Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts.

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.

The sword of damocles: Understanding the carbon abatement effects of top-down environmental management practices -- insights from China's campaign-style governance.

Inspection, standing for top-down environmental management practices, also known as campaign-style governance, is used by central governments to lessen local environmental pollution. However, there is no causal evidence for carbon abatement. Employing staggered difference-in-differences (DiD), I find that inspected cities mitigate carbon intensity and carbon emissions by 3.72% and 2.34%, respectively, with economic significance. Conducting a triple difference strategy, I suggest the channels are the local people's congresses and political consultative conferences' proposals, government attention, environmental regulation, industrial structure, and green innovation. Also, the heterogeneous effects suggest that municipal party secretaries assigned to their birthplace, the older the party standing and age, and those with natural sciences majors, are more conducive to the inspection achieving carbon mitigation. An alternative DiD specification shows that the "look-back" inspection achieves sustained carbon reduction. I support the argument that top-down inspection helps achieve resilience to climate change and reduce greenhouse gas emissions.

Evaluating the impact of biodiversity offsetting on native vegetation.

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade-offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria's Native Vegetation Framework (2002-2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near-complete baseline woody vegetation cover ("avoided loss", 2702 ha) and with incomplete cover ("regeneration", 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self-selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%-3.6%/year more than non-offset sites (138-180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%-1.9%/year more than non-offset sites; 19-97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of 'net gain' (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self-selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.

Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models.

The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the within- spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.

Educational expansion and trends in intergenerational social mobility among Korean men.

Scholars have explored three channels through which educational expansion contributes to increased intergenerational social mobility: the compositional effect, educational equalization, and class returns to education. Existing literature on impacts of educational expansion on intergenerational social mobility is primarily based on experiences of European societies and the United States. We expand the existing literature by investigating the relationship between educational expansion and intergenerational mobility in Korea showing an exceptional degree of educational expansion over the last few decades. Log-linear models show that social fluidity has increased across birth cohorts of Korean men born between 1950 and 1984, with the recent cohorts experiencing it considerably. Utilizing a counterfactual decomposition method, our study shows that educational expansion has played a crucial role in promoting social fluidity mainly through educational equalization for earlier cohorts and through the compositional effect for more recent cohorts. The role played by the class returns to education was minor.

The Long-Term Impact of Natural Mentoring Relationships: A Counterfactual Analysis.

Previous research suggests that youth's natural mentoring relationships are associated with better academic, vocational, and psychosocial functioning. However, little is known about the extent to which the impact of mentoring endures beyond adolescence and early adulthood. Furthermore, most natural mentoring research is confounded by selection bias. In this study, we examined the long-term impact of mentoring using the nationally representative, longitudinal Add Health dataset. We conducted counterfactual analysis, a more stringent test of causality than regression-based approaches. Compared to their unmentored counterparts, adults (ages 33-42) who had a natural mentor during adolescence or emerging adulthood reported higher educational attainment, more time spent volunteering, and more close friends, after controlling for a range of confounding factors. However, outcomes differed when mentors were classified as "strong ties" (e.g., grandparents, friends) or "weak ties" (e.g., teachers, coaches, employers). Having a strong-tie mentor was associated with having more close friends and a lower income. In contrast, having a weak-tie mentor was associated with higher educational attainment, higher income, and more time spent volunteering. These findings suggest that natural mentoring relationships can exert lasting influence on young people's developmental trajectories, providing strong rationale for efforts to expand their availability and scope.

The relationship between societal change and rising support for gender egalitarianism among men and women: Results from counterfactual analyses in the Netherlands, 1979-2012.

This study examines the relationship between important social, cultural, economic, and demographic changes and the rise of support for gender egalitarianism within the Dutch population between 1979 and 2012. Cohort replacement, educational expansion, secularization, and the feminization of the labor force are important processes that have taken place in western societies in ways that may have fostered support for gender egalitarianism. Using unique data from 16 repeated cross-sectional surveys in the Netherlands, we estimate age-period-cohort regression models, and the outcomes are subsequently applied in counterfactual simulation designs. Our results show that the social, cultural, economic, and demographic changes explain only a small part of the modest rise in support for gender egalitarianism for men, while they provide a much better explanation of the stronger rise among women. Especially the replacement of older female cohorts by younger ones seems to have propelled support for gender egalitarianism among women throughout the years.

Disentangling the respective roles of the early environment and parental BMI on BMI change across childhood: A counterfactual analysis using the Millennium Cohort Study.

This study has two objectives. First, to analyse the respective roles of parental BMI and the wider environment on children's BMI across childhood, using a counterfactual analysis. Second, to determine if the correlations between parents and offspring BMI are partly environmental. We used data on 4437 girls and 4337 boys born in 2000-2001 in the UK and included in the Millennium Cohort Study. Children's BMI was measured at ages 3years, 5years, 7years, and 11years. We described the environment using social class and behaviours within the family. At the age of 3, there was no link between the environment and children's BMI. In contrast, there was a clear link between the environment and BMI slopes between 3 and 11years of age. At the age of 11, we calculated that if all children had the most favourable environment, mean BMI would be reduced by 0.91kg/m(2) (95% CI: 0.57-1.26) for boys and by 1.65kg/m(2) (95% CI: 1.28-2.02) for girls. Associations between parents' and offspring BMI remained unchanged after adjustment for environmental variables. Conversely, the link between the environment and children's BMI is partly reduced after adjustment for parental BMI. This confirms that parental BMI is partly a broad proxy of the environment. We highlighted that if every child's environment was at its most favourable, the mean BMI would be significantly reduced. Thus, the recent rise is likely to be reversible.

Impact of High Covid-19 Vaccination Rate in an Aging Population: Estimating Averted Hospitalizations and Deaths in the Basque Country, Spain Using Counterfactual Modeling.

COVID-19 vaccines have demonstrated significant efficacy in reducing severe symptoms and fatalities, although their effectiveness in preventing transmission varies depending on the population's age profile and the dominant variant. This study evaluates the impact of the COVID-19 vaccination campaign in the Basque Country region of Spain, which has the fourth highest proportion of elderly individuals worldwide. Using epidemiological data on hospitalizations, ICU admissions, fatalities, and vaccination coverage, we calibrated four versions of an ordinary differential equations model with varying assumptions on the age structure and transmission function. Counterfactual no-vaccine scenarios were simulated by setting the vaccination rate to zero while all other parameters were held constant. The initial vaccination rollout is estimated to have prevented 46,000 to 75,000 hospitalizations, 6,000 to 11,000 ICU admissions, and 15,000 to 24,000 deaths, reducing these outcomes by 73-86%. The most significant impact occurred during the third quarter of 2021, coinciding with the Delta variant's dominance and a vaccination rate exceeding 60%. Sensitivity analysis revealed that vaccination coverage had a more substantial effect on averted outcomes than vaccine efficacy. Overall, the vaccination campaign in the Basque Country significantly reduced severe COVID-19 outcomes, aligning with global estimates and demonstrating robustness across different modeling approaches.

The impact of the European Union emissions trading system on carbon dioxide emissions: a matrix completion analysis.

Despite the negative externalities on the environment and human health, today's economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005-2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions.

Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach.

This paper analyzes the impact of automatic wage indexation on employment. To boost competitiveness and increase employment, Belgium suspended its automatic wage indexation system in 2015. This resulted in a 2% fall in real wages for all workers. In the absence of a suitable control group, we use machine learning for the counterfactual analysis. We artificially construct the control group for a difference-in-difference analysis based on the pre-treatment evolution of treated firms. We find a positive impact on employment of 1.2%, which corresponds to a labor demand elasticity of - 0.6. This effect is more pronounced for manufacturing firms, where the elasticity reaches - 1. These results show that a suspension of the automatic wage indexation mechanism can be effective in preserving employment.

A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach.

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017-2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago.

The emergence of social soft skill needs in the post COVID-19 era.

Social soft skills are crucial for workers to perform their tasks, yet it is hard to train people on them and to readapt their skill set when needed. In the present work, we analyze the possible effects of the COVID-19 pandemic on social soft skills in the context of Italian occupations related to 88 economic sectors and 14 age groups. We leverage detailed information coming from ICP (i.e. the Italian equivalent of O*Net), provided by the Italian National Institute for the Analysis of Public Policy, from the microdata for research on the continuous detection of labor force, provided by the Italian National Institute of Statistics (ISTAT), and from ISTAT data on the Italian population. Based on these data, we simulate the impact of COVID-19 on workplace characteristics and working styles that were more severely affected by the lockdown measures and the sanitary dispositions during the pandemic (e.g. physical proximity, face-to-face discussions, working remotely). We then apply matrix completion-a machine-learning technique often used in the context of recommender systems-to predict the average variation in the social soft skills importance levels required for each occupation when working conditions change, as some changes might be persistent in the near future. Professions, sectors, and age groups showing negative average variations are exposed to a deficit in their social soft-skills endowment, which might ultimately lead to lower productivity.

Assessing the Dynamic Outcomes of Containment Strategies against COVID-19 under Different Public Health Governance Structures: A Comparison between Pakistan and Bangladesh.

COVID-19 scenarios were run using an epidemiological mathematical model (system dynamics model) and counterfactual analysis to simulate the impacts of different control and containment measures on cumulative infections and deaths in Bangladesh and Pakistan. The simulations were based on national-level data concerning vaccination level, hospital capacity, and other factors, from the World Health Organization, the World Bank, and the Our World in Data web portal. These data were added to cumulative infections and death data from government agencies covering the period from 18 March 2020 to 28 February 2022. Baseline curves for Pakistan and Bangladesh were obtained using piecewise fitting with a consideration of different events against the reported data and allowing for less than 5% random errors in cumulative infections and deaths. The results indicate that Bangladesh could have achieved more reductions in each key outcome measure by shifting its initial lockdown at least five days backward, while Pakistan would have needed to extend its lockdown to achieve comparable improvements. Bangladesh's second lockdown appears to have been better timed than Pakistan's. There were potential benefits from starting the third lockdown two weeks earlier for Bangladesh and from combining this with the fourth lockdown or canceling the fourth lockdown altogether. Adding a two-week lockdown at the beginning of the upward slope of the second wave could have led to a more than 40 percent reduction in cumulative infections and a 35 percent reduction in cumulative deaths for both countries. However, Bangladesh's reductions were more sensitive to the duration of the lockdown. Pakistan's response was more constrained by medical resources, while Bangladesh's outcomes were more sensitive to both vaccination timing and capacities. More benefits were lost when combining multiple scenarios for Bangladesh compared to the same combinations in Pakistan. Clearly, cumulative infections and deaths could have been highly impacted by adjusting the control and containment measures in both national settings. However, COVID-19 outcomes were more sensitive to adjustment interventions for the Bangladesh context. Disaggregated analyses, using a wider range of factors, may reveal several sub-national dynamics. Nonetheless, the current research demonstrates the relevance of lockdown timing adjustments and discrete adjustments to several other control and containment measures.

Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens.

OBJECTIVES: Antimicrobial resistance (AMR) is the next big pandemic that threatens humanity. The One Health approach to AMR requires quantification of interactions between health, demographic, socioeconomic, environmental, and geopolitical factors to design interventions. This study is focused on learning health system factors on global AMR. METHODS: This study analysed longitudinal data (2004-2017) of AMR having 6 33 820 isolates from 70 middle and high-income countries. We integrated AMR data with the Global Burden of Disease (GBD), Governance (WGI), and Finance data sets to find AMR's unbiased and actionable determinants. We chose a Bayesian decision network (BDN) approach within the causal modelling framework to quantify determinants of AMR. Further, we integrated Bayesian networks' global knowledge discovery approach with discriminative machine learning to predict individual-level antibiotic susceptibility in patients. RESULTS: From MAR (multiple antibiotic resistance) scores, we found a non-uniform spread pattern of AMR. Components-level analysis revealed that governance, finance, and disease burden variables strongly correlate with AMR. From the Bayesian network analysis, we found that access to immunization, obstetric care, and government effectiveness are strong, actionable factors in reducing AMR, confirmed by what-if analysis. Finally, our discriminative machine learning models achieved an individual-level AUROC (Area under receiver operating characteristic curve) of 0.94 (SE = 0.01) and 0.89 (SE = 0.002) to predict Staphylococcus aureus resistance to ceftaroline and oxacillin, respectively. CONCLUSION: Causal machine learning revealed that immunisation strategies and quality of governance are vital, actionable interventions to reduce AMR.

Disruption evaluation in end-to-end semiconductor supply chains via interpretable machine learning.

COVID-19 has posed unprecedented challenges to global health and the world economy. Two years into the pandemic, the widespread impact of COVID-19 continues to deepen, impacting different industries such as the automotive industry and its supply chain. This study presents a hybrid approach combining simulation modeling and tree-based supervised machine learning techniques to explore the implications of end-market demand disruptions. Specifically, we apply the concept of born-again tree ensembles, which are powerful and, at the same time, easily interpretable classifiers, to the case of the semiconductor industry. First, we show how to use born-again tree ensembles to explore data generated by a supply chain simulation model. To this end, we demonstrate the influence of varying behavioral and structural parameters and show the impact of their variation on specific key performance indicators, e.g., the inventory level. Finally, we leverage a counterfactual analysis to identify detailed managerial insights for semiconductor companies to mitigate adverse impacts on one echelon or the entire supply chain. Our hybrid approach provides a simulation model enhanced by a tree-based supervised machine learning model that companies can use to determine optimal measures for mitigating the adverse effects of end-market demand disruptions. We close the loop of our analysis by integrating the findings of the counterfactual analysis backward into the simulation model to understand the overall dynamics within the multi-echelon supply chain.

Vaccination strategies and transmission of COVID-19: Evidence across advanced countries.

Given limited supply of approved vaccines and constrained medical resources, design of a vaccination strategy to control a pandemic is an economic problem. We use time-series and panel methods with real-world country-level data to estimate effects on COVID-19 cases and deaths of two key elements of mass vaccination - time between doses and vaccine type. We find that new infections and deaths are both significantly negatively associated with the fraction of the population vaccinated with at least one dose. Conditional on first-dose coverage, an increased fraction with two doses appears to offer no further reductions in new cases and deaths. For vaccines from China, however, we find significant effects on both health outcomes only after two doses. Our results support a policy of extending the interval between first and second doses of vaccines developed in Europe and the US. As vaccination progresses, population mobility increases, which partially offsets the direct effects of vaccination. This suggests that non-pharmaceutical interventions remain important to contain transmission as vaccination is rolled out.

Do Start-Up Subsidies for the Unemployed Affect Participants' Well-Being? A Rigorous Look at (Un-)Intended Consequences of Labor Market Policies.

BACKGROUND: The literature on start-up subsidies (SUS) for the unemployed finds positive effects on objective outcome measures such as employment or income. However, little is known about effects on subjective well-being of participants. Knowledge about this is especially important because subsidizing the transition into self-employment may have unintended adverse effects on participants' well-being due to its risky nature and lower social security protection, especially in the long run. OBJECTIVE: We study the long-term effects of SUS on subjective outcome indicators of well-being, as measured by the participants' satisfaction in different domains. This extends previous analyses of the current German SUS program ("Gründungszuschuss") that focused on objective outcomes-such as employment and income-and allows us to make a more complete judgment about the overall effects of SUS at the individual level. RESEARCH DESIGN: Having access to linked administrative-survey data providing us with rich information on pretreatment characteristics, we base our analysis on the conditional independence assumption and use propensity score matching to estimate causal effects within the potential outcomes framework. We perform several sensitivity analyses to inspect the robustness of our findings. RESULTS: We find long-term positive effects on job satisfaction but negative effects on individuals' satisfaction with their social security situation. Supplementary findings suggest that the negative effect on satisfaction with social security may be driven by negative effects on unemployment and retirement insurance coverage. Our heterogeneity analysis reveals substantial variation in effects across gender, age groups, and skill levels. Estimates are highly robust.

Lessons from COVID-19 for GCR governance: a research agenda.

The Lessons from Covid-19 Research Agenda offers a structure to study the COVID-19 pandemic and the pandemic response from a Global Catastrophic Risk (GCR) perspective. The agenda sets out the aims of our study, which is to investigate the key decisions and actions (or failures to decide or to act) that significantly altered the course of the pandemic, with the aim of improving disaster preparedness and response in the future. It also asks how we can transfer these lessons to other areas of (potential) global catastrophic risk management such as extreme climate change, radical loss of biodiversity and the governance of extreme risks posed by new technologies. Our study aims to identify key moments- 'inflection points'- that significantly shaped the catastrophic trajectory of COVID-19. To that end this Research Agenda has identified four broad clusters where such inflection points are likely to exist: pandemic preparedness, early action, vaccines and non-pharmaceutical interventions. The aim is to drill down into each of these clusters to ascertain whether and how the course of the pandemic might have gone differently, both at the national and the global level, using counterfactual analysis. Four aspects are used to assess candidate inflection points within each cluster: 1. the information available at the time; 2. the decision-making processes used; 3. the capacity and ability to implement different courses of action, and 4. the communication of information and decisions to different publics. The Research Agenda identifies crucial questions in each cluster for all four aspects that should enable the identification of the key lessons from COVID-19 and the pandemic response.

Matching theory and evidence on Covid-19 using a stochastic network SIR model.

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under-reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution and the timing of early interventions in the United Kingdom and Germany.