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Natural mortality: Mean age-, race-, sex-adjusted multiple sclerosis mortality rates among United States residents aged 25 and older (1968–2009; no prevention known)

Natural mortality: Mean age-, race-, sex-adjusted multiple sclerosis mortal...

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in A Social History of Disease: Contextualizing the Rise and Fall of Social Inequalities in Cause-Specific Mortality > Demography
Published: 16 August 2016
Fig. 2 Natural mortality: Mean age-, race-, sex-adjusted multiple sclerosis mortality rates among United States residents aged 25 and older (1968–2009; no prevention known) More
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Reduced mortality: Mean age-, race-, sex-adjusted thymoma mortality rates among United States residents aged 25 and older (1968–2009; tpk = 1900s)

Reduced mortality: Mean age-, race-, sex-adjusted thymoma mortality rates a...

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in A Social History of Disease: Contextualizing the Rise and Fall of Social Inequalities in Cause-Specific Mortality > Demography
Published: 16 August 2016
Fig. 5 Reduced mortality: Mean age-, race-, sex-adjusted thymoma mortality rates among United States residents aged 25 and older (1968–2009; t pk = 1900s) More
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Fig. 1 Age-adjusted mortality rates by rural–urban classification and race and ethnicity in the United States, 1999–2016

Age-adjusted mortality rates by rural–urban classification and race and eth...

Open Access
in Research Note Showing That the Rural Mortality Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016 > Demography
Published: 01 December 2023
Fig. 1 Age-adjusted mortality rates by rural–urban classification and race and ethnicity in the United States, 1999–2016 More
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Fig. 4 Rural–urban rate ratios for age-adjusted mortality by race and ethnicity and U.S. Census region of residence, 1999–2016. Ratios were calculated by dividing the rural rate by the urban rate and multiplying by 100. The red dashed lines represent the point of similar mortality rates. Deviations from these line are illustrative of the direction and magnitude of the rural–urban differences. Direction: Above represents a rural mortality penalty, and vice versa. Magnitude: Values farther away indicate a larger difference.

Rural–urban rate ratios for age-adjusted mortality by race and ethnicity an...

Open Access
in Research Note Showing That the Rural Mortality Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016 > Demography
Published: 01 December 2023
Fig. 4 Rural–urban rate ratios for age-adjusted mortality by race and ethnicity and U.S. Census region of residence, 1999–2016. Ratios were calculated by dividing the rural rate by the urban rate and multiplying by 100. The red dashed lines represent the point of similar mortality rates More
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Fig. 1 Age-adjusted excess all-cause mortality by sex, race, and ethnicity, April 2020 through March 2021. Excess mortality is the difference between the observed population-standardized mortality rate and the expected population-standardized mortality rate. Expected mortality rates for April 2020 through March 2021 were estimated using the prediction equation from a negative binomial regression predicting age-adjusted sex-, race-, and ethnicity-specific deaths for April 2010 through March 2020 as a function of a linear year term, a categorical month term, and a population exposure term. Sources: Census Numident (Q3, 2021); Decennial Census (2000, 2010); and ACS (2001–2019). Data were restricted to individuals aged 15–99 at the start of any given month with a nonmissing self-reported or household proxy race and ethnicity response.

Age-adjusted excess all-cause mortality by sex, race, and ethnicity, April ...

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in Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic > Demography
Published: 01 February 2024
Fig. 1 Age-adjusted excess all-cause mortality by sex, race, and ethnicity, April 2020 through March 2021. Excess mortality is the difference between the observed population-standardized mortality rate and the expected population-standardized mortality rate. Expected mortality rates for April More
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Fig. 4 Age- and sex-adjusted excess all-cause mortality by state and pandemic wave, April 2020 through March 2021. Excess mortality is the difference between the observed population-standardized mortality rate and the expected population-standardized mortality rate. Expected mortality rates for April 2020 through March 2021 were estimated using the prediction equation from a negative binomial regression predicting age- and sex-adjusted state- and wave-specific deaths for April 2010 through March 2020 as a function of a linear year term, a categorical wave term, and a population exposure term. Note that choropleth scales differ across panels. The estimates mapped are also reported in online appendix 5. Sources: Census Numident (Q3, 2021); Decennial Census (2000, 2010); ACS (2001-2019); and various administrative and third-party location data (2010–2020). Data are restricted to individuals aged 15–99 at the start of any given month with a nonmissing self-reported or household proxy race and ethnicity response and a nonmissing state location for any given year.

Age- and sex-adjusted excess all-cause mortality by state and pandemic wave...

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in Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic > Demography
Published: 01 February 2024
Fig. 4 Age- and sex-adjusted excess all-cause mortality by state and pandemic wave, April 2020 through March 2021. Excess mortality is the difference between the observed population-standardized mortality rate and the expected population-standardized mortality rate. Expected mortality rates More
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Fig. 5 Age- and sex-adjusted Black–White gaps in all-cause mortality by state and wave, April 2020 through March 2021. Dashed lines represent the 45-degree diagonal where y = x. Solid lines are the linear trend through plotted state points. Observed mortality gaps are the adjusted differences between mortality rates for NH Black alone and NH White alone individuals, where negative values indicate gaps in favor of NH Blacks. Expected mortality gaps for April 2020 through March 2021 were estimated using the prediction equation from a negative binomial regression predicting age- and sex-adjusted race- and ethnicity-specific deaths for April 2010 through March 2020 as a function of a linear year term, a categorical wave term, and a population exposure term. Note that axes differ across panels, and states in which the NH Black alone population share is less than 4% are omitted. Sources: See Figure 4.

Age- and sex-adjusted Black–White gaps in all-cause mortality by state and ...

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in Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic > Demography
Published: 01 February 2024
Fig. 5 Age- and sex-adjusted Black–White gaps in all-cause mortality by state and wave, April 2020 through March 2021. Dashed lines represent the 45-degree diagonal where y  =  x . Solid lines are the linear trend through plotted state points. Observed mortality gaps are the adjusted differences More
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Fig. 6 Age- and sex-adjusted Hispanic–White gaps in all-cause mortality by state and wave, April 2020 through March 2021. Dashed lines represent the 45-degree diagonal where y = x. Solid lines are the linear trend through plotted state points. Observed mortality gaps are the adjusted differences between excess mortality rates for Hispanic and NH White alone individuals, where negative values indicate gaps in favor of Hispanics. Expected mortality rates for April 2020 through March 2021 were estimated using the prediction equation from a negative binomial regression predicting age- and sex-adjusted race- and ethnicity-specific deaths for April 2010 through March 2020 as a function of a linear year term, a categorical wave term, and a population exposure term. Note that axes differ across panels, and states in which the Hispanic population share is less than 4% are omitted. Sources: See Figure 4.

Age- and sex-adjusted Hispanic–White gaps in all-cause mortality by state a...

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in Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic > Demography
Published: 01 February 2024
Fig. 6 Age- and sex-adjusted Hispanic–White gaps in all-cause mortality by state and wave, April 2020 through March 2021. Dashed lines represent the 45-degree diagonal where y  =  x . Solid lines are the linear trend through plotted state points. Observed mortality gaps are the adjusted More
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Fig. 2 Rural–urban rate ratios for age-adjusted mortality rates by race and ethnicity in the United States, 1999–2016. Ratios were calculated by dividing the rural rate by the urban rate and multiplying by 100. The red dashed line represents the point of similar mortality rates.

Rural–urban rate ratios for age-adjusted mortality rates by race and ethnic...

Open Access
in Research Note Showing That the Rural Mortality Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016 > Demography
Published: 01 December 2023
Fig. 2 Rural–urban rate ratios for age-adjusted mortality rates by race and ethnicity in the United States, 1999–2016. Ratios were calculated by dividing the rural rate by the urban rate and multiplying by 100. The red dashed line represents the point of similar mortality rates. More
Journal Article

Reply

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Samuel H. Preston
Demography (1977) 14 (3): 375–377.
DOI: https://doi.org/10.2307/2060794
Published: 01 August 1977
...Samuel H. Preston 30 12 2010 © Population Association of America 1977 1977 Family Size Total Fertility Rate Baby Boom Mortality Adjustment Sibship Size References Coale , Ansley J. , & Zelnick , M. ( 1963 ). New Estimates of Fertility and Population...
View articletitled, Reply View PDF for article titled, Reply
Journal Article

Birth Order and Mortality: A Population-Based Cohort Study

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Kieron Barclay, Martin Kolk
Demography (2015) 52 (2): 613–639.
DOI: https://doi.org/10.1007/s13524-015-0377-2
Published: 17 March 2015
...-time survival analysis using a within-family comparison, and the estimates are adjusted for age, mother’s age at the time of birth, and cohort. Focusing on sibships ranging in size from two to six, we find that mortality risk in adulthood increases with later birth order. The results show...
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View articletitled, Birth Order and <span class="search-highlight">Mortality</span>: A Population-Based Cohort Study View PDF for article titled, Birth Order and <span class="search-highlight">Mortality</span>: A Population-Based Cohort Study
Includes: Supplementary data
Journal Article

Estimation of Covariate Effects With Current Status Data and Differential Mortality

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Alberto Palloni, Jason R. Thomas
Demography (2013) 50 (2): 521–544.
DOI: https://doi.org/10.1007/s13524-012-0160-6
Published: 18 November 2012
... the magnitude of the bias attributable to the selection problem, and (b) suggest an adjustment procedure that corrects for this bias. We find that the proposed adjustment procedure considerably reduces the bias arising from differential mortality. 20 9 2012 18 11 2012 © Population Association...
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View articletitled, Estimation of Covariate Effects With Current Status Data and Differential <span class="search-highlight">Mortality</span> View PDF for article titled, Estimation of Covariate Effects With Current Status Data and Differential <span class="search-highlight">Mortality</span>
Journal Article

Birth Intervals and Health in Adulthood: A Comparison of Siblings Using Swedish Register Data

Open Access
Kieron J. Barclay, Martin Kolk
Demography (2018) 55 (3): 929–955.
DOI: https://doi.org/10.1007/s13524-018-0673-8
Published: 21 May 2018
... and women, mortality. In models that do not adjust carefully for family background, we find that short and long birth intervals are clearly associated with height, physical fitness, being overweight or obese, and mortality. However, after carefully adjusting for family background using a within-family...
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View articletitled, Birth Intervals and Health in Adulthood: A Comparison of Siblings Using Swedish Register Data View PDF for article titled, Birth Intervals and Health in Adulthood: A Comparison of Siblings Using Swedish Register Data
Includes: Supplementary data
Journal Article

Widowhood and Mortality: A Meta-Analysis and Meta-Regression

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Eran Shor, David J. Roelfs, Misty Curreli, Lynn Clemow, Matthew M. Burg ...
Demography (2012) 49 (2): 575–606.
DOI: https://doi.org/10.1007/s13524-012-0096-x
Published: 17 March 2012
... adjustment, and study quality. 13 2 2012 17 3 2012 © Population Association of America 2012 2012 Widowhood Marital status Mortality Meta-analysis References Alter G. , Dribe M. , &amp; van Poppel F. W. A. ( 2007 ). Widowhood, family size, and post...
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View articletitled, Widowhood and <span class="search-highlight">Mortality</span>: A Meta-Analysis and Meta-Regression View PDF for article titled, Widowhood and <span class="search-highlight">Mortality</span>: A Meta-Analysis and Meta-Regression
Includes: Supplementary data
Journal Article

Methods for Comparing the Mortality Experience of Heterogeneous Populations

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Kenneth G. Manton, Eric Stallard, James W. Vaupel
Demography (1981) 18 (3): 389–410.
DOI: https://doi.org/10.2307/2061005
Published: 01 August 1981
... of the degree of population heterogeneity it is possible to adjust comparisons of mortality risks across populations for the effects of population heterogeneity, differential mortality selection, and different age trajectories of the force of mortality. These methods are demonstrated by applying a variety...
View articletitled, Methods for Comparing the <span class="search-highlight">Mortality</span> Experience of Heterogeneous Populations View PDF for article titled, Methods for Comparing the <span class="search-highlight">Mortality</span> Experience of Heterogeneous Populations
Journal Article

Research Note Showing That the Rural Mortality Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016

Open Access
Danielle Rhubart, Alexis Santos
Demography (2023) 60 (6): 1699–1709.
DOI: https://doi.org/10.1215/00703370-11078239
Published: 01 December 2023
...Fig. 1 Age-adjusted mortality rates by rural–urban classification and race and ethnicity in the United States, 1999–2016 ...
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View articletitled, Research Note Showing That the Rural <span class="search-highlight">Mortality</span> Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016 View PDF for article titled, Research Note Showing That the Rural <span class="search-highlight">Mortality</span> Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016
Includes: Supplementary data
Journal Article

The Contribution of Smoking to Black-White Differences in U.S. Mortality

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Jessica Y. Ho, Irma T. Elo
Demography (2013) 50 (2): 545–568.
DOI: https://doi.org/10.1007/s13524-012-0159-z
Published: 20 October 2012
... adjustment for socioeconomic characteristics. These findings advance our understanding of the contribution of smoking to contemporary mortality trends and differences and reinforce the need for interventions that better address the needs of all groups. 20 9 2012 20 10 2012 © Population...
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View articletitled, The Contribution of Smoking to Black-White Differences in U.S. <span class="search-highlight">Mortality</span> View PDF for article titled, The Contribution of Smoking to Black-White Differences in U.S. <span class="search-highlight">Mortality</span>
Includes: Supplementary data
Journal Article

A Simple method for estimating age-specific rates from sequential cross sections

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Carl P. Schmertmann
Demography (2002) 39 (2): 287–310.
DOI: https://doi.org/10.1353/dem.2002.0018
Published: 01 May 2002
... in the 1 state differential mortality is easily analyzed. If the researcher has available a quantitative estimate of mortality differences, there is a simple adjustment to Eq. (14). If such an estimate is unavailable but the overall mortality level is known, it is still possible to establish upper...
View articletitled, A Simple method for estimating age-specific rates from sequential cross sections View PDF for article titled, A Simple method for estimating age-specific rates from sequential cross sections
Journal Article

Estimation of vital rates by means of monte carlo simulation

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Bennett Dyke, Jean W. MacCluer
Demography (1973) 10 (3): 383–403.
DOI: https://doi.org/10.2307/2060846
Published: 01 August 1973
... simulation model was used in which fertility, nuptiality, and mortality were random variables with probability distributions. Preliminary estimates of fertility were made from the birth records and used as input to this simulation program. Birth probabilities were adjusted empirically from one set...
View articletitled, Estimation of vital rates by means of monte carlo simulation View PDF for article titled, Estimation of vital rates by means of monte carlo simulation
Journal Article

Estimating the Effect of Smoking on Slowdowns in Mortality Declines in Developed Countries

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Brian L. Rostron, John R. Wilmoth
Demography (2011) 48 (2): 461–479.
DOI: https://doi.org/10.1007/s13524-011-0020-9
Published: 26 April 2011
... of the indirect Peto-Lopez method, which uses lung cancer mortality rates as a proxy for smoking exposure, to analyze this trend. The modified method estimates smoking-attributable mortality for more-specific age groups than does the Peto-Lopez method. An adjustment factor is also introduced to account for low...
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View articletitled, Estimating the Effect of Smoking on Slowdowns in <span class="search-highlight">Mortality</span> Declines in Developed Countries View PDF for article titled, Estimating the Effect of Smoking on Slowdowns in <span class="search-highlight">Mortality</span> Declines in Developed Countries