In this study, we found sociodemographic and socioeconomic differences between the reference and the Swedish IDEFICS populations supporting our first hypothesis. Those with disadvantageous socioeconomic and sociodemographic backgrounds were underrepresented in the population in the IDEFICS study. Our second hypothesis was not supported, since we found no selection effect related to the children’s BMI at the time and age when the IDEFICS children were included.
Our findings are supported by several other studies. Low level of education is known to have a selection impact according to several studies in adult populations [7, 24]. Non-participation in a parental support program for underage drinking in adolescents was strongly associated with low education . Other reported obstacles for participation are single parenthood and immigrant background, related to busy personal schedules, inconvenient times, and logistical difficulties . Participation, on the other hand, was related to non-smoking habits, higher education, and co-habiting parents . These associations are in line with the inverse care law, i.e. medical care tends to vary inversely with the severity of the health problem . The inverse equity hypothesis is a consequence of this . Accordingly, new public health interventions may increase inequity in health initially by having a stronger impact on the well-to-do families than poorer ones. However, the gap will close over time, and disadvantaged families may catch up .
Sweden is a reasonably homogenous society with comparatively equal income distribution , consequently we found no health inequity when using BMI as a health indicator . Still there was a distinct unequal distribution of sociodemographic backgrounds between the two populations in our study. Also, an uneven distribution was evident despite that the three IDEFICS municipalities were largely a bit above or similar to the average national socioeconomic level. All the participants at the eight centres in the IDEFICS study were convenient samples, not nationally representative. The educational level of the various populations appeared to vary largely between the centres , indicating that the selection mechanisms might differ. We find it likely that a selection bias occurred in all countries but the pattern is probably unique for each one.
Our second hypothesis related to BMI was not supported. The BMI SDS Index at age of inclusion did not differ significantly between the populations. The IDEFICS study was devoted to young children aged 2–9 years. In this age group, the well-known stigma of childhood obesity may be less severe than for older school children . Parent’s lack of perceiving their children’s accurate overweight or obese weight status is another possible explanation for attendance in the IDEFICS study. A previous study within IDEFICS showed that between 51% and 77% of parents to children with overweight classified their children as normal weight, and about 57% to 85% of parents of children in the obese category classified their children as “slightly too overweight” .
We found that the growth characteristics of the study populations at birth up to 5.5 years of age were very similar. At 8 years of age, the BMI SDS and BMI categories according to IOTF differed significantly. Growth data collected from the health care records showed that 2.9% of the IDEFICS population were in the obese category, whereas the prevalence in the referent population was 4.5% (p = 0.03) at 8 years of age (Table 2). Our interpretation is that the age-related development of BMI differed between the two populations. A possible explanation for this could be diverse effects on the populations over time of the “obesogenic environment” . In two studies of Swedish pre-school children, the growth development was different in populations according to differences in socioeconomic characteristics [33, 34]. In one of these studies, growth data did not differ by socioeconomic factors at birth, whereas children at 4 years of age in the more disadvantaged areas had a significantly higher prevalence of overweight and obesity .
In the present study, a selection bias in the IDEFICS population was demonstrated. Not one but several socioeconomic characteristics pointed towards a clear difference between the populations. Sociodemographic background and multiple adverse circumstances are interrelated in a complex pattern . In the reference population, lower education and incomes and more financial support from society were present. Families with these characteristics may have less capacity to resist environmental influences and protect their children from them . Development of obesity in children and a higher prevalence of smoking among mothers in the referent population reflect a social patterning in agreement with others . Immigrant families, especially if living in a deprived area, have a higher prevalence of overweight and obesity compared with Swedish adults . The reference population had a higher prevalence of immigrants in this study. This was also the strongest determinant for belonging to the reference population (Table 4), and could be an important factor in the diverse development of higher BMI at 8 years of age that was demonstrated in this study.
Considering our results, we propose the following strategies to increase representativeness in health surveys and community interventions: exploit all available socio- demographic and municipality statistics; make use of focus groups consisting of local community officials with inside knowledge of the community; to overcome culture barriers, use culture bearers, adapt and translate written and oral information to residents with foreign background and short education; single parents may benefit from flexible time-schedules in time and setting; survey and study personnel might also perform their work in the geographic vicinity of the target populations.
The municipalities chosen to participate in the IDEFICS study were not randomly selected, although efforts were made to choose municipalities corresponding to the average Swedish municipality. The distribution of participants was necessary to adjust to the intervention design of the IDFICS study, with recruitment of one half of the participants from one municipality and the other half from two others. Ethical approval to study the 934 non-participating families was not granted. It would have been of great value to determine the characteristics of this group. Another limitation is the relatively low AUC (0.59) of our model. However, many other circumstances that are not possible to measure and include in the model may contribute to the outcome.
In Sweden, the unique PIN makes it possible to link different national official register data at an individual level . Using the PIN, each child in the IDEFICS study was closely matched to the referent child living in the same municipality, with only ± 1 month’s differences in age. The Swedish registers are very complete, derived directly from the authorities and have very little missing data, granting the validity of information. Further, the measured longitudinal child growth data from the health records at CHC and SHC were available for 95% of the eligible IDEFICS and 89% of the referent populations. An important strength was the unique opportunity to link the growth and register data.