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Assessing longitudinal data linkage results in the COMPASS study

Assessing longitudinal data linkage results in the COMPASS study (PDF)

COMPASS technical report series, volume 3, issue 4, August 2015

Table of contents

Acknowledgements
Introduction
Methods
Results
Discussion
References
Appendix

Acknowledgements

Authors

Wei Qian, MSc. (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON)
Katelyn Battista, MMath (Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON Canada.)
Chad Bredin, BA (Propel Centre for Population Health Impact, University of Waterloo, Waterloo, ON)
K. Stephen Brown, PhD (Propel Centre for Population Health Impact, University of Waterloo, Waterloo, ON)
Scott T. Leatherdale, PhD (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON)

Report funded by

The COMPASS study was supported by a bridge grant from the Canadian Institutes of Health Research (CIHR) Institute of Nutrition, Metabolism and Diabetes (INMD) through the “Obesity – Interventions to Prevent or Treat” priority funding awards (OOP-110788; grant awarded to S. Leatherdale) and an operating grant from the Canadian Institutes of Health Research (CIHR) Institute of Population and Public Health (IPPH) (MOP-114875; grant awarded to S. Leatherdale).

Suggested citation

Qian W, Battista K, Bredin C, Brown KS, Leatherdale ST. Assessing longitudinal data linkage results in the COMPASS study: Technical Report Series. 2015; 3(4). Waterloo, Ontario: University of Waterloo. Available at: https://uwaterloo.ca/compass-system/publications#technical

Contact

COMPASS research team University of Waterloo 200 University Ave West, Waterloo, ON Canada N2L 3G1  uwaterloo.ca/compass-system.

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Introduction

COMPASS is a longitudinal study (started in 2012-13) designed to follow a cohort of grade 9 to 12 students attending a convenience sample of Ontario secondary schools for four years to understand how changes in school environment characteristics (policies, programs, built environment) are associated with changes in youth health behaviours [1]. COMPASS originated to provide school stakeholders with the evidence to guide and evaluate school-based interventions related to obesity, healthy eating, tobacco use, alcohol and marijuana use, physical activity, sedentary behaviour, school connectedness, bullying, and academic achievement. COMPASS has been designed to facilitate multiple large-scale school-based data collections and uses in-class whole-school sampling data collection methods consistent with previous research [2-5]. COMPASS also facilitates knowledge transfer and exchange by annually providing each participating school with a school-specific feedback report that highlights the school-specific prevalence for each outcome, comparisons to provincial and national norms or guidelines, and provides evidence-based suggestions for school-based interventions (programs and/or policies) designed to address the outcomes covered in the feedback report (refer to: https://uwaterloo.ca/compass-system/).

One challenge associated with the COMPASS data is to link student-level data over years since these data are self-reported by students anonymously. COMPASS includes a series of questions in the student questionnaire (Cq) that are designed for linkage purposes only (see Appendix A), and then uses the answers to these questions to create a unique code for each student in a school. This method was designed to be simple-to-complete and able to ensure students’ anonymity while still allowing us to link each student’s unique identifier data over multiple years [6-7]. The generated code allows us to link student-level data within each school using an algorithm developed by the COMPASS team, led by Brown. This linkage method was tested as part of the COMPASS validation study. The method was found to be robust and to produce sufficiently high linkage rates [6]. The linkage process has been completed for Year 1 (Y1) and Year 2 (Y2) data.

We have created a longitudinal sample of 11,049 students with the responses from Y1 (2012/2013) and Y2 (2013/2014). Provided with this longitudinal sample, users may ask how this sample represents the study population:

COMPASS contains both longitudinal and cross-sectional components; it is important to distinguish between them. The target population of our longitudinal sample covers students who are expected to attend Ontario high schools in both Y1 and Y2. This definition excludes most grade 12 students in Y1 who would graduate from high school in Y2.

It is also important to understand the potential sources of bias during the creation of the final sample, including bias due to convenience sampling, bias due to non-response (including absence, refusal, and drop-out), and bias due to linkage. The quasi-experimental design of COMPASS assumes the convenience sample does not introduce sampling bias. The potential bias due to linkage is the main interest of this document, and the response bias will be explored in another report. The linkage bias in terms of the dynamic trend is difficult to assess; instead, we evaluate the linkage bias in terms of a snapshot at Y1.

This technical reports provides a detailed description of the linkage of Y1 and Y2 longitudinal student data, with the aim of helping data users understand the benefits and limitations of using these linked data.

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Methods

Obtaining the Linked Sample

In Y1, 30,147 grade 9 to 12 students were enrolled in the 43 participating schools and 24,173 of them (80.2%) completed the Cq. In Y2, 29,945 grade 9 to 12 students were enrolled and 23,424 of them (78.2%) completed the Cq. Missing respondents resulted primarily from scheduled spares or absenteeism at the time of the Cq, and partially from student or parent refusal (see Table 2).

The longitudinal sample is created by linking Y1 and Y2 student responses to a six digit alpha-numeric code generated for each completed questionnaire using the responses to five specifically-designed questions along with the response to the question regarding the student’s sex. Bredin and Leatherdale [6] provide more information on the creation of the identification questions. Within each school, Y1 and Y2 codes are compared by record. If the code for record A in Y1 matches the code for record B in Y2 on at least 5 out of 6 digits, A and B are considered to be a match. Note that students who answered “No” to the question “Did you attend this school last year?” in the Y2 Cq are excluded from the linkage process.

Additional restrictions are then imposed to reduce false-linkage error. Using information from other questions in each record, the match is dissolved if:

  • the difference in grade between Y1 and Y2 is less than zero or greater than one
  • the difference in age is greater than two

Assessing the Quality of Linkage

The linkage process is subject to two types of errors: missing linkage error (matched pair is not identified) and false-linkage error (unmatched pair is identified as pair); see the highlighted cells (B&C) as shown in Table 1.

Table 1: Possibilities for matches and false matches

Actual

Outcome

Matched

Unmatched

Matched

True match (A)

False non-match (B)

(Missing linkage error)

Unmatched

False match (C)

(False-linkage error)

True non-match (D)

The false-linkage error rate is defined as

False Linkage Rate =   C / (A + C)  x 100%

This false linkage error is difficult to evaluate since we cannot know the number of false matches (C) without a validation study; however, before the COMPASS survey, a validation study was conducted and data were collected from a convenience sample of 204 students [6] in which 132 matches were found and none of them were false matches. Thus, we may assume the false-linkage error is negligible, and later we will further verify this by looking at the consistency between Y1 and Y2 regarding student characteristics.

The matching rate is often used to measure the missing linkage error.  The matching rate is defined as

Matching Rate =        A / (A + B)  x 100%

where A = 11,049 and B remains unknown. The denominator is the number of students who participated in both Y1 and Y2 Cq’s and is unknown. We roughly estimate it from the number of Y1 students or Y2 students by subtracting the number of students who did not participate in both Cq’s and thus were not expected to be linked.

Year 1 students not expected to be linked include:

  1. students not participating in Y1 Cq (5,672 students due to spares and absenteeism, 302 due to student or parent refusal)
  2. students absent on the Y2 Cq date (the Y2 data show around 21.8% students were absent on the Cq date)
  3. Grade 12 students graduating from the high school (5,669 grade 12 students in Y1, and 283 linked to Y2 grade 12)
  4. students transferring out to other schools (this number is unknown)
  5. students dropping out of school in Y2 (Ontario 2012 high school student drop-out rate was 6.6% [8])

Year 2 students not expected to be linked include:

  1. students not participating in the Y2 Cq (6,192 due to spares and absenteeism, 329 due to student or parental refusal)
  2. students absent on the Y1 Cq date (the Y1 data show around 18.8% students were absent on the Cq date)
  1. Grade 9 students newly admitted into high school (6,342 grade 9 students in Y2, and 12 remaining in grade 9 in Y2)
  2. students transferring in from other schools (this number is unknown)

Table 2 shows the breakdown of the number of students expected to be linked via the linking process. As shown, we have a raw matching rate of 80.5% for Y1 and 79.6% for Y2. As we mentioned before, this is a rough estimate, but it shows the linkage strategy worked well.

Table 2: Number of students linked and unlinked over two years
 

Year 1

Year 2

Total Students Enrolled

30,147

29,945

Less: Missing Due to Spares and Absenteeism

-  5,672

-  6,192

Less: Missing Due to Student or Parent Refusal

-  302

-  329

Students Completing Survey

=24,173

=23,424

Less: Students in Grade 12

-  5,669

N/A

Plus: Students remaining in Grade 12

+  283

N/A

Less: Students transferring out to other schools

N/A [1]

N/A

Percentage of students present on Y2 survey date

X 78.2%[2]

N/A

Percentage of not dropping out of schools in Y2

X 93.4%

N/A

Less: Students in Grade 9

N/A

-  6,342

Plus: Grade 9 students remaining in Grade 9 in Y2

N/A

+ 12

Less: Students transferred in from other schools

N/A

N/A

Percentage of students present on Y1 survey date

N/A

X 81.2%[3]

Total Students Expected to be Linked

13,722

13,880

Total Students Linked

11,049

11,049

Linkage Rate

80.5%

79.6%

 

To validate the accuracy of the linkage process in terms of false-linkage error, we examined characteristics of the matched students from Y1 to Y2. Tables 3 and 4 show the sex and grade distribution of matched students in Y1 and Y2. The majority of linked students provided consistent sex and grade information across both years. Fewer than 0.15% of matches have contradictory sex information, and no matches have contradictory grade information (a difference greater than 1 year). Only 3.0% of students reported staying in the same grade as the previous year, with the vast majority being grade 12 students. The consistency in the information for matched students suggests a very low false-linkage rate.

Table 3: Validating linkage accuracy using sex variables

Sex

Year 2

Year 1

Female

Male

Missing

Total

Female

5782

5

31

5818

Male

10

5157

32

5199

Missing

16

16

0

32

Total

5808

5178

63

11049

Table 4: Validating linkage accuracy using grade variables

Grade

Year 2

Year 1

9

10

11

12

Missing

Total

9

12

3979

0

0

6

3997

10

0

12

3727

0

8

3747

11

0

0

24

2982

5

3011

12

0

0

0

283

6

289

Missing

0

4

0

1

0

5

Total

12

3995

3751

3266

25

11049

As further validation of the sample, Table 5 shows the distribution of body mass index (BMI) for matched students in Y1 and Y2. Of the 11,049 matches, 7,722 had complete BMI information. Of the 7,722 matched students, 6,471 (83.8%) reported to be in the same BMI category across both years. Only 103 students (1.3%) reported moving by more than one weight category. Note that the BMI calculation requires complete height and weight information: Students not knowing or not reporting either one of these accounts for the lower-than-usual response rate.

Table 5: Assessing validity of linkage accuracy using BMI variable

BMI

Year 2

Year 1

Underweight

Heathy Weight

Overweight

Obese

Total

Underweight

38

94

2

0

134

Heathy Weight

80

5266

388

47

5781

Overweight

4

358

796

126

1284

Obese

3

47

102

371

523

Total

125

5765

1288

544

7722

Tables 6 and 7 show the binge drinking and marijuana use status for the linked students in Y1 and Y2. Students are classified as either current, non-current, or never-users. A small but significant percentage of students reported contradictory responses; that is, students reporting to be current or non-current users in Y1, but reporting having never used in Y2. This amounts to 488 students (3.4%) for binge drinking and 208 students (1.9%) for marijuana use. While this result is initially surprisingly, it is similar to results often seen in other longitudinal studies where individuals’ responses across time are compared. [9] More information on the classification is provided in the Substance Use section of the results.

Table 6: Binge-drinking status in Y1 and Y2: illustrating contradictory responses for alcohol-use over time

Binge Drinking

Year 2

Year 1

Never

Non-Current

Current

Total

Never

4635

1560

589

6784

Non-Current

374

1250

849

2473

Current

104

387

1244

1735

Total

5113

3197

2682

10992

Table 7: Marijuana-use status in Y1 and Y2: illustrating contradictory responses for marijuana-use over time

Marijuana Use

Year 2

Year 1

Never

Non-Current

Current

Total

Never

6898

946

522

8366

Non-Current

141

698

385

1224

Current

67

249

812

1128

Total

7106

1893

1719

10718

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Results

Using Y1 data and excluding grade 12 students, we compare linked respondents with non-linked respondents to show the potential bias for a group of selected variables. We break down the comparison by sex and grade; as a result, students with missing grade or sex information are also excluded. A total of 18,280 grade 9 to 11 students with complete grade and sex information are compared, 10,730 (58.7%) students are linked and 7,550 (42.3%) are not linked.  Table 8 shows the distribution of the 18,280 students.

Table 8: Linkage rates by grade and sex

Sex

 

Grade (Y1)

9

10

11

Total

Total

Eligible Students

6270

6144

5866

18280

Percentage Linked

63.6%

60.8%

51.2%

58.7%

Female

Eligible Students

3133

3099

2893

9125

Percentage Linked

68.0%

64.6%

54.7%

62.6%

Male

Eligible Students

3137

3045

2973

9155

Percentage Linked

59.2%

57.0%

47.8%

54.8%

Difference between genders

8.8%*

7.6%*

6.9%*

7.8%*

*: p-value <0.0001

The variables we selected to test for potential bias are grouped into five categories that represent the primary COMPASS study outcomes: obesity, physical activity, sedentary behaviour, substance use, and bullying and academics. For each variable analyzed, we compare the distributions of categorical variables using a Chi-square test or the means of continuous variables using a t-test separately for each sex and grade group. Students not reporting the information are excluded.  A p-value of less than 0.05 is considered statistically significant in assessing whether differences exist between the linked samples and non-linked samples.

As a result of the analyses, obesity-related measures do not show significant differences between linked and non-linked samples. Significant differences are, however, consistently seen on measures of sedentary behaviour, substance use, and bullying and academics, and to a lesser degree on measures of physical activity.
 

Obesity

Obesity-related measures include a student’s body mass index (BMI), as well as measures of whether students are receiving the Canada Food Guide recommended number of servings for each food group. The results showed no significant differences between the linked and non-linked samples on any of the obesity-related measures, with the exception of a significant difference in ‘meats and alternatives’ consumption for grade 10 males only.

Body Mass Index (BMI)

BMI is a measure of healthy body weight, calculated from a student’s self-reported height and weight. Based on BMI scores, students are classified into four groups: Underweight, Healthy Weight, Overweight, and Obese, according to the BMI classification system defined by the World Health Organization. [10]

Of the 18,280 eligible students who completed the questionnaire in Y1, 14,207 had complete BMI information. Of these students, 8,394 were linked and 5,633 were not linked. Table 9 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. No significant differences were observed between the linked and non-linked samples.

Table 9: Statistical evaluation of linkage rates for students responding to BMI questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF* = 3

p-value

Female

9

2188

1515

673

2.6

0.466

10

2398

1581

817

4.8

0.186

11

2310

1290

1020

1.6

0.654

Male

9

2313

1383

930

0.8

0.851

10

2401

1430

971

6.0

0.111

11

2417

1195

1222

2.5

0.472

*DF: degree of freedom

Table 10 shows the percentage of linked and non-linked students in each BMI class by grade and sex. Consistent with the results of the Chi-square tests, the distribution of BMI classification is very similar between linked and non-linked students in each sex and grade group.

Table 10: Comparing distribution of linked and non-linked data for BMI categories, by grade and sex

Female

Male

Grade

BMI

Linked

Non-Linked

Grade

BMI

Linked

Non-Linked

9

Underweight

2

2.5

9

Underweight

2.2

1.9

Heathy Weight

80

77.3

Heathy Weight

66.1

65.1

Overweight

14.3

16.5

Overweight

20.8

21.2

Obese

3.7

3.7

Obese

10.9

11.8

10

Underweight

1.6

2.2

10

Underweight

1.2

2.1

Heathy Weight

81.2

78.8

Heathy Weight

67.6

64.8

Overweight

13.5

13.7

Overweight

19.8

22.6

Obese

3.7

5.3

Obese

11.5

10.6

11

Underweight

1.6

2.3

11

Underweight

2.3

1.6

Heathy Weight

78.9

77.9

Heathy Weight

67.3

69.1

Overweight

14.4

14.5

Overweight

20.6

19.6

Obese

5.1

5.3

Obese

9.8

9.7

Fruit and Vegetable Consumption

Fruit and vegetable consumption is assessed according whether students received the Canada Food Guide recommended servings of fruits and vegetables in the previous day. Students are categorized according to whether they consumed at least the recommended number of servings, which is 7 servings for females and 8 servings for males. [11]

Of the eligible students, 17,795 completed the fruit and vegetable consumption question, including 10,536 linked students and 7,259 non-linked students. Table 11 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. No significant differences were observed between the linked and non-linked samples.

Table 11: Statistical evaluation of linkage rates for students responding to fruit and vegetable consumption questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square
(DF = 1)

p-value

Female

9

3073

2096

977

0.5

0.499

10

3053

1979

1074

2.1

0.145

11

2831

1560

1271

0.7

0.411

Male

9

3029

1818

1211

3.5

0.060

10

2941

1698

1243

0.0

0.930

11

2868

1385

1483

0.8

0.379

Table 12 shows the percentage of linked and non-linked students in each category for fruit and vegetable consumption by grade and sex. Consistent with the results of the Chi-square tests, the results are similar between linked and non-linked students. The majority of students do not meet the recommended serving levels.

Table 12: Comparing distribution of linked and non-linked data for fruit and vegetable consumption variables, by grade and guideline categorization

Female

Male

Grade

Recommended Servings

Linked

Non-Linked

Grade

Recommended Servings

Linked

Non-Linked

9

Does Not Meet

94.4

93.8

9

Does Not Meet

96.1

94.7

Meets/Exceeds

5.6

6.2

Meets/Exceeds

3.9

5.3

10

Does Not Meet

94.3

93.0

10

Does Not Meet

95.6

95.6

Meets/Exceeds

5.7

7.0

Meets/Exceeds

4.4

4.4

11

Does Not Meet

94.8

94.1

11

Does Not Meet

95.5

94.7

Meets/Exceeds

5.2

5.9

Meets/Exceeds

4.5

5.3

Grain Product Consumption

Grain product consumption is assessed according whether students received the Canada Food Guide recommended servings of grain products (breads, cereals, rice, and pasta) in the previous day. Students are categorized according to whether they consumed at least the recommended number of servings, which is 6 servings for females and 7 servings for males. [11]

Of the eligible students, 17,794 completed the grain product consumption question, including 10,534 linked students and 7,260 non-linked students. Table 13 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. No significant differences were observed between the linked and non-linked samples.

Table 13: Statistical evaluation of linkage rates for students responding to grain consumption questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3072

2095

977

0.5

0.458

10

3053

1979

1074

0.5

0.487

11

2831

1561

1270

1.2

0.266

Male

9

3030

1818

1212

2.7

0.099

10

2937

1696

1241

2.0

0.157

11

2871

1385

1486

1.6

0.202

Table 14 shows the percentage of linked and non-linked students in each grain consumption category by grade and sex. Consistent with the results of the Chi-square tests, the results are similar between linked and non-linked students. The majority of students do not meet the recommended serving levels.

Table 14: Comparing distribution of linked and non-linked data for grain consumption variables, by grade and guideline categorization

Female

Male

Grade

Recommended Servings

Linked

Non-Linked

Grade

Recommended Servings

Linked

Non-Linked

9

Does Not Meet

93.7

93.0

9

Does Not Meet

91.9

90.2

Meets/Exceeds

6.3

7.0

Meets/Exceeds

8.1

9.8

10

Does Not Meet

93.9

93.3

10

Does Not Meet

91.2

89.6

Meets/Exceeds

6.1

6.7

Meets/Exceeds

8.8

10.4

11

Does Not Meet

93.7

92.6

11

Does Not Meet

91.0

89.6

Meets/Exceeds

6.3

7.4

Meets/Exceeds

9.0

10.4

Meats and Alternatives Consumption

Meat and meat alternative consumption is assessed according whether students received the Canada Food Guide recommended servings of meats and alternatives in the previous day. One serving of meat and alternatives includes cooked fish, chicken, beef, pork, or game meat, eggs, nuts or seeds, peanut butter or nut butters, legumes (beans), and tofu. Students are categorized according to whether they consumed at least the recommended number of servings, which is 2 servings for females and 3 servings for males. [11]

Of the eligible students, 17,786 completed the meats and alternatives consumption question, including 10,525 linked students and 7,261 non-linked students. Table 15 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. No significant differences were observed between the linked and non-linked samples, except for the male grade 10 group.

Table 15: Statistical evaluation of linkage rates for students responding to meats and alternatives consumption questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3073

2093

980

0.9

0.330

10

3047

1977

1070

3.7

0.053

11

2834

1563

1271

1.5

0.218

Male

9

3027

1812

1215

1.1

0.293

10

2938

1697

1241

5.3

0.021

11

2867

1383

1484

0.0

0.837

Table 16 shows the percentage of linked and non-linked students in each meats and alternatives category by grade and sex. Consistent with the results of the Chi-square tests, the results are similar between linked and non-linked students. A higher percentage of females than males meet the recommended number of servings.
 

Table 16: Comparing distribution of linked and non-linked data for meats and alternatives consumption variables, by grade and guideline categorization

Female

Male

Grade

Recommended Servings

Linked

Non-Linked

Grade

Recommended Servings

Linked

Non-Linked

9

Does Not Meet

38.4

40.2

9

Does Not Meet

53.5

55.5

Meets/Exceeds

61.6

59.8

Meets/Exceeds

46.5

44.5

10

Does Not Meet

36.2

39.7

10

Does Not Meet

50.3

54.6

Meets/Exceeds

63.8

60.3

Meets/Exceeds

49.7

45.4

11

Does Not Meet

35.1

37.3

11

Does Not Meet

48.2

47.8

Meets/Exceeds

64.9

62.7

Meets/Exceeds

51.8

52.2

Milk and Alternatives Consumption

Milk and milk alternatives consumption is assessed according whether students received the Canada Food Guide recommended servings of milks and alternatives in the previous day. Students are categorized according to whether they consumed at least the recommended number of servings, which is 3 servings for both females and males. [11]

Of the eligible students, 17,794 completed the milk and alternatives consumption question, including 10,535 linked students and 7,259 non-linked students. Table 17 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. No significant differences were observed between the linked and non-linked samples.

Table 17: Statistical evaluation of linkage rates for students responding to milk and alternatives consumption questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3075

2096

979

0.0

0.891

10

3051

1979

1072

0.3

0.574

11

2832

1562

1270

1.7

0.193

Male

9

3026

1816

1210

1.9

0.174

10

2939

1696

1243

0.5

0.494

11

2871

1386

1485

0.7

0.413

Table 18 shows the percentage of linked and non-linked students in each milk and alternatives category by grade and sex. Consistent with the results of the Chi-square tests, the results are similar between linked and non-linked students. A higher percentage of males than females meet the recommended number of servings.

Table 18: Comparing distribution of linked and non-linked data for milk and alternatives consumption variables, by grade and guideline categorization

Female

Male

Grade

Recommended Servings

Linked

Non-Linked

Grade

Recommended Servings

Linked

Non-Linked

9

Does Not Meet

57.9

57.6

9

Does Not Meet

38.9

41.4

Meets/Exceeds

42.1

42.4

Meets/Exceeds

61.1

58.6

10

Does Not Meet

62.0

60.9

10

Does Not Meet

42.1

43.4

Meets/Exceeds

38.0

39.1

Meets/Exceeds

57.9

56.6

11

Does Not Meet

61.7

64.1

11

Does Not Meet

42.9

44.4

Meets/Exceeds

38.3

35.9

Meets/Exceeds

57.1

55.

Physical Activity

The measure of students’ physical activity levels showed varying results for significant differences between the linked and non-linked samples. Significant differences were only found for grade 9 females and grade 10 males, but linked samples consistently showed lower percentages of students meeting physical activity guidelines. Due to the consistency of these differences (regardless of significance), we decided the same test should be conducted simply by sex and simply by grade to ascertain if the differences were significant in a larger group break-down. Except for grade 11 students in these larger groups, the differences in the rate of meeting PA guidelines between linked sample and non-linked sample are significant.

Students are dichotomized according to whether or not they meet the Canadian Society for Exercise Physiology guidelines of at least 60 minutes of combined moderate and vigorous physical activity per day. [12] This is based on a student’s reported number of minutes of spent doing vigorous and/or moderate physical activity in the last seven days.

Of the eligible students, 17,783 completed the physical activity questions, including 10,486 linked students and 7,297 non-linked students. The following table shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. Interestingly, the linked samples consistently showed lower percentages of students meeting physical activity guidelines, though this difference was only significant for grade 9 females and grade 10 males.

Table 19: Statistical evaluation of linkage rates for students responding to physical activity questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3051

2079

972

6.4

0.012

10

3036

1969

1067

2.4

0.119

11

2825

1551

1274

3.6

0.058

Male

9

3033

1808

1225

0.3

0.602

10

2943

1693

1250

15.7

0.000

11

2895

1386

1509

0.1

0.745

Table 20 shows the percentage of linked and non-linked students meeting physical activity guidelines.

Table 20: Comparing distribution of linked and non-linked data for physical activity variables, by grade and guideline categorization

Female

Male

Grade

PA Guideline

Linked

Non-Linked

Grade

PA Guideline

Linked

Non-Linked

9

Yes

43.5

48.7

9

Yes

58.9

59.8

No

56.5

51.3

No

41.1

40.2

10

Yes

39.8

42.9

10

Yes

54

61.4

No

60.2

57.1

No

46

38.6

11

Yes

36.9

40.3

11

Yes

55.4

54.8

No

63.1

59.7

No

44.6

45.2


Sedentary Behaviour

The measure of students’ sedentary behaviour levels showed significant differences between the linked and non-linked samples by gender. Significant differences were found for females, with linked students reporting fewer minutes of daily sedentary behaviour, while no significant differences were found for males.

Sedentary behaviour is measured as the total number of minutes per day spent on: watching TV shows or movies, playing computer or video games, talking on the phone, surfing the internet, texting, messaging and emailing. To avoid over-reporting behaviours that are often conducted simultaneously, time spent texting or messaging is excluded from the final results. In addition, responses with total time exceeding 24 hours less time spent sleeping, and responses reporting the maximum time for each behaviour (9 hours and 45 minutes), are treated as erroneous and excluded from the analysis.

Of the eligible students, 17,584 completed the sedentary behaviour questions, including 10,427 linked students 7,157 non-linked students. Table 21 shows the total responses by sex and grade, as well as the p-values from the Satterthwaite t test. The linked samples showed significantly fewer minutes of daily sedentary activity for females in all grades, and no significant difference for males.

Table 21 Statistical evaluation of linkage rates for students responding to sedentary behaviour questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

t Statistic

p-value

Female

9

3034

2075

959

3.61

.0003

10

2992

1963

1029

3.11

.0019

11

2815

1549

1266

2.53

.0116

Male

9

2998

1793

1205

0.38

.7060

10

2903

1673

1230

-0.14

.8865

11

2842

1374

1468

-0.80

.4214

Table 22 shows the average number of minutes of sedentary behaviour per student per day, in each grade and sex category. Females have fewer daily minutes of sedentary behaviour than males, on average.

Table 22: Comparing mean minutes of sedentary behaviour, by grade and sex

Female

Male

Grade

Linked

Non-Linked

Grade

Linked

Non-Linked

9

318

348

9

351

354

10

323

348

10

366

365

11

312

331

11

363

356


Substance Use

Measures related to substance use include students’ smoking status, binge drinking status, and marijuana use. Significant differences were observed between the linked and non-linked samples across all substance use measures for all grades and genders.

Tobacco Use

Students’ smoking status is derived using two survey questions:

  1. Have you ever smoked 100 or more whole cigarettes in your like? (Yes/No)
  2. On how many of the last 30 days did you smoke one or more cigarettes? (0, 1, 2-3, 4-5, …)

Students who answer yes to the first question and 1 or greater to the second question are classified as Current Smokers. Students who answer yes to the first question and 0 to the second question are classified as Non-Current Smokers. Students who answered no to the first question are classified as Never Smokers.

All of the 18,280 eligible students completed the smoking questions, including 10,730 linked students and 7,550 non-linked students. Table 23 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with more linked students being Never Smokers and more non-linked students being Current Smokers.

Table 23: Statistical evaluation of linkage rates for students responding to tobacco-use questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 2

p-value

Female

9

3133

2130

1003

55.3

<0.001

10

3099

2003

1096

33.3

<0.001

11

2893

1581

1312

39.8

<0.001

Male

9

3137

1858

1279

58.0

<0.001

10

3045

1736

1309

55.6

<0.001

11

2973

1422

1551

48.4

<0.001

Table 24 shows the percentage of linked and non-linked students in each category by grade and sex. Across all grades and sexes, a higher percentage of non-linked students were considered Current Smokers and a higher percentage of linked students were considered Never Smokers.

Table 24: Comparing distribution of linked and non-linked data for tobacco-use variables, by grade and smoking status

Female

Male

Grade

Smoker Status

Linked

Non-Linked

Grade

Smoker Status

Linked

Non-Linked

9

Never

98.9

95.0

9

Never

98.7

93.8

Non-Current

0.2

0.2

Non-Current

0.1

0.8

Current

0.9

4.8

Current

1.2

5.6

10

Never

97.5

93.4

10

Never

96.8

90.7

Non-Current

0.3

1.0

Non-Current

0.6

0.8

Current

2.2

5.6

Current

2.4

8.5

11

Never

96.3

80.7

11

Never

92.6

84.5

Non-Current

0.8

1.4

Non-Current

0.6

1.7

Current

2.9

7.9

Current

6.8

13.8

Binge Drinking

Students’ binge drinking is classified according to their answers to the survey question “In the last 12 months, how often did you have 5 drinks of alcohol or more on any one occasion?” Students who answer “I have never done this” are classified as Never Binger Drinkers. Students who answer “I did not have 5 or more drinks on one occasion in the last 12 months” or “Less than once a month” are classified as Non-Current Binger Drinkers. Students who answer “Once a Month” or more frequently are classified as Current Binge Drinkers.

Of the eligible students, 18,203 completed the question on binge drinking, including 10,700 linked students and 7,503 non-linked students. Table 25 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with fewer linked students classified as Current Binge Drinkers.

Table 25: Statistical evaluation of linkage rates for students responding to binge-drinking questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 2

p-value

Female

9

3125

2125

1000

48.4

<0.001

10

3085

1994

1091

34.0

<0.001

11

2880

1577

1303

32.1

<0.001

Male

9

3126

1856

1270

25.2

<0.001

10

3032

1732

1300

34.9

<0.001

11

2955

1416

1539

31.5

<0.001

Table 26 shows the percentage of linked and non-linked students in each category by grade and sex. Across all grades and sexes, a higher percentage of non-linked students were considered Current Binge Drinkers and a higher percentage of linked students were considered Never Binge Drinkers. The overall percentage of Current and Non-Current Binge Drinkers increases considerably as grade increases, for both females and males.

Table 26: Comparing distribution of linked and non-linked data for binge-drinking variables, by grade and binge-drinking status

Female

Male

Grade

Binge Drinker Status

Linked

Non-Linked

Grade

Binge Drinker Status

Linked

Non-Linked

9

Never

76.4

66.2

9

Never

77.8

71.1

Non-Current

16.4

19.7

Non-Current

15.4

17.4

Current

7.2

14.1

Current

6.8

11.5

10

Never

59.7

50.0

10

Never

60.0

54.2

Non-Current

24.0

26.2

Non-Current

23.6

20.8

Current

16.2

23.7

Current

16.4

25.1

11

Never

45.1

37.1

11

Never

45.6

38.1

Non-Current

31.8

30.9

Non-Current

26.6

24.5

Current

23.1

32.0

Current

27.9

37.4

Marijuana Use

Students’ marijuana use is classified according to their answers to the survey question “In the last 12 months, how often did you use marijuana or cannabis?” Students who answer “I have never used marijuana” are classified as Never Users. Students who answer “I have used marijuana but not in the last twelve months” or “Less than once a month” are classified as Non-Current Users. Students who answer “Once a Month” or more frequently are classified as Current Users.

Of the eligible students, 17,869 completed the question on marijuana-use, including 10,568 linked students and 7,301 non-linked students. Table 27 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with fewer linked students classified as Current Users.
 

Table 27: Statistical evaluation of linkage rates for students responding to marijuana-use questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 2

p-value

Female

9

3085

2111

974

102.2

<0.001

10

3053

1982

1071

73.1

<0.001

11

2831

1559

1272

68.8

<0.001

Male

9

3048

1818

1230

85.7

<0.001

10

2963

1708

1255

77.2

<0.001

11

2889

1390

1499

88.5

<0.001

Table 28 shows the percentage of linked and non-linked students in each category by grade and sex. Across all grades and sexes, a higher percentage of non-linked students were considered Current Users and a higher percentage of linked students were considered Never Users. The overall percentage of Current and Non-Current Users increases considerably as grade increases, with more females considered Never Users.

Table 28: Comparing distribution of linked and non-linked data for marijuana-use variables, by grade and marijuana-use status

Female

Male

Grade

Marijuana-Use Status

Linked

Non-Linked

Grade

Marijuana-Use Status

Linked

Non-Linked

9

Never

89.2

75.6

9

Never

87.2

75.0

Non-Current

5.6

10.3

Non-Current

6.2

8.5

Current

5.2

14.2

Current

6.6

16.4

10

Never

79.3

66.5

10

Never

75.6

62.0

Non-Current

11.4

14.3

Non-Current

11.1

12.7

Current

9.3

19.2

Current

13.3

25.3

11

Never

69.0

55.8

11

Never

64.0

47.2

Non-Current

18.8

21.1

Non-Current

16.6

20.3

Current

12.3

23.0

Current

19.4

32.4


Bullying and Academics

Bullying and academic-related measures include whether students have been bullied or have bullied others, how often students skip classes, and students’ educational expectations. Significant differences were observed between the linked and non-linked samples across all bullying and academic measures for all grades and genders.

Being Bullied

Students are dichotomized according to whether or not they have been bullied by other students in the last 30 days, based on their answers to the question “In the last 30 days, in what ways were you bullied by other students?” Answers are recorded for all eligible students, with missing values recorded as “I have not been bullied in the last 30 days”. Table 29 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with fewer linked students reporting being bullied.

Table 29: Statistical evaluation of linkage rates for students responding to questions about being bullied, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3133

2130

1003

32.6

<0.001

10

3099

2003

1096

8.0

0.0047

11

2893

1581

1312

24.9

<0.001

Male

9

3137

1858

1279

5.5

0.0193

10

3045

1736

1309

14.5

<0.001

11

2973

1422

1551

8.6

0.0033

Table 30 shows the percentage of linked and non-linked students in each category by grade and sex. Across all grades, more females report being bullied than males.

Table 30: Comparing distribution of linked and non-linked data for being-bullied variables, by grade and bullied status

Female

Male

Grade

Bullied

Linked

Non-Linked

Grade

Bullied

Linked

Non-Linked

9

No

75.3

65.5

9

No

80.8

77.4

Yes

24.7

34.5

Yes

19.2

22.6

10

No

76.0

71.4

10

No

82.4

76.9

Yes

24.0

28.6

Yes

17.6

23.1

11

No

79.1

71.0

11

No

82.2

77.9

Yes

20.9

29.0

Yes

17.8

22.1


Bullying Others

Students are dichotomized according to whether or not they have bullied other students in the last 30 days, based on their answers to the question “In the last 30 days, in what ways did you bully other students?” Answers are recorded for all eligible students, with missing values recorded as “I did not bully other students in the last 30 days”. Table 31 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with fewer linked students reporting bullying others.

Table 31: Statistical evaluation of linkage rates for students responding to questions about bullying others, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 1

p-value

Female

9

3133

2130

1003

22.7

<0.001

10

3099

2003

1096

6.8

0.009

11

2893

1581

1312

9.5

0.002

Male

9

3137

1858

1279

9.7

0.002

10

3045

1736

1309

10.8

0.001

11

2973

1422

1551

9.5

0.002

Table 32 shows the percentage of linked and non-linked students in each category by grade and sex.
 

Table 32: Comparing distribution of linked and non-linked data for bullying-others variables, by grade and bullying status

Female

Male

Grade

Bullied Others

Linked

Non-Linked

Grade

Bullied Others

Linked

Non-Linked

9

No

90.6

84.8

9

No

87.5

83.6

Yes

9.4

15.2

Yes

12.5

16.4

10

No

88.5

85.2

10

No

86.1

81.7

Yes

11.5

14.8

Yes

13.9

18.3

11

No

89.1

85.3

11

No

83.8

79.4

Yes

10.9

14.7

Yes

16.2

20.6

Skipping Class

Students are categorized based on the number of classes they report skipping in the last four weeks. Students who report skipping 0-2 classes are categorized as Rarely/Never, students who report skipping 3-5 classes are categorized as Sometimes, and students who report skipping 6 or more classes are categorized as Often.

Of the eligible students, 17,841 completed the question on skipping class, including 10,539 linked students and 7,302 non-linked students. Table 33 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples, with fewer linked students skipping more than two classes.

Table 33: Statistical evaluation of linkage rates for students responding to truancy questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 2

p-value

Female

9

3081

2104

977

50.1

<0.001

10

3049

1976

1073

43.4

<0.001

11

2837

1557

1280

54.1

<0.001

Male

9

3052

1810

1242

54.9

<0.001

10

2935

1700

1235

53.3

<0.001

11

2887

1392

1495

30.3

<0.001

Table 34 shows the percentage of linked and non-linked students in each category by grade and sex. Overall, higher grade students report skipping more classes. Across all grades, a higher percentage of linked students reported rarely or never skipping class, and a higher percentage of non-linked students reported skipping class sometimes or often.

Table 34: Comparing distribution of linked and non-linked data for truancy variables, by grade and frequency of skipping class

Female

Male

Grade

Skipping Class

Linked

Non-Linked

Grade

Skipping Class

Linked

Non-Linked

9

Rarely/Never

96.3

90.7

9

Rarely/Never

97.6

92.0

Sometimes

2.8

4.9

Sometimes

1.5

3.9

Often

1.0

4.4

Often

0.8

4.1

10

Rarely/Never

95.0

88.9

10

Rarely/Never

94.9

87.9

Sometimes

3.8

7.2

Sometimes

3.6

6.6

Often

1.2

3.9

Often

1.5

5.6

11

Rarely/Never

91.7

83.8

11

Rarely/Never

90.9

84.2

Sometimes

6.6

9.5

Sometimes

5.0

8.6

Often

1.8

6.6

Often

4.1

7.2

Educational Expectations

Educational expectation is defined as the highest level of education students expect they will achieve. Expected education levels are categorized as High School Diploma or Less, College/Trade/Bachelor’s Degree, Master’s Degree or Higher, and Unsure.

Of the eligible students, 17,724 completed the question on educational expectation, including 10,478 linked students and 7,246 non-linked students. Table 35 shows the total responses by sex and grade, as well as the p-values from the Chi-square tests. The results showed significant differences between the linked and non-linked samples.

Table 35: Statistical evaluation of linkage rates for students responding to educational expectations questions, by grade and sex

Sex

Grade

Total

Linked

Non-Linked

Chi-square

DF = 3

p-value

Female

9

3037

2073

964

18.2

<0.001

10

3025

1962

1063

18.3

<0.001

11

2832

1557

1275

33.3

<0.001

Male

9

3035

1808

1227

36.8

<0.001

10

2919

1692

1227

34.9

<0.001

11

2876

1386

1490

11.9

0.008

Table 36 shows the percentage of linked and non-linked students in each category by grade and sex. Generally, linked students have higher educational expectations, and fewer linked students expect to achieve only a high school diploma or less. Students in lower grades more often report they are unsure. Females report more often than males that they expect to achieve a master’s degree or higher.

Table 36: Comparing distribution of linked and non-linked data for educational expectations variables, by grade and expected education achievement

Female

Male

Grade

Education Level

Linked

Non-Linked

Grade

Education Level

Linked

Non-Linked

9

Unsure

30.8

27.3

9

Unsure

24.2

24.4

High School or Less

6.5

9.8

High School or Less

5.4

11.3

College/Bachelor

32.0

35.9

College/Bachelor

46.1

41.6

Master or Higher

30.7

27.1

Master or Higher

24.3

22.7

10

Unsure

17.5

18.7

10

Unsure

14.8

15.8

High School or Less

3.1

6.0

High School or Less

4.3

9.2

College/Bachelor

42.1

42.2

College/Bachelor

52.0

51.1

Master or Higher

37.3

33.0

Master or Higher

29.0

23.9

11

Unsure

12.0

13.3

11

Unsure

12.8

11.5

High School or Less

2.8

5.6

High School or Less

4.7

7.7

College/Bachelor

47.8

52.4

College/Bachelor

58.3

57.4

Master or Higher

37.3

28.6

Master or Higher

24.2

23.4

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Discussion

Despite decades of primary prevention efforts being targeted at improving the health of Canadian youth, those efforts in many domains seemed to be failing as evident by the current risk behavioural profile of Canadian youth [2]. Available evidence suggests that one of the major challenges inhibiting successful population prevention among youth in Canada was that no one was systematically collecting the necessary data to inform and evaluate prevention activities in a comprehensive or ongoing fashion. As such, the value of a longitudinal dataset such as COMPASS cannot be overstated. It is the ability to track a defined cohort of students and the schools they attend over time that allows COMPASS researchers to effectively evaluate the efficacy of natural experiments (programs and polices implemented in schools to improve student health), in ways that cross-sectional surveys cannot. Implementing policies and practises that have not been evaluated for effectiveness can potentially be a waste of time and valuable resources, while providing little or no improvement to student health (and at worst, can actually have a detrimental effect). Knowing what programs and policies work best, the populations for whom they work best, and the environments in which they work best, is paramount to implementing efficient and effective policies and practices that will have lasting impacts on youth health.

If the strength of a longitudinal dataset is the linkage of student data at multiple time points of a study, then the failure to link all student data is its limitation. While a significant portion of student data can be linked from one year to the next (~80% success rate), there is a smaller—but still significant—portion of student data that are not linked each year. As this report has illustrated, it does not appear to be a random collection of students whose data cannot be linked over time, but rather students who are more likely to drink, smoke, use marijuana, and be involved with bullying. Furthermore, those same students report skipping classes significantly more and are, therefore, more likely to be absent on a data collection day. If it is surmised based on these analyses that students who exhibit similar behaviours are more likely to skip school, then there should be concern that a specific subsample of a school population will be absent at any given time, as it introduces a level of bias to the data.  This has two major implications:

First, researchers must account for differences in the linked vs non-linked data. If researchers wish to measure changes in eating habits, BMI scores, and other obesity-related outcomes over time, they can use the linked data without concern for in-school representativeness, knowing that there are no significant differences between linked and non-linked student data. When measuring changes in substance-use behaviours, (tobacco-, alcohol-, and marijuana-use), however, researchers must account for the fact that a significant portion of students who report using these substances will not be included in the linked dataset (for example, smoking rates amongst linked students in grades 9 to 11 are so low that tracking any sort of behavioural change in that group over time is near-impossible). As such, measurable changes in behaviours will be more difficult to assess over time in these cases. Likewise, bullying, academic ambition, and—to a lesser degree—physical activity and sedentary behaviour data must also be used with some caution.

Second, knowing that on any given data collection day a larger proportion of substance-users than non-users will be absent from school—and will, therefore, not be included in any resulting datasets—suggests that it is likely that existing cross-sectional surveys (the current norm in surveillance research) are systematically under-reporting youth substance-use data. This is a further illustration of the value of the COMPASS system’s design: Using passive permission protocols to minimize in-school sample bias [13], and being able to identify data bias by virtue of being longitudinal (even if it remains difficult to control for that bias), the COMPASS system is able to mitigate potentially serious shortcomings in the collection of student data in a way that cross-sectional studies cannot (especially those that utilize active consent protocols), and thus provide researchers with a more realistic idea of what student health behaviours actually are.

While this technical report has been created to illustrate which data show bias or not (so as to be a guide for data-users), there is additional work to be done to try to ascertain why these data are shown to be biased. It is interesting to note that while measures such as substance-use are both significantly and consistently biased, other measures are either consistent but not significantly biased (such as physical activity), or are significant but not consistently biased. When this linkage method is applied to the third wave of data, we will perhaps be better able to explain failed linkage based on how many students we are able to link in 2 out of 3 years, and how we may be able to infer for missing data. 

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References

  1. Leatherdale ST, Brown KS, Carson, V, et al: The COMPASS study: a longitudinal hierarchical research platform for evaluating natural experiments related to changes in school-level programs, policies and built environment resources. BMC Public Health. 2014,14,331. doi:10.1186/1471-2458-14-331
  2. Leatherdale ST, Burkhalter R: The substance use profile of Canadian youth: exploring the prevalence of alcohol, drug and tobacco use by gender and grade. Addict Behav 2012, 37:318-322.
  3. Leatherdale ST, Manske S, Faulkner G, Arbour K, Bredin C: A multi-level examination of school programs, policies and resources associated with physical activity among elementary school youth in the PLAY-ON study. Int J Behav Nutr Phys Act 2010, 25;6. doi: 10.1186/1479 -5868-7-6.
  4. Leatherdale ST, McDonald PW, Cameron R, Brown KS: A multi-level analysis examining the relationship between social influences for smoking and smoking onset. Am J Health Behav 2005, 29:520-530.
  5. Leatherdale ST, Papadakis S: A multi-level examination of the association between older social models in the school environment and overweight and obesity among younger students. J Youth Adolesc 2011, 40:361 - 372.
  6. Bredin C, Leatherdale ST. Methods for linking COMPASS student-level data over time. COMPASS Technical Report Series. 2013;1(2). Waterloo, Ontario: University of Waterloo. Available at: https://uwaterloo.ca/compass-system/publications#technical
  7. Kearney K, Hopkins RH, Mauss AL and Weisheit RA: Self-Generated Identification Codes for Anonymous Collection of Longitudinal Questionnaire Data. The Public Opinion Quarterly, Vol. 48,No. 1 (Spring, 1984), pp. 370-378.
  8. Statistics Canada. Labour Force Survey 2012. Ottawa: Statistics Canada, 2012.
  9. Leatherdale ST, McDonald PW. Are the Recommended Taxonomies for the Stages of Youth Smoking Onset Consistent with Youth’s Perceptions of Their Smoking Status? Canadian Journal of Public Health; Jul/Aug 2006; 97, 4; Research Library pg. 316
  10. WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. WHO Technical Report Series 854. Geneva: World Health Organization, 1995.
  11. Health Canada: Eating Well with Canada’s Food Guide. Minister of Health; 2011. http://www.hc-sc.gc.ca/fn-an/alt_formats/hpfb-dgpsa/pdf/food-guide-aliment/print_eatwell_bienmang-eng.pdf [accessed July 2015]
  12. Canadian Society for Exercise Physiology. Canadian Physical Activity Guidelines for Youth – 12 to 17 years. 2013. http://www.csep.ca/CMFiles/Guidelines/CSEP-InfoSheets-youth-ENG.pdf.
  13. Thompson-Haile A, Bredin C, Leatherdale ST. Rationale for using an Active-Information Passive-Consent Permission Protocol in COMPASS. COMPASS Technical Report Series. 2013;1(6). Waterloo, Ontario: University of Waterloo. Available at: www.compass.uwaterloo.ca

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Appendix

Appendix A: Questions used to create self-generated code for the purpose of tracking data over time:


Please read each sentence below carefully. Write the correct letter, number, or work on the line and then fill in the corresponding circle.

The first letter of your middle name (if you have more than one middle name use your first middle name; if you don't have a middle name , use "z":____

The name of the  month you were born in:______

The last letter of your full last  name:____

The second letter of your full first name:____

The first initial of your mother's first name (think about the mother you see most):____

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January

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Back page of report

University of Waterloo

200 University Ave. W., Waterloo, Ontario, Canada N2L 3G1

Telephone: (519) 888-4567

uwaterloo.ca/compass-system

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[1] Because we are unable to accurately quantify the percentage of students who move to a different school in a given year, we have not included this in the equation. The rate is likely significant, however, as 5.8% of 2012-13 participants in grades 10-12 reported in the Cq having not attended their current school the previous year.

[2] In Y2, 21.8% of students were absent for the Cq. Students in Y1 were assumed to have the same absentee rate in Y2.

[3]In Y1, 19.8% of students were absent for the Cq. Students in Y2 were assumed to have the same absentee rate in Y1