Tracking Stroke Hospitalization Clusters Over Time and Associations With County-Level Socioeconomic and Healthcare Characteristics

Abstract

Our study included hospital claims data for fee-for-service Medicare beneficiaries aged ≥65 for 1995, 1996, 2005, and 2006 from the Centers for Medicare and Medicaid Services' Medicare Provider Analysis and Review file, Part A. We defined a stroke hospitalization Background and Purpose-This study evaluated clustering of stroke hospitalization rates, patterns of the clustering over time, and associations with community-level characteristics. Methods-We used Medicare hospital claims data from 1995-1996 to 2005-2006 with a principal discharge diagnosis of stroke to calculate county-level stroke hospitalization rates. We identified statistically significant clusters of highand low-rate counties by using local indicators of spatial association, tracked cluster status over time, and assessed associations between cluster status and county-level socioeconomic and healthcare profiles. Results-Clearly defined clusters of counties with high-and low-stroke hospitalization rates were identified in each time.Approximately 75% of counties maintained their cluster status from 1995-1996 to 2005-2006. In addition, 243 counties transitioned into high-rate clusters, and 148 transitioned out of high-rate clusters. Persistently high-rate clusters were located primarily in the Southeast, whereas persistently low-rate clusters occurred mostly in New England and in the West. In general, persistently low-rate counties had the most favorable socioeconomic and healthcare profiles, followed by counties that transitioned out of or into high-rate clusters. Persistently high-rate counties experienced the least favorable socioeconomic and healthcare profiles. Conclusions-The persistence of clusters of high-and low-stroke hospitalization rates during a 10-year period suggests that the underlying causes of stroke in these areas have also persisted. In this study, we refer to cerebrovascular disease as stroke. Beneficiaries were determined from the Medicare Denominator Files and were excluded if they were members of a health maintenance organization, died before July 1, or were <65 years on July 1 for each of the study years. All stroke hospitalizations and Medicare beneficiaries were assigned to the county of the patient's residence in the Medicare claims files. The study years were combined to create 2 periods: 1995-1996 and 2005-2006. Two-year stroke hospitalization rates were calculated for each period and age-adjusted by using the 2000 US standard population weights.10 Data for independent cities in Virginia were merged with their surrounding counties. Only counties located in the 48 contiguous United States that had at least 1 neighboring county were included (n=3074). Stroke hospitalization rates were calculated by using SAS software (version 9.1.3).11 Maps were created by using ArcMap software (version 10.0). 12 Spatial Clustering AnalysesWe undertook a 2-step process to assess positive spatial autocorrelation, or clustering. All spatial clustering analyses were conducted by using GeoDa software (version 0.9.5-i), 13 and the Empirical-B...

Reassessing the Stroke Belt

“…The stability of spatial patterns of mortality over time is not known, but a prior study found that 75% of stroke hospitalization clusters were stable over 10 years. 9 This cross-sectional, population-level analysis cannot assess causality. Population-level associations may not apply at the individual level.…”

Health disparities and stroke: the influence of insurance status on the prevalence of patient safety indicators and hospital-acquired conditions

“…3,[6][7][8]10,13,[16][17][18]20 In a recent retrospective chart review of the New Jersey Acute Stroke Registry of patients presenting with acute ischemic stroke, uninsured patients had higher mortality and longer lengths of stay than insured patients. a higher level of neurological impairment, higher mortality, and longer lengths of stay for uninsured patients.…”

Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old

“…Regarding income, in some countries such as USA, variables such as the median household income or the percentage of population in poverty are currently available at the county (municipal) level through official sources such as the Area Resource File (HRSA, 2015), and have been used in studies focused on stroke such as that by Schieb et al (2013). In Portugal, however, this is not available at the municipal level, not even as part of the census information.…”

“…These time periods were chosen primarily in order to understand the evolution of the relationship between socioeconomic factors and stroke between two consecutive and relatively recent decades, and also bearing in mind that the first period registers much more mortality in persons <65 years old (almost the double) than the second. These periods were also devised in order to assess the relationship between socioeconomic data from the 1991 and 2001 census and the aggregated mortality in ensuing years, not spanning more than half a decade from each census year, as suggested in works by Thrift et al (2006) and Schieb et al (2013). A third period would have been useful, but the necessary data were not available, since mortality data were available only up to 2012 following the 2011 census and the situation at the time of data gathering.…”