Complementary and alternative medicine use in families of children with cerebral palsy

Abstract

In order to assess patterns of usage of complementary and alternative medicine (CAM) in families of children with cerebral palsy (CP), 213 families with a child (0 to 18 years) with CP were recruited at the university medical center in Ann Arbor, MI, USA as part of a descriptive survey. Two hundred and thirty‐five surveys were distributed. Mean age of the child was 8 years 6 months (SD 4y: 9mo) and 56% of the sample was male with 35% full‐time independent ambulators, while the rest used an assistive device or a wheelchair. Fifty‐four percent were in special education classrooms. Families were given a survey on functional status of the child with CP, CAM usage of the child and the parent, factors influencing the decision to use CAM, demographics, and clinical information. Of the families, 56%, used one or more CAM techniques. Massage therapy (25%) and aquatherapy (25%) were the most common. Children of families that used CAM were significantly younger (7y: 9mo, SD 4y: 7mo) than non‐users (9y: 6mo, SD 4y: 6mo: t‐test p < 0.01 two‐tailed). Children with quadriplegic CP, with spasticity, and those who could not walk independently were more commonly exposed to CAM (Pearson's X2 [PX2] p=0.01 two‐tailed; for mobility, odds ratio [OR] of 2.5 with regression). Mothers with a college degree had a greater tendency to use CAM for their child than those without (PX2p=0.01 two‐tailed). Fathers of children who used CAM were older than fathers of those who did not (37y: 9mo versus 33y: 2mo, p=0.04 two‐tailed). There was no significant difference between groups for mother's age, father's education, income, or for population of home town. Parents who used CAM for themselves were more likely to try CAM for their child (70% versus 47%, OR 2.1), and were much more likely to be pleased with the outcome (71% versus 42%, OR 3.5). Child's age (younger), lack of independent mobility, and parental use of CAM were the most significant predictive factors identified via logistic regression.