scholarly journals An Italian individual-level data study investigating on the association between air pollution exposure and Covid-19 severity in primary-care setting

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Valeria Pegoraro ◽  
Franca Heiman ◽  
Antonella Levante ◽  
Duccio Urbinati ◽  
Ilaria Peduto
Keyword(s):  
Air Pollution ◽  
Index Date ◽  
Linear Mixed Model ◽  
Mixed Effects ◽  
Atmospheric Pollutants ◽  
Exposure Level ◽  
Individual Level ◽  
Level Data ◽  
Starting Point

Abstract Background Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study’s objective was to estimate the association between ≤10 μm diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy. Methods Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients’ consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 – June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP’s office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia. Results Among 6483 Covid-19 patients included, 1079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled. Conclusion The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

scholarly journals An Italian individual-level data study investigating on the association between air pollution exposure and Covid-19 severity in primary-care setting

2021 ◽  
Author(s):  
Valeria Pegoraro ◽  
Franca Heiman ◽  
Antonella Levante ◽  
Duccio Urbinati ◽  
Ilaria Peduto
Keyword(s):  
Air Pollution ◽  
Index Date ◽  
Linear Mixed Model ◽  
Mixed Effects ◽  
Atmospheric Pollutants ◽  
Exposure Level ◽  
Individual Level ◽  
Level Data ◽  
Starting Point

Abstract BACKGROUND: Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study’s objective was to estimate the association between ≤10 micrometers diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy.METHODS: Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients’ consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 – June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP’s office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia.RESULTS: Among 6,483 Covid-19 patients included, 1,079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled.CONCLUSION: The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

scholarly journals Income disparities in life expectancy in the City of Toronto and Region of Peel, Ontario

2012 ◽  
Vol 32 (4) ◽  
pp. 208-215 ◽  
Author(s):  
J. Stratton ◽  
D.L. Mowat ◽  
R. Wilkins ◽  
M. Tjepkema
Keyword(s):  
Life Expectancy ◽  
Immigrant Status ◽  
Individual Level ◽  
Probability Of Survival ◽  
Income Disparities ◽  
Income Adequacy ◽  
Level Data ◽  
Calculated Period ◽  
The City

Introduction To understand the lack of a gradient in mortality by neighbourhood income in a previous study, we used individual-level data from the 1991–2001 Canadian census mortality follow-up study to examine income-related disparities in life expectancy and probability of survival to age 75 years in the City of Toronto and Region of Peel. Methods We calculated period life tables for each sex and income adequacy quintile, overall and separately for immigrants and non-immigrants. Results For all cohort members of both sexes, including both immigrants and non-immigrants, there was a clear gradient across the income quintiles, with higher life expectancy in each successively richer quintile. However, the disparities by income were much greater when the analysis was restricted to non-immigrants. The lesser gradient for immigrants appeared to reflect the higher proportion of recent immigrants in the lower income quintiles. Conclusion These findings highlight the importance of using individual-level ascertainment of income whenever possible, and of including immigrant status and period of immigration in assessments of health outcomes, especially for areas with a high proportion of immigrants.

scholarly journals Do Suicide Attempts of Mood Disorder Patients Directly Increase the Risk for a Reattempt?

2020 ◽  
Vol 11 ◽  
Author(s):  
Kari I. Aaltonen ◽  
Tom Rosenström ◽  
Pekka Jylhä ◽  
Irina Holma ◽  
Mikael Holma ◽  
...  
Keyword(s):  
Suicide Attempt ◽  
Mood Disorder ◽  
Major Depressive Episode ◽  
Suicide Attempts ◽  
Ordinal Number ◽  
Major Depressive ◽  
Individual Level ◽  
Level Data ◽  
Depressive States

Background: Preceding suicide attempts strongly predict future suicidal acts. However, whether attempting suicide per se increases the risk remains undetermined. We longitudinally investigated among patients with mood disorders whether after a suicide attempt future attempts occur during milder depressive states, indicating a possible lowered threshold for acting.Methods: We used 5-year follow-up data from 581 patients of the Jorvi Bipolar Study, Vantaa Depression Study, and Vantaa Primary Care Depression Study cohorts. Lifetime suicide attempts were investigated at baseline and during the follow-up. At follow-up interviews, life-chart data on the course of the mood disorder were generated and suicide attempts timed. By using individual-level data and multilevel modeling, we investigated at each incident attempt the association between the lifetime ordinal number of the attempt and the major depressive episode (MDE) status (full MDE, partial remission, or remission).Results: A total of 197 suicide attempts occurred among 90 patients, most during MDEs. When the dependencies between observations and individual liabilities were modeled, no association was found between the number of past suicide attempts at the time of each attempt and partial remissions. No association between adjusted inter-suicide attempt times and the number of past attempts emerged during follow-up. No indication for direct risk-increasing effects was found.Conclusion: Among mood disorder patients, repeated suicide attempts do not tend to occur during milder depressive states than in the preceding attempts. Previous suicide attempts may indicate underlying diathesis, future risk being principally set by the course of the disorder itself.

scholarly journals Suicide, overdose and worker exit in a cohort of Michigan autoworkers

2020 ◽  
pp. jech-2020-214117
Author(s):  
Ellen A Eisen ◽  
Kevin T Chen ◽  
Holly Elser ◽  
Sally Picciotto ◽  
Corinne A Riddell ◽  
...  
Keyword(s):  
Automobile Industry ◽  
Age Groups ◽  
College Education ◽  
General Motors ◽  
Individual Level ◽  
Fatal Overdose ◽  
Level Data ◽  
Working Age ◽  
Stable Employment

BackgroundIn recent decades, suicide and fatal overdose rates have increased in the US, particularly for working-age adults with no college education. The coincident decline in manufacturing has limited stable employment options for this population. Erosion of the Michigan automobile industry provides a striking case study.MethodsWe used individual-level data from a retrospective cohort study of 26 804 autoworkers in the United Autoworkers-General Motors cohort, using employment records from 1970 to 1994 and mortality follow-up from 1970 to 2015. We estimated HRs for suicide or fatal overdose in relation to leaving work, measured as active or inactive employment status and age at worker exit.ResultsThere were 257 deaths due to either suicide (n=202) or overdose (n=55); all but 21 events occurred after leaving work. The hazard rate for suicide was 16.1 times higher for inactive versus active workers (95% CI 9.8 to 26.5). HRs for suicide were elevated for all younger age groups relative to those leaving work after age 55. Those 30–39 years old at exit had the highest HR for suicide, 1.9 (95% CI 1.2 to 3.0). When overdose was included, the rate increased by twofold for both 19- to 29-year-olds and 30- to 39-year-olds at exit. Risks remained elevated when follow-up was restricted to 5 years after exit.ConclusionsAutoworkers who left work had a higher risk of suicide or overdose than active employees. Those who left before retirement age had higher rates than those who left after, suggesting that leaving work early may increase the risk.

scholarly journals Cognitive development Respiratory Tract Illness and Effects of eXposure (CORTEX) project: Data processing challenges in combining high spatial resolution pollution level data with individual level health and education data

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Jane Lyons ◽  
Amy Mizen ◽  
Sarah Rodgers ◽  
Damon Berridge ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Pollution Level ◽  
Individual Level ◽  
Education Data ◽  
Level Data ◽  
Ozone Levels ◽  
Daily Time

Background and ObjectivesThere is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality-related health conditions. The CORTEX project combined routinely collected health and education data, high spatial resolution air pollution modelling, and daily pollen measurements for 18,241 pupils living in Cardiff, UK, between 2009 and 2015, to investigate the acute effects of air quality and respiratory conditions on education attainment. DatasetsAir pollutants PM2.5, PM10, NO2, and ozone levels were modelled for 157,361 home and school locations, anonymised into the Secure Anonymised Information Linkage (SAIL) Databank, and summarised into minimum, average and maximum readings for 4 daily time periods reflecting pupil home/school exposure. Adding a unique Residential Anonymised Linking Field (RALF) allowed linkage of pollution estimates to individual level data. Annual pollution datasets contained 369 columns and 472,083-rows, with one column per location, pollutant, daily time-period and day of year. Dataset transformation produced a 5 column, 3,446,205,900-row matrix per year. Methods and ConclusionsAn algorithm using Structured Query Language (SQL) to manage data held within a relational database management system, was designed to reduce dimensionality from 24 billion to 18,241 rows of data. The algorithm calculated average means for each pollutant (PM2.5, PM10, NO2, and ozone levels) over the revision and examination periods, and summarised data into one row per pupil. The algorithm adjusted for weekends, school, and bank holidays, it calculated daily pollutant exposure for each pupil, and successfully linked 95% of pupil pollution exposures to their health and education data.

Employment Insecurity, Incumbent Partisanship, and Voting Behavior in Comparative Perspective

2016 ◽  
Vol 50 (11) ◽  
pp. 1489-1523 ◽  
Author(s):  
Agnar Freyr Helgason ◽  
Vittorio Mérola
Keyword(s):  
Voting Behavior ◽  
Left Wing ◽  
Economic Insecurity ◽  
Potential Conflict ◽  
Bayesian Hierarchical ◽  
Welfare Policies ◽  
Individual Level ◽  
Employment Insecurity ◽  
Level Data

We argue that occupational unemployment rates, by informing perceptions of economic insecurity, serve as a salient and powerful heuristic for aggregate economic performance. Consequently, high and rising occupational unemployment leads to negative evaluations of the economy and reduces the probability of supporting the incumbent government. Simultaneously, however, such changes shift support toward left-wing parties. Thus, economic insecurity serves as a valence issue, but is also inherently a positional issue, due to the distributional consequences of welfare policies. This brings about a potential conflict as under left-wing incumbent governments the economically insecure are cross-pressured, which increases their likelihood of exiting the electoral arena completely. We test our hypotheses using a Bayesian hierarchical multinomial model, with individual-level data from 43 elections in 21 countries. We find support for the hypothesized effects of employment insecurity on voting behavior, with a follow-up analysis supporting the posited informational mechanism.

scholarly journals Employment and crime: A longitudinal follow-up of organized crime offenders

2020 ◽  
pp. 147737082094128
Author(s):  
Vere van Koppen ◽  
Victor van der Geest ◽  
Edward Kleemans ◽  
Edwin Kruisbergen
Keyword(s):  
Organized Crime ◽  
Financial Support ◽  
Fixed Effects ◽  
Leadership Positions ◽  
Individual Level ◽  
Self Employment ◽  
Level Data ◽  
Percent Increase

Employment is considered to help offenders desist from crime. Studies focusing on organized crime offenders, however, have suggested that employment may promote rather than inhibit crime for these offenders, but lacked quantitative individual-level data to confirm this finding. Using a large sample of organized crime offenders ( N = 1921) and longitudinal individual data on offending, employment, income and financial support, the current study aims to clarify the role of employment in the offending careers of these offenders. Fixed effects models show the effects of employment, self-employment and employment on the payroll. For organized crime offenders, being employed is associated with a 10 percent increase in offending and having their own business is associated with a 23 percent increase in offending. For organized crime offenders in leadership positions, employment is associated with a 47 percent increase in offending and owning a business is associated with a 68 percent increase in offending.

scholarly journals SNP heritability: What are we estimating?

2020 ◽  
Author(s):  
Konrad Rawlik ◽  
Oriol Canela-Xandri ◽  
John Woolliams ◽  
Albert Tenesa
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Individual Level ◽  
Correct Model ◽  
Level Data ◽  
Genomic Variance ◽  
Class Of Estimators ◽  
Theoretical Results

The SNP heritability has become a central concept in the study of complex traits. Estimation of based on genomic variance components in a linear mixed model using restricted maximum likelihood has been widely adopted as the method of choice were individual level data are available. Empirical results have suggested that this approach is not robust if the population of interest departs from the assumed statistical model. Prolonged debate of the appropriate model choice has yielded a number of approaches to account for frequency- and linkage disequilibrium dependent genetic architectures. Here we analytically resolve the question of how these estimates relate to of the population from which samples are drawn. In particular, we show that the correct model for the purpose of inference about does not require knowledge of the true genetic architecture of a trait. More generally, our results provide a complete perspective of these class of estimators of , highlighting practical shortcomings of current practise. We illustrate our theoretical results using simulations and data from UK Biobank.

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