COVID-19 Death Rates and County Subdivision Level Contextual Characteristics: A Connecticut Case Study
As of July 15th, 2020, at least 3,483,832 and 136,938 confirmed COVID-19 cases and deaths have been reported respectively in the U.S.A., posing unprecedented socioeconomic and health challenges to the country. Existing empirical evidence examining the spatial association between contextual factors and COVID-19 death rates, however, remains sparse and ambiguous. The objective of this research is to examine the spatial relationship between COVID-19 death rates and contextual characteristics at the county subdivision level in the State of Connecticut, U.S.A. The analysis shows that explanatory variables, such as income, race, age, type of housing, and underlying medical condition indicators, are associated with COVID-19 death rates in the state. Most importantly, the association between COVID-19 death rates and the explanatory variables in our analysis significantly varies over space, highlighting the need for local and context-specific COVID-19 prevention and intervention programs..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2021 |
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| Enthalten in: |
Cybergeo - (2021) |
| Sprache: |
Deutsch ; Englisch ; Französisch ; Italienisch ; Portugiesisch |
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| Beteiligte Personen: |
Yunliang Meng [VerfasserIn] |
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| Links: |
doaj.org [kostenfrei] |
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| Themen: |
COVID 19 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ006548504 |
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| 520 | |a As of July 15th, 2020, at least 3,483,832 and 136,938 confirmed COVID-19 cases and deaths have been reported respectively in the U.S.A., posing unprecedented socioeconomic and health challenges to the country. Existing empirical evidence examining the spatial association between contextual factors and COVID-19 death rates, however, remains sparse and ambiguous. The objective of this research is to examine the spatial relationship between COVID-19 death rates and contextual characteristics at the county subdivision level in the State of Connecticut, U.S.A. The analysis shows that explanatory variables, such as income, race, age, type of housing, and underlying medical condition indicators, are associated with COVID-19 death rates in the state. Most importantly, the association between COVID-19 death rates and the explanatory variables in our analysis significantly varies over space, highlighting the need for local and context-specific COVID-19 prevention and intervention programs. | ||
| 650 | 4 | |a disease | |
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