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Who benefits from government fiscal policies? Providing an answer to this question is a crucial element in maintaining the sustainability of government policies. In this paper we extend traditional tax incidence studies to benefit incidence studies, focusing especially on the incidence of government-provided public goods in the form of urban transportation infrastructure spending. We develop a new approach to benefit incidence studies—a “time-savings approach”—that allows us both to measure individual access to government infrastructure programs and to estimate the time savings to individual households from these infrastructure programs. We then apply this time-savings approach in two detailed applications, using micro-level and geo-spatial data from Indonesia and Mozambique. We find that the time-savings approach has the potential to provide estimates at the household level of the monetary value of government urban infrastructure improvements via the value of reduced travel time, illustrating the power of this approach in allocating and valuing the benefits of public goods like transportation infrastructure spending for individual households. Fully realizing the potential of the time-savings approach requires access to data with accurate time and distance variables at the household level.

Supply chain risk management embroils quite a lot of situations of managerial decision-making under uncertainties. As contemporary supply chains are intricate networks exposed to ample vulnerabilities, a resilient supply chain with inbuilt capabilities for responding to unanticipated events can assume significance. This paper proposes a decision support model for managers for knowing, measuring and improving the level of resilience in manufacturing supply chains. A novel computational methodology involving the incidence analysis and grey theory is proposed in this study. Using the methodology of advanced analysis of grey incidences, the level of resilience of supply chains can be measured. Various strategies and attributes imparting resilience, particularly relevant to the manufacturing industry are analyzed in this research. A framework considering five strategies and twenty-three attributes contributing to supply chain resilience is also constructed. And a case evaluation has been conducted to implementing the proposed methodology. Managers can ascertain their supply chain resilience capabilities by means of synthetic resilience index, as recommended in this study. From the measures of resilience for the bygone period, top management can assess and improve the resilience capabilities of their supply chain, by taking strategic level decisions.Graphic abstract

Aims/hypothesis There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations. Methods For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5–19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence. Results Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS’s HR was 1.27 per SD ( p =1.6 × 10 −8 ; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 ( p =4.3 × 10 −8 ; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 ( p =2.8 × 10 −16 ; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA 1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention. Conclusions/interpretation This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA 1c ). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages. Graphical abstract

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng’s grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Background In the last two decades, cesarean section (CS) deliveries in India have increased by six-fold and created economic hardship for families and households. Although several schemes and policies under the National Health Mission (NHM) have reduced the inequality in the use of maternal care services in India, the distributive effect of public health subsidies on CS deliveries remains unclear. In this context, this paper examines the usage patterns of CS delivery and estimates the share of public health subsidies on CS deliveries among mothers by different background characteristics in India. Data Data from the fourth round of the National Family Health Survey (NFHS-4) was used for the study. Out-of-pocket (OOP) payment for CS delivery was used as a dependent variable and was analyzed by level of care that is, primary (PHC, UHC, other) and secondary (government/municipal, rural hospital). Descriptive statistics, binary logistic regression, benefit incidence analysis, concentration curve and concentration index were used for the analysis. Results A strong economic gradient was observed in the utilization of CS delivery from public health facilities. Among mothers using any public health facility, 23% from the richest quintile did not pay for CS delivery compared to 13% from the poorest quintile. The use of the public subsidy among mothers using any type of public health facility for CS delivery was pro-rich in nature; 9% in the poorest quintile, 16.1% in the poorer, 24.5% in the middle, 27.5% among richer and 23% in the richest quintile. The pattern of utilization and distribution of public subsidy was similar across the primary and secondary health facilities but the magnitude varied. The findings from the benefit-incidence analysis are supported by those obtained from the inequality analysis. The concentration index of CS was 0.124 for public health centers and 0.291 for private health centers. The extent of inequality in the use of CS delivery in public health centers was highest in the state of Mizoram (0.436), followed by Assam (0.336), and the lowest in Tamil Nadu (0.060), followed by Kerala (0.066). Conclusion The utilization of CS services from public health centers in India is pro-rich. Periodically monitoring and evaluating of the cash incentive schemes for CS delivery and generating awareness among the poor would increase the use of CS delivery services in public health centers and reduce the inequality in CS delivery in India.

This study aims to provide a comprehensive overview of the clinical characteristics and epidemiology of uveal melanoma (UM) in the Portuguese population, evaluated at the National Reference Centre (NRC). A prospective observational study was conducted, involving patients consecutively diagnosed with UM at the Portuguese NRC between July 2013 and December 2022. The study collected data on demographic and tumour characteristics, clinical staging according to the American Joint Committee on Cancer (AJCC), treatment approaches, local disease control, patient survival, and the occurrence of distant metastases. The study included a total of 316 patients, 53.8% female. The mean age at diagnosis was 61.8±14.2 years, and 75.0% of patients presented with symptoms. The mean annual age-adjusted incidence of uveal melanoma in Portugal between 2014 and 2022 was 2.4 cases per million (95% confidence interval [CI]: 2.1–2.8). For choroidal/ciliary body tumours, the overall cumulative survival and distant metastases-free survival (DMFS) rates at 5 years were 84.9% (95% CI: 78.7–91.1) and 79.4% (95%CI: 72.8–86.0), respectively. Notably, higher AJCC stages at presentation, the need for enucleation, and increased tumour thickness were associated with lower DSS and DMFS rates. This study represents the most extensive analysis of UM epidemiology within the Portuguese population. The findings underscore the importance of early diagnosis and treatment in UM, as lower AJCC stages and smaller tumour thickness at diagnosis correlate with improved DSS and DMFS. •First study, to the best of our knowledge, to analyze the uveal melanoma survival and incidence in Portugal.•Mean annual age-adjusted incidence of 2.4 cases per million between 2014 and 2022.•Incidence inferior to that observed in Northern Countries.•Lower AJCC stages correlated with better survival.

Unfair differences in healthcare access, utilisation, quality or health outcomes exist between and within countries around the world. Improving health equity is a stated objective for many governments and international organizations. We provide an overview of the major tools that have been developed to measure, evaluate and promote health equity, along with the data required to operationalise them. Methods are organised into four key policy questions facing decision-makers: (i) what is the current level of inequity in health; (ii) does government health expenditure benefit the worst-off; (iii) can government health expenditure more effectively promote equity; and (iv) which interventions provide the best value for money in reducing inequity. Benefit incidence analysis can be used to estimate the distribution of current public health sector expenditure, with geographical resource allocation formulae and health system reform being the main government policy levers for improving equity. Techniques from the economic evaluation literature, such as extended and distributional cost-effectiveness analysis can be used to identify ‘best buy’ interventions from a health equity perspective. A range of inequality metrics, from gap measures and slope indices to concentration indices and regression analysis, can be applied to these approaches to evaluate changes in equity. Methods from the economics literature can provide policymakers with a toolkit for addressing multiple aspects of health equity, from outcomes to financial protection, and can be adapted to accommodate data commonly available in low- and middle-income settings.

Online reviews of products have a significant impact on consumers' purchasing decisions, making it important for both platform retailers and consumers to rank products, and eventually purchase products. With respect to the problem of product ranking that consists of the information contained in online reviews; by considering the sentiment intensity distribution of online reviews, we establish a fine-grained sentiment intensity analysis and then exploit grey incidence analysis and TOPSIS to establish a multi-attribute approach for product ranking. Finally, a case study of laptop purchases verifies the applicability and effectiveness of the proposed approach.

PurposeWith the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (εij), relative degree of grey incidence (rij) or synthetic degree of grey incidence (ρij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.Design/methodology/approachTo achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).FindingsIt turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.Research limitations/implicationsLimitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.Originality/valueIn this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.

Background: The epidemiology of retinopathy of premature infants (ROP) in Poland is a topic rarely discussed in the literature. Single publications (Pubmed) concern only specific regions of Poland and date back to 2000–2006, which prompted the authors to update Polish epidemiological data. Methods: Data from the 2012–2021 screening tests were analyzed for: diagnosis of ROP, severe ROP and the percentage of ROP undergoing treatment (laser-diode, anti-VEGF, and complications such as retinal detachment). The Polish results were compared with the available data from Europe in the same period. Results: The analysis of the GOCC data confirmed that the prevalence of ROP in Poland for 2016–2019 was 15.1%; in 2012–2021 (15.6%). Polish epidemiological data shows a lower percentage of ROP diagnosis compared to other European countries (15.6% vs. 23.8% in Portugal, 28.3% in the Netherlands, 25.1% in Genoa, Italy, 38% in Florence, Italy). Conclusions: In order to obtain and create an epidemiological database, it is important to undertake activities aimed at a detailed verification of the analyzed parameters and the collection of similar or the same data from all centers in Poland. There is no economic data available on the cost of caring for a child with ROP.