Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States

Abstract

Introduction All-cause mortality in the United States declined from 1935 through 2014, with a recent uptick in 2015. This national trend is composed of disparate local trends. We identified distinct groups of all-cause mortality rate trajectories by grouping US counties with similar temporal trajectories. Methods We used all-cause mortality rates in all US counties for 1999 through 2016 and estimated discrete mixture models by using county level mortality rates. Proc Traj in SAS was used to detect how county trajectories clustered into groups on the basis of similar intercepts, slopes, and higher order terms. Models with increasing numbers of groups were assessed on the basis of model fit. We created county-level maps of mortality trajectory groups by using ArcGIS. Results Eight unique trajectory groups were detected among 3,091 counties. The average mortality rate in the most favorable trajectory group declined 29.4%, from 592.3 deaths per 100,000 in 1999 to 418.2 in 2016. The least favorable mortality trajectory group declined 3.4% over the period, from 1,280.3 deaths per 100,000 to 1,236.9. We saw significant differences in the demographic and socioeconomic profiles and geographic patterns across the trajectory categories, with favorable mortality trajectories in the Northeast, Midwest, and on the West Coast and unfavorable trajectories concentrated in the Southeast. Conclusions County-level disparities in all-cause mortality rates widened over the past 18 years. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by our research could inform interventions to achieve equitable distribution of county mortality rates.

Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice

“…This is a time of great advances in the development and application of spatial statistics, spatial tools, spatially referenced data sets, and spatial data visualization -all of which enable public health professionals to more precisely understand and address existing inequities in chronic diseases. Many studies in this collection use state-of-the-art spatial statistics, including Bayesian spatial smoothing (10,11) and the spatial Durbin econometric model (3), along with other advanced spatial analytic techniques, such as hot spot analysis (12) and spatial scan statistics for spatial clustering (13), and trajectory analysis (14). Furthermore, the development of 2 spatial analysis tools is included in this collection -The Peel Walkability Composite Index (6) and the Rate Stabilizing Tool (RST) (11).…”

Modeling Community Health with Areal Data: Bayesian Inference with Survey Standard Errors and Spatial Structure

“…Thus the quintile-based RII ( ) with counties ordered by mortality rates is , whereas the comparable quantity for counties ordered by the ICE is . Two studies comparing the bottom to the top quartile of counties ( ) ordered by socioeconomic variables [ 45 , 46 ] found values of 1.22 and 1.41, respectively; those comparing the first to fifth quintiles of counties ( ) ordered by socioeconomic variables [ 47 , 48 , 49 ] found between 1.5 and 1.8; and, those reporting RIIs by decile [ 50 ] or other tail-area grouping [ 51 , 52 ] found RIIs between 1.6 and 2.7.…”

Space Time Trends of Community Onset Staphylococcus Aureus Infections in Children Living in Southeastern United States: 2002-2010

“…Another way to analyze trends of population is through group based trajectory models (GBTM), which incorporates information from all time points and examines non linear (quadratic, cubic, and other higher order) rate trends. In this manner, GBTM determine if groups of study units have similar trajectory patterns and can predict outcome trends of individual units which are grouped together into patterns (14)(15)(16). There are currently no studies to model or analyze multiyear trends of MRSA or MSSA rates over a longitudinal time period, identifying high or low infection trends based on location.…”

“…In group-based trajectory modeling, discrete underlying groups within the population are assumed to have their own case intercept, slope and possibly higher order terms. Proc Traj requires speci cation of the number of groups the model will t. We used a process of evaluating model t while simultaneously identifying informative similar trajectory groups as previously described by Baltrus et al (16). We estimated a zero-in ated Poisson model with a rst order, quadratic term for each outcome, and an independent variable of time (years) for each group.…”

“…Descriptive analyses by individual-level and area-level variables across the trajectory groups: Characteristics from individual level data were strati ed by group -based trajectory patterns for CO-MRSA and CO-MSSA. We grouped the individual level data into two periods, designated as early period (2002)(2003)(2004)(2005) Local 'clusters' of aggregated patients' point data were determined by applying the local indicators of spatial association (LISA) technique as previously described (16,24,25). From this, hot and cold spot analyses were performed using GetisOrd Gi* statistic.…”