# Determinants of Attrition to Follow-Up in a Multicentre Cohort Study in Children-Results from the IDEFICS Study

Authors: Sabrina Hense; Hermann Pohlabeln; Nathalie Michels; Staffan Mårild; Lauren Lissner; Eva Kovacs; Luis A. Moreno; Charalampos Hadjigeorgiou; Toomas Veidebaum; Licia Iacovello; Yannis Pitsiladis; Lucia Reisch; Alfonso Siani; Wolfgang Ahrens
Journal: Epidemiology Research International
 (2013)

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## Abstract

Cohort participant retention is a crucial element and may depend on several factors. Based on data from a multicentre cohort of European children, the effect of baseline participation on attrition and the association with and the impact of single determinants in relation to the extent of attrition were investigated. Data was available for 16,225 children from the IDEFICS baseline survey (2007/2008). Attrition was defined as nonparticipation in the first follow-up examination (2009/2010). Determinants of attrition were analysed by logistic regression. The statistical significance level was set atα=0.01 to account for the large sample size. The strongest associations were seen for baseline item non-response, especially when information on migration background (odds ratio (OR) = 1.55; 99% confidence interval (CI): 1.04, 2.31), single parenthood (OR = 1.37; 99% CI: 1.12, 1.67), or well-being (OR = 1.46; 99% CI: 1.19, 1.79) was lacking. Drop-out proportion rose with the number of missing items. Overweight, low education, single parenthood and low well-being scores were independent determinants of attrition. Baseline participation, and the individual determinant effects seemed unrelated to the variation of the extent of attrition between study centres. A high level of item nonresponse as well as overweight and disadvantageous sociodemographic conditions were identified as main attrition determinants, suggesting the consideration of these aspects in conduct and analysis of cohort studies in childhood obesity research.


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## Body

## 1. Introduction

In longitudinal studies, participant adherence is a crucial element of study organisation that requires considerable effort and time. However, attrition is an inevitable problem in almost every epidemiological cohort study, which leads to a loss of power and potentially introduces selection bias when drop-out is related to the exposures or outcomes of interest. The most commonly reported types of attrition are noncontact or refusal [1]. In fact, the motivation of study participants to remain involved in the study activities may decline over time and will depend on several factors. To apply appropriate analysis strategies and to allow for a correct interpretation of the results from longitudinal studies, knowledge on the determinants of attrition is important. Additionally, information about factors affecting attrition is crucial to improve retention of cohort participants. One aspect that has been reported to affect attrition is baseline response and concomitant recruitment efforts [2]. With regard to longitudinal studies in children and adolescents, where parental consent is commonly required, low parental education level, migration background, and single parenthood have been suggested to mainly determine attrition rates [3, 4]. However, there is little experience regarding the effects of different determinants of attrition in a multicentre study where the same standardized study protocols are applied. Such data would facilitate the comparison between study centres that have different attrition proportions and baseline responses and the extent of potential selection effects.This report presents results regarding the proportionate attrition from baseline (T0) to the first follow-up examination (T1) of the multi-centre IDEFICS (identification and prevention of dietary- and lifestyle-induced health effects in children and infants) study. We investigated systematic drop-out related to weight status as well as to known obesity risk factors, such as low educational level and migration background. Also, behavioural factors that have been identified as determinants of overweight, such as screen time [5], sleep duration [6], or dietary behaviour [7, 8], and certain psychosocial aspects [9] were examined as possible attrition determinants. Selective drop-out with regard to these factors might affect the interpretability of results in longitudinal studies of obesity research. Additionally, allocation to either the intervention or the control region (see the following for more details) might play a role in this context and was therefore another determinant of interest in the present study.In this paper we (1) assess determinants of attrition and evaluate whether (2) the baseline participation in the study centres is associated with attrition and (3) whether the impact of attrition determinants is stronger in centres with a high proportionate attrition.


## 2. Methods

### 2.1. Study Population

The IDEFICS project is a population-based multicentre intervention study which included children aged 2 to 9 years from eight European countries.Between September 2007 and May 2008, 31 543 children from schools and preschools in selected regions in Italy, Estonia, Cyprus, Belgium, Sweden, Hungary, Germany, and Spain were asked to participate in the baseline survey (T0) based on a two-stage random sampling strategy with schools presenting the first and classes presenting the second level of sampling units. Of those children 16864 (53.4%) accepted the invitation, with 16225 (51.4%) providing the parental questionnaire and measurement of weight and height [10]. They thus fulfilled the preset inclusion criteria and were eligible for the present analysis. The baseline survey was followed by a community- and setting-based intervention program concerning diet, physical activity, and stress coping, which was implemented in intervention regions in each of the participating countries, while no intervention was offered in the control regions [11]. Two years later, every child who had participated in the T0 survey was automatically reinvited to take part in the T1 survey (September 2009 to June 2010). Both, T0 and T1 involved a similarly extensive study protocol including anthropometric examinations, collection of biosamples (urine, saliva, blood), fitness, and sensory tests as well as questionnaires on diet, psychosocial, behavioural, and environmental factors to be filled in by the parents. More detailed information on the study protocol and procedures can be found elsewhere [12].Two age groups were defined with one group including children aged 2 to <6 years (preschool children) and the other group including children aged 6 to 9 years (school children) at baseline.In each country, the participating centres obtained ethical approval by their responsible authority. All children and their parents provided oral and written informed consent, respectively, for all examinations and/or the collection of samples, subsequent analysis, and storage of personal data and collected samples. More detailed information on the study design, applied instruments, and measurements as well as baseline response rates with regard to the respective study modules has been presented elsewhere [10].


### 2.2. Measurements

#### 2.2.1. Attrition

The outcome measure was a dichotomous variable for attrition at two-year follow-up (T1 participant versus T1 nonparticipant). The amount of attrition per study centre was expressed as a percentage of the number of baseline participants.


#### 2.2.2. Baseline Characteristics

All measurements were conducted according to a manual of standardized procedures in all centres. Body height was measured without shoes by trained staff using a portable stadiometer (SECA 225). Weight was measured by means of an adapted version of electronic scale TANITA BC 420 SMA, wearing only underwear. BMI was calculated and then categorized according to the criteria of the International Obesity Task Force [13]. Our reference category included normal and underweight children. Data on personal, social, environmental, and behavioural factors of each child, such as parental education level, family status, migration background, time spent in front of a TV or PC screen (screen time), and well-being was collected by means of a standardized parental self-completion questionnaire. Education level was categorized according to the International Standard Classification of Education (ISCED). Four levels of education (low versus medium-low versus medium-high versus high) were created out of the six ISCED levels, with ISCED levels 0–2 being defined as low education, level 3 being defined medium-low, level 4 being defined as medium-high, and level 5 and higher being defined as high education. Migration background was defined as either mother or father of the child or both parents being born in a foreign country. Family status was defined as single versus two-parent families.A well-being scale was developed, including information on emotional well-being, self-esteem, family, and friends. Though the scale itself was not validated, these questions were based on the respective subscales from the Kiddy-KINDLR  for parents of children aged 4–7 years, a validated questionnaire for measuring health-related quality of life in children [14]. Answers were given according to a 4-point Likert scale (never, rarely, sometimes, often/all the time), that was adapted to the original 5-point Likert scale of the KINDLR. Since the IDEFICS parental questionnaire did not comprise the whole subscale set from KINDLR (questions on physical well-being and everyday functioning were excluded in IDEFICS) no total instrument score, but only sum scores based on the included subscales were created as proposed by the authors of the original instrument [15]. Based on these scores we created four well-being categories (low, medium-low, medium-high, and high). More detailed information on the KINDLR scores can be found at http://www.kindl.org/ [15].Information on dietary habits was obtained from a validated standardized Food Frequency Questionnaire (CEHQ-FFQ) [16, 17]. A variable representing the weekly consumption of junk food (including sweetened drinks, chocolate or nut based spreads, snacks) was created based on this data. Furthermore, the parents were asked if soft drinks were available during meals (yes versus no).Sleep duration was assessed using a 24-hour dietary recall (SACINA). SACINA is a computer-based instrument that was filled out by parents or guardian of each participating child and contained questions on the time at which the child got up in the morning and went to bed on the previous day. Nocturnal sleep duration was calculated as the difference between bed and get-up time in the SACINA interview resulting in a continuous variable. More detailed information on the sleep duration assessment in IDEFICS is given elsewhere [6, 18]. Two sleep duration categories were created (≤11 hours of sleep versus >11 hours of sleep).A “non-response-score” was developed, taking into account the amount of item non-response per participant with regard to the variables of the baseline survey, resulting in four categories: none (no item non-response); low (1 item non-response); medium (2 or more item non-responses).Baseline participation rate was included in the analysis as a dichotomous variable per study centre with the categories low (<50%) versus high (≥50%).


### 2.3. Statistical Analysis

Descriptive analyses describe attrition proportion and sample characteristics in the participating study centres (countries). Chi square statistics were used for comparison of categorical variables. To analyse the association between potential determinants and individual attrition (as a dichotomous dependant variable) we used separate logistic regression models. More precisely, we applied the procedure GLIMMIX (SAS Institute, Cary, North Carolina, USA) and included adjustments for the second-level random effect (country) as well as the simultaneous consideration of age and sex (Model 1). In a further step, we defined a multivariate logistic regression model (Model 2), which combined all variables showing a statistically significant effect, or an estimated odds ratio (OR) ≥ 1.50, in the separate Model 1, to identify the strength and independence of the impact of single determinants on attrition. For sensitivity analysis, forward selection of covariates was additionally applied for model development to investigate if this approach would lead to inclusion of the same variables in the model. In order to compare whether the impact of determinants is related to the variation in the extent of attrition between study centres we conducted a country-stratified analysis (separate models for each country based on model 1), where country was used as a proxy for the degree of attrition. The statistical significance level was set atα=0.01 to account for the large sample size.


## 2.1. Study Population

The IDEFICS project is a population-based multicentre intervention study which included children aged 2 to 9 years from eight European countries.Between September 2007 and May 2008, 31 543 children from schools and preschools in selected regions in Italy, Estonia, Cyprus, Belgium, Sweden, Hungary, Germany, and Spain were asked to participate in the baseline survey (T0) based on a two-stage random sampling strategy with schools presenting the first and classes presenting the second level of sampling units. Of those children 16864 (53.4%) accepted the invitation, with 16225 (51.4%) providing the parental questionnaire and measurement of weight and height [10]. They thus fulfilled the preset inclusion criteria and were eligible for the present analysis. The baseline survey was followed by a community- and setting-based intervention program concerning diet, physical activity, and stress coping, which was implemented in intervention regions in each of the participating countries, while no intervention was offered in the control regions [11]. Two years later, every child who had participated in the T0 survey was automatically reinvited to take part in the T1 survey (September 2009 to June 2010). Both, T0 and T1 involved a similarly extensive study protocol including anthropometric examinations, collection of biosamples (urine, saliva, blood), fitness, and sensory tests as well as questionnaires on diet, psychosocial, behavioural, and environmental factors to be filled in by the parents. More detailed information on the study protocol and procedures can be found elsewhere [12].Two age groups were defined with one group including children aged 2 to <6 years (preschool children) and the other group including children aged 6 to 9 years (school children) at baseline.In each country, the participating centres obtained ethical approval by their responsible authority. All children and their parents provided oral and written informed consent, respectively, for all examinations and/or the collection of samples, subsequent analysis, and storage of personal data and collected samples. More detailed information on the study design, applied instruments, and measurements as well as baseline response rates with regard to the respective study modules has been presented elsewhere [10].


## 2.2. Measurements

### 2.2.1. Attrition

The outcome measure was a dichotomous variable for attrition at two-year follow-up (T1 participant versus T1 nonparticipant). The amount of attrition per study centre was expressed as a percentage of the number of baseline participants.


### 2.2.2. Baseline Characteristics

All measurements were conducted according to a manual of standardized procedures in all centres. Body height was measured without shoes by trained staff using a portable stadiometer (SECA 225). Weight was measured by means of an adapted version of electronic scale TANITA BC 420 SMA, wearing only underwear. BMI was calculated and then categorized according to the criteria of the International Obesity Task Force [13]. Our reference category included normal and underweight children. Data on personal, social, environmental, and behavioural factors of each child, such as parental education level, family status, migration background, time spent in front of a TV or PC screen (screen time), and well-being was collected by means of a standardized parental self-completion questionnaire. Education level was categorized according to the International Standard Classification of Education (ISCED). Four levels of education (low versus medium-low versus medium-high versus high) were created out of the six ISCED levels, with ISCED levels 0–2 being defined as low education, level 3 being defined medium-low, level 4 being defined as medium-high, and level 5 and higher being defined as high education. Migration background was defined as either mother or father of the child or both parents being born in a foreign country. Family status was defined as single versus two-parent families.A well-being scale was developed, including information on emotional well-being, self-esteem, family, and friends. Though the scale itself was not validated, these questions were based on the respective subscales from the Kiddy-KINDLR  for parents of children aged 4–7 years, a validated questionnaire for measuring health-related quality of life in children [14]. Answers were given according to a 4-point Likert scale (never, rarely, sometimes, often/all the time), that was adapted to the original 5-point Likert scale of the KINDLR. Since the IDEFICS parental questionnaire did not comprise the whole subscale set from KINDLR (questions on physical well-being and everyday functioning were excluded in IDEFICS) no total instrument score, but only sum scores based on the included subscales were created as proposed by the authors of the original instrument [15]. Based on these scores we created four well-being categories (low, medium-low, medium-high, and high). More detailed information on the KINDLR scores can be found at http://www.kindl.org/ [15].Information on dietary habits was obtained from a validated standardized Food Frequency Questionnaire (CEHQ-FFQ) [16, 17]. A variable representing the weekly consumption of junk food (including sweetened drinks, chocolate or nut based spreads, snacks) was created based on this data. Furthermore, the parents were asked if soft drinks were available during meals (yes versus no).Sleep duration was assessed using a 24-hour dietary recall (SACINA). SACINA is a computer-based instrument that was filled out by parents or guardian of each participating child and contained questions on the time at which the child got up in the morning and went to bed on the previous day. Nocturnal sleep duration was calculated as the difference between bed and get-up time in the SACINA interview resulting in a continuous variable. More detailed information on the sleep duration assessment in IDEFICS is given elsewhere [6, 18]. Two sleep duration categories were created (≤11 hours of sleep versus >11 hours of sleep).A “non-response-score” was developed, taking into account the amount of item non-response per participant with regard to the variables of the baseline survey, resulting in four categories: none (no item non-response); low (1 item non-response); medium (2 or more item non-responses).Baseline participation rate was included in the analysis as a dichotomous variable per study centre with the categories low (<50%) versus high (≥50%).


## 2.2.1. Attrition

The outcome measure was a dichotomous variable for attrition at two-year follow-up (T1 participant versus T1 nonparticipant). The amount of attrition per study centre was expressed as a percentage of the number of baseline participants.


## 2.2.2. Baseline Characteristics

All measurements were conducted according to a manual of standardized procedures in all centres. Body height was measured without shoes by trained staff using a portable stadiometer (SECA 225). Weight was measured by means of an adapted version of electronic scale TANITA BC 420 SMA, wearing only underwear. BMI was calculated and then categorized according to the criteria of the International Obesity Task Force [13]. Our reference category included normal and underweight children. Data on personal, social, environmental, and behavioural factors of each child, such as parental education level, family status, migration background, time spent in front of a TV or PC screen (screen time), and well-being was collected by means of a standardized parental self-completion questionnaire. Education level was categorized according to the International Standard Classification of Education (ISCED). Four levels of education (low versus medium-low versus medium-high versus high) were created out of the six ISCED levels, with ISCED levels 0–2 being defined as low education, level 3 being defined medium-low, level 4 being defined as medium-high, and level 5 and higher being defined as high education. Migration background was defined as either mother or father of the child or both parents being born in a foreign country. Family status was defined as single versus two-parent families.A well-being scale was developed, including information on emotional well-being, self-esteem, family, and friends. Though the scale itself was not validated, these questions were based on the respective subscales from the Kiddy-KINDLR  for parents of children aged 4–7 years, a validated questionnaire for measuring health-related quality of life in children [14]. Answers were given according to a 4-point Likert scale (never, rarely, sometimes, often/all the time), that was adapted to the original 5-point Likert scale of the KINDLR. Since the IDEFICS parental questionnaire did not comprise the whole subscale set from KINDLR (questions on physical well-being and everyday functioning were excluded in IDEFICS) no total instrument score, but only sum scores based on the included subscales were created as proposed by the authors of the original instrument [15]. Based on these scores we created four well-being categories (low, medium-low, medium-high, and high). More detailed information on the KINDLR scores can be found at http://www.kindl.org/ [15].Information on dietary habits was obtained from a validated standardized Food Frequency Questionnaire (CEHQ-FFQ) [16, 17]. A variable representing the weekly consumption of junk food (including sweetened drinks, chocolate or nut based spreads, snacks) was created based on this data. Furthermore, the parents were asked if soft drinks were available during meals (yes versus no).Sleep duration was assessed using a 24-hour dietary recall (SACINA). SACINA is a computer-based instrument that was filled out by parents or guardian of each participating child and contained questions on the time at which the child got up in the morning and went to bed on the previous day. Nocturnal sleep duration was calculated as the difference between bed and get-up time in the SACINA interview resulting in a continuous variable. More detailed information on the sleep duration assessment in IDEFICS is given elsewhere [6, 18]. Two sleep duration categories were created (≤11 hours of sleep versus >11 hours of sleep).A “non-response-score” was developed, taking into account the amount of item non-response per participant with regard to the variables of the baseline survey, resulting in four categories: none (no item non-response); low (1 item non-response); medium (2 or more item non-responses).Baseline participation rate was included in the analysis as a dichotomous variable per study centre with the categories low (<50%) versus high (≥50%).


## 2.3. Statistical Analysis

Descriptive analyses describe attrition proportion and sample characteristics in the participating study centres (countries). Chi square statistics were used for comparison of categorical variables. To analyse the association between potential determinants and individual attrition (as a dichotomous dependant variable) we used separate logistic regression models. More precisely, we applied the procedure GLIMMIX (SAS Institute, Cary, North Carolina, USA) and included adjustments for the second-level random effect (country) as well as the simultaneous consideration of age and sex (Model 1). In a further step, we defined a multivariate logistic regression model (Model 2), which combined all variables showing a statistically significant effect, or an estimated odds ratio (OR) ≥ 1.50, in the separate Model 1, to identify the strength and independence of the impact of single determinants on attrition. For sensitivity analysis, forward selection of covariates was additionally applied for model development to investigate if this approach would lead to inclusion of the same variables in the model. In order to compare whether the impact of determinants is related to the variation in the extent of attrition between study centres we conducted a country-stratified analysis (separate models for each country based on model 1), where country was used as a proxy for the degree of attrition. The statistical significance level was set atα=0.01 to account for the large sample size.


## 3. Results

Among the 16225 children who participated in the IDEFICS baseline survey, the proportion of boys and girls was nearly equal in all survey centres, while slightly more school than preschool children participated in the survey in most countries, except for Sweden and Belgium. Striking country differences were seen in the percentage of obese and overweight children (e.g., Italy: 19.5% and 22.5%; Belgium: 2.3% and 6.4%, resp.) as well as in other potential determinants of attrition. Thus, for example, the proportion of children with migration background and parents with low educational level was very low in Estonia (4.6% and 1.9%, resp.) as compared to Germany (34.1% and 35.5%, resp.). The proportion of single parents was low in Sweden (8.5%) and relatively high in Italy (28.9%). A detailed overview of all potential attrition determinants under investigation is given in Table1.Table 1
Baseline Response and Baseline Characteristics of Children in the IDEFICS Study by Country.
Estonia
Sweden
Germany
Belgium
Hungary
Italy
Spain
Cyprus
All
n
%
n
%
n
%
n
%
n
%
n
%
n
%
n
%
n
%
Region
Intervention
793
46.1%
902
49.9%
1179
57.1%
976
50.7%
1277
49.7%
1182
52.5%
798
53.0%
1373
57.7%
8480
52.3%
Control
926
53.9%
907
50.1%
886
42.9%
950
49.3%
1292
50.3%
1068
47.5%
709
47.0%
1007
42.3%
7745
47.7%
Sex
Male
850
49.4%
934
51.6%
1051
50.9%
978
50.8%
1286
50.1%
1165
51.8%
773
51.3%
1226
51.5%
8236
50.8%
Female
869
50.6%
875
48.4%
1014
49.1%
948
49.2%
1283
49.9%
1085
48.2%
734
48.7%
1154
48.5%
7962
49.1%
Age
Pre-school
859
50.0%
918
50.7%
876
42.4%
1043
54.2%
1039
40.4%
974
43.3%
715
47.4%
954
40.1%
7378
45.5%
School
860
50.0%
891
49.3%
1189
57.6%
883
45.8%
1530
59.6%
1276
56.7%
792
52.6%
1426
59.9%
8847
54.5%
Weight status
Obese
69
4.0%
36
2.0%
92
4.5%
44
2.3%
149
5.8%
438
19.5%
88
5.8%
211
8.9%
1127
6.9%
Overweight
178
10.4%
160
8.8%
237
11.5%
123
6.4%
288
11.2%
507
22.5%
223
14.8%
345
14.5%
2061
12.7%
Normal weight
1472
85.6%
1613
89.2%
1736
84.1%
1759
91.3%
2132
83.0%
1305
58.0%
1196
79.4%
1824
76.6%
13037
80.4%
Migration background
No
1572
91.4%
1433
79.2%
1304
63.1%
1726
89.6%
2398
93.3%
1846
82.0%
1310
86.9%
1404
59.0%
12993
80.1%
Partly
69
4.0%
180
10.0%
210
10.2%
72
3.7%
83
3.2%
329
14.6%
75
5.0%
377
15.8%
1395
8.6%
Full
11
0.6%
146
8.1%
493
23.9%
42
2.2%
19
0.7%
54
2.4%
86
5.7%
240
10.1%
1091
6.7%
Missing
67
3.9%
50
2.8%
58
2.8%
86
4.5%
69
2.7%
21
0.9%
36
2.4%
359
15.1%
746
4.6%
Educational level
Low
33
1.9%
32
1.8%
734
35.5%
78
4.0%
78
3.0%
484
21.5%
142
9.4%
78
3.3%
1659
10.2%
Medium-low
674
39.2%
331
18.3%
513
24.8%
513
26.6%
1107
43.1%
1338
59.5%
409
27.1%
285
12.0%
5170
31.9%
Medium-high
725
42.2%
180
10.0%
410
19.9%
430
22.3%
255
9.9%
0
0.0%
160
10.6%
518
21.8%
2678
16.5%
High
230
13.4%
1215
67.2%
321
15.5%
850
44.1%
1069
41.6%
410
18.2%
767
50.9%
1144
48.1%
6006
37.0%
Missing
57
3.3%
51
2.8%
87
4.2%
55
2.9%
60
2.3%
18
0.8%
29
1.9%
355
14.9%
712
4.4%
Family status
Two parents
1414
82.3%
1606
88.8%
1675
81.1%
1677
87.1%
2036
79.3%
1477
65.6%
1322
87.7%
1086
45.6%
12293
75.8%
Single parent
214
12.4%
153
8.5%
309
15.0%
184
9.6%
394
15.3%
651
28.9%
132
8.8%
391
16.4%
2482
15.3%
Missing
91
5.3%
50
2.8%
81
3.9%
65
3.4%
139
5.4%
122
5.4%
53
3.5%
903
37.9%
1504
9.3%
Well being
Low
217
12.6%
156
8.6%
333
16.1%
272
14.1%
847
33.0%
543
24.1%
210
13.9%
449
18.9%
3027
18.7%
Medium-low
414
24.1%
352
19.5%
436
21.1%
371
19.3%
906
35.3%
617
27.4%
442
29.3%
427
17.9%
3965
24.4%
Medium-high
524
30.5%
646
35.7%
666
32.3%
668
34.7%
598
23.3%
649
28.8%
541
35.9%
356
15.0%
4648
28.6%
High
406
23.6%
540
29.9%
314
15.2%
431
22.4%
48
1.9%
273
12.1%
183
12.1%
97
4.1%
2292
14.1%
Missing
158
9.2%
115
6.4%
316
15.3%
184
9.6%
170
6.6%
168
7.5%
131
8.7%
1051
44.2%
2293
14.1%
Weekly screen time
≤7 hours
312
18.2%
444
24.5%
756
36.6%
632
32.8%
820
31.9%
622
27.6%
586
38.9%
502
21.1%
4674
28.8%
7 to ≤14 hours
603
35.1%
839
46.4%
699
33.8%
732
38.0%
937
36.5%
817
36.3%
554
36.8%
805
33.8%
5986
36.9%
>14 hours
717
41.7%
446
24.7%
448
21.7%
466
24.2%
706
27.5%
721
32.0%
288
19.1%
625
26.3%
4417
27.2%
Missing
87
5.1%
80
4.4%
162
7.8%
96
5.0%
106
4.1%
90
4.0%
79
5.2%
448
18.8%
1148
7.1%
Junk food
Never
121
7.0%
276
15.3%
30
1.5%
26
1.3%
227
8.8%
184
8.2%
126
8.4%
84
3.5%
1074
6.6%
Occasionally
778
45.3%
1160
64.1%
431
20.9%
534
27.7%
1132
44.1%
831
36.9%
810
53.7%
673
28.3%
6349
39.1%
Often
588
34.2%
272
15.0%
1369
66.3%
1118
58.0%
937
36.5%
979
43.5%
465
30.9%
477
20.0%
6205
38.2%
Missing
232
13.5%
101
5.6%
235
11.4%
248
12.9%
273
10.6%
256
11.4%
106
7.0%
1146
48.2%
2597
16.0%
Soft drink available
Yes
423
24.6%
32
1.8%
1060
51.3%
486
25.2%
579
22.5%
321
14.3%
64
4.2%
158
6.6%
3123
19.2%
No
1224
71.2%
1690
93.4%
896
43.4%
1300
67.5%
1892
73.6%
1906
84.7%
1395
92.6%
930
39.1%
11233
69.2%
Missing
72
4.2%
87
4.8%
109
5.3%
140
7.3%
98
3.8%
23
1.0%
48
3.2%
1292
54.3%
1869
11.5%
Sleep duration
≤11 hours
1289
75.0%
848
46.9%
937
45.4%
209
10.9%
865
33.7%
1578
70.1%
464
30.8%
908
38.2%
7098
43.7%
>11 hours
42
2.4%
366
20.2%
649
31.4%
199
10.3%
36
1.4%
64
2.8%
40
2.7%
45
1.9%
1441
8.9%
Missing
388
22.6%
595
32.9%
479
23.2%
1518
78.8%
1668
64.9%
608
27.0%
1003
66.6%
1427
60.0%
7686
47.4%
Item nonresponse
None
1490
86.7%
1644
90.9%
1649
79.9%
1628
84.5%
2276
88.6%
1907
84.8%
1299
86.2%
1150
48.3%
13043
80.4%
Low
159
9.2%
104
5.7%
296
14.3%
204
10.6%
214
8.3%
295
13.1%
163
10.8%
249
10.5%
1684
10.4%
High
70
4.1%
61
3.4%
120
5.8%
94
4.9%
79
3.1%
48
2.1%
45
3.0%
981
41.2%
1498
9.2%
Baseline response
54.5%
65.6%
48.2%
58.3%
43.7%
60.0%
41.4%
49.9%
51.4%The average proportionate attrition in the IDEFICS follow-up was 32.6%, but it varied significantly between countries (P<0.001), ranging from 18.2% in Sweden to 51.5% in Hungary (Figure 1).Figure 1
Attrition and participation proportion by country.Table2 presents proportionate attrition and effect estimates for each category of the investigated factors applying two levels of adjustment (Models 1 and 2). Each potential determinant variable includes a category which represents the missing values (non-response) for the respective item. Attrition proportions in the single categories of possible determinants ranged from 27.7% (children from highly educated parents) to 46.8% (children whose parents did not report their educational level). After control for country, age, and sex, the strongest impact on attrition was observed when baseline variable values were missing and children with non-response to certain items in T0 were about two times as likely to drop out of follow-up as children with information on these aspects. Thus, a high combined non-response score showed the highest impact of all potential determinants under investigation with a clear dose-effect relationship (low score: OR = 1.45; 99% confidence interval (CI): 1.26, 1.68; high score: OR = 2.63; 99% CI: 2.10; 3.29).Table 2
Proportionate attrition and effect estimates for determinants of attrition in the IDEFICS study.
Proportionate attrition
Model 1a
Model 2b
OR
99% CI
OR
99% CI
Sex
Male
32.8%
Reference
Female
32.3%
0.97
0.89, 1.06
Age group
Preschool
33.5%
1.15
1.07, 1.23
1.22
1.11, 1.34
School
31.8%
Reference
Reference
Region
Intervention
33.0%
Reference
Control
32.1%
0.96
0.88, 1.05
Weight status
Obese
31.7%
1.36
1.14, 1.62
1.28
1.07, 1.54
Overweight
35.9%
1.30
1.14, 1.49
1.27
1.10, 1.45
Normal weight
36.8%
Reference
Reference
Migration background
No
31.0%
Reference
Reference
Partly
33.0%
1.26
1.07, 1.48
1.23
1.04, 1.45
Full
40.6%
1.57
1.31, 1.89
1.15
0.91, 1.45
Missing
46.6%
2.44
1.98, 3.00
1.55
1.04, 2.32
Educational level
Low
41.4%
1.31
1.11, 1.53
1.19
1.01, 1.40
Medium-low
34.0%
Reference
Reference
Medium-high
31.4%
1.04
0.90, 1.20
1.06
0.92, 1.22
High
27.7%
0.84
0.75, 0.94
0.88
0.78, 1.00
Missing
46.8%
2.15
1.72, 2.69
1.09
0.72, 1.68
Family status
Two parents
30.9%
Reference
Reference
Single parent
36.3%
1.28
1.13, 1.45
1.21
1.06, 1.38
Missing
40.0%
1.96
1.66, 2.32
1.37
1.12, 1.67
Well-being
Low
37.5%
1.34
1.14,
1.59
1.26
1.07, 1.49
Medium-low
33.0%
1.18
1.00, 1.38
1.15
0.97, 1.35
Medium-high
29.0%
1.05
0.89, 1.22
1.04
0.89, 1.22
High
25.4%
Reference
Reference
Missing
39.7%
1.97
1.65, 2.36
1.46
1.19, 1.79
Weekly screen time
≤7 hours
31.6%
Reference
Reference
7 to ≤14 hours
31.9%
1.14
1.01, 1.27
1.11
0.99, 1.24
>14 hours
31.9%
1.16
1.03, 1.32
1.06
0.94, 1.21
Missing
42.3%
1.96
1.63, 2.35
1.10
0.86, 1.39
Junk food
Never
29.0%
Reference
Occasionally
29.7%
1.00
0.83, 1.23
Often
34.0%
1.00
0.82, 1.22
Missing
37.8%
1.53
1.23, 1.91
Soft drink available
Yes
38.7%
1.11
0.98, 1.25
No
30.0%
Reference
Missing
37.3%
1.97
1.66, 2.33
Sleep duration
≤11 hours
29.0%
0.86
0.72, 1.03
>11 hours
25.5%
Reference
Missing
35.3%
1.08
0.91, 1.29
Item non-response
No missing
30.8%
Reference
Low
39.0%
1.45
1.26, 1.68
High
40.4%
2.18
1.83, 2.58
Baseline participation
Low
27.3%
1.50
0.54, 4.16
1.33
0.46, 3.89
High
37.3%
Reference
Reference
CI: confidence interval; OR: odds ratio.
aAdjusted for country, sex, and age group.
bAdjusted for country, age group, weight status, parental education level, migration background, family status, well-being, screen time, and baseline proportionate participation.Other, in particular sociodemographic and psychosocial factors were attributed to attrition as well. Hence, children from families where both parents were born in a foreign country were more likely to drop out as were children with parents of low educational background, while children from highly educated families showed a particularly high probability to participate in T1 (Table2). Effects on attrition could be revealed also for some psychosocial aspects such as a low well-being score or a long screen time. Similarly, overweight and obese children participated less often in the follow-up examinations than normal weight children. In Model 2, age group, overweight/obesity, parental education level, migration background, family status, well-being, screen time, and the baseline proportionate participation were included. The non-response score variable was omitted from Model 2 to avoid overadjustment, since a missing category was already included for each of the variables in the model. The strongest effect on attrition was found for non-response regarding the items migration background, family status, and well-being. Generally, most of the effects (except for screen time) observed in model 1 persisted as statistically significant after multivariate adjustment although the associations were somewhat attenuated. Of note, sex, allocation in intervention or control region, dietary behaviour (junk food consumption and availability of soft drinks), sleep duration, and participation rate at baseline were not related to attrition in these models. Also considering baseline response as a continuous variable in sensitivity analysis, no correlation was found with proportionate attrition (Pearson’s r=-0.37; P=0.36). Sensitivity analysis for model development applying forward selection of covariates resulted in the inclusion of the same variables as in the model based on the originally used selection procedure. The hierarchy for inclusion of the selected covariates was as follows: country, migration background, family status, well-being of the child, socioeconomic status, age group, and overweight.Country-stratified comparisons regarding the main determinants for drop-out are given in Figure2. No specific patterns or clustering of the determinants of attrition in the different countries were identified, indicating that the impact of determinants is not substantially stronger in centres with a high attrition proportion.Effect estimates (OR, 99% CI) for the main determinants by country (ordered by the extent of proportionate attrition).
(a)
Migration background
(b)
Low education
(c)
Overweight
(d)
Item-Non-Response


## 4. Discussion

Our results confirmed the hypothesis that overweight/obesity and factors related to obesity risk affect attrition to follow-up in a cohort study of children. This association was independent from differences in baseline proportionate participation and attrition to follow-up. The strongest effects in the adjusted models were seen for overweight/obesity and for item non-response at baseline, the latter especially with regard to migration background, family status, and well-being. A dose-effect relationship was found within the study centres between the numbers of item non-responses and the drop-out. Generally, the extent of attrition does not seem to be related to the size of specific determinants for drop-out. The suggestion that selective drop-out due to a differential loss of individuals with specific patterns of obesity-related factors is stronger in study centres with high attrition proportions was thus not confirmed by our study. Sensitivity analysis excluding data from Spain and Belgium in Model 1 was conducted to account for the extremely high values in these countries with respect to the item non-response score. However, the results were only slightly attenuated but not essentially different (e.g., high non-response score: OR = 2.15; 99% CI: 1.69, 2.73).Determinants of attrition in longitudinal population-based studies have been discussed in several publications [3, 19–21]. In studies of young populations, where parental consent is usually required, sociodemographic factors, such as parental education or single parenthood, have been reported as the most relevant attrition determinants [4, 22]. These reports are in line with our results, where socio-demographic aspects including parental education, single parenthood, and migration background also affected nonparticipation in the follow-up study. This suggests that during the conduct of follow-up studies, special attention has to be paid to these groups in order to optimize their study adherence.Behavioural factors, such as sedentary behaviour or diet, were not or only weakly associated with attrition, which corroborates findings from the Danish Youth Cohort study [3]. However, our data revealed an impact of the children’s well-being on attrition. To the authors’ knowledge no such association has been previously reported in other studies of young populations. The same applies to the impact of item non-response, in particular to the dose-effect relationship between the number of item non-responses and attrition that was observed in the analysis. This seems to suggest that parents who were reluctant to completely answer the questionnaire or to participate in the respective study modules already at baseline were more likely to not participate after two years. In this context, results from a German cohort should be noted where late invitation response and the intensity of recruitment efforts were determinants of attrition to follow-up [2]. The authors suppose that late response is just an indicator for reluctance to participation and thus probably associated with a high value of the item non-response scores. Unfortunately, local variations in the organization of recruitment procedures in the different study centres and the different settings in each country precluded more detailed analyses of recruitment efforts. Nevertheless, intensified motivation procedures may need to be considered for participants in the cohort panel who show high item non-response scores.In previous studies it has been discussed that individuals might select themselves out of studies due to treatment or intervention [23]. Interestingly, no differences in attrition were seen with regard to being allocated to the intervention or the control region in the present study, which may indicate that the IDEFICS intervention programme was either well accepted or so low threshold that the decision to take part in the follow-up was unaffected.An important aspect in this context is the potential for biased estimates in longitudinal analyses due to selective attrition. Selective attrition threatens to impair the generalizability of findings and to bias the estimates of associations [24]. Although evidence for this is limited and conflicting [2, 19], this aspect needs to be considered in the interpretation of our results, in particular regarding how the systematic drop-out in the present study threatens to induce bias in the longitudinal analysis of certain outcomes. Particularly the elevated probability of drop-out for overweight or obese children, or with factors related to the development of overweight, is of particular concern here. Thus, in a prospective analysis using the incidence of obesity as a dichotomous outcome, the differential drop-out of children in the overweight (and even more so in the upper overweight) category, where the progression to obesity is more likely to occur than in children with a lower BMI, introduces an elevated probability of missing the outcome of interest in the follow-up examinations; this might lead to a biased estimation of the association under investigation. Therefore, alternative analytic approaches, for example, by using the relative change in BMI, and thereby identifying changes in the BMI within the whole range of the BMI distribution, as the outcome of interest, could be one approach to diminish the observed limitations in this cohort.Another example, where the selective drop-out might impair longitudinal analysis, is the study of the incidence of secondary diseases that are more likely to occur in overweight or obese children, for example, type 2 diabetes [25]. The elevated drop-out of children from this group could lead to a relevant loss of statistical power as the number of incident cases may become too low to observe significant effects since these conditions are generally rare (although increasing) within the age group of interest [26]. We suppose that the prospective analysis of observational studies within the context of obesity research in children should conscientiously take these aspects into consideration. While the results concerning obesity-related determinants of attrition cannot be generalized to cohort studies with a different research focus, the identification of item non-response as the factor with the strongest impact might be relevant for most longitudinal studies conducted. This special population group can be identified based on baseline data and extensive recruitment strategies might be developed and applied in order to increase study retention among this population.Data of the reasons for attrition (lack of interest, illness, lack of time, etc.) would have deepened the analyses in this report. However, no or only partial information on reasons for loss to follow-up was assessed during the field work and therefore no such analysis was feasible in the context of the present study.


## 5. Conclusion

Our results indicate selective attrition in cohort studies of children which are independent from baseline participation in the study centres. The main determinants of loss to follow-up were a high level of item non-response at baseline, especially lack of information on sociodemographic and psychosocial factors, followed by children’s overweight and disadvantageous sociodemographic and psychosocial conditions. Exposure or nonexposure to the intervention programme did not affect participation in follow-up examinations. Observational studies of obesity research in children should provide for these aspects in the longitudinal analysis of data, but preferably already during the planning and conduct of follow-up surveys.



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*Source: 936365-2013-05-09.xml*