S. Tikka, Jussi Hakanen, Mirka Saarela, J. Karvanen
We propose a framework for realistic data generation and the simulation of complex systems and demonstrate its capabilities in a health domain example. The main use cases of the framework are predicting the development of variables of interest, evaluating the impact of interventions and policy decisions, and supporting statistical method development. We present the fundamentals of the framework by using rigorous mathematical definitions. The framework supports calibration to a real population as well as various manipulations and data collection processes. The freely available opensource implementation in R embraces efficient data structures, parallel computing, and fast random number generation, hence ensuring reproducibility and scalability. With the framework, it is possible to run dailylevel simulations for populations of millions of individuals for decades of simulated time. An example using the occurrence of stroke, type 2 diabetes, and mortality illustrates the usage of the framework in the Finnish context. In the example, we demonstrate the data collection functionality by studying the impact of nonparticipation on the estimated risk models and interventions related to controlling excessive salt consumption. DOI: https:// doi. org/ 10. 34196/ ijm. 00240
{"title":"Sima – an Open-source Simulation Framework for Realistic Large-scale Individual-level Data Generation","authors":"S. Tikka, Jussi Hakanen, Mirka Saarela, J. Karvanen","doi":"10.34196/ijm.00240","DOIUrl":"https://doi.org/10.34196/ijm.00240","url":null,"abstract":"We propose a framework for realistic data generation and the simulation of complex systems and demonstrate its capabilities in a health domain example. The main use cases of the framework are predicting the development of variables of interest, evaluating the impact of interventions and policy decisions, and supporting statistical method development. We present the fundamentals of the framework by using rigorous mathematical definitions. The framework supports calibration to a real population as well as various manipulations and data collection processes. The freely available opensource implementation in R embraces efficient data structures, parallel computing, and fast random number generation, hence ensuring reproducibility and scalability. With the framework, it is possible to run dailylevel simulations for populations of millions of individuals for decades of simulated time. An example using the occurrence of stroke, type 2 diabetes, and mortality illustrates the usage of the framework in the Finnish context. In the example, we demonstrate the data collection functionality by studying the impact of nonparticipation on the estimated risk models and interventions related to controlling excessive salt consumption. DOI: https:// doi. org/ 10. 34196/ ijm. 00240","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45019194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Dynamic Microsimulation Model for Ageing and Health in England: The English Future Elderly Model","authors":"","doi":"10.34196/ijm.00239","DOIUrl":"https://doi.org/10.34196/ijm.00239","url":null,"abstract":"","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47529186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we introduce UKMOD, a new tax- benefit model for England, Wales, Scot-land, Northern Ireland and the whole of the UK. The model originates and replaces as a stand- alone model the UK component of EUROMOD, the tax- benefit model for the European Union member states, which from 2021 is not updated anymore. We describe the main departures from EUROMOD, discuss some key assumptions including data issues, and provide information on the nowcasting and macro- validation procedure applied.
{"title":"UKMOD – A new tax-benefit model for the four nations of the UK","authors":"Matteo G. Richiardi, D. Collado, Daria","doi":"10.34196/ijm.00231","DOIUrl":"https://doi.org/10.34196/ijm.00231","url":null,"abstract":"In this paper we introduce UKMOD, a new tax- benefit model for England, Wales, Scot-land, Northern Ireland and the whole of the UK. The model originates and replaces as a stand- alone model the UK component of EUROMOD, the tax- benefit model for the European Union member states, which from 2021 is not updated anymore. We describe the main departures from EUROMOD, discuss some key assumptions including data issues, and provide information on the nowcasting and macro- validation procedure applied.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44703832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the development of a microsimulation model to simulate the long-term impact of the conversion of agricultural land to forestry at individual farm level. Reflecting the negative externalities associated with agriculture in terms of carbon emissions and the positive forest externalities in terms of carbon sequestration, we model both private returns and social returns. These reflect respectively the return from the market and the wider return to society, which incorporates both market returns and the public good returns associated with carbon emissions/sequestration. The modelling assumptions used in developing the model are described in detail, along with the validation of model components against other analyses and the testing of the sensitivity of the results to different assumptions. The paper considers the distributional impact of the private and social returns to agriculture finding significant heterogeneity between the private and social return across farms, with the incorporation of carbon value, resulting in many more farms with positive social return than private returns.
{"title":"The Complexity of Incorporating Carbon Social Returns in Farm Afforestation: A Microsimulation Approach","authors":"M. Ryan, C. O’Donoghue","doi":"10.34196/ijm.00232","DOIUrl":"https://doi.org/10.34196/ijm.00232","url":null,"abstract":"This paper describes the development of a microsimulation model to simulate the long-term impact of the conversion of agricultural land to forestry at individual farm level. Reflecting the negative externalities associated with agriculture in terms of carbon emissions and the positive forest externalities in terms of carbon sequestration, we model both private returns and social returns. These reflect respectively the return from the market and the wider return to society, which incorporates both market returns and the public good returns associated with carbon emissions/sequestration. The modelling assumptions used in developing the model are described in detail, along with the validation of model components against other analyses and the testing of the sensitivity of the results to different assumptions. The paper considers the distributional impact of the private and social returns to agriculture finding significant heterogeneity between the private and social return across farms, with the incorporation of carbon value, resulting in many more farms with positive social return than private returns.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43912752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Owen Tan, D. Schofield, T. O’Brien, T. Trahair, R. Shrestha
Precision medicine is a new approach to identify the best treatment available to patients based on their genomic information. However, no economic evaluation of genome sequencing has been reported for the treatment of childhood cancers, which is critical to evaluate the feasibility of implementing patient’s genome sequencing as part of a publicly funded treatment strategy. We have developed a microsimulation model, PeCanMOD, to evaluate the cost and benefit of applying the Next Generation Sequencing (NGS) in the management of childhood cancer. This paper describes the construction of PeCanMOD. We used linked datasets of children under 18 year of age, living in New South Wales (NSW), Australia, who have had cancer, as a base population. Their records were extracted from the NSW Central Cancer Registry and were linked to mortality and hospital datasets. In addition, we simulated the genomic landscape of the cancer registry population, through information obtained from 1,200 molecularly profiled paediatric cancer from the Foundation Medicine. The model simulated the number of individuals eligible for precision medicine, and the incremental cost of treatment per life year gained if precision medicine was introduced for late stage cancer patients as a final treatment option. Cost of drugs, and hospital admission were included in the model. Data on response rate and probability of survival was imputed based on the latest available evidence. Each unit record in the model was weighted using input from the Australian Institute of Health and Welfare (AIHW) to reflect total paediatric cancer population in Australia. The model demonstrates the application of microsimulation modelling to simulate the impacts of NGS and precision medicine on costs and health outcomes for childhood cancer. JEL classification: C1, C3, I1 DOI: https:// doi. org/ 10. 34196/ ijm. 00230
{"title":"Modelling the Economic Impact of Next Generation Sequencing and Precision Medicine on Childhood Cancer Management—a Microsimulation Approach","authors":"Owen Tan, D. Schofield, T. O’Brien, T. Trahair, R. Shrestha","doi":"10.34196/ijm.00230","DOIUrl":"https://doi.org/10.34196/ijm.00230","url":null,"abstract":"Precision medicine is a new approach to identify the best treatment available to patients based on their genomic information. However, no economic evaluation of genome sequencing has been reported for the treatment of childhood cancers, which is critical to evaluate the feasibility of implementing patient’s genome sequencing as part of a publicly funded treatment strategy. We have developed a microsimulation model, PeCanMOD, to evaluate the cost and benefit of applying the Next Generation Sequencing (NGS) in the management of childhood cancer. This paper describes the construction of PeCanMOD. We used linked datasets of children under 18 year of age, living in New South Wales (NSW), Australia, who have had cancer, as a base population. Their records were extracted from the NSW Central Cancer Registry and were linked to mortality and hospital datasets. In addition, we simulated the genomic landscape of the cancer registry population, through information obtained from 1,200 molecularly profiled paediatric cancer from the Foundation Medicine. The model simulated the number of individuals eligible for precision medicine, and the incremental cost of treatment per life year gained if precision medicine was introduced for late stage cancer patients as a final treatment option. Cost of drugs, and hospital admission were included in the model. Data on response rate and probability of survival was imputed based on the latest available evidence. Each unit record in the model was weighted using input from the Australian Institute of Health and Welfare (AIHW) to reflect total paediatric cancer population in Australia. The model demonstrates the application of microsimulation modelling to simulate the impacts of NGS and precision medicine on costs and health outcomes for childhood cancer. JEL classification: C1, C3, I1 DOI: https:// doi. org/ 10. 34196/ ijm. 00230","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41652867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Belgium has implemented, following the example of other countries, in- work benefit policies since the early 2000’s, with the objective of increasing employment rates and fighting poverty. Belgian in- work benefits differ from most other in- work benefits as eligibility requires low hourly earnings. We study the effects extensions of those benefits would have both on labour supply and welfare, using a random- utility - random- opportunity model estimated on cross- sectional SILC datasets. Results show that further increasing the benefits would slightly increase labour supply and welfare of low- to- middle income deciles, but at very high net cost per job created. We compare our results with existing research and explain some mechanisms that possibly led to an underestimation of negative intensive margin labour supply responses in previous simulations.
{"title":"In-work Benefits in Belgium: Effects on Labour Supply and Welfare","authors":"Antoine de Mahieu","doi":"10.34196/ijm.00229","DOIUrl":"https://doi.org/10.34196/ijm.00229","url":null,"abstract":"Belgium has implemented, following the example of other countries, in- work benefit policies since the early 2000’s, with the objective of increasing employment rates and fighting poverty. Belgian in- work benefits differ from most other in- work benefits as eligibility requires low hourly earnings. We study the effects extensions of those benefits would have both on labour supply and welfare, using a random- utility - random- opportunity model estimated on cross- sectional SILC datasets. Results show that further increasing the benefits would slightly increase labour supply and welfare of low- to- middle income deciles, but at very high net cost per job created. We compare our results with existing research and explain some mechanisms that possibly led to an underestimation of negative intensive margin labour supply responses in previous simulations.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44362973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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