Dataset Information

Tracking stroke hospitalization clusters over time and associations with county-level socioeconomic and healthcare characteristics.

ABSTRACT: This study evaluated clustering of stroke hospitalization rates, patterns of the clustering over time, and associations with community-level characteristics.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 high- and 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.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.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. The associations found between cluster status (persistently high, transitional, persistently low) and socioeconomic and healthcare profiles shed new light on the contributions of community-level characteristics to geographic disparities in stroke hospitalizations.

SUBMITTER: Schieb LJ

PROVIDER: S-EPMC4533978 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Tracking stroke hospitalization clusters over time and associations with county-level socioeconomic and healthcare characteristics.

Schieb Linda J LJ Mobley Lee R LR George Mary M Casper Michele M

Stroke 20121127 1

<h4>Background and purpose</h4>This study evaluated clustering of stroke hospitalization rates, patterns of the clustering over time, and associations with community-level characteristics.<h4>Methods</h4>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 high- and low-rate counties by using local indicators of spatial associatio ...[more]

PMID: 23192758

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Project description:BackgroundHeat exposure, which will increase with global warming, has been linked to increased risk of a range of types of cause-specific hospitalizations. However, little is known about socioeconomic disparities in vulnerability to heat. We aimed to evaluate whether there were socioeconomic disparities in vulnerability to heat-related all-cause and cause-specific hospitalization among Brazilian cities.Methods and findingsWe collected daily hospitalization and weather data in the hot season (city-specific 4 adjacent hottest months each year) during 2000-2015 from 1,814 Brazilian cities covering 78.4% of the Brazilian population. A time-stratified case-crossover design modeled by quasi-Poisson regression and a distributed lag model was used to estimate city-specific heat-hospitalization association. Then meta-analysis was used to synthesize city-specific estimates according to different socioeconomic quartiles or levels. We included 49 million hospitalizations (58.5% female; median [interquartile range] age: 33.3 [19.8-55.7] years). For cities of lower middle income (LMI), upper middle income (UMI), and high income (HI) according to the World Bank's classification, every 5°C increase in daily mean temperature during the hot season was associated with a 5.1% (95% CI 4.4%-5.7%, P < 0.001), 3.7% (3.3%-4.0%, P < 0.001), and 2.6% (1.7%-3.4%, P < 0.001) increase in all-cause hospitalization, respectively. The inter-city socioeconomic disparities in the association were strongest for children and adolescents (0-19 years) (increased all-cause hospitalization risk with every 5°C increase [95% CI]: 9.9% [8.7%-11.1%], P < 0.001, in LMI cities versus 5.2% [4.1%-6.3%], P < 0.001, in HI cities). The disparities were particularly evident for hospitalization due to certain diseases, including ischemic heart disease (increase in cause-specific hospitalization risk with every 5°C increase [95% CI]: 5.6% [-0.2% to 11.8%], P = 0.060, in LMI cities versus 0.5% [-2.1% to 3.1%], P = 0.717, in HI cities), asthma (3.7% [0.3%-7.1%], P = 0.031, versus -6.4% [-12.1% to -0.3%], P = 0.041), pneumonia (8.0% [5.6%-10.4%], P < 0.001, versus 3.8% [1.1%-6.5%], P = 0.005), renal diseases (9.6% [6.2%-13.1%], P < 0.001, versus 4.9% [1.8%-8.0%], P = 0.002), mental health conditions (17.2% [8.4%-26.8%], P < 0.001, versus 5.5% [-1.4% to 13.0%], P = 0.121), and neoplasms (3.1% [0.7%-5.5%], P = 0.011, versus -0.1% [-2.1% to 2.0%], P = 0.939). The disparities were similar when stratifying the cities by other socioeconomic indicators (urbanization rate, literacy rate, and household income). The main limitations were lack of data on personal exposure to temperature, and that our city-level analysis did not assess intra-city or individual-level socioeconomic disparities and could not exclude confounding effects of some unmeasured variables.ConclusionsLess developed cities displayed stronger associations between heat exposure and all-cause hospitalizations and certain types of cause-specific hospitalizations in Brazil. This may exacerbate the existing geographical health and socioeconomic inequalities under a changing climate.

Project description:Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space-time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Project description:Background and Purpose- Recent national and state-level trends show a stalling or reversal of previously declining stroke death rates. These national trends may mask local geographic variation and changes in stroke mortality. We assessed county-level trends in stroke mortality among adults aged 35 to 64 and ≥65 years. Methods- We used data from National Vital Statistics Systems and a Bayesian multivariate space-time conditional autoregressive model to estimate age-standardized annual stroke death rates for 2010 through 2016 among middle-aged adults (35-64 years) and older adults (≥65 years) in US counties. We used log-linear regression models to estimate average annual and total percent change in stroke mortality during the period. Results- Nationally, the annual percent change in stroke mortality from 2010 to 2016 was -0.7% (95% CI, -4.2% to 3.0%) among middle-aged adults and -3.5% (95% CI, -10.7% to 4.3%) among older adults, resulting in 2016 rates of 15.0 per 100 000 and 259.8 per 100 000, respectively. Increasing county-level stroke mortality was more prevalent among middle-aged adults (56.6% of counties) compared with among older adults (26.1% of counties). About half (48.3%) of middle-aged adults, representing 60.2 million individuals, lived in counties in which stroke mortality increased. Conclusions- County-level increases in stroke mortality clarify previously reported national and state-level trends, particularly among middle-aged adults. Roughly 3×as many counties experienced increases in stroke death rates for middle-aged adults compared with older adults. This highlights a need to address stroke prevention and treatment for middle-aged adults while continuing efforts to reduce stroke mortality among the more highly burdened older adults. Efforts to reverse these troubling local trends will likely require joint public health and clinical efforts to develop innovative and integrated approaches for stroke prevention and care, with a focus on community-level characteristics that support stroke-free living for all.

Dataset Information

Tracking stroke hospitalization clusters over time and associations with county-level socioeconomic and healthcare characteristics.

Publications

Tracking stroke hospitalization clusters over time and associations with county-level socioeconomic and healthcare characteristics.

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