School Absence Among Children with Chronic Illness

Barbara A. Cook, Karin Schaller, Jeffrey P. Krischer

ABSTRACT This paper reports school attendance for 336 chronically ill, Medicaid-eligible

children living in rural areas of northern Florida. Demographic data were obtained by a questionnaire administered in a home interview. Attendance data were collcded directly from the schools. The mean number of days absent the previous year was 16.9; the mean percentage of days absent was 9.4%. Regression analysis indicated that lower education level of parents and the child’s inability to participate in physical activities were significant in predicting days missed from school.

No individual diagnostic category was predictive of school absence. Thus, the chronicity of an illness and its impact on the child may be a more significant influence on school attendance than the actual diagnosis of the illness.

INTRODUCTION The importance of assessing the functional impact of

chronic illness in children, as well as the medical aspects, has been emphasized both by researchers and clinicians.’ School attendance is a sensitive measure of functioning level and should be considered when assessing how well the child is coping with chronic illness. However, surprisingly little litera- ture about children with chronic illness deals with school absence.

In a study of 200 asthmatic children of low-income families in New York City, the children had absence rates 24Vo greater than the overall rates for the school district studied.’ Parcel et all compared school absence in students with asthma with a random sample of nonasthmatic school children. Students with asthma had a significantly higher average absence rate (p < .001) than, did nonasthmatic students (absent 8.4% of days vs. 5.9%). To determine the effects of mothers’ percep- tions of severity of asthma on school attendance, mothers were asked to describe the severity of their child’s asthma. School absence rates varied directly with maternal perceptions of the severity of the child’s illness.

Klein‘ found that 12% of a sample of chronically ill patients from a university pediatric renal clinic missed more than five days of school each month, whereas only 1% of normal siblings missed that many days a month. In a study by Sultz et al,’ 390 chronically ill children with a range of diagnoses were interviewed to ascertain the effects of their illness; 26% were absent from school a total of at least six weeks during the year, and 35% who required special home care experienced such frequent or prolonged absences.

METHOD Sample Selection

The study population was comprised of 3,228 chronically ill children less than 17 years old, residing in a 24 county area of northern Florida (Figure 1). The children received care under the auspices of Children’s Medical Services (CMS), Florida State Dept. of Health and Rehabilitative Services, and were Medicaid-eligible during the period June 1983-June- 1984.

Barbara A. Cook, MS. and Jeffrey P. Krischer, PhD, Dept. of Pediatrics, College of Medicine, Box 5-296, J. Hillis Miller Health Center, University of Florida, Gainesville, FL 32601; and Karin Schaller.

This work was supported in part by Children’s Medical Services, Florida Slate Dept. of Health and Rehabilitative Services, and in part by the Robert Wood Johnson Foundation.

CMS eligibility includes children younger than age 21 who have a chronic, disabling, or potentially disabling medical con- dition (any condition that hinders normal growth or develop- ment), and whose family can not afford to pay for all needed treatment (family of four earning wages less than $9,000 gross a year). To be eligible for Medicaid in Florida, a family must meet eligibility criteria for one of the three main programs: aid to families with dependent children (AFDC); federal supple- mentary security income (SSI); or family services foster care for children. AFDC income eligibility in Florida is $3,276 a year for a four-person family.

The differences among the 24 study counties concerning availability of health resources may influence health care and ultimately school attendance. Thus, the population was strati- fied by county of residence and a random sample was selected within each stratum to ensure a representative sample. A sample of 713 (22% of the population) was obtained; 449 (63%) reportedly attended school during the previous year and attendance records were obtained directly from the school for 336 (75Vo). This report focuses on data collected for these 336 subjects.

Procedures All cases in the sample were classified into 17 diagnostic

categories based on groupings of ICD-9-CM codes to divide the patients into homogeneous subgroups for analysis. Stand- ardized questionnaires were administered in a home interview to elicit information from parents about a variety of circum- stances surrounding the chronically ill children. The interviews included questions that addressed basic demographics and school problems. Parents provided data on school attendance during the previous year and also signed a release of information form that granted permission to obtain school information directly from the school.

Figure 1 24 County Study Area (shaded)

Journal of School Health September 1985, Vol. 55, No. 7 . 265

The percentage of days absent was calculated by dividing the number of days absent by the number of days in the school year (180). Multiple linear stepwise regression analysis was used to identify and investigate the factors influencing school absence. Variables assessed to determine if they influenced the number of days absent were demographic data on the child and the family such as county of residence, age, sex, race, number of siblings, and education of caretaker; information about the child’s illness such as diagnosis, time since diagnosis, and family expectations of the condition; and health care utilization.

RESULTS Characteristics of the sample are presented in Table 1. The

age distribution was 13.4% aged six years or less, 53.3% aged seven-11 years, and 33.3% aged 12-16 years. The racial composition was 65.2% black, 34.2% white, and 0.6% other. The ratio of males to females was 1.2:l; 50.3% of the sample had never been retained in a grade. Overall, 91% of the cases had primary caretakers who had at least eight years of education. Only 11.1 To had caretakers with more than 12 years of education.

Illness duration was expressed as a percentage of the child’s life span. For instance, a 10-year-old child with a duration of 80% would indicate that the illness had been present for eight years. Information in Table 1 indicates that 68.7% of the sample had their illness for at least 50% of their life span.

The distribution of chronic disease (Table 1) reveals that diseases of the nervous system and sense organs comprised the single largest grouping of disorders (25.6%). Ill-defined condi- tions and congenital anomalies represented the next largest groupings, 18.5% and 11.6%, respectively.

According to the attendance data obtained from the schools, the mean number of total days absent in the previous year was 16.9 (SD = 20.7) and the mean percent of days absent was 9.4% (SD = 11 3). Attendance patterns varied markedly, ranging from 0 to 158 days; 4.2% of the children had perfect attendance and 3% missed more than one-third of the school year. School absences varied considerably by diagnostic category (Table 2). Children with injuries/poison- ings or congenital anomalies, and the few with no recorded diagnosis had high absentee rates, while those with digestive disorders or infections were less likely to be absent from school. Yet, though the means differed, the large standard deviations indicate considerable overlap of the underlying dis- tributions. Overall, 10.4% missed more than 20% of the school year.

A stepwise linear regression procedure was used to con- struct a model for predicting the number of days a child missed from school. Based on the findings of other investigators, various independent variables were selected to test the hypothesis that the variables play a significant role. The step- wise procedure sequentially selects variables that contribute significantly to the prediction of school absences after adjusting statistically for variables already entered into the model.

Variables relating to background and demographic infor- mation about the child included the child’s age, sex, race, and county of residence. Also, family status (single vs. two parents), years of education of the primary caretaker “Education,” and the number of siblings were considered. Variables pertaining directly to the chronic illness were diagnosis, represented by 16 categorical variables, and duration of the illness expressed as a percentage of the child’s life “Duration.” The interviewee’s expectations of the child’s condition, the child’s appearance compared to other children, and the ability of the child to take part in physical activities,

“Active-All,” if the child “almost always” takes part and “Active-Some” if the child “sometimes/somewhat” takes part. The reference category that the child ‘never” takes part also was included in the analysis. Finally, the number of emergency room visits during the previous year was con- sidered.

The stepwise procedure selected three independent variables for entry into the model (Table 3). The value of R2, 17.06%, was low despite the high statistical significance of the model (p < .oooS). This result indicates a fairly low predictive power as it is difficult to predict with great accuracy the number of school days missed using these variables alone. Yet, a highly significant statistical association was detected.

On the average, the predicted number of school days missed will be less for children who at least sometimes partici- pate in physical activities. The predicted number of days absent decreases as the education level of the primary care- taker increases.

A comparison of the parents’ and the schools’ reporting of school absences is in Table 4. Overall, the percentage of total responses in agreement (agreement divided by the sum of agreements plus disagreements) is quite low, 27.6%; 54.5% of the parents underreported school absences and, as can be seen, parental reporting is significantly biased in terms of under- reporting.

Table 1 Characteristics of the Sample

Age 6 years or less 7 to 11 years 12 to 16 years Sex Female Male Race Black White Other Retained a Grade Yes No Educatlon of Primary Caretaker less than 8 years 8 lo 12 years 13 to 17 years Duration of illness 0.29% of Lifespan 30.49% of Lifespan 50.79% of Lifespan 80-100% of Lifespan Prlmary Olagnosis Infectious (1-139) Neoplasms (140239) Endocrine, Nutritional and Metabolic

Diseases of Blood and Blood-Forming

Mental Disorders (290-319) Diseases of the Nervous System and Sense

Diseases of the Circulatory System (390-459) Diseases of the Respiratory System (460-519) Diseases of the Digestive System (520-579) Diseases of the Genitourinary Systsm (580-629) Diseases of the Skin and Subcutaneous

Tissue (680-709) Diseases of the Musculoskeletal System and

Connective Tissue (710-739) Congenital Anomalies (740-759) Certain Conditions Originating in the Perinatal

Period (760-779) Symptoms, Signs and Ill-Defined Conditions (780-799) Injury and Poisoning (800-999) None

Diseases and immunity Disorders (240-279)

Organs (280-289)

Organs (320-389)

N 45

179 112

151 185

219 115

2

164 162

30 266

37

69 32 67

155

6 8

10

11 13

86 10 28 10 12

1

24 39

1 62 13

2

Percent 13.4 53.3 33.3

44.9 55.1

65.2 34.2

0.6

50.3 49.7

9.0 79.9 11.1

21.4 9.9

20.7 48.0

1.8 2.4

3.0

3.3 3.9

25.6 3.0 8.3 3.0 3.6

0.3

7.1 11.6

0.3 18.5 3.9 0.6

266 Journal of School Health September 1985, Vol. 55, No. 7

Table 2 Mean School Absences by Diagnostic Category

Infectious Neoplasms Endocrine, Nutritional and Metabolic

Diseases and Immunity Disorders Diseases of Blood and Blood-Forming Organs Mental Disorders Diseases of the Nervous System and

Sense Organs Diseases 01 the Circulatory System Diseases 01 the Respiratory System Diseases of the Digestive System Diseases of the Genitourinary System Diseases of the Skin and Subcutaneous Tissue Diseases of the Musculoskeletal System and

Connective Tissue Congenital Anomalies Certain Conditions Originating in the

Perinatal Period Symptoms, Signs and 111 Defined Conditions Injury and Poisoning None

MEan N (Days) SD 6 9.2 5.6 8 16.9 26.6

10 16.1 9.2 1 1 14.2 16.7 13 8.6 10.4

86 16.7 20.2 10 17.8 13.4 28 17.4 11.7 10 7.8 8.0 12 13.5 11.6

- 1 11.0

24 16.1 13.2 39 20.1 27.1

- 1 2.0 62 18.7 25.9 13 23.9 35.1 2 26.0 11.3

Table 3 Summary of Stepwise Regression Analysis

of Total School Days Missed .. . . variable

Education Activeall Activesome

B -.87”

-19.03’ -14.89*’

Constant: 47.48 R’: ,1706 (unadjusted)

The model had an overall significance level of ,0004. The significance levels of the individual estimates were: * = p c .05, ” * = p c .Ol. The individual significance levels should be interpreted with caution, because the tests reflect the Significance of each coefficient taking into account all the other variables in the model.

Table 4 Comparison of Parents’ and Schools’

Reporting of School Absences School

Parent 0 1-3 4-7 8-12 13-20- 21-30 31-40 41-60 * 60 Total 0 1 -3 4-7 8-12 13-20 21-30 31-40 41-60 =. 60 Total

9 1 0 1 0 7 8 2 2 0 1 5 5 2 1 6 1 9 7 9 0 0 2 0 5 6 2 10 20 22 21 3 2 0 0 81 0 5 9 1 4 1 5 9 2 0 0 5 6 1 3 4 7 1 2 1 3 4 4 0 4 6 0 0 1 2 5 9 2 1 5 26 0 0 0 0 1 2 4 2 1 1 0 0 0 0 1 1 1 0 6 0 9 0 0 0 0 0 0 1 2 3 7 14 44 63 60 72 39 17 17 10 336

DISCUSSION According to data from the 1981 National Health Inter-

view Survey, children in the US ages six-16 were absent an

average of 4.9 days from school each year.6 Parcel’ found that children with asthma were absent an average of 8.4% of total days. In this sample, the mean number of days absent was 16.9%; the mean percent of days absent was 9.4%. The mean number of days absent was higher than the US average, while the mean percent was only slightly higher than that found in Parcel’s group of asthmatic children.

The degree of underreporting of school absences by parents in this study emphasizes the importance of obtaining attendance data directly from the schools.

Excessive absence from school is a hindrance to progress in basic educational and social skills. Several children in this study missed more than 50% of the school year. Such excessive absenteeism would contribute further to the child’s problems in coping with a chronic illness by interfering with learning skills necessary to achieve a productive life.

Factors that were significant in predicting days missed from school were parents’ education level and the child’s ability to participate in physical activities. The inability to participate in physical activities most likely indicates a multitude of physical problems that interfere with the child’s ability to attend school. The inverse relationship between absenteeism and caretaker’s education indicates an ability to overcome some problems associated with chronic illness that can lead to better attendance. A corollary is the possible role of the school to assist families in meeting the needs of the chronically ill to improve school attendance.

The finding that diagnosis was not predictive of school absences concurs with Pless and Pinkerton’s’ belief that the chronicity of an illness and its impact on the child and family is more significant than the actual diagnosis of the illness. Hence, there seems to be more variability within the diagnostic categories than between them. The findings of this study sup- port previous studies. The findings indicate a need for collaboration among families, health care professionals, and school personnel to appreciate the additional social and educational burdens of chronic illness and to work to reduce the high level of school absences among chronically ill chil- dren.

References

1. Pless IB, Pinkerton P: Chronic Childhood Disorders - Pro- moting Patterns for Adjustment. London, Henry Kimpton Publishers, 1975.

2. Freudenberg N, Clark N, Feldman C. et al: The Impact of Bronchial Asthma on School Attendance and Performance. Paper read before the 1979 annual meeting of the American Public Health Association, New York.

3. Parcel GS, Silman S, Nader P, et al: A comparison of absentee rates of elementary school children with asthma and nonasthmatic schoolmates. Pediatrics 1979;64:878-881.

4. Klein SD: Chronic Kidney Disease: Impact on the Child and Family and Strategies for Coping, doctoral thesis. University of Minnesota, Minneapolis, 1975.

5. Sultz HA, et al: Long Term Childhood fllness. Pittsburgh, University of Pittsburgh Press, 1972.

6. National Center for Health Statistics: Current Estimates from the Health Interview Survey, 1981, series number 141. Hyattsville, Md, 1983.

1 Input ’859, Humber College, in cooperation with the Canadian Addiction Foundation, will present

Input ’85, the sixth biennial Canadian Conference on Employee Assistance Programs in the Workplace, Oct. 27-30 at the Chateau Laurier Hotel in Ottawa. For further information, con- tact: Input ’85 Headquarters, Humber College, 205 Humber College Blvd., Rexdale, Ontario, Canada M9W 5L7.

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