Explicit inference: A meta-replication of SEER cancer registry research evaluating the Affordable Care Act's Medicaid expansion.

OBJECTIVES: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state-based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state-based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion's impact on staging, treatment, and mortality in cancer patients. DATA SOURCES: Cancer case data (2000-2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical). STUDY DESIGN: A Difference-in-Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster Bootstrap. DATA COLLECTION: Data was accessed via SEER*Stat. PRINCIPAL FINDINGS: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster Bootstrap techniques never rejected the null hypotheses. CONCLUSIONS: This study reiterates the importance of explicit inference. Future state-based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy.

Adult Drug Treatment Courts and Community-Level Drug Possession Arrests.

Though the literature largely recognizes adult drug treatment courts (ADCs) as beneficial to participants, with lower rates of recidivism and drug use, the question remains of how ADCs impact communities and how other institutions (e.g., law enforcement) react to their presence. This study extends previous work estimating higher arrests associated with ADCs, particularly for crimes involving higher degrees of law enforcement discretion. Results indicate lower drug possession arrest rates for White residents in rural communities, and higher in urban areas, generally, but especially for Black citizens. Though the exact source of these changes has yet to be determined, current analysis indicates larger effect sizes for arrests scaled per officer, as compared to per population, pointing toward changes in law enforcement behavior.

The effect of fine particulate matter (PM2.5) pollution on health inequality: an intergenerational perspective.

Air pollution poses a serious challenge to public health and simultaneously exacerbating regional & intergenerational health inequality. This research introduces PM2.5 pollution into the intergenerational health transmission model, and estimates its impact on health inequality in China using Ordered Logit Regression (OLR) and Multi-scale Geographically Weighted Regression (MGWR) model. The results indicate that PM2.5 pollution exacerbate the intergenerational health inequality, and its impacts show inconsistency across family income levels, parental health insurance status, and area of residence. Specifically, it is more difficult for offspring in low-income families to escape from the influence of unhealthy family to become upwardly mobile. Additionally, this health inequality is more significant in households in which at least one parent does not have health insurance. Moreover, the intergenerational solidification caused by PM2.5 pollution is higher in the east and lower in the west. Both the PM2.5 level and solidification effect are high in Beijing-Tianjin-Hebei region, Yangtze River Delta region and central areas of China, which is the focus of air pollution management. These findings suggest that more emphasis should be placed on family-based health promotion. In areas with high PM2.5 pollution levels, resources, subsidies and air pollution protection should be provided for less healthy families with lower incomes and no health insurance.

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis.

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators.

The class of doubly robust (DR) functionals studied by Rotnitzky et al. (2021) is of central importance in economics and biostatistics. It strictly includes both (i) the class of mean-square continuous functionals that can be written as an expectation of an affine functional of a conditional expectation studied by Chernozhukov et al. (2022b) and the class of functionals studied by Robins et al. (2008). The present state-of-the-art estimators for DR functionals ψ are double-machine-learning (DML) estimators (Chernozhukov et al., 2018). A DML estimator ψ^1 of ψ depends on estimates p^(x) and b^x of a pair of nuisance functions p(x) and bx, and is said to satisfy "rate double-robustness" if the Cauchy-Schwarz upper bound of its bias is o(n-1/2). Were it achievable, our scientific goal would have been to construct valid, assumption-lean (i.e. no complexity-reducing assumptions on b or p) tests of the validity of a nominal (1-α) Wald confidence interval (CI) centered at ψ^1. But this would require a test of the bias to be o(n-1/2), which can be shown not to exist. We therefore adopt the less ambitious goal of falsifying, when possible, an analyst's justification for her claim that the reported (1-α) Wald CI is valid. In many instances, an analyst justifies her claim by imposing complexity-reducing assumptions on b and p to ensure "rate double-robustness". Here we exhibit valid, assumption-lean tests of H0: "rate double-robustness holds", with non-trivial power against certain alternatives. If H0 is rejected, we will have falsified her justification. However, no assumption-lean test of H0, including ours, can be a consistent test. Thus, the failure of our test to reject is not meaningful evidence in favor of H0.

Local sources of vulnerability to climate change and armed conflicts in East Africa.

While socioeconomic and institutional factors are crucial in explaining the onset and evolution of conflicts, recent research suggests that climate change is a further indirect driver acting as a "threat multiplier". This paper focuses on the concept of vulnerability to both climate change and conflicts to explain why some locations are more likely to engage in armed conflicts than others in the presence of a similar level of exposure to climatic changes. In particular, by means of a Spatial Autoregressive Model, we identify a set of local-specific vulnerability factors that increase conflict risk in East Africa. We employ a georeferenced database with a resolution of 25 × 25 km, covering the period 1997-2016. Results from our analysis provide some interesting insights: first, climate change does not increase conflict risk per se, but only in the presence of pre-existing vulnerabilities. Second, resource access and socioeconomic factors play a key role in driving the climate-conflict nexus especially in urban areas. In particular, vulnerability is increased whenever power is not distributed in such a way as to ensure an equitable distribution of resources. Overall, our findings suggest that, by addressing vulnerability factors that prevent adaptive capacity and an equitable distribution of resources, societies may benefit in terms of both diminished conflict risk and alleviation of climate change impacts.

The impact of health on economic growth: A narrative literature review.

The nexus between health and economic growth is a dynamic and complex relationship. This article reviews the empirical evidence that has sought to assess the causal impact of health on growth, understood as growth in GDP per capita, and focusing on cross-country and selected single country studies. The review largely provides evidence in favour of a positive effect of population health on economic growth. However, the multitude of the factors at play and the possible bidirectional relationship between health and growth pose a challenge for the quantification of the effect and for the relative importance of the underlying mechanisms. There is notable heterogeneity between studies in the magnitude and, in some cases, even in the sign of the effect. The evidence suggests that the health-growth relationship may depend on three main factors: the sample composition (i.e. a country's demographic stage or GDP per capita); the health dimension considered (e.g. health improvements at different life stages may affect productivity differently); and the model specification (e.g. whether or not initial life expectancy is controlled for in the analysis or the quality of the instrument). These findings advocate for a policy approach that integrates health considerations into economic strategies and emphasizes intersectoral collaboration to maximize the economic returns from improved health outcomes.

Measuring the unmeasurable multidimensional poverty for economic development: Datasets, algorithms, and models from the poorest region of Luzon, Philippines.

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. This work presents a processed and analyzed dataset from a huge community-based monitoring system of Goa, Camarines Sur. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.

Fresh air in the city: the impact of air pollution on the pricing of real estate.

This study investigates the impact of ambient air pollution on housing prices in Warsaw, Poland, by examining spatial dependencies. The high concentration of particulate matter (PM10, PM2.5 and PM1) is expected to reduce real estate values. Using a hedonic model with approximately 15,000 observations and a spatial error model, we did not find evidence of this impact. Standard and premium housing submarkets differ in price determinants, but both are insensitive to environmental issues. This could be explained by the lack of comprehensive intra-urban historical information on air pollution, which limits investors' rationality and their ability to properly value real estate based on environmental issues. Additionally, measurement and aggregation issues, along with low pollution variability within the city, may contribute to the insignificance of this information in real estate sales prices. Our empirical research confirms a strong link between air pollution and weather conditions within the city, where low temperatures and low-speed southern winds worsen contamination levels, while high temperatures and westerly winds improve air quality. Furthermore, we find that incorporating pollution data using PM yearly mean concentration works better in modelling than the PCA-reduced air pollution index.

For whom the bell tolls. A spatial analysis of the renewable energy transition determinants in Europe in light of the Russia-Ukraine war.

The ongoing invasion of Russia of Ukraine and energy crises have sparked concern about economic and geopolitical stability all over the world. In Europe, the war has destabilized and endangered the energy cooperation and transition between European countries within and outside of the EU. This emergency has shown once more the importance of energy resilience policies to offset the vulnerability of energy systems and energy insecurity at the national and regional levels. Consilience has been reached on the necessity of enhancing EU energy security as an adaptation strategy. This work contributes to the existing scholarship on renewable energy transition and citizens' perception, focusing on European Union member states. Key characteristics of the renewable energy transition in the EU prior to the energy crisis and the war in Ukraine are examined. To this end, we analyze selected economic, environmental, social, policy and political variables on energy sorting from the Eurobarometer studying European citizens' perceptions. The exercise makes use of spatially-clustered regression to explore spatial heterogeneity and to elicit determinant information on specific regional groups. We learn that southern Europeans attribute less importance to energy infrastructure to facilitate the renewable energy transition and repute the EU solidity not a requirement for energy security access. Conversely, northern European citizens tend not to associate the responsibility of the EU in guiding competitiveness and policy toward green energy sources transformation. Robustness tests confirm our hypothesis. Regardless of regional differences, the EU energy and ecological transition will thrive with industrial and political cohesion. This process will pass through increased trust in institutions and dedicated energy policy action which will smooth the risks and disruptions coming from current and future shocks.

Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China.

Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM2.5 and PM10 on a monthly, seasonal, and annual scale for 2273 cities in China from 2000 to 2020. Subsequently, we analyze the influence of urban characteristics using a combined approach of a two-way fixed effects model and a spatial Durbin model. Results show a strong downward trend in the UAPI intensity since 2013, with reductions ranging from 42 % to 61 % until 2020, for both pollutants and in summer as well as winter. Consistently, the proportion of the cities experiencing the UAPI phenomenon decreased from 94.5 % to 77.3 % for both PM2.5 and PM10. We find a significant influence of urban morphology on UAPI. Specifically, urban sprawl, polycentric development, and an increase in urban green spaces are associated with a reduction in UAPI, while dense urban areas intensify it. Our study also reveals a robust inverted U-shaped relationship between stages of economic development and UAPI. Moreover, economic development and air pollution itself show spillover effects that oppose their direct impacts. These results suggest that urban and regional planning and more ambitious climate change mitigation policies could be more effective strategies for mitigating air pollution in cities than end-of-pipe control.

The joint determination of morbidity and vaccination in the spatiotemporal epidemiology of COVID-19.

This paper examines the mutual dependence between COVID-19 morbidity and vaccination rollout. A theory of endogenous immunization is proposed in which the decision to become vaccinated varies directly with the risks of contagion, and the public self-selects into self-protection. Hence, COVID-19 morbidity varies inversely with vaccination rollout, and vaccination rollout varies directly with COVID-19 morbidity. The paper leverages the natural sequencing between morbidity and immunization to identify the causal order in the dynamics of this relationship. A modified SIR model is estimated using spatial econometric methods for weekly panel data for Israel at a high level of spatial granularity. Connectivity between spatial units is measured using physical proximity and a unique mobility-based measure. Spatiotemporal models for morbidity and vaccination rollout show that not only does morbidity vary inversely with vaccination rollout, vaccination rollout varies directly with morbidity. The utility of the model for public health policy targeting, is highlighted.

Instrumental variables methods reconcile intention-to-screen effects across pragmatic cancer screening trials.

Pragmatic cancer screening trials mimic real-world scenarios in which patients and doctors are the ultimate arbiters of treatment. Intention-to-screen (ITS) analyses of such trials maintain randomization-based apples-to-apples comparisons, but differential adherence (the failure of subjects assigned to screening to get screened) makes ITS effects hard to compare across trials and sites. We show how instrumental variables (IV) methods address the nonadherence challenge in a comparison of estimates from 17 sites in five randomized trials measuring screening effects on colorectal cancer incidence. While adherence rates and ITS estimates vary widely across and within trials, IV estimates of per-protocol screening effects are remarkably consistent. An application of simple IV tools, including graphical analysis and formal statistical tests, shows how differential adherence explains variation in ITS impact. Screening compliers are also shown to have demographic characteristics similar to those of the full trial study sample. These findings argue for the clinical relevance of IV estimates of cancer screening effects.

The future of feed formulation for poultry: Toward more sustainable production of meat and eggs.

Current methods for feed formulation are based on minimizing costs, not maximizing profits. Complex models of bird growth and reproduction as functions of genetic, feed and other environmental variables are being developed, but their adaptation has been slow. The development of profit maximizing models will evolve to center on the production functions of broilers and layers. The production functions are the relationship between the value of products (mainly meat and eggs) and the cost of feed. The production function is the tool used to maximize profits subject to all the various inputs, not just feed or nutrition. The production function is subject to the law of diminishing returns. The most profitable output levels are those where the marginal value (price) of the meat or eggs is just equal to the marginal cost of the inputs including feed, housing, processing and all other costs. Anything that affects the production function, bird genetics, feed quality, housing and environment, will be considered to maximize profits for the poultry firm. The profit maximizing models of poultry firms will improve as various technical improvements are made: metabolizable energy to describe ingredients will evolve to net energy systems that consider that the heat production (and therefore energetic efficiency) of broilers is different depending on the ingredients used to formulate the feed and the environmental temperatures under which they are reared. Amino acid needs will include a method to find the birds' needs for the non-essential amino acids. "Digestible" amino acid assays will differentiate between digestion and absorption to best balance various sources. The carbohydrate fractions of feed ingredients will be determined to optimize the use of exogenous enzymes. The value of meat and egg co-products will reduce overall costs (e.g., organic fertilizer for crop enhancement). Future profit maximizing production models will be ever evolving processes where field conditions and results are continually being utilized to re-calibrate the technical models so that the management team can use them with cost and return projections to decide on the best choices of inputs and outputs.

Capital structure determinants across sectors: Comparison of observed evidences from the use of time series and panel data estimators.

This comparative study is an attempt to explore the determinants of capital structure for Malaysian firms listed in various sectors level. Within the framework of traditional and moderate dynamic capital structure theories, the key determinants such as fixed assets, current assets, return on equity, size, earning per share and total assets are tested in relation to the debt-equity ratio. The large-scale study entails data collected from 551 listed firms of Bursa Malaysia main market over 12 years period i.e. 2005-2016. Notably, this study combines Time Series econometrics with Panel Data analysis to enhance methodological robustness. Moreover, the comparative analysis approach is designated to recognize the most persistent capital structure determinants. In the first place, the Multiple Regression analysis (MRA) is selected as a baseline estimation method. Subsequently, the Auto Regression Distributed Lag model (ARDL), the Panel Data Static models, and Dynamic model via the Generalized Method of Moments (GMM) are employed to identify the capital structure determinants for the firms listed at Bursa Malaysia. The outcomes are surprising and indicate that the entire market is primarily controlled by the studied determinant total assets, which is significant in both construction and property sectors through MRA, ARDL, and GMM analysis. Technically, the significant role of tangibility and the existence of speed of adjustment across sectors imply that the Dynamic Capital Structure is the most prominent among all, followed by the Dynamic Trade-off theory.

Under-spending, over-spending or substitution among services? Spatial patterns of unexplained shares of health care expenditures.

Using individual-level administrative data, we investigate the spatial patterns of unexplained shares of health care expenditures (HCE) at the municipality level. The focus is on the elderly population in the Italian Region Friuli-Venezia Giulia observed over the period 2017-2019. The empirical analysis comprises two steps. First, random-effects two-part models are estimated to analyze the effect of age, morbidity, and death on the probability and amount of positive individual total HCE and its components. Second, the unexplained shares of HCE at the municipality level are examined to identify areas with under- or over-spending and substitution among services. Results confirm the existing findings on the determinants of HCE and reveal geographic patterns in the unexplained shares of expenditures. We identify clusters of municipalities with observed HCE higher than predicted for each type of service and clusters with substitution between home care and all other services. These findings are associated with the degree of urbanization of these areas and, consequently, with the ease of access to health care. This is crucial from a policy perspective, as it indicates specific policy targets for public health intervention.

A Nuanced Examination of Primate Capture and Consumption and Human Socio-Economic Well-Being in Kirindy Mitea National Park, Madagascar.

The futures of human and nonhuman primates are closely tied in protected areas. Understanding this interconnectedness is especially urgent in Madagascar, one of the world's most impoverished biodiversity hotspots. Yet, no study has evaluated the relationship between poverty and lemur hunting and consumption using a composite poverty metric that includes health, education, and living standards. To address this gap, and to inform primate conservation practice and policy, we administered annual surveys to 81 households over six consecutive months (September 2018 to March 2019) in a village on the border of Kirindy Mitea National Park, Madagascar. We observed extreme deprivation scores across multiple dimensions of poverty and identified ninety-five percent of households as 'impoverished'. Of these, three-quarters (77%) of households were identified as being in 'severe poverty'. One-fifth (19%) of all households hunted lemurs and half (49%) of households consumed lemurs. While poverty eradication is an urgent need in communities around Kirindy Mitea National Park, our findings show no relationship between poverty and lemur hunting and consumption, perhaps due to the lack of variance in poverty. Our results highlight the need to investigate other contributory factors to lemur hunting and consumption locally. Because food insecurity is a known driver of lemur hunting and consumption among the study community, and because domestic meats can be preferred over protected species, we recommend testing the efficacy of livestock interventions near Kirindy Mitea National Park.

How the energy technology influences the total factor of energy efficiency?: evidence from China.

Technological progress is of great importance to total-factor energy efficiency (TFEE). However, previous research has not narrowed technological progress into the energy field, generating rough and ambiguous empirical evidence for policymakers. In addition, technological progress is often discussed from a conventional perspective as a whole, ignoring its heterogeneity and spillover effect between regions. This study applies the stock of energy patents to reflect the effect of technological progress in the energy field on TFEE at first. The dynamic models are then employed to investigate if and how technological progress influences TFEE from the conventional and spatial perspectives for China's over the period of 2000-2016. The conventional analysis shows that energy technology is of great importance to TFEE. However, the creation-type of technology coming from businesses specifically is shown to have more success in enhancing TFEE than other types of energy technology. Further evidence coming from the spatial econometrics demonstrates that technology spillovers across regions are rather common and have significant effects on TFEE.

The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States.

The extant literature on the U.S. SARS-CoV-2 virus indicates that the vaccination campaign was lagging, insufficient, and uncoordinated. This study uses the spatial model to identify the drivers of vaccine hesitancy (in the middle of the pandemic), one of the critical steps in creating impactful and effective interventions to influence behavioral changes now and in the future. The applied technique accounted for observed and unobserved homogeneity and heterogeneity among counties. The results indicated that political and religious beliefs, quantified by Cook's political bipartisan index and the percentage of the population affiliated with the main Christian groups, were the main drivers of the SARS-CoV-2 vaccine hesitancy. The past vaccination experience and other variables determining the demand and supply of vaccines were also crucial in influencing hesitancy. The results imply that vaccination campaigns require engaging community leaders at all levels rather than depending on politicians alone and eliminating barriers to the supply and demand of vaccines at all levels. Coordination among religious and community leaders would build a practical institutional arrangement to facilitate (rather than frustrate) the vaccination drives.

Estimating health care costs at scale in low- and middle-income countries: Mathematical notations and frameworks for the application of cost functions.

Appropriate costing and economic modeling are major factors for the successful scale-up of health interventions. Various cost functions are currently being used to estimate costs of health interventions at scale in low- and middle-income countries (LMICs) potentially resulting in disparate cost projections. The aim of this study is to gain understanding of current methods used and provide guidance to inform the use of cost functions that is fit for purpose. We reviewed seven databases covering the economic and global health literature to identify studies reporting a quantitative analysis of costs informing the projected scale-up of a health intervention in LMICs between 2003 and 2019. Of the 8725 articles identified, 40 met the inclusion criteria. We classified studies according to the type of cost functions applied-accounting or econometric-and described the intended use of cost projections. Based on these findings, we developed new mathematical notations and cost function frameworks for the analysis of healthcare costs at scale in LMICs setting. These notations estimate variable returns to scale in cost projection methods, which is currently ignored in most studies. The frameworks help to balance simplicity versus accuracy and increase the overall transparency in reporting of methods.