The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States
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{"title"=>"The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States", "type"=>"journal", "authors"=>[{"first_name"=>"Majid", "last_name"=>"Ezzati", "scopus_author_id"=>"6701418638"}, {"first_name"=>"Ari B.", "last_name"=>"Friedman", "scopus_author_id"=>"7401879795"}, {"first_name"=>"Sandeep C.", "last_name"=>"Kulkarni", "scopus_author_id"=>"36004905600"}, {"first_name"=>"Christopher J L", "last_name"=>"Murray", "scopus_author_id"=>"55481130700"}], "year"=>2008, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-43249087616", "pui"=>"351649255", "doi"=>"10.1371/journal.pmed.0050066", "isbn"=>"10.1371/journal.pmed.0050066", "sgr"=>"43249087616", "pmid"=>"18433290"}, "id"=>"c2ae1fd4-91d3-3755-8120-ce13c4347acd", "abstract"=>"BACKGROUND: Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.\\n\\nMETHODS AND FINDINGS: We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.\\n\\nCONCLUSIONS: There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.", "link"=>"http://www.mendeley.com/research/reversal-fortunes-trends-county-mortality-crosscounty-mortality-disparities-united-states", "reader_count"=>180, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>12, "Librarian"=>1, "Student > Doctoral Student"=>11, "Researcher"=>33, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>6, "Student > Master"=>30, "Other"=>15, "Student > Bachelor"=>14, "Lecturer > Senior Lecturer"=>3, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>12, "Librarian"=>1, "Student > Doctoral Student"=>11, "Researcher"=>33, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>6, "Student > Master"=>30, "Other"=>15, "Student > Bachelor"=>14, "Lecturer > Senior Lecturer"=>3, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>5, "Arts and Humanities"=>2, "Philosophy"=>1, "Business, Management and Accounting"=>2, "Veterinary Science and Veterinary Medicine"=>1, "Chemistry"=>1, "Computer Science"=>3, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>4, "Engineering"=>2, "Environmental Science"=>2, "Nursing and Health Professions"=>12, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>2, "Medicine and Dentistry"=>66, "Sports and Recreations"=>1, "Physics and Astronomy"=>1, "Psychology"=>5, "Social Sciences"=>54, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>66}, "Social Sciences"=>{"Social Sciences"=>54}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>5}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>12}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"New Zealand"=>1, "Canada"=>1, "United States"=>7, "South Africa"=>2, "United Kingdom"=>4, "Italy"=>1, "Kenya"=>1, "Switzerland"=>2, "Spain"=>2}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/933285"], "description"=>"<div><p>Counties are categorized into six groups on the basis of how their life expectancy changed in relation to national sex-specific change in life expectancy (4.1 y for men and 4.8 y for women in 1961–1983; 3.1 y for men and 1.3 y for women in 1983–1999). Actual life expectancies are shown in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050066#pmed-0050066-sg001\" target=\"_blank\">Figure S1</a>, and absolute changes in life expectancy are shown in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050066#pmed-0050066-sg002\" target=\"_blank\">Figure S2</a>.</p>\n <p>Group 1, life expectancy increased at a level significantly higher than the national sex-specific mean; group 2, life expectancy increased at a level significantly higher than zero but not significantly distinguishable from the national sex-specific mean; group 3, life expectancy increased at a level significantly higher than zero but significantly less than the national sex-specific mean; group 4, life expectancy change was statistically indistinguishable from zero and from the national sex-specific mean; group 5, life expectancy change was statistically indistinguishable from zero and was significantly less than the national sex-specific mean; group 6, life expectancy had a statistically significant decline. All statistical significance was assessed at 90%.</p></div>", "links"=>[], "tags"=>["expectancy"], "article_id"=>603736, "categories"=>["Biotechnology"], "users"=>["Majid Ezzati", "Ari B Friedman", "Sandeep C Kulkarni", "Christopher J. L Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050066.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Change_in_County_Life_Expectancy_in_1961_8211_1983_and_1983_8211_1999_/603736", "title"=>"Change in County Life Expectancy in 1961–1983 and 1983–1999", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-22 01:02:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/933400"], "description"=>"<div><p>The total height of each column shows the change in the probability of dying (from all causes) in the age range shown, divided into the probability of dying from specific diseases and injuries. The change is calculated as the probability of death in the end year minus that of the initial year. Therefore, a positive number indicates an increase in mortality, and a negative number indicates a decline in mortality (disease-specific or all-cause for the net effects of all diseases). Group 6 for females in 1983–1999 is shown on a different scale to increase resolution for all other groups.</p>\n <p>Notes: Results are not shown for 5–14 y because there are few deaths in these ages in the United States. Groups with less than 0.2% of the country's population (groups 4 and 6 for both sexes in 1961–1983, and group 4 for males in 1983–1999) have not been shown because the results are based on too few deaths. COPD and lung cancer are presented together and changed in the same direction for all age and county group. The other noncommunicable disease group includes diabetes, for which the direction of change in probability of death is identical to other noncommunicable diseases exclusive of diabetes.</p></div>", "links"=>[], "tags"=>["probability", "ranges", "1961", "1983", "counties", "grouped", "expectancy"], "article_id"=>603844, "categories"=>["Biotechnology"], "users"=>["Majid Ezzati", "Ari B Friedman", "Sandeep C Kulkarni", "Christopher J. L Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050066.g004", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Change_in_Probability_of_Dying_in_Specific_Age_Ranges_between_1961_and_1983_and_between_1983_and_1999_with_Counties_Grouped_on_the_Basis_of_the_Level_of_Change_in_Life_Expectancy_as_in_Figure_3_/603844", "title"=>"Change in Probability of Dying in Specific Age Ranges between 1961 and 1983 and between 1983 and 1999, with Counties Grouped on the Basis of the Level of Change in Life Expectancy as in Figure 3", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-22 01:04:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/933128"], "description"=>"<div><p>(A) Life expectancy for counties that make up the 2.5% of the US population with the highest and lowest county life expectancies in each year.</p>\n <p>(B) Difference between highest and lowest life expectancies in (A). Data for each year show the combined life expectancy for counties that make up 2.5% of the US population and have the highest/lowest life expectancy for that year. The mean number of years that each county was in the highest/lowest life expectancy group was 7.9/10.5 y for men and 8.6/8.6 y for women.</p>\n <p>The vertical lines show the 90% confidence interval (CI) for the estimated life expectancy or life expectancy difference. In each year, the 5% and 95% confidence limits correspond to the 5th and 95th percentiles of the distribution of life tables, calculated using the population-weighted average of death rates of constituent counties in 100 independent draws (see <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050066#s2\" target=\"_blank\">Methods</a>).</p></div>", "links"=>[], "tags"=>["expectancy", "disparity", "counties", "highest", "lowest"], "article_id"=>603573, "categories"=>["Biotechnology"], "users"=>["Majid Ezzati", "Ari B Friedman", "Sandeep C Kulkarni", "Christopher J. L Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050066.g002", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Annual_Absolute_Life_Expectancy_Disparity_between_the_Counties_with_the_Highest_and_Lowest_Life_Expectancies_/603573", "title"=>"Annual Absolute Life Expectancy Disparity between the Counties with the Highest and Lowest Life Expectancies", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-22 00:59:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/476057", "https://ndownloader.figshare.com/files/476067", "https://ndownloader.figshare.com/files/476085", "https://ndownloader.figshare.com/files/476145", "https://ndownloader.figshare.com/files/476178", "https://ndownloader.figshare.com/files/476240"], "description"=>"<div><h3>Background</h3><p>Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.</p> <h3>Methods and Findings</h3><p>We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.</p> <h3>Conclusions</h3><p>There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.</p> </div>", "links"=>[], "tags"=>["reversal", "trends", "cross-county", "disparities", "united", "states"], "article_id"=>153766, "categories"=>["Biotechnology"], "users"=>["Majid Ezzati", "Ari B Friedman", "Sandeep C Kulkarni", "Christopher J. L Murray"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.0050066.sd001", "https://dx.doi.org/10.1371/journal.pmed.0050066.sd002", "https://dx.doi.org/10.1371/journal.pmed.0050066.sg001", "https://dx.doi.org/10.1371/journal.pmed.0050066.sg002", "https://dx.doi.org/10.1371/journal.pmed.0050066.sv001", "https://dx.doi.org/10.1371/journal.pmed.0050066.sv002"], "stats"=>{"downloads"=>7, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Reversal_of_Fortunes_Trends_in_County_Mortality_and_Cross_County_Mortality_Disparities_in_the_United_States_/153766", "title"=>"The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-04-22 01:02:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/932965"], "description"=>"<p>Inequality in family income (e.g., as measured by the Gini coefficient) declined in the United States between the 1920s and 1970s, and has increased after that period [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050066#pmed-0050066-b049\" target=\"_blank\">49</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050066#pmed-0050066-b050\" target=\"_blank\">50</a>].</p>", "links"=>[], "tags"=>["expectancies", "units", "united", "states"], "article_id"=>603407, "categories"=>["Biotechnology"], "users"=>["Majid Ezzati", "Ari B Friedman", "Sandeep C Kulkarni", "Christopher J. L Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050066.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SD_of_Life_Expectancies_of_the_2_068_County_Units_in_the_United_States_by_Sex_/603407", "title"=>"SD of Life Expectancies of the 2,068 County Units in the United States by Sex", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-22 00:56:47"}

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  • {"unique-ip"=>"27", "full-text"=>"26", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2015", "month"=>"10"}
  • {"unique-ip"=>"48", "full-text"=>"55", "pdf"=>"20", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2015", "month"=>"11"}
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  • {"unique-ip"=>"20", "full-text"=>"24", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
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  • {"unique-ip"=>"20", "full-text"=>"14", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"2"}
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Relative Metric

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