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The article discusses a recent study on mortality attributable to COVID-19 in Italy and the need for further analysis. The study used a reliable methodology to estimate excess deaths due to the pandemic. However, there are still questions about the specific effects of COVID-19 compared to other factors, such as delayed or missing access to treatment for other illnesses. Analyzing the time course of excess deaths could reveal such effects. There are also open questions about how COVID-19 deaths are classified and reported, which could lead to over or under-diagnosing cases. The article notes that occupational physicians have played an important role in preventing the spread of COVID-19 among workers. A recent study found that personal protective equipment (PPE), particularly masks, effectively reduced the risk of infection among healthcare workers. However, it is still unclear whether Occupational Medicine should incorporate infectious diseases as a major concern or return to its historically agnostic attitude toward communicable diseases. More data on mortality from specific diseases will be needed for further analysis and understanding of the pandemic's effects on mortality rates in Italy. [ABSTRACT FROM AUTHOR]

Objectives: The epidemiologic Health Impact Assessment (eHIA) process is receiving growing attention in Italy. In the context of such an approach, the present paper has three objectives: to review the computational aspects of eHIA for stressing strengths and weaknesses of methods and formulas; to discuss which rate at baseline could be used for the estimation of attributable cases; how to use the results of eHIA to make decisions regarding the realization of industrial projects. Methods and Results: Using a linear formulation of the relationship between exposure and disease occurrence: a) formulas have been derived to compute attributable cases (AC) using both Relative Risk (RR) and Excess Risk (ER) approaches; b) a discussion is made of the use as baseline rate of the rate that is caused by all the risk factors for a particular disease and a suggestion is made to use the rate that is caused simply by the risk factors that are under evaluation; c) under assumptions and approximations that must be validated in any specific situation, formulas are derived to compute Incremental Lifetime Cumulative Risk (ILCR), an indicator that can be used to compare the results coming from the eHIA approach with the levels of action used by USEPA and others (10-6, 10-5, 10-4). Conclusion: In this paper, the methodology and the formulas commonly used in eHIA have been enlarged to consider the case in which the baseline rate is equal to zero, suggesting to use Excess Risk (ER) estimates instead of Relative Risk (RR) estimates. Using different baseline rates produces very different estimates of AC, and work needs to be done on this topic. Lastly, due to assumptions, approximations, and uncertainty of eHIA computations, prudence and caution should be exercised in using eHIA results in decision making, particularly if hard decisions have to be made. [ABSTRACT FROM AUTHOR]

Abstract 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.

Copyright of La Medicina del Lavoro is the property of Mattioli 1885 SpA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of La Medicina del Lavoro is the property of Mattioli 1885 SpA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)