| Title |
A Flexible Bayesian Model for Estimating Subnational Mortality
|
|---|---|
| Published in |
Demography, October 2017
|
| DOI | 10.1007/s13524-017-0618-7 |
| Pubmed ID | |
| Authors |
Monica Alexander, Emilio Zagheni, Magali Barbieri |
| Abstract |
Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities. |
X Demographics
The data shown below were collected from the profiles of 40 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 28% |
| France | 2 | 5% |
| Germany | 2 | 5% |
| Spain | 2 | 5% |
| Mexico | 2 | 5% |
| United Kingdom | 1 | 3% |
| Australia | 1 | 3% |
| Austria | 1 | 3% |
| Canada | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 14 | 35% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 27 | 68% |
| Scientists | 13 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 66 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 21% |
| Researcher | 13 | 20% |
| Student > Bachelor | 7 | 11% |
| Student > Doctoral Student | 6 | 9% |
| Other | 4 | 6% |
| Other | 12 | 18% |
| Unknown | 10 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 26 | 39% |
| Medicine and Dentistry | 5 | 8% |
| Mathematics | 5 | 8% |
| Economics, Econometrics and Finance | 5 | 8% |
| Immunology and Microbiology | 2 | 3% |
| Other | 9 | 14% |
| Unknown | 14 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2021.
All research outputs
#1,461,960
of 25,416,581 outputs
Outputs from Demography
#394
of 1,994 outputs
Outputs of similar age
#29,254
of 333,731 outputs
Outputs of similar age from Demography
#7
of 23 outputs
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