Musculoskeletal Complaints Among 11-Year-Old Children and Associated Factors

The PIAMA Birth Cohort Study

Gerben Hulsegge; Sandra H. van Oostrom; H. Susan J. Picavet; Jos W. R. Twisk; Dirkje S. Postma; Marjan Kerkhof; Henriëtte A. Smit; Alet H. Wijga

Disclosures

Am J Epidemiol. 2011;174(8):877-884. 

In This Article

Materials and Methods

Study Design and Study Population

Data from the population-based PIAMA Study, which was originally designed to investigate asthma, were used. Details of the study design have been published previously.[16] The baseline cohort consisted of 4,146 pregnant women selected at prenatal health clinics in the Netherlands in 1996 and 1997 who agreed to participate and gave written informed consent. A total of 183 participants were lost to follow-up before any data on their children had been obtained, so the study started with 3,963 newborn children. Questionnaires were sent to the participating parents during the last trimester of pregnancy, at the child's age of 3 months, at the child's age of 1 year, and annually until age 8 years. At the age of 11 years, questionnaires were sent to both the parents and the child. Our study population consisted of the 2,638 children who completed the questionnaire at age 11 years. Although a few (3.9%) of the children were aged 10 or 12 years when they completed the questionnaire, we refer to them here as 11-year-olds.

Musculoskeletal Complaints

Complaints of musculoskeletal pain in the back, upper extremities, and lower extremities were measured as part of a 15-item list of diseases and complaints, which started with: "Could you mark for the following diseases or conditions whether you have had it in the past 12 months (= last year) and whether you visited your doctor in the past 12 months for that reason?" The questionnaire further specified, "If 'long-lasting' is stated, we mean conditions which bothered you in total for more than 1 month," using the following 3 items: "long-lasting back complaints," "long-lasting complaints of the neck, shoulder, elbow, wrist, or hand," and "long-lasting complaints of the hip, knee, ankle, or foot." Children were able to answer "no," "yes, but I did not visit the doctor," or "yes, I visited the doctor for this complaint." "Any MSC" was defined as complaints in one or more of the 3 categories of complaints.

Determinants of MSC

Many variables were measured in the PIAMA Study, but we could only use a limited number of variables because of statistical power issues. We chose variables from different domains: sociodemographic characteristics, growth and development factors, psychosocial factors, and lifestyle.

Maternal educational level when the child was 1 year old was used as a proxy for socioeconomic status. Highest attained educational level was divided into 3 categories: primary school, lower vocational education, or lower secondary education (low); intermediate vocational education or intermediate/higher secondary education (intermediate); and higher vocational education or university degree (high).

Children were classified as non-Western migrants if 1 or both parents had been born in a non-Western country. Children were classified as native Dutch if both parents had been born in the Netherlands. The remaining children were classified as Western migrants.

The child's body weight (in kilograms), height (in centimeters), and date of last measurement were reported at ages 8 and 11 years. Since the height and weight of a child change with age, measures of body mass index, height, or weight alone are inappropriate for comparisons of groups of children.[17] For that reason, we calculated sex-specific standard deviation scores (z scores) for height-for-age and weight-for-height using Growth Analyser software (http://www.growthanalyser.org), based on the reference growth curves of the Dutch Fourth Nationwide Growth Study.[18] Weight change in kilograms per year and height change in centimeters per year were defined as the change in weight and height per year between ages 8 and 11 years.

Pubertal status was measured with the Pubertal Development Scale,[19] including growth spurt, pubic hair, and skin change for boys and girls. Data on facial hair growth and voice change were recorded only for boys and on breast development and menarche only for girls. The response options were scored on a 4-point ordinal scale which ranged from 1 (no development) to 4 (development already completed). The menarche item was scored 1 if no menstrual periods had occurred and 4 if menstrual periods had already begun. The scores were summed and divided by 5 in order to preserve the original 1–4 metric.

Mental health status and daytime tiredness were included as indicators of psychosocial factors. The Five-Item Mental Health Inventory (MHI-5) is a brief screening questionnaire for mood and anxiety disorders that has been validated in adults.[20] This questionnaire has been used previously to assess mental health status among adolescents.[14,21,22] The MHI-5 consists of the following 5 questions: "How much of the time during the past month 1) have you been a very nervous person?; 2) have you felt calm and peaceful?; 3) have you felt downhearted and blue?; 4) have you been a happy person?; and 5) have you felt so down in the dumps that nothing could cheer you up?." We used a 5-point Likert scale ranging from 1 (all the time) to 5 (never).[23] All scores were summed and transformed into a scale ranging from 0 to 10, with higher scores indicating better mental health.

Two questions were used to assess daytime tiredness. Children were asked how many times a week they felt tired during the day and fell asleep at school. Children were classified as daytime tired when they answered 1 or both questions positively (≥2 times per week).

The children's level of physical activity was assessed at ages 5, 7, and 8 years through questionnaires completed by the parents. Children were categorized as "active" when they engaged in at least 1 hour of activity of moderate-to-vigorous intensity on a normal weekday or when they walked/bicycled to school for at least 1 hour on a normal weekday. At age 11 years, the children filled out the questionnaire themselves. The first question referred to active movement, and examples were given (sports, gym classes at school, cycling, walking, rope jumping, playing tag, walking a dog, delivering newspapers, etc.). The question was formulated as: "How many days a week are you active (altogether) for at least 1 full hour a day?". If a child responded "5 or more days," he/she was classified as being active. A second question was asked: "How many minutes are you normally walking or cycling from home to school and back?". Children were classified as being physically active when they responded "more than 60 minutes." We constructed 2 variables for physical activity—physical activity at age 11 years and a variable comparing the children who were "active" at all ages (5–11 years) with those who were inactive at one or more ages.

Watching television and using a computer during leisure time were combined to study sedentary lifestyle at age 11 years. Since recommendations for the duration of screen time among children are set at a maximum of 2 hours per day,[24] we dichotomized the mean daily duration of television-watching and/or after-school computer use at 2 hours/day (≤2 hours/day vs. >2 hours/day).

Statistical Analyses

The statistical analyses included variables that were measured either once or several times during PIAMA follow-up. Firstly, univariable logistic regression analyses were conducted for all variables, and a P value lower than 0.05 was considered statistically significant. Results from all univariable analyses were adjusted for age (in months) and sex. Secondly, the contributions of all factors were explored by means of multivariable analyses using a stepwise forward selection procedure. Age (months) and sex were included in every step of the multivariable analyses, and an entry probability for each variable was set at 0.05. SAS software, version 9.2 (SAS Institute, Inc., Cary, North Carolina), was used. Odds ratios and 95% confidence intervals are reported.

Data on most cross-sectional variables contained only a few missing values (<5.2%), but combining prospectively measured data and multivariable models may lead to exclusion of a substantial proportion of observations. Furthermore, the missing data are unlikely to be "missing completely at random," and a complete-case analysis may lead to biased results. Therefore, missing data were multiply imputed using the "multivariate imputation by chained equations" method in the statistical program R, version 2.5.0 (http://rss.acs.unt.edu/Rdoc/library/mice/html/mice.html). This technique is considered the best available method for dealing with missing data[25,26] The imputation matrix consisted of all original outcome variables and independent variables included in this study. Geographic region, other health problems, general health, and television/computer use at ages 5, 7, and 8 years were also included in the imputation matrix. Twenty imputed data sets were created and analyzed by standard logistic regression analysis. Results from these analyses were combined using PROC MIANALYZE in SAS. In addition, we conducted sensitivity analysis to compare the univariable odds ratios from the complete-case analyses with the odds ratios obtained in the imputed-data analyses. Only results from the imputed-data analyses are presented.

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