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- Am J Public Health
- v.100(8); Aug 2010
- PMC2901298
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Am J Public Health. 2010 August; 100(8): 1462–1469.
PMCID: PMC2901298
NIHMSID: NIHMS298246
PMID: 20558800
Yu-Chu Shen, PhD
and Renee Y. Hsia, MD, MSc
Author information Article notes Copyright and License information PMC Disclaimer
Abstract
Objectives. We analyzed how ease of geographic access to emergency departments (EDs), defined as driving time to the closest ED, changed between 2001 and 2005, and whether access deterioration was more likely to occur in vulnerable communities.
Methods. We classified communities on the basis of American Hospital Association and Census data into 3 categories according to driving time to the nearest ED: no increase, less than a 10-minute increase, and a 10-minute or more increase. We estimated a multinomial logit model to examine the relative risk ratio (RRR) of various community characteristics.
Results. More than 95% of communities experienced no ED access deterioration. However, 11.4 million people experienced increased driving time to their nearest ED. Low-income communities had a higher risk of facing deteriorating access compared with high-income communities (urban: RRR = 3.67; P < .01; rural: RRR = 1.75; P < .10), and communities with higher shares of Hispanics also had higher risks of facing declines (urban: RRR = 3.41; P < .10; rural: RRR = 2.67; P < .01).
Conclusions. Deteriorating access to EDs is more likely to occur in communities with economic hardship and high shares of Hispanic populations. The uneven access to critical services warrants increased attention from policymaking bodies.
Emergency departments (EDs) are an integral part of the safety net services in the United States. Though EDs were originally designed for treating urgent and emergent conditions, they are increasingly being used by certain segments of the population as a provider of last resort for nonurgent care.1–5 Between 1992 and 2001, ED visits in the United States increased by 20% to 108 million visits, whereas the number of EDs decreased by 15%,6 and it is well documented that access to emergency care, in terms of crowding and waiting times, has deteriorated since the 1990s.7–12
There is little information, however, on whether the decreased access is even across different communities, especially in places with high proportions of vulnerable populations. The Institute of Medicine and other public health institutions have voiced growing unease that there could be systemic disparities in access among traditionally vulnerable patients, including those of racial/ethnic minorities, the economically disadvantaged, the elderly, and rural populations.13
There are different dimensions of access to ED care, including but not limited to geographic, financial, or language barriers. Barriers in access emanating from any of these areas can have a detrimental effect on patient health. Ease of geographic access is especially important for time-sensitive interventions, such as treatment of acute myocardial infarction or early goal-directed therapy in sepsis.14 We focused on geographic access. Specifically, we analyzed how driving times to the nearest ED have changed between 2001 and 2005 for communities in the continental United States, and we examined whether geographic access to EDs for vulnerable populations has become more difficult between 2001 and 2005 in both rural and urban communities.
The definition of vulnerable population varies considerably in the literature; it can be as narrow as including only children or senior citizens, or as broad as including any personal traits that make the individual vulnerable to changes in health care access. For the purpose of this study, we defined vulnerable populations as those whose vulnerability is attributable to demographic characteristics (racial/ethnic minority, foreign-born, senior citizens) or economic status (low-income, unemployed). We also identified other health care market–level factors that may contribute to more difficult geographic access to EDs over time. We defined urban communities as those in the metropolitan statistical areas (MSAs).
METHODS
We obtained characteristics of communities by using zip code–level data from the 2000 Census.15 We further supplemented our zip code data set with longitude and latitude coordinates of each zip code by using Mailer's software (available at: http://www.MelissaData.com/software.htm; Melissa Data, Rancho Santa Margarita, CA). Using the longitude and latitude coordinates of the hospital's heliport (if one existed) or the hospital's physical address provided by Jill Horwitz, PhD, and Austin Nichols, PhD, we calculated driving time between each community to the nearest ED. We extracted data regarding ED availability and hospital characteristics between 2001 and 2005 from the American Hospital Association Annual Surveys.16 In addition, we added county-level health care market data from the 2005 Area Resource Files.17
Because urban and rural communities have different distributions of vulnerable populations and face significantly different access barriers to ED care,11,14,18 we performed separate analyses for urban and rural areas according to whether the zip code was in an MSA.
Changes in Access Between 2001 and 2005
Our main outcome of interest was each community's distance to the nearest ED and, more importantly, whether geographic access to the ED worsened during the study period. We first calculated the distance between each community to the nearest ED by using the population centroid location of the zip code, separately for 2001 and 2005. The distance calculation based on longitude and latitude coordinates is highly correlated with actual driving distance.19,20 Next, we computed the change in distance between the 2 years for each community. To give a better sense of the extent of change each community faces and to provide clarity in presenting the multivariate results, we translated changes in distance to changes in driving time with the formula by Phibbs and Luft.20,21 Finally, we classified the communities according to whether the driving time between a community and the nearest ED satisfied the following conditions between 2001 and 2005: (1) did not increase, (2) increased by less than 10 minutes, or (3) increased by 10 minutes or more.
Our unit of analysis was community as defined by zip code. We estimated a multinomial logit model to examine the odds that access to the nearest ED had changed by the 3 categories defined previously. The regression model was weighted by the population of each community to obtain population-based estimates of the effect. We estimated robust standard errors to account for the fact that some health care market variables are measured at the aggregated county level.
Community Economic Condition and Share of Vulnerable Populations
Our key variables of interest were categories of vulnerable populations defined in this section. We divided the share of each subpopulation into tertile distributions. The categorical variables are much easier to interpret when we present the logistic regression results. In addition, we controlled for population size.
Economically disadvantaged communities.
Economically disadvantaged groups are extremely vulnerable to changes in ED access, as poverty has been shown to be an independent risk factor associated with frequent use of the ED.22 We captured each community's economic condition by using income distribution and unemployment rate. We divided communities into 3 income categories based on per capita income distribution of the whole sample and defined 3 income groups: low-income community (lower third of the distribution, estimated separately for urban and rural), medium-income community (middle third), and high-income community (upper third, the reference group). We also included the percentage unemployed, divided into tertiles, as a separate category.23,24
Race/ethnicity.
Racial/ethnic minorities have been shown to have higher use of certain emergency services25 and comprise a larger proportion of patients who are using safety-net services.26 We considered the following minority groups: African Americans, White Hispanics, and other non-White populations. These were compared with non-Hispanic Whites.
Foreign-born.
Foreign-born populations have been shown to have poorer health and to have more limited access to health care.27–29 We included the share of foreign-born population to capture this potential vulnerable population.
Elderly.
The elderly use emergency services at a higher rate than the nonelderly population30 and are therefore vulnerable to changes in access to health care services. We considered elderly populations as individuals aged 65 years or older.
County-Level Primary Care Market Characteristics
We captured several types of primary care characteristics that can potentially affect the demand of ED service. First, total general physicians per capita: individuals with changes in their usual source of care are more likely to use emergency services31 and we therefore hypothesized that areas with better access to physicians might have less demand for EDs. Second, the number of federally qualified health centers and rural clinics: the presence of these facilities might indicate that these areas are already given special attention for needing health care resources and have less demand for EDs. Third, indicators for whether the whole or part of the county is designated as health professional shortage areas (HPSAs): we hypothesized that populations in HPSAs might be more vulnerable to changes in ED access because they already suffer from health professional shortages.
Hospital Market Characteristics
Finally, to examine whether there are hospital market-level characteristics that might contribute to deteriorating access to EDs, we defined the hospital market as the 15-mile radius surrounding the hospital, as is standard.20,32 We examined the following characteristics at the hospital market level: (1) number of EDs within the same hospital market at baseline to control for the possibility that a higher number of EDs in the baseline might suggest that the community already has adequate or excess ED supply, and (2) presence of core safety-net hospitals in the market, as some public hospitals and all critical access hospitals are legally obligated to provide safety-net services whereas not-for-profit hospitals might adopt an explicit mission to provide certain services, and teaching hospitals are less likely to close down their EDs.26,33 Therefore, we included presence of for-profit, government, teaching, and critical access hospitals as separate variables.
RESULTS
A total of 28 520 zip codes were included in our sample, with an estimated population size of 272 million persons. Our focus was on change in access between 2001 and 2005, but it is useful to first understand the baseline access distribution. For illustrative purposes, we categorized the access distribution into 3 discrete categories of access: the closest ED is less than a 10-minute drive away; the closest ED is at least 10 and less than 30 minutes away; and the closest ED is at least 30 minutes away. Figure 1 shows the distribution of the 2001 access categories by urban and rural areas. Not surprisingly, the urban population had much better access to EDs: only 5% of the urban population (equivalent to 11.3 million persons) was more than 30 minutes away from the nearest ED, whereas 24% of the rural population (equivalent to 11.8 million persons) was more than 30 minutes away from the nearest ED.
FIGURE 1
Population access to emergency departments (EDs): United States, 2001.
Changes in Access Between 2001 and 2005
Between 2001 and 2005, access to the nearest ED deteriorated for some populations. We show the distribution of the 3 access change categories (no increase, increased driving time < 10 minutes, increased driving time ≥ 10 minutes) by the 4 Census regions. Figure 2 shows that more than 95% of the population had the same or improved geographic access to EDs between 2001 and 2005 (including 4% that had improved access). Access change was not uniform across the regions: the Midwest had the smallest share of population with deteriorating access for both urban and rural communities, whereas the South appears to have had more communities that faced increased driving time beyond 10 minutes. Figure 2 also shows that rural communities had worse deterioration than did urban communities: whereas less than 1% of urban communities faced an increase of at least 10 minutes in driving time, between 1.6% and 2.6% of rural communities experienced an increase in driving time by at least 10 minutes. Table 1 shows that a total of 9.8 million urban population confronted at least some increase in driving time (an average increase of 4.6 minutes), and a total of 1.6 million rural residents had an average increase in the driving time to the nearest ED of 26 minutes.
FIGURE 2
Changes in population access to emergency departments (EDs) between 2001 and 2005: United States.
TABLE 1
Descriptive Statistics of Community and Health Market Characteristics, by ED Access Change Categories: United States, 2001 and 2005
| Urban Communities | Rural Communities | |||
| No Increase in Driving Time, Mean (SD) or No. | Driving Time Increased, Mean (SD) or No. | No Increase in Driving Time, Mean (SD) or No. | Driving Time Increased, Mean (SD) or No. | |
| Average increase in driving time to the nearest ED between 2001 and 2005 (in minutes) | 4.61 (6.99) | 25.58 (24.21) | ||
| Zip code population characteristics in 2000 | ||||
| Per capita income, $ | 22 867.11 (9769.09) | 20 333.69*** (8456.63) | 17 005.97 (3790.14) | 16 366.19*** (3241.17) |
| % unemployed | 0.03 (0.02) | 0.03 (0.01) | 0.03 (0.01) | 0.03* (0.02) |
| % African American population | 0.13 (0.20) | 0.16*** (0.23) | 0.09 (0.15) | 0.13*** (0.19) |
| % Hispanic population | 0.14 (0.19) | 0.23*** (0.24) | 0.05 (0.11) | 0.05 (0.12) |
| % other non-White populationa | 0.14 (0.15) | 0.20*** (0.17) | 0.06 (0.10) | 0.05*** (0.07) |
| % elderly population (aged ≥ 65 y) | 0.07 (0.03) | 0.06*** (0.02) | 0.08 (0.03) | 0.08 (0.03) |
| % foreign-born population | 0.13 (0.13) | 0.16*** (0.15) | 0.03 (0.04) | 0.03** (0.04) |
| Community population | 31 523.61 (18 865.79) | 35 585.96*** (21 327.71) | 13 603.46 (11 947.07) | 11 253.75*** (11 215.72) |
| County-level primary care market characteristics in 2001 | ||||
| No. of total physicians per capita | 2.86 (1.79) | 2.91 (1.41) | 1.20 (0.97) | 1.01*** (0.88) |
| No. of general physicians per capita | 0.23 (0.09) | 0.22** (0.07) | 0.27 (0.15) | 0.28 (0.15) |
| No. of federally qualified health clinics and rural health clinics | 5.67 (8.69) | 7.92*** (9.09) | 1.99 (2.77) | 1.64*** (1.94) |
| Part of county designated HPSA | 0.02 (0.14) | 0.01 (0.12) | 0.18 (0.38) | 0.25*** (0.43) |
| Whole county designated HPSA | 0.22 (0.41) | 0.17*** (0.38) | 0.28 (0.45) | 0.26 (0.44) |
| Hospital market characteristics in 2001 | ||||
| No. of hospitals within 15 miles | 11.85 (15.21) | 16.06*** (15.60) | 1.28 (0.87) | 1.54*** (1.07) |
| No. of for-profit hospitals within 15 miles | 2.23 (3.91) | 4.41*** (6.14) | 0.16 (0.40) | 0.24*** (0.48) |
| No. of government hospitals within 15 miles | 1.25 (2.46) | 1.27 (1.97) | 0.33 (0.56) | 0.35 (0.61) |
| No. of teaching hospitals within 15 miles | 2.64 (5.25) | 3.17** (4.67) | 0.01 (0.13) | 0.00 (0.05) |
| No. of critical access hospitals in county | 0.10 (0.33) | 0.14*** (0.36) | 0.28 (0.54) | 0.32* (0.64) |
| Total population size | 213 236 118 | 9 804 470 | 47 763 487 | 1 572 257 |
| Number of zip codes | 13 684 | 625 | 13 668 | 543 |
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Note. ED = emergency department; HPSA = health professional shortage area. Population characteristics derived from 2000 US Census.15
aIncluding Asian, American Indian or Alaskan Native, Native Hawaiian, or Other Pacific Islander populations.
*P < .10; **P < .05; *** P < .01, for statistically significant differences between access categories.
Community and Health Care Market Characteristics
Table 1 also compares population and health care market characteristics between zip codes that had no deterioration in access, and zip codes that faced increased driving time, separately for urban and rural areas. The first 2 columns show that, in urban areas, communities that experienced increased driving times tended to be poor (as measured by per capita income) and had higher proportions of unemployed residents (3.2% vs 2.8%; P < .01). Those communities also had higher proportions of minority groups such as African Americans (16% compared with 13% in communities with no deterioration in access; P < .01), Hispanics (23% vs 14%; P < .01), other non-Whites (20% vs 14%; P < .01), and foreign-born populations (16% vs 13%; P < .01). Rural communities facing increased driving time were also poorer compared with rural communities with no deterioration in access (average per capita income $16 366 vs $17 006; P < .01), and had a higher share of African Americans (13% vs 9%; P < .01).
We also compared health care market characteristics. Although we have analyzed urban and rural access to care separately, it is worthwhile to note that rural communities had more difficult health care access overall as evidenced by fewer physicians, higher proportions of residents in HPSAs, and significantly fewer hospitals within a 15-mile radius.
Within urban areas, communities that experienced an increased distance to the nearest ED actually had better access to other health care resources in the baseline such as more hospitals (16 vs 12; P < .01), and more federally qualified health centers (7.9 vs 5.7; P < .01). These communities also were more likely to be in markets with more for-profit hospitals (4.4 vs 2.2; P < .01).
Multivariate Results on Community Characteristics and Changes in Access
Table 1 shows that communities facing increased driving time were poorer and had higher shares of vulnerable populations, but correlated factors were not controlled. Therefore, we estimated a multinomial logit model. For clarity of presentation, we present only the results comparing communities with at least a 10-minute increase in driving time relative to the reference group in Table 2, because results from this comparison have more meaningful policy implications (results from the other comparison are available upon request from the authors).
TABLE 2
Multinomial Logit Regression Results on the Likelihood That Driving Time to the Nearest ED Increases by at Least 10 Minutes: United States, 2001 and 2005
| Urban Communities, RRR (95% CI) | Rural Communities, RRR (95% CI) | |
| Community economic condition at baseline | ||
| Income distribution | ||
| High-income community (Ref) | 1.00 | 1.00 |
| Medium-income community | 4.78*** (1.95, 11.74) | 1.82* (0.98, 3.39) |
| Low-income community | 3.67*** (1.37, 9.85) | 1.75* (0.92, 3.32) |
| Share of unemployed population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share | 1.83* (0.97, 3.44) | 1.25 (0.80, 1.95) |
| High share | 2.58** (1.18, 5.64) | 1.39 (0.86, 2.22) |
| Community vulnerable population at baseline | ||
| Share of African American population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share (middle third) | 1.60 (0.84, 3.04) | 1.12 (0.67, 1.89) |
| High share (upper third) | 0.76 (0.34, 1.74) | 1.46 (0.88, 2.43) |
| Share of Hispanic population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share | 0.67 (0.23, 2.01) | 1.31 (0.83, 2.06) |
| High share | 3.41* (0.78, 14.88) | 2.67*** (1.50, 4.76) |
| Share of other non-White population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share | 0.47 (0.14, 1.59) | 1.07 (0.67, 1.72) |
| High share | 0.37 (0.05, 2.71) | 0.78 (0.42, 1.42) |
| Share of elderly population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share | 2.18* (0.99, 4.80) | 0.79 (0.48, 1.31) |
| High share | 1.19 (0.53, 2.68) | 0.76 (0.49, 1.17) |
| Share of foreign-born population | ||
| Low share (Ref) | 1.00 | 1.00 |
| Medium share | 0.61 (0.25, 1.45) | 1.44 (0.92, 2.26) |
| High share | 0.35 (0.06, 2.02) | 0.70 (0.36, 1.36) |
| County-level health care market characteristics at baseline | ||
| No. of general practitioners per capita | 0.76 (0.04, 13.34) | 4.10*** (1.68, 10.01) |
| No. of federally qualified health clinics and rural health clinics | 1.01 (0.98, 1.04) | 0.88*** (0.81, 0.95) |
| Part of county designated HPSA | 0.80 (0.44, 1.47) | 2.04** (1.11, 3.75) |
| Whole county designated HPSA | 1.32 (0.41, 4.20) | 2.78*** (1.46, 5.30) |
| Hospital market characteristics at baseline | ||
| Market has 1 ED | 0.54 (0.25, 1.15) | 1.29 (0.76, 2.17) |
| Market has more than 1 ED | 0.43** (0.22, 0.85) | 1.51 (0.75, 3.04) |
| Presence of for-profit hospitals in market | 3.17*** (1.64, 6.14) | 1.77* (0.94, 3.33) |
| Presence of government hospitals in market | 0.96 (0.49, 1.89) | 0.86 (0.54, 1.36) |
| Presence of teaching hospitals in market | 0.64 (0.32, 1.27) | 0.59 (0.20, 1.79) |
| Presence of critical access hospitals in county | 1.18 (0.43, 3.18) | 1.49** (1.02, 2.17) |
| Log (zip code–level population) | 0.61*** (0.49, 0.77) | 0.78*** (0.67, 0.90) |
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Notes. CI = confidence interval; ED = emergency department; HPSA = health professional shortage area; RRR = relative risk ratio. The reference groups were those that experienced no increase in driving time. A hospital market was defined as everything within a 15 mile radius of the hospital. The number of zip codes falling under the urban community definition was 14 309 and the number of zip codes falling under the rural community definition was 14 211. Results on the comparison between communities with driving time increased by less than 10 minutes and those with no increased driving time is available upon request from the authors.
*P < .10; **P < .05; ***P < .01.
Table 2 presents the multinomial logit results separately for urban and rural communities, and categorizes the results into 4 groups: economic condition, share of vulnerable populations, other community characteristics, and hospital market characteristics. To interpret the relative risk ratio (RRR) of low-income communities in urban areas, for example, the 3.67 indicates that residents in low-income communities were 3.67 times more likely to face increased driving time by at least 10 minutes to the nearest ED compared with residents in high-income communities (the reference group). The first group of results on economic condition shows communities in lower income categories had high RRRs (4.78 and 3.67 for medium- and low-income communities, respectively; P < .01 for both) of experiencing deterioration in access. Communities with higher shares of unemployed population were also more likely to face increased driving time of at least 10 minutes (RRR = 1.83 and RRR = 2.58 for the medium and high proportions, respectively; P < .10 and P < .05, respectively). For urban areas, there did not appear to be uneven distribution of vulnerable population in terms of access deterioration with 1 exception: areas with high shares of Hispanics had a higher RRR of facing increased driving time by at least 10 minutes (3.41; P < .10).
We observed similar trends, although of a slightly different magnitude, in rural areas. Rural communities with lower income also encountered more deterioration in access compared with high-income communities (RRR = 1.82 and RRR = 1.75 for medium- and low-income communities, respectively; both P < .10). In addition, rural communities with a high share of Hispanics were 2.67 times more likely to confront an increased driving time of at least 10 minutes compared with communities with low shares of Hispanics (P < .01).
In terms of health care market characteristics, in urban and rural areas, communities with for-profit hospitals at baseline were 3.17 and 1.77 times more likely, respectively, to face increased driving time of at least 10 minutes to the nearest ED compared with communities with no for-profit hospitals nearby (P < .01 and P < .10, respectively). It is worrisome that urban communities with better ED access at baseline (measured by the presence of 1 or more EDs in the hospital market) also had a lower likelihood of facing increased driving time between 2001 and 2005 than did rural communities, suggesting that there is an increasing disparity in access to emergency care for communities that have poorer access to EDs at baseline. In rural areas, communities that were already in HPSA counties at baseline were at much higher risk of facing deteriorating access than communities that did not suffer from health professional shortage (for partly HPSA: RRR = 2.04; P < .05; for whole county as HPSA: RRR = 2.78; P < .01). On the other hand, greater number of federally qualified health centers and rural clinics was associated with a smaller risk of deteriorating ED access. The presence of these facilities might indicate that the government is already actively addressing the health care need in these areas, and such attention might have a spillover effect on ED access.
DISCUSSION
According to the National Center for Health Statistics, the number of hospital EDs in the United States decreased from 4176 to 3195 between 1995 and 2005, while annual ED visits increased from 96.5 million to 115.3 million during the same period, implying that each ED is facing a heavier patient load.34 From the perspective of geographic access, closure is not necessarily detrimental if there are numerous EDs in close proximity that can absorb the patient load. We examined this issue from the perspective of population access by examining whether driving time to the closest ED increased between 2001 and 2005. We found that access deterioration only occurred in a very small percentage of communities. Between 2001 and 2005, more than 95% of the US population experienced no increase in driving time to EDs. However, 11.4 million people had to drive farther to reach their nearest ED, with the average increase in driving time being 5 minutes for those in urban and 26 minutes for those in rural communities. For critical conditions such as certain types of heart attacks and strokes, minutes matter.
More importantly, we found that the declines in geographic access were uneven across communities, and tended to occur in poor communities. In particular, once we controlled for correlating factors, lower-income communities (both urban and rural) and urban communities with higher proportions of unemployed residents had a much higher relative risk of facing a substantial increase in driving time. In addition, communities with high proportions of Hispanics had a much higher relative risk of deteriorating access. This is particularly concerning when one considers previous literature showing that certain minority populations and the poor also have a higher rate of use of EDs,35 and, thus, these changes affect them disproportionately.
It is important to recognize that we only examined 1 type of access—namely, geographic access. Although this is an important aspect of access, especially for illness in which time is critical, there are other barriers to care that we cannot address in this study, such as financial or cultural barriers. Persons in communities with “easy” geographic access to EDs still would face disparity in care if they cannot overcome other types of barriers to care. In addition, geographic access does not capture waiting times in EDs. Emergency department closures may still occur in communities that experience no notable increase in driving time. Patients are therefore likely to face longer waiting times because of the increased patient loads among the remaining open EDs.
The study has several limitations. First, the actual affected population is likely to be much larger because we cannot account for temporary unavailability of the nearest ED if the ED is on diversion. Second, our distance variable is based on the longitude and latitude information of the zip code's population center. Even though this distance measure is highly correlated with driving distance,20 2 people from the same zip code might have very different access to the same ED, especially in rural areas. Third, we identified the nearest ED by using the American Hospital Association survey, which is self-reported and is susceptible to reporting errors. As long as the errors do not systematically differ by the community characteristics we examined, we do not expect to have a bias in our estimated ED access effect. Finally, we defined rural communities as zip codes that are outside the MSAs. In some cases, this metropolitan-based definition mixes highly urbanized and highly rural regions together, so the gap in health care resources we observed between urban and rural could potentially be larger.
The findings of this study have important implications regarding the continued evolution of access to EDs for certain populations. Because EDs are a crucial part of the health care safety net that disproportionately serves vulnerable populations, it is concerning that the very populations that depend on these safety-net services are experiencing sharper declines in access to emergency services. As patient-level studies have shown that minority and low-income patients have poorer outcomes,36–38 public health experts have raised the question of whether these differences are because of individual treatment of patients or systems issues such as decreased access to emergency care.39,40 Our findings bolster this latter hypothesis.
Although we focused on geographic access to EDs, it is important to recognize that EDs cannot operate in isolation without the support of community health centers or primary care clinics. The expansion of federally qualified health centers seems to have had a positive spillover effect on ED availability in rural areas, suggesting that the government's attention to the health care need in these areas might not be limited to primary care clinics.
In addition, our study shows that rural communities have poorer access on all fronts compared with urban communities, and, that, even within the rural areas, Hispanic and poorer communities are at higher risk of experiencing further declines in access. Moreover, we found that presence of for-profit hospitals is associated with greater declines in access. This observation is consistent with findings from other studies that for-profit hospitals have a greater likelihood of closing down their safety net services.41,42 Another concerning finding was that rural communities that were designated HPSAs also had higher odds of deteriorating ED access. Taken together, these results suggest that there are market-level factors that are associated with systems-level access to care, and should be considered in policymaking decisions. With more than 11 million people facing some degree of access deterioration to emergency care, further research is warranted to determine and quantify whether such deteriorating geographic access affects patient outcomes for a host of time-sensitive conditions, such as acute coronary syndrome, sepsis, stroke, and trauma.
Acknowledgments
This project was supported by the Robert Wood Johnson Foundation's Changes in Health Care Financing and Organization initiative (grant 63974). In addition, R. Y. Hsia was supported in part by a grant under the Robert Wood Johnson Foundation's Physician Faculty Scholars Program and the National Institutes of Health/National Center for Research Resources, University of California, San Francisco Clinical and Translational Science (KL2 RR024130).
We thank Jill Horwitz, PhD, from the University of Michigan, and Austin Nichols, PhD, from the Urban Institute, for providing us with geographic coordinates of all hospitals in the study.
Human Participant Protection
The study was approved by the Naval Postgraduate School's institutional review board.
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