Childhood Obesity and Health Care Utilization: Empirical Evidence from Austrian Administrative Data

Abstract

Background:

Increases in pediatric obesity have been associated with higher levels of health care utilization. There is currently a lack of knowledge on the therapeutic drivers of increased health care use.

Objective:

To examine the association between different measures of health care utilization and BMI among children.

Methods:

We linked cross-sectional administrative data from a regional health insurance fund in Austria with objectively measured BMI from routine school health examinations in 6–15-year-old children (n = 13,493). Differences in probabilities of annual health care utilization (drug prescriptions by therapeutic classification, physician visits by medical specialty, and hospitalizations) were compared between children with normal weight, overweight, or obesity using Probit regressions.

Results:

Children with obesity had a 1.6 and 8.6 percentage points (pp) higher probability of outpatient doctor visits and prescribed medication, respectively (all p < 0.05). Children with overweight were intermediate. There was a higher probability of consulting a general practitioner, pediatrician, or orthopedist, and higher levels of prescribing for children with obesity across most common drug groups. Children with obesity were ∼40% more likely to receive medication for musculoskeletal and for mental health problems. This was reflected in orthopedic clinic appointments but not in psychology clinics. There were no major differences by gender or age, or parental socioeconomic status.

Conclusions:

Our data show clear and objective evidence of higher health care utilization by children with obesity. This highlights the importance of policy interventions to curb obesity in children and young people.

Introduction

The World Obesity Federation states that, “Childhood obesity is one of the most serious global public health challenges of the 21st century, affecting every country in the world.”¹ The 10-fold increase in prevalence in just 40 years is concerning because of the number of children affected (124 million by 2016 estimates) and the implications for adult health. Obesity in childhood tracks into adulthood² and impacts morbidity and mortality later in life.³ This has significant cost implications to individuals psychologically and to health care systems in managing physical comorbidities.

There is accumulating evidence of the early health care needs of children with obesity. This can be seen directly in the consumption of, and expenditure on, health care services for children with obesity. This is evidenced in the outpatient sector,^4–8 in hospitalization,^5,8 prescription drugs,^8–11 and in total medical expenditure.^4,6,12–14 A recent systematic review concluded there was an overall positive association of obesity in children with increased outpatient and emergency department visits.¹⁵ However, variation in the direction and strength of this association was also noted. Some studies find no greater use of health care services^{4,7,9,16–18} or in total expenditure.¹⁸ Importantly, there has been little systematic examination of the impact or socioeconomic status (SES) and no analysis of reported health care use by children's sex.¹⁵

The inconsistency in results is a likely consequence of differences in the outcome measures, methods of data collection, and the underlying data themselves. Many studies rely mainly on self-reported height, weight, and health care utilization. Self-reports have inherent measurement error.^19,20 Some assess health care utilization over a relatively short time period or over a narrow range of children's age. Additionally, studies using administrative data (e.g., routine clinical data) often apply convenience sampling for which external validity is unclear. For example, families are recruited only when they seek medical care. There is therefore a need for high-quality and detailed information on actual health care utilization from large and representative samples of children.

We report on health care utilization by children from different weight groups using two linked data sources. One yielded data on outpatient clinical attendance, hospitalization, and drug prescriptions. These were linked to the second, which held objectively measured weight and height of children. We were able to distinguish types of outpatient visit according to clinical discipline, and drug categories (as per the Anatomical Therapeutic Chemical [ATC] classification groups). This permitted information on the broad types of health problems being addressed, by medical specialty and prescribed drug. In addition, we were able to include information on family SES and children's sex and age in the analysis. We hypothesized that children with obesity would have a greater number of outpatient visits and higher levels of drug prescriptions in comparison with their normal-weight peers.

Methods

Data

We used health care utilization data from 2015 provided by the Regional Health Insurance Fund of Upper Austria. This fund covers all private-sector employees, unemployed individuals, social benefit recipients, and their coinsured dependents in the province of Upper Austria, one of the nine regional states in Austria. The health insurance fund covers 75% of the state population. Membership cannot be freely chosen as affiliation is determined by an individual's occupation and place of residence. The fund register includes detailed information on outpatient visits, hospitalization episodes, and medication. In addition to health care utilization, the register provides information on the SES characteristics of insured persons such as their labor market status, occupation, and regional indicators for place of residence.

Anthropometric information came from routine school examinations in which school physicians assessed the health of children on behalf of the regional government. Health examination data were available in 2015 for 130 primary and secondary schools in Upper Austria, when children were between 6 and 15 years of age.

Outcome Variables and Weight Status

We used BMI-for-age z-scores provided by the World Health Organization, defining underweight [< −2 standard deviations (SD) from the median], normal weight (−2 to +1 SD), overweight (>1 SD), and obesity (>2 SD) among children.²¹ The outcome variables were binary indicators regarding whether (1) the child attended an outpatient clinic (in primary or secondary care), (2) had any hospital stay, or (3) received a medical prescription in 2015. We distinguished outpatient attendance by clinical specialty, and medication was further differentiated by therapeutic drug groups according to the ATC classification system.

Study Sample Characteristics

Our sample comprised 13,557 children 6–15 years of age for whom information on their BMI and health care utilization was available in 2015. We excluded from the sample 64 children with unrealistically high or low BMIs (>50 or <11) or who were extreme outliers with more than 10,000 € health care expenditures per year. The final sample was 13,493 children with a mean age of 122 months (∼10 years), 47% of whom were female (see Table 1). Some 13% of the children had obesity, 19% were overweight, 65% were of normal weight, and 3% were underweight.

Table 1.

Sociodemographic Characteristics of the Sample of Children and Their Parents

	Total sample	With obesity	Overweight	Normal weight	Underweight
N	13,493	1763	2554	8827	349
Age (years)	10.2	10.5	10.6	10.0	10.3
Female (%)	47	37	49	49	43
Labor market status of main insured adult family member (%)
White-collar worker	40	25	34	44	46
Blue-collar worker	37	46	41	34	35
Unemployed	9	12	11	9	8
Other	14	16	15	14	11

Table 2 shows the percentages of obesity and overweight for boys and girls with different age. Overall, the prevalence of overweight and obesity increased with age in both sexes. With the exception of 6-year-olds, more than a quarter of children in every age group were overweight or with obesity. Obesity was more prevalent among boys while overweight percentages were higher for girls. Table 1 further reveals that the children's weight was associated with the SES of the family. For example, the proportion of white-collar workers among parents of children with normal weight was 44%, compared with only 25% for children with obesity.

Table 2.

Overweight and Obesity by Age

	Boys		Girls
Age (years)	Overweight (%)	Obesity (%)	Overweight (%)	Obesity (%)
6	12.48	9.49	13.99	7.84
7	13.09	12.64	15.96	7.55
8	15.82	13.42	17.33	10.76
9	16.06	17.50	19.67	9.24
10	22.18	17.97	20.73	12.95
11	23.32	19.49	24.43	12.22
12	21.04	16.86	23.23	11.71
13	21.58	16.11	25.70	11.89
14	20.73	16.64	21.85	10.82
15	27.52	20.13	22.22	9.26

Statistical Analysis

We compared health care utilization between children of normal weight, with overweight, and with obesity based on Probit models. For all outcomes, we estimated a baseline model that controlled for age and sex of the child only (Model A). In Model B, we additionally controlled for the labor market status of the main insured person (white collar, blue collar, unemployed, other) and indicators for the political district of the school to allow for potential confounding effects of families' SES. To study effect heterogeneity, we estimated separate regressions for boys and girls and also for younger (6–10 years old) and older (11–15 years old) children. As the focus of this study was on the utilization of health care services by children with obesity, we did not include children with underweight in the regression analyses. This research has been approved by the research cooperation unit of the Austrian Health Insurance Fund and the steering committee of the Upper Austrian State Healthcare Fund.

Results

BMI and Health Care Utilization

Table 3 summarizes the regression results. For both estimated models, we found statistically significant differences in outpatient visits and prescribed medication between the weight groups. Results from model A revealed that the probability that a child with obesity making an outpatient visit was increased by 1.6 percentage points (pp). The probability of receiving prescription drugs was 8.6 pp higher than for children with normal weight. In contrast, we did not find statistically significant differences in hospitalization among the analyzed weight groups.

Table 3.

Health Care Utilization of Children With Overweight and Obesity

	Mean	Model A		Model B
	Mean	Obesity	Overweight	Obesity	Overweight
Any physician	0.900	0.016^** (0.008)	0.005 (0.007)	0.015^* (0.008)	0.004 (0.006)
Hospitalization	0.067	0.003 (0.006)	−0.001 (0.006)	−0.001 (0.006)	−0.004 (0.006)
Prescription drugs	0.484	0.086^*** (0.013)	0.047^*** (0.011)	0.070^*** (0.013)	0.039^*** (0.011)
Clinician visits
GP/pediatrician	0.774	0.055^*** (0.011)	0.015 (0.009)	0.046^*** (0.011)	0.011 (0.009)
ENT	0.110	0.015^* (0.008)	0.011 (0.007)	0.016^** (0.008)	0.012^* (0.007)
Orthopedic	0.045	0.014^*** (0.005)	0.013^*** (0.004)	0.013^** (0.005)	0.012^*** (0.004)
Laboratory	0.078	0.008 (0.007)	−0.010 (0.006)	0.004 (0.006)	−0.011^* (0.006)
Psychology	0.015	0.000 (0.003)	0.002 (0.003)	0.001 (0.003)	0.002 (0.002)
Drug prescriptions for main ATC groups^a
Anti-infectives	0.273	0.058^*** (0.011)	0.036^*** (0.010)	0.052^*** (0.012)	0.034^*** (0.010)
Respiratory system	0.145	0.058^*** (0.009)	0.031^*** (0.008)	0.045^*** (0.009)	0.024^*** (0.008)
Musculoskeletal system	0.083	0.032^*** (0.007)	0.017^*** (0.006)	0.017^*** (0.006)	0.009^* (0.005)
Dermatologicals	0.057	0.019^*** (0.006)	0.014^*** (0.005)	0.013^** (0.006)	0.011^** (0.005)
Alimentary tract	0.053	0.015^*** (0.006)	0.000 (0.005)	0.008 (0.005)	−0.003 (0.005)
Nervous system	0.044	0.018^*** (0.005)	0.004 (0.005)	0.009^** (0.004)	−0.001 (0.004)

The table includes marginal effects of a Probit estimation and their standard errors in parentheses for the baseline and extended models A and B. The second column shows the mean of the dependent variable. ^*, ^**, and ^*** indicate statistical significance at the 10%, 5%, and 1% levels.

Anti-infectives include all drug prescriptions of ATC main group J. Respiratory system drugs include ATC group R, musculoskeletal system drugs include ATC group M, dermatologicals include ATC group D, alimentary tract drugs include ATC group A, and nervous system drugs include ATC group N.

ATC, Anatomical Therapeutic Chemical; ENT, ear–nose–throat; GP, general practitioner.

The separate analysis of visits by clinical specialty shows that children with obesity had a higher probability of visiting general practitioners (GPs) or pediatricians (5.5 pp) and orthopedists (1.4 pp). With regard to medication for therapeutic use, the regression results indicate higher prescribing for children with obesity in all common ATC groups. The largest differences were in antibiotics and drugs for the respiratory system (5.8 pp), followed by medication for the musculoskeletal system (3.2 pp) and dermatologicals (1.9 pp).

We also found higher health care utilization in children with overweight compared with children with normal weight. They received more prescription drugs in general, and in the drug groups anti-infectives, respiratory system, musculoskeletal system, and dermatologicals. Children with overweight were also more likely to visit orthopedists. The effects were quantitatively and/or qualitatively smaller than for children with obesity, indicating that in terms of health care utilization, children with overweight were an intermediate group between children of normal weight and with obesity. The estimates in Model B remained qualitatively unchanged, although the point estimates of the obesity and overweight coefficients were slightly lower overall (see Table 3). This suggests that the SES of families explained (a small) part of the variation in health care utilization among the weight groups.

Effect Heterogeneity

Table 4 depicts separate estimation results for younger (6–10 years old) and older (11–15 years old) children. For both age groups, we found children with obesity or who were overweight were more likely to receive prescription drugs. However, the quantitative effect for children with obesity from the younger age group (10.8 pp) was substantially higher than that in the older group (6.8 pp). This effect was mainly driven by the higher utilization of antibiotics in the younger cohort (8.5 pp vs. 3.5 pp). The use of physician services by children with obesity was also significantly higher for the younger cohort than that for older children. This applied to services from GPs and pediatricians as well as orthopedics and laboratory services. The effects of overweight on the demand for medical services were quantitatively and qualitatively smaller than those of obesity in both age groups. Of note, the increase in spending on medications for the respiratory and musculoskeletal system and also for dermatologicals in children with overweight occurred only in the older age group.

Table 4.

Heterogeneous Results (Model A)—by Children's Age

	6–10 Years (N = 7039)			11–15 Years (N = 6105)
	Mean	Obesity	Overweight	Mean	Obesity	Overweight
Any physician	0.916	0.022^** (0.011)	0.015^* (0.009)	0.881	0.010 (0.012)	0.004 (0.010)
Hospitalization	0.065	−0.000 (0.009)	0.000 (0.008)	0.069	0.007 (0.009)	−0.002 (0.008)
Prescription drugs	0.494	0.108^*** (0.019)	0.048^*** (0.016)	0.474	0.068^*** (0.018)	0.046^*** (0.016)
Clinician visits
GP/Pediatrician	0.798	0.072^*** (0.016)	0.021 (0.013)	0.747	0.043^*** (0.016)	0.010 (0.014)
ENT	0.123	0.003 (0.012)	0.020^* (0.010)	0.096	0.023^** (0.010)	0.002 (0.009)
Orthopedic	0.039	0.017^*** (0.007)	0.015^*** (0.006)	0.052	0.008 (0.008)	0.009 (0.007)
Laboratory	0.067	0.020^** (0.009)	0.005 (0.008)	0.091	−0.004 (0.010)	−0.025^*** (0.009)
Psychology	0.016	0.007^* (0.004)	0.008^** (0.004)	0.015	−0.009^* (0.005)	−0.004 (0.004)
Drug prescriptions for main ATC groups^a
Anti-infectives	0.290	0.085^*** (0.017)	0.037^** (0.015)	0.253	0.035^** (0.016)	0.034^** (0.013)
Respiratory system	0.141	0.060^*** (0.012)	0.016 (0.011)	0.149	0.055^*** (0.012)	0.044^*** (0.011)
Musculoskeletal system	0.075	0.030^*** (0.009)	0.011 (0.008)	0.091	0.036^*** (0.010)	0.023^*** (0.009)
Dermatologicals	0.050	0.017^** (0.007)	0.009 (0.007)	0.067	0.022^*** (0.009)	0.019^*** (0.007)
Alimentary tract	0.049	0.020^*** (0.007)	0.004 (0.007)	0.058	0.011 (0.008)	−0.003 (0.007)
Nervous system	0.035	0.021^*** (0.006)	−0.002 (0.007)	0.054	0.015^* (0.008)	0.007 (0.007)

The table includes marginal effects of a Probit estimation and their standard errors in parentheses for different age groups. The second column shows the mean of the dependent variable. ^*, ^**, and ^*** indicate statistical significance at the 10%, 5%, and 1% levels.

As can be seen from Table 5, boys with obesity were significantly more likely to see a GP or pediatrician (6 pp), an ear–nose–throat (ENT) specialist (1.9 pp), or an orthopedist (1.6 pp) compared with the normal-weight reference group. A similar effect on GP/pediatrician visits was found in girls with obesity (4.9 pp), whereas their impact on ENT and orthopedic services remained insignificant. Boys and girls with obesity had a nearly 9 pp higher utilization of medications. The corresponding effects for boys and girls with overweight were 5and 4.4 pp, respectively. Boys and girls with obesity had a higher prescription probability for the most frequently used ATC categories such as antibiotics, drugs for the respiratory system, those for the musculoskeletal system, dermatologicals, and for the nervous system.

Table 5.

Heterogeneous Results (Model A)—by Children's Sex

	Boys (N = 6925)			Girls (N = 6219)
	Mean	Obesity	Overweight	Mean	Obesity	Overweight
Any physician	0.900	0.024^** (0.009)	−0.002 (0.009)	0.900	0.004 (0.012)	0.014 (0.010)
Hospitalization	0.070	0.001 (0.009)	−0.001 (0.008)	0.064	0.006 (0.010)	−0.001 (0.008)
Prescription drugs	0.484	0.088^*** (0.017)	0.050^*** (0.016)	0.485	0.086^*** (0.021)	0.044^*** (0.016)
Clinician visits
GP/pediatrician	0.782	0.060^*** (0.014)	0.010 (0.013)	0.765	0.049^*** (0.018)	0.020 (0.014)
ENT	0.110	0.019^* (0.010)	0.017^* (0.010)	0.105	0.009 (0.013)	0.005 (0.010)
Orthopedic	0.044	0.016^** (0.006)	0.013^** (0.006)	0.047	0.010 (0.009)	0.012^* (0.007)
Laboratory	0.076	0.009 (0.009)	−0.011 (0.009)	0.080	0.007 (0.011)	−0.009 (0.009)
Psychology	0.020	0.001 (0.005)	0.006 (0.004)	0.010	−0.000 (0.004)	−0.001 (0.003)
Drug prescriptions for main ATC groups^a
Anti-infectives	0.265	0.053^*** (0.015)	0.045^*** (0.014)	0.282	0.068^*** (0.018)	0.026^* (0.014)
Respiratory system	0.159	0.068^*** (0.012)	0.036^*** (0.011)	0.130	0.043^*** (0.013)	0.027^** (0.010)
Musculoskeletal system	0.086	0.032^*** (0.009)	0.019^** (0.009)	0.078	0.033^*** (0.010)	0.016^* (0.008)
Dermatologicals	0.051	0.017^** (0.007)	0.008 (0.007)	0.064	0.019^** (0.010)	0.020^*** (0.007)
Alimentary tract	0.052	0.011^* (0.007)	−0.009 (0.007)	0.055	0.019^** (0.009)	0.009 (0.007)
Nervous system	0.050	0.020^*** (0.007)	0.002 (0.007)	0.037	0.016^** (0.007)	0.005 (0.006)

The table includes marginal effects of a Probit estimation and their standard errors in parentheses for boys and girls. The second column shows the mean of the dependent variable. ^*, ^**, and ^*** indicate statistical significance at the 10%, 5%, and 1% levels.

Summarizing our heterogeneity analysis, the greater likelihood of children with obesity making an outpatient clinical visit was apparent only in boys and in the group of children who were between 6 and 10 years of age. The quantitative effect amounts to slightly more than 2 pp each. However, the detailed analysis of physician visits revealed a higher probability of consulting a GP or pediatrician in both sexes and age groups. Drug use was increased in boys and girls with overweight and obesity and for both age groups with quantitative effects between 4.4 and 10.8 pp.

Discussion

As hypothesized, there were significantly more outpatient visits and prescribed medication given to children with obesity. Looking in detail at clinical specialty, there was a higher probability of consulting a GP, pediatrician, or orthopedist, but no major differences by gender or age. With regard to medication, we found significantly higher levels of prescribing for children with obesity across most common ATC codes. Children with overweight were intermediary in health care utilization, between those of normal weight and those with obesity.

The main novelties of this study are in the objective nature of the data (actual health care utilization and measured weight), the representativeness of the sample, the age range of children included, the time period over which health care utilization was monitored (1 year), and the distinction between clinical specialties (and prescribing). Only two studies have previously reported on detailed therapeutic prescribing. They show mixed outcomes. One Canadian study observed that children with overweight or obesity were prescribed respiratory and nervous system drugs more frequently. It also found a decreased use of drugs related to the alimentary tract.¹⁰ A study from the United Kingdom observed increased prescribing for respiratory conditions and infections, but no significant difference in other drug groups.¹¹

Both studies relied on survey data where respondents had to recall and report the medication used. In addition, they captured medication taken either only in the last month¹¹ or regular current medication.¹⁰ In contrast, by considering records of prescribing over a full year we found significantly higher levels of prescribing for children with obesity in most ATC codes, suggesting generally poorer physical health rather than the presence of a specific set of health problems.

Our findings are consistent with a large literature on comorbid conditions and complications associated with obesity. Already during childhood, obesity affects virtually every organ system in an adverse manner, including the endocrine, gastrointestinal, pulmonary, cardiovascular, and musculoskeletal systems.^22–24 By way of example, a study among more than 1 million 17-year-olds in Israel found that obesity was related to the presence of high blood pressure, diabetes, and hyperlipidemia.²⁵ Medical treatment of such conditions is a plausible explanation for the increases in health care utilization. The existing literature also provides mixed results about the importance of sex in the association between childhood obesity and comorbidities.²² We found similarities among boys, girls, and different age groups. More research with larger samples is needed to further assess potential heterogeneities in health care utilization.

Although we observed higher levels of obesity among children from families with lower SES, the results indicate that SES does not explain the association between health care utilization and childhood obesity. SES may affect nutrition, physical activity, and other aspects of lifestyle, and there is clear evidence that low SES and obesity are associated with higher income countries.²⁶ At the same time, there is strong evidence that parental SES affects child health.²⁷ We found higher levels of health care utilization for children with obesity even after allowing for SES, suggesting that there is an independent association between obesity and health care utilization. Furthermore, neither children's sex nor their age affected these associations.

In addition to the study novelties referred to above, a further study strength was the large sample size. We also avoided nonresponse issues, which are common in survey studies. An important limitation is that we have no information on health care utilization that was not covered by health insurance such as over-the-counter drugs or private health care. Our data come from a single province in Austria, limiting generalization to countries with different income levels and health care systems. In addition, we have no information on how health care usage differs between families and children from different ethnic backgrounds.

The cross-sectional nature of the study cannot identify causal effects of BMI on health care utilization. There may be unobserved confounding variables and problems of reverse causation, that is, diseases or their treatment can lead to increases in body weight.^* A recent literature has aimed to estimate the causal effect of BMI using the BMI of a child's biological parents in instrumental variable approaches.^28–30 The idea is that children inherit genes from their parents, and genes affect the probability of becoming overweight and obese. These studies find significantly larger effects of BMI on health care utilization compared with conventional regression analysis. This suggests that studies like ours, which merely aim to estimate the association of BMI and health care utilization, may underestimate the true impact of child overweight and obesity. However, a concern related to these approaches is a potential violation of the underlying assumption that the instrument affects a child's health care utilization only through its effect on BMI.²⁸ For example, common environmental factors could affect weight and health care utilization of all family members.

A promising new avenue is the direct use of detailed genetic information. A recent study constructed genetic risk scores (GRSs) based on known genetic variants associated with BMI and found significant effects of maternal and paternal transmitted GRSs on child weight.³¹ Interestingly, the study also found large effects of parents' BMI on child overweight even after allowing for parental GRS. This indicates that other genetic or environmental factors play an important role in the intergenerational transmission of overweight and obesity.

In conclusion, our data show clear and objective evidence of significantly high health care utilization, clinician time, and prescribed medication for children with obesity. While the difference in percentage points in comparison with healthy weight children may seem small, then the base rates need to be taken into consideration. For example, over the 12 months of recording, only 7.4% of children with normal weight received medication for musculoskeletal problems. The percentage point difference of 3.5 means that children with obesity were 47% (3.5/7.4) more likely than peers with normal weight to receive this medication. They were also 53% more likely to receive medication acting on the nervous system. This is a broad categorization but includes treatments for ADHD and depression. It is of note however, that while children with obesity were more likely to be seen in orthopedic clinics, they were no more likely to be seen in psychology clinics (and only 6% more likely seen by a GP or pediatrician). This is surprising given the early burden of obesity in terms of mood disorder, low self-esteem, and quality of life.^32,33 This may indicate either an unmet health care need or one fulfilled by private (self-funded) health care. Overall, these findings add to our understanding of the burden of poor health for children with obesity and their families and adds evidence to the need for policy measures to prevent obesity and intervene early for those most at risk.

Footnotes

Acknowledgments

The authors gratefully acknowledge financial support from the Austrian Federal Ministry of Science, Research, and Economic Affairs (BMWFW) and the National Foundation of Research, Technology, and Development. They thank Felix Glaser for excellent research assistance.

Funding Information

No funding was received for this article.

Author Disclosure Statement

A.J.H. receives payment as an advisor for Slimming World (United Kingdom). Otherwise, no competing financial interests exist.

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