Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates.
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ABSTRACT:
SUBMITTER: Rader B
PROVIDER: S-EPMC7239151 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background Exercise-based cardiac rehabilitation (CR) is known to reduce morbidity and mortality for patients with cardiac conditions. Sociodemographic disparities in accessing CR persist and could be related to the distance between where patients live and where CR facilities are located. Our objective is to determine the association between sociodemographic characteristics and geographic proximity to CR facilities. Methods and Results We identified actively operating CR facilities across Los Angeles County and used multivariable Poisson regression to examine the association between sociodemographic characteristics of residential proximity to the nearest CR facility. We also calculated the proportion of residents per area lacking geographic proximity to CR facilities across sociodemographic characteristics, from which we calculated prevalence ratios. We found that racial and ethnic minorities, compared with non-Hispanic White individuals, more frequently live ≥5 miles from a CR facility. The greatest geographic disparity was seen for non-Hispanic Black individuals, with a 2.73 (95% CI, 2.66-2.79) prevalence ratio of living at least 5 miles from a CR facility. Notably, the municipal region with the largest proportion of census tracts comprising mostly non-White residents (those identifying as Hispanic or a race other than White), with median annual household income <$60 000, contained no CR facilities despite ranking among the county's highest in population density. Conclusions Racial, ethnic, and socioeconomic characteristics are significantly associated with lack of geographic proximity to a CR facility. Interventions targeting geographic as well as nongeographic factors may be needed to reduce disparities in access to exercise-based CR programs. Such interventions could increase the potential of CR to benefit patients at high risk for developing adverse cardiovascular outcomes.
| S-EPMC9683686 | biostudies-literature
Project description:ObjectiveTo characterize the quantity and quality of hospital capacity across the United States.Data sourcesWe combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data.Study designThis study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity.Data collection/extraction methodsAll data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included.Principal findingsNon-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas.ConclusionsThis study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.
| S-EPMC9441275 | biostudies-literature
Project description:Supported by the 10% set-aside funds in the Community Mental Health Block grant, distributed at the state level, coordinated specialty care (CSC) have been widely disseminated throughout the U.S. This study explores variations in the geographical accessibility of CSC programs by neighborhood level characteristics in Washington State. CSC locations were geocoded. Socioeconomic neighborhood deprivation (i.e., Area deprivation index) and rurality (i.e., Rural-Urban Commuting Area codes) were neighborhood level characteristics extracted from the 2018 American Community Survey. Geographic accessibility of CSC was assessed using a two-step floating catchment area technique and multilevel linear models were used to examine the association between specific neighborhood characteristics and geographic accessibility. The association between access and socioeconomically deprived neighborhoods varied differentially by neighborhood rurality (an interaction effect). Model estimates indicated that the least deprived, metropolitan neighborhoods had the best access (M = 0.38; CI: 0.34, 0.42) and rural neighborhoods in the second most deprived quartile had the worst access (M = 0.16; CI: 0.11, 0.21) to CSC. There was a clear decrease in accessibility for more rural neighborhoods, regardless of other neighborhood characteristics. In conclusions, findings provide important insight into how resource distribution contributes to geographic disparities in access to CSC. The use of spatial analytic techniques has the potential to identify specific neighborhoods and populations where there is a need to expand and increase availability of CSC to ensure access to rural and socioeconomically deprived neighborhoods.
| S-EPMC9119810 | biostudies-literature
Project description:Background Although methamphetamine abuse is associated with the development of heart failure (HF), nationwide data on methamphetamine-associated HF (MethHF) hospitalizations are limited. This study evaluates nationwide HF hospitalizations associated with substance abuse to better understand MethHF prevalence trends and the clinical characteristics of those patients. Methods and Results This cross-sectional period-prevalence study used hospital discharge data from the National Inpatient Sample to identify adult primary HF hospitalizations with a secondary diagnosis of abuse of methamphetamines, cocaine, or alcohol in the United States from 2002 to 2014. All 2014 MethHF admissions were separated by regional census division to evaluate geographical distribution. Demographics, payer information, and clinical characteristics of MethHF hospitalizations were compared with all other HF hospitalizations. Total nationwide MethHF hospitalizations increased from 547 in 2002 to 6625 in 2014 with a predominance on the West Coast. Methamphetamine abuse was slightly more common among primary HF hospitalizations compared with all-cause hospitalizations (7.4 versus 6.4 per 1000; Cohen h=0.012; P<0.001). Among HF hospitalizations, patients with MethHF were younger (mean age, 48.9 versus 72.4 years; Cohen d=1.93; P<0.001), more likely to be on Medicaid (59.4% versus 8.8%; Cohen h=1.16; P<0.001) or uninsured (12.0% versus 2.6%; Cohen h=0.36; P<0.001), and more likely to present to urban hospitals (43.8% versus 28.3%; Cohen h=0.32; P<0.001) than patients with non-methamphetamine associated HF. Patients with MethHF had higher rates of psychiatric comorbidities and were more likely to leave the hospital against medical advice. Conclusions MethHF hospitalizations have significantly increased in the United States, particularly on the West Coast. Coordinated public health policies and systems of care are needed to address this rising epidemic.
| S-EPMC8475042 | biostudies-literature
Project description:Background & aimsSignificant geographic variability in gastrointestinal (GI) cancer-related death has been reported in the United States. We aimed to evaluate both modifiable and nonmodifiable factors associated with intercounty differences in mortality due to GI cancer.MethodsData from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research platform were used to calculate county-level mortality from esophageal, gastric, pancreatic, and colorectal cancers. Multivariable linear regression models were fit to adjust for county-level covariables, considering both patient (eg, sex, race, obesity, diabetes, alcohol, and smoking) and structural factors (eg, specialist density, poverty, insurance prevalence, and colon cancer screening prevalence). Intercounty variability in GI cancer-related mortality explained by these covariables was expressed as the multivariable model R2.ResultsThere were significant geographic disparities in GI cancer-related county-level mortality across the US from 2010-2019 with the ratio of mortality between 90th and 10th percentile counties ranging from 1.5 (pancreatic) to 2.1 (gastric cancer). Counties with the highest 5% mortality rates for gastric, pancreatic, and colorectal cancer were primarily in the Southeastern United States. Multivariable models explained 43%, 61%, 14%, and 39% of the intercounty variability in mortality rates for esophageal, gastric, pancreatic, and colorectal cancer, respectively. Cigarette smoking and rural residence (independent of specialist density) were most strongly associated with GI cancer-related mortality.ConclusionsBoth patient and structural factors contribute to significant geographic differences in mortality from GI cancers. Our findings support continued public health efforts to reduce smoking use and improve care for rural patients, which may contribute to a reduction in disparities in GI cancer-related death.
| S-EPMC9703359 | biostudies-literature
Project description:BackgroundWhile artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.MethodsWe performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.ResultsOur search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).InterpretationU.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.
| S-EPMC9931338 | biostudies-literature
Project description:ObjectiveWe test whether nursing homes serving predominately low-income and racial minority residents (compositional explanation) or located in neighborhoods with higher concentrations of low-income and racial minority residents (contextual explanation) have worse financial outcomes and care quality.Data sourcesHealthcare Cost Report Information System, Nursing Home Compare, Online Survey Certification and Reporting Certification, and American Community Survey.Study designA cross-sectional study design of nursing homes within U.S. metropolitan areas.Data collection/extraction methodsData were obtained from Centers for Medicare & Medicaid Services and U.S. Census Bureau.Principal findingsMedicaid-dependent nursing homes have a 3.5 percentage point lower operating ratio. Those serving primarily racial minorities have a 2.64-point lower quality rating. A 1 percent increase in the neighborhood population living in poverty is associated with a 1.20-point lower quality rating, on a scale from 10 to 50, and a 1 percent increase in the portion of neighborhood black residents is associated with a 0.8 percentage point lower operating ratio and a 0.37 lower quality rating.ConclusionsMedicaid dependency (compositional effect) and concentration of racial minority residents in neighborhoods (contextual effect) are associated with higher fiscal stress and lower quality of care, indicating that nursing homes' geographic location may exacerbate long-term care inequalities.
| S-EPMC6056600 | biostudies-literature
Project description:Measuring rates of coverage and spatial access to healthcare services is essential to inform policies for development. These rates tend to reflect the urban-rural divide, typically with urban areas experiencing higher accessibility than rural ones. Especially in Sub-Saharan Africa (SSA), a region experiencing high disease burden amid fast urbanisation and population growth. However, such assessment has been hindered by a lack of updated and comparable geospatial data on urbanisation and health facilities. In this study, we apply the UN-endorsed Degree of Urbanisation (DoU or DEGURBA) method to investigate how geographic access to healthcare facilities varies across the urban-rural continuum in SSA as a whole and in each country, for circa 2020. Results show that geographic access is overall highest in cities and peri-urban areas, where more than 95% of inhabitants live within 30 min from the nearest HCF, with this share decreasing to 80-90% in towns. This share is lowest in villages and dispersed rural areas (65%), with about 10-15% of population more than 3 h away from any health post. Challenges in geographic access seem mostly determined by high travel impedance, since overall spatial densities of HCF are comparable to European levels.
| S-EPMC10630936 | biostudies-literature
Project description:COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.
| S-EPMC10116449 | biostudies-literature
Project description: Not available
| S-EPMC10631173 | biostudies-literature