Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area

Background Geographical imbalances in the health workforce, particularly the shortage of health care workers in rural areas, is an issue of social and political concern in most countries. Estimating the number of required doctors is essential for evidence-based health policy planning. In this study, we propose two methods for estimating the number of required doctors using a simple method. One is counting by unit and the other is incorporating access to medical institutions. The purpose of this study is to verify the need to incorporate access to medical institutions when estimating the number of required physicians in a region by comparing both estimation methods from the viewpoint of regional population density. Methods We calculated the ratio of outpatients who can access medical institutions and the number of required physicians using the travel time by car and the number of patients who can be treated per doctor per day (estimation method for the number of physicians based on the access simulation, hereinafter referred to as EAS). We compared the results of this estimation with those of a conventional method, such as the number of doctors per population (estimation method for the number of physicians based on the number of patients, hereinafter referred to as ENP) to show how important it is to incorporate the element of accessibility in such a simulation analysis. Based on the results, we discussed the applicability of the proposed method. Results ENP estimated that 38,685 outpatient primary care (PC) physicians were required and EAS estimated that 46,378 were required. There was a difference of about 8000. A comparison of the EAS-estimated number of physicians and the ENP-estimated number of physicians showed that the ENP-estimated number was small, particularly in areas with low population density. Conclusions The results showed that it is effective to use the proposed EAS method for the estimation of PC physicians, particularly in areas with low population density. We showed that the method of allocating the number of physicians in proportion to the number of patients in a certain unit requires paying attention to the setting of the unit.

The Regional Determinants of the New Venture Formation in China’s Car-Sharing Economy

New ventures play an important role in promoting regional economic growth, employment and innovative development. In China, the new business model centered on the sharing economy has grown rapidly in a short time, especially with regard to car-sharing, which has become one of the new governmental strategies to promote economic development in China. New car-sharing ventures have been recognized as the leading sector in sustainable development for the circular economy and resource reuse. This paper explores the regional determinants of new firm formation in the nascent car-sharing industry in China. We used panel data from 449 car-sharing new ventures established in 257 cities in China from 2011 to 2019 to conduct an empirical analysis. The findings show that the urbanization economy, human capital and venture capital in the region have a positive impact on the establishment of new ventures. At the same time, the regional population density, localization economy, innovation capacity and competitive environment have no significant relationships with the establishment of new car-sharing firms. This paper provides insights for startups entering the field of car-sharing and a theoretical basis for the subsequent research on startups in the sharing economy industry.

A new air pollution management method based on the integration of evidential reasoning and slacks-based measure

Air pollution management is becoming a major topic of political concern, and many studies have devoted to the efficiency measurement of air pollution management. However, several drawbacks must be overcome for better applying efficiency measurement to improve air pollution management, including neglect of the importance of different indicators, non-integrity of indicator information for efficiency measurement, and lack of analyzing regional factors in the efficiency of air pollution management. Accordingly, by utilizing the evidential reasoning (ER) approach with entropy weighting method to propose an ER-based indicator integration and introducing the slacks-based measure (SBM) model with consideration of undesirable outputs and the regression model to propose an SBM-based efficiency analysis, a new air pollution management method, called integrated ER-SBM method, is developed in the present study. In the case study of Chinese 29 provinces, the application procedure and results are provided to illustrate how to apply the integrated ER-SBM method to integrate various air pollution indicators with different importance and further analyze the influence of regional factors, such as technological innovation, regional population density, import-export values, number of industries, and energy resources, on the efficiency of air pollution management. In addition, the policy recommendations targeting the results are concluded as well.

Convergence of regional population density in Russia over 120 years

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Climate change as an incentive for future human migration

Human migration is both motivated and constrained by a multitude of socioeconomic and environmental factors, including climate-related factors. Climatic factors exert an influence on local and regional population density. Here, we examine the implications of future motivation for humans to migrate by analyzing today's relationships between climatic factors and population density, with all other factors held constant. Such “all other factors held constant” analyses are unlikely to make quantitatively accurate predictions, but the order of magnitude and spatial pattern that come out of such an analysis can be useful when considering the influence of climate change on the possible scale and pattern of future incentives to migrate. Our results indicate that, within decades, climate change may provide hundreds of millions of people with additional incentive to migrate, largely from warm tropical and subtropical countries to cooler temperate countries, with India being the country with the greatest number of people with additional incentive to migrate. These climate-driven incentives would be among the broader constellation of incentives that influence migration decisions. Areas with the highest projected population growth rates tend to be areas that are likely to be most adversely affected by climate change.

Estimating Variation of Covid-19 infection in the Population:Results from Understanding Society (UKHLS) first monthly covid-19 survey

The analysis in this paper uses the new Understanding Society COVID-19 survey. The key advantage of these data is that they allow us to examine infection rates for people with particular characteristics. We study how reported symptoms vary in the population and relate reported symptoms to a positive Covid-19 test in the small sample in the survey who were tested. Combining these probabilities we find that the chances of infection increase with a persons education level, are lower and declining with age among those aged over 55, and were higher in the West Midlands and London and lower in the North East than in the rest of the country, and tended to increase with regional population density. There is also evidence that the infection rate was lower among those of a Caribbean origin. A suitably cautious estimate of the mean infection rate is that, during the period up to the end of April 2020, it was between 2% and 8%, with a central rate of about 5%.

Climate change as a driver of future human migration

Human migration is both motivated and constrained by a multitude of socioeconomic and environmental factors, including climate-related factors. Climatic factors exert an influence on local and regional population density. Here, we examine implications for future motivation for humans to migrate by analyzing today’s relationships between climatic factors and population density, with all other factors held constant. Such all other factors held constant analyses are unlikely to make quantitatively accurate predictions but the order-of-magnitude and spatial pattern that come out of such an analysis can be useful for thinking about the influence of climate change on the possible scale and pattern of future incentives to migrate. Our results indicate that, within decades, climate change may provide to hundreds of millions of people additional incentive to migrate, largely from warm tropical and subtropical countries to cooler temperate countries, with India being the country with the greatest number of people with additional incentive to migrate. These climate-driven incentives would be among the broader constellation of incentives that influence migration decisions. Areas with the highest projected population growth rates tend to be areas that are likely to be most adversely affected by climate change.