Comparison of Gross Motor Outcomes Between Children With Cerebral Palsy From Appalachian and Non-Appalachian Counties : Pediatric Physical Therapy

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RESEARCH REPORTS

Comparison of Gross Motor Outcomes Between Children With Cerebral Palsy From Appalachian and Non-Appalachian Counties

Bican, Rachel PT, DPT, PhD; Noritz, Garey MD; Heathcock, Jill PT, MPT, PhD

Author Information
Pediatric Physical Therapy 35(1):p 66-73, January 2023. | DOI: 10.1097/PEP.0000000000000971
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Abstract

PURPOSE

Appalachia is a geographical and cultural region in the United States, with 32 counties within Ohio.1,2 Health outcomes in Appalachia are poorer than in the rest of the United States.2 People from Appalachia have higher mortality rates from cardiovascular disease, lung cancer, diabetes, kidney disease, suicide, unintentional injuries, and drug overdoses.1 The difference in health outcomes between people from non-Appalachian and Appalachian counties may be due to a shortage of health care providers, limited access to health care and health care facilities, reduced health insurance coverage because of high unemployment rates, and a cultural notion of individualism, which may prevent people living in Appalachia from reaching out to health care providers for help.2,3

Mothers and infants from Appalachia also demonstrate poorer health outcomes than those from non-Appalachian counties.2,3 Mothers from Appalachia have decreased perinatal care, poorer health literacy, higher instances of teenage pregnancy, poorer education, and higher unemployment that all contribute to decreased health outcomes for themselves and their children.4–6 Infants born to mothers in Appalachia have higher rates of prematurity, low birth weight, and infant mortality when compared with the rest of the United States.1,2,4 Similar to adults, infants and children in Appalachia have limited access to medical care and may have unmet health needs.2,7

Cerebral palsy (CP) incidence rates and motor outcomes for children with CP from Appalachia are not available due to the lack of a national registry.8 However, based on available research for health outcomes for people living in Appalachia, it could be hypothesized that Appalachian counties may have a higher prevalence of CP and that children with CP from Appalachian counties may have poorer motor outcomes compared with the rest of the United States. This is because infants from Appalachia have a higher prevalence of prematurity and low birth weight,1,2,4 are from rural counties of habitancy,6 and are more likely to come from a family of low socioeconomic status, which are all risk factors for CP.6 They may also have limited access to medical care, which may prevent early diagnosis of CP and subsequent early rehabilitation services for optimal motor outcomes.9

The primary purpose of this study was to evaluate gross motor outcomes between children with CP from non-Appalachian and Appalachian counties in Ohio who were seen at a single pediatric hospital. The secondary purposes of this study were to (1) compare gestational age and birth weight, and (2) evaluate the age of the participant when CP was first noted in their electronic medical record (EMR) between children with CP from non-Appalachian and Appalachian counties.

METHODS

Participants

The procedures described in this study were approved by the Institutional Review Board (Buck-IRB at The Ohio State University 2016N0031). Written consent was waived because data were retrospectively sourced from EMR at a single pediatric hospital system. For this retrospective, descriptive and matched-case controlled study (N = 114) participants were included (N = 57 children from non-Appalachian and N = 57 children from Appalachian counties; mean age: 5.6 ± 4.3 years at the time of their first encounter). The groups were matched by age and Gross Motor Function Classification System (GMFCS) level.10 Inclusion criteria were (1) ages birth to less than 18 years, (2) diagnosis of CP, and (3) seen at Nationwide Children's Hospital (NCH) between 2010 and 2020. Exclusion criteria were (1) no motor scores available, and (2) county-level habitancy was not recorded. Participant demographics are shown in Table 1.

TABLE 1 - Demographic Informationa
Participants Non-Appalachian Counties Appalachian Counties Test for Equal Variance P
Mean SD Mean SD
Age, y 5.1 4.1 6.2 4.5 1.00
N % N %
Sex (male) 16 47.1 24 64.9 .86
GMFCS level .99
Level I 7 12.3 7 12.3
Level II 5 8.8 5 8.8
Level III 9 15.8 9 15.8
Level IV 17 29.8 17 28.8
Level V 19 33.3 19 33.3
Type of CP N/A
Hypotonic 3 5.3 2 3.5
Hypertonic/spastic 50 87.6 52 91.2
Dystonic 0 0.0 0 0.0
Athetoid 0 0.0 0 0.0
Ataxic 1 1.8 0 0.0
Unspecified 3 5.3 3 5.3
Topography
Left hemiplegia 4 7.0 0 0.0 N/A
Right hemiplegia 1 1.8 3 5.3
Diplegia 7 12.3 3 5.3
Quadriplegia 29 50.9 33 57.9
Triplegia 1 1.8 1 1.8
Unspecified 15 33.2 14 29.7
Race N/A
Asian 1 1.8 0 0.0
Black (Non-Hispanic) 9 15.8 1 1.8
White (Non-Hispanic) 41 71.8 49 85.9
Multiple race 3 5.3 5 8.8
Other 3 5.3 2 3.5
Abbreviations: CP, cerebral palsy; GMFCS, Gross Motor Function Classification System; N, participant count; N/A, not applicable.
aEqual variance was accounted for between children from non-Appalachian and Appalachian counties for age, sex, and GMFSC level and is provided in the last column (α < 0.05 for significance). N = 114 total participants (N = 57 participants from non-Appalachian counties; N = 57 participants from Appalachian counties). Type and topography of CP were determined by the ICD medical diagnosis.

Motor Testing

The Gross Motor Function Measure, 66 (GMFM-66) was selected as the primary motor outcome measure. This measure is consistently used in clinical care at NCH and is a valid and reliable measure of gross motor development for children with CP.11–16 The GMFM-66 was completed by trained physical or occupational therapists employed at NCH. Motor testing was completed during routine clinical care.

Data Collection

All data were retrospectively sourced from EMR through Epic Systems17 from a single pediatric hospital system (NCH). This hospital system includes 1 main hospital and 11 “close-to-home” outpatient locations throughout central Ohio.. Data were sourced from 28 outpatient clinics in the hospital system that routinely perform motor assessments for children with CP. These clinics include specialty clinics, such as cerebral palsy clinic and early development clinic, as well as all outpatient physical therapy clinics. A total number of 9962 encounters (N = 1916 unique individuals with CP) were found within the hospital system from 2010 to 2020 who had complete data sets. A complete data set was identified as children who had their age, sex, GMFCS level, GMFM-66 scores, gestational age, and birth weight recorded. To obtain the study sample of 114 participants, we randomly selected 57 children from Appalachian counties from the EMR. These participants were then randomly matched by age and GMFCS level to children from non-Appalachian counties who had complete data sets. Figure 1 graphs the flowchart of the final selection of 114 participants. Data sourced included age at encounter, sex, GMFCS level, GMFM-66 scores, gestational age and weight at birth, and age that CP was first noted within their EMR.

F1
Fig. 1.:
Data collection: The inclusion and exemption process used to obtain the sample. GMFCS indicates Gross Motor Function Classification System; GMFM-66, Gross Motor Function Measure, 66.

Data Quality

PEDSnet outlines 4 important components to assess data quality: fidelity, consistency, accuracy, and completeness.18 To examine fidelity, we descriptively report the department and year the GMFM-66 were recorded between our sample data (N = 114) and total unique individuals with complete data sets (N = 1916). We descriptively examine consistency and accuracy of the sample data. Finally, to examine completeness, we report the total number of complete data sets.

Statistical Analyses

A priori power analyses were completed for both samples to determine sample size. A total sample size of 114 children with CP (N = 57 from non-Appalachian counties and N = 57 from Appalachian counties) was included in this study. The sample size was determined using the G*Power, linear multiple regression: fixed model, R2 deviation from zero, and F test (effect size: 0.10; ∝: 0.05; β = 0.80; predictors: age, sex, GMFCS level, and county-level habitancy). Effect size was determined using the GMFM,16,19,20 and a small effect size was chosen to increase the total sample size to obtain a representative sample.

Descriptive statistics are used to describe data quality, gestational age, birth weight, gross motor outcomes, and the age that a CP diagnosis was first recorded within the EMR. A 1-way analysis of variance was used to evaluate the difference between gestational age and birth weight by county-level habitancy (α = 0.05). Correlation and multiple linear regression analyses were conducted to examine the relationship between gross motor function (GMFM-66 total score) and location of habitancy (non-Appalachia and Appalachia) (α = 0.05). A 1-way analysis of variance was also used to evaluate the difference between age of CP recorded in the participant's EMR, as measured by the first date it is in the participant's EMR, by county-level habitancy (non-Appalachian and Appalachian counties) (α = 0.05).

RESULTS

Data Quality

Fidelity and Completeness. There were a total number of 1916 complete data sets (99.3%) of 1929 total unique individuals. One hundred four individuals (5.4%) of the complete individual data set (N = 1916) came from Appalachian counties. Comparison percentages of clinics where the data were recorded and years the data were recorded between the sample data and total individual data are shown in Table 2.

TABLE 2 - Comparison of Sample Data and Total Unique Individual Data Setsa
Sample Data Total Data
N % N %
Clinic 114 100.0 1916 100.0
CP clinic 5 4.4 89 4.6
CP ortho clinic 94 82.5 1025 53.5
Early development clinic 6 5.1 48 2.5
PT outpatient 8 7.0 540 28.2
Other 1 1.0 214 11.2
Year 114 100.0 1916 100.0
2010-2015 48 42.1 976 50.9
2016-2020 66 57.9 940 49.1
Sample Data: Appalachian Sample Data: Non-Appalachian
N % N %
Clinic 57 100 57 100
CP clinic 1 1.7 4 7.0
CP ortho clinic 47 82.5 47 82.5
Early development clinic 3 5.3 3 5.3
PT outpatient 6 10.5 2 3.5
Other 0 0.0 1 1.7
Year 57 100 57 100
2010-2015 33 57.9 15 26.3
2016-2020 24 42.1 42 73.7
Abbreviations: CP, cerebral palsy; PT, physical therapy.
aDepartment is representative of which department recorded the Gross Motor Function Measure, 66 (GMFM-66) score. The year denotes when the GMFM-66 score was recorded.

Consistency. All GMFM-66 data were recorded in the same flow sheet, regardless of the clinic or the individual who input the score.

Accuracy. We are unable to identify the number of unique providers who recorded the GMFM-66 score into the flow sheet, but it is standard of care that all providers who perform the GMFM-66 are clinically trained on this outcome assessment. Standard training includes mentorship from another clinician who has worked in the clinic and has performed the GMFM.

Motor Scores

Mean scores on the GMFM-66 are shown in Table 3 by location of habitancy and GMFCS level. Minimal clinical important differences (MCID) in the table were established by Oeffinger et al.15 Participants (GMFCS levels I-III) from Appalachian counties had mean scores on the GMFM that were higher than the MCID compared with participants from non-Appalachian counties.

TABLE 3 - Mean GMFM-66 Scores by Location of Habitancy
GMFCS Level Non-Appalachian County Appalachian County MCID Difference Between Mean Scores
Mean Score (GMFM-66) SD Mean Score (GMFM-66) SD
Level I 68.9 11.0 73.6 16.9 1.7 4.7a
Level II 51.7 5.6 63.1 10.7 1.0 11.4a
Level III 42.6 8.4 48.6 7.6 0.7 6.0a
Level IV 29.9 6.6 33.2 6.9 N/A 3.3
Level V 17.0 7.5 19.0 6.33 N/A 2.0
Abbreviations: GMFCS, Gross Motor Function Classification System; GMFM-66, Gross Motor Function Measure, 66; MCID, minimal clinical important difference; N/A, not applicable.
aThe difference between mean scores is greater than the MCID. MCID established by Oeffinger et al21 for ambulatory children with CP.

Relationship Between County-Level Habitancy and Motor Outcomes

Age at the time of the encounter (P < .001), GMFCS level (P < .001), and county-level habitancy (P = .021) were all significant predictors of gross motor function (GMFM-66). Children from Appalachian counties had significantly higher GMFM-66 scores than children from non-Appalachian counties when controlling for age and GMFCS level. The multiple linear regression model with all predictors produced R2 = 0.86, F(3,6 = 106.14; P < .001. Table 3 summarizes the analysis results and Figure 2a presents GMFM-66 score by GMFCS level for children with CP from non-Appalachian and Appalachian counties.

F2
Fig. 2.:
(a) GMFM-66 by GMFCS level and county-level habitancy: intervals represent a 95% confidence interval around the mean and individual standard deviations are used to calculate the interval. N = 114 total participants (N = 57 participants from non-Appalachian counties on the left; N = 57 participants from Appalachian counties on the right). (b) Gestational age by county-level habitancy: Intervals represent a 95% confidence interval around the mean and individual standard deviations are used to calculate the interval. N = 111 total participants (N = 57 participants from non-Appalachian counties on the left; N = 54 participants from Appalachian counties on the right). N = 3 participants did not have gestational age recorded within their electronic medical record (EMR). (c) Weight at birth by county-level habitancy: Intervals represent a 95% confidence interval around the mean and individual standard deviations are used to calculate the interval. N= 102 total participants (N = 57 participants from non-Appalachian counties on the left; N = 45 participants from Appalachian counties on the right). N = 12 participants did not have birth weight recorded within their EMR. (d) Age of CP was recorded in the EMR by county-level habitancy: EMR. Intervals represent a 95% confidence interval around the mean and individual standard deviations are used to calculate the interval. N = 109 total participants (N = 54 participants from non-Appalachian counties on the left; N = 55 participants from Appalachian counties on the right). N = 5 participants did not have age of CP diagnosis recorded within their EMR. GMFCS indicates Gross Motor Function Classification System; GMFM-66, Gross Motor Function Measure, 66.

Difference Between County-Level Habitancy, Gestational Age, and Weight at Birth

There was no difference in gestational age (F1,109 = 0.21; P = .65) between children from non-Appalachian counties and children from Appalachian counties. The mean gestational age for children from non-Appalachian counties was 32.36 ± 5.57 weeks of gestation (95% confidence interval [CI]: 33.89-35.82 wk) (range: 23.29-41.00 wk; median: 37.00 wk). The mean gestational age for children from Appalachian counties was 33.87 ± 5.64 weeks of gestation (95% CI: 32.89-35.38 wk) (range: 24.43-41.29 wk; median: 34.93 wk) (Figure 2b).

There was no difference in birth weight (F1,100 = 1.41; P = .24) between children from non-Appalachian and Appalachian counties. The mean birth weight for children from non-Appalachian counties was 2357 ± 1194 g (95% CI: 2,043-2,672 g) (range: 476-4686 g; median: 2608 g). The mean birth weight for children from Appalachian counties was 2073 ± 1201 g (95% CI: 1728-2428 g) (range: 550-4848 g; median: 1880 g) (Figure 2c).

Difference Between Age of CP Recorded Within the EMR by County-Level Habitancy

Children from Appalachian counties have a CP diagnosis recorded within their EMR significantly later than children from non-Appalachian counties (F1,107 = 7.46; P = .001). The mean age of CP recorded within the EMR for children from non-Appalachian counties was 4.33 ± 3.90 years (95% CI: 3.10-5.55 years). The mean age of CP recorded within the EMR for children from Appalachian counties was 6.70 ± 5.08 years (95% CI: 5.49-7.91 years) (Figure 2d).

CONCLUSION

Families and children from Appalachian counties have decreased access to quality health care services, which has resulted in documented poorer overall health outcomes.2,22 Surprisingly, our study found that children with CP from non-Appalachian counties scored significantly lower on the GMFM-66 across GMFCS levels when compared with children from Appalachian counties. Prior to the study, we hypothesized that children from Appalachia would have lower motor scores, as measured by the GMFM-66. Our hypothesis was based on previous research that children from Appalachia may not have access to rehabilitation and other health care services1,2,4 that are important for optimal motor outcomes for children with CP.

The results of this study reinforce the need to evaluate health disparities for children with CP, not only by location but by other psychosocial factors. These factors may include socioeconomic status, race, health literacy, parent education, and access to health care services.8 It is important to consider that both urban (non-Appalachian counties in this study) and rural (Appalachian counties in this study) areas face both health inequities23 and decreased access to health care secondary to not only location of habitancy22 but also other psychosocial factors that were not accounted for in this study.22,24,25 Possible confounding factors not accounted for may have included income, insurance coverage, and health services provided outside of the institution where the data were collected. Without accounting for other psychosocial factors, it is difficult to assess whether children from non-Appalachian counties lived in low-resource communities and faced health inequity challenges at a higher rate than children from Appalachian counties.

Children from Appalachian counties had GMFM-66 scores that were higher than the MCID across GMFCS levels when compared with children from non-Appalachian counties. Although GMFCS levels typically remain stable,26–28 these results suggest that location of habitancy may influence clinically meaningful differences in motor outcomes. One possibility for the difference in motor outcomes is that children from Appalachia may not receive environmental modifications to the same extent as children from non-Appalachian counties, due to the rurality of their habitancy. Other studies have found that children with CP from low-resource areas have high rates of independence and social engagement within their communities.29,30 Without environmental modifications and an Appalachian cultural notion of “taking care of their own,”2,5 children with CP in these communities may be unintentionally improving their motor abilities through routine, every day experiences. These factors may have contributed to children from non-Appalachian counties having lower motor outcomes compared with children from Appalachian counties.

It is also important to distinguish the difference between motor outcomes and health outcomes for children with CP. Previous studies have found that children from low-resource communities may have a higher prevalence of and more severe CP with higher incidences of secondary comorbidities.8,31–37 Children with CP in low-resource communities face secondary health challenges including malnutrition,31,38 poor dental health,39 and musculoskeletal concerns, such as untreated hip migration and scoliosis, which may ultimately contribute to decreased motor outcomes and quality of life.35,36 In addition, families of children with CP in low-resource communities may experience higher stress and lower quality of life due to the distance from and relatively inaccessibility of health care providers to support their needs and the needs of their child.34 Although this study is unable to determine prevalence rates of CP and secondary comorbidities, it will be critical that future studies evaluate the unmet needs of children with CP in both urban and rural counties in Ohio to maximize developmental outcomes, quality of life, and participation in meaningful activities.31,40

The mean and range of gestational age and birth weight were lower in children with CP from Appalachian counties compared with non-Appalachian counties, although the difference was not significant. Previous studies have demonstrated that infants born in Appalachia had higher rates of prematurity and lower birth weight compared with the rest of the country.1,2,4 We may not have found a significant difference between children from Appalachian and non-Appalachian counties for several reasons. First, we may not have had enough children in the sample to find a significant difference, even though the mean and range for gestational age and birth weight from children born in Appalachian counties were lower. Second, we did not account for where the child was born, only where the child lived at the time of the encounter. This means that some children may have not been predisposed to lack of access to health care if their mothers gave birth outside of Appalachia and then moved to Appalachia after the child was born. Finally, not all Appalachian counties have the same level of poverty and lack of access to health care. The Appalachian Regional Commission categorizes Appalachian counties into “distressed,” “at-risk,” and “transitional” based on unemployment rates, per capita market income, and poverty rates.4,41 Only 5 children came from “distressed” Appalachian counties. Future studies may consider different stratification of groups. For example, instead of stratifying groups as only non-Appalachian and Appalachian counties,4,41 researchers may consider stratifying by distress level of Appalachian counties, distance from health services, or income42 level of the family.

Finally, our study also found that children from non-Appalachian counties had a diagnosis of CP recorded within their medical record significantly later than children from Appalachian counties. Importantly, the age of CP recorded in the medical record may be related, but not a direct, indicator of age of CP diagnosis. The results may have occurred for several reasons. First, children who lived geographically closer to the hospital system, or children from the non-Appalachian counties, may have been able to access care sooner than those who lived farther away, such as the children from Appalachian counties. It would also be plausible that children from non-Appalachian counties first received care at a local institution before transitioning to NCH, a very large medical institution, once their needs were unable to be met locally. It will be important for future studies to better understand the implications of a delayed diagnosis being recorded within an EMR: such as whether a delay in recording the medical diagnosis delays important medical and rehabilitation care.

Limitations and Future Directions

There are several limitations in this study. First, the participants were recruited from the same pediatric hospital system, preventing generalizability of the results to all children with CP. We feel that the data collected captured children with CP from Columbus (non-Appalachian counties) but did not fully capture children from Appalachian counties. This is due to the small percentage of children seen at NCH who were from Appalachia (5.4%), and an even smaller subset who came from distressed counties in Appalachia (less than 1%). These percentages may be so low if the children with CP from low-resourced areas of Appalachia are not receiving health services.37 Second, only structured data were sourced from the EMR, leaving gaps in data, which may unintentionally influence the results of this study. Third, we used a small effect size (0.1) to estimate our sample size for this study. Previous studies have determined that the effect size of the GMFM may range from 0.719 to 0.8.20 However, these studies evaluate the change in GMFM-66 scores over time or after an intervention. For this study, we were not interested in the change in GMFM-66 scores, rather in whether there was any difference between groups. We wanted to ensure that a representative sample was included for this study, and using a smaller effect size increased the total sample of participants needed to power this study. However, it is important to note that this may increase the probability of finding a significant difference in motor scores between groups that may not be clinically meaningful. We included information on MCID to help mitigate these limitations. Finally, groups were stratified by whether the child was living in a non-Appalachian or Appalachian County at the time of their first encounter. Future studies should consider stratifying participants by distress level of the county, income level, or access to health services.

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Keywords:

cerebral palsy; motor outcomes; pediatrics

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