Title |
Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records
|
---|---|
Published in |
Demography, July 2018
|
DOI | 10.1007/s13524-018-0695-2 |
Pubmed ID | |
Authors |
Carl P. Schmertmann, Marcos R. Gonzaga |
Abstract |
High sampling variability complicates estimation of demographic rates in small areas. In addition, many countries have imperfect vital registration systems, with coverage quality that varies significantly between regions. We develop a Bayesian regression model for small-area mortality schedules that simultaneously addresses the problems of small local samples and underreporting of deaths. We combine a relational model for mortality schedules with probabilistic prior information on death registration coverage derived from demographic estimation techniques, such as Death Distribution Methods, and from field audits by public health experts. We test the model on small-area data from Brazil. Incorporating external estimates of vital registration coverage though priors improves small-area mortality estimates by accounting for underregistration and automatically producing measures of uncertainty. Bayesian estimates show that when mortality levels in small areas are compared, noise often dominates signal. Differences in local point estimates of life expectancy are often small relative to uncertainty, even for relatively large areas in a populous country like Brazil. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 2 | 10% |
United States | 2 | 10% |
Sweden | 1 | 5% |
Mexico | 1 | 5% |
South Africa | 1 | 5% |
United Kingdom | 1 | 5% |
Austria | 1 | 5% |
Canada | 1 | 5% |
Kenya | 1 | 5% |
Other | 2 | 10% |
Unknown | 8 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 14 | 67% |
Scientists | 7 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 60 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 22% |
Student > Ph. D. Student | 12 | 20% |
Student > Master | 5 | 8% |
Student > Bachelor | 4 | 7% |
Student > Postgraduate | 4 | 7% |
Other | 9 | 15% |
Unknown | 13 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 15 | 25% |
Medicine and Dentistry | 7 | 12% |
Mathematics | 5 | 8% |
Nursing and Health Professions | 3 | 5% |
Agricultural and Biological Sciences | 2 | 3% |
Other | 11 | 18% |
Unknown | 17 | 28% |