Dataset Information

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

ABSTRACT: To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010.We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty.The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods.The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates.

SUBMITTER: Vaughan AS

PROVIDER: S-EPMC4397179 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

Vaughan Adam S AS Kramer Michael R MR Waller Lance A LA Schieb Linda J LJ Greer Sophia S Casper Michele M

Annals of epidemiology 20150219 5

<h4>Purpose</h4>To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010.<h4>Methods</h4>We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographi ...[more]

PMID: 25776848

Similar Datasets

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.

Project description:Although annual mortality trends for prostate cancer were stabilized in recent years, understanding the exact treatment changes is necessary for optimal management. Utilization of not-otherwise specified (NOS) treatments for prostate cancer was unclear. Thus, this study aimed to analyze trends in treatment for prostate cancer in the U.S. from 2010 to 2015 and examine whether the treatment for the prostate cancer in the U.S. is compliant with clinical practice guidelines. Using joinpoint regression models, we examined trends in the rate and proportion of age-standardized utilization (ASUR and ASUP) of treatments for prostate cancer diagnosed during 2010-2015 in the U.S. based on the data from the Surveillance, Epidemiology, and End Results (SEER, 2018 data-release, with linkage to active surveillance/watchful waiting [AS/WW]) cancer registry program. Among 316,690 men with prostate cancer diagnosed during 2010-2015, ASUR and ASUP for radical prostatectomy, radiotherapy, AS/WW and NOS treatment were 32.7, 34.4, 10.0 and 40.1 per 100,000, and 27.9%, 29.3%, 8.5% and 34.2%, respectively. Trends in the overall ASUR for prostate cancer treatments differed by cancer risk group, patients' age, race/ethnicity, Gleason score, insurance status, and the average education level, average poverty-level and foreign-born person percentage of the patient's residence-county, but not by rural-urban continuum or region. ASUP of radical prostatectomy decreased from 9.8% in 2010 to 4.8% in 2015 (annual percent change [APC] = -12.0%, 95% CI, -15.9 to -7.9%), and the decrease was observed in all different risk groups. ASUP of AS/WW increased from 16.4% in 2010 to 30.2% in 2013 (APC = 22.7%, 95% CI, 4.6 to 44.0%) and then remained stable through 2013 to 2015 (APC = 1.9%, 95% CI, -24.1 to 36.9%). The increasing tendency of AS/WW only occurred in the low-risk and intermediate-risk groups. The ASUP of NOS treatment has increased from 32.3% in 2010 to 36.8% in 2015 (P<0.01). In conclusion, ASUR and ASUP for prostate cancer treatments, including NOS treatment, had changed during 2010-2015. Their trends appeared to differ by cancer risk-group, age, race/ethnicity, Gleason score and socioeconomic factors. Future studies are warranted to understand the impacts of upward trends in ASUP of NOS treatments and AS/WW on patient survival and prostate cancer mortality.

Project description:Key Points There is substantial variation in county-level ESKD mortality across the United States, with highest rates seen in the Southeastern United States. County characteristics explain approximately 19% of variation in ESKD mortality. Counties with high ESKD-related mortality may benefit from targeted and multilevel interventions. Visual Abstract Background Geographic and neighborhood-level factors, such as poverty and education, have been associated with an increased risk for incident ESKD, likelihood of receiving pre-ESKD care, and likelihood of receiving a transplant. However, few studies have examined whether these same factors are associated with ESKD mortality. In this study, we examined county-level variation in ESKD mortality and identified county-level characteristics associated with this variation. Methods We identified 1,515,986 individuals (aged 18–84 years) initiating RRT (dialysis or transplant) between 2010 and 2018 using the United States Renal Data System. Among 2781 counties, we estimated county-level, all-cause, age-standardized mortality rates (ASMR) among patients with ESKD. We then identified county-level demographic (e.g., percent female), socioeconomic (e.g., percent unemployed), healthcare (e.g., percent without health insurance), and health behavior (e.g., percent current smokers) characteristics associated with ASMR using multivariable hierarchic linear mixed models and quantified the percentage of ASMR variation explained by county-level characteristics. Results County-level ESKD ASMR ranged from 45 to 1022 per 1000 person-years (PY) (mean, 119 per 1000 PY). ASMRs were highest in counties located in the Tennessee Valley and Appalachia regions, and lowest in counties located in New England, the Pacific Northwest, and Southern California. In fully adjusted models, county-level characteristics significantly associated with higher ESKD mortality included a lower percentage of Black residents (−4.94 per 1000 PY), lower transplant rate (−4.08 per 1000 PY), and higher healthcare expenditures (5.21 per 1000 PY). Overall, county-level characteristics explained 19% of variation in ESKD mortality. Conclusions Counties with high ESKD-related mortality may benefit from targeted and multilevel interventions that combine knowledge from a growing evidence base on the interplay between individual and community-level factors associated with ESKD mortality.

Dataset Information

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

Publications

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets