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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. [ABSTRACT FROM AUTHOR]

Summary: Globally, there has been an increasing trend toward electricity production from renewables, hence promoting the opportunity for the electrification of the residential heating sector as opposed to fossil fuels' combustion. Decentralized heat pumps and district heating networks could play a key role in the Swiss future approach to heating. This study provides a detailed enviro‐economic assessment of the application of heat pumps and district heating systems based on renewable sources in different Swiss building archetypes on different scales. The results show that the levelized costs of heat from individual heat pumps and large‐scale district heating networks (using industrial excess heat with a lower energy price than for the other energy carriers) are 7% to 20% and 65% to 75% higher than individual fossil fuel boilers for single‐family houses, respectively. However, for multifamily houses, individual heat pumps, small thermal grids, and large‐scale district heating networks are competitive. The district heating network coupled with a large centralized water source heat pump is found to be the most expensive solution (0.24‐0.29 CHF/kWh) for residential heating today. The sensitivity analysis suggests the economic feasibility of different heating technologies significantly overlaps with each other depending on the boundary conditions, hence justifying not only the mixed opinions by experts in Switzerland but also the disagreements over costs of these technologies in international literature. It can be concluded that, based on the levelized cost of heat, there are sufficient economically viable options for decarbonization and electrification of the residential heating sector. While this is an important finding for policymakers, the barriers, for example, high upfront cost, need to be addressed. Highlights: Low‐carbon heating technologies are investigated for residential heating supply.Levelized costs of heat from heat pumps and district heating networks are computed.Levelized costs of the heating technologies significantly overlap with each other. [ABSTRACT FROM AUTHOR]