ABSTRACT
Background: In the current global COVID-19 pandemic, terminal care in a patient's home has been expanded as a positive choice even for patients suffering from hematological malignancy (HM). Although there are many tools for predicting the prognosis of patients in the terminal phase of solid tumors, there is little information about prognostic factors in patients in the terminal phase of HM, especially patients receiving home medical care (HMC). In comparison to patients with solid tumors, those with HM are more likely to have acute diseases such as acute bleeding and acute infection leading to death. A previous report revealed that HM was a factor associated with aggressive end-of-life care. Because of the various complications associated with HM, it was reported to be difficult to predict the prognosis for patients with HM. Providing patients with accurate information about prognosis is important for them to consider how to spend their remaining life. Aims: In patients in the terminal phase of HM who received HMC, we aimed to validate the usefulness of two prognostic models: Palliative Prognostic Index (PPI), which is an established prognostic model for patients in the terminal phase of a solid tumor, and the prognostic model reported by Kripp et al., which is a prognostic model for patients with HM in a palliative care unit. In addition, we aimed to determine prognostic factors for patients in the terminal phase of HM who received HMC and to develop a more detailed prognostic scoring system. Methods: We retrospectively evaluated 136 patients in the terminal phase of HM who were receiving HMC provided by 6 clinics between 2008 and 2022. Medical records relevant to prognosis were collected by a chart review. The effects of possible factors associated with overall survival (OS) were determined by the Kaplan-Meier method and univariate and multivariate Cox regression models. This study was approved by the IRB of Hokkaido University Hospital. Results: Patients characteristics were as follows: male/female, 78/58;age, 25 to 94 years;median age, 79 years;AML, 50 patients;B-NHL, 32;MDS, 24;MM, 13;T-NHL 6;ALL, 5;ATL 2, CMML 2, and PV, 2. According to PPI, there was no significant difference in OS between the intermediate-risk group and the low-risk group (panel A;P = 0.15). By using the prognostic model reported by Kripp et al., we could stratify the patients into 3 risk groups with significantly different survival times (panel B;P < 0.01). However, there was a wide range of survival times in the high-risk group (OS, 0 to 125 days;median OS, 24 days). In our investigation of factors associated with OS, multivariate analyses revealed that there were 7 factors associated with poor OS (panel C). For the development of our prognostic scoring system, each variable was weighed according to the value of the hazard ratio (panel C) and 4 risk groups were shown to clearly discriminate survival (P < 0.01): low-risk group (n = 25, median OS of 434 days), intermediate-low risk group (n = 60, median OS of 112 days), intermediate-high risk group (n = 31, median OS of 31 days), and high-risk group (n = 20, median OS of 9 days). (Figure Presented ) Summary/Conclusion: This is the first investigation of prognostic factors that influence the overall survival of patients in the terminal phase of HM who received home medical care. In comparison to previously reported prognostic models, our scoring system could stratify patients in more detail. Providing patients and medical staff with accurate information about prognosis will lead to a higher quality of life in the terminal phase and better support by medical staff.
ABSTRACT
Japan is now facing the new disaster of the COVID-19 pandemic in the midst of the recovery from the Great East Japan Earthquake. We identified the following unique characteristics of the COVID-19 pandemic. (A) Unlike many past infectious diseases, the epicenters of the most severe outbreaks until the first half of 2020 were the wealthiest metropolises in the USA and Europe. (B) The virus of COVID-19 is mostly transmitted through face-to-face (F2F) communication. Thus, despite the great advancement of ICT today, the pandemic has been causing grave negative socioeconomic effects throughout the world. (C) The propagation of virus transmission occurs in 3C-environments (i.e., crowded places, close-contact settings, and confined and closed spaces) in big cities. Thus, large cities that have grown in the past with intensive agglomeration of socioeconomic activities in 3C environments now face the paradox of relying less on such 3C environments to avoid the risk of infection diseases in the future. The propagation of virus transmission in 3C environments also yields the scale-effect of population concentration in large cities. (D) Given that the functioning of metropolises in the USA and Europe is supported by a large mass of so-called essential workers, their working and living conditions in 3C-environments triggered the COVID-19 explosions there. The spread of COVID-19 infections in Japan has proceeded under the strong influence of the Tokyo monopolar land system. In particular, we identified a strong population-scale effect in infection growth in Tokyo. In the USA and Europe, in the early phase of the COVID-19 pandemic, the epicenters of the outbreaks were around respective economic centers called “the parallelogram” in the North-West USA and around the area called the “Blue Banana” in Western Europe. The explosion of the pandemic in the USA was largely caused by the fierce presidential campaign, suggesting that in emergency situations such as the COVID-19 pandemic, decisive political leadership at the national level based on scientific evidence is indispensable for controlling the situation. We examined the possible impact of the pandemic on the spatial structure of cities and regions in the future, focusing on the practice of working from home (WFH), which became rapidly popular in many countries for mitigating the 3C-environments in large cities. By using an analytical model, we have shown why the WFH productivity relative to working with commuting (WWC) is much lower in Japan in comparison with other major countries and how to improve the WFH productivity in Japan. To build back better large cities in Japan after the pandemic, each firm needs to transform its traditional working system centered around the HQ office in Central Business District (CBD) to an Activity Based Working system (ABW system) by fully utilizing ICT. With the prolonged pandemic in Japan, we witness that a weakening of the Tokyo monopolar system both in migration pattern and firm location choice. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
For a systematic understanding of the concentration of economic activities in large cities and industrial areas, we first introduce the basic concept of spatial economics. The spatial structure of the economy stems from self-organization that occurs from the balance between countering agglomeration forces and dispersion forces within historical path dependence. Decreased transportation costs reveal the endogenously generated agglomeration and dispersion forces hidden behind the agglomeration and dispersion forces based on natural characteristics. Remarkable improvement of transportation access among cities might cause the production of highly differentiated goods and services absorbed from small cities to large cities (straw effect), or small cities to disappear under the influence of larger cities’ shadows (shadow effect). In this view, we understand that Japan’s national land system increased concentration through self-reinforcing agglomeration 8 economies in the core area, mirrored by a negative feedback of population decrease and weakening agglomeration forces in the periphery (rural) areas. The population in many rural prefectures in the post-war era had already reached a peak in the 1950s, long before Japan’s population peaked in 2008. As Japan has entered a “knowledge-creating society” as the post-industrialization society, agglomeration to the Tokyo metropolitan area has become more intense, attracting highly skilled and higher income people. Concentration of political powers and outstanding diversity in public infrastructure for science, transportation, culture, and amenity further strengthened the dominant position of the Tokyo metropolitan area. It should not be dismissed, however, that the Tokyo monopolar concentration has already shown negative effects. A lower childbirth rate in urban living and working conditions is an example. The exodus of young people from unique local areas and concentration in the Tokyo metropolitan area results in the assimilation of ways of thinking and a loss of diversity in people. The latter is detrimental in the knowledge-creating society. Furthermore, excessive concentration of economic and political powers increases the systemic risk, as evidenced during the Tokyo-centered COVID-19 pandemic. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.