SEARCH

SEARCH BY CITATION

Keywords:

  • barriers to care;
  • behavioral model;
  • disease severity;
  • Duchenne and Becker muscular dystrophy;
  • healthcare services

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES

Introduction: In progressive conditions, such as Duchenne and Becker muscular dystrophy (DBMD), the need for care may outpace care use. We examined correlates that contribute to utilization of needed care. Methods: Structured interviews were conducted on use of care among 34 young men with DBMD who were born before 1982. Results: Disease severity, per capita income, and presence of other relatives with DBMD predicted greater use of services. Race/ethnicity, acculturation, and level of caregiver education did not significantly predict service utilization. Conclusions: We identified disparities in receipt of healthcare and related services in adult men with DBMD that can affect quality of life. Despite the high disease severity identified in this population, these men utilized only half of the services available to individuals with significant progressive conditions. Providers should be aware of low service utilization and focus on awareness and assistance to ensure access to available care. Muscle Nerve 49: 21–25, 2014

Abbreviations
DBMD

Duchenne and Becker muscular dystrophy

BMD

Becker muscular dystrophy

DMD

Duchenne muscular dystrophy

EK

Egen Klassifikation

MD STARnet

Muscular Dystrophy Surveillance Tracking and Research Network

In progressive conditions, such as Duchenne and Becker muscular dystrophies (DBMD), the need for care increases over time. DBMD are genetic conditions that result in progressive muscle weakness, decreasing mobility, and respiratory and cardiac problems, with Duchenne muscular dystrophy (DMD) progressing at a faster rate than Becker muscular dystrophy (BMD).[1, 2] Advances in care have extended the lifespan of young men with DBMD,[3, 4] and young adults with muscular dystrophy report high levels of life satisfaction.[3, 5-7] As lifespan increases for individuals with DBMD, it is important to maximize their health and well-being.

Access to and use of services should logically increase in tandem with disease severity for progressive conditions. However, this is not necessarily the case. Parker and colleagues found discrepancies in service utilization in their study of 25 adult patients with DMD.[8] Healthcare and other services may lag behind increases in severity for a variety of reasons. For example, the families of affected individuals may not be aware of existing services, or they may not understand the medical and related implications of the young man's increased disease severity. Availability of services, family resources, and other socioeconomic variables may all influence the utilization of services by individuals with DBMD and their families. The purpose of this investigation was to evaluate the correlates of service utilization among young men with DBMD.

METHODS

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES

Sample

Primary caregivers of young men with DBMD were recruited from Arizona, Colorado, Iowa, and New York and through listservs, websites, and publications.[9] Study eligibility was limited to caregivers of young men who were born before 1982. The survey instrument was developed in collaboration with the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet), funded by the Centers for Disease Control and Prevention.[10] The survey included questions about early development, disease progression, and use of services. The study was approved by the institutional review board of the University of Arizona. Complete survey details have been presented by Arias et al.[9]

Forty-eight families were recruited and consented; 34 respondents (32 caregivers and 2 of the young men) completed a structured interview by telephone and received $20 for participation. Nearly two-thirds (22 of 34) of the participants resided in Arizona, and the others were from New York (10), Iowa (1), and Kansas (1).

The average age for caregivers was 50.6 (SD = 8.4) years, and their average level of education was 12 (SD = 3.6) years. Nine caregivers were Hispanic; 2 spoke more Spanish than English. Most (28 of 32) of the primary caregivers were women, and half (16 of 32) worked outside of the home. The average family income was $41,000 (SD = $25,000). Twenty percent (7 of 34) of the individuals with DBMD had cousins or uncles with muscular dystrophy. Over 90% (31 of 34) of the young men had siblings. Nearly 75% (25 of 34) of the young men lived in 2-parent homes. Twelve of the young men had died by the time of the interview; for these young men, all survey questions were answered retrospectively, based on their last year of care. Of the 22 living men, the average age was 27.8 (SD = 3.17) years, with a range of 24–34 years. For the 12 men who died prior to the interview, the average age at time of their last neuromuscular visit was 21.8 (SD = 5.41) years, with a range of 12–29 years.

Model

We evaluated the contributions of disease severity, family characteristics, and resources to total service utilization. The need for services (disease severity) should be associated with their use. Ideally, family characteristics and resources should not be associated with access to and use of services. This model may be considered a subset of the Anderson model,[11] which evaluates the impact of environment, population characteristics, and health behavior on outcomes. Equitable access occurs when an individual's physical need accounts for most of the variance in use of services. Inequitable access is measured by the extent to which resources and social structure contribute to the use of health services.

Disease Severity

Rasch analyses were used to develop a scale of disease severity for DBMD.[12] Rasch modeling jointly estimates the severity of the persons and the items simultaneously. The survey instrument included questions related to medical device or intervention use, as well as 2 clinical markers of severity, diagnosis and death; this yielded 17 total contributors to the model. Each variable included in the model is measured at the age at which the intervention occurred or the medical device was first introduced. The severity items included use of medical equipment (braces and splints, wheelchairs, transfer boards, hospital beds, cough assist, ventilator) and surgical procedure (tendon release, scoliosis instrumentation, tracheotomy). Table 1 lists all of the indicators included in the disease severity measure. A score of 5 indicates the individual used a motorized wheelchair; a score approaching 8 indicates use of a hospital bed or transfer board; and a score of >10 indicates the individual had received a tracheotomy or was using a cough assist device. The severity measure developed for this study demonstrated high reliability and concurrent validity. Detailed methodological information regarding development of the disease severity scale was presented by Davis et al.[13] The disease severity scale was based on the most severe symptoms for each individual and had a reliability coefficient of 0.83.[13] The scale also correlated significantly (0.68) with the Egen Klassifikation (EK) scale, a measure of disease severity in DBMD.[14]

Table 1. Starting age for disease severity indicators (N = 34).a
EventAge, years [mean (SD)]nPercentage
  1. a

    Adapted with permission from Davis et al.[13] (Table 1).

Diagnosis of DBMD5.1 (3.1)34100%
Manual wheelchair10.7 (2.0)3294.1%
Motorized wheelchair13.6 (4.2)2985.3%
Hospital bed/special mattress17.4 (4.6)2779.4%
Splints/braces8.5 (2.1)2676.5%
Shower chair13.6 (5.2)2573.5%
Transfer board/Hoyer lift15.8 (4.5)2161.8%
Assisted ventilation20.5 (4.5)2058.8%
Bedside commode13.7 (4.7)1647.1%
Death22.4 (5.3)1235.3%
Tendon release surgery9.5 (2.2)1132.4%
Walker10.0 (2.1)1029.4%
Stander/lift chair12.3 (2.7)926.5%
Tracheotomy23.2 (3.2)926.5%
Back brace13.9 (5.7)823.5%
Scoliosis surgery14.6 (1.8)823.5%
Cough assist device25.4 (2.7)823.5%
Family Characteristics and Resources

Several demographic questions were used to generate family characteristics variables for inclusion in the model. Most demographics relate to the caregiver responsible for completing the interview and include measures for marital status, education level, and Hispanic ethnicity. Per capita income was calculated using the annual household income divided by the number of children and adults supported by that income. Last, a listing of siblings and relatives with DBMD was collected. However, information related to the timing of family knowledge preceding the diagnosis of the subject of the interview was not available.

Service Utilization

Young men with DBMD typically receive services from physicians, ancillary health professionals, and home-based care. The services used in these analyses are those frequently utilized by young men with DBMD. Questions regarding their use were developed in collaboration with the MD STARnet, which subsequently implemented a baseline parent interview.[15] The services included: healthcare services and therapy (neuromuscular, cardiology, pulmonology, primary care, other); therapies (occupational, physical, speech); supportive services (respiratory care, dietary counseling, mental health, social service, case management, pain management, pastoral care); and ancillary services (attendant care, respite care, transportation, skilled nursing, hospice, home meal delivery, household assistance, other). The latter 2 service categories are typical of palliative care services.[9]

Acculturation

Acculturation was estimated using a 5-item language scale.[16] Items included language spoken, read, used at home, for thinking, and used with friends. Response options were on a 5-point scale from “English only” to “Another language only.” Most of the caregivers were monolingual English speakers, and only 3 caregivers reported using more Spanish than English.

Statistical Analyses

A hierarchical general linear model was used to evaluate the contributions of disease severity, family characteristics, and resources to service utilization. The dependent variable was the sum of services that each young man had ever received. The predictors and their order of entry were disease severity, marital status, education, per capita income, Hispanic ethnicity, acculturation, and relatives with DBMD. Imputation procedures were used for the few missing demographic items.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES

The types of medical care and services and the number of caregivers reporting their use are described in Table 2. In this investigation, we examined the total number of services that were ever used by the family. Respondents indicated an average lifetime use of 9.85 of the 19 (SD = 3.7) services, with a range of 2–15 services.

Table 2. Types and frequency of reported use for healthcare and other services accessed by families.
ServiceCaregivers reporting use (N = 34) [n (%)]
Healthcare visits and therapies
Neurologists34 (100%)
Primary care providers32 (94%)
Pulmonologists28 (82%)
Cardiologists27 (79%)
Physical therapy27 (79%)
Respiratory therapy23 (68%)
Orthopedists23 (67%)
Occupational therapy22 (65%)
Speech therapy11 (32%)
Palliative care
Nursing care17 (50%)
Attendant or respite care16 (47%)
Case management15 (44%)
Nutritional services14 (41%)
Social services12 (35%)
Mental health services9 (27%)
Pastoral care9 (27%)
Transportation5 (15%)
Pain management through hospice4 (12%)
Hospice2 (6%)

On the 12-point scale of disease severity, the young men had an average severity of 9.2 (SD = 1.8), with scores ranging from 1.8 to 10.5. More than half had a severity level of >10.

A general linear model was used to examine correlates of service utilization. The final model accounted for 63.4% of the variance in service utilization. The young men received an average of 9.85 services. Disease severity, per capita income, and relatives with DBMD were associated with service utilization (Table 3). Each increase of 1 on a 12-point severity scale resulted in an increase of 1.73 services. Each additional $10,000 in per capita income resulted in 0.15 additional service. If other relatives had muscular dystrophy, the young man received 4.90 additional services.

Table 3. Hypothesis tests for predictors of service utilization.
PredictorbβF(1, 33)p
  1. b, unstandardized beta weight; β, standardized beta weight.

Disease severity1.730.8216.050.001
Marital status1.440.172.640.116
Education0.320.302.760.108
Per capita income ($10,000)0.150.408.920.006
Hispanic ethnicity−0.54−0.073.150.088
Acculturation0.030.011.550.225
Relatives with muscular dystrophy4.90−0.5410.020.004

Follow-up analyses were conducted to investigate the specific types of services that families with DBMD relatives were more likely to use. Families with uncles or cousins with DBMD used only 1 service at significantly higher rates; the young men were more likely to see a mental health practitioner (F = 4.59, P < 0.05).

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES

In this study we determined correlates of service utilization in the progressive medical condition of DBMD. Both clinical severity–based measures and socioeconomic characteristics that typically influence service utilization were tested in a general linear model. Twenty-three percent (22.6%) of the variance was explained by the individual's need for services (disease severity). The remainder of the variance was associated with financial resources and diagnosis in maternal relatives. Service utilization models reported previously in the literature typically explain <25% of the variance for the model, indicating that factors other than the predictors selected have a significant influence on service utilization. The total model for our study predicted 63% of the variance in service utilization, which indicates that more than half of the variation in service utilization can be explained by disease severity, per capita household income, and family history.[17-21] Predictors of the utilization of individual services were presented in a study by Arias et al. but have not been addressed in this study.[9]

In an attempt to minimize the potentially large effect that frequency of access to care might have in the model, the medical and ancillary services variable was calculated as a sum of the types of services the young man ever received rather than the frequency of use. For example, a single visit to any of the service providers is sufficient to increment the lifetime sum by 1, and this approach should minimize the effect of family resources. Despite this, the level of income continues to predict that utilization of services with lower levels of income contributes to fewer resources accessed at any point in time. The other culturally and socioeconomically based variables did not contribute significantly to the model.

Significant disparities in healthcare exist in many U.S. population groups, including minorities and low-income families. Access to healthcare services and providers represents a major area of disparity in underserved populations. Overall, racial and ethnic minorities and the poor have less access to care than majority and economically advantaged groups. Persons who speak a foreign language at home are less likely to have an identified primary care provider compared with individuals who speak English at home.[22] About 50% of people who are linguistically Spanish-dominant report communication problems with their healthcare provider.[23] Studies also show doctors may not recognize symptoms in racial or ethnic minority groups when compared with the racial and ethnic majority.[24] In this study we did not observe a significant effect of race/ethnicity and the measure of acculturation after adjustment for the other variables considered in the model. The lack of any effect of acculturation and race/ethnicity may be attributable to the limited diversity in the sample. “Only English” or “mostly English” speakers accounted for 80% of the sample, and 74% were not of Hispanic ethnic background. Replicating the study in a more rural population with language and other socioeconomic barriers would provide insight into how significant the effect of language and racial or ethnic origin may actually be with regard to access and use of available services in this population.

There were 37 million people in poverty (12.6%) in the USA in 2005. Poverty rates varied by race and ethnicity, with 21.8% of Hispanics classified as poor.[25] Individuals living in poverty do not have as much access to high-quality healthcare, are more likely to die prematurely, and are more likely to be uninsured. Research shows parents from low-income families are concerned about getting and keeping health insurance coverage for themselves and their children. In our study, after adjustment for the other sociodemographic variables of race/ethnicity and acculturation, income was found to be a significant correlate of service utilization.

The clinical progression of DBMD, particularly DMD, becomes more rapid and more complex as the individual ages, beginning with proximal muscle weakness in the extremities and progressing toward complete muscle wasting, respiratory and cardiac compromise, scoliosis, and other secondary conditions related to chronic wheelchair use and weakness. It stands to reason that increasing severity of disease will lead to increased access of services to maintain quality of life and prolong vitality. We found that the measure of severity in these individuals significantly influences utilization of services, accounting for the largest percentage of the variance in services utilization after adjustment for the effects of other variables.

Having additional family members affected by DBMD was also found to contribute to an increase in services; however, mental health practitioners were the only service providers utilized at higher rates by young men who had uncles or cousins with DBMD. Families who have uncles or cousins with muscular dystrophy are more likely to be aware of services that can help the young man and his family through the disease process. It should be noted that the survey collected information about the caregiver's current knowledge of other affected family members. There was no way to identify whether presence of other affected individuals was indicative of prior knowledge of the condition in the family or if some of these individuals (e.g., cousins) were diagnosed later than the individual about whom the interview was completed.

The limitations of this study are similar to those reported previously.[9] They include: small sample size; voluntary and non-random nature of the sample; and non-inclusion of certain factors in the model, such as availability of palliative care services. The sample was also predominantly Arizona-based, with 65% of the respondents from this state. To address the sample size issue, the study employed several data aggregation techniques to increase power. These include the creation of a disease severity measure from medical equipment and treatment variables and a composite service utilization measure. By increasing reliability, composite measures can increase the power of a study to detect differences if they exist.[26] The use of Rasch modeling to create a disease severity measure from available data has substantial potential. The severity measure is not intended to replace clinician ratings. However, it can be used to augment studies, particularly in the case of limited or missing data.[27] A limitation of the disease severity measure is that some of the items in the scale may themselves be related to access to healthcare, such as use of medical equipment and surgical procedures. We were unable to parse out the possible service-seeking behavior of some parents, which could have inflated the scores.

Service utilization in our model was unaffected by factors usually associated with utilization of healthcare, such as race/ethnicity, and cultural factors, such as language used. However, the data suggest that income still serves as a barrier to utilization of healthcare in progressively debilitating terminal conditions such as DBMD. Most families are not accessing or using the majority of services available to them, with the average use of services numbering less than half of those available despite the relative severity of disease in these young men with DBMD (score of 9.2 on the 12-point disease severity scale).

We conclude that financial burdens continue to hinder access to healthcare for families of young men with DBMD. Providers need to educate families more thoroughly about the resources available to them and the types of services that can increase the quality of life in late-stage DBMD.

ACKNOWLEDGEMENT

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES

The authors thank the individuals with Duchenne and Becker muscular dystrophy and their families for participating in the survey. The Palliative Care Group of MD STARnet assisted in the development of the questionnaire for the study and included Susan Apkon, Melinda F. Davis, Jane Karwoski, Dennis Matthews, F. John Meaney, Timothy Miller, Shree Pandya, and Christina Trout. Finally, the authors acknowledge the assistance of Shawnell Damon and Rebeca Arias, who conducted the interviews, and Kathy Pettit, who provided technical assistance with database development and management.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGEMENT
  7. REFERENCES
  • 1
    McDonald CM, Abresch RT, Carter GT, Fowler WM, Johnson ER, Kilmer DD, et al. Profiles of neuromuscular diseases. Duchenne muscular dystrophy. Am J Phys Med Rehabil 1995;74(suppl):S7092.
  • 2
    McDonald CM, Abresch RT, Carter GT, Fowler WM, Johnson ER, Kilmer DD. Profiles of neuromuscular diseases. Becker's muscular dystrophy. Am J Phys Med Rehabil 1995;74(suppl):S93103.
  • 3
    Eagle M, Baudouin SV, Chandler C, Giddings DR, Bullock R, Bushby K. Survival in Duchenne muscular dystrophy: improvements in life expectancy since 1967 and the impact of home nocturnal ventilation. Neuromuscul Disord 2002;12:926929.
  • 4
    Kohler M, Clarenbach CF, Bahler C, Brack T, Russi EW, Bloch KE. Disability and survival in Duchenne muscular dystrophy. J Neurol Neurosurg Psychiatry 2009;80:320325.
  • 5
    Miller JR, Colbert AP, Osberg JS. Ventilator dependency: decision-making, daily functioning and quality of life for patients with Duchenne muscular dystrophy. Dev Med Child Neurol 1990;32:10781086.
  • 6
    Bach JR, Campagnolo DI, Hoeman S. Life satisfaction of individuals with Duchenne muscular dystrophy using long-term mechanical ventilatory support. Am J Phys Med Rehabil 1991;70:129135.
  • 7
    Kohler M, Clarenbach CF, Böni L, Brack T, Russi EW, Bloch KE. Quality of life, physical disability, and respiratory impairment in Duchenne muscular dystrophy. Am J Respir Crit Care Med 2005;172:10321036.
  • 8
    Parker AE, Robb SA, Chambers J, Davidson AC, Evans K, O'Dowd J, et al. Analysis of an adult Duchenne muscular dystrophy population. Q J Med 2005;98:729736.
  • 9
    Arias R, Andrews J, Pandya S, Pettit K, Trout C, Apkon S, et al. Palliative care services in families of males with Duchenne muscular dystrophy. Muscle Nerve 2011;44:93101.
  • 10
    Miller LA, Romitti PA, Cunniff C, Druschel C, Mathews KD, Meaney FJ, et al. The Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet): surveillance methodology. Birth Defects Res A Clin Mol Teratol 2006;76:793797.
  • 11
    Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav 1995;36:110.
  • 12
    Rasch G. Probabilistic models for some intelligence and attainment tests (Copenhagen, Danish Institute for Educational Research). Chicago: University of Chicago Press; 1960.
  • 13
    Davis MF, Scherer K, Miller TM, Meaney FJ. Measuring disease severity in Duchenne and Becker muscular dystrophy. J Methods Meas Soc Sci 2010;1:818.
  • 14
    Steffensen B, Hyde S, Lyager S, Mattsson E. Validity of the EK scale: a functional assessment of non-ambulatory individuals with Duchenne muscular dystrophy or spinal muscular atrophy. Physiother Res Int 2001;6:119134.
  • 15
    Nabukera SK, Romitti PA, Campbell KA, Meaney FJ, Caspers KM, Mathews KD, et al. Use of complementary and alternative medicine by males with Duchenne or Becker muscular dystrophy. J Child Neurol 2012;27:734740.
  • 16
    Unger JB, Gallaher P, Shakib S, Ritt-Olson A, Palmer PH, Johnson CA. The AHIMSA Acculturation Scale: a new measure of acculturation for adolescents in a multicultural society. J Early Adolesc 2002;22:225.
  • 17
    Bass DM, Noelker LS. The influence of family caregivers on elder's use of in-home services: an expanded conceptual framework. J Health Soc Behav 1987;28:184196.
  • 18
    Balkrishnan R, Naughton M, Smith BP, Manuel J, Koman LA. Parent caregiver-related predictors of health care service utilization by children with cerebral palsy enrolled in Medicaid. J Pediatr Health Care 2002;16:7378.
  • 19
    Smith GC. Aging families of adults with mental retardation: patterns and correlates of service use, need, and knowledge. Am J Ment Retard 1997;102:1326.
  • 20
    Magaña S, Seltzer MM, Krauss MW. Service utilization patterns of adults with intellectual disabilities: a comparison of Puerto Rican and non-Latino white families. J Gerontol Soc Work 2002;37:6586.
  • 21
    Pruchno RA, McMullen WF. Patterns of service utilization by adults with a developmental disability: type of service makes a difference. Am J Ment Retard 2004;109:362378.
  • 22
    Devoe JE, Baez A, Angier H, Krois L, Edlund C, Carney PA. Insurance + access not equal to health care: typology of barriers to health care access for low-income families. Ann Fam Med 2007;5:511518.
  • 23
    Center KFFKPH. Health care experiences—2002 National Survey of Latinos survey brief. Washington, DC: KFF Pew Hispanic Center; 2004.
  • 24
    Tarshis TP, Jutte DP, Huffman LC. Provider recognition of psychosocial problems in low-income Latino children. J Health Care Poor Underserved 2006;17:342357.
  • 25
    DeNavas-Walt C, Proctor BD, Lee CH, US Census Bureau. Current population reports, P60–231, income, poverty, and health insurance coverage in the United States: 2005. Washington, DC: US Census Bureau; 2006.
  • 26
    Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum; 1988.
  • 27
    Davis MF, Sechrest LB, Shapiro D. Measuring progress toward smoking cessation. J Appl Meas 2005;6:164172.