Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey

BACKGROUND: Administrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources. METHODS: Data from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment. RESULTS: Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs' estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources. CONCLUSION: This study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement

Neuron 83, this issue

found in this control experiment: an offset in the motion coherence of the real stimulus biases the monkeys' choices and confidence ratings just like microstimulation did. In an elegant control experiment, Fetsch et al. (2014) sought to break the system apart. Instead of using low currents to stimulate a small patch of neurons with similar preferred orientations, the authors now injected a large amount of current that recruited a wider population of neurons including disparate preferred motion directions. This widespread activation resulted in a large increase in the number of sure bet choices, indicating that monkeys experienced noisy motion information and less confident decisions. The result illustrates at least two important issues. First, it demonstrates that monkeys are capable of reporting a large decrease in confidence and, second, it shows that the behavioral consequences of microstimulation are exquisitely dependent on the selectivity of the stimulated neurons. Large stimulation currents, instead of injecting additional information, indiscriminately recruit neuronal populations whose contributions can mask subtle sensory representations. The results reported by Fetsch et al. (2014) demonstrate that the mechanisms that read sensory evidence have access to the additional information added by microstimulation at the level of MT/MST. Future experiments should be aimed to identify the downstream neuronal circuits that read this evidence to decide whether to choose a safe bet or to risk for a larger reward. Importantly, these circuits must have learned, during behavioral training, the association between the amount of accumulated evidence and the likelihood that a given answer will be correct. What are the neuronal correlates of this learning? The answer will likely include the orchestrating functions of the frontal cortices, and also the modulatory effects of subcortical projection systems (de Lafuente and Romo, 2011; Schultz, 2013)