Teaming up census and patient data to delineate fine-scale hospital service areas and identify geographic disparities in hospital accessibility.
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ABSTRACT:
SUBMITTER: Jia P
PROVIDER: S-EPMC6598966 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. Without appropriate early resuscitation interventions, the prospect of survival is limited. This means that an effective community response is a critical enabler of increasing the number of people who survive. However, while OHCA incidence is higher in more deprived areas, propensity to volunteer is, in general, associated with higher socioeconomic status. In this context, we consider whether there are socioeconomic disparities in geographic accessibility to volunteer community first responders (CFRs) in Ireland, where CFR groups have developed organically and communities self-select to participate. We use geographic information systems and propensity score matching to generate a set of control areas with which to compare established CFR catchment areas. Differences between CFRs and controls in terms of the distribution of catchment deprivation and social fragmentation scores are assessed using two-sided Kolmogorov-Smirnov tests. Overall we find that while CFR schemes are centred in more deprived and socially fragmented areas, beyond a catchment of 4 min there is no evidence of differences in area-level deprivation or social fragmentation. Our findings show that self-selection as a model of CFR recruitment does not lead to more deprived areas being disadvantaged in terms of access to CFR schemes. This means that community-led health interventions can develop to the benefit of community members across the socioeconomic spectrum and may be relevant for other countries and jurisdictions looking to support similar models within communities.
| S-EPMC9249950 | biostudies-literature
Project description:PURPOSE:To address locally relevant cancer-related health issues, health departments frequently need data beyond that contained in standard census area-based statistics. We describe a geographic information system-based method for calculating age-standardized cancer incidence rates in non-census defined geographical areas using publically available data. METHODS:Aggregated records of cancer cases diagnosed from 2009 through 2013 in each of Chicago's 77 census-defined community areas were obtained from the Illinois State Cancer Registry. Areal interpolation through dasymetric mapping of census blocks was used to redistribute populations and case counts from community areas to Chicago's 50 politically defined aldermanic wards, and ward-level age-standardized 5-year cumulative incidence rates were calculated. RESULTS:Potential errors in redistributing populations between geographies were limited to <1.5% of the total population, and agreement between our ward population estimates and those from a frequently cited reference set of estimates was high (Pearson correlation r = 0.99, mean difference = -4 persons). A map overlay of safety-net primary care clinic locations and ward-level incidence rates for advanced-staged cancers revealed potential pathways for prevention. CONCLUSIONS:Areal interpolation through dasymetric mapping can estimate cancer rates in non-census defined geographies. This can address gaps in local cancer-related health data, inform health resource advocacy, and guide community-centered cancer prevention and control.
| S-EPMC5839327 | biostudies-literature
Project description:ObjectiveThe aims of the study were to demonstrate a method for developing rehabilitation service areas and to compare service areas based on postacute care rehabilitation admissions to service areas based on acute care hospital admissions.DesignWe conducted a secondary analysis of 2013-2014 Medicare records for older patients in Texas (N = 469,172). Our analysis included admission records for inpatient rehabilitation facilities, skilled nursing facilities, long-term care hospitals, and home health agencies. We used Ward's algorithm to cluster patient ZIP Code Tabulation Areas based on which facilities patients were admitted to for rehabilitation. For comparison, we set the number of rehabilitation clusters to 22 to allow for comparison to the 22 hospital referral regions in Texas. Two methods were used to evaluate rehabilitation service areas: intraclass correlation coefficient and variance in the number of rehabilitation beds across areas.ResultsRehabilitation service areas had a higher intraclass correlation coefficient (0.081 vs. 0.076) and variance in beds (27.8 vs. 21.4). Our findings suggest that service areas based on rehabilitation admissions capture has more variation than those based on acute hospital admissions.ConclusionsThis study suggests that the use of rehabilitation service areas would lead to more accurate assessments of rehabilitation geographic variations and their use in understanding rehabilitation outcomes.
| S-EPMC8262929 | biostudies-literature
Project description:OBJECTIVE:To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. DATA SOURCES:The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project. STUDY DESIGN:A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. PRINCIPAL FINDINGS:The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization. CONCLUSIONS:The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions.
| S-EPMC5785331 | biostudies-other
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.
| S-EPMC8649262 | biostudies-literature
Project description:ObjectiveTo develop an automated, reproducible method for delineating hospital service areas (HSAs).Data sources/settingDischarge data from all Swiss acute care hospitals for the years 2013 to 2016.Study designWe derived HSAs and hospital referral regions for Switzerland using a newly developed flow-based, automated, objective, and reproducible method using all discharge data. We compared our method to the classical, partially subjective approach used to delineate the Swiss Health Care Atlas by delineating four sets of intervention-specific HSAs.Principal findingsBased on 4 105 885 discharges, the fully automated method delineated 63 HSAs. Comparison with existing HSAs reveals good overlap and comparable measures of health utilization between the methods and shows that in the Swiss setting, our method outperforms a cluster-based approach to defining HSAs. While the classical method potentially takes an entire day to delineate the regions, our method took approximately 10 minutes.ConclusionsHospital service areas are used to analyze differences in use of health care that may indicate underuse and overuse. Our new, fully automated, objective, and reproducible method provides a useful tool for hospital services researchers that will enable them to delineate and update patient-flow-based HSAs.
| S-EPMC7240760 | biostudies-literature
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.
| S-EPMC9549490 | biostudies-literature
Project description:To measure the contribution of market-level prices, utilization, and health risk to medical spending variation among the Blue Cross Blue Shield of Texas (BCBSTX) privately insured population and the Texas Medicare population.Claims data for all BCBSTX members and publicly available CMS data for Texas in 2011.We used observational data and decomposed overall and service-specific spending into health status and health status adjusted utilization and input prices and input prices adjusted for the BCBSTX and Medicare populations.Variation in overall BCBSTX spending across HRRs appeared driven by price variation, whereas utilization variation factored more prominently in Medicare. The contribution of price to spending variation differed by service category. Price drove inpatient spending variation, while utilization drove outpatient and professional spending variation in BCBSTX. The context in which negotiations occur may help explain the patterns across services.The conventional wisdom that Medicare does a better job of controlling prices and private plans do a better job of controlling volume is an oversimplification. BCBSTX does a good job of controlling outpatient and professional prices, but not at controlling inpatient prices. Strategies to manage the variation in spending may need to differ substantially depending on the service and payer.
| S-EPMC4254133 | biostudies-literature
Project description:BackgroundThe strong geographic variations in the incidence rates of renal replacement therapy (RRT) for end-stage renal disease are not solely related to variations in the population's needs, such as the prevalence of diabetes or the deprivation level. Inequitable geographic access to health services has been involved in different countries but never in France, a country with a generous supply of health services and where the effect of the variability of medical practices was highlighted in an analysis conducted at the geographic scale of districts. Our ecological study, performed at the finer scale of townships in a French area of 8,370,616 inhabitants, investigated the association between RRT incidence rates, socioeconomic environment and geographic accessibility to healthcare while adjusting for morbidity level and medical practice patterns.MethodsUsing data from the Renal Epidemiology and Information Network registry, we estimated age-adjusted RRT incidence rates during 2010-2014 for the 282 townships of the area. A hierarchical Bayesian Poisson model was used to examine the association between incidence rates and 18 contextual variables describing population health status, socioeconomic level and health services characteristics. Relative risks (RRs) and 95% credible intervals (95% CrIs) for each variable were estimated for a 1-SD increase in incidence rate.ResultsDuring 2010-2014, 6,835 new patients ≥18 years old (4231 men, 2604 women) living in the study area started RRT; the RRT incidence rates by townships ranged from 21 to 499 per million inhabitants. In multivariate analysis, rates were related to the prevalence of diabetes [RR (95% CrI): 1.05 (1.04-1.11)], the median estimated glomerular filtration rate at dialysis initiation [1.14 (1.08-1.20)], and the proportion of incident patients ≥ 85 years old [1.08 (1.03-1.14)]. After adjusting for these factors, rates in townships increased with increasing French deprivation index [1.05 (1.01-1.08)] and decreased with increasing mean travel time to reach the closest nephrologist [0.92 (0.89-0.95]).ConclusionThese data confirm the influence of deprivation level, the prevalence of diabetes and medical practices on RRT incidence rates across a large French area. For the first time, an association was found with the distance to nephrology services. These data suggest possible inequitable geographic access to RRT within the French health system.
| S-EPMC7386572 | biostudies-literature
Project description:We examine whether the proportion of the US population with ?60 minute access to Primary Stroke Centers (PSCs) varies based on geographic and demographic factors.Population level access to PSCs within 60 minutes was estimated using validated models of prehospital time accounting for critical prehospital time intervals and existing road networks. We examined the association between geographic factors, demographic factors, and access to care. Multivariable models quantified the association between demographics and PSC access for the entire United States and then stratified by urbanicity.Of the 309 million people in the United States, 65.8% had ?60 minute PSC access by ground ambulance (87% major cities, 59% minor cities, 9% suburbs, and 1% rural). PSC access was lower in stroke belt states (44% versus 69%). Non-whites were more likely to have access than whites (77% versus 62%), and Hispanics were more likely to have access than non-Hispanics (78% versus 64%). Demographics were not meaningfully associated with access in major cities or suburbs. In smaller cities, there was less access in areas with lower income, less education, more uninsured, more Medicare and Medicaid eligibles, lower healthcare utilization, and healthcare resources.There are significant geographic disparities in access to PSCs. Access is limited in nonurban areas. Despite the higher burden of cerebrovascular disease in stroke belt states, access to care is lower in these areas. Selecting demographic and healthcare factors is strongly associated with access to care in smaller cities, but not in other areas, including major cities.
| S-EPMC4282182 | biostudies-literature