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Teaming up census and patient data to delineate fine-scale hospital service areas and identify geographic disparities in hospital accessibility.

ABSTRACT: The number of hospital beds per capita, an important measure of equity in healthcare availability and resource allocation, was found to vary across geographic areas in many countries, including the USA. The hospital service areas (HSAs) have proven to be more meaningful spatial units for studying health-seeking behaviors and health resource allocation and service utilization. However, when evaluating the geographical balance in ratios of hospital beds to population (HBtP), no existing HSA delineation methods directly consider the underlying population distribution. Using Geographic Information Systems (GIS), this study incorporated the State Inpatient Database with census data to develop a population-based HSA delineation method. The census-derived HSAs were produced for Florida and were validated by aggregating and comparing with the traditional flow-based HSAs. The difference in current ratios of HBtP between the most over- and under-served HSAs was approximately 60 times. Significant clusters of high and low ratios were found in Miami and Jacksonville metropolitan areas, respectively. Such results may be of interest to relevant stakeholders and contribute to planning and optimization of hospital resource allocation and healthcare policy-making. Furthermore, the discovery of a strong correlation between the numbers of hospital discharges and the population at ZIP code level holds a remarkable potential for affordable population estimation, especially in non-census years.

SUBMITTER: Jia P

PROVIDER: S-EPMC6598966 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Rates of major lower extremity amputation in patients with peripheral artery disease are higher in rural communities with markers of low socioeconomic status, but most Americans live in metropolitan areas. Whether amputation rates vary within US metropolitan areas is unclear, as are characteristics of high amputation rate urban communities. Methods and Results We estimated rates of major lower extremity amputation per 100 000 Medicare beneficiaries between 2010 and 2018 at the ZIP code level among ZIP codes with ≥100 beneficiaries. We described demographic characteristics of high and low amputation ZIP codes, and the association between major amputation rate and 3 ZIP code-level markers of socioeconomic status-the proportion of patients with dual eligibility for Medicaid, median household income, and Distressed Communities Index score-for metropolitan, micropolitan, and rural ZIP code cohorts. Between 2010 and 2018, 188 995 Medicare fee-for-service patients living in 31 391 ZIP codes with ≥100 beneficiaries had a major lower extremity amputation. The median (interquartile range) ZIP code-level number of amputations per 100 000 beneficiaries was 262 (75-469). Though nonmetropolitan ZIP codes had higher rates of major amputation than metropolitan areas, 78.2% of patients undergoing major amputation lived in metropolitan areas. Compared with ZIP codes with lower amputation rates, top quartile amputation rate ZIP codes had a greater proportion of Black residents (4.4% versus 17.5%, P<0.001). In metropolitan areas, after adjusting for clinical comorbidities and demographics, every $10 000 lower median household income was associated with a 4.4% (95% CI, 3.9-4.8) higher amputation rate, and a 10-point higher Distressed Communities Index score was associated with a 3.8% (95% CI, 3.4%-4.2%) higher amputation rate; there was no association between the proportion of patients eligible for Medicaid and amputation rate. These findings were comparable to the associations identified across all ZIP codes. Conclusions In metropolitan areas, where most individuals undergoing lower extremity amputation live, markers of lower socioeconomic status and Black race were associated with higher rates of major lower extremity amputation. Development of community-based tools for peripheral artery disease diagnosis and management targeted to communities with high amputation rates in urban areas may help reduce inequities in peripheral artery disease outcomes.

Project description:BackgroundEstimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models of accessibility and competition to blood transfusion services in Bungoma County, Western Kenya.MethodsHospitals providing blood transfusion services in Bungoma were identified from an up-dated geo-coded facility database. AccessMod was used to define care-seeker's travel times to the nearest blood transfusion service. A spatial accessibility index for each enumeration area (EA) was defined using modelled travel time, population demand, and supply available at the hospital, assuming a uniform risk of emergency occurrence in the county. To identify populations marginalized from transfusion services, the number of people outside 1-h travel time and those residing in EAs with low accessibility indexes were computed at the sub-county level. Competition between the transfusing hospitals was estimated using a spatial competition index which provided a measure of the level of attractiveness of each hospital. To understand whether highly competitive facilities had better capacity for blood transfusion services, a correlation test between the computed competition metric and the blood units received and transfused at the hospital was done.Results15 hospitals in Bungoma county provide transfusion services, however these are unevenly distributed across the sub-counties. Average travel time to a blood transfusion centre in the county was 33 min and 5% of the population resided outside 1-h travel time. Based on the accessibility index, 38% of the EAs were classified to have low accessibility, representing 34% of the population, with one sub-county having the highest marginalized population. The computed competition index showed that hospitals in the urban areas had a spatial competitive advantage over those in rural areas.ConclusionThe modelled spatial accessibility has provided an improved understanding of health care gaps essential for health planning. Hospital competition has been illustrated to have some degree of influence in provision of health services hence should be considered as a significant external factor impacting the delivery, and re-design of available services.

Project description:BackgroundMany rural, low-income, and historically underrepresented minority communities lack access to trauma center services, including surgical care and injury prevention efforts. Along with features of the built and social environment at injury incident locations, geographic barriers to trauma center services may contribute to injury disparities. This study sought to classify injury event locations based on features of the built and social environment at the injury scene, and to examine patterns in individual patient demographics, injury characteristics, and mortality by location class.MethodsData from the 2015 Maryland Adult Trauma Registry and associated prehospital records (n = 16,082) were used in a latent class analysis of characteristics of injury event locations, including trauma center distance, trauma center characteristics, land use, community-level per capita income, and community-level median age. Mortality effects of location class were estimated with logistic regression, with and without adjustment for individual patient demographics and injury characteristics.ResultsEight classes were identified: rural, exurban, young suburban, aging suburban, inner suburban, urban fringe, high-income urban core, and low-income urban core. Patient characteristics and odds of death varied across classes. Compared with inner suburban locations, adjusted odds of death were highest at rural (odds ratio [OR], 1.98; 95% confidence interval [CI], 1.36-2.88), young suburb (OR, 1.57; 95% CI, 1.14-2.17), aging suburb (OR, 1.36; 95% CI, 1.04-1.78), and low-income urban core (OR, 1.38; 95% CI, 1.04-1.83) locations.ConclusionInjury incident locations can be categorized into distinguishable classes with varying mortality risk. Identification of location classes may be useful for targeted primary prevention and treatment interventions, both by identifying geographic areas with the highest risk of injury mortality and by identifying patterns of individual risk within location classes.Level of evidencePrognostic and epidemiological, level III.

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Teaming up census and patient data to delineate fine-scale hospital service areas and identify geographic disparities in hospital accessibility.

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