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Background Late‐stage diagnosis of oral cavity cancer (OCC) often results from diagnostic delays due to healthcare access limitations or financial barriers. This study investigates how area‐level (“place”) and individual‐level (“pocketbook”) income influence the stage at OCC diagnosis. Methods This retrospective cohort study analyzed data on patients diagnosed with OCC between 2010 and 2013 sourced from Taiwan's National Cancer Registry and National Health Insurance databases. The primary outcome was late‐stage diagnosis, defined as those initially diagnosed at stage III or IV. Multivariable analyses were conducted to estimate the association between area‐level, individual‐level, and late‐stage diagnosis. Results This study included 16,652 incident OCC patients, with 6639 (39.9%) diagnosed at a late stage and 10,013 (60.1%) at an early stage. Patients with low individual incomes residing in low‐income areas had 1.48 times higher odds (95% CI = 1.09 to 2.00, p = 0.011) of late‐stage diagnosis compared to high‐income patients in high‐income areas. High‐income patients; however, in low‐income areas had 1.34 times higher odds (95% CI = 1.05 to 1.71, p = 0.02) to be diagnosed at a late stage compared to high‐income patients in high‐income areas. Conclusions While area‐level income plays a significant role in late‐stage OCC diagnosis, higher individual income does not fully protect against late‐stage diagnosis in low‐income areas.

The number of patients who survive for a long time after cancer diagnosis is rapidly increasing; however, such patients experience major problems such as returning to work and changes in their income. This study aimed to determine the extent of income changes of cancer patients during the first year after cancer diagnosis and identify the influencing factors. From November 2019 through January 2020, we conducted a multicenter, self-administered anonymous survey of cancer patients in Kagawa Prefecture, Japan. The number of questionnaires collected was 483 (recovery rate 60.4%), and the number of participants who met the inclusion criteria was 72. Mean year-on-year income level one year since cancer diagnosis was 66% (SD: 32%; median: 70%). Cancer stage (p = 0.016), employment status at diagnosis (p = 0.006), and continued employment at the same workplace (p = 0.001) were associated with income change. Findings from this study showed that cancer patients lost one-thirds of their income one year after their diagnosis. It was related to the stage of their illness, employment status, and continued employment at their workplace just before the diagnosis. Employers should provide cancer patients with the support they need to keep them employed.

This study investigates the relationship between certain economic and psychological factors and demographic characteristics of Slovene taxpayers, such as gender, income level and size of settlement as it is becoming important for a country's tax compliance framework to align with the tax recommendations of global institutions. The results show some gender differences, with males being less likely to feel guilty or bad if taxes are not paid in full than females, whereas females tend to have the opinion that working for cash-in-hand payment without paying tax is not a trivial offence. Taxpayers with low incomes tend to agree that tax evasion is morally acceptable if tax rates are too high. Taxpayers from rural settlements exhibit a higher tendency to feel morally obligated to pay their taxes than taxpayers from urban settlements. The findings indicate that the vast majority of taxpayers feel morally obligated to pay their taxes.

Specialist groups have often advised health ministers and other decision makers in developing countries on the use of social health insurance (SHI) as a way of mobilizing revenue for health, reforming health sector performance, and providing universal coverage. This book reviews the specific design and implementation challenges facing SHI in low- and middle-income countries and presents case studies on Ghana, Kenya, Philippines, Colombia, and Thailand.

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.

Background: Census-based measures of income often are used as proxies for individual-level income. Yet, the validity of such area-based measures relative to 'true' individual-level income has not been fully characterized. Objectives: The objectives of this study were (1) to determine whether area-based measures of household income are a suitable proxy for self-reported household income and (2) to assess whether these measures are associated with outcomes in a cardiac disease cohort. Research Design: We used a prospective cohort from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH©) cardiac catheterization registry. Subjects: A total of 4372 patients having undergone cardiac catheterization and who also completed a 1-year follow-up questionnaire on self-reported income level were studied. Measures: Our measurements were survival to 2.5 years after catheterization and health-related quality of life (EuroQoL). Results: Agreement between the 2 income measures generally was poor (unweighted Kappa = 0.07), particularly for the low-income patients. Despite this poor agreement, both income measures were positively associated with survival and EuroQoL scores. An outcome analysis that simultaneously considered individual level income and area-based income revealed that low-income individuals have poorer survival and lower quality of life scores if they live in low income neighborhoods, but not if they live in high income neighborhoods. Conclusions: The area-based estimates of household income in these data demonstrate poor agreement with self-reported household income at the level of individual patients, particularly for low-income patients. Despite this, both income measures appear to be prognostically relevant, perhaps because individual and neighborhood income measure different constructs.

Many scholars have argued that once “basic needs” have been met, further rises in income are not associated with further increases in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between well-being and income is roughly log-linear and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it.

Objective To quantify the relation between food prices and the demand for food with specific reference to national and household income levels.Design Systematic review with meta-regression.Data sources Online databases of peer reviewed and grey literature (ISI Web of Science, EconLit, PubMed, Medline, AgEcon, Agricola, Google, Google Scholar, IdeasREPEC, Eldis, USAID, United Nations Food and Agriculture Organization, World Bank, International Food Policy Research Institute), hand searched reference lists, and contact with authors.Study selection We included cross sectional, cohort, experimental, and quasi-experimental studies with English abstracts. Eligible studies used nationally representative data from 1990 onwards derived from national aggregate data sources, household surveys, or supermarket and home scanners.Data analysis The primary outcome extracted from relevant papers was the quantification of the demand for foods in response to changes in food price (own price food elasticities). Descriptive and study design variables were extracted for use as covariates in analysis. We conducted meta-regressions to assess the effect of income levels between and within countries on the strength of the relation between food price and demand, and predicted price elasticities adjusted for differences across studies.Results 136 studies reporting 3495 own price food elasticities from 162 different countries were identified. Our models predict that increases in the price of all foods result in greater reductions in food consumption in poor countries: in low and high income countries, respectively, a 1% increase in the price of cereals results in reductions in consumption of 0.61% (95% confidence interval 0.56% to 0.66%) and 0.43% (0.36% to 0.48%), and a 1% increase in the price of meat results in reductions in consumption of 0.78% (0.73% to 0.83%) and 0.60% (0.54% to 0.66%). Within all countries, our models predict that poorer households will be the most adversely affected by increases in food prices.Conclusions Changes in global food prices will have a greater effect on food consumption in lower income countries and in poorer households within countries. This has important implications for national responses to increases in food prices and for the definition of policies designed to reduce the global burden of undernutrition.

In high-income countries, the percent of the population covered under mandatory old-age pension programs is typically high but often incomplete; in low- and middle-income countries, coverage is low and even stagnant. At the same time, older people are less able to rely on family and community support as a result of growing urbanization and migration. And low-income workers and the poor simply cannot save enough to prepare for their old age. As a response, many countries are considering or have already implemented various forms of retirement income transfers aiming to guarantee a minimum level of income during old age. Despite the growing popularity of these programs, research assessing their success has been limited. 'Closing the Coverage Gap: The Role of Social Pensions and Other Retirement Income Transfers' brings together a group of renowned academics, policy analysts, and policy makers working in the area of pensions and public policy. They discuss how social pensions and other retirement income transfers can be used to close the coverage gap of mandatory pension systems: how they operate, when they can be appropriate, and how to make them work. The book reviews the experiences of low-, middle-, and high-income countries with the design and implementation of retirement income transfers. The book analyzes design issues related to financing, incentives, targeting mechanisms, and administration, and also identifies the role of promising instruments such as matching contributions to reach parts of the informal sector.

Promoting electric vehicles (EVs) adoption has become one of the important paths for countries around the world to address climate change and accelerate the transformation of energy system for achieving sustainable development. As one of the important psychological factors, the research on the explanatory power of emotions to EVs purchase intention is still insufficient. This paper collected 400 valid questionnaires all around China. By incorporating emotions and moral norms into the Theory of Planned Behavior (TPB) model, this study used structural equation model to estimate the impact of positive anticipated emotion (PAE), negative anticipated emotion (NAE), and moral norms together with TPB elements on EVs purchase intention. In order to explore the heterogeneity effect of the above factors on EVs purchase intention among consumers of different income groups, we divided the total sample into high-income subsample and low-income subsample according to the household monthly disposable income. We concluded as follows: for the total sample, PAE has the greatest impact on EVs purchase intention, followed by attitude, NAE, and perceived behavioral control (PBC). In particular, the purchase intention of high-income consumers mainly depends on NAE, while the purchase intention of low-income consumers mainly depends on PAE. Additionally, PBC has more significant impact on EVs purchase intention of high-income group. Finally, targeted policy implications are proposed to promote EVs purchase.