The present study aimed to investigate possible associations between maintenance vs. change in body weight and subsequent outpatient health care utilisation in terms of number of visits to physicians. GP utilisation by participants who stayed in the same BMI category increased linearly with BMI category. Consistent with previous cross-sectional studies [23
], while among those who moved to a higher category, all overweight groups reported more GP visits than those who had maintained a normal weight (especially those who had been moderately obese), among those who lost weight, those who had been preobese and especially those who had been severely obese at baseline reported higher utilisation. Internist utilisation was highest among the moderately obese at baseline. After inclusion of incident diabetes and first cancer in the model, those in this group who had either maintained their weight or gained weight by follow-up reported significantly more visits than those who stayed in the normal weight group. Concerning other physicians, only participants that switched from the moderate to severe obesity group reported significantly more visits than those who had stayed in the normal weight group. When summing up utilisation for all physicians, all BMI groups except the preobese and severely obese who lost weight showed increased utilisation.
Several study limitations should be taken into account. First, it is possible that utilisation is underestimated in the present analysis since both baseline and follow-up survey non-response may be associated with higher burdens of morbidity. For instance, a non-response analysis comparing participants in the S4 survey and 49% of its non-responders showed that non-responders more often had a lower level of education (German Hauptschule, i.e. low academic level secondary school: 65% vs. 54%) and fair or poor self-rated health (28% vs. 21%), were more often unmarried (34% vs. 29%) and smokers (29% vs. 26%), and more frequently reported physician visits in the last four weeks (46% vs. 38%), myocardial infarction (6% vs. 3%) and diabetes (7% vs. 4%). It is possible that similar patterns may be found for the 3,031 baseline participants who dropped out of the follow-up surveys in the present analysis. For example, among baseline participants aged 64 or less (n=7,296), 37.3% fell into the normal weight range, 42.3% were preobese, 15.2% were moderately obese, and 5.3% were severely obese. At follow-up drop-out was slightly higher among moderately and severely obese groups (14.4% and 4.8%) than among those in the normal weight group. In addition, caution should also be taken when interpreting the effects of changes from normal weight to moderate or severe obesity, preobesity to severe obesity, moderate obesity to normal weight, and severe obesity to preobesity or normal weight due to subsample sizes of 20 participants or less.
Second, the present surveys are restricted to participants of German nationality. Since studies have repeatedly shown that obesity is more prevalent in migrant populations [35
], the results cannot be extrapolated to the total resident population of Germany without further assumptions.
Third, utilisation data in follow-up surveys F3 and F4 were assessed retrospectively over time horizons of 12 and 3 months, respectively. Thus, inaccuracies in the self-reported data cannot be excluded. On one hand, the validity of recalls of physician visits over a time period as long as 12 months is uncertain and may render underestimations. On the other hand, the 3-months F4 data were extrapolated to 12 months. Again, underestimation is possible since a response of “no utilisation” was coded as zero for one year even if, e.g. a participant had visited a physician four months ago. This may also explain the significant effects of the study sample in terms of lower outpatient utilisation in F4 compared to F3. On the other hand, if someone had 10 physician visits in the last three months due to acute illness, this was extrapolated as 40 visits in the year preceding the survey, in which case overestimation cannot be excluded. However, there are no indications that these limitations have biased utilisation differentials between the groups defined by maintenance of or changes in BMI category. Also, the 3-months time-slot in F4 did not refer to one and the same time of the year for all participants of the survey since it was conducted over a time period of 18 months.
Fourth, utilisation was assessed for the year before follow-up only, i.e. a single time period, implying that no changes in utilisation from baseline to follow-up could be modelled and tested for their association with changes in body weight. Thus, utilisation habits – especially those with regards to GP utilisation – which may be only partially related to need factors could not be controlled for. However, we did control for proxy variables for such habits (i.e. sex, age and SES), suggesting that the effects found for the BMI development variable may be attributed specifically to it and possible alternative explanations (i.e. confounding factors), such as aging leading to both weight gain and to higher utilisation, may be ruled out.
Fifth, the present analysis did not adjust for health-related behaviours such as smoking, alcohol consumption, physical activity and diet/nutrition. The rationale for this restriction was twofold. On one hand, such behaviours are antecedents of BMI development, and thus rather more distal than proximal factors in the hypothetical causal chain of behaviours leading to ill-health leading to health care utilization. On the other hand, to account for these factors in a way parallel to that regarding BMI, developments in smoking, alcohol consumption, physical activity and diet/nutrition would have to be modelled, which would have implied an analytical complexity going beyond the present examination. Sixth, incident diseases other than diabetes and first cancer (e.g. gastrointestinal disorders) were not accounted for. However, it is planned to scrutinize the role of a wider range of physical (co-)morbidities using the Physical Functional Comorbidity Index (PFCI) [21
], which however is not available for the S3/F3 and S4/F4 longitudinal cohorts as yet.
Lastly, in January 2004, a German health care reform introduced a €10 charge for the first outpatient visit to a physician in each quarter for all adults covered by statutory health insurance. One analysis concerning differences by SES has demonstrated that avoiding a physician visit due to these charges is comparatively common among low income groups [36
]. Since utilisation data for the present study had been collected in 2004/05 (F3) and 2006/08 (F4) after the introduction of the €10 fee, it is possible for these charges to have had some impact on participants’ visits to physicians, at least at the beginning of the observation period. However, contrary to expectations and public opinion, the effects of this new copayment on decisions to visit physicians have been shown to be rather limited [37
], so potential bias should be minor and may not have affected the differences between BMI development subgroups. It should also be noted that this study did not analyse the reasons for utilisation, and did not focus on the referral system from general practitioners to medical specialists (as one specificity of the German health care system).
Despite these limitations, the findings of this study are largely consistent with the results of previous analyses on associations between predisposing and enabling factors, such as sex, age and SES, and outpatient utilisation. Women had more visits to GPs and “other physicians” than men, which may be explained by the finding that communication and shared decision making are more common between GPs and female patients [39
]. Also, similar to the findings of [40
], significantly more GP visits were reported by lower SES groups in this study. Interestingly, none of the significant age effects found in the regression analyses were considerably changed by inclusion of BMI development or incident comorbidity into the models, probably reflecting the fact that these selected health conditions are only part of the age-related morbidity spectrum in the German population. At the same time, future studies (optimally with larger samples) should further examine the association between age and utilization for different BMI- and body weight development groups using appropriate indicators (contrast variables).
The present study also found that compared to participants who were not overweight at baseline and follow-up, those who went from being moderately obese (baseline) to severely obese (follow-up) showed the most consistent excess utilisation rates across all types of physicians, which was not explained by incident diabetes or first cancer. This finding could suggest that preventing weight gain in already obese groups may potentially prevent comorbidities as well. Those remaining moderately obese tended to visit internists comparatively often, as did those who lost weight after moderate obesity, an effect which became insigificant after inclusion of incident first cancer. Incident first cancer also affected excess utilisation of non-GPs among those with stable severe obesity (again, the latter effect was insignificant after inclusion of this comorbidity). Participants that lost weight after baseline severe obesity were the group with the highest GP utilisation rate, which only partially changed after inclusion of incident diabetes. Future research should examine the needs of this specific subgroup, especially since this group did not report excess utilisation of other physicians and reported fewer (though insignificantly fewer) visits to internists. It is also unclear whether these patterns reflect high utilisation of other health care services (e.g. inpatient health care services) and/or health gains associated with this weight loss.