Excess body weight increases the burden of age-associated chronic diseases and their associated health care expenditures.

Aging and excessive adiposity are both associated with an increased risk of developing multiple chronic diseases, which drive ever increasing health costs. The main aim of this study was to determine the net (non-estimated) health costs of excessive adiposity and associated age-related chronic diseases. We used a prevalence-based approach that combines accurate data from the Health Search CSD-LPD, an observational dataset with patient records collected by Italian general practitioners and up-to-date health care expenditures data from the SiSSI Project. In this very large study, 557,145 men and women older than 18 years were observed at different points in time between 2004 and 2010. The proportion of younger and older adults reporting no chronic disease decreased with increasing BMI. After adjustment for age, sex, geographic residence, and GPs heterogeneity, a strong J-shaped association was found between BMI and total health care costs, more pronounced in middle-aged and older adults. Relative to normal weight, in the 45-64 age group, the per-capita total cost was 10% higher in overweight individuals, and 27 to 68% greater in patients with obesity and very severe obesity, respectively. The association between BMI and diabetes, hypertension and cardiovascular disease largely explained these elevated costs.

proxies of medical expenditures [5][6][7][8][9][10][11]. In this study, we used a patient-based approach combining health care cost data and accurate anthropometric and clinical informations collected by general practitioners in a large representative longitudinal sample of more than 550 thousand Italian men and women, homogeneously distributed across all Italian regions. Moreover, Italy is an ideal setting for this type of analysis, since its National Health Service provides universal and substantially free health care access to all citizens, with 87% of medical services publicly financed [12], avoiding problems of patient selection associated with insurance based health care systems. Finally, by modelling this data with a seemingly unrelated regression equation (SURE) statistical method, we were able to disentangle the direct and indirect (i.e. obesity-associated diseases) impact of BMI on health care system spending. Table 1 summarizes the breakdown of the variables according to BMI classes, showing large differences in demographic and clinical parameters such as age and prevalence of comorbidities. Hypertension and type 2 diabetes were the most common BMI-associated health conditions and their prevalence shows a strong increase with increasing BMI (p=0.0001 for all BMI categories with respect to normal weight individuals). In addition, the prevalence of dyslipidaemia, CVD and arthrosis were higher in individuals with overweight and obesity than in normal weight individuals in both younger (<55 yrs) and older (>55 yrs) patients (p=0.0001 for all BMI categories with respect to normal weight individuals). There was a clear negative association between BMI and the proportion of individuals with no chronic disease (p=0.0001 for all BMI categories with respect to normal weight individuals). In contrast, the proportion of individuals affected by 2 or more chronic diseases increased sharply with raising BMI. Table 2 and Table 3 present the coefficient estimates of indirect (i.e. obesity-associated diseases), direct and differential effects of BMI on outpatient and total health care costs for each age group. The tables report marginal effects for each BMI category within each age specific subsample, as well as the percentage differences of the marginal effect estimate with respect to the annual average expenditure of normo-weight individuals.

BMI and healthcare costs
As shown in Figure 1, after adjusting for age, sex, geographic residence, and GPs heterogeneity, there was a J-shaped association between BMI and overall (direct and indirect) total health care expenditure, which was stronger among middle-aged and elderly individuals. Total health care expenditures among the middle-aged (45-64 yrs old) individuals with overweight, obesity, severe obesity and very severe obesity were 10%, 27%, 52% and 68% higher, respectively, than among those with BMI of 18.5 to 24.9 (p=0.0001) (third panel of Table 3). In absolute terms, outpatient costs were more strongly related to BMI among individuals aged 45 to 64 years. The annual mean costs among the overweight, obesity, severe obesity and very severe obesity patients were 76, 159, 237, and 310 euro higher, respectively, than in normo-weight individuals, which translates in a cost increase of about 18%, 38%, 57% and 75% for each BMI category, respectively (third panel of Table 2). In contrast, total costs were more strongly related to BMI among individuals aged 65+ years. The annual differential mean costs among the patients with overweight, obesity, severe obesity and very severe obesity were 75, 302, 719, and 790 euro higher, respectively, than in normal weight individuals (fourth panel of Table 3). Total overall (direct and indirect) costs were also significantly higher in underweight individuals than in normal weight individuals in all subsamples (Table 3). In particular, annual mean total costs among all underweight individuals were 138 euro higher than in the normo-weight subjects, which translates in a cost increase of 13% (first panel of Table 3).

Indirect and direct costs
The share of indirect costs within overall outpatient costs was the largest in overweight and obese men and women aged 45-64 years (Table 2). Moreover, the indirect costs of the underweight subjects were lower than those of the normo-weight individuals for each age group, and this differential was more pronounced in the elderly, amounting to 11% (Table 2).
In terms of total outpatient and inpatient health expenditure, direct costs were negative in the overweight and obesity groups, suggesting that after correcting for BMI-related pathologies, these patients on average had lower health care expenditures than normo-weight individuals. This direct cost differential turned positive for the category with severe and very severe obesity, and was particularly pronounced in the elderly (Table 3). Moreover, the total indirect costs in the underweight individuals were significantly lower than in the normoweight subjects, and this differential was particularly high in the elderly (fourth panel of Table 3). Finally, the total direct costs in the underweight men and women were substantially higher than in the elderly normoweight individuals. www.impactaging.com

Relative effects of BMI and adiposity-associated comorbidities on health care costs
As shown in figure 1, based on SURE analysis, the indirect costs of excess body weight in individuals with overweight and obesity explain the great majority of overall BMI-related costs both in terms of outpatient and total health care expenditures. Moreover, as shown in figure 2, when hypertension, diabetes and CVD were accounted for, the age-adjusted relation between BMI and total and outpatient health care costs was to a large extent eliminated.  www.impactaging.com

DISCUSSION
In this study, we evaluated the disease burden, and the direct and indirect effects of BMI on health care costs, in a large population of 557,145 men and women for whom accurate anthropometric, clinical and medical cost data collected by general practitioners were available. First, our data show a sharp increase in the proportion of individuals affected by 2 or more ageassociated chronic diseases with raising BMI. Second, our data show that after adjusting for age, sex, geographic residence, and GPs heterogeneity, BMI still has a relevant effect on both inpatient and outpatient health care expenditure. We found a strong J-shaped association between BMI and total health care costs, which was more pronounced in middle-aged and older adults. Third, our results demonstrated that hypertension, diabetes and CVD account for the largest share of outpatient and total health care expenditures.
Many studies have attempted to estimate the health care costs attributable to excess body weight. However, most published studies so far have estimated BMI-related costs by using a top-down approach which quantifies attributable fractions of costs associated with adiposityrelated diseases by modelling group and individual level data collected from National Health interview surveys [5][6][7][8][9][10][11]. Moreover, in these studies the determinants of BMI and the prevalence of chronic diseases were mostly self-reported and, therefore, subjected to bias [31]. To our knowledge, this is the first study to determine the direct and indirect health care costs by using a prevalence-based approach that combines data from a large observational dataset, containing computer-based patient records with accurate anthropometric and clinical information and precise medical cost data collected by general practitioners. Our data are generally in line with the estimates of other studies [21,22,25] showing that total health care expenditure of the overweight is around 3% higher than in normal weight individuals, while patients with obesity, severe obesity and very severe obesity spend respectively 18%, 41% and 50% more than their normal-weight counterparts.
Interestingly, we found that BMI-related costs vary substantially across age groups. With respect to normoweight individuals, the highest overall (direct plus indirect) outpatient expenditure differentials in absolute terms were found in the 45-64 age group, and in the 65+ age group for total out-and in-patient health care costs. This finding suggests that outpatient health care utilization in terms of drugs, medical visits and diagnostic tests resulting from excess body weight is particularly higher in the 45-64 age group. However, when inpatient expenditure (hospitalizations) was accounted for, the 65+ age group patients generated in absolute terms the majority of the BMI related health care costs.
It is essential, however, to establish the extent to which one or more obesity-related medical conditions may account for the variation in health care costs by BMI. Our sophisticated multivariate regression SURE analysis indicated that much of the increased costs can be attributed to three very prevalent chronic diseases: hypertension, type 2 diabetes and CVD. These data are in agreement with previous estimates of other studies [32,33]. However, this does not mean that BMI is not related to increased health care expenditure through other channels, because in our study individuals with severe and very severe obesity had high direct health care costs, net of the frequent adiposity-related medical conditions.
The prevalence of overweight and obesity in Italy, as in many other developed and developing countries, has been increasing steadily in the last few years [1,2,34]. Our data show that the rise in body weight is associated with a strong increase in the prevalence of several chronic diseases, including type 2 diabetes, hypertension, dyslipidemia, CVD, depression and arthrosis, especially in individuals aged 55+ years. Moreover, our data show that as BMI increases, the percentage of younger and older adults with two or more adiposityrelated medical conditions increases several fold, whereas the proportion of individuals with no chronic diseases diminishes by 2 fold in the 18 to 55-year-old age group and by 3 folds in individuals over 55-yearsold.
The results of our study provide firm evidence that the impact that excess body weight has on a set of chronic diseases, represents the largest component of health care expenditures. Considering our marginal BMI-related costs and the official statistics of obesity prevalence of the Italian adult population [35], we estimated that the overall BMI-related costs amount approximately to 4% of total health care expenditure of the Italian national health service (i.e. 4.5 billions of euro per year). This estimate, based on the self-and under-reported official prevalence rates of BMI among the Italian population represents a lower bound of the real costs of excessive adiposity in Italy. In fact, this estimate is slightly lower than the 5-10% found for the USA [23,26,29,30], and 4.5% for the UK [28], while somewhat higher than 2.3-3.5% found for Switzerland [27].

Strengths and limitations
It is important to highlight the strengths and limitations of this study. The use of clinical data (e.g. weights, www.impactaging.com heights, chronic disease diagnosis, test results, drug prescriptions, outpatient diagnostic tests, specialist visits and hospital admissions), collected for a large sample of patients and entered in an up-to-date computer-based database by trained GPs, is a major strength of this study. In addition, it is important to stress that the Italian National Health Service is a public and universalistic system, which provides substantially free health services for all citizens. According to OECD Health Data, in 2012 about 87% of medical services in Italy was publicly financed. This setting favours the external validity of the study and minimizes the selection problems related to presence of private insurance plans. Finally, the use of a multi-equation recursive model (SURE) to calculate the relative effects of BMI and adiposity-associated chronic diseases on health care costs represents another strength of this study, since it provides better and more efficient estimates of the cost of obesity and allows to study direct and indirect effects of BMI of health care expenditure. One major limitation of this study is the lack of alternative measures of adiposity, such as waist circumference or waist-hip ratio. Moreover, the analysis could potentially benefit by introducing covariates such as smoking or socio-economic status, enhancing the precision of the coefficient estimates, which are not available within this dataset. Finally, the estimates might suffer from a downward bias, as the control group includes individuals who, while not being affected by the diseases included in this analysis, may have had other pathologies, which may increase health expenditure with respect to the overall population.

METHODS
Sources of data. The present analysis was based on data of 557,145 men and women, older than 18 years, who were observed at different times between 2004 and 2010, amounting to a total of 2,705,211 observations. The anthropometric and clinical data have been extracted from the Health Search/CSD Patient Database (HS), an Italian general practice registry that includes data obtained from computer-based patient records of a selected group of 700 general practitioners (GP), homogeneously distributed across all Italian regions, covering a patient population of over a 1.8 million between 2004-2010. The GPs voluntarily agreed to collect patient information and to attend specific training courses for data entry [13]. The HS database contains patient demographic data that are linked through the use of an encrypted patient code with their medical records (diagnoses, prescribed tests results), drug prescription information (medication name, date of filled prescription, and number of days' supply), hospital admissions, and date of death. To be considered for participation in epidemiological studies, GPs should meet "up-to-standard" quality criteria pertaining to the levels of coding, prevalence of well-known diseases, mortality rates, and years of recording [13]. The HS database complies with the European Union guidelines on the use of medical data for research, and has previously been demonstrated to be a valid data source for scientific research [14][15][16]. GPs collected this information on daily basis. However, in this study the records have been collapsed to obtain yearly aggregates. Finally, these data have been merged, at patient level, with data from the SiSSi (Simulazione Spesa Sanitaria Italiana -Simulation of the Italian Health Care Expenditure) project, which includes information on prices and tariffs for drugs, outpatient visits, diagnostic tests and hospitalization visits. By multiplying health care service utilization data from the HS with price and tariff data from the SiSSi project we obtained detailed information on public health care expenditure at the patient level [17][18][19].

CONCLUSION
Based on one of the largest datasets used in this literature, the results of this study reinforce the concept that overweight and obesity increases the risk of developing multiple and costly chronic diseases. Our findings demonstrate the adverse impact of increased BMI on outpatient and total healthcare costs, especially in middle-aged and elderly individuals. They also show that hypertension, type 2 diabetes and CVD are responsible for a large part of these BMIrelated health care expenditures. The knowledge of these costs will be useful for future economic analysis of preventive and treatment interventions, such as long-term, comprehensive national initiatives that tackle the basic causes of poor diet quality and sedentary lifestyles.

Funding
Supported in part by the Italian National Institute of Health (ISS), Rome, Italy. ISS had no involvement in the design of the study, collection, analysis, or interpretation of the data, nor decision to approve publication of the finished manuscript. www.impactaging.com