Disparities in Patterns of Health Care Travel Among Inpatients Diagnosed With Congestive Heart Failure, Florida, 2011

The geography of Medicare's hospital value-based purchasing in relation to market demographics

OBJECTIVE

To illustrate the association between the sociodemographic characteristics of hospital markets and the geographic patterns of Medicare hospital value-based purchasing (HVBP) scores.

DATA SOURCES AND STUDY SETTING

This is a secondary analysis of United States hospitals with a HVBP Total Performance Score (TPS) for 2019 in the Centers for Medicare and Medicaid Services (CMS) Hospital Compare database (4/2021 release) and American Community Survey (ACS) data for 2015-2019.

STUDY DESIGN

This is a cross-sectional study using spatial multivariable autoregressive models with HVBP TPS and component domain scores as dependent variables and hospital market demographics as the independent variables.

DATA COLLECTION/EXTRACTION METHODS

We calculated hospital market demographics using ZIP code level data from the ACS, weighted the 2019 CMS inpatient Hospital Service Area file.

PRINCIPAL FINDINGS

Spatial autoregressive models using eight nearest neighbors with diversity index, race and ethnicity distribution, families in poverty, unemployment, and lack of health insurance among residents ages 19-64 years provided the best model fit. Diversity index had the highest statistically significant contribution to lower TPS (ß = -12.79, p < 0.0001), followed by the percent of the population coded to "non-Hispanic, some other race" (ß = -2.59, p < 0.0023), and the percent of families in poverty (ß = -0.26, p < 0.0001). Percent of the population was non-Hispanic American Indian/Alaskan Native (ß = 0.35, p < 0.0001) and percent non-Hispanic Asian (ß = 0.12, p < 0.02071) were associated with higher TPS. Lower predicted TPS was observed in large urban cities throughout the US as well as in states throughout the Southeastern US. Similar geographic patterns were observed for the predicted Patient Safety, Person and Community Engagement, and Efficiency and Cost Reduction domain scores but are not for predicted Clinical Outcomes scores.

CONCLUSIONS

The lower predicted scores seen in cities and in the Southeastern region potentially reflect an inherent-that is, structural-association between market sociodemographics and HVBP scores.

Socioeconomic position and the health gradient in Cuba: dimensions and mechanisms

Background

To throw light on the under-researched association between socioeconomic position (SEP) and health in Cuba, this study examined SEP gradients in health and their underlying mechanisms among urban Cuban adults aged 18–65.

Methods

By applying linear regressions to data from the 2010 National Survey on Risk Factors and Chronic Diseases, the analysis explored the SEP-health gradient along three SEP dimensions − education, occupation, and skin colour − using ten health measures: self-reported health (SRH), general and abdominal obesity, hypertension, high glucose, high cholesterol, high triglycerides, low high-density lipoprotein cholesterol, metabolic syndrome, and cumulative risk factors. Regressions also included behaviours and health-related risk perceptions (tobacco and alcohol consumption, diet, physical activity, and risk-related behaviours). It thus investigated the SEP-health gradient and its underlying mechanisms via both behaviours and health-related risk perceptions.

Results

Once controlling for gender, age, marital status, region and provincial dummies, the analysis detected educational gradients in SRH (estimated coefficient [95% CI]: middle-level education = 3.535 [1.329, 5.741], p < 0.01; high-level education = 5.249 [3.050, 7.448], p < 0.01) that are partially explainable by both health-affecting behaviours (tobacco and alcohol consumption, diet, physical and sedentary activity) and risk perceptions. Using objective measures of health, however, it found no SEP-health gradients other than hypertension among people identified as having Black skin color (adjusted for demographic variables, 0.060 [0.018, 0.101], p < 0.01) and high cholesterol among those identified as having Mulatto or Mestizo skin color (adjusted for demographic variables, − 0.066 [− 0.098, − 0.033], p < 0.01).

Conclusions

In terms of objective health measures, the study provides minimal evidence for an SEP-health gradient in Cuba, results primarily attributable to the country’s universal healthcare system − which offers full coverage and access and affordable medications − and its highly developed education system.

Landscapes of healthcare utilization during a dengue fever outbreak in an urban environment of Colombia

The well-being of a population and its health are influenced by a myriad of socioeconomic and environmental factors that interact across a wide range of scales, from the individual to the national and global levels. One of these factors is the provision of health services, which is regulated by both demand and supply. Although an adequate provision can significantly improve health outcomes of a population, lopsided flow of patients to specific health centers can result in serious disparities and potentially delay the timeliness of a diagnosis. In this paper, utilization patterns during an epidemic of dengue fever in the city of Cali, Colombia for the year 2010 are investigated. Specifically, the objectives are to (1) identify health facilities that exhibit patterns of over- and underutilization, (2) determine where patients who are being diagnosed at a particular facility originate from, and (3) whether patients are traveling to their closest facility and hence (4) estimate how far patients are willing to travel to be diagnosed and treated for dengue fever. Analysis is further decomposed by age group and by gender, in an attempt to test whether utilization patterns drastically change according to these variables. Answers to these questions can help health authorities plan for future epidemics, for instance, by providing guidelines as to which facilities require more resources and by improving the organization of health prevention campaigns to direct population seeking health assistance to use facilities that are underutilized.

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

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.

Differential effects of distance decay on hospital inpatient visits among subpopulations in Florida, USA

Understanding patients' travel behavior for seeking hospital care is fundamental for understanding healthcare market and planning for resource allocation. However, few studies examined the issue comprehensively across populations by geographical, demographic, and health insurance characteristics. Based on the 2011 State Inpatient Database in Florida, this study modeled patients' travel patterns for hospital inpatient care across geographic areas (by average affluence, urbanicity) and calendar seasons, and across subpopulations (by age, gender, race/ethnicity, and health insurance status). Overall, travel patterns for all subpopulations were best captured by the log-logistic function. Patients in more affluent areas and rural areas tended to travel longer for hospital inpatient care, so did the younger, whites, and privately insured. Longer travel distances may be a necessity for rural patients to cope with lack of accessibility for local hospital care, but for the other population groups, it may indicate rather better mobility and more healthcare choices. The results can be used in various healthcare analyses such as accessibility assessment, hospital service area delineation, and healthcare resource planning.

Impact of Travel Time on Health Care Costs and Resource Use by Phase of Care for Older Patients With Cancer

PURPOSE

Many community cancer clinics closed between 2008 and 2016, with additional closings potentially expected. Limited data exist on the impact of travel time on health care costs and resource use.

METHODS

This retrospective cohort study (2012 to 2015) evaluated travel time to cancer care site for Medicare beneficiaries age 65 years or older in the southeastern United States. The primary outcome was Medicare spending by phase of care (ie, initial, survivorship, end of life). Secondary outcomes included patient cost responsibility and resource use measured by hospitalization rates, intensive care unit admissions, and chemotherapy-related hospitalization rates. Hierarchical linear models with patients clustered within cancer care site (CCS) were used to determine the effects of travel time on average monthly phase-specific Medicare spending and patient cost responsibility.

RESULTS

Median travel time was 32 (interquartile range, 18-59) minutes for the 23,382 included Medicare beneficiaries, with 24% of patients traveling longer than 1 hour to their CCS. During the initial phase of care, Medicare spending was 14% higher and patient cost responsibility was 10% higher for patients traveling longer than 1 hour than those traveling 30 minutes or less. Hospitalization rates were 4% to 13% higher for patients traveling longer than 1 hour versus 30 minutes or less in the initial (61 v 54), survivorship (27 v 26), and end-of-life (310 v 286) phases of care (all P < .05). Most patients traveling longer than 1 hour were hospitalized at a local hospital rather than at their CCS, whereas the converse was true for patients traveling 30 minutes or less.

CONCLUSION

As health care locations close, patients living farther from treatment sites may experience more limited access to care, and health care spending could increase for patients and Medicare.

Applications of geographic information systems (GIS) data and methods in obesity-related research

Geographic information systems (GIS) data/methods offer good promise for public health programs including obesity-related research. This study systematically examined their applications and identified gaps and limitations in current obesity-related research. A systematic search of PubMed for studies published before 20 May 2016, utilizing synonyms for GIS in combination with synonyms for obesity as search terms, identified 121 studies that met our inclusion criteria. We found primary applications of GIS data/methods in obesity-related research included (i) visualization of spatial distribution of obesity and obesity-related phenomena, and basic obesogenic environmental features, and (ii) construction of advanced obesogenic environmental indicators. We found high spatial heterogeneity in obesity prevalence/risk and obesogenic environmental factors. Also, study design and characteristics varied considerably across studies because of lack of established guidance and protocols in the field, which may also have contributed to the mixed findings about environmental impacts on obesity. Existing findings regarding built environment are more robust than those regarding food environment. Applications of GIS data/methods in obesity research are still limited, and related research faces many challenges. More and better GIS data and more friendly analysis methods are needed to expand future GIS applications in obesity-related research.