Estimating Life Expectancy in Small Areas, with an Application to Recent Changes in Life Expectancy in US Counties

2014 ◽  
pp. 15-33
Author(s):  
Peter Congdon
Keyword(s):  
Life Expectancy ◽  
Small Areas ◽  
Us Counties

scholarly journals Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Jordi Perez-Panades ◽  
Paloma Botella-Rocamora ◽  
Miguel Angel Martinez-Beneito
Keyword(s):  
Life Expectancy ◽  
Age Groups ◽  
Dependence Structure ◽  
Risk Indicator ◽  
Small Areas ◽  
Life Expectancy At Birth ◽  
First Case ◽  
Wide Range ◽  
Standardized Mortality Ratios

Abstract Background Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies. Methods To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age–space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates. Results Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age–space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered. Conclusion Our age–space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.

scholarly journals Impact of nursing home deaths on life expectancy calculations in small areas

2004 ◽  
Vol 58 (11) ◽  
pp. 958-962 ◽  
Author(s):  
E. S Williams
Keyword(s):  
Nursing Home ◽  
Life Expectancy ◽  
Small Areas

scholarly journals Falling behind: life expectancy in US counties from 2000 to 2007 in an international context

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Sandeep C Kulkarni ◽  
Alison Levin-Rector ◽  
Majid Ezzati ◽  
Christopher JL Murray
Keyword(s):  
Life Expectancy ◽  
International Context ◽  
Us Counties

scholarly journals Widening life expectancy inequalities across small areas of England

2021 ◽  
Author(s):  
David A Leon ◽  
Vladimir M Shkolnikov
Keyword(s):  
Life Expectancy ◽  
Small Areas

scholarly journals Inequalities in Life Expectancy Among US Counties, 1980 to 2014

2017 ◽  
Vol 177 (7) ◽  
pp. 1003 ◽  
Author(s):  
Laura Dwyer-Lindgren ◽  
Amelia Bertozzi-Villa ◽  
Rebecca W. Stubbs ◽  
Chloe Morozoff ◽  
Johan P. Mackenbach ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Us Counties

scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2020 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Small Area ◽  
Standardized Mortality Ratio ◽  
Small Areas ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Small Area Level ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small-areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small-areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small-areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small-area.

scholarly journals Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records

Demography ◽  
2018 ◽  
Vol 55 (4) ◽  
pp. 1363-1388 ◽  
Author(s):  
Carl P. Schmertmann ◽  
Marcos R. Gonzaga
Keyword(s):  
Life Expectancy ◽  
Bayesian Estimation ◽  
Small Areas ◽  
Vital Records ◽  
Specific Mortality

scholarly journals Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies.

2020 ◽  
Author(s):  
Jordi Perez-Panades ◽  
Paloma Botella-Rocamora ◽  
Miguel Angel Martinez-Beneito
Keyword(s):  
Life Expectancy ◽  
Age Groups ◽  
Dependence Structure ◽  
Risk Indicator ◽  
Small Areas ◽  
Life Expectancy At Birth ◽  
First Case ◽  
Wide Range ◽  
Standardized Mortality Ratios

Abstract Background: Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR)the omnipresent risk indicator for small areas epidemiologic studies.Methods:To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age-space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates.Results: Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of consideringage-space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered.Conclusion: Our age-space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas

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