tetano
Editor, Senior Moderator
Crit Care Explor
. 2020 Sep 15;2(9):e0221.
doi: 10.1097/CCE.0000000000000221. eCollection 2020 Sep.
Tokyo and New York: A Study in the Contrasting Effects of Socioeconomic Status on Coronavirus Disease 2019 Outcomes
Toru Hifumi[SUP] 1 [/SUP], Yohei Ishikawa[SUP] 1 2 [/SUP], Norio Otani[SUP] 1 [/SUP], Shinichi Ishimatsu[SUP] 1 [/SUP], Mitsuyoshi Urashima[SUP] 2 [/SUP]
Affiliations
Abstract
Objectives: It is still not known why cases of coronavirus disease 2019 during the first wave in Tokyo have fallen without lockdown restrictions. People with low socioeconomic status are not dominant among coronavirus disease 2019 patients in Tokyo in contrast with New York, where the opposite demographics have been in play. Thus, we set out to examine the association between socioeconomic status and the rate of coronavirus disease 2019 infections using public data from Tokyo.
Design: We obtained data from each of the 23 wards of Tokyo, showing population size, density, age, sex, number of graduates, income, and hospital attendance numbers. Coronavirus disease 2019 infections were gathered for 2 separate days: April 9, 2020, when new daily coronavirus disease 2019 infections were at their peak during the first wave in Japan; and May 9, 2020, to observe any changes in incidence over the preceding month.
Setting: The primary outcome was set as the number of coronavirus disease 2019 infections per 100,000 population.
Interventions: None.
Measurements and main results: By conducting simple linear regression modeling, the incidence of cases on April 9 was associated significantly with four variables: population age greater than 65 years (%), university rate, hospital, and income. Using these four variables, multivariate linear regression analyses demonstrated that only income remained significant (p = 0.006 at April 9 and p = 0.03 at May 9). This indicates that the highest case numbers were dominant in high-income areas, and affected fewer patients in districts in the low-income areas.
Conclusions: The result of the current study is exactly opposite to the data from New York. This may be considered one of the main reasons why the rate of death and new patients of coronavirus disease 2019 has been so low in Tokyo. That is, appropriate hygienic status, free access to hospital by ambulance, and universal health insurance system may contribute to the outcome in such low-income areas.
Keywords: New York; Tokyo; coronavirus disease 2019.
. 2020 Sep 15;2(9):e0221.
doi: 10.1097/CCE.0000000000000221. eCollection 2020 Sep.
Tokyo and New York: A Study in the Contrasting Effects of Socioeconomic Status on Coronavirus Disease 2019 Outcomes
Toru Hifumi[SUP] 1 [/SUP], Yohei Ishikawa[SUP] 1 2 [/SUP], Norio Otani[SUP] 1 [/SUP], Shinichi Ishimatsu[SUP] 1 [/SUP], Mitsuyoshi Urashima[SUP] 2 [/SUP]
Affiliations
- PMID: 32984839
- PMCID: PMC7498128
- DOI: 10.1097/CCE.0000000000000221
Abstract
Objectives: It is still not known why cases of coronavirus disease 2019 during the first wave in Tokyo have fallen without lockdown restrictions. People with low socioeconomic status are not dominant among coronavirus disease 2019 patients in Tokyo in contrast with New York, where the opposite demographics have been in play. Thus, we set out to examine the association between socioeconomic status and the rate of coronavirus disease 2019 infections using public data from Tokyo.
Design: We obtained data from each of the 23 wards of Tokyo, showing population size, density, age, sex, number of graduates, income, and hospital attendance numbers. Coronavirus disease 2019 infections were gathered for 2 separate days: April 9, 2020, when new daily coronavirus disease 2019 infections were at their peak during the first wave in Japan; and May 9, 2020, to observe any changes in incidence over the preceding month.
Setting: The primary outcome was set as the number of coronavirus disease 2019 infections per 100,000 population.
Interventions: None.
Measurements and main results: By conducting simple linear regression modeling, the incidence of cases on April 9 was associated significantly with four variables: population age greater than 65 years (%), university rate, hospital, and income. Using these four variables, multivariate linear regression analyses demonstrated that only income remained significant (p = 0.006 at April 9 and p = 0.03 at May 9). This indicates that the highest case numbers were dominant in high-income areas, and affected fewer patients in districts in the low-income areas.
Conclusions: The result of the current study is exactly opposite to the data from New York. This may be considered one of the main reasons why the rate of death and new patients of coronavirus disease 2019 has been so low in Tokyo. That is, appropriate hygienic status, free access to hospital by ambulance, and universal health insurance system may contribute to the outcome in such low-income areas.
Keywords: New York; Tokyo; coronavirus disease 2019.