Geographic health inequalities in Norway: a Gini analysis of cross-county differences in mortality from 1980 to 2014.
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ABSTRACT:
SUBMITTER: Skaftun EK
PROVIDER: S-EPMC5968669 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
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Geographic health inequalities in Norway: a Gini analysis of cross-county differences in mortality from 1980 to 2014.
Skaftun Eirin K EK Verguet Stéphane S Norheim Ole F OF Johansson Kjell A KA
International journal for equity in health 20180524 1
<h4>Background</h4>This study aims at quantifying the level and changes over time of inequality in age-specific mortality and life expectancy between the 19 Norwegian counties from 1980 to 2014.<h4>Methods</h4>Data on population and mortality by county was obtained from Statistics Norway for 1980-2014. Life expectancy and age-specific mortality rates (0-4, 5-49 and 50-69 age groups) were estimated by year and county. Geographic inequality was described by the absolute Gini index annually.<h4>Res ...[more]
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Project description:ImportanceCardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized.ObjectiveTo identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories.Design, setting, and participantsThis longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021.ExposuresCounty-level characteristics, collected from 5 county-level data sets.Main outcomes and measuresCardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership.ResultsAmong 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory.Conclusions and relevanceCardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.
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Project description:ImportanceSubstance use disorders, including alcohol use disorders and drug use disorders, and intentional injuries, including self-harm and interpersonal violence, are important causes of early death and disability in the United States.ObjectiveTo estimate age-standardized mortality rates by county from alcohol use disorders, drug use disorders, self-harm, and interpersonal violence in the United States.Design and settingValidated small-area estimation models were applied to deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 for alcohol use disorders, drug use disorders, self-harm, and interpersonal violence.ExposuresCounty of residence.Main outcomes and measuresAge-standardized mortality rates by US county (N = 3110), year, sex, and cause.ResultsBetween 1980 and 2014, there were 2 848 768 deaths due to substance use disorders and intentional injuries recorded in the United States. Mortality rates from alcohol use disorders (n = 256 432), drug use disorders (n = 542 501), self-harm (n = 1 289 086), and interpersonal violence (n = 760 749) varied widely among counties. Mortality rates decreased for alcohol use disorders, self-harm, and interpersonal violence at the national level between 1980 and 2014; however, over the same period, the percentage of counties in which mortality rates increased for these causes was 65.4% for alcohol use disorders, 74.6% for self-harm, and 6.6% for interpersonal violence. Mortality rates from drug use disorders increased nationally and in every county between 1980 and 2014, but the relative increase varied from 8.2% to 8369.7%. Relative and absolute geographic inequalities in mortality, as measured by comparing the 90th and 10th percentile among counties, decreased for alcohol use disorders and interpersonal violence but increased substantially for drug use disorders and self-harm between 1980 and 2014.Conclusions and relevanceMortality due to alcohol use disorders, drug use disorders, self-harm, and interpersonal violence varied widely among US counties, both in terms of levels of mortality and trends. These estimates may be useful to inform efforts to target prevention, diagnosis, and treatment to improve health and reduce inequalities.
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Project description:ImportanceExamining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity.ObjectiveTo estimate annual life tables by county from 1980 to 2014; describe trends in geographic inequalities in life expectancy and age-specific risk of death; and assess the proportion of variation in life expectancy explained by variation in socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.Design, setting, and participantsAnnual county-level life tables were constructed using small area estimation methods from deidentified death records from the National Center for Health Statistics (NCHS), and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Measures of geographic inequality in life expectancy and age-specific mortality risk were calculated. Principal component analysis and ordinary least squares regression were used to examine the county-level association between life expectancy and socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.ExposuresCounty of residence.Main outcomes and measuresLife expectancy at birth and age-specific mortality risk.ResultsCounties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110. In 2014, life expectancy at birth for both sexes combined was 79.1 (95% uncertainty interval [UI], 79.0-79.1) years overall, but differed by 20.1 (95% UI, 19.1-21.3) years between the counties with the lowest and highest life expectancy. Absolute geographic inequality in life expectancy increased between 1980 and 2014. Over the same period, absolute geographic inequality in the risk of death decreased among children and adolescents, but increased among older adults. Socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors explained 60%, 74%, and 27% of county-level variation in life expectancy, respectively. Combined, these factors explained 74% of this variation. Most of the association between socioeconomic and race/ethnicity factors and life expectancy was mediated through behavioral and metabolic risk factors.Conclusions and relevanceGeographic disparities in life expectancy among US counties are large and increasing. Much of the variation in life expectancy among counties can be explained by a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Policy action targeting socioeconomic factors and behavioral and metabolic risk factors may help reverse the trend of increasing disparities in life expectancy in the United States.
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Project description:IntroductionCancer is a leading cause of morbidity and mortality in the United States and results in a high economic burden.ObjectiveTo estimate age-standardized mortality rates by US county from 29 cancers.Design and settingDeidentified death records from the National Center for Health Statistics (NCHS) and population counts from the Census Bureau, the NCHS, and the Human Mortality Database from 1980 to 2014 were used. Validated small area estimation models were used to estimate county-level mortality rates from 29 cancers: lip and oral cavity; nasopharynx; other pharynx; esophageal; stomach; colon and rectum; liver; gallbladder and biliary; pancreatic; larynx; tracheal, bronchus, and lung; malignant skin melanoma; nonmelanoma skin cancer; breast; cervical; uterine; ovarian; prostate; testicular; kidney; bladder; brain and nervous system; thyroid; mesothelioma; Hodgkin lymphoma; non-Hodgkin lymphoma; multiple myeloma; leukemia; and all other cancers combined.ExposureCounty of residence.Main outcomes and measuresAge-standardized cancer mortality rates by county, year, sex, and cancer type.ResultsA total of 19 511 910 cancer deaths were recorded in the United States between 1980 and 2014, including 5 656 423 due to tracheal, bronchus, and lung cancer; 2 484 476 due to colon and rectum cancer; 1 573 593 due to breast cancer; 1 077 030 due to prostate cancer; 1 157 878 due to pancreatic cancer; 209 314 due to uterine cancer; 421 628 due to kidney cancer; 487 518 due to liver cancer; 13 927 due to testicular cancer; and 829 396 due to non-Hodgkin lymphoma. Cancer mortality decreased by 20.1% (95% uncertainty interval [UI], 18.2%-21.4%) between 1980 and 2014, from 240.2 (95% UI, 235.8-244.1) to 192.0 (95% UI, 188.6-197.7) deaths per 100 000 population. There were large differences in the mortality rate among counties throughout the period: in 1980, cancer mortality ranged from 130.6 (95% UI, 114.7-146.0) per 100 000 population in Summit County, Colorado, to 386.9 (95% UI, 330.5-450.7) in North Slope Borough, Alaska, and in 2014 from 70.7 (95% UI, 63.2-79.0) in Summit County, Colorado, to 503.1 (95% UI, 464.9-545.4) in Union County, Florida. For many cancers, there were distinct clusters of counties with especially high mortality. The location of these clusters varied by type of cancer and were spread in different regions of the United States. Clusters of breast cancer were present in the southern belt and along the Mississippi River, while liver cancer was high along the Texas-Mexico border, and clusters of kidney cancer were observed in North and South Dakota and counties in West Virginia, Ohio, Indiana, Louisiana, Oklahoma, Texas, Alaska, and Illinois.Conclusions and relevanceCancer mortality declined overall in the United States between 1980 and 2014. Over this same period, there were important changes in trends, patterns, and differences in cancer mortality among US counties. These patterns may inform further research into improving prevention and treatment.
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Project description:BackgroundA recent study from Japan suggested that geographic inequalities in all-cause premature adult mortality have increased since 1995 in both sexes even after adjusting for individual age and occupation in 47 prefectures. Such variations can arise from compositional effects as well as contextual effects. In this study, we sought to further examine the emerging geographic inequalities in all-cause mortality, by exploring the relative contribution of composition and context in each prefecture.MethodsWe used the 2005 vital statistics and census data among those aged 25 or older. The total number of decedents was 524,785 men and 455,863 women. We estimated gender-specific two-level logistic regression to model mortality risk as a function of age, occupation, and residence in 47 prefectures. Prefecture-level variance was used as an estimate of geographic inequalities in mortality, and prefectures were ranked by odds ratios (ORs), with the reference being the grand mean of all prefectures (value = 1).ResultsOverall, the degree of geographic inequalities was more pronounced when we did not account for the composition (i.e., age and occupation) in each prefecture. Even after adjusting for the composition, however, substantial differences remained in mortality risk across prefectures with ORs ranging from 0.870 (Okinawa) to 1.190 (Aomori) for men and from 0.864 (Shimane) to 1.132 (Aichi) for women. In some prefectures (e.g., Aomori), adjustment for composition showed little change in ORs, while we observed substantial attenuation in ORs in other prefectures (e.g., Akita). We also observed qualitative changes in some prefectures (e.g., Tokyo). No clear associations were observed between prefecture-level socioeconomic status variables and the risk of mortality in either sex.ConclusionsGeographic disparities in mortality across prefectures are quite substantial and cannot be fully explained by differences in population composition. The relative contribution of composition and context to health inequalities considerably vary across prefectures.
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Project description:BackgroundCounties 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.Methods and findingsWe 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.ConclusionsThere 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.
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