The economic burden of obesity in Italy: a cost-of-illness study

Abstract

Background: Obesity is a complex health disorder that significantly increases the risk of several chronic diseases, and it has been associated with a 5-20-year decrease in life expectancy. The prevalence of obesity is increasing steadily worldwide and Italy follows this trend with an increase of almost 30% in the adult obese population in the last 3 decades. Previous studies estimated that 2-4% of the total health expenditure in Europe is attributed to obesity and it is projected to double by 2050. Currently, there is a lack of sufficient knowledge on the burden of obesity in Italy and most relevant estimates are derived from international studies. The aim of this study is to estimate the direct and indirect costs of obesity in Italy, taking 2020 as the reference year.

Methods: Based on data collected from the literature, a quantitative cost-of-illness (COI) study was performed from a societal perspective focussing on the adult obese population (Body Mass Index (BMI) ≥ 30 kg/m²) in Italy.

Results: The study indicated that the total costs attributable to obesity in Italy amounted to €13.34 billion in 2020 (95% credible interval: €8.99 billion < µ < €17.80 billion). Direct costs were €7.89 billion, with cardiovascular diseases (CVDs) having the highest impact on costs (€6.66 billion), followed by diabetes (€0.65 billion), cancer (€0.33 billion), and bariatric surgery (€0.24 billion). Indirect costs amounted to €5.45 billion, with almost equal contribution of absenteeism (€2.62 billion) and presenteeism (€2.83 billion).

Conclusions: Obesity is associated with high direct and indirect costs, and cost-effective prevention programmes are deemed fundamental to contain this public health threat in Italy.

Keywords: Cancer; Cardiovascular diseases (CVD); Cost analysis; Cost-of-illness (COI); Diabetes; Obesity.

Fig. 1

Deterministic (one-way) sensitivity analysis and tornado diagram. Seven variables were tested to address uncertainty of the following parameter values when estimating the economic burden of obesity: (1) obesity prevalence, (2) total costs of bariatric surgery, (3) total costs of obesity-attributable CVDs, (4) total costs of obesity-attributable diabetes, (5) total costs of obesity-attributable cancer, (6) total costs of obesity-associated productivity losses, and (7) rate of eligible patients receiving bariatric surgery. Parameter values are changed through upper and lower bounds to estimate minimum and maximum total obesity costs

Fig. 2

Probabilistic sensitivity analysis (PSA) performed to address uncertainty of parameter values when estimating the total burden of obesity. The PSA was performed adopting the Monte Carlo method (second order) and calculation of the total obesity costs was replicated with 1,000 simulations