Explaining the spatial variation in American life expectancy

Dobis, Elizabeth A.; Stephens, Heather; Skidmore, Mark; Goetz, Stephan J.

doi:10.1016/j.socscimed.2019.112759

Cited by 25 publications

(30 citation statements)

References 31 publications

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“…These differences may be due to regional variation in factors that influence COVID-19 risks, including various individual (e.g., age, pre-existing health conditions), household (e.g., poverty, household size), and community (e.g., presence of group quarters such as correctional facilities or nursing homes) factors (10). This is consistent with a long line of research on regional variation in health and mortality (11)(12)(13)(14)(15)(16). Despite apparent regional differences in COVID-19 outcomes, there has been little focus on how physical distancing behavior varies by region, which is an important omission because physical distancing trends are predictive of later COVID-19 outcomes (3).…”

Section: Introductionsupporting

confidence: 80%

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U.S. Regional Differences in Physical Distancing: Evaluating Racial and Socioeconomic Divides During the COVID-19 Pandemic

Zang

West

Kim

et al. 2021

Preprint

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Health varies by U.S. region of residence. Despite regional heterogeneity in the outbreak of COVID-19, regional differences in physical distancing behaviors over time are relatively unknown. This study examines regional variation in physical distancing trends during the COVID-19 pandemic and investigates variation by race and socioeconomic status (SES) within regions.Data from the 2015-2019 five-year American Community Survey were matched with anonymized location pings data from over 20 million mobile devices (SafeGraph, Inc.) at the Census block group level. We visually present trends in the stay-at-home proportion by Census region, race, and SES throughout 2020 and conduct regression analyses to statistically examine these patterns.From March to December, the stay-at-home proportion was highest in the Northeast (0.25 in March to 0.35 in December) and lowest in the South (0.24 to 0.30). Across all regions, the stay-at-home proportion was higher in block groups with a higher percentage of Blacks, as Blacks disproportionately live in urban areas where stay-at-home rates were higher (0.009 [CI: 0.008, 0.009]). In the South, West, and Midwest, higher-SES block groups stayed home at the lowest rates pre-pandemic; however, this trend reversed throughout March before converging in the months following. In the Northeast, lower-SES block groups stayed home at comparable rates to higher-SES block groups during the height of the pandemic but diverged in the months following.Differences in physical distancing behaviors exist across U.S. regions, with a pronounced Southern and rural disadvantage. Results can be used to guide reopening and COVID-19 mitigation plans.

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Section: Introductionsupporting

confidence: 80%

“…Decades of research has documented regional variation in health outcomes, life expectancy, and mortality (11)(12)(13)(14)(15)(16). Recent research indicates that regional variation has remained a crucial part of understanding health-related patterns in the U.S. during the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

U.S. Regional Differences in Physical Distancing: Evaluating Racial and Socioeconomic Divides During the COVID-19 Pandemic

Zang

West

Kim

et al. 2021

Preprint

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“…Several studies have shown the role of air flows, including that of Bowen and Laroe (2006) who studied the SARS epidemic in 2003 and the Zika epidemic in 2015, both of which mainly affected developing countries (Lourenco et al, 2017). Moreover, in medical geography (Meade, 2014;Dobis, 2020), health infrastructures have also been shown to play a role in the number of cases recorded in the epidemics observed. Finally, in order to understand the extent to which the spread of COVID-19 is due to economic globalization, we also examine export volumes (in constant dollars) per country.…”

Section: Methodsmentioning

confidence: 99%

Economic globalization and the COVID-19 pandemic: global spread and inequalities

Jeanne¹,

Bourdin²,

Nadou³

et al. 2020

Preprint

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“…The factor that determines the healthy lifestyle includes the smoking (X1), drinking (X2), physical activities (X3) (excise), good sleep (X4) and proper food (X5). 31 The equation follows the normal distribution and we assumed a mean value 1 unit for all the factors. While smoking and drinking negatively influencing factor denoted by θ 1 θ 2 .…”

Section: Life Expectancymentioning

confidence: 99%

Modeling Trade Openness and Life Expectancy in China

Shah¹,

Ullah²,

Xiao³

et al. 2021

RMHP

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This study investigates life expectancy and trade openness in China for the period 1960-2018. Methods: We purposed a theoretical model that is tested for China by applying regimeswitching regression. Results: Our findings suggest that trade openness increases life expectancy in China; trade affects life expectancy from two aspects; firstly, trade expansion and industrialization lead to high economic activities and resulted in raise the income of the people in society leading to improve life expectancy. Secondly, industrial expansion increases the CO 2 emissions which leads to imposes a negative implication on human health and thus reduces life expectancy. Conclusion: Thus, the net effect of trade liberalization depends on the value of income effect and volume of CO 2 emissions. Therefore, the government needs to support the trade policies which causes a low level of CO 2 emissions, the government may provide incentives to exports and industrialists to adopted green energy in the production process. Besides, the government may impose some regulations such as carbon tax to mitigate the CO 2 emissions in society.

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Explaining the spatial variation in American life expectancy

Cited by 25 publications

References 31 publications

U.S. Regional Differences in Physical Distancing: Evaluating Racial and Socioeconomic Divides During the COVID-19 Pandemic

U.S. Regional Differences in Physical Distancing: Evaluating Racial and Socioeconomic Divides During the COVID-19 Pandemic

Economic globalization and the COVID-19 pandemic: global spread and inequalities

Modeling Trade Openness and Life Expectancy in China

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