Sage Journals: Discover world-class research

Abstract

PURPOSE:

Studies have shown that children with muscular dystrophy are at increased risk for falls, however there is insufficient information about what predicts the first and subsequent events. The purpose of this study was to describe the experience of injury with emphasis on identifying risk factors for fall-related injuries.

METHODS:

We studied 269 boys with muscular dystrophy describing their injury experience and identifying risk and protective factors associated with 281 non-simultaneous injuries and 127 falls that resulted in Emergency Department visits and/or inpatient hospitalization during the period 1998–2014. We used a Cox model to estimate the predictors of an initial fall and a zero-inflated Poisson model to identify the predictors for the number of falls.

RESULTS:

Falls accounted for the greatest number of injury occurrences; The most frequent injury type was contusion. The factors that were protective for falls were steroid use, wheelchair use, or having a heart condition. Baseline age was negatively associated with the risk of having any fall, but not significantly related to subsequent falls.

CONCLUSION:

Wheelchair use and heart conditions associated with reduced risk of falls likely reflects decreased mobility. Clinicians should help families identify factors associated with falls among those who remain ambulatory.

Keywords

Duchenne muscular dystrophy risk factors for falls corticosteroid wheelchair boys

1. Introduction

Muscular Dystrophy (MD) is a group of genetic diseases that result in degenerated muscular function. Duchenne and Becker muscular dystrophy (DBMD) is the most common form of MD in children and adolescents [1, 2]. These muscular dystrophies are both due to defects in the production of dystrophin that result in progressive muscular weakness [3]. The two types of MD are similar, except that Duchenne MD (DMD) exhibits an earlier onset and a more aggressive course than Becker MD (BMD). Both MD types are transmitted with the same x-linked, recessive pattern and are therefore far more common in males [4]. The mean diagnosis age for DMD is 3.2 years old [5], whereas the age of diagnosis for BMD is older, typically during adolescence [6]. In 2010, the estimated prevalence of DBMD in the United States was 1.38 per 10,000 males aged 5 to 24, of which 1.02 per 10,000 were males with DMD and 0.36 per 10,000 were males with BMD [7].

Individuals affected by DBMD experience a decline in strength and ambulatory ability, with eventual loss of independent ambulation at a young age in Duchenne MD [8, 9], though ambulation is often preserved into adulthood for individuals with Becker MD [10]. Injuries are common among males with DBMD. A recent study using medical record reviews and patient interviews reported that 20.9% of male MD patients had experienced injuries in their lifetime, and about 41% of the injuries had occurred in patients between 8 and 11 years of age [11]. In another study that focused on fractures, conducted by the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARNET), 249 of 747 young males with DBMD had experienced at least one fracture [12]. Impaired balance is part of the clinical course of DBMD, as lower extremity strength becomes increasingly impaired [13, 14, 15, 16]. There is substantial evidence that patients with MD are more likely to experience injuries that result in fractures than those without MD [5, 6, 7].

Although there has been a fair amount of research about the risk of fractures in children and adolescents with DBMD, we are not aware of any studies that have examined the full range of injury experiences and specifically falls, that result in fractures and other outcomes such as contusions, sprains, strains, etc. It stands to reason that given the effects of DBMD on lower extremity strength and balance, people with DBMD may be at risk for a range of injuries (not just fractures) related to falls. Though it is expected that falls are a key cause of injury, other causes of injury (for example, motor vehicles or violence) may also be important. Likewise, other types of injury in addition to fractures (such as contusions and burns) may also be areas of concern. In this study, we aimed to describe the experience of male children and adolescents with MD, presumably DBMD, including the types of injury (fracture, dislocation, contusion, etc.), the body region of the injury (face, lower extremities, etc.), and the cause of the injury (vehicle crash, falls, poisoning, etc.). We examined the entire range of injuries that bring boys with DBMD to the Emergency Department or hospital inpatient unit, and we focused on the most prevalent event – falls. The aim of the study was to identify factors associated with an initial fall, as well as predictors for number of falls.

There is conflicting evidence about whether corticosteroid use (a mainstay of DBMD treatment) might influence the risk of having fractures. Some studies have reported that corticosteroid treatments improve muscle strength and function compared to controls [17], while other studies have shown short term improvements in muscle strength without any significant effects in long term use [18]. Full time wheelchair use has also been associated with risk of fractures due to limited weight bearing activity and its effects on bone density [12, 17, 19, 20, 21]. Finally, it is well established that individuals with DBMD are at increased risk of heart disease (such as cardiomyopathy and dysrhythmias) [22] and pulmonary insufficiency [23, 24]. We hypothesized that these complications could be associated with reduced risk of falls due to reductions in exertional capacity and resultant reduced activity levels. Therefore, we included cardiac and respiratory diagnoses as potential predictors of falls, in addition to age, race, wheelchair use and steroid use. Our goal for the study was to identify opportunities for injury prevention in this potentially high-risk group.

2. Methods

The study is a retrospective cohort that used secondary data over a 17-year period.

2.1 Data sources

Data during the 1998–2014 study period were from three sources: uniform-billing hospital discharge data including inpatient hospitalizations and Emergency Department visits (UB), medical and pharmacy claims for individuals insured by South Carolina Medicaid, and medical and pharmacy claims for individuals insured by the State Health Plan. South Carolina Revenue and Fiscal Affairs (RFA), Health and Demographics Section, a central repository of health and human service for the state, compiled data from these three resources. To “link across” data sources, RFA developed a series of algorithms to create a unique global identifier that was used in lieu of personal identifiers to enable analysis of the data while protecting confidentiality. This study was approved by the South Carolina Data Oversight Council and the participating agencies from which the data originated [25].

Identification of DBMD was accomplished from the medical claims data based on ICD-9 code 359.1, the code for hereditary progressive muscular dystrophy (including all muscular dystrophies). We limited our study subjects to males up to 18 years of age having at least 12 months of health insurance for one documented year throughout the study period. The insurance criteria assured that we had sufficient information about the diagnosis of muscular dystrophy and the injury events. The other criteria increased the likelihood that most of the cases we captured had DBMD. The UB data provided information on injury, including the timing, type, and place of occurrence. The UB data included all acute-care visits to civilian hospitals within South Carolina irrespective of payment source; therefore it was able to capture the utilization for enrollees who were identified in our cohort.

2.2 Outcomes

Injury was defined based on ICD-9 codes: 800–848, 850–854, 860–887, 890–897, 900–951, 953–994 (for more details refer to https://www.cdc.gov/nchs/data/ injury/barell.pdf). All included injuries were identified in the ED or inpatient hospital, thus injuries not seen in either of these settings were missed. Multiple injuries could happen simultaneously and were grouped as one injury event. A hierarchical decision rule was applied to define the injury type based on ICD-9 codes when the simultaneous injuries occurred. The causes of injury were identified and categorized based on E-codes in the ICD-9-CM classification associated with injury. The injuries resulting from falls (E880–E889) were then selected as the primary interest for analyses.

Table 1
Causes and type of injury by the number of injuries for males $\leqslant$ 18 years of age with muscular dystrophy ( $N=$ 281)

Causes	ICD-9 E-Code	1 injury ( $N=$ 68)	2 or more injuries ( $N=$ 60)
Falls	E880–888	28	99
Striking or struck accidentally by objects or persons	E916–917	12	28
Other causes*		28	86
Injury type	ICD-9	1 injury ( $N=$ 68)	2 or more injuries ( $N=$ 60)
Contusion with intact skin surface	920–924	18 (26.5%)	45 (21.1%)
Open wound	870–897	13 (19.1%)	39 (18.3%)
Other types ${}^{**}$		37 (54.4%)	129 (60.6%)
Total		68	213

*Other causes include other accidents (E928, E918, E925), vehicle related accidents (E810–849), cutting and piercing objects (E920), submersion, suffocation and foreign bodies (E910–915), overexertion/strenuous movements (E927), injury caused by animal (E905–906), hot substance or object caustic or corrosive material and steam (E924), poisoning (E850–860), drugs causing adverse effects in therapeutic use (E930–949), suicide or homicide (E950–969), other undetermined causes (E980–989), and missing causes. **Other types include superficial injury (910–919), sprains and strains (840–848), fracture (800–829), intracranial injury (850–854), traumatic complications unspecified injuries (958–959), foreign body entering through orifice (930–939), burns (940–949), toxic effect (980–989), poisoning (960–979), unspecified effects of external causes (990–994, 995.50–995.54, 995.59, 995.80, 995.85), and dislocation (830–839).

Table 2

Injury types and body regions of injury by the number of falls males $\leqslant$ 18 years of age with muscular dystrophy ( $N=$ 72)

Injury type	Number of falls
	1 fall ( $N=$ 40)	2 or more falls ( $N=$ 32)	Total
Contusion with intact skin surface	14 (35%)	26 (29.9%)	40
Other injury types*	26 (65%)	61 (70.1%)	87
Body region	1 fall ( $N=$ 40)	2 or more falls ( $N=$ 32)	Total
Extremities	16 (40%)	57 (65.5%)	73
Head, neck, spine, back and torso	24 (60%)	30 (34.5%)	44
Total	40	87	127

*Other injury types include fracture, open wound, superficial injury, traumatic complications unspecified injuries, intracranial injury, and sprains and strains.

We were interested in the time of the first fall, which was calculated from the date that the first medical record appeared. Patients without a fall were treated as censored and time was calculated from the date of entry until the last record date or the patients reached 18 years old, whichever came first. This is followed by the analysis of the number of falls for each subject during the study period using a zero-inflated Poisson model.

2.3 Explanatory variables

Age at baseline was defined as the patient age when he was first observed on record from UB data, Medicaid insured claims, or SHP insured claims during the study period, 1998–2014. Race was categorized into White, Black and other (Asian, American Indian, Hispanic, Other, and missing).

Respiratory conditions included acute respiratory infections (ICD-9: 460–466), other diseases of upper respiratory tract (ICD-9: 470–478), pneumonia and influenza (ICD-9: 480–488), chronic obstructive pulmonary disease and allied conditions (ICD9: 490–496), pneumoconiosis, other lung diseases due to external agents (ICD9: 500–508), and other diseases of respiratory system (ICD9: 510–519). Heart conditions included cardiomyopathy (ICD9: 425), conduction disorders (ICD9: 426), cardiac dysrhythmias (ICD9: 427) and heart failure (ICD9: 428). Corticosteroids included prednisone (brand name includes Deltasone), Dexamethasone, or prednisolone (brand names include Orapred, Pediapred, Prelone). Respiratory conditions, heart conditions, corticosteroid use, and wheelchair use were dichotomized into yes/no. We also used a decision rule to categorize the cases (including only corticosteroid or wheelchairs used or having respiratory or heart conditions) prior to the first fall as a positive response. If there were no corticosteroid prescriptions filled or there was no purchase or rental of a wheelchair prior to the first fall, then the code was categorized as a negative response. A similar decision rule was also applied to regrouped respiratory conditions and heart conditions.

2.4 Statistical analysis

The distribution of injuries in our study subjects was summarized based on the causes (such as falls, accidents, etc.), and injury types (such as fractures, contusions, etc.). Then we focused on falls, and described the resulting injury types, the body regions impacted by them, and stratified by the number of incidents. We also described the baseline characteristics of the boys with DBMD grouped by those with and without falls. Mean $\pm$ standard error was used for continuous variables. Frequencies with percentages were used for categorical variables. Two sample t-test and Chi-square test (or Fisher’s exact test) were used to compare the differences for continuous and categorical variables, respectively.

Cox proportional hazard (PH) models were used to calculate hazard ratio and 95% confidence intervals (CI) for the time to the first fall. The validity of PH assumption was checked by visualizing the Kaplan-Meier curves. We first fitted the full model including age at baseline, wheelchair use, heart condition, respiratory condition, steroid use, and interactions between baseline age and any other risk factors. These variables were selected based on our knowledge of the literature and the factors that have clinical significance. A backward model selection procedure was then conducted to identify the final optimal model including all covariates with $p$ -value $<$ 0.05 or with significant clinical meaning.

To better understand the relationships between risk factors and multiple falls, we identified the risk factors that are associated with the number of falls. Due to the excessive number of individuals with 0 falls in the data, a zero-inflated Poisson (ZIP) model was used to model the risk of having falls. The ZIP model is a mixture of two models where the first model deals with the extra zeros which cannot be explained by the Poisson model, and the second Poisson model is used to fit the rest of the data. We considered age at baseline as the risk factor for modeling the probability of having excessive zero falls in the data. Then, we fitted the Poisson model including age at baseline, wheelchair use, heart condition, respiratory condition, steroid use, and interactions between age and any other risk factors. Similar model selection procedures as in the Cox model were applied to achieve the optimal model.

Data management and statistical analyses were performed using SAS, version 9.4 (SAS Institute Inc., Cary, NC, USA). The significance level $\alpha$ was set at 0.05.

Table 3
Characteristics of MD male cases $\leqslant$ 18 years of age, with and without falls

Risk factors	Sample size	0 fall ( $N=$ 197)	1 or more falls ( $N=$ 72)	$P$ value
Wheelchair use
No	160	103 (52.28%)	57 (79.17%)	$<$ 0.0001
Yes	109	94 (47.71%)	15 (20.83%)
Heart conditions
No	196	$>$ 120	$<$ 70	$<$ 0.0001
Yes	73	$>$ 60	$<$ 10
Respiratory conditions
No	32	$>$ 20	$<$ 10	0.2752
Yes	237	$>$ 160	$<$ 80
Steroid use
No	167	111 (56.35%)	56 (77.78%)	0.0013
Yes	102	86 (43.65%)	16 (22.22%)
Race
Non-hispanic white	159	112 (56.85%)	47 (65.28%)	0.001
Non-hispanic black	66	$>$ 30	$<$ 30
Other/Missing	44		$<$ 10
Age		Mean $\pm$ SD	Mean $\pm$ SD
		6.39 $\pm$ 5.63	3.82 $\pm$ 4.22	$<$ 0.0001

Table 4

Cox proportional hazard model for the time to first fall for males $\leqslant$ 18 years of age with muscular dystrophy ( $N=$ 269)

Variables	Parameter estimates	Standard error	$P$ value	Hazard ratios and 95% CIs
Age at baseline	$-$ 0.07	0.03	0.03	0.94 (0.88–0.98)
Heart conditions (yes vs. no)	$-$ 1.33	0.43	0.002	0.26 (0.11–0.62)
Wheelchair use (yes vs. no)	$-$ 1.01	0.30	0.0007	0.37 (0.21–0.65)
Steroid use (yes vs. no)	$-$ 0.95	0.29	0.001	0.39 (0.22–0.68)

Table 5

Zero-inflated Poisson model for the number of falls for males $\leqslant$ 18 years of age with muscular dystrophy ( $N=$ 269)

Variables	Parameter estimates	Standard error	$P$ value	Rate ratios and 95% CIs
Intercept	0.46	0.20	0.02	1.59 (1.08–2.34)
Age	$-$ 0.0007	0.03	0.92	1.00 (0.94–1.06)
Heart conditions (yes vs. no)	$-$ 0.82	0.34	0.01	0.44 (0.23–0.85)
Wheelchair use (yes vs. no)	$-$ 0.85	0.26	0.001	0.43 (0.26–0.71)
Steroid use (yes vs. no)	$-$ 0.89	0.25	0.0004	0.41 (0.25–0.67)

3. Results

Of 269 male subjects with an MD diagnosis code, 128 (47.6%) had at least one injury that received treatment in an ED or an inpatient hospital setting. The total number of non-simultaneous injuries was 281. Among the 128 males with an injury, 68 had only one injury, and 60 had two or more injuries. The median duration of follow-up was 9.75 years, ranging from 0.9 to 16.9 years. The average injury rate based on the observations was 10.8%. There were various causes of injuries evidenced as shown in Table 1. One of the major causes was falls. In addition, we examined the injury types by the number of injuries shown in the second half of Table 1. Contusions and open wounds were the two most common injury types for both groups of subjects with only one injury or with two or more injuries. Fractures made up less than 10% of all types of injuries for subjects with one injury compared to 16% for subjects with two or more injuries, requiring hospital services (results not shown).

Among all the injury causes, we focused on the falls and the injuries resulting from them. There were 72 boys with at least one fall. The total number of falls was 127, shown in Table 2. Among the 72 boys, there were 40 boys with only one fall, and 32 with two or more falls. Similarly, contusions and open wounds were the most common injury types resulting from falls for all the subjects. Fractures occurred in greater than 20% of those with one fall, and 31% for those with two or more falls (results not shown). In general, over half of the injuries resulting from falls occurred in the extremities.

Characteristics between the subjects with at least one fall compared to those with none are shown in Table 3. The mean baseline age for subjects with at least one fall was 3.8 years old (with standard deviation 4.2), and 6.4 years old for subjects without any fall (with standard deviation 5.6). The age when the child first was identified in the medical record with a MD diagnosis was statistically significantly younger for subjects with at least one fall compared to subjects without any fall ( $P<$ 0.0001). Compared to the subjects without any falls, the proportion of using a wheelchair for subjects with at least one fall was significantly lower ( $P<$ 0.0001). Similarly, the proportion of males with a heart condition and steroid use for those with at least one fall was significantly lower than those for subjects without any falls.

Table 4 showed the results from the final Cox PH model following the model selection procedure. Age at baseline was negatively associated with the time to the first fall. The risk of having the first fall was highest for those who entered Medicaid or had a hospitalization at a younger age. Wheelchair use had a significantly negative association with the first fall occurrence in MD patients ( $P$ $=$ 0.0009), meaning that patients who had ever used wheelchairs had a longer time to first fall compared to those who had never used wheelchairs. The hazard ratio with 95% CI is 0.37 (0.21–0.65) after adjusting for other risk factors and this result was consistent with a Kaplan-Meier curve that showed (Kaplan-Meier results not shown). Similarly, having a heart condition and taking steroids had negative associations with the time to the first fall after controlling for other covariates, indicating protective effects to the risk of getting the first fall. Race groups were not included in the model due to the high proportion of unknown. The presence of a respiratory condition was not significantly associated with falls, so it was removed from the model.

To better understand the effect of multiple falls, a zero-inflated Poisson model was used (shown in Table 5). Baseline age is an important predictor of the severity of the MD, therefore it was used to adjust for the zero-inflated part of the model. The odds of having excessive zero falls were increased by 1.13 for each 1-year increase in baseline age, implying that the later a subject appeared in the record there was a lower risk of falls (results not shown). This result is consistent with what we had in the Cox model. However, the number of falls was not related to the age when a subject appeared in the study. Wheelchair use had a protective effect on the number of falls. In fact, the expected number of falls for the wheelchair users was 57% lower compared to the subjects who did not use wheelchairs. Similar protective effects for taking steroids or having a heart conditions were observed (59% and 56% lower number of falls, respectively). The protective effect from these factors was consistent with the results from the Cox model.

4. Discussion

To our knowledge, this is the first study to thoroughly describe the injury experience of male children and adolescents with MD. Our analyses included a total of 269 males with MD, who experienced a total of 281 non-simultaneous injuries seen in the Emergency Department or inpatient unit during the time period of observation. It is noteworthy that 52% of the boys followed for the study did not experience any injury event requiring the Emergency Department or inpatient treatment. Among those who had an injury requiring hospital care, the majority experienced only one episode. However, those with three or more episodes accounted for the majority of total injuries in the cohort.

As we anticipated, falls were the most frequent type of injury occurrence, accounting for 127 of 281 (45%) of injury events. This is in line with expectations, given that the most common type of MD in male children and adolescents is DBMD, which leads to a reduction in lower body strength and balance over time. The progression of DBMD typically includes reduced function in the proximal lower limbs, followed by weakness in the upper limbs and distal muscles [9, 26]. Loss of ambulation is a widely accepted milestone to describe progression of DBMD and early loss of ambulation is seen as a more severe form of the disease [8]. The findings from this study suggest that falls are associated with younger age, which is likely during the period preceding loss of ambulation.

In general, contusions were the most frequent injury type, followed closely by open wounds among all injury causes. Fractures, which have been the subject of most injury risk research among young males with MD, were the third most frequent injury type in our dataset for all injury causes. Looking only at injuries due to falls, the most frequent injury types were contusions, followed by fractures, and then open wounds. The body parts most frequently injured by falls were the extremities, followed by the head and neck.

The dataset used for this study was created using all injuries occurring after a MD diagnosis. In looking at predictors of injuries due to falls, younger age at baseline was associated with the risk of experiencing any injury, but was not significantly associated with risk of subsequent injuries. The other predictors of fall related injury were corticosteroid use, wheelchair use, and heart disease. All of these were inversely associated with fall related injury risk. It is likely that younger age at baseline, wheelchair use, and heart disease in the data is partially a proxy for Duchenne versus Becker MD, since Medicaid eligibility can be attained on the basis of disability, which occurs at a younger age in patients with Duchenne MD and is more severe. The fact that all of these risk factors are associated with reduced risk of falls likely reflects decreased mobility among boys with greater levels of disability, leading to fewer opportunities for falls. The inverse association between corticosteroid use and risk of fall related injury is more difficult to interpret. It could represent a beneficial effect or may be reflective of an increased likelihood of corticosteroid treatment among individuals with more severe disease. We favor the latter interpretation, as it is consistent with the other study findings indicative of reduced fall risk with reduced exertional and ambulatory capacity.

The greatest limitations of this study are our reliance on administrative data for identification of individuals with DBMD, assessment of the outcome of injury, and identification of explanatory variables. We were dependent upon diagnosis codes in the billing data for identifying young males with DBMD. In fact, the ICD-9 code used to identify cases in this study (359.1) also includes other types of MD. It should be noted that future studies in the US will have the opportunity to use the ICD-10 codes that were implemented in October 2015 and now include a specific code for DBMD. Our reliance on billing data for identifying episodes of injury is also a limitation, since we only have access to information about episodes of injury for which medical treatment was received in the Emergency Department or inpatient hospital unit. Clearly, less severe injuries that were treated in outpatient settings were missed. We are also limited by the nature of the data in terms of the level of detail with which we are able to describe explanatory factors. For example, we have information about wheelchair purchases and rentals, but we are not able to ascertain whether the individual used the wheelchair full time (and therefore presumably was not able to ambulate at all), most of the time, or part time. Access to clinical data and perhaps participant interviews would be needed to fully explore these details. Race groups were not included in the models due to the high proportion of unknown/missing self-identifiers. The overall proportion of unknown race is approximately 16%, with the majority having no injury.

Additionally, the steroid Deflazacort (brand name Emflaza or Calcort) was approved by US FDA in February 2017, so it was not on the pharmacy records during our study period. However, patients might be able to have access to Deflazacort from other sources that we did not capture. This can be considered as another limitation of this study.

The primary clinical and public health implications of this study are that male children and adolescents with MD are at risk of injuries that result in hospital visits, especially due to falls. Injury risk appears to decline among those with advanced disability. As such, clinicians and families who are interested in preventing injuries in this patient population would be wise to focus on reducing the risk of falls among those who remain ambulatory and active. Anticipatory guidance for parents is important for addressing the risks and optimizing the home environment to reduce tripping hazards and other risk factors for falls.

Additional research is needed to confirm our findings. Ideally, this research would incorporate ICD-10 codes as well as clinical and/or survey data that can effectively identify individuals specifically with DBMD. It would be helpful if future research could differentiate between Duchenne and Becker MD, and include measures to assess ambulatory and exertional capacity. Further longitudinal research is needed in order to clarify the role of steroid use on injury occurrence and to identify additional risk and protective factors associated with injuries in children with DBMD. Based on our findings, it would be prudent for practitioners in neuromuscular clinics to discuss fall prevention strategies for boys who are ambulatory, since they are a high-risk group for fall-associated injuries that result in hospital care.

Footnotes

Acknowledgments

This publication was supported by the Grant or Cooperative Agreement Number, 5 U01 DP001117–03 funded by the Center for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Center for Disease Control and Prevention or the Department of Health and Human Services, the South Carolina Department of Health and Human Services, the South Carolina Public Employee Benefit Authority, the South Carolina Revenue and Fiscal Affairs Office or the South Carolina Data Oversight Council.

Conflict of interest

The authors have no conflict of interest to report.

References

Mathews

, Cunniff

, Kantamneni

, Ciafaloni

, Miller

, Matthews

, et al. Muscular dystrophy surveillance tracking and research network (MD STARnet): Case definition in surveillance for childhood-onset Duchenne/Becker muscular dystrophy. J Child Neurol. 2010; 25(9): 1098-102 doi: 10.1177/0883073810371001.

Romitti

, Puzhankara

, Mathews

, Zamba

, Cunniff

, Matthews

, et al. Prevalence of Duchenne/Becker muscular dystrophy among males aged 5-24 years – four states, 2007. MMWR Morb Mortal Wkly Rep. 2009; 58(40): 1119-22.

Mah

, Korngut

, Dykeman

, Day

. A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscul Disord. 2014; 24(6): 482-91. doi: 10.1016/j.nmd.2014.03.008.

Emery

AEH

. The muscular dystrophies. Lancet. 2002; 359(9307): 687-95.

Ciafaloni

, Fox

, Pandya

, Westfield

, Puzhankara

, Romitti

, et al. Delayed diagnosis in Duchenne muscular dystrophy: Data from the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet). J Pediatr. 2009; 155(3): 380-5. doi: 10.1016/j.jpeds.2009.02.007.

Piña-Garza

. Fenichel’s Clinical Pediatric Neurology: A signs and symptoms approach. Elsevier Health Sciences. 2009.

Romitti

, Zhu

, Puzhankara

, James

, Nabukera

, Zamba

GKD

, et al. Prevalence of Duchenne and Becker muscular dystrophies in the United States. Pediatrics. 2015; 135(3): 513-21. doi: 10.1542/peds.20142044.

Ciafaloni

, Kumar

, Liu

, Pandya

, Westfield

, Deborah

, et al. Age at onset of first signs or symptoms predicts age at loss of ambulation in Duchenne and Becker muscular dystrophy: Data from the MD STARnet. J Pediatr Rehabil Med. 2018; 9(1): 5-11. doi: 10.3233/PRM160361.

Yiu

, Kornberg

. Duchenne muscular dystrophy. J Paediatr Child Health. 2015; 51: 759-64.

10.

Bushby

, Gardner-Medwin

. The clinical, genetic and dystrophin characteristics of Becker muscular dystrophy. I. Natural history. J Neurol. 1993; 240(2): 98-104.

11.

Mcdonald

DGM

, Kinali

, Gallagher

, Mercuri

, Muntoni

, Roper

, et al. Fracture prevalence in Duchenne muscular dystrophy. Dev Med Child Neurol. 2002; 44: 695-8.

12.

James

, Cunniff

, Apkon

, Mathews

, Lu

, Holtzer

, et al. Risk factors for first fractures among males with Duchenne or Becker muscular dystrophy. J Pediatr Orthop. 2015; 35(6): 640-4. doi: 10.1097/BPO.0000000000000348.

13.

Alkan

, Mutlu

, Fırat

, Bulut

, Karaduman

, Yılmaz

. Effects of functional level on balance in children with Duchenne Muscular Dystrophy. Eur J Pediatr Neurol. 2017; 21: 635-8. doi: 10.1016/j.ejpn.2017.02.005.

14.

Kelly

, Redford

, Zilber

, Madden

. Standing balance in healthy boys and in children with Duchenne muscular dystrophy. Arch Phys Med Rehabil. 1981; 62(7): 324-7.

15.

Baptista

, Costa

, Pizzato

, Souza

, Mattiello-Sverzut

. Postural alignment in children with Duchenne muscular dystrophy and its relationship with balance. Braz J Phys Ther. 2014; 18(2): 119-26.

16.

Alfano

, Lowes

, Flanigan

, Mendell

. Correlation of knee strength to functional outcomes in Becker muscular dystrophy. Muscle Nerve. 2013; 47(4): 550-4. doi: 10.1002/mus.23660.

17.

Bell

, Shields

, Watters

, Hamilton

, Beringer

, Elliott

, et al. Interventions to prevent and treat corticosteroid-induced osteoporosis and prevent osteoporotic fractures in Duchenne muscular dystrophy (Review). 1, Cochrane Database Syst Rev. 2017. doi: 10.1002/14651858.CD010899.

18.

Matthews

, Brassington

, Kuntzer

, Jichi

, Manzur

. Corticosteroids for the treatment of Duchenne muscular dystrophy (Review). Cochrane Database Syst Rev. 2016; (5). doi: 10.1002/14651858.CD003725.

19.

Perera

, Sampaio

, Woodhead

, Farrar

. Fracture in Duchenne muscular dystrophy: Natural history and Vitamin D deficiency. J Child Neurol. 2016; 31(9): 1181-7. doi: 10.1177/0883073816650034.

20.

Kim

, Zhu

, Romitti

, Fox

, Sheehan

, Valdez

, et al. Associations between timing of corticosteroid treatment initiation and clinical outcomes in Duchenne muscular dystrophy. Neuromuscul Disord. 2017; 27(8): 730-7. doi: 10.1016/j.nmd.2017.05.019.

21.

Henderson

, Berglund

, May

, Zemel

, Grossberg

, Johnson

, et al. The relationship between fractures and DXA measures of BMD in the distal femur of children and adolescents with cerebral palsy or muscular dystrophy. J Bone Miner Res. 2010; 25(3): 520-6. doi: 10.1359/jbmr.091007.

22.

Silvestri

, Ismail

, Zimetbaum

, Raynor

. Cardiac involvement in the muscular dystrophies. Muscle Nerve. 2018; 57(5): 707-15. doi: 10.1002/mus.26014.

23.

Hutchinson

, Whyte

. Neuromuscular disease and respiratory failure. Pr Neurol. 2008; 8(4): 229-37. doi: 10.1136/pn.2008.152611.

24.

Finder

, Mayer

, Sheehan

, Sawnani

, Abresch

, Benditt

, et al. Pulmonary endpoints in Duchenne muscular dystrophy a workshop summary. Am J Respir Crit Care Med. 2017; 196(4): 512-9. doi: 10.1164/rccm.2017030507WS.

25.

Royer

, Hardin

, Mcdermott

, Ouyang

, Mann

, Ozturk

, et al. Use of state administrative data sources to study adolescents and young adults with rare conditions. J Gen Intern Med. 2014; 29(Suppl 3): 732-8. doi: 10.1007/s11606-014-2925-7:

26.

Mercuri

, Muntoni

. Muscular dystrophies. Lancet. 2013; 381: 845-60.

Risk factors for falls among boys under 18 years with muscular dystrophy

Abstract

PURPOSE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Data sources

2.2 Outcomes

Table 1 Causes and type of injury by the number of injuries for males ⩽ 18 years of age with muscular dystrophy ( N = 281)

2.4 Statistical analysis

Table 3 Characteristics of MD male cases ⩽ 18 years of age, with and without falls

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Causes and type of injury by the number of injuries for males $\leqslant$ 18 years of age with muscular dystrophy ( $N=$ 281)

Table 3
Characteristics of MD male cases $\leqslant$ 18 years of age, with and without falls