{"title":"From the climate crisis to the health crisis and vice versa – a COVID-19 update","authors":"M. Jungmann","doi":"10.4324/9781003145646-15","DOIUrl":"https://doi.org/10.4324/9781003145646-15","url":null,"abstract":"","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123007925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Insights for policymakers","authors":"M. Jungmann","doi":"10.4324/9781003145646-16","DOIUrl":"https://doi.org/10.4324/9781003145646-16","url":null,"abstract":"","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Republic of Korea","authors":"M. Jungmann","doi":"10.4324/9781003145646-10","DOIUrl":"https://doi.org/10.4324/9781003145646-10","url":null,"abstract":"0 Early Childhood Education 2,214,560 01 Early Childhood Educational Development Programmes 997,093 02 Pre‐Primary Education 1,217,467 1 Primary Education 2,715,568 One Year Before Than Official Primary Entry Age 424,993 Last Grade Of Primary Education 455,973 2 Lower Secondary Education 1,364,086 2T3 Secondary Education 2,761,169 3 Upper Secondary Education 1,397,083 5T8 Tertiary Education 2,922,111","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133348760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The United Kingdom of Great Britain and Northern Ireland","authors":"M. Jungmann","doi":"10.18356/5aa2ffa0-en","DOIUrl":"https://doi.org/10.18356/5aa2ffa0-en","url":null,"abstract":"0 Early Childhood Education 3,869,295 01 Early Childhood Educational Development Programmes 2,274,631 02 Pre‐Primary Education 1,594,664 1 Primary Education 4,942,571 One Year Before Than Official Primary Entry Age 804,442 Last Grade Of Primary Education 821,249 2 Lower Secondary Education 2,423,732 2T3 Secondary Education 5,464,803 3 Upper Secondary Education 3,041,071 4 Post‐Secondary Non‐Tertiary Education 821,083 5T8 Tertiary Education 3,934,961","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122322946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Regression results","authors":"M. Jungmann","doi":"10.1787/9789264232969-12-en","DOIUrl":"https://doi.org/10.1787/9789264232969-12-en","url":null,"abstract":"RESULTS This chapter describes the results of our analyses to account for the patient and facility characteristics that influence the VA's patient care costs. These results were generated by applying the regression analysis techniques described in the preceding chapter. Additionally, the chapter presents the results of simulations, based on the regression models, of how VISN allocations would change if VERA incorporated various factors that affect patient care costs but that it currently omits. In this chapter, we have focused our discussion on the set of results that we believe to be most relevant for policy purposes. Complete findings from both the regression and simulation analyses are contained in Appendix D. In this section, we summarize the results from the patient-and facility-level regression models. We focus our discussion on the results from the policy model; however, we also include a brief discussion of the key differences between the results of the policy model and those of the fully specified model. As noted in the previous chapter, we restricted the sample in our primary analysis to those veterans who are funded via the current VERA allocation methodology. Specifically , the primary analysis sample excludes Priority 7 veterans who are in the Basic Care patient categories that are currently excluded from VERA workload estimates and patients who are not veterans, such as non-veteran employees. The final sample for FY 2000 includes 3,000,563 veterans. Descriptive statistics are presented in Table 3.1. Approximately 44 percent of the sample is 65 years of age or older, 95 percent is male, 52 percent is married, and 59 percent reported an annual income of $20,000 or less. However, the income data must be interpreted with caution, because the data are based on voluntary self-reports and are missing for 13 percent of the sample. Missing data are also an issue for a number of the demographic variables. For example, we do not have information on race for 32 percent of the sample or on marital status for 5 percent. Again, these data are based on self-reports, and the information is not required for treatment within the system. A plurality of veterans (48 percent) is in Priority Group 5 (see Table 1.1 for definitions of patient priority groups). Priority Groups 1 and 3 are the second","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131238306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Japan","authors":"M. Jungmann","doi":"10.4324/9781003145646-11","DOIUrl":"https://doi.org/10.4324/9781003145646-11","url":null,"abstract":"","PeriodicalId":134456,"journal":{"name":"The Politics of the Climate Change-Health Nexus","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}