Background: Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice. Methods: Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths. Results: Seven relevant models for health impacts forecasting were identified, consisting of I) comparative risk assessment (CRA), II) time series analysis (TSA), III) compartmental models (CM), IV) structural models (SM), V) agent-based models (ABM), VI) microsimulations (MS), and VII) artificial intelligence (AI)/ machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. Conclusion: The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.
Background We developed an integrated model called Microsimulation for Income and Child Health (MICH) that provides a tool for analysing the prospective effects of fiscal policies on childhood health in European countries. The aim of this first MICH study is to evaluate the impact of alternative fiscal policies on childhood overweight and obesity in Italy. Methods MICH model is composed of three integrated modules. Firstly, module 1 (M1) simulates the effects of fiscal policies on disposable household income using the tax-benefit microsimulation program EUROMOD fed with the Italian EU-SILC 2010 data. Secondly, module 2 (M2) exploits data provided by the Italian birth cohort called Nascita e Infanzia: gli Effetti dell’Ambiente (NINFEA), translated as Birth and Childhood: the Effects of the Environment study, and runs a series of concatenated regressions in order to estimate the prospective effects of income on child body mass index (BMI) at different ages. Finally, module 3 (M3) uses dynamic microsimulation techniques that combine the population structure and incomes obtained by M1, with regression model specifications and estimated effect sizes provided by M2, projecting BMI distributions according to the simulated policy scenarios. Results Both universal benefits, such as universal basic income (BI), and targeted interventions, such as child benefit (CB) for poorer households, have a significant effect on childhood overweight, with a prevalence ratio (PR) in 10-year-old children—in comparison with the baseline fiscal system—of 0.88 (95%CI 0.82–0.93) and 0.89 (95%CI 0.83–0.94), respectively. The impact of the fiscal reforms was even larger for child obesity, reaching a PR of 0.67 (95%CI 0·50–0.83) for the simulated BI and 0.64 (95%CI 0.44–0.84) for CB at the same age. While both types of policies show similar effects, the estimated costs for a 1% prevalence reduction in overweight and obesity with respect to the baseline scenario is much lower with a more focalised benefit policy than with universal ones. Conclusions Our results show that fiscal policies can have a strong impact on childhood health conditions. Focalised interventions that increase family income, especially in the most vulnerable populations, can help to prevent child overweight and obesity. Robust microsimulation models to forecast the effects of fiscal policies on health should be considered as one of the instruments to reach the Health in All Policies (HiAP) goals.
In year 1991 regional governments in Spain started a period of implementation of a law that rose the Minimum Legal Drinking Age from 16 to 18 years old. This process was fully completed in year 2015. To evaluate the effects of this change on consumption of legal drugs and its related morbidity outcomes, we construct a regional panel dataset on alcohol consumption and hospital entry registers and compare variation in several measures of prevalence between the treatment group (16-18 years old individuals) and the control group (20-22 years old individuals). Our findings show important differences by gender. Firstly, our main result regarding overall drinking prevalence show reductions ranging from-11.57% for the subsample including both genders to-14.31% for the subsample of males. Secondly, effects on males are driven mainly by reductions in beer with alcohol consumption (-8.98%). Thirdly, effects on wine and/or cava drinking prevalence range from-12.62% for the subsample including both genders to-9.65% for the subsample of females. No effects regarding overall smoking prevalence are found. Fourthly, we do not find evidence that these reductions in alcohol consumption are translated into hospitalizations related to alcohol overdose. To our knowledge, this is the first paper providing evidence on gender-based differences to policies aimed at reducing alcohol consumption. Our results have important policy implications for countries currently considering changes in the Minimum Legal Drinking Age.
In year 1991, regional governments in Spain started a period of implementation of a law that rose the minimum legal drinking age from 16 to 18 years old. To evaluate the effects of this change on the consumption of legal drugs and its related morbidity outcomes, we construct a regional panel dataset on alcohol consumption and hospital entry registers and compare variation in several measures of prevalence between the treatment group (16–18 years old) and the control group (20–22 years old). Our findings show important differences by gender. Our main result regarding overall drinking prevalence shows a reduction of −21.37% for the subsample that includes males and females altogether. This effect on drinking is mainly driven by a reduction of −44.43% in mixed drinks and/or liquors drinking prevalence corresponding to the subsample of males. No causal effects regarding overall smoking prevalence and hospitalizations due to alcohol overdose or motor vehicle traffic accidents were found. To our knowledge, this is the first paper providing evidence on gender‐based differences to policies aimed at reducing alcohol consumption. Our results have important policy implications for countries currently considering changes in the minimum legal drinking age.
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