U.S. State Life Tables, 2018

Arias, Elizabeth; Bastian, Brigham; Xu, Jiaquan; Tejada-Vera, Betzaida

doi:10.15620/cdc:101128

Cited by 69 publications

(83 citation statements)

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“…US life tables for 2010-18 were obtained from the National Center for Health Statistics. 20 21 22 23 24 25 26 27 28 Weekly age specific death counts for all men and women in the US and for Black, White, and Hispanic men and women in the US for the years 2018 and 2020 were obtained from the National Center for Health Statistics’ AH (ad hoc) Excess Deaths by Sex, Age, and Race file. 29 Mid-year population estimates by age, sex, and race and ethnicity for men and women in the US for 2015-19 were obtained from the US Census Bureau.…”

Section: Methodsmentioning

confidence: 99%

“…. 90-94, 95-99, ≥100, were estimated separately for men and women in the US and for men and women in specific race and ethnic group populations by multiplying 2018 m x 28 by the 2020-18 rate ratio estimates derived from data from the National Center for Health Statistics and US Census Bureau, and calculating q x =(m x ×n)/(1+m x ×a x ), where q x is the age specific probability of death, m x is the age specific mortality rate, n is the width of the age interval, and a x is the age specific person years lived by the deceased. 34 Probabilities of death for each peer country in 2020 were estimated by multiplying q x in the Human Mortality Database life tables by the 2020-18 rate ratios in the Human Mortality Database Short Term Mortality Fluctuations data.…”

Section: Methodsmentioning

confidence: 99%

“…We used Python (version 3.9.1) to simulate 50 000 five year abridged 2020 life tables for each US subpopulation, with the estimated q x for 2020, a x derived from 2018 official life tables, 28 and random 10% error in the q x estimate. For each peer country population, 50 000 five year abridged 2020 life tables were simulated with the estimated 2020 q x and 2018 a x values in the Human Mortality Database 2018 life tables, and random 10% error in the q x estimate.…”

Section: Methodsmentioning

confidence: 99%

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Effect of the covid-19 pandemic in 2020 on life expectancy across populations in the USA and other high income countries: simulations of provisional mortality data

Woolf

Masters

Aron

2021

BMJ

133

128

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Objective To estimate changes in life expectancy in 2010-18 and during the covid-19 pandemic in 2020 across population groups in the United States and to compare outcomes with peer nations. Design Simulations of provisional mortality data. Setting US and 16 other high income countries in 2010-18 and 2020, by sex, including an analysis of US outcomes by race and ethnicity. Population Data for the US and for 16 other high income countries from the National Center for Health Statistics and the Human Mortality Database, respectively. Main outcome measures Life expectancy at birth, and at ages 25 and 65, by sex, and, in the US only, by race and ethnicity. Analysis excluded 2019 because life table data were not available for many peer countries. Life expectancy in 2020 was estimated by simulating life tables from estimated age specific mortality rates in 2020 and allowing for 10% random error. Estimates for 2020 are reported as medians with fifth and 95th centiles. Results Between 2010 and 2018, the gap in life expectancy between the US and the peer country average increased from 1.88 years (78.66 v 80.54 years, respectively) to 3.05 years (78.74 v 81.78 years). Between 2018 and 2020, life expectancy in the US decreased by 1.87 years (to 76.87 years), 8.5 times the average decrease in peer countries (0.22 years), widening the gap to 4.69 years. Life expectancy in the US decreased disproportionately among racial and ethnic minority groups between 2018 and 2020, declining by 3.88, 3.25, and 1.36 years in Hispanic, non-Hispanic Black, and non-Hispanic White populations, respectively. In Hispanic and non-Hispanic Black populations, reductions in life expectancy were 18 and 15 times the average in peer countries, respectively. Progress since 2010 in reducing the gap in life expectancy in the US between Black and White people was erased in 2018-20; life expectancy in Black men reached its lowest level since 1998 (67.73 years), and the longstanding Hispanic life expectancy advantage almost disappeared. Conclusions The US had a much larger decrease in life expectancy between 2018 and 2020 than other high income nations, with pronounced losses among the Hispanic and non-Hispanic Black populations. A longstanding and widening US health disadvantage, high death rates in 2020, and continued inequitable effects on racial and ethnic minority groups are likely the products of longstanding policy choices and systemic racism.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Effect of the covid-19 pandemic in 2020 on life expectancy across populations in the USA and other high income countries: simulations of provisional mortality data

Woolf

Masters

Aron

2021

BMJ

133

128

View full text Add to dashboard Cite

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“…DALYs were estimated as a combination of years of life lost from premature death due to COVID-19 and disability associated with the acute infection period (see Appendix). We estimated mean years of life lost per death in each risk group by averaging estimates of years of life remaining for individuals in the group given their age, sex and race/ethnicity from US life tables (28). We applied disability weights for the acute infection period based on weights for episodes of "mild", "moderate" and "severe" acute illness from previous literature (29), and did not include long-term sequelae of illness.…”

Section: Study Outcomesmentioning

confidence: 99%

Comparison of COVID-19 vaccine prioritization strategies

Chapman

Shukla

Rodríguez-Barraquer

et al. 2021

Preprint

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Background: For countries that have only recently started COVID-19 vaccinations, there remains a key public health question of who should be prioritized for early vaccination. Most vaccine prioritization analyses have only considered variation in risk of infection and death by age. We provide a more granular analysis with stratification by demographics, risk factors, and location. Methods: We used a simulation model to compare the impact of different prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs) over the first 6 months of vaccine rollout. We calibrated the model to demographic and location data on 28,175 COVID-19 deaths in California up to December 30, 2020, and incorporated variation in risk by occupation and comorbidity status using published estimates. We estimated the proportion of clinical cases, deaths and DALYs averted relative to a scenario of no vaccination for strategies prioritizing vaccination by a single risk factor (special population status (e.g. incarcerated individual), age, essential worker status, comorbidity status) or multiple risk factors (e.g. age and location). Results: We found that age-based targeting averted the most deaths (65% for 5 million individuals vaccinated) and DALYs (40%) of strategies targeting by a single risk factor and targeting essential workers averted the least deaths (33%) and DALYs (25%) over the first 6 months of vaccine rollout. However, targeting by two or more risk factors simultaneously averted up to 40% more DALYs. Conclusions: Our findings highlight the potential value of multiple-risk-factor targeting of COVID-19 vaccination. Where vaccine supply is limited and logistical challenges in vaccine delivery persist, age-based targeting offers a means of ensuring that vaccines reach those most at risk of poor health outcomes. If operational challenges can be overcome, more granular vaccination strategies that overlap age with other risk factors can be adopted.

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“…Therefore, from issues of chronic care, we turn to the case study of pregnancy vis-à-vis the COVID-19 pandemic. With over six million pregnancies per year in the USA, pregnant and breastfeeding women constitute a significant portion of the population impacted by COVID-19 (Arias and Xu, 2018). As with managing chronic conditions, eHealth may be a tool to achieve social distancing and nonetheless maintain healthcare management in a timely manner.…”

Section: Ehealth and Pregnancymentioning

confidence: 99%

The COVID-19 pandemic: new concerns and connections between eHealth and digital inequalities

Khilnani

Schulz

Robinson

2020

JICES

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Purpose Telemedicine has been advancing for decades and is more indispensable than ever in this unprecedented time of the COVID-19 pandemic. As shown, eHealth appears to be effective for routine management of chronic conditions that require extensive and repeated interactions with healthcare professionals, as well as the monitoring of symptoms and diagnostics. Yet much needs to be done to alleviate digital inequalities that stand in the way of making the benefits of eHealth accessible to all. The purpose of this paper is to explore the recent shift in healthcare delivery in response to the COVID-19 pandemic towards telemedicine in healthcare delivery and show how this rapid shift is leaving behind those without digital resources and exacerbating inequalities along many axes. Design/methodology/approach Because the digitally disadvantaged are less likely to use eHealth services, they bear greater risks during the pandemic to meet ongoing medical care needs. This holds true for both medical conditions necessitating lifelong care and conditions of particular urgency such as pregnancy. For this reason, the authors examine two case studies that exemplify the implications of differential access to eHealth: the case of chronic care diseases such as diabetes requiring ongoing care and the case of time-sensitive health conditions such as pregnancy that may be compromised by gaps in continuous care. Findings Not only are the digitally disadvantaged more likely to belong to populations experiencing greater risk – including age and economic class – but they are less likely to use eHealth services and thereby bear greater risks during the pandemic to meet ongoing medical care needs during the pandemic. Social implications At the time of writing, almost 20% of Americans have been unable to obtain medical prescriptions or needed medical care unrelated to the virus. In light of the potential of telemedicine, this does not need to be the case. These social inequalities take on particular significance in light of the COVID-19 pandemic. Originality/value In light of the COVID-19 virus, ongoing medical care requires exposure to risks that can be successfully managed by digital communications and eHealth advances. However, the benefits of eHealth are far less likely to accrue to the digitally disadvantaged.

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U.S. State Life Tables, 2018

Abstract: National Vital Statistics Report; includes complete period life tables for each of the 50 states and the District of Columbia by sex based on age-specific death rates in 2018

Cited by 69 publications

References 0 publications

Effect of the covid-19 pandemic in 2020 on life expectancy across populations in the USA and other high income countries: simulations of provisional mortality data

Effect of the covid-19 pandemic in 2020 on life expectancy across populations in the USA and other high income countries: simulations of provisional mortality data

Comparison of COVID-19 vaccine prioritization strategies

The COVID-19 pandemic: new concerns and connections between eHealth and digital inequalities

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