Aging is a strong risk factor for many chronic diseases. However, the impact of an aging population on the prevalence of chronic diseases and related healthcare costs are not known. We used a prevalence‐based approach that combines accurate clinical and drug prescription data from Health Search CSD‐LPD. This is a longitudinal observational data set containing computer‐based patient records collected by Italian general practitioners (GP) and up‐to‐date healthcare expenditures data from the SiSSI Project. The analysis is based on data collected by 900 GP on an unbalanced sample of more than 1 million patients aged 35+, observed in different time periods between 2005 and 2014. In 2014, 86% of the Italian adults older than 65 had at least one chronic condition, and 56.7% had two or more. Prevalence of multiple chronic diseases and healthcare utilization increased among older and younger adults between 2004 and 2014. Indeed, in the last 10 years, average number of prescriptions increased by approximately 26%, while laboratory and diagnostic tests by 27%. The average number of DDD prescribed increased with age in all the observed years (from 114 in 2005 to 119.9 in 2014 for the 35–50 age group and from 774.9 to 1,178.1 for the 81+ patients). The alarming rising trends in the prevalence of chronic disease and associated healthcare costs in Italy, as well as in many other developed countries, call for an urgent implementation of interventions that prevent or slow the accumulation of metabolic and molecular damage associated with multiple chronic disease.

xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fixed-and randomeffects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total marginal effects and related standard errors for linear (in variables) specifications, and to exploit a wide range of postestimation features, extending to the panel data case the predictors proposed by Kelejian and Prucha (2007). Moreover, it also allows the use of margins to compute total marginal effects in presence of nonlinear specifications obtained using factor variables. This paper describes the command and all its functionalities using both simulated and real data.

xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fixed-and randomeffects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total marginal effects and related standard errors for linear (in variables) specifications, and to exploit a wide range of postestimation features, extending to the panel data case the predictors proposed by Kelejian and Prucha (2007). Moreover, it also allows the use of margins to compute total marginal effects in presence of nonlinear specifications obtained using factor variables. This paper describes the command and all its functionalities using both simulated and real data.

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.

No abstract