access, but most elevators cannot accommodate an ambulance stretcher with a patient in a supine position.4 More than 50% of out-of-hospital cardiac arrest (OHCA) events occur in private homes, a location that is associated with poor survival outcomes.5 The proportion of domestic households consisting solely of people aged ≥65 years has increased by approximately 24% between 2011 and 2016, from 8.4% to 10.4%.6 Considering these demographic changes, there is a need for improved overall understanding of the prehospital management of cardiac arrests that involve older adults in homes and other locations. This study investigated patient characteristics, types of bystanders involved, and prehospital interventions that were associated with differences in survival outcomes among cardiac arrests involving older adults in homes, compared with cardiac arrests on streets and in public areas excluding streets (PAES).

This secondary analysis focused on a historical cohort from a previous study.5 The Emergency Ambulance Service of the Fire Services Department (FSD) provides most emergency medical services (EMS) in Hong Kong through a one-tiered system that serves the entire 1104 km² region. At the time of data collection, the population was around 7.1 million.7 Ambulance personnel are required to perform cardiopulmonary resuscitation (CPR) on and transfer most cases of OHCA to hospitals. A small number of patients with obvious post-mortem changes (eg, rigor mortis) may be directly transferred to the public mortuary; such patients were not included in this study. Fire Services Department ambulances will only transfer patients to emergency departments under the Hospital Authority. At the time of data collection, callers requesting for EMS for OHCA patients were not provided with post-dispatch instructions to perform CPR.

This secondary analysis included all patients with OHCA who were transferred to the Emergency Departments (EDs) by FSD ground ambulances from 1 August 2012 to 31 July 2013. Exclusion criteria were cardiac arrests caused by trauma, patients not transferred by ground ambulance, and patients directly transferred to the public mortuary. After patient selection from the primary dataset, the following additional exclusions were made: cardiac arrests that involved patients aged <65 years, occurred within residential care homes for the elderly, or occurred in the ambulance en route to hospital.

Data regarding patient characteristics and prehospital management were prospectively collected by EMS personnel who were directly involved in prehospital care for patients who experienced OHCA. The collected data included patient age and sex, location of cardiac arrest, whether the cardiac arrest was witnessed and the identity of the witness, whether bystander CPR was performed and who performed it, whether defibrillation with an automated external defibrillator (AED) was performed, what electrocardiogram rhythm was first detected, the timings of prehospital events (recognition of cardiac arrest, receipt of EMS call, initiation of bystander CPR, initiation of first defibrillation, EMS arrival at patient’s side, initiation of CPR by EMS personnel, and arrival at the ED), and return of spontaneous circulation (ROSC) before ED arrival.

Electronic medical records at the relevant ED (Accident and Emergency Information System, Hong Kong Hospital Authority) were reviewed to determine the time of defibrillation and time of ROSC at the ED, as well as whether a patient survived until admission. A patient was assumed to have received no resuscitative intervention unless specific documentation was present in the ED record. Neurological status upon discharge and survival at 30 days after cardiac arrest were determined from a territory-wide electronic medical record database (Clinical Management System, Hong Kong Hospital Authority).

Streets were defined as paved thoroughfares for pedestrians, including sidewalks. Public areas excluding streets were other areas that were accessible by the public throughout the day; these included outdoors (eg, parks and markets) and indoor facilities (eg, eateries, places of recreation, and day care facilities for older adults). Bystanders were defined in accordance with the guidelines of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest.8 Fire Services Department first responders dispatched to the scene were classified as EMS personnel. Older adult care workers (OACWs) are individuals who care for residents in various private and public housing arrangements for older adults. Older adult care workers accompanying patients were not dispatched as part of the organised emergency rescue team; thus, they were classified as bystanders. Public access defibrillation (PAD) was defined as a defibrillation shock delivered from an AED when a bystander performed CPR. Shocks delivered when FSD first responders performed CPR were excluded.

Time intervals were rounded to the nearest minute. The decision interval was the interval between recognition of cardiac arrest and receipt of EMS call. Call-to-bystander CPR was the interval between receipt of EMS call and initiation of bystander CPR. Call-to-EMS arrival was the interval between receipt of EMS call and EMS arrival at the patient’s side. Time of first defibrillation was defined as the time of the earliest of the following three events: PAD, defibrillation by EMS, or defibrillation in the ED. Call-to-bystander CPR intervals were grouped as 0-2, 3-5, 6-8, 9-11, and 12-31 minutes, as well as no bystander CPR. Call-to-first defibrillation intervals were grouped as 0-5, 6-10, 11-15, 16-20, and 21-55 minutes, as well as no defibrillation (>55 minutes/not applicable).

Post-cardiac arrest neurological status was classified using the 5-point Glasgow-Pittsburgh Cerebral Performance Categories (CPC) scale. In the scale, CPC 1 represents patients with good cerebral performance; CPC 2 includes patients who can manage activities of daily living independently or participate in part-time work in a sheltered environment; CPC 3 to CPC 5 ranges from patients who are unable to live independently because of cerebral disability to patients who have experienced brain death. Patients with CPC 1 or CPC 2 were presumed to have a favourable neurological outcome.

Patient characteristics, interventions, and outcomes were analysed using descriptive statistics. Pearson’s χ² test was used to compare categorical variables; Fisher’s exact test was used if >20% of expected counts were <5. The Kruskal–Wallis rank sum test was used to compare non-parametric time intervals. A P value of <0.05 was considered statistically significant. Predictors of 30-day survival were analysed using univariate and multivariate logistic regression; findings were reported as odds ratios (ORs) with 95% confidence intervals. Adjusted variables included age; sex; arrest location; person witnessing the arrest (relative, OACW or other bystanders, EMS personnel, or unwitnessed); person performing bystander CPR (no bystander CPR, OACW, relative, or other); PAD (yes or no); first monitored rhythm (asystole, pulseless electrical activity, ventricular fibrillation/ventricular tachycardia); and call-to-EMS arrival, call-to-bystander CPR, and call-to-first defibrillation intervals.

Statistical analysis was performed using R software, version 3.6.1 (R Foundation for Statistical Computing, Austria). The original study was approved by the Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (Ref No.: UW 15-599). No new data were collected for secondary analysis. This manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines.

Figure 1 describes patient selection from the primary dataset. The original cohort comprised 5154 patients with OHCA who were transferred to the ED by FSD ground ambulances. After the application of exclusion criteria, 2255 patients were included in the analysis. Table 1 compares the patient and bystander characteristics, interventions, and outcomes of OHCA occurring in homes, in PAES, and on streets. Patients who experienced cardiac arrest in homes were significantly older (approximately 5 years; P<0.001) than patients who experienced cardiac arrest on streets or in PAES. In all groups, there were more male patients; the sex disparity was the greatest in the streets group, followed by the PAES group.

Furthermore, most cardiac arrests (66.4% among all patients; P<0.001; Table 1) were unwitnessed. Relatives were the most common type of bystander present in witnessed arrests, whereas there were significant differences in the involvement of OACWs, EMS personnel, and other individuals at the three locations. Compared with EMS personnel, there were more OACWs as bystanders in PAES and more bystanders, represented by the ‘other’ group, in PAES and on streets. There was a significant difference in the proportion of bystanders performing CPR among the three locations (P<0.001), as illustrated in Figure 2. The bystander CPR rate was the highest in PAES and the lowest in homes. Among the nine patients who received PAD, six received it when OACWs provided CPR in PAES.

Notably, asystole was the most common initial monitored rhythm (81.2% among all patients; P<0.001; Table 1). However, cardiac arrests on streets and in PAES had significantly higher rates of shockable rhythm and PEA, compared with cardiac arrests in homes. The prevalences of shockable rhythm relative to the time from receipt of EMS call and the location of cardiac arrest are shown in Figure 3. The highest rates of shockable initial rhythm (SIR) were observed within the first 5 and 10 minutes after receipt of EMS call for cardiac arrests on streets, which were 47% (8/17) and 41% (17/42), respectively.

Patients with cardiac arrest in homes had significantly longer intervals in terms of receipt of EMS call, initiation of bystander CPR, and receipt of defibrillation (all P<0.001; Table 1). The median interval for EMS to reach patients was 3 minutes longer in homes than on streets. The interval between recognition of cardiac arrest and receipt of EMS call was 0 minutes in 57.5% of patients (1297/2255).

Additionally, patients with cardiac arrest in homes had significantly lower rates of ROSC, 30-day survival, and favourable neurological outcomes (all P<0.001; Table 1).

Independent predictors of 30-day survival are shown in Table 2. Older age and longer call-to-EMS arrival interval both decreased the overall likelihood of survival (ORs of 0.92 and 0.87, respectively). Pulseless electrical activity and ventricular fibrillation/ventricular tachycardia increased the likelihood of survival compared with asystole (ORs of 6.4 and 15.6, respectively). Cardiac arrest witnessed by EMS personnel and defibrillation within 15 minutes after receipt of EMS call increased the overall likelihood of survival (ORs of 6.23 and 4.07, respectively).

The relationship among the location, timing of defibrillation, and 30-day survival of cardiac arrest is shown in Figure 4. Overall, patients who received defibrillation within 5 minutes and at 6 to 10 minutes after receipt of EMS call had survival rates of 33% (2/6) and 17% (15/86), respectively. For patients who received defibrillation on streets/in PAES within 5 minutes and at 6 to 10 minutes after receipt of EMS call, the survival rates were 50% (2/4) and 22% (10/45), respectively. Two patients in the homes group received defibrillation within 5 minutes; the survival rate was 0% (0/2).

Cardiac arrest at home was a predictor of survival in univariate analysis (OR=0.076, 95% confidence interval [CI]=0.038-0.15) but not in multivariable analysis (OR=0.65, 95% CI=0.22-1.90). The effect of location on survival was mediated by the first monitored rhythm, and the call-to-EMS arrival interval.

This study investigated factors that affect the prevalences of shockable rhythm and survival outcomes among cardiac arrests involving older adults in Hong Kong. The patient characteristics, proportion of witnessed arrests, and rates of SIR and PAD for cardiac arrests involving older adults in homes were similar between the present study and a previous analysis in Japan.9 Unlike many western countries, EMS personnel in Hong Kong and Japan generally do not terminate resuscitation in the field; this similarity facilitates comparison of data between the two studies. A notable difference was that in Japanese homes, 45% of older patients received bystander CPR; this receipt of CPR was associated with rate of ROSC, 30-day survival, and favourable neurological outcomes that were threefold higher than the corresponding rates in Hong Kong.

The bystander CPR rate in Hong Kong homes was low (3.8%) [Table 1], and there was a substantial delay in its initiation. Although the type of relatives involved as bystanders was not recorded in the present study, considering the proportion of older adult households in Hong Kong,6 many of the relatives presumably were cohabiting older adults. Such individuals may not be able to follow telephone instructions to perform CPR because of physical limitations or emotional distress10; thus, the provision of post-dispatch instructions and enhancement of community-wide CPR training will not improve survival among these patients.11 Although high-rise apartments create barriers to EMS personnel, they also increase the likelihood that trained volunteers will be present in the vicinity, where they may be dispatched using mobile applications.12 13 14

In non-residential locations, most bystanders performing CPR were not relatives of the patients. Fear of legal consequences is reportedly a major cause for intervention inertia in this situation.15 A previous survey in Hong Kong, in which one-third of respondents had prior first aid training, revealed that nearly all respondents were willing to call for help but only one-fifth were willing to perform bystander CPR.16 These findings suggest that knowledge transfer is insufficient to overcome bystander inertia in Hong Kong. Training programmes should ensure that factors inhibiting intervention (eg, legal concerns, fear of disease transmission, and bystander effect) are addressed.17 18

Previous studies in Hong Kong revealed low rates of SIR in patients with OHCA, ranging from 5% to 14%, along with dismal survival rates of 0.6% to 3%.1 19 20 These low rates imply that aggressive bystander interventions (eg, defibrillation for older adults) are futile. However, the findings of the present study indicate that older adults in non-residential locations have much higher SIR rates in the initial 10 minutes after receipt of EMS call; moreover, early defibrillation is an independent predictor of survival among such patients, and high survival rates can be achieved with early defibrillation.

The present study revealed a 2% per-minute decrease in the rate of SIR. This is similar to the findings in a large multinational study from northern Europe.21 Differences in SIR rates between residential and non-residential locations may be partly related to patient factors (eg, age and presence of co-morbidities); they could also be related to differences in the decision interval (ie, time elapsed between recognition of cardiac arrest [as reported by a bystander] and receipt of EMS call). A previous study in Hong Kong showed that efforts to seek advice from relatives often contributed to delayed receipt of EMS call.4 Longer decision intervals and consequential delays in EMS arrival lead to interactions with later parts of the shockable rhythm downslope and lower SIR rates. In practice, the recall of decision intervals by bystanders is unreliable. This is consistent with the decision interval of 0 minutes reported by most bystanders in the present study. Despite this confounding factor, the findings in this study indicate that bystanders should not hesitate to provide aggressive resuscitation and early defibrillation for older patients.

Notably, very few patients received PAD in this study, and most instances of PAD administration were performed by OACWs in PAES. According to a nationwide study in Japan, 16.5% of patients received PAD during witnessed ventricular fibrillation cardiac arrest.22 Estimation of the AED coverage rate in Hong Kong using a horizontal level walking route distance model revealed that only 11% of patients with OHCA would have an AED within 100 m.23 Considering the large number of OHCA events occurring within high-rise buildings, the actual coverage rate is presumably lower. Furthermore, there is evidence that most people in Hong Kong do not know the location of the AED nearest to their home or workplace.16 Unless AEDs are easy to locate and readily accessible at all times, PAD rates will remain low.24

In a previous study in Hong Kong, the proportions of patients with OHCA who could be accessed by elevator or stairs and by stairs alone were 74% and 14%, respectively.4 In the present study, the median interval for EMS to reach patients was 3 minutes longer in homes than on streets. This represents the ‘vertical response time’ component of the call-to-EMS arrival interval.25 In a previous study, survival was lower among patients who experienced cardiac arrest at higher levels within buildings.26 Nearly 70% of lifts in Hong Kong do not have sufficient area to accommodate the ambulance stretcher.4 Therefore, the vertical response time leads to a delay in EMS interventions and deterioration in CPR quality, both of which may contribute to the poor outcomes of cardiac arrests that occur in homes. The use of circulatory adjuncts to enhance cerebral perfusion during head-up position CPR within lifts should be considered.27

Importantly, only patients transported to hospital by FSD ground ambulances were included in this study; a small number of patients with OHCA may have been transported to hospital by other means.

Furthermore, data regarding the timings of recognition of cardiac arrest, bystander CPR, and PAD obtained from bystanders may have been subject to response bias. The lack of blinding of emergency department personnel towards patient factors (eg, absence of shockable rhythm and prehospital defibrillation, longer time to ROSC, co-morbidities, and advanced age) may have led to selection bias regarding treatment decisions, including the termination of resuscitation, arrangement of intensive care unit resources, and coronary angiography; such bias has been reported to negatively influence the survival rate.28 Data regarding pre-arrest co-morbidity and functional status were not available, which may have resulted in a confounding effect on survival outcomes. Additionally, a small number of patients received defibrillation within 5 minutes. All of the factors listed here may have affected the accuracy of conclusions drawn from this subset.

This study was based on territory-wide data collected in 2012 to 2013. Thus, it may not reflect the current situation because of changes in patient demography, prevalence of shockable rhythm, and survival enhancement interventions introduced in the past several years. A large multinational study in northern Europe investigated the rate of SIR among OHCA events occurring in residential and public locations from 2006 to 2015. The rate of SIR in public locations remained stable during that period. A decrease was observed in residential locations between 2006 and 2010, but the proportion has remained stable since 2011.21 Therefore, despite these limitations, the findings of the present study add to the broader understanding of OHCA involving older adults.

This study revealed significant differences in the patient and bystander characteristics and prehospital interventions among cardiac arrests involving older adults that occurred in homes, on streets, and in other public locations. Many older adults who experienced cardiac arrest in non-residential locations had a shockable rhythm in the early period after receipt of EMS call. Early defibrillation, an independent predictor of survival, was associated with favourable survival outcomes in older adults. These findings suggest that bystanders should provide aggressive resuscitation, including early defibrillation. Additionally, low rates of shockable rhythm and significant delays in bystander and EMS processes were observed within homes. New interventions (eg, volunteer dispatch via mobile applications) are needed to overcome unfavourable factors that affect cardiac arrests occurring within older adult households. Finally, the overall bystander CPR rate was low, indicating that additional measures are needed to overcome bystander inertia. The insights from this study will help to improve survival outcomes in OHCAs involving older adults.

The author contributed to the concept or design, analysis or interpretation of data, drafting of the manuscript, and critical revision of the manuscript for important intellectual content. The author had full access to the data, contributed to the study, approved the final version for publication, and takes responsibility for its accuracy and integrity.

The author has no conflicts of interest to disclose.

The author thanks Dr Ling-pong Leung, Emergency Medicine Unit of The University of Hong Kong, for providing the original dataset and permitting its use for secondary analysis in the study. The author also thanks Mr Min Fan and Ms Lujie Chen, both from Emergency Medicine Unit of The University of Hong Kong, for their technical support in this study.

This research received no specific grant from any funding in the public, commercial, or not-for-profit sectors.

This study is a secondary analysis of a historical cohort study which was approved by the Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (Ref No.: UW 15-599). The requirement for informed patient consent was waived because of the retrospective study design. All patient data in the dataset were anonymous.

1. Census and Statistics Department, Hong Kong SAR Government. Hong Kong population projections 2020-2069. Available from: https://www.censtatd.gov.hk/hkstat/sub/sp190.jsp?productCode=B1120015. Accessed 7 Nov 2020.

2. Census and Statistics Department, Hong Kong SAR Government. The profile of Hong Kong population analysed by District Council district, 2018. Table 2. Proportion of land-based non-institutional population by District Council district and age group, 2017. Available from: https://www.censtatd.gov.hk/en/data/stat_report/ product/FA100096/att/B71807FB2018XXXXB0100.pdf. Accessed 24 Mar 2020.

3. Census and Statistics Department, Hong Kong SAR Government. 2016 Population By-census Office. Main tables (demographic). Population density by District Council district and year. 2017. Available from: https://www.bycensus2016.gov.hk/en/bc-mt.html?search=A202. Accessed 24 Mar 2020.

4. Leung LP, Wong TW, Tong HK, Lo CB, Kan PG. Out-of-hospital cardiac arrest in Hong Kong. Prehosp Emerg Care 2001;5:308-11. Crossref

5. Fan KL, Leung LP, Siu YC. Out-of-hospital cardiac arrest in Hong Kong: a territory-wide study. Hong Kong Med J 2017;23:48-53. Crossref

6. Census and Statistics Department, Hong Kong SAR Government. 2016 Population By-census. Domestic households in Hong Kong. Available from: https://www.bycensus2016.gov.hk/en/Snapshot-04.html. Accessed 24 Mar 2020.

7. Census and Statistics Department, Hong Kong SAR Government. 2011 Hong Kong Population Census. Census results. Available from: https://www.censtatd.gov.hk/en/scode170.html. Accessed 17 Mar 2023.

8. Perkins GD, Jacobs IG, Nadkarni VM, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation. Circulation 2015;132:1286-300. Crossref

9. Okabayashi S, Matsuyama T, Kitamura T, et al. Outcomes of patients 65 years or older after out-of-hospital cardiac arrest based on location of cardiac arrest in Japan. JAMA Netw Open 2019;2:e191011. Crossref

10. Dami F, Carron PN, Praz L, Fuchs V, Yersin B. Why bystanders decline telephone cardiac resuscitation advice. Acad Emerg Med 2010;17:1012-5. Crossref

11. Kiyohara K, Nishiyama C, Matsuyama T, et al. Out-of-hospital cardiac arrest at home in Japan. Am J Cardiol 2019;123:1060-8. Crossref

12. Ringh M, Rosenqvist M, Hollenberg J, et al. Mobile-phone dispatch of laypersons for CPR in out-of-hospital cardiac arrest. N Engl J Med 2015;372:2316-25. Crossref

13. Smith CM, Wilson MH, Ghorbangholi A, et al. The use of trained volunteers in the response to out-of-hospital cardiac arrest—the GoodSAM experience. Resuscitation 2017;121:123-6. Crossref

14. Mao DR, Ong ME. High-rise residential resuscitation: scaling the challenge. CMAJ 2016;188:399-400. Crossref

15. Coons SJ, Guy MC. Performing bystander CPR for sudden cardiac arrest: behavioral intentions among the general adult population in Arizona. Resuscitation 2009;80:334-40. Crossref

16. Fan KL, Leung LP, Poon HT, Chiu HY, Liu HL, Tang WY. Public knowledge of how to use an automatic external defibrillator in out-of-hospital cardiac arrest in Hong Kong. Hong Kong Med J 2016;22:582-8. Crossref

17. Resuscitation Council UK. Cardiopulmonary resuscitation, automated defibrillators and the law. 2018. Available from: https://www.resus.org.uk/sites/default/files/2020-05/CPR%20AEDs%20and%20the%20law%20%285%29.pdf. Accessed 24 Mar 2020. Crossref

18. Sayre MR, Barnard LM, Counts CR, et al. Prevalence of COVID-19 in out-of-hospital cardiac arrest: implications for bystander cardiopulmonary resuscitation. Circulation 2020;142:507-9. Crossref

19. Wong TW, Yeung KC. Out-of-hospital cardiac arrest: two and a half years experience of an accident and emergency department in Hong Kong. J Accid Emerg Med 1995;12:34-9. Crossref

20. Lau CL, Lai JC, Hung CY, Kam CW. Outcome of out-of-hospital cardiac arrest in a regional hospital in Hong Kong. Hong Kong J Emerg Med 2005;12:224-7. Crossref

21. Oving I, de Graaf C, Karlsson L, et al. Occurrence of shockable rhythm in out-of-hospital cardiac arrest over time: a report from the COSTA group. Resuscitation 2020;151:67-74. Crossref

22. Kitamura T, Kiyohara K, Sakai T, et al. Public-access defibrillation and out-of-hospital cardiac arrest in Japan. N Engl J Med 2016;375:1649-59. Crossref

23. Fan M, Fan KL, Leung LP. Walking route–based calculation is recommended for optimizing deployment of publicly accessible defibrillators in urban cities. J Am Heart Assoc 2020;9:e014398 Crossref

24. Agerskov M, Nielsen AM, Hansen CM, et al. Public access defibrillation: great benefit and potential but infrequently used. Resuscitation 2015;96:53-8. Crossref

25. Silverman RA, Galea S, Blaney S, et al. The “vertical response time”: barriers to ambulance response in an urban area. Acad Emerg Med 2007;14:772-8. Crossref

26. Drennan IR, Strum RP, Byers A, et al. Out-of-hospital cardiac arrest in high-rise buildings: delays to patient care and effect on survival. CMAJ 2016;188:413-9. Crossref

27. Moore JC, Segal N, Debaty G, Lurie KG. The “do’s and don’ts” of head up CPR: lessons learned from the animal laboratory. Resuscitation 2018;129:e6-e7. Crossref

28. Winther-Jensen M, Kjaergaard J, Hassager C, et al. Resuscitation and post resuscitation care of the very old after out-of-hospital cardiac arrest is worthwhile. Int J Cardiol 2015;201:616-23. Crossref

A diagnosis of cancer is disheartening news for every patient in terms of both disease and treatment. Anticancer treatments for common cancers (eg, chemotherapy and radiotherapy) are gonadotoxic and detrimental to future fertility.1 Advances in medical treatment have improved the 5-year survival rates of some cancers to >80% in children and adolescents.2 However, most surviving patients experience illness-related infertility.3 Infertility is also a concern for patients with severe endometriosis, patients with poor ovarian reserve, patients with benign medical conditions requiring chemotherapy, and transgender individuals undergoing gender-affirming surgery.4 Fortunately, advancements in fertility preservation (FP) technologies offer these patients the opportunity to have biological offspring in the future. In Hong Kong, only 45.6% of clinicians,5 22.2% of medical students,6 and 21.7% of the general public7 are familiar with FP. Therefore, FP technologies require greater attention in Hong Kong. Both clinicians and the public should be aware where and how to seek help when patients are diagnosed with cancer and need to use FP technologies to preserve their fertility.

For patients who cannot undergo the ovarian stimulation necessary for oocyte or embryo freezing as well as prepubertal girls (all ineligible for oocyte freezing), ovarian tissue cryopreservation (OTC) and subsequent orthotopic or heterotopic ovarian tissue transplantation (OTT) are ideal options for FP after recovery.8 Many European countries have provided OTC for patients with various medical reasons.4 Although OTC and OTT are widely available in Belgium, Denmark, Spain, France,9 Japan, Singapore,10 the United States, India, Australia, the Philippines, Korea,11 and some parts of China,12 these FP technologies remain unavailable in Hong Kong. Thus far, >130 babies worldwide have been born via OTC and OTT,13 and the American Society for Reproductive Medicine removed the ‘experimental’ designation for these technologies in 2019.14 Ovarian tissue cryopreservation can be performed via slow freezing or vitrification. Slow freezing has been the standard treatment for ovarian tissues,15 but there is increasing evidence to support the use of vitrification.16 17 Nevertheless, controversies remain concerning subsequent oocyte viability and the preservation of morphological integrity after ovarian tissues have been processed using these two cryopreservation techniques.18

The development of OTC, which can improve quality of life among young female cancer survivors,19 is urgently needed in Hong Kong. Here, we performed a pilot study to assess the feasibility of OTC and subsequent OTT in Hong Kong via xenografts in nude mice. In this study, we collected ovarian tissues, established both slow freezing and vitrification protocols, and evaluated tissue viability and follicle preservation after OTC and OTT in a nude mouse xenograft model. This mouse model provided important insights that will support the clinical implementation of OTC and OTT in Hong Kong. Our primary outcome was ovarian tissue viability after slow freezing or vitrification, as determined by histological analysis and immunohistochemistry. Our secondary outcomes were follicular density and the morphological integrity of grafted ovarian tissues.

This study was conducted between July 2019 and December 2021 at the Prince of Wales Hospital, a university-affiliated tertiary hospital in Hong Kong. All participants received a detailed explanation of the study, then provided written consent for inclusion. All researchers involved in the animal experiments were licensed by the Department of Health of the Hong Kong SAR Government.

Ovarian tissues were collected from women or transgender individuals who underwent laparoscopic or open, unilateral or bilateral, ovarian cystectomy or salpingo-oophorectomy as treatment for benign ovarian cysts or tumours. During the operation, each patient underwent removal of a small section of ovarian tissue or the whole ovary; each specimen of donated ovarian tissue was retrieved from a routine surgical specimen or directly removed during surgery. To prevent thermal injury during tissue removal, cold scissors were used and diathermy was avoided. The amount of donated tissue varied among patients, depending on their age and clinical condition. For example, larger volumes of ovarian tissue were often collected from patients undergoing oophorectomy. Each patient was assigned a unique identification number linked to an encrypted file containing the patient’s data and demographic information; during analyses of tissue from each patient, the pathologist and research staff who conducted histology and immunohistochemistry analyses were blinded to the contents of the encrypted files.

Tissues were transported to the laboratory in a standardised culture medium at 4°C and processed within 30 minutes after collection. After removing the medullary region, the ovarian tissue was frozen in accordance with a controlled-rate slow freezing machine protocol (Ovarian Tissue Cryopreservation Scientific Roundup; Planer, UK)20 or a vitrification manual (Ova Cryo Kit Type M, VT301S; Kitazato Corporation, Japan).21 The cortical region was cut into small fragments with a thickness of approximately 1 mm. Some collected ovarian tissues were very small and could not be sectioned for parallel slow freezing and vitrification fresh tissue controls; these small tissues were either frozen using the standard slow freezing method or vitrification.22 Larger ovarian tissues (typically collected from transgender individuals during oophorectomy) were cut into smaller fragments prior to slow freezing and vitrification, or prior to use as fresh tissue controls. Before the xenograft procedure, fragments were removed from fresh tissue, thawed slow-frozen tissue, and thawed vitrified tissue; these fragments were subsequently compared with grafted tissues to identify any differences related to engraftment.

A subset of fresh ovarian tissue fragments was fixed and subjected to histological analysis. When a large amount of ovarian tissue was available from a single patient, we compared cryopreservation methods using tissue from that patient; we also compared the cryopreserved tissue with fresh tissue.

Slow freezing was performed in accordance with a validated protocol.23 24 25 Collected ovarian cortices were equilibrated at 4°C on a tilting shaker for 30 minutes in freezing solution (1.5 mol/L ethylene glycol and 0.1 mol/L sucrose in G-MOPS PLUS; Vitrolife, Sweden). After equilibration, the ovarian tissue pieces were placed into 1.8-mL cryogenic vials that had been pre-filled with 1 mL of freezing solution (two tissue pieces per vial). The cryogenic vials were then placed into an automated, computer-controlled freezing system (Kryo-360; Planer, UK).20 The slow freezing protocol was performed in accordance with the method described by Dolmans et al26 and the Planer Ovarian Tissue Cryopreservation Scientific Roundup.20

For vitrification of ovarian cortices, the Ovarian Tissue Vitrification Kit (Ova Cryo Kit Type M, VT301S; Kitazato Corporation, Japan)21 was used. The collected ovarian cortices were cut into 1 × 1 × 1 cm³ cubes using a surgical knife and a square measuring device provided in the kit. Vitrification was then performed in accordance with the kit manufacturer’s protocol, and the ovarian tissues were stored in liquid nitrogen.

Slow-frozen tissues were removed from liquid nitrogen and exposed to room temperature air for 5 seconds, then placed in 37°C water for 2 minutes. Subsequently, they were transferred to thawing solution 1 (0.75 mol/L ethylene glycol and 0.25 mol/L sucrose in G-MOPS PLUS) for 10 minutes, then to thawing solution 2 (0.25 mol/L sucrose in G-MOPS PLUS) for 10 minutes, and finally to a handling medium (G-MOPS) for 10 minutes. Vitrified ovarian tissue fragments were thawed using Ova Thawing Kit Type M (V302S; Kitazato Corporation, Japan), in accordance with the manufacturer’s instructions.21

The nude mouse xenograft model is ideal for assessment of OTC and OTT outcomes. Ovarian xenografts in immunodeficient nude mice can be used to test follicular viability and development. This approach can reveal whether freezing and thawing cause damage to ovarian tissue; it can also demonstrate the ability of cryopreserved tissue to support the development of large antral follicles.27 Considering the higher rate of immune leakiness in severe combined immunodeficient mice,28 we used BALB/c athymic nude mice to validate our OTC and OTT protocols before clinical implementation. Thirty-four female BALB/c athymic nude mice (age, 4-6 weeks; Laboratory Animal Services Centre, The Chinese University of Hong Kong) were used for this study. To prevent fighting between engrafted mice, only three mice were housed in individually ventilated cages at 28°C under controlled sterile conditions, with a 12-hour light/dark cycle and free access to an autoclaved pelleted diet and water. Mice were anaesthetised by intraperitoneal injection of ketamine (75 mg/kg)/xylazine (10 mg/kg) (AlfaMedic Limited, Hong Kong; manufactured in Holland). Ovarian tissues collected from patients were grafted onto nude mice. During ovarian tissue engraftment, the cortical surface was carefully oriented outward and tightly attached to the subcutaneous tissue or abdominal wall. There were two phases in our study, as described in the following sections (Fig 1).

To maintain endogenous hormone secretion and avoid the risk of ovariectomy-related death, mice in this phase were not subjected to ovariectomy. One fresh, slow-frozen, or vitrified tissue of approximately 4 × 6 × 1 mm³ was engrafted into the subcutaneous site on the neck of nine nude mice.29 The mice were then sacrificed by intraperitoneal injection of overdose of the anaesthetic. One mouse engrafted with vitrified tissue, two engrafted with slow-frozen tissues and one engrafted with fresh tissue were sacrificed after 2 weeks. Two mice engrafted with vitrified tissues, one engrafted with slow-frozen tissue and two engrafted with fresh tissues were sacrificed after 5 weeks.

To promote graft survival and growth, mice in this phase were subjected to ovariectomy. Fresh, slow-frozen, or vitrified tissues of approximately 4 × 6 × 1 mm³ were either engrafted into the subcutaneous site on the neck of ovariectomised nude mice, or used for intraperitoneal engraftment in the left abdomen of those mice.29 Mice in this phase were divided into a saline group and a treatment group after 2 or 6 weeks of engraftment. The presence of gonadotrophins can optimise graft establishment and stimulate follicle growth.30 To promote folliculogenesis, mice in the treatment group underwent intraperitoneal injection (in the right abdomen) of 1 IU (100 μL) of follitropin alfa (GONAL-f; Merck Serono, Geneva, Switzerland) every other day for 5 to 8 weeks after 2 or 6 weeks’ engraftment.30 During the same period, mice in the saline group underwent intraperitoneal injection (in the right abdomen) of an equal volume of physiological saline every other day. Thirty-six hours before the mice were sacrificed, both groups of mice received a single dose of 10 international units of human chorionic gonadotrophin (Sigma-Aldrich, St Louis [MO], US) by injection to promote ovulation.

All grafted tissues were fixed in buffered formalin and embedded in paraffin wax, then sectioned and stained for analysis.

Microscopic observations up to 400 times the original magnification (Leica DMIRB; Leica Microsystem, Wetzlar, Germany) of fresh and thawed ovarian tissues were performed after the tissues had been stained with haematoxylin and eosin (H&E). All follicles from the entire grafted tissue specimen on every slide were counted; section thickness and the presence/absence of a nucleolus were also considered.

Stromal tissue viability was determined by assessing the morphologies of stromal cells on H&E-stained sections. Viability was defined as the presence of spindle cells with consistent cellularity; an intact nuclear membrane; the absence of pyknotic figures, apoptosis, or necrosis; and the absence of fibrosis or calcification. Viability was confirmed by immunohistochemical analyses using antibodies to cluster of differentiation 10 (CD10) and oestrogen receptors. Anti-CD10 antibody (clone NCL-CD10-270; Novocastra, Newcastle upon Tyne, UK) was used at a dilution of 1:50 with an incubation time of 30 minutes at a sustained temperature of 37°C. Anti–oestrogen receptor antibody (RM-9101; Thermo Fisher Scientific, Fremont [CA], US) was used at a dilution of 1:150 with an incubation time of 32 minutes at a sustained temperature of 37°C. Antigen retrieval was performed using ethylenediaminetetraacetic acid and microwave. Antibody detection was performed using Roche Diagnostics OptiView DAB IHC Detection Kit (Thermo Fisher Scientific, Waltham [MA], US). Immunohistochemical staining (original magnification × 400) was semi-quantitative and based on signal intensity (absent, weak, moderate, and strong). The presence of at least weak staining intensity in ovarian stromal tissue was regarded as a positive result.

All follicles from the entire grafted tissue specimen on every H&E-stained slide were counted on multiple levels within thick sections (≥20 μm). The digital images were annotated on QuPath,31 obtaining the two-dimensional area of the slide and number of follicles. Follicular density was calculated by established methods described previously.32 33 Ovarian follicles were classified as primordial, primary, or secondary follicles according to morphological assessment of H&E-stained sections.33 Evaluation of grafted follicle quality was based on basement membrane integrity, cellular density, presence or absence of pyknotic bodies, and oocyte integrity. Only morphologically normal (ie, viable) follicles were counted. The results of gross anatomical examinations were confirmed by histological assessments. Gross tissue integrity was defined as the presence of a distinct vascularised tissue fragment that exhibited firmness and perfusion. Microscopic findings indicating viability were the presence of an intact nuclear membrane and the absence of necrosis, apoptosis, and pyknotic nuclei.

In total, 52 ovarian tissues were collected from 12 patients aged 29 to 41 years. Ovarian tissues from different patients were cut into several pieces according to tissue size. These tissues were treated by vitrification or slow freezing, then engrafted into 34 mice as shown in Figure 1. Tables 1 and 2 only show data from patients with follicles to facilitate readability. Although there were nine tissues from four patients (Patients 1, 6, 7, and 10) in Phase I, Table 1 only shows data from the two patients with follicles (Patients 1 and 7). In Phase II, there were 25 tissues from six patients (Patients 1, 5, 8, 9, 11, and 12), but Table 2 only shows data from the four patients with follicles. In total, 18 control tissues were collected from 11 patients (Patients 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, and 13). One patient (Patient 5) provided sufficient ovarian tissue for a comparison of cryopreservation methods using tissue from a single patient. Additionally, we compared fresh and slow-frozen tissues from Patient 5, and compared fresh and vitrified tissues from Patients 1, 5, and 12.

In Phase I, all xenografts were successfully retrieved from the experimental mice. Macroscopic observations of fresh and thawed tissues did not show substantial differences between cryopreservation methods in terms of tissue integrity or morphology (Fig 2a). Microscopic findings showed the presence of viable nuclei and the absence of necrosis, apoptosis, and pyknotic nuclei.

In Phase II, all xenografts were successfully retrieved from the experimental mice, with the exception of two calcified tissues. Most subcutaneous sites contained soft tissue fragments that were completely encased in membranes; the graft–murine tissue interface was vascularised (Fig 2b). Intraperitoneal sites contained soft tissue fragments with small vessels visible on the graft surface; the fragments were attached to surrounding tissue, and some grafts were encased in abdominal adipose tissue (Fig 2c).

Immunohistochemical staining showed that all retrieved grafts had maintained viability, with the exception of two calcified tissues (Fig 3). Haematoxylin and eosin staining, CD10 staining, and oestrogen receptor staining showed no between-group differences (fresh vs slow-frozen, fresh vs vitrified, and slow-frozen vs vitrified). Moreover, stromal tissue viability did not differ between the treatment and saline groups in Phase II.

Retrieved grafts were embedded with paraffin and sectioned at a thickness of 4 or 30 μm. Microscopy analysis revealed primordial, primary, and secondary follicles (Fig 4). Tables 1 and 2 show the follicular densities of retrieved grafts from Phases I and II, respectively. Primordial follicles were observed in fresh and cryopreserved grafts from the same patient (Patient 5), regardless of cryopreservation method (slow freezing or vitrification) or gonadotrophin injection status.

Our pilot study demonstrated the feasibility of OTC with subsequent OTT in Hong Kong via xenografts of fresh and cryopreserved ovarian tissues in nude mice. Most grafted ovarian tissues remained viable after engraftment, as demonstrated by CD10 and oestrogen receptor staining results in stromal tissue, along with the presence of viable nuclei and the absence of necrosis, apoptosis, and pyknotic nuclei. Regardless of cryopreservation method, primordial follicles were observed in thawed ovarian tissues after engraftment; thus, both cryopreservation methods are feasible and effective. There were no differences in folliculogenesis after gonadotrophin injection. Overall, these findings validate our protocol for surgical collection of ovarian tissue, cryopreservation via slow freezing or vitrification, and subsequent tissue engraftment into mice; this protocol successfully generated primordial follicles in the xenografts. To our knowledge, this type of protocol was not previously validated in Hong Kong.

The benefits of OTC and OTT are not limited to gynaecology patients; they are also useful for patients in other specialties4 (eg, medicine, oncology, and paediatrics), including adolescents,34 as well as women who cannot undergo ovarian stimulation. To our knowledge, this is the first study to demonstrate the feasibility of OTC and OTT in Hong Kong; our findings support the clinical implementation of these technologies at medical centres in Hong Kong.

Ovarian tissue cryopreservation has become an accepted FP technology in many fertility centres since the removal of its experimental designation.11 14 Notably, OTC allows the preservation of thousands of primordial follicles in a single procedure; compared with mature oocytes, preserved primordial follicles are more resistant to cryodamage.35 This technology is also appropriate for patients who cannot undergo ovulation stimulation because they require urgent chemotherapy or must avoid the enhancement of a hormone-sensitive malignancy36; it is also the only available FP technology for prepubertal girls.36 Furthermore, OTC allows natural conception; several spontaneous pregnancies have been reported after successful orthotopic autotransplantation.9 37 In some instances, both fertility and gonadal function are restored.34 According to a meta-analysis,38 endocrine function was restored in 63.9% of patients; the combined rate of pregnancies and live births was 28.4%. Dolmans et al9 also reported that 26% of women became pregnant and gave birth to one or two infants after the transplantation of frozen-thawed ovarian tissue; the live birth rate was 30.6%.

Despite its advantages, there are multiple barriers to the clinical implementation of OTC. Effective use of this technology involves two surgical procedures: the initial removal of ovarian tissue (prior to cryopreservation) and a future transplantation procedure, which may cause surgical and ethical problems (particularly in prepubertal patients).39 The technology also requires expertise that is not available in some parts of Asia. A Japanese group reported a live birth in 201540; another successful live birth was reported by a Chinese group in 2021,41 involving the cryopreserved ovarian tissue bank established by the Beijing Obstetrics and Gynecology Hospital.12 However, OTC is not widely available in Hong Kong. Reproductive health centres in Hong Kong may lack sufficient surgical expertise and/or an optimal cryopreservation environment.11 Thus, there is a need to reduce the obstacles to clinical implementation of OTC. From our experience, in terms of laboratory requirements, the protocol, equipment, and consumables can be incorporated into most assisted reproductive technology units. However, practical education is needed regarding OTC, including tissue management (eg, tissue thinning during removal of the medulla) and specific aspects of cryopreservation. Proper records of success measures (eg, freeze-thaw outcomes and graft survival rate) are essential; these data should be carefully documented in laboratory records. Additional equipment is also needed for the clinical implementation of OTC as a routine service because the harvesting surgery may be performed on an urgent basis that differs from the routine assisted reproductive technology laboratory programme.11 While planning for this study, we found that there have been inconsistencies in terms of selection criteria, cryopreservation methods, laboratory management of harvested tissue, and the transplantation technique itself. Although we found no differences in the morphological integrity of ovarian tissue after cryopreservation via slow freezing or vitrification, further studies with larger numbers of patients are needed to confirm the feasibility of follicular stimulation in vivo.

Human OTT remains unavailable in Hong Kong. Notably, our analysis of tissue engraftment was conducted in a mouse model. After the clinical implementation of OTC in Hong Kong, OTT involving autotransplantation could be established as a routine service. Ovarian autotransplantation is performed when a patient has fully recovered from disease, but this approach may carry a small risk of reintroducing malignant cells in patients with cancer.42 The results of some studies have suggested that the risk of reintroducing malignant cells could be minimised by meticulous examination of representative biopsy samples via histology, immunohistochemistry, and molecular biology techniques.43 Moreover, optical coherence tomography can be used to assess malignant cells in thawed ovarian tissue before transplantation.44

There were several limitations in this study. First, tissue engraftment was not conducted in humans because of ethical concerns; thus, we analysed tissue engraftment in a nude mouse xenograft model, which was the best available model. Second, some ovarian tissues were collected from transgender individuals who had undergone testosterone replacement therapy, which might have affected the hormonal milieu of the ovarian tissue.45 Third, we only retrieved small fragments of ovarian tissue (~1 cm) from random locations in the ovaries of included patients; this may have led to sampling error if the sampled cortical layers did not contain primordial follicles. Fourth, the small number of included patients hindered our ability to compare the effects of cryopreservation methods on tissue from a single patient. Moreover, the small sample size might have reduced the strength of the findings. Finally, there are no standardised protocols for freezing, gonadotrophin stimulation, or transplantation in nude mouse xenograft models. However, our study demonstrated the feasibility of OTC in our centre. Further randomised controlled trials are needed to confirm our findings.

We plan to conduct a randomised controlled trial of the two cryopreservation methods used in this study to determine which is best for clinical implementation. From the experience of the Danish group Rosendahl et al24 on OTC, they suggested that before applying the technique to humans, each laboratory should thoroughly test and validate the OTC method. In the future, implantation of artificial ovaries or the engraftment of human ovarian tissue into mice may enable fertility restoration without the potential reintroduction of malignant cells. These approaches may be particularly useful in women with a high risk of blood-borne leukaemia or cancers with a high risk of ovarian metastasis, as well as women who cannot undergo autotransplantation.46

Our study demonstrated the feasibility and viability of OTC with subsequent OTT in Hong Kong via xenografts in nude mice. These findings support the clinical implementation of OTC and subsequent OTT in Hong Kong, particularly for prepubertal young girls and for women who cannot undergo the ovarian stimulation necessary for oocyte or embryo freezing. Further studies are needed to clarify optimal cryopreservation and engraftment protocols.

Concept or design: JPW Chung, DYL Chan.
Acquisition of data: All authors.
Analysis or interpretation of data: JPW Chung, DYL Chan, Y Song, MBW Leung, JJX Li, CC Wang.
Drafting of the manuscript: All authors.
Critical revision of the manuscript for important intellectual content: JPW Chung, DYL Chan, CC Wang.

All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

As an editor of the journal, JPW Chung was not involved in the peer review process of the article. All other authors have no conflicts of interest to disclose.

This research was supported by Basecare Medical Device Co., Ltd. and the Theme-based Research Scheme funded by the Research Grants Council of the Hong Kong SAR Government (Ref No.: T13-602/21-N).

The research was approved by the Institutional Review Board of the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (Ref No.: 2019.356) and overseen by an independent data and safety monitoring committee. The trial was registered with the World Health Organization Primary Registry–Chinese Clinical Trials Registry (Trial No.: ChiCTR2100041611). The experimental animal protocol was approved by The Chinese University of Hong Kong Animal Experimentation Ethics Committee (Ref No.: 19-214-MIS). All participants received a detailed explanation of the study and provided written consent for inclusion. All researchers involved in animal experiments were licensed by the Department of Health of the Hong Kong SAR Government.

1. Maltaris T, Seufert R, Fischl F, et al. The effect of cancer treatment on female fertility and strategies for preserving fertility. Eur J Obstet Gynecol Reprod Biol 2007;130:148-55. Crossref

2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34. Crossref

3. Brydøy M, Fosså SD, Dahl O, Bjøro T. Gonadal dysfunction and fertility problems in cancer survivors. Acta Oncol 2007;46:480-9. Crossref

4. ESHRE Guideline Group on Female Fertility Preservation, Anderson RA, Amant F, et al. ESHRE guideline: female fertility preservation. Hum Reprod Open 2020;2020:hoaa052. Crossref

5. Chung JP, Lao TT, Li TC. Evaluation of the awareness of, attitude to, and knowledge about fertility preservation in cancer patients among clinical practitioners in Hong Kong. Hong Kong Med J 2017;23:556-61. Crossref

6. Ng EY, Ip JK, Mak DR, Chan AY, Chung JP. Awareness of fertility preservation among Chinese medical students. Hong Kong Med J 2020;26:184-91. Crossref

7. Yeung SY, Ng EY, Lao TT, Li TC, Chung JP. Fertility preservation in Hong Kong Chinese society: awareness, knowledge and acceptance. BMC Womens Health 2020;20:86. Crossref

8. Dolmans MM, Donnez J. Fertility preservation in women for medical and social reasons: oocytes vs ovarian tissue. Best Pract Res Clin Obstet Gynaecol 2021;70:63-80. Crossref

9. Dolmans MM, von Wolff M, Poirot C, et al. Transplantation of cryopreserved ovarian tissue in a series of 285 women: a review of five leading European centers. Fertil Steril 2021;115:1102-15. Crossref

10. Harzif AK, Santawi VP, Maidarti M, Wiweko B. Investigation of each society for fertility preservation in Asia. Front Endocrinol (Lausanne) 2019;10:151. Crossref

11. Takae S, Lee JR, Mahajan N, et al. Fertility preservation for child and adolescent cancer patients in Asian countries. Front Endocrinol (Lausanne) 2019;10:655. Crossref

12. Jin F, Ruan X, Du J, et al. Analysis on the characteristics of the patients and effects of ovarian tissue cryopreservation in the first ovarian tissues cryopreservation bank in China [in Chinese]. J Capital Univ Med Sci 2021;42:521-5.

13. Donnez J, Dolmans MM. Fertility preservation in women. N Engl J Med 2017;377:1657-65. Crossref

14. Practice Committee of the American Society for Reproductive Medicine. Fertility preservation in patients undergoing gonadotoxic therapy or gonadectomy: a committee opinion. Fertil Steril 2019;112:1022-33. Crossref

15. Rivas Leonel EC, Lucci CM, Amorim CA. Cryopreservation of human ovarian tissue: a review. Transfus Med Hemother 2019;46:173-81. Crossref

16. Marques LS, Fossati AA, Rodrigues RB, et al. Slow freezing versus vitrification for the cryopreservation of zebrafish (Danio rerio) ovarian tissue. Sci Rep 2019;9:15353. Crossref

17. Glujovsky D, Riestra B, Sueldo C, et al. Vitrification versus slow freezing for women undergoing oocyte cryopreservation. Cochrane Database Syst Rev 2014;2014:CD010047. Crossref

18. Bianchi V, Macchiarelli G, Borini A, et al. Fine morphological assessment of quality of human mature oocytes after slow freezing or vitrification with a closed device: a comparative analysis. Reprod Biol Endocrinol 2014;12:110. Crossref

19. Turan V, Oktay K. Sexual and fertility adverse effects associated with chemotherapy treatment in women. Expert Opin Drug Saf 2014;13:775-83. Crossref

20. Planer Ovarian Tissue Cryopreservation Scientific Roundup. Ovarian Tissue Cryopreservation Scientific Roundup. 2019. Available from: https://mail.planer.com/__80258426005B6A5E.nsf/0/C4C23666734672DF802584B1002F3777/$File/Ai091V1-Ovarian-Tissue-Cryopreservation-Scientific-Round-Up-28th-October-2019.pdf?OpenElement. Accessed 1 Mar 2022. Crossref

21. Kitazato Corporation. Ova Cryo Kit—Ovarian Tissue Vitrification Kit. 2021. Available from: https://www.kitazato.co.jp/en/products/vitrification/ova-cryo-kit-type-m. Accessed 31 January 2023.

22. Huang L, Mo Y, Wang W, Li Y, Zhang Q, Yang D. Cryopreservation of human ovarian tissue by solid–surface vitrification. Eur J Obstet Gynecol Reprod Biol 2008;139:193-8. Crossref

23. Jensen AK, Kristensen SG, Macklon KT, et al. Outcomes of transplantations of cryopreserved ovarian tissue to 41 women in Denmark. Hum Reprod 2015;30:2838-45.Crossref

24. Rosendahl M, Greve T, Andersen CY. The safety of transplanting cryopreserved ovarian tissue in cancer patients: a review of the literature. J Assist Reprod Genet 2013;30:11-24. Crossref

25. Macklon KT, Jensen AK, Loft A, Ernst E, Andersen CY. Treatment history and outcome of 24 deliveries worldwide after autotransplantation of cryopreserved ovarian tissue, including two new Danish deliveries years after autotransplantation. J Assist Reprod Genet 2014;31:1557-64. Crossref

26. Dolmans MM, Luyckx V, Donnez J, Andersen CY, Greve T. Risk of transferring malignant cells with transplanted frozen–thawed ovarian tissue. Fertil Steril 2013;99:1514-22. Crossref

27. Nisolle M, Casanas-Roux F, Qu J, Motta P, Donnez J. Histologic and ultrastructural evaluation of fresh and frozen–thawed human ovarian xenografts in nude mice. Fertil Steril 2000;74:122-9. Crossref

28. Bosma GC, Fried M, Custer RP, Carroll A, Gibson DM, Bosma MJ. Evidence of functional lymphocytes in some (leaky) scid mice. J Exp Med 1988;167:1016-33. Crossref

29. Dath C, Van Eyck AS, Dolmans MM, et al. Xenotransplantation of human ovarian tissue to nude mice: comparison between four grafting sites. Hum Reprod 2010;25:1734-43. Crossref

30. Dittrich R, Lotz L, Fehm T, et al. Xenotransplantation of cryopreserved human ovarian tissue—a systematic review of MII oocyte maturation and discussion of it as a realistic option for restoring fertility after cancer treatment. Fertil Steril 2015;103:1557-65. Crossref

31. Bankhead P, Loughrey MB, Fernández JA, et al. QuPath: open source software for digital pathology image analysis. Sci Rep 2017;7:16878. Crossref

32. Schmidt KL, Byskov AG, Nyboe Andersen A, Müller J, Yding Andersen C. Density and distribution of primordial follicles in single pieces of cortex from 21 patients and in individual pieces of cortex from three entire human ovaries. Hum Reprod 2003;18:1158-64. Crossref

33. Gougeon A, Chainy GB. Morphometric studies of small follicles in ovaries of women at different ages. J Reprod Fertil 1987;81:433-42. Crossref

34. Anderson RA, Mitchell RT, Kelsey TW, Spears N, Telfer EE, Wallace WH. Cancer treatment and gonadal function: experimental and established strategies for fertility preservation in children and young adults. Lancet Diabetes Endocrinol 2015;3:556-67. Crossref

35. Anderson RA, Wallace WH, Baird DT. Ovarian cryopreservation for fertility preservation: indications and outcomes. Reproduction 2008;136:681-9. Crossref

36. Jeruss JS, Woodruff TK. Preservation of fertility in patients with cancer. N Engl J Med 2009;360:902-11. Crossref

37. Anderson RA, Wallace WH, Telfer EE. Ovarian tissue cryopreservation for fertility preservation: clinical and research perspectives. Hum Reprod Open 2017;2017:hox001. Crossref

38. Pacheco F, Oktay K. Current success and efficiency of autologous ovarian transplantation: a meta-analysis. Reprod Sci 2017;24:1111-20. Crossref

39. Wallace WH, Kelsey TW, Anderson RA. Fertility preservation in pre-pubertal girls with cancer: the role of ovarian tissue cryopreservation. Fertil Steril 2016;105:6-12. Crossref

40. Suzuki N, Yoshioka N, Takae S, et al. Successful fertility preservation following ovarian tissue vitrification in patients with primary ovarian insufficiency. Hum Reprod 2015;30:608-15. Crossref

41. Sun N, Li Z, Pang W, Wang L, Li W. Live birth after transplantation of cryopreserved ovarian tissue with two-year follow-up: report of the first Chinese case [in Chinese]. Chin J Reprod Contraception 2021;41:1026-30.

42. Peek R, Eijkenboom LL, Braat DD, Beerendonk CC. Complete purging of Ewing sarcoma metastases from human ovarian cortex tissue fragments by inhibiting the mTORC1 signaling pathway. J Clin Med 2021;10:4362. Crossref

43. Lotz L, Dittrich R, Hoffmann I, Beckmann MW. Ovarian tissue transplantation: experience from Germany and worldwide efficacy. Clin Med Insights Reprod Health 2019;13:1179558119867357. Crossref

44. Peters IT, Stegehuis PL, Peek R, et al. Noninvasive detection of metastases and follicle density in ovarian tissue using full-field optical coherence tomography. Clin Cancer Res 2016;22:5506-13. Crossref

45. Irwig MS. Testosterone therapy for transgender men. Lancet Diabetes Endocrinol 2017;5:301-11. Crossref

46. Telfer EE, Andersen CY. In vitro growth and maturation of primordial follicles and immature oocytes. Fertil Steril 2021;115:1116-25. Crossref

The World Health Organization announced that the coronavirus disease 2019 (COVID-19) outbreak had become an international public health emergency on 30 January 2020; on 11 March 2020, it declared that the outbreak had become a pandemic.1 Governments and public health authorities worldwide implemented public health policies to reduce the risk of viral transmission, including strict physical distancing, severe travel restrictions, and the closure of many businesses and schools. On 25 January 2020, China’s Central Government announced a nationwide travel ban and quarantine policy2; it initiated nationwide school closures as an emergency measure to prevent the spread of COVID-19.3 Thus, >220 million school-aged children and adolescents were confined to their homes; online classes were offered and delivered via the internet.4

Vision problems are public health challenges; among school-aged children, these problems often involve asthenopia and vision impairment. Asthenopia is defined as a subjective sensation of visual fatigue, eye weakness, or eyestrain; it can manifest through various symptoms, including epiphora, ocular pruritis, diplopia, eye pain, and dry eye.5 Vision impairment is defined as visual acuity (VA) of 6/12 or worse in either eye6; it is often caused by uncorrected refractive errors, and its estimated prevalence is 43%.7 Although both asthenopia and vision impairment have negative effects on students, the effects of vision impairment are greater. A previous global analysis revealed that vision impairment was present in 12.8 million children aged 5 to 15 years, half of whom lived in China.8 Moreover, students with vision impairment have lower scores on various motor and cognitive tests.9 10

Excessive use of digital devices contributes to increases in asthenopia prevalence and vision impairment among school-aged children.4 11 12 13 14 15 The COVID-19 pandemic has led to increased use of digital device–supported online classes,16 17 18 which require extended exposure to those devices.19 20 Importantly, long durations of exposure to digital devices can contribute to many vision problems in children.14

Asthenopia and vision impairment related to the excessive use of digital devices during the COVID-19 pandemic have been investigated in developed countries and urban China.4 11 12 To our knowledge, no similar studies have been conducted in western rural China. Additionally, online classes are increasingly implemented in rural areas, and the use of digital devices is becoming more prevalent11; thus, there is a need for research that focus on vision health in students.

The primary purpose of this study was to assess screen time, asthenopia prevalence, and vision impairment progression during the COVID-19 pandemic among students in western rural China. To achieve this goal, we first conducted a general descriptive analysis of student characteristics and screen time trends before and during the pandemic. We then investigated the prevalence of asthenopia and progression of vision impairment. Finally, we explored factors influencing the prevalence of asthenopia and progression of vision impairment before and during the pandemic.

This study focused on areas that were broadly representative of rural western China because of limited resources. Thus, the study was conducted in Shaanxi and Ningxia regions in western China. In 2019, the per capita gross domestic product in Shaanxi Province was US$10 167; this is similar to that in Ningxia Autonomous Region (US$8236).21

Vision data were acquired from local vision care centres (VCs), which had been established by the Center for Experimental Economics in Education at Shaanxi Normal University, in cooperation with county-level organisations such as the local education ministries and hospitals.

Before the pandemic, VC screenings were performed in each county, except during summer and winter vacations. Staff conducted one to two screenings per week (covering 2 to 4 schools); they completed one round of screening in one town each month. In practice, approximately 1 year is needed to complete one round of vision screening for all eligible children in a particular county. The second round and subsequent rounds of vision screening were performed using a similar workflow. After the completion of vision screening, students who required further assessment were referred to the VC for full eye and refractive examinations. This study included students who had visited the VC 3 months before the beginning of the COVID-19 pandemic.

During the pandemic, VC staff could not attend schools to perform vision screenings. To maintain vision screening services for students, we telephoned all students who had visited the VC before the pandemic. Participants in this panel study were students who participated in data collection before and during the COVID-19 pandemic.

We conducted two cycles of surveys in the VC. The first survey cycle was conducted from October to December 2019 (before the pandemic); the second survey cycle was conducted among a group of students who visited the VC for follow-up from July to December 2020 (during the pandemic), based on their enrolment in the study before the pandemic. The same information was collected during the two survey cycles. During the vision screening process, VC staff administered questionnaires to students for collection of the following information: sex (male=1), age, ethnicity (Han=1), residence (non-rural=1), only-child status (yes=1), parental education (parents with ≥12 years of education=1), and parental migration status (one or both out-migrated=1; defined as one or both parents worked away from home during the semester). Household assets were calculated by summing the values of 13 items owned by the family, in accordance with the China Rural Household Survey Yearbook.22

The survey also included the collection of information regarding screen time and asthenopia. Students completed a previously described, self-administered questionnaire concerning mean time spent throughout the day on near activities (including computer and smartphone use, television viewing, and studying/homework after school). Reports of time spent on near activities during different parts of the day were categorised as screen time while learning and screen time while playing. Information regarding asthenopia was collected via three questions focused on ocular discomfort: whether the student had experienced dry eyes (yes=1), eye pain and swelling (yes=1), and eye fatigue and watery eyes (yes=1). Asthenopia was defined as the presence of at least one of these three types of vision health problems (yes=1).23 Furthermore, information regarding VA was collected when students visited the VC. The optometrist in the VC conducted a VA test to measure the clarity of each student’s vision. All students completed VA tests without refractive correction; students with spectacles completed VA tests with their routine method of vision correction.

The questionnaire regarding asthenopia was developed and reviewed by a group of health experts from Shaanxi Normal University and Zhongshan Ophthalmic Center, a well-known ophthalmology institution in China. The included questions were constructed to ensure that they could be clearly understood by students aged 9 to 17 years with the aid of trained VC staff. These three questions can serve as good indicators of symptoms representing different degrees of asthenopia in students, and they have been used in previous research.23

Visual acuity was assessed using Early Treatment Diabetic Retinopathy Study tumbling-E charts (Precision Vision, La Salle [IL], United States). In an indoor area with sufficient light, VA was separately assessed for each eye without refraction at a distance of 4 m. Students were first examined using a 6/60 line; if they correctly identified the orientation of at least four of five optotypes, they were examined using a 6/30 line, followed by a 6/15 line and a 6/3 line. In this manner, the VA for an eye was defined as the lowest line on which four of five optotypes were correctly identified. If the participant could not read the top line at a distance of 4 m, they were tested at a distance of 1 m, and the VA result was divided by 4.

In this study, VA levels were calculated and compared using the logarithm of the minimum angle of resolution (logMAR) scale, which is a linear scale with regular increments that offers a reasonably intuitive interpretation of VA measurement.24 In this study, vision impairment was defined as logMAR ≥0.3 (ie, VA of 6/12 or worse) in either eye.

This balanced panel study compared student data between two periods (before and during the COVID-19 pandemic). Mean screen time, asthenopia prevalence, and vision impairment progression were compared among students using t tests, after stratification according to various demographic and behavioural factors. Fixed-effects logistic and regression models were used to explore factors influencing the prevalence of asthenopia and progression of vision impairment before and during the pandemic. Fixed-effects models were adjusted for sex, age, ethnicity, rural or non-rural residence, only-child status, parental migration status, parental education level, household assets, screen time while learning, and screen time while playing. All analyses were performed using Stata Statistical Software, version 14.1 (StataCorp, College Station [TX], United States). All tests were two-sided, and P values <0.05 were considered statistically significant.

This study included 128 students from western rural China (mean age before pandemic, 11.82 ± 1.46 years; mean age during pandemic, 12.32 ± 1.54 years; 80 girls [62.5%] and 48 boys [37.5%]). All participants had vision impairment and were attending online classes (Table 1).

During the pandemic, screen time significantly increased because of enrolment in online classes. The mean total screen time during the pandemic was 3.22 hours per day, compared with 1.97 hours during the pre-pandemic period (P<0.001). The mean screen time while learning during the pandemic was 1.70 hours per day, compared with 0.90 hours during the pre-pandemic period (P<0.001); the mean screen time while playing during the pandemic was 1.52 hours per day, compared with 1.33 hours during the pre-pandemic period (P=0.019). Additionally, rural students had significantly greater screen time while learning during the pandemic, compared with the pre-pandemic period (P<0.001); there was no such difference among non-rural students (Table 1).

The prevalence of asthenopia and progression of vision impairment significantly differed between the pandemic and pre-pandemic periods. The prevalence of asthenopia during the pandemic was 55% (71/128), whereas it was 27% (35/128) during the pre-pandemic period (P<0.001). The mean logMAR VA was worse during the pandemic compared with the pre-pandemic period (0.81 vs 0.65; P<0.001). The prevalence of asthenopia was higher during the pandemic than during the pre-pandemic period, regardless of the characteristics used to stratify participants. The mean logMAR VA was worse during the pandemic than during the pre-pandemic period, although the difference being insignificant among participants with non-Han ethnicity and participants in the top quartile of household assets (Table 2).

Fixed-effects logistic models for asthenopia revealed that screen time while learning was associated with asthenopia prevalence, and the probability of asthenopia increased by 24.6% for each 1-hour increase in screen time while learning (95% confidence interval [CI]=1.02-1.53; P=0.034). Additionally, older age (odds ratio [OR]=2.073, 95% CI=1.13-3.81, P=0.019), Han ethnicity (OR=2.405, 95% CI=1.22-4.74; P=0.011), and only-child status (OR=0.488, 95% CI=0.21-1.13; P=0.095) were factors associated with asthenopia; screen time while playing was not (Table 3).

Fixed-effects regression models showed that residence in a non-rural area (OR=-0.200, 95% CI=-0.355 to -0.046; P=0.011) and only-child status (OR=-0.099, 95% CI=-0.197 to 0.000; P=0.049) were factors associated with logMAR VA. The probability of worse logMAR VA increased by 0.200 in non-rural areas, compared with rural areas. However, screen time while learning and screen time while playing were not associated with vision impairment (Table 4).

The global spread of the COVID-19 pandemic has affected the education of >1.5 billion children and adolescents worldwide.25 The participants in our study were representative of this important population. They demonstrated declines in VA and vision health during the pandemic, in relation to the excessive use of digital devices; these findings were consistent with the results of previous studies.19 26

All students in our study were attending online classes during the pandemic. We observed an increase in the mean daily time spent on digital devices between the pre-pandemic and pandemic periods; these results are consistent with international findings that screen time was greater during the pandemic than before the pandemic.19 Notably, we found that total screen time and screen time while learning significantly changed among rural students but not among non-rural students; these results are also consistent with previous findings.19 This difference presumably occurred because, compared with rural students, non-rural students were more likely to use digital devices and online classes before the pandemic.

We observed a significant difference in asthenopia prevalence among students in low-income areas of western China before and during the pandemic; this finding supports the results of previous studies.26 27 Although the risk of asthenopia reportedly increases with screen time,28 there is no published literature concerning changes in asthenopia among students in relation to the COVID-19 pandemic. Similar to previous studies,14 we found that the prevalence of asthenopia was approximately twofold greater among students aged 13 to 17 years than among those aged 9 to 12 years. Furthermore, Moon et al26 reported that symptoms of dry eye diseases were more common among older children than among younger children. Older children spend more time using digital devices, leading to a higher prevalence of asthenopia.29

This study showed significant progression of vision impairment in relation to the pandemic; similarly, a study in eastern China revealed that students had worse vision during the pandemic, compared with their vision at pre-pandemic examinations.4 However, screen time has not been associated with vision impairment among students. Furthermore, evidence regarding the impact of digital devices use on vision impairment has been inconsistent,30 31 with computer screen time made students’ vision worse while television viewing had no effect. We speculate that the association will become clearer as school-aged children spend increasing amounts of time using these devices.

This study had three important limitations. First, the screen time data were retrospectively collected through a self-reporting mechanism, which may have led to recall bias. However, considering the resource and measurement limitations that researchers encountered during the pandemic, self-reported recall was regarded as the optimal method for collection of screen time data in the present study. Second, the selection of students with poor vision may lead to underestimation of screen time effects on the general population, and the results should be generalised with caution. Third, the study was not designed to accurately distinguish between vision impairment caused by intrinsic factors and vision impairment caused by pandemic-related eye strain.

Our findings provide new evidence regarding the effects of increased screen time on asthenopia and vision impairment among students in western rural China during the pandemic; they can also serve as a basis for future research. Although pandemic-related school closures are temporary, the increasing popularity of online classes may accelerate the overall acceptance of digital devices. The use of online learning approaches is associated with multiple vision problems, which merit attention in future studies.

The present study demonstrated that asthenopia and vision impairment among students in western rural China were also affected by the pandemic; these findings provide critical insights regarding the effects of the pandemic on vision health in rural students. Moreover, the findings highlight important issues related to childhood vision health during the pandemic; parents, teachers, and eye care providers should consider evidence-based measures to avoid asthenopia and vision impairment in children. The current pace of economic and technological development is leading to increased use of digital devices and online learning approaches, but vision problems in rural China have not received sufficient consideration. Thus, there is a critical need for greater efforts to monitor VA and vision health among students in this region.

Concept or design: All authors.
Acquisition of data: Y Ding, H Guan, K Du.
Analysis or interpretation of data: Y Ding, H Guan, K Du, Y Shi.
Drafting of the manuscript: Y Ding, Y Zhang, Z Wang.
Critical revision of the manuscript for important intellectual content: H Guan, Y Shi.

All authors contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

As an International Editorial Advisory Board member of the journal, Y Shi was not involved in the peer review process. Other authors have disclosed no conflicts of interest.

We thank Dr Wenting Liu, Dr Jiaqi Zhu, and staff from the Center for Experimental Economics in Education of Shaanxi Normal University, China for their valuable contributions.

H Guan received funding for this study from the National Natural Science Foundation of China (Grant No.: 7180310) and Soft Science Project of Shaanxi Province (Grant No.: 2023-CX-RKX-127). Y Ding received funding for this study from the Fundamental Research Funds for the Central Universities (Grant No.: 2020CSWY018). This study was supported by the 111 Project (Grant No.: B16031). The funders had no role in designing the study, collecting, analysing or interpreting the data, or in drafting this manuscript.

This study protocol was approved by Sun Yat-sen University, China (Registration No.: 2013MEKY018) and all procedures followed the principles of the Declaration of Helsinki. Permission was obtained from the local boards of education in the study area, as well as the principals of all participating schools. All participating children provided oral assent before baseline data collection, and legal guardians provided written informed consent for their children to be enrolled in the study.

1. World Health Organization. WHO timeline—COVID-19. 2020. Available from: https://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19. Accessed 13 Sep 2021.

2. Li D, Liu Z, Liu Q, et al. Estimating the efficacy of quarantine and traffic blockage for the epidemic caused by 2019-nCoV (COVID-19): a simulation analysis. medRxiv [Preprint]. 25 Feb 2020. Available from: https://doi.org/10.1101/2020.02.14.20022913. Accessed 13 Sep 2021. Crossref

3. Wang G, Zhang Y, Zhao J, Zhang J, Jiang F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. Lancet 2020;395:945-7. Crossref

4. Wang J, Li Y, Musch DC, et al. Progression of myopia in school-aged children after COVID-19 home confinement. JAMA Ophthalmol 2021;139:293-300. Crossref

5. Kowalska M, Zejda JE, Bugajska J, Braczkowska B, Brozek G, Malińska M. Eye symptoms in office employees working at computer stations [in Polish]. Med Pr 2011;62:1-8.

6. Cumberland PM, Peckham CS, Rahi JS. Inferring myopia over the lifecourse from uncorrected distance visual acuity in childhood. Br J Ophthalmol 2007;91:151-3. Crossref

7. Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol 2012;96:614-8. Crossref

8. Resnikoff S, Pascolini D, Mariotti SP, Pokharel GP. Global magnitude of visual impairment caused by uncorrected refractive errors in 2004. Bull World Health Organ 2008;86:63-70. Crossref

9. Jan C, Li SM, Kang MT, et al. Association of visual acuity with educational outcomes: a prospective cohort study. Br J Ophthalmol 2019;103:1666-71. Crossref

10. Roch-Levecq AC, Brody BL, Thomas RG, Brown SI. Ametropia, preschoolers’ cognitive abilities, and effects of spectacle correction. Arch Ophthalmol 2008;126:252-8. Crossref

11. Zhang Z, Xu G, Gao J, et al. Effects of e-learning environment use on visual function of elementary and middle school students: a two-year assessment—experience from China. Int J Environ Res Public Health 2020;17:1560. Crossref

12. Wong CW, Tsai A, Jonas JB, et al. Digital screen time during COVID-19 pandemic: risk for a further myopia boom? Am J Ophthalmol 2021;223:333-7. Crossref

13. Kim J, Hwang Y, Kang S, et al. Association between sexposure to smartphones and ocular health in adolescents. Ophthalmic Epidemiol 2016;23:269-76. Crossref

14. Mohan A, Sen P, Shah C, Jain E, Jain S. Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: digital eye strain among kids (DESK study-1). Indian J Ophthalmol 2021;69:140-4. Crossref

15. Guan H, Yu NN, Wang H, et al. Impact of various types of near work and time spent outdoors at different times of day on visual acuity and refractive error among Chinese school-going children. PLoS One 2019;14:e0215827.Crossref

16. Sultana A, Tasnim S, Hossain MM, Bhattacharya S, Purohit N. Digital screen time during the COVID-19 pandemic: a public health concern. Available from: https://f1000research.com/articles/10-81. Accessed 13 Sep 2021. Crossref

17. Nigg CR, Wunsch K, Nigg C, et al. Are physical activity, screen time, and mental health related during childhood, preadolescence, and adolescence? 11-year results from the German Montorik–Modul Longitudinal Study. Am J Epidemiol 2021;190:220-9. Crossref

18. Schmidt SC, Anedda B, Burchartz A, et al. Physical activity and screen time of children and adolescents before and during the COVID-19 lockdown in Germany: a natural experiment. Sci Rep 2020;10:21780. Crossref

19. Aguilar-Farias N, Toledo-Vargas M, Miranda-Marquez S, et al. Sociodemographic predictors of changes in physical activity, screen time, and sleep among toddlers and preschoolers in Chile during the COVID-19 pandemic. Int J Environ Res Public Health 2020;18:176. Crossref

20. Bates LC, Zieff G, Stanford K, et al. COVID-19 impact on behaviors across the 24-hour day in children and adolescents: physical activity, sedentary behavior, and sleep. Children (Basel) 2020;7:138. Crossref

21. National Bureau of Statistics of China, PRC Government. China Statistical Yearbook 2020. Available from: http://www.stats.gov.cn/tjsj/ndsj/2020/indexch.htm. Accessed 14 Sep 2021.

22. National Bureau of Statistics of China, PRC Government. China Statistical Yearbook 2013. Beijing, China: China State Statistical Press; 2013.

23. Seguí Mdel M, Cabrero García J, Crespo A, Verdú J, Ronda E. A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace. J Clin Epidemiol 2015;68:662-73. Crossref

24. Yi H, Zhang L, Ma X, et al. Poor vision among China’s rural primary school students: prevalence, correlates and consequences. China Econ Rev 2015;33:247-62. Crossref

25. United Nations International Children’s Emergency Fund. Don’t let children be the hidden victims of COVID-19 pandemic. Available from: https://www.unicef.org/press-releases/dont-let-children-be-hidden-victims-covid-19-pandemic. Accessed 6 Oct 2020.

26. Moon JH, Kim KW, Moon NJ. Smartphone use is a risk factor for pediatric dry eye disease according to region and age: a case control study. BMC Ophthalmol 2016;16:188. Crossref

27. Moon JH, Lee MY, Moon NJ. Association between video display terminal use and dry eye disease in school children. J Pediatr Ophthalmol Strabismus 2014;51:87-92. Crossref

28. Rechichi C, De Mojà G, Aragona P. Video game vision syndrome: a new clinical picture in children? J Pediatr Ophthalmol Strabismus 2017;54:346-55.Crossref

29. Mowatt L, Gordon C, Santosh AB, Jones T. Computer vision syndrome and ergonomic practices among undergraduate university students. Int J Clin Pract 2018;72:e13035. Crossref

30. Terasaki H, Yamashita T, Yoshihara N, Kii Y, Sakamoto T. Association of lifestyle and body structure to ocular axial length in Japanese elementary school children. BMC Ophthalmol 2017;17:123. Crossref

31. Fernández-Montero A, Olmo-Jimenez JM, Olmo N, et al. The impact of computer use in myopia progression: a cohort study in Spain. Prev Med 2015;71:67-71. Crossref

Patients with breast cancer receiving (neo)adjuvant treatment exhibit improved prognoses.1 However, chemotherapy regimens for breast cancer are associated with various degrees of chemotherapy-induced nausea and vomiting (CINV). The doxorubicin/cyclophosphamide (AC) regimen is one of the most frequently prescribed regimens for patients with breast cancer who are receiving (neo) adjuvant chemotherapy; AC is among the highly emetogenic chemotherapies with ≥90% risk of nausea and vomiting.

In situations where a neurokinin-1 receptor antagonist (NK1RA) is accessible, most current guidelines for AC(-like) chemotherapy recommend the use of a prophylactic triplet antiemetic regimen that consists of an NK1RA, a 5-hydroxytryptamine type-3 receptor antagonist (5HT3RA), and a corticosteroid, with or without olanzapine.2 3 4 In addition to earlier NK1RAs (eg, aprepitant, fosaprepitant, and rolapitant), netupitant/palonosetron (NEPA) [Akynzeo], which is a combination of an NK1RA (netupitant 100 mg) and a second-generation 5HT3RA (palonosetron 0.5 mg), has been available in the past decade. Although palonosetron constitutes a more potent 5HT3RA,5 it also has synergistic interactions with netupitant that include interference with 5HT3 receptor cross-talk and enhancement of the netupitant-mediated effect on NK1 receptor internalisation.6 7

In a recent systematic review and meta-analysis, Yokoe et al8 compared different antiemetic regimens to assess their control of CINV in patients receiving highly emetogenic chemotherapy regimens. The authors arbitrarily defined the ‘conventional’ regimen as a three-drug regimen that contained dexamethasone, a first- or second-generation secondgeneration 5HT3RA, and an earlier NK1RA compound (aprepitant, fosaprepitant, or rolapitant); they defined ‘new’ regimens as regimens that contained NEPA or olanzapine. The results indicated that, compared with conventional regimens, new regimens containing NEPA were more effective in terms of producing a complete response (ie, absence of vomiting and no use of rescue therapy). Additionally, Yokoe et al8 showed that olanzapine-containing regimens were most effective in terms of producing a complete response, particularly when olanzapine was added to a triplet regimen of an NK1RA, a 5HT3RA, and dexamethasone. These findings were supported by the results of a prospective randomised study published in 2020, which directly compared an olanzapine-containing four-drug regimen with a standard triplet antiemetic regimen (consisting of aprepitant, ondansetron, and dexamethasone) for the prevention of CINV in patients receiving AC chemotherapy.9

Here, we conducted a post-hoc analysis through retrospective assessment of individual patient data from two previously reported prospective antiemetic studies that involved Chinese patients with breast cancer.9 10 We hypothesised that a four-drug antiemetic regimen (consisting of an NK1RA, a 5HT3RA, dexamethasone, and olanzapine) would remain superior to a three-drug regimen (consisting of an NK1RA, a 5HT3RA, and dexamethasone) that included NEPA as a combination NK1RA and 5HT3RA agent. The primary objective was to compare the efficacies of olanzapine- and NEPA-containing antiemetic regimens in terms of controlling CINV during the first cycle of AC. The secondary objectives were: (1) to assess quality of life (QOL) outcomes in patients receiving these treatments during the first cycle of AC, and (2) to assess emesis control outcomes in patients receiving these treatments over multiple cycles of AC.

This study constituted a post-hoc analysis of data from two recently reported prospective studies. The first prospective study investigated emesis outcomes in patients with breast cancer who received a standard triplet antiemetic regimen (ie, aprepitant, ondansetron, and dexamethasone) with or without olanzapine9; after the first study, a second prospective study was conducted to assess the antiemetic efficacy of NEPA and dexamethasone.10 These studies were conducted with institutional ethics approval and were registered at ClinicalTrials.gov (NCT03386617 and NCT03079219, respectively). For the post-hoc analysis, data were extracted from the first study regarding patients who received an olanzapine plus aprepitant-containing four-drug antiemetic regimen; data were extracted from the second study regarding patients who received NEPA and dexamethasone. These patients were categorised into the ‘olanzapine’ and ‘NEPA’ groups, respectively.

Inclusion criteria were similar for the two studies. Specifically, patients were eligible if they were women of Chinese ethnicity, were aged >18 years, had early-stage breast cancer, and planned to receive a regimen of (neo)adjuvant AC. All study participants were required to read, understand, and complete study questionnaires and diaries in Chinese. Exclusion criteria included abnormal bone marrow, renal, or hepatic functions; receipt or planned receipt of radiation therapy to the abdomen or pelvis within 7 days prior to initial administration of study treatment; presence of grade 2 to 3 nausea, as defined by the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0,11 or vomiting within 24 hours prior to initial administration of the study treatment; presence of an active infection or any uncontrolled disease; a history of illicit drugs, including marijuana or alcohol abuse; mental incapacitation; and/or presence of a clinically significant emotional or psychiatric disorder. Written consent was provided by eligible patients prior to enrolment in the studies.

Patients in the olanzapine group received olanzapine 10 mg, aprepitant 125 mg, dexamethasone 12 mg, and ondansetron 8 mg before chemotherapy on day 1; they also received ondansetron 8 mg 8 hours after chemotherapy. Subsequently, they received aprepitant 80 mg daily on days 2-3 and olanzapine 10 mg daily on days 2-5.

Patients in the NEPA group received one capsule of NEPA (netupitant 300 mg/palonosetron 0.50 mg) with dexamethasone 12 mg before chemotherapy on day 1. Subsequently, they received dexamethasone 4 mg twice per day on days 2-3.

At the initiation of chemotherapy on day 1, individual patients were provided a diary to record the date and time of their symptoms of vomiting and nausea for 120 hours after the AC infusion; the use of any rescue medication was also recorded. On days 2-6, patients rated their symptoms of nausea for the previous 24 hours using a visual analogue scale (in which 0 mm implied no nausea, whereas 100 mm implied nausea that was ‘as bad as it could be’). Additionally, on day 1 (before infusion of AC) and day 6 (after completion of the diary), patients completed the Functional Living Index-Emesis (FLIE) questionnaire. A research nurse/assistant called individual patients on days 2-6 to remind them to take the study medications, complete the patient diary, and complete the FLIE questionnaire.

Antiemetic efficacy was measured across three overlapping time periods. The ‘acute’ phase comprised 0 to 24 hours from the infusion of AC; the ‘delayed’ phase comprised 24 to 120 hours from the infusion of AC; the ‘overall’ phase comprised 0 to 120 hours from the infusion of AC.

Variables used to assess antiemetic efficacy were ‘complete response’, ‘no vomiting’, ‘no significant nausea’, ‘no nausea’, ‘no use of rescue therapy’, ‘complete protection’, and ‘total control’; definitions of these variables are provided in Table 1. The proportions of patients who exhibited these variables were recorded separately. Additionally, the ‘time to first vomiting’ in cycle 1 was determined using information recorded in patients’ diaries.

Quality of life was evaluated using the Chinese version of self-reported FLIE questionnaires from individual patients.12 The FLIE questionnaire consists of a nausea domain (9 items) and a vomiting domain (9 items). All scores were transformed to ensure that higher scores indicated worse impact on QOL.

A modified intention-to-treat approach was used for all efficacy analyses; specifically, analyses included patients who had received chemotherapy, had completed the study procedures from 0 to 120 hours in cycle 1 of AC, and had no major protocol violations.

To achieve the primary objective of this study, the efficacies of the two antiemetic regimens were based on the proportions (including 95% confidence intervals) of patients who achieved complete response during the acute, delayed, and overall phases after AC infusion in cycle 1. Other parameters compared in cycle 1 of AC were ‘time to first vomiting’, ‘no vomiting’, ‘no significant nausea’, no nausea’, ‘no use of rescue therapy’, ‘complete protection’, and ‘total control’.

To achieve the secondary objectives, QOL was compared between the two antiemetic regimens based on assessments of the nausea domain, vomiting domain, and total score (sum of nausea and vomiting domains) of the FLIE questionnaire during cycle 1 of AC. Emesis control over multiple cycles was compared between the two antiemetic regimens by assessing the proportions (including 95% confidence intervals) of patients who achieved ‘complete response’, ‘complete protection’, and ‘total control’ in the acute, delayed, and overall phases.

Comparisons between the two antiemetic regimens were made using the Wilcoxon rank-sum test for continuous data and Pearson’s Chi squared test for dichotomous data. Two-sided P values <0.05 were considered statistically significant. The SAS Software version 9.4 (SAS Institute, Cary [NC], United States) was used for analyses.

Data from 120 patients were included in this study; 60 patients each were enrolled in the NEPA and olanzapine groups. Fifty-six patients (93.3%) in the olanzapine group completed all four cycles of AC, whereas 60 patients (100%) in the NEPA group completed all four cycles of AC.

Patient characteristics, including characteristics that could potentially affect CINV, are shown in Table 2. The olanzapine and NEPA groups had very similar patient characteristics, with median ages of 54.5 and 56 years, respectively. Nearly two-thirds of patients in each group had Stage II breast cancer (63.3% and 66.7%, respectively). The percentage of patients with a history of motion sickness was higher in the NEPA group (35%) than in the olanzapine group (16.7%). Furthermore, 30% of patients in the NEPA group and 20% of patients in the olanzapine group received AC as neoadjuvant treatment.

Antiemetic efficacies during cycle 1 of AC in the olanzapine and NEPA groups are shown in Table 3. Complete response rates in acute, delayed, and overall phases in cycle 1 did not differ between groups. In the acute phase, the olanzapine group exhibited a higher rate of ‘no use of rescue therapy’ (olanzapine vs NEPA: 96.7% vs 85.0%, P=0.0225). No parameters differed between groups in the delayed phase. In the overall phase, the olanzapine group exhibited significantly higher rates of ‘no use of rescue therapy’ (91.7% vs 76.7%, P=0.0244) and ‘no significant nausea’ (91.7% vs 78.3%, P=0.0408).

The median time to first vomiting was not reached in either group (P=0.3902). Quality of life results during cycle 1 of AC in the olanzapine and NEPA groups, determined using the FLIE questionnaire, are shown in the Figure. There were no significant differences in the nausea domain, vomiting domain, or total score of the FLIE questionnaire between the two groups.

Antiemetic efficacies over multiple cycles of AC in the olanzapine and NEPA groups are shown in Table 4. In the acute phase, the NEPA group exhibited significantly higher rates of total control in cycle 2 (olanzapine vs NEPA: 59.6% vs 81.7%, P=0.0087) and cycle 4 (63.2% vs 86.7%, P=0.0032). No parameters differed between groups in the delayed phase. In the overall phase, the NEPA group exhibited significantly higher rates of total control in cycle 3 (55.4% vs 73.3%, P=0.0430) and cycle 4 (54.4% vs 75.0%, P=0.0195).

Chemotherapy-induced nausea and vomiting is a frustrating adverse effect for patients receiving anticancer treatment.13 The administration of optimal antiemetic prophylaxis can help to maintain QOL, while potentially improving patient compliance in terms of completing planned therapies. In current antiemetic prophylaxis guidelines, the European Society of Medical Oncology/Multinational Association of Supportive Care in Cancer, the American Society of Clinical Oncology, and the United States National Comprehensive Cancer Network offer several options regarding antiemetic regimens for patients receiving AC(-like) chemotherapy. These options mainly involve the combination of a 5HT3RA and corticosteroids, with or without an NK1RA and olanzapine.2 3 4 In particular, the incorporation of olanzapine, an antipsychotic drug with antagonistic effects on various receptors (eg, dopamine and serotonin receptors),14 is increasingly regarded as a component of antiemetic prophylaxis for patients receiving anticancer treatment.

In an attempt to identify the best antiemetic regimen, Yokoe et al8 conducted a meta-analysis of randomised trials that tested various antiemetic regimens. The results indicated that olanzapine-based regimens demonstrated the best efficacy. Specifically, olanzapine in combination with an NK1RA, a 5HT3RA, and dexamethasone exhibited the greatest efficacy; other olanzapine-containing regimens (consisting of a 5HT3RA and dexamethasone) were also superior to regimens that lacked olanzapine. Moreover, even in the presence of earlier NK1RAs (eg, aprepitant, fosaprepitant, or rolapitant), regimens lacking olanzapine remained inferior.

Similar to the findings with olanzapine, Yokoe et al8 reported that triplet antiemetics involving NEPA were superior to conventional NK1RAs (eg, aprepitant, fosaprepitant, or rolapitant). Furthermore, Zhang et al15 directly compared NEPA-based antiemetic regimens with aprepitant-based triplet regimens in a randomised study that involved 800 patients who underwent administration of a cisplatin-containing regimen. Their results revealed that patients receiving NEPA and dexamethasone exhibited similar control of CINV, compared with patients receiving aprepitant, granisetron, and dexamethasone; however, NEPA-treated patients had a significantly lower requirement for rescue therapy. Additionally, in a recent study focused on patients with breast cancer who were undergoing AC chemotherapy, patients who received NEPA and dexamethasone demonstrated significantly higher rates of complete response, complete protection, and total control with enhanced QOL, compared to historical controls who received aprepitant, ondansetron, and dexamethasone; these benefits persisted over multiple cycles of chemotherapy.10

To our knowledge, no study has directly compared olanzapine- and NEPA-containing regimens. Using an indirect comparison approach, the present study showed that the olanzapine-based regimen had higher rates of ‘no use of rescue therapy’ and ‘no significant nausea’ in cycle 1 of AC, compared to the NEPA-based regimen. In contrast, assessments in subsequent cycles revealed that the NEPA-based regimen led to higher rates of total control in the acute phase (cycles 2 and 4) and the overall phase (cycles 3 and 4). The lack of difference in QOL between the two groups of patients may be related to the difference in adverse-effect profiles of the antiemetics used. For instance, the continued use of dexamethasone on days 2-3 in the NEPA group may have affected QOL among those patients because of its effects on mood, insomnia, gastrointestinal symptoms, and metabolic profiles.16 Indeed, a recent meta-analysis showed that, among patients receiving AC or moderately emetogenic chemotherapy, 3 days of dexamethasone did not provide additional benefit compared to 1 day of the agent.17 However, olanzapine has been associated with sedation and somnolence.18 Thus, after the completion of a phase 2 trial in Japan that suggested olanzapine was more effective at 5 mg than at 10 mg,19 the same group of investigators conducted a phase 3 study in which they tested the addition of daily olanzapine 5 mg to an aprepitant-based three-drug regimen; the results showed that, even at a lower dose of olanzapine, the olanzapine-containing regimen remained more efficacious than the olanzapine-free regimen for patients receiving cisplatin.20 Other adverse effects have been reported. Our analysis of olanzapine in combination with aprepitant, ondansetron and dexamethasone revealed a significantly higher incidence of grade ≥2 neutropenia in the olanzapine arm than in the standard arm, although this altered incidence was not associated with a significant difference in neutropenic fever.9 A few cases of olanzapine-induced neutropenia have been reported21; additionally, a recent randomised antiemetic study showed that patients who received an olanzapine-containing regimen had a higher frequency of severe neutropenia (without an increased incidence of neutropenic fever).22 Although the underlying mechanism remains unknown, the results of the aforementioned Japanese study20 suggest that olanzapine 5 mg could reduce the incidence of neutropenia. In contrast, in our previous trial regarding a NEPA-based regimen, we found that patients in the NEPA arm had significantly lower incidences of grade ≥2 neutropenia and neutropenic fever, compared to historical controls who received an aprepitant-based regimen.9 10

This study had some potential limitations. First, dexamethasone was only used for 1 day in the olanzapine-based regimen, whereas it was administered for 3 days in the NEPA-based regimen; this difference may have influenced the findings. Second, the use of data from two separate studies may have affected the generalisability of the findings because of slight variations in patient characteristics; the lack of blinding in both studies also increased the potential for patient-related reporting biases. Nonetheless, the original studies were consecutively conducted during the period from 2017 to 2019; both the data from Chinese patients enrolled in a homogenous group with early-stage breast cancer who were receiving (neo)adjuvant AC chemotherapy and the present analysis were analysed based on individual patient data. These factors support the validity of our comparison approach.

In conclusion, the present findings do not conclusively support the superiority of either the olanzapine-based regimen or the NEPA-based regimen in terms of antiemetic efficacy or QOL among patients with breast cancer who are receiving AC. Our previous study demonstrated that aprepitant has a limited effect when used with a 5HT3RA and dexamethasone23; we also found that NEPA was superior to aprepitant.10 Overall, the available data suggest that olanzapine-containing antiemetic regimens can be used without aprepitant, particularly when seeking to reduce medical expenses. Moreover, the available data support the previous conclusion that, in parts of the world where socio-economic limitations restrict the availability of NK1RAs, the use of olanzapine combined with a 5HT3RA and dexamethasone may be an effective low-cost alternative antiemetic regimen.8 24 Antiemetic efficacy may be enhanced if NEPA is administered in combination with dexamethasone and olanzapine as a four-drug antiemetic regimen; however, the efficacy of an olanzapine plus NEPA regimen in terms of controlling CINV should be confirmed in a trial setting.

Concept or design: W Yeo.
Acquisition of data: FKF Mo, W Yeo.
Analysis or interpretation of data: W Yeo, CCH Yip, FKF Mo.
Drafting of the manuscript: W Yeo, CCH Yip.
Critical revision of the manuscript for important intellectual content: L Li, TKH Lau, VTC Chan, CCH Kwok, JJS Suen, FKF Mo.

All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

W Yeo has been involved in the Chemotherapy-Induced Nausea and Vomiting (CINV) Network in Asia and has provided lectures on CINV at events organised by Mundipharma International Limited, which supported the design of the NEPA study10 analysed in this post-hoc analysis but had no role in the present comparative analysis, data collection and analysis, decision to publish, or preparation of the manuscript.

We thank Ms Dong KT Lai, Ms Elizabeth Pang, Ms Vivian Chan, and Ms Maggie Cheung of the Department of Clinical Oncology, The Chinese University of Hong Kong for their support in contributing to patient enrolment and study monitoring.

Data from this study were presented at the European Society of Medical Oncology Asia Virtual Congress 2020 on 27 November 2020.

This study was supported by an education grant from Madam Diana Hon Fun Kong Donation for Cancer Research (Grant No.: CUHK Project Code 7104870). The Donation had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The studies examined in this post-hoc analysis were approved by The Joint Chinese University of Hong Kong–New Territories East Cluster Institution Review Board of The Chinese University of Hong Kong and the Hong Kong Hospital Authority, and the Kowloon West Cluster Research Ethics Committee of the Hong Kong Hospital Authority (Ref No.: CREC 2016.013, CREC 2017.1609 and KW/FR-18-019[119-19]). All patient data in this study were anonymous and were based on the abovementioned reported studies. There was no additional work on retrieving patient records in this study.

1. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG); Peto R, Davies C, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012;379:432-44. Crossref

2. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology, Antiemesis Version 1; 2019.

3. Herrstedt J, Roila F, Warr D, et al. 2016 updated MASCC/ESMO consensus recommendations: prevention of nausea and vomiting following high emetic risk chemotherapy. Support Care Cancer 2017;25:277-88. Crossref

4. Hesketh PJ, Kris MG, Basch E, et al. Antiemetics: American Society of Clinical Oncology Clinical Practice Guideline update. J Clin Oncol 2017;35:3240-61. Crossref

5. Popovic M, Warr DG, Deangelis C, et al. Efficacy and safety of palonosetron for the prophylaxis of chemotherapy-induced nausea and vomiting (CINV): a systematic review and meta-analysis of randomized controlled trials. Support Care Cancer 2014;22:1685-97. Crossref

6. Thomas AG, Stathis M, Rojas C, Slusher BS. Netupitant and palonosetron trigger NK1 receptor internalization in NG108-15 cells. Exp Brain Res 2014;232:2637-44. Crossref

7. Stathis M, Pietra C, Rojas C, Slusher BS. Inhibition of substance P-mediated responses in NG108-15 cells by netupitant and palonosetron exhibit synergistic effects. Eur J Pharmacol 2012;689:25-30. Crossref

8. Yokoe T, Hayashida T, Nagayama A, et al. Effectiveness of antiemetic regimens for highly emetogenic chemotherapy-induced nausea and vomiting: a systematic review and network meta-analysis. Oncologist 2019;24:e347-57. Crossref

9. Yeo W, Lau TK, Li L, et al. A randomized study of olanzapine-containing versus standard antiemetic regimens for the prevention of chemotherapy-induced nausea and vomiting in Chinese breast cancer patients. Breast 2020;50:30-8. Crossref

10. Yeo W, Lau TK, Kwok CC, et al. NEPA efficacy and tolerability during (neo)adjuvant breast cancer chemotherapy with cyclophosphamide and doxorubicin. BMJ Support Palliat Care 2022;12:e264-70.

11. National Cancer Institute. Cancer Therapy Evaluation Program. 2021. Available from: https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm#ctc_40. Accessed 3 Feb 2023.

12. Martin AR, Pearson JD, Cai B, Elmer M, Horgan K, Lindley C. Assessing the impact of chemotherapy-induced nausea and vomiting on patients’ daily lives: a modified version of the Functional Living Index-Emesis (FLIE) with 5-day recall. Support Care Cancer 2003;11:522-7. Crossref

13. Griffin AM, Butow PN, Coates AS, et al. On the receiving end. V: patient perceptions of the side effects of cancer chemotherapy in 1993. Ann Oncol 1996;7:189-95. Crossref

14. Bymaster FP, Nelson DL, DeLapp NW, et al. Antagonism by olanzapine of dopamine D1, serotonin2, muscarinic, histamine H1 and alpha 1-adrenergic receptors in vitro. Schizophr Res 1999;37:107-22. Crossref

15. Zhang L, Lu S, Feng J, et al. A randomized phase III study evaluating the efficacy of single-dose NEPA, a fixed antiemetic combination of netupitant and palonosetron, versus an aprepitant regimen for prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy (HEC). Ann Oncol 2018;29:452-8. Crossref

16. Roila F, Ruggeri B, Ballatori E, et al. Aprepitant versus metoclopramide, both combined with dexamethasone, for the prevention of cisplatin-induced delayed emesis: a randomized, double-blind study. Ann Oncol 2015;26:1248-53. Crossref

17. Okada Y, Oba K, Furukawa N, et al. One-day versus three-day dexamethasone in combination with palonosetron for the prevention of chemotherapy-induced nausea and vomiting: a systematic review and individual patient data-based meta-analysis. Oncologist 2019;24:1593-600. Crossref

18. Navari RM, Qin R, Ruddy KJ, et al. Olanzapine for the prevention of chemotherapy-induced nausea and vomiting. N Engl J Med 2016;375:134-42. Crossref

19. Abe M, Hirashima Y, Kasamatsu Y, et al. Efficacy and safety of olanzapine combined with aprepitant, palonosetron, and dexamethasone for preventing nausea and vomiting induced by cisplatin-based chemotherapy in gynecological cancer: KCOG-G1301 phase II trial. Support Care Cancer 2016;24:675-82. Crossref

20. Hashimoto H, Abe M, Tokuyama O, et al. Olanzapine 5 mg plus standard antiemetic therapy for the prevention of chemotherapy-induced nausea and vomiting (J-FORCE): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 2020;21:242-9. Crossref

21. Malhotra K, Vu P, Wang DH, Lai H, Faziola LR. Olanzapine-induced neutropenia. Ment Illn 2015;7:5871. Crossref

22. Gjafa E, Ng K, Grunewald T, et al. Neutropenic sepsis rates in patients receiving bleomycin, etoposide and cisplatin chemotherapy using olanzapine and reduced doses of dexamethasone compared to a standard antiemetic regimen. BJU Int 2021;127:205-11. Crossref

23. Yeo W, Mo FK, Suen JJ, et al. A randomized study of aprepitant, ondansetron and dexamethasone for chemotherapy-induced nausea and vomiting in Chinese breast cancer patients receiving moderately emetogenic chemotherapy. Breast Cancer Res Treat 2009;113:529-35. Crossref

24. Babu G, Saldanha SC, Kuntegowdanahalli Chinnagiriyappa L, et al. The efficacy, safety, and cost benefit of olanzapine versus aprepitant in highly emetogenic chemotherapy: a pilot study from South India. Chemother Res Pract 2016;2016:3439707. Crossref

The coronavirus disease 2019 (COVID-19) pandemic, which began in early 2020, has resulted in unprecedented large-scale public health responses. Stringent regional social distancing measures (eg, quarantine, school closures, and restrictions at work and recreation destinations) were rapidly implemented during the early days of the pandemic as forms of non-pharmacological intervention.1 Although there is evidence that such measures can temporarily contain the spread of severe acute respiratory syndrome coronavirus 2,2 collateral effects among non–COVID-19–related conditions have also been reported.3 Trauma is the leading cause of death and disability among young adults worldwide,4 but the effects of the COVID-19 pandemic on injuries and fracture incidence within Hong Kong have not been fully elucidated. This uncertainty has hindered healthcare resource deployment and clinical service demand estimation in times of stringency. We sought to address this problem using ‘big data’ sources and regional clinical data repositories, which allow researchers to rapidly delineate epidemiological associations with potential applications in forecasting models, while avoiding resource-intensive collection of conventional epidemiological information and protecting patient anonymity.

We presumed that restrictions on citizen mobility, in concert with social distancing, were associated with reductions in musculoskeletal injuries during the early days of the COVID-19 pandemic. Specifically, we hypothesised that reduced population mobility was associated with reductions in fracture incidence and fracture-related healthcare needs during the early days of the pandemic. We investigated these relationships by analysing daily multicentre hospital data registries in Hong Kong, along with digital population mobility datasets published by a technology company. Our main outcome measurement was skeletal fractures, which served as a specific surrogate for musculoskeletal trauma.

This study was conducted in Hong Kong, a highincome region (with gross domestic product per capita of HK$357 667 in 20205) that was among the first areas affected by COVID-19; social distancing measures were implemented during the early days of the pandemic.

Using the Clinical Data Analysis and Reporting System of the Hospital Authority, anonymised patient records were retrieved from all 43 public hospitals in Hong Kong for the period from 22 November 2016 to 20 May 2020. In Hong Kong, up to 90% of hospital bed-days occur in public hospitals, which manage nearly all critical emergencies in the region.6 Anonymised clinical data were retrieved, including time of initial injury presentation, emergency department triage, trauma category, hospital admission, diagnosis, and surgical procedures. Diagnoses and procedures were encoded in accordance with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) by treating physicians based on clinical and radiological investigations, intraoperative findings, and date of hospital discharge. The ICD-9-CM codes that met the inclusion criteria (which included fractures under the purview or commonly admitted under the care of orthopaedic and traumatology service) were all codes from 805 to 829 (inclusive). Duplicate records from fracture reassessment related to follow-up attendances, hospitalisation after emergency department attendance, and elective hospital re-admissions (ie, episodes assigned to the same patient unique identifier with identical diagnostic codes, which occurred within 30 days of the index episode) were regarded as a single event to avoid double counting. Pathological fractures and records with missing diagnosis codes or admission times were excluded from the analysis.

The ‘COVID-19 epoch’ was defined as 25 January 2020 (activation of the government’s ‘emergency’ response and commencement of social distancing policies7) to 26 March 2020; this arbitrarily chosen 62-day period included all patients with fractures who presented during that period. This epoch was compared with the 9 weeks preceding the onset of the COVID-19 pandemic (ie, 22 November 2019 to 24 January 2020), as well as the same period over the past 3 years to adjust for seasonality-related variations8 (ie, 25 January to 26 March in 2017, 2018, and 2019). Differences between actual and projected daily fracture incidences were calculated based on mean values at the same time of year over the past 3 years. Fracture-related mortality rates, defined as the numbers of deaths within 30 days after initial fracture presentation per 100 000 person-years, were compared. The Chi squared test was used to detect differences in fracture incidence and fracture-related mortality during the COVID-19 pandemic and pre-pandemic epochs.

Surrogate data concerning population mobility were retrieved from Mobility Trends Reports9—an aggregate daily measure of geographical direction requests on Apple Maps, a service established by Apple Inc, which holds the largest market share of electronic mobile devices (including smartphones and tablets) in Hong Kong.10 Walking index was regarded as an index of population mobility, considering the smartphone penetration of 91.5%11 among the 7.50 million residents of Hong Kong.12

Associations between daily fracture incidence and population mobility were determined by incidence rate ratios (IRRs) using quasi-Poisson regression. Secondary analysis involved associations between mobility index and fracture repair surgeries, all types of orthopaedic emergency department attendances, orthopaedic hospital admissions, and emergency orthopaedic surgeries.

Because medical records were timestamped in Hong Kong time (8 hours ahead of Greenwich Mean Time), they were converted to Pacific Time to match the time intervals listed in Mobility Trends Reports; this conversion ensured that data were temporally matched for analysis.

To determine whether mobility associations simply reflected health-seeking behaviour, we included analyses of diseases which lacked a physiological basis and were not associated with population mobility—these ‘controls’ included appendicitis, cellulitis, and abscess (ICD-9-CM diagnosis codes 540 and 682). Statistical analysis was performed using R software, version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria). Quasi-Poisson regression was used to model the relationship between the population mobility index and the daily incidences of fractures and fracture-related events; the population mobility index was the explanatory variable, whereas the

daily incidences of various events were response variables. A quasi-Poisson distribution was preferred over a Poisson distribution, considering the presence of significant overdispersion among some response variables (in the form of count data) when a dispersion parameter was included. In accordance with standard statistical methods, the natural logarithm was utilised as the link function. Incidence rate ratios were reported and represented by the following formula:

Estimated incidence = IRR^PMI × BIR

where IRR represents the incidence rate ratio, PMI represents the population mobility index, and BIR represents the baseline incidence rate. The IRR, which quantifies the relationship between the mobility index and fracture incidence, is multiplicative in nature—for every unit increase in the mobility index, there is a corresponding multiplicative increase in the IRR. If the IRR is <1, it is expected to decrease in a multiplicative manner for every unit decrease in the mobility index. Multiple comparisons were adjusted by Bonferroni correction, and the threshold for statistical significance was regarded as P<0.00227 (0.05/22).

In total, 59 931 fracture-related medical records from orthopaedic emergency department attendances, hospital admissions, and surgeries were reviewed. After exclusion of 11 498 linked episodes, 284 pathological fractures, 786 follow-up attendances, 175 hospital re-admissions, and two episodes with missing admission times, 47 186 fractures were included in the analysis. Descriptive statistics regarding daily fracture incidences, controls, and fracture-related surgeries during COVID-19 social distancing are shown in Table 1. Intra-year and inter-year comparison cohorts are presented in Table 2.

A reduction of 1748 fractures in the actual versus projected incidence (321.9 vs 459.1 per 100 000 person-years, P<0.001) was observed during the COVID-19 epoch; the relative risk was 0.690 (95% confidence interval [CI]=0.678-0.702), compared with mean incidences in the previous 3 years (ie, inter-year cohort) [Table 2]. Differences in fracture incidence between the pandemic and pre-pandemic epochs are shown in Figure 1.

Fracture incidences, population mobilities, and controls are depicted in Figure 2. The first two COVID-19 cases in Hong Kong were reported on 23 January 202013; three additional cases were reported on 24 January 2020. Social distancing measures were implemented on 25 January 2020; these included suspension of schools, initiation of ‘work from home’ measures among civil servants, and suspension of hospital visitations. Mandatory border quarantine was enforced on 8 February 2020. The sharpest decrease in mobility was observed on 24 January 2020; population mobility subsequently remained at low levels, in conjunction with cancellations of large-scale social and sporting events, as well as the imposition of travel restrictions with quarantine measures for returning travellers.7

Fracture incidence was positively associated with the population mobility index (IRR=1.0055, 95% CI=1.0044-1.0066, P<0.001). Analyses of fracture incidence according to anatomical location revealed associations of the population mobility index with upper limb fractures (IRR=1.0073, 95% CI=1.0057-1.0088, P<0.001) and lower limb fractures (IRR=1.0045, 95% CI=1.0030-1.0060, P<0.001) [Fig 3].

The population mobility index was associated with the incidences of fractures involving the radius and ulna (IRR=1.0079, 95% CI=1.0057-1.0101, P<0.001), hand and fingers (IRR=1.0069, 95% CI=1.0039-1.0098, P<0.001), femoral neck (IRR=1.0065, 95% CI=1.0035-1.0095, P<0.001), and tibia and fibula (IRR=1.0097, 95% CI=1.0044-1.0151, P<0.001) [Fig 4]. However, after Bonferroni correction, the population mobility index did not exhibit statistically significant associations with trochanteric hip fractures (IRR=1.0008, P=0.683), spine fractures (IRR=0.996, P=0.183), or pelvic fractures (IRR=1.0064, P=0.00799).

Stronger associations were observed among fractures, such that some patients presented at a younger age (eg, patients with tibia, fibula, hand, and finger fractures), whereas other patients presented at an older age (eg, patients with femoral neck fractures). Digital literacy, manual dexterity and visual acuity, and higher internet and smartphone usage among younger residents11 are among the factors that cause the population mobility index to have increased sensitivity for analysis in such age-groups.

The incidences of cellulitis, abscesses, and appendicitis were not associated with the population mobility index (P>0.00227). These findings support the hypothesis that changes in associations between fracture incidence and population mobility were not solely caused by changes in health-seeking behaviour; if they had been caused by changes in such behaviour, corresponding reductions in those conditions would have been observed.

The daily population mobility index was associated with the number of patients admitted on a particular day who subsequently underwent fracture repair surgeries (IRR=1.0041, 95% CI=1.0020-1.0062, P<0.001). The population mobility index was also associated with all types of emergency orthopaedic surgeries (IRR=1.0040, 95% CI=1.0021-1.0058, P<0.001), attendances at orthopaedic emergency departments (IRR=1.0076, 95% CI 1.0064-1.0087, P<0.001), and emergency orthopaedic hospital admissions (IRR=1.0054, 95% CI=1.0043-1.0064, P<0.001). Additionally, the numbers of orthopaedic patients triaged as critical, emergent, and urgent (ie, patients who require physician attention within 30 minutes of attendance) were also associated with the population mobility index (IRR=1.0063, 95% CI=1.0054-1.0073, P<0.001). Whereas the numbers of traffic-related and sports-related trauma cases were associated with the population mobility index (IRR=1.008, 95% CI=1.0063-1.0097 and IRR=1.013, 95% CI=1.0092-1.0158, respectively, both P<0.001), the number of assault-related trauma cases was not (P=0.238).

Forty-nine patients with fractures died within 30 days of presentation during the COVID-19 epoch. This constituted a mortality rate of 3.22 deaths per 100 000 person-years, which was lower than the rate of 4.70 deaths per 100 000 person-years during the period before the pandemic (P<0.001); thus, there were around 19 fewer fracture-related deaths in the Hong Kong population during the 62-day study period. Four patients with fractures had COVID-19 (ie, they had positive results in nasopharyngeal swab reverse transcriptase-polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2) and survived beyond 30 days after initial fracture presentation. The change in mortality was presumably explained by reduced fracture incidence: 30-day mortality among patients with fractures did not significantly differ between the COVID-19 epoch (1.2%, 49 deaths in 4101 patients) and the preceding period (1.0%, 175 deaths in 17 198 patients) [P=0.305].

This study analysed 47 186 fractures in Hong Kong, prior to and during the early days of the COVID-19 pandemic. Population mobility was assessed through aggregate digital footprints using the volume of location service requests as a surrogate marker, considering the high smartphone and internet penetration in Hong Kong11; importantly, datasets of aggregate digital footprints have been published to facilitate efforts to control COVID-19.9 The findings support our hypothesis in terms of the relationship between fracture incidence and population mobility.

Fractures incur substantial healthcare costs; for example, fragility fracture-related costs incurred costs of 37.5 billion euros, along with the loss of 1.0 million quality-adjusted life years, among the six largest European countries in 2017.14 Some fractures (eg, hip fractures) warrant early surgical management to mitigate the morbidity and mortality associated with surgical delays.15 Guidance regarding early surgical management remained in effect, even during the early days of the COVID-19 pandemic.16 Despite the best available tools, fracture prediction remains difficult; there are additional challenges associated with epidemiological projections of specific time points when such fractures occur. Accordingly, hospitals and public health entities experience difficulties in terms of estimating emergency trauma service load and allocating limited healthcare resources. Our findings suggest that population mobility indices, which are freely and publicly accessible, can provide insights regarding fracture incidence. Population mobility may be useful in quantitative modelling of fracture-related inpatient and surgical theatre service demand, using the IRRs described in this study.

Although there is evidence to support the efficacy of social distancing measures with respect to COVID-19 transmission,2 our findings emphasise the collateral impacts of pandemic-related interventions on non-communicable diseases. We found that fracture incidence decreased when population mobility was hindered by social distancing measures; the relative reduction in overall fractures appeared to be similar to the effect of established pharmacological interventions on fragility fractures.17 Although this relationship appears to contradict the common notion that physical activity confers a protective effect against fractures in both young and old age-groups, 18 19 associations of increased fracture risk with specific types of exercises (eg, bicycling), or regular participation in other exercise and sports activities, have been described.20 Thus, long-term benefits (eg, increased bone mineral density) may be accrued at the expense of increased exposure to fracture risk when engaging in physical activity. Although the long-term impact of reduced population mobility on fracture incidence remains unclear, vitamin D deficiency caused by prolonged time indoors (ie, without sunlight exposure) is an established risk factor for future fractures.21

The strengths of our study include its inclusion of data from all public hospitals in Hong Kong, which allowed extensive analysis of rare events such as fractures. Our database has a high (>96%) positive predictive value for fractures,22 presumably because data entry is conducted by impartial registered medical practitioners. Furthermore, high internet and smartphone penetration increased the sensitivity of the population mobility analysis, such that the mobility index was geographically specific to the study population. Pedestrian and road traffic densities, which are indirectly represented by the population mobility index, could also precipitate accidents, falls, and subsequent fracture risk. Additionally, potential confounding based on health-seeking behaviour was partially mitigated by the inclusion of ‘control’ groups. Fortunately, all hospitals involved in the study maintained full emergency service during the early days of the COVID-19 pandemic23; this maintenance of emergency service minimised potential confounding by hospitals that were unable to provide service to patients with fractures.

Limitations of the study involved deficiencies in the population mobility index. For example, travel between familiar places and travel where navigation guidance is unnecessary, as well as the usage of alternative electronic service providers, were not considered. Therefore, the population mobility index served as a more specific (rather than sensitive) tool for assessment of population mobility. Global positioning system (GPS)–based mobility tracking would theoretically allow more extensive data collection, thus providing greater detection sensitivity; however, such mobility tracking would cause substantial privacy issues, resulting in legal and ethical challenges.

Notably, older adults are less adept in smartphone usage (62.2% of residents aged ≥65 years reported internet usage in 202011), and the digital population mobility index does not adequately illustrate this division in the population. Furthermore, fractures in older adults are largely caused by osteoporosis, whereas high-energy injury mechanisms are observed in younger individuals.24 Therefore, social distancing may have a negligible effect on the incidences of osteoporotic fractures sustained indoors. We caution against using population mobility data as the sole source of estimates for health service planning because that approach could underestimate fragility fracture service demand.

Additionally, the use of fracture incidence data from a public healthcare database only included approximately 90% of the population health demand. During the early days of the COVID-19 pandemic, instances of diversion to the private sector, attendances in private clinics, and visits to alternative practitioners were not coded; the lack of these data may have led to underestimation of total fracture incidence. Finally, we caution against generalising these findings to regions with less internet and smartphone penetration.

During the early days of the COVID-19 pandemic, fracture incidence and fracture-related mortality considerably decreased with the implementation of government social distancing measures that targeted population mobility. This unique opportunity enabled the identification of collateral associations and revealed that population mobility could be used (along with many other factors) to estimate clinical service demand.

Concept or design: JSH Wong, DKH Yee.
Acquisition of data: JSH Wong.
Analysis or interpretation of data: JSH Wong, ALH Lee, DKH Yee, CX Fang.
Drafting of the manuscript: JSH Wong, ALH Lee, CX Fang.
Critical revision of the manuscript for important intellectual content: CX Fang, DKH Yee, FKL Leung, KMC Cheung.

All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

All authors have disclosed no conflicts of interest.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethics approval was granted by the Institutional Review Board of The University of Hong Kong/ Hospital Authority Hong Kong West Cluster (HKU/HA HKW IRB Ref No.: UW 20-275), and investigations were carried out in accordance with the Declaration of Helsinki. The requirement for patient informed consent was waived by the Board because the study used anonymised data and the risk of identification was low.

1. Leung K, Wu JT, Liu D, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet 2020;395:1382-93. Crossref

2. Cousins S. New Zealand eliminates COVID-19. Lancet 2020;395:1474. Crossref

3. De Filippo O, D’Ascenzo F, Angelini F, et al. Reduced rate of hospital admissions for ACS during Covid-19 outbreak in Northern Italy. N Engl J Med 2020;383:88-9. Crossref

4. Krug EG, Sharma GK, Lozano R. The global burden of injuries. Am J Public Health 2000;90:523-6. Crossref

5. Census and Statistics Department, Hong Kong SAR Government. Table 31: Gross Domestic Product (GDP), implicit price deflator of GDP and per capita GDP. 2020. Available from: https://www.censtatd.gov.hk/en/web_table.html?id=31. Accessed 26 Apr 2020.

6. Food and Health Bureau, Hong Kong SAR Government. Report of the Strategic Review on Healthcare Manpower Planning and Professional Development. 2017. Available from: https://www.fhb.gov.hk/en/press_and_publications/otherinfo/180500_sr/srreport.html. Accessed 20 May 2020.

7. Leung GM, Cowling BJ, Wu JT. From a sprint to a marathon in Hong Kong. N Engl J Med 2020;382:e45. Crossref

8. Yee DK, Fang C, Lau TW, Pun T, Wong TM, Leung F. Seasonal variation in hip fracture mortality. Geriatr Orthop Surg Rehabil 2017;8:49-53. Crossref

9. Apple Inc. COVID-19–mobility trends reports. Available from: https://www.apple.com/covid19/mobility. Accessed 26 Apr 2020.

10. Statcounter GlobalStats. Mobile & tablet vendor market share Hong Kong. Jan – Mar 2020. Available from: https://gs.statcounter.com/vendor-market-share/mobile-tablet/hong-kong/#monthly-202001-202003. Accessed 26 Apr 2020.

11. Census and Statistics Department, Hong Kong SAR Government. Thematic Household Survey Report No. 69: Personal Computer and Internet Penetration. 2020. Available from: https://www.ogcio.gov.hk/en/about_us/facts/doc/householdreport2020_69.pdf. Accessed 5 May 2020.

12. Census and Statistics Department, Hong Kong SAR Government. Population– Overview. 2020. Available from: https://www.censtatd.gov.hk/hkstat/sub/so20.jsp. Accessed 12 Apr 2020.

13. Centre for Health Protection, Department of Health, Hong Kong SAR Government. Latest situation of novel coronavirus infection in Hong Kong. Available from: https://chp-dashboard.geodata.gov.hk/covid-19/en.html. Accessed 12 Apr 2022.

14. Borgström F, Karlsson L, Ortsäter G, et al. Fragility fractures in Europe: burden, management and opportunities. Arch Osteoporos 2020;15:59. Crossref

15. Leung F, Lau TW, Kwan K, Chow SP, Kung AW. Does timing of surgery matter in fragility hip fractures? Osteoporos Int 2010;21 Suppl 4:S529-34. Crossref

16. British Orthopaedic Association. COVID BOAST-Management of patients with urgent orthopaedic conditions and trauma during the coronavirus pandemic. Available from: https://www.boa.ac.uk/resources/covid-19-boasts-combined1.html. Accessed 13 Feb 2023.

17. Tsuda T, Hashimoto Y, Okamoto Y, Ando W, Ebina K. Meta-analysis for the efficacy of bisphosphonates on hip fracture prevention. J Bone Miner Metab 2020;38:678-86. Crossref

18. Fritz J, Cöster ME, Nilsson JA, Rosengren BE, Dencker M, Karlsson MK. The associations of physical activity with fracture risk—a 7-year prospective controlled intervention study in 3534 children. Osteoporos Int 2016;27:915-22. Crossref

19. Morseth B, Ahmed LA, Bjørnerem Å, et al. Leisure time physical activity and risk of non-vertebral fracture in men and women aged 55 years and older: the Tromsø study. Eur J Epidemiol 2012;27:463-71. Crossref

20. Appleby PN, Allen NE, Roddam AW, Key TJ. Physical activity and fracture risk: a prospective study of 1898 incident fractures among 34,696 British men and women. J Bone Miner Metab 2008;26:191-8. Crossref

21. Nilson F, Moniruzzaman S, Andersson R. A comparison of hip fracture incidence rates among elderly in Sweden by latitude and sunlight exposure. Scand J Public Health 2014;42:201-6. Crossref

22. Sing CW, Woo YC, Lee AC, et al. Validity of major osteoporotic fracture diagnosis codes in the Clinical Data Analysis and Reporting System in Hong Kong. Pharmacoepidemiol Drug Saf 2017;26:973-6. Crossref

23. Hospital Authority, Hong Kong SAR Government. HA adjusts service provision to focus on combatting epidemic. 2020. Press Release. Available from: https://www.info.gov.hk/gia/general/202002/10/P2020021000711.htm. Accessed 20 May 2020.

24. Bergh C, Wennergren D, Möller M, Brisby H. Fracture incidence in adults in relation to age and gender: a study of 27,169 fractures in the Swedish Fracture Register in a well-defined catchment area. PLoS One 2020;15:e0244291. Crossref