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Estimating the population exposed to a risk factor over a time window: A microsimulation modelling approach from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury
[journal article]
Abstract Objectives: Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours f... view more
Objectives: Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours for one country (Italy). Methods: We developed a three-model approach: Model 1, a multilevel model, estimates exposure to the risk factor at the first year of the time window (yeart). Model 2, a regression model, estimates transition probabilities between exposure categories during the time window (yeart to yeart+n). Model 3, a microsimulation model, estimates the exposed population over the time window, using the Monte Carlo method. The microsimulation is carried out in three steps: (a) a representative synthetic population is initiated in the first year of the time window using prevalence estimates from Model 1, (b) the exposed population is simulated over the time window using the transition probabilities from Model 2; and (c) the population is censored for deaths during the time window. Results: We estimated the population exposed to long working hours (i.e. 41-48, 49-54 and ≥55 hours/week) over a 10-year time window (2002-11) in Italy. We populated all three models with official data from Labour Force Surveys, United Nations population estimates and World Health Organization life tables. Estimates were produced of populations exposed over the time window, disaggregated by sex and 5-year age group. Conclusions: Our modelling approach for estimating the population exposed to a risk factor over a time window is simple, versatile, and flexible. It however requires longitudinal exposure data and Model 3 (the microsimulation model) is stochastic. The approach can improve accuracy and transparency in exposure and burden of disease estimations. To improve the approach, a logical next step is changing Model 3 to a deterministic microsimulation method, such as modelling of microflows.... view less
Keywords
risk assessment; population; illness; assault; work-related illness; stress; simulation; working hours; Italy; model
Classification
Health Policy
Working Conditions
Free Keywords
EU-LFS 1983-2018
Document language
English
Publication Year
2022
Page/Pages
p. 1-16
Journal
PLOS ONE, 17 (2022) 12
DOI
https://doi.org/10.1371/journal.pone.0278507
ISSN
1932-6203
Status
Published Version; peer reviewed