Geographic variation in the demand for emergency care: A local population-level analysis

Socioeconomic Barriers to Care for Pediatric Airways Utilizing Geographic Information Systems

OBJECTIVE

Geographic information systems (GIS) provide a unique set of tools to spatially analyze health care and identify patterns of health outcomes to help optimize delivery. Our goal is to create maps of pediatric tracheostomy patients using GIS to assess socioeconomic and other factors that impact postoperative care after discharge to home.

METHODS

A retrospective study was performed on patients (≤21 years old) who underwent tracheostomy at a tertiary care pediatric hospital from January 1, 2015 to December 31, 2020. Using GIS, we geocoded patient addresses and conducted spatial analyses of the relationship between patients and access to health care providers as well as vulnerable population factors including poverty, educational attainment, and single-parent households.

RESULTS

A total of 156 patients were included. Patients initially discharged to transitional care (108/156, 69.2%) had significantly higher likelihood of presenting to the ED regardless of socioeconomic status (OR: 2.28, 95% CI: 1.03-5.05; p = 0.042). There was no relationship between ED visit rate and median household income, poverty level, and percentage of uneducated adults (p = 0.490; p = 0.424; p = 0.752). Median distance to the tertiary care pediatric hospital was significantly longer for patients with no ED visit (median = 61.28 miles; SD = 50.90) compared with those with an ED visit (median = 37.75 miles; SD = 35.92) (p = 0.002).

CONCLUSION

The application of GIS could provide geo-localized data to better understand the healthcare barriers to access for children with tracheostomies. This study uniquely integrates medical record data with socioeconomic factors and social determinants of health.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:1919-1925, 2024.

The role of mesolevel characteristics of the health care system and socioeconomic factors on health care use - results of a scoping review

BACKGROUND

Besides macrolevel characteristics of a health care system, mesolevel access characteristics can exert influence on socioeconomic inequalities in healthcare use. These reflect access to healthcare, which is shaped on a smaller scale than the national level, by the institutions and establishments of a health system that individuals interact with on a regular basis. This scoping review maps the existing evidence about the influence of mesolevel access characteristics and socioeconomic position on healthcare use. Furthermore, it summarizes the evidence on the interaction between mesolevel access characteristics and socioeconomic inequalities in healthcare use.

METHODS

We used the databases MEDLINE (PubMed), Web of Science, Scopus, and PsycINFO and followed the 'Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols extension for scoping reviews (PRISMA-ScR)' recommendations. The included quantitative studies used a measure of socioeconomic position, a mesolevel access characteristic, and a measure of individual healthcare utilisation. Studies published between 2000 and 2020 in high income countries were considered.

RESULTS

Of the 9501 potentially eligible manuscripts, 158 studies were included after a two-stage screening process. The included studies contained a wide spectrum of outcomes and were thus summarised to the overarching categories: use of preventive services, use of curative services, and potentially avoidable service use. Exemplary outcomes were screening uptake, physician visits and avoidable hospitalisations. Access variables included healthcare system characteristics such as physician density or distance to physician. The effects of socioeconomic position on healthcare use as well as of mesolevel access characteristics were investigated by most studies. The results show that socioeconomic and access factors play a crucial role in healthcare use. However, the interaction between socioeconomic position and mesolevel access characteristics is addressed in only few studies.

CONCLUSIONS

Socioeconomic position and mesolevel access characteristics are important when examining variation in healthcare use. Additionally, studies provide initial evidence that moderation effects exist between the two factors, although research on this topic is sparse. Further research is needed to investigate whether adapting access characteristics at the mesolevel can reduce socioeconomic inequity in health care use.

North to south gradient and local waves of influenza in Chile

Influenza seasonality is caused by complex interactions between environmental factors, viral mutations, population crowding, and human travel. To date, no studies have estimated the seasonality and latitudinal patterns of seasonal influenza in Chile. We obtained influenza-like illness (ILI) surveillance data from 29 Chilean public health networks to evaluate seasonality using wavelet analysis. We assessed the relationship between the start, peak, and latitude of the ILI epidemics using linear and piecewise regression. To estimate the presence of incoming and outgoing traveling waves (timing vs distance) between networks and to assess the association with population size, we used linear and logistic regression. We found a north to south gradient of influenza and traveling waves that were present in the central, densely populated region of Chile. Our findings suggest that larger populations in central Chile drive seasonal influenza epidemics.

The analysis of the geographical distribution of emergency departments' frequent users: a tool to prioritize public health policies?

Background

The individual factors associated to Frequent Users (FUs) in Emergency Departments are well known. However, the characteristics of their geographical distribution and how territorial specificities are associated and intertwined with ED use are limited. Investigating healthcare use and territorial factors would help targeting local health policies. We aim at describing the geographical distribution of ED’s FUs within the Paris region.

Methods

We performed a retrospective analysis of all ED visits in the Paris region in 2015. Data were collected from the universal health insurance’s claims database. Frequent Users (FUs) were defined as having visited ≥3 times any ED of the region over the period. We assessed the FUs rate in each geographical unit (GU) and assessed correlations between FUs rate and socio-demographics and economic characteristics of GUs. We also performed a multidimensional analysis and a principal component analysis to identify a typology of territories to describe and target the FUs phenomenon.

Results

FUs accounted for 278,687 (11.7%) of the 2,382,802 patients who visited the ED, living in 232 GUs. In the region, median FUs rate in each GU was 11.0% [interquartile range: 9.5–12.5]. High FUs rate was correlated to the territorial markers of social deprivation. Three different categories of GU were identified with different profiles of healthcare providers densities.

Conclusion

FUs rate varies between territories and is correlated to territorial markers of social deprivation. Targeted public policies should focus on disadvantaged territories.

The Influence of Social Determinants of Health on Emergency Departments Visits in a Medicaid Sample

STUDY OBJECTIVE

We evaluate the relationship between social determinants of health and emergency department (ED) visits in the Medicaid Cohort of the District of Columbia.

METHODS

We conducted a retrospective cohort analysis of 8,943 adult Medicaid beneficiaries who completed a social determinants of health survey at study enrollment. We merged the social determinants of health data with participants' Medicaid claims data for up to 24 months before enrollment. Using latent class analysis, we grouped our participants into 4 distinct social risk classes based on similar responses to the social determinants of health questions. We classified ED visits as primary care treatable or ED care needed, using the Minnesota algorithm. We calculated the adjusted log relative primary care treatable and ED care needed visit rates among the social risk classes by using generalized linear mixed-effects models.

RESULTS

The majority (71%) of the 49,111 ED visits made by the 8,943 participants were ED care needed. The adjusted log relative rate of both primary care treatable and ED care needed visit rates increased with each higher (worse) social risk class compared with the lowest class. Participants in the highest social risk class (ie, unemployed and many social risks) had a log relative primary care treatable and ED care needed rate of 39% (range 28% to 50%) and 29% (range 21% to 38%), respectively, adjusted for age, sex, and illness severity.

CONCLUSION

There is a strong relationship between social determinants of health and ED utilization in this Medicaid sample that is worth investigating in other Medicaid samples and patient populations.

Children’s Health Insurance Status and Emergency Room Utilization: An Examination of Complex Survey Data

Since the Children’s Health Insurance Program’s passage into law in 1997, the program has increased in cost to over $15 billion in recent years. Emergency room usage has also increased throughout the United States, leading to nationwide issues in overcrowding and surges in service costs. This study seeks to examine emergency room utilization of children insured under Children’s Health Insurance Program to determine if Children’s Health Insurance Program enrollees use the emergency room more or less frequently than their privately insured counterparts. The data used in this study were from the 2017 National Health Interview Survey. SAS statistical software was used to conduct a multinomial regression assessing the relationship between insurance type (private v. Children’s Health Insurance Program) and frequency of emergency room utilization over the last 12 months. The analysis results indicate no statistically significant difference between Children’s Health Insurance Program insured and privately insured children in terms of frequency of emergency room utilization and suggest a need to explore other factors that more directly influence Children’s Health Insurance Program costs.

Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York

OBJECTIVES

Some of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level.

DESIGN

We performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York's rural Sullivan County.

SETTING

Sullivan County, a rural county ranked second-to-last for health outcomes in New York State.

PARTICIPANTS

Adult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017-2018 or had at least one ED visit in 2011-2015.

OUTCOME MEASURES

We compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas.

RESULTS

Our county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011-2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23-0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses.

CONCLUSIONS

For select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease.

Using geographic information systems to map older people's emergency department attendance for future health planning

Objectives

This study aimed to assess the pattern of use of EDs, factors contributing to the visits, geographical distribution and outcomes in people aged 65 years or older to a large hospital in Dublin.

Methods

A retrospective analysis of 2 years of data from an urban university teaching hospital ED in the southern part of Dublin was reviewed for the period 2014–2015 (n=103 022) to capture the records of attenders. All ED presentations by individuals 65 years and older were extracted for analysis. Address-matched records were analysed using QGIS, a geographic information systems (GIS) analysis and visualisation tool to determine straight-line distances travelled to the ED by age.

Results

Of the 49 538 non-duplicate presentations in the main database, 49.9% of the total are women and 49.1% are men. A subset comprised of 40 801 had address-matched records. When mapped, the data showed a distinct clustering of addresses around the hospital site but this clustering shows different patterns based on age cohort. Average distances travelled to ED are shorter for people 65 and older compared with younger patients. Average distances travelled for those aged 65–74 was 21 km (n=4177 presentations); for the age group 75–84, 18 km (n=2518 presentations) and 13 km for those aged 85 and older (n=2104 presentations). This is validated by statistical tests on the clustered data. Self-referral rates of about 60% were recorded for each age group, although this varied slightly, not significantly, with age.

Conclusions

Health planning at a regional level should account for the significant number of older patients attending EDs. The use of GIS for health planning in particular can assist hospitals to improve their understanding of the origin of the cohort of older ED patients.

Variation in Hospitalization Rates Following Emergency Department Visits in Children with Medical Complexity

OBJECTIVES

To evaluate factors associated with admission from emergency department (ED) encounters for children with medical complexity (CMC) and to quantify the hospital admission rate as well as variation in adjusted hospital admission rates across EDs.

STUDY DESIGN

Retrospective study of 271 806 visits to 37 EDs in freestanding children's hospitals from January 1, 2014, to June 30, 2017, for patients of all ages with a complex chronic condition. Associations between patient demographic, clinical, and health services characteristics and the likelihood of hospital admission were identified using generalized linear models, which were then used to calculate adjusted hospital admission rates.

RESULTS

Hospital admission occurred with 25.7% of ED visits. Characteristics with the greatest aOR of hospitalization were ≥3 compared with 0 prior hospitalizations in 365 days (4.7; 95% CI, 4.5-4.9), ED arrival overnight compared with during workday 3.2 (95% CI, 3.1-3.3)], and ≥6 vs 0-1 chronic conditions (1.6; 95% CI, 1.5-1.6). Adjusted hospital admission rates varied significantly (P < .001) across EDs (21.1% [10th percentile]) and 30.0% [90th percentile]). Significant variation remained when excluding low-intensity ED visits, excluding hospitalizations requiring surgery and/or intensive care, or restricting the cohort to overnight ED arrival and to children with ≥3 prior hospitalizations.

CONCLUSIONS

CMC are frequently admitted from the ED. Substantial variation in CMC hospital admission rates across EDs exists after case-mix adjustment.

Using Geospatial Analysis and Emergency Claims Data to Improve Minority Health Surveillance

Traditional methods of health surveillance often under-represent racial and ethnic minorities. Our objective was to use geospatial analysis and emergency claims data to estimate local chronic disease prevalence separately for specific racial and ethnic groups. We also performed a regression analysis to identify associations between median household income and local disease prevalence among Black, Hispanic, Asian, and White adults in New York City. The study population included individuals who visited an emergency department at least once from 2009 to 2013. Our main outcomes were geospatial estimates of diabetes, hypertension, and asthma prevalence by Census tract as stratified by race and ethnicity. Using emergency claims data, we identified 4.9 million unique New York City adults with 28.5% of identifying as Black, 25.2% Hispanic, and 6.1% Asian. Age-adjusted disease prevalence was highest among Black and Hispanic adults for diabetes (13.4 and 13.1%), hypertension (28.7 and 24.1%), and asthma (9.9 and 10.1%). Correlation between disease prevalence maps demonstrated moderate overlap between Black and Hispanic adults for diabetes (0.49), hypertension (0.57), and asthma (0.58). In our regression analysis, we found that the association between low income and high disease prevalence was strongest for Hispanic adults, whereas increases in income had more modest reductions in disease prevalence for Black adults, especially for diabetes. Our geographically detailed maps of disease prevalence generate actionable evidence that can help direct health interventions to those communities with the highest health disparities. Using these novel geographic approaches, we reveal the underlying epidemiology of chronic disease for a racially and culturally diverse population.

Using Indirect Measures to Identify Geographic Hot Spots of Poor Glycemic Control: Cross-sectional Comparisons With an A1C Registry

OBJECTIVE

Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control.

RESEARCH DESIGN AND METHODS

Census tracts in New York City (NYC) were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the NYC A1C Registry data.

RESULTS

Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9%, 89.6%, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which had 86.2% accuracy.

CONCLUSIONS

Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.

Identifying Geographic Disparities in Diabetes Prevalence Among Adults and Children Using Emergency Claims Data

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Geospatial analysis of emergency department visits for targeting community-based responses to the opioid epidemic

The opioid epidemic in the United States carries significant morbidity and mortality and requires a coordinated response among emergency providers, outpatient providers, public health departments, and communities. Anecdotally, providers across the spectrum of care at Massachusetts General Hospital (MGH) in Boston, MA have noticed that Charlestown, a community in northeast Boston, has been particularly impacted by the opioid epidemic and needs both emergency and longer-term resources. We hypothesized that geospatial analysis of the home addresses of patients presenting to the MGH emergency department (ED) with opioid-related emergencies might identify “hot spots” of opioid-related healthcare needs within Charlestown that could then be targeted for further investigation and resource deployment. Here, we present a geospatial analysis at the United States census tract level of the home addresses of all patients who presented to the MGH ED for opioid-related emergency visits between 7/1/2012 and 6/30/2015, including 191 visits from 100 addresses in Charlestown, MA. Among the six census tracts that comprise Charlestown, we find a 9.5-fold difference in opioid-related ED visits, with 45% of all opioid-related visits from Charlestown originating in tract 040401. The signal from this census tract remains strong after adjusting for population differences between census tracts, and while this tract is one of the higher utilizing census tracts in Charlestown of the MGH ED for all cause visits, it also has a 2.9-fold higher rate of opioid-related visits than the remainder of Charlestown. Identifying this hot spot of opioid-related emergency needs within Charlestown may help re-distribute existing resources efficiently, empower community and ED-based physicians to advocate for their patients, and serve as a catalyst for partnerships between MGH and local community groups. More broadly, this analysis demonstrates that EDs can use geospatial analysis to address the emergency and longer-term health needs of the communities they are designed to serve.