Sector of Employment and Mortality: A Cohort Based on Different Administrative Archives.

Administrative data can be precious in connecting information from different sectors. For the first time, we used data from the National Social Insurance Agency (INPS) to investigate the association between the occupational sectors and both non-accidental and accidental mortality. We retrieved information on occupational sectors from 1974 to 2011 for private sector workers included in the 2011 census cohort of Rome. We classified the occupational sectors into 25 categories and analyzed occupational exposure as ever/never have been employed in a sector or as the lifetime prevalent sector. We followed the subjects from the census reference day (9 October 2011) to 31 December 2019. We calculated age-standardized mortality rates for each occupational sector, separately in men and women. We used Cox regression to investigate the association between the occupational sectors and mortality, producing hazard ratios (HRs) and 95% confidence intervals (95%CI). We analyzed 910,559 30+-year-olds (53% males) followed for 7 million person-years. During the follow-up, 59,200 and 2560 died for non-accidental and accidental causes, respectively. Several occupational sectors showed high mortality risks in men in age-adjusted models: food and tobacco production with HR = 1.16 (95%CI: 1.09-8.22), metal processing (HR = 1.66, 95%CI: 1.21-11.8), footwear and wood (HR = 1.19, 95%CI: 1.11-1.28), construction (HR = 1.15, 95%CI: 1.12-1.18), hotels, camping, bars, and restaurants (HR = 1.16, 95%CI: 1.11-1.21) and cleaning (HR = 1.42, 95%CI: 1.33-1.52). In women, the sectors that showed higher mortality than the others were hotels, camping, bars, and restaurants (HR = 1.17, 95%CI: 1.10-1.25) and cleaning services (HR = 1.23, 95%CI: 1.17-1.30). Metal processing and construction sectors showed elevated accidental mortality risks in men. Social Insurance Agency data have the potential to characterize high-risk sectors and identify susceptible groups in the population.

SARS-CoV-2 spread and area economic disadvantage in the italian three-tier restrictions: a multilevel approach.

BACKGROUND: To face the second wave of COVID-19, Italy implemented a tiered restriction system with different limitation levels (yellow = medium; orange = medium-high, red = high) at the beginning of November 2020. The restrictions systematically reduced the transmission of SARS-CoV-2 with increasing strength for increasing tier. However, it is unknown whether the effect of limitations was equal between provinces with different socioeconomic levels. Therefore, we investigated the association between the province's socioeconomic level and SARS-CoV-2 infection daily reproduction number in each restriction level. METHODS: We measured the province's socioeconomic level as the percentage of individuals whose 2019 total yearly income was lower than 10,000€, using the measure as a proxy of economic disadvantage. We estimated the daily reproduction number (Rt) at the province level using the SARS-CoV-2 daily incidence data from November 2020 to May 2021. We then used multilevel linear regression models with random intercepts stratified by restriction level to estimate the association between economic disadvantage and Rt. We also adjusted the analyses for potential confounders of the association between the province's economic disadvantage and the Rt: the percentage of people with 0-5 years, the quartiles of population density, and the geographical repartition. RESULTS: Overall, we found increasing Rt in yellow (+ 0.004 p < 0.01, from Rt = 0.99 to 1.08 in three weeks) and containing effects for the orange (-0.005 p < 0.01, from Rt = 1.03 to 0.93) and the red tier (-0.014 p < 0.01, from Rt = 1.05 to 0.76). More economically disadvantaged provinces had higher Rt levels in every tier, although non-significantly in the yellow level (yellow = 0.001 p = 0.19; orange = 0.002 p = 0.02; red = 0.004 p < 0.01). The results showed that the association between economic disadvantage and Rt differed by level of restriction. The number of days into the restriction and the economic disadvantage had statistically significant interactions in every adjusted model. Compared to better off, more economically disadvantaged provinces had slower increasing trends in yellow and steeper Rt reductions in orange, but they showed slower Rt reductions in the highest tier. CONCLUSION: Lower tiers were more effective in more economically disadvantaged provinces, while the highest restriction level had milder effects. These results underline the importance of accounting for socioeconomic level when implementing public health measures.

Socioeconomic inequalities in health status and survival: a cohort study in Rome.

OBJECTIVES: To analyse the association between individual and contextual socioeconomic position (SEP) with health status and to investigate the role of SEP and baseline health status on survival. DESIGN: Cross-sectional and cohort study. SETTING: Rome, Italy. PARTICIPANTS, PRIMARY AND SECONDARY OUTCOMES: We selected the 25-99 year-olds included in the Rome 2011 census cohort. As a measure of health status on the census reference date (09 October 2011), we used the presence of chronic or rare conditions from the Disease-Related Co-payment Exemption Registry, a database implemented to provide free care to people with chronic or rare diseases. We used logistic regression to analyse the association between both individual (educational attainment) and contextual SEP (neighbourhood real estate price quintiles) with baseline health status. We analysed the role of SEP and the presence of chronic or rare conditions on 5-year survival (until 31 December 2016) using accelerated failure time models with Weibull distribution, reporting time ratios (TRs; 95% CI). RESULTS: In middle-aged, subjects with low SEP (either individual or contextual) had a prevalence of chronic conditions comparable with the prevalence in high SEP individuals 10 years older. Adjusted logistic models confirmed the direct association between SEP and baseline health status in both women and men. The lowest educated were up to 67% more likely to have a chronic condition than the highest educated, while the difference was up to 86% for lowest versus highest contextual SEP. Low SEP and the presence of chronic conditions were associated with shorter survival times in both sexes, lowest versus highest educated TR was TR=0.79 for women (95% CI: 0.77 to 0.81) and TR=0.71 for men (95% CI: 0.70 to 0.73). The contextual SEP shrunk survival times by about 10%. CONCLUSION: Inequalities were present in both baseline health and survival. The association between SEP and survival was independent of baseline health status.

Differences in mortality between temporary and permanent workers: results from the Rome Longitudinal Study.

OBJECTIVE: Precarious employment is characterised by instability, lack of protection and economic vulnerability. The objective of this study was to assess the association between temporary contracts and mortality. DESIGN: Cohort study. SETTING: Rome, Italy. PARTICIPANTS: All employees, aged 25-65 years, from the Rome Longitudinal Study, followed from 21 October 2001 to the first date among death, migration from Rome, or 31 December 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: We investigated all-cause, cardiovascular and accidental mortality. We considered gender, age, place of birth, education, temporary versus permanent contract and sector of employment. We used Cox models to investigate the association between type of contract and total, cardiovascular and accidental mortality in men and women, overall and by employment sector. RESULTS: We analysed 597 834 subjects. The proportion of temporary contracts varied by gender (9.6% in men and 13.3% in women) and by employment sector, ranging from 4.5% (public administration) to 27% (recreational, cultural, sports activities) in men. During the study period, 21 136 subjects died. Men with temporary contracts, compared with those with permanent positions, had greater overall mortality risk (HR=1.16, 95% CI 1.09 to 1.24), cardiovascular mortality (HR=1.29, 95% CI 1.14 to 1.45) and accidental mortality (HR=1.27, 95% CI 1.04 to 1.57). In men, the association varied widely among different economic sectors, with greater risks in the industry, building constructions and social services sectors. In women, there was no evidence of association between temporary contracts and mortality. A statistically significant association between temporary contracts and mortality in women was found in the sector of sales and transports only. CONCLUSIONS: Temporary work should be considered a determinant of health, particularly for specific economic sectors.