Article, Cardiology

Narrowing performance gap between rural and urban hospitals for acute myocardial infarction care

a b s t r a c t

Background: Rural communities experience significant barriers to quality healthcare, including dispari- ties in medical care following acute myocardial infarctions (AMI). This study sought to determine if the population density of the county where Medicare patients were hospitalized following AMI predicted Short-term outcomes and to quantify longitudinal changes in Hospital performance on quality of care metrics.

Methods: Hospital-level data was queried from the 2012 and 2018 Centers for Medicare & Medicaid Services archives. Each hospital was classified based on residing county using the National Center for Health Statistics Rural-Urban Continuum Codes (RUCC). Variations and longitudinal changes in risk- adjusted outcomes and quality of care metrics were stratified by RUCC classification and analyzed.

Results: Among the 4798 hospitals identified, Rural hospitals had significantly higher risk-adjusted 30- day mortality (rs = 0.095, p < 0.001) and decreased statin prescribed at discharge (rs = –0.066, p = 0.004). Only aspirin (R2 = 0.003, p = 0.024) and statin (R2 = 0.006, p = 0.001) prescribed at discharge were correlated with improved 30-day mortality. Despite these differences, from 2012 to 2018 the per- formance gap between rural and urban hospitals narrowed for all but one quality of care metric, with concurrent 1.83% [95% CI 1.76-1.90] and 3.37% [95% CI 3.30-3.44] reductions in mortality and Hospital readmissions, respectively.

Conclusions: In the United States, only modest variations currently exist between rural and urban hospi- tals in the medical care of AMI. Although the performance gap has narrowed, new strategies to improve timely and effective care are necessary to alleviate residual cardiovascular healthcare disparities in rural communities.

(C) 2019

Introduction

Rural communities in the United States experience significant barriers to quality healthcare, resulting in higher incidence of dis- ease and lower Life expectancy [1,2]. One demonstrative example of such disparity is the variation in medical care following acute myocardial infarctions (AMI), which affect approximately 720,000 people in the United States each year and have aggregate hospital costs of $12.1 billion [3,4]. Previous studies have reported that Medicare beneficiaries in rural hospitals were less likely to

Abbreviations: AMI, Acute myocardial infarction; CMS, Centers for Medicare & Medicaid Services; HRRP, Hospital Readmissions Reduction Program; MSA, metropolitan statistical areas; RUCC, Rural-Urban Continuum Code.

* Corresponding author at: Central Michigan University College of Medicine,

1280 East Campus Drive, Mount Pleasant, MI 48858, USA.

E-mail addresses: [email protected] (F. Alghanem), [email protected] (J.M. Clements).

receive recommendED treatments for AMI both during hospitaliza- tion and at discharge, resulting in significantly higher post-AMI mortality [5,6]. This includes decreased utilization of aspirin, beta-blockers, nitroglycerin, heparin, thrombolytics, and percuta- neous coronary angioplasty. These findings are corroborated by numerous state-level, national, and international studies analyzing similar quality of care metrics [7-12].

Fortunately, since the 1990s, AMI Incidence rates and mortality have steadily declined by 30-50% due to improved and earlier therapies [13-16]. The development of standardized American Heart Association and American College of Cardiology guidelines may have narrowed the performance gap between rural and urban hospitals, while contemporary medical advances, often concen- trated within urban populations, may have simultaneously widened the performance gap [17-19]. Unfortunately, interpreting healthcare trends between existing studies is complex due to differences in study design, changing criteria for diagnosis of AMI, and demographic differences in patient populations [20,21].

https://doi.org/10.1016/j.ajem.2019.04.030

0735-6757/(C) 2019

Despite significant changes in the management of AMI over the last decade, to our knowledge there are no recent longitudinal analyses examining changes in hospital performance across geo- graphic locations following hospitalizations for AMI. Therefore, this study sought to use a single Centers for Medicare & Medicaid Ser- vices (CMS) national database to determine if the population den- sity of the county where Medicare beneficiaries were hospitalized following AMI still predicts quality of care and short-term out- comes and quantify how contemporary advances in medical prac- tice were distributed among rural and urban hospitals. Identifying where healthcare disparities persist will allow physicians and healthcare policy-makers to optimize resource allocation and tar- get areas where greatest opportunity for improvement exists.

Methods

The data that support the findings of this study are available from the corresponding author upon request. All datasets used originate from the Medicare.gov Hospital Compare Website pro- vided by the CMS [22]. Conclusions from this study should not give the false impression of government endorsement.

Data sources

For all Medicare-certified hospitals, publicly available CMS datasets on hospital-level ‘Timely and Effective Care’ and ‘Compli- cations and Death’ were collected from the 05/23/2018 (median reporting dates 7/1/2013 to 6/30/2016) and 07/01/2012 (median reporting dates 07/01/2008 to 06/30/2011) online archives. These include both hospitals with and without percutaneous coronary intervention capability. This data is available through the Hospital Inpatient Quality Reporting Program, which allows CMS to reim- burse hospitals that successfully report designated quality mea- sures a higher annual update to their payment rates. Data is reported from the most recent archive, unless otherwise specified. Patient inclusion was based on discharges with an ICD Principal Diagnosis Code for any AMI, including both STEMI and NSTEMI. Excluded populations comprised patients <65 years of age, patients

with incomplete administrative data, cases with a length of stay

<=1 day discharged alive, and cases with a total hospital length of stay exceeding one year. Outcome metrics included risk-adjusted 30-day mortality and 30-day readmission rate. CMS estimates these hospital-specific risk-adjusted rates using hierarchical gener- alized linear models incorporating age, sex, clinically relevant comorbidities and past medical history. Quality of care metrics included median time to electrocardiogram (ECG), aspirin given at arrival, aspirin prescribed at discharge, statin prescribed at dis- charge, Primary percutaneous coronary intervention received within 90 min of hospital arrival, and Fibrinolytic therapy received within 30 min of emergency department (ED) arrival.

Quality of care metrics for PCI and fibrinolytic therapy have additional inclusion criterion of ST-elevation or left-bundle- branch-block on ECG and exclusion criterion of transfer from another facility. Furthermore, these metrics are only reported as the fraction of patients whose time from hospital arrival to therapy is timely, divided by the number of patients who received that therapy. Stated otherwise, the reported metric’s denominator is AMI patients with ST-elevation or LBBB on ECG who received pri- mary PCI or fibrinolytics. These additional requirements (ECG changes plus receiving therapy) result in a significantly smaller number of hospitals with an included population sufficient for public reporting (>11 cases per year).

Each hospital was also assigned a Rural-Urban Continuum Code (RUCC) based on its residing county and the 2013 National Center for Health Statistics’ Urban-Rural Classification Scheme for Coun- ties [23]. [Fig. 1] This is a county-level scheme based on 2010 cen- sus population data with six levels: four metropolitan (large central metro, large fringe metro, medium metro, and small metro) and two nonmetropolitan (micropolitan and noncore). [Table 1] The RUCC classification distinguishes metropolitan counties by the population size of their metropolitan statistical areas (MSA) and principle city (large: >=1,000,000; medium: 250,000-999,999; small: <250,000) and nonmetropolitan counties by degree of urbanization and adjacency to a metropolitan area. Large metropolitan counties are subdivided into Large Central counties (either contain the entire population of the largest principal city

Fig. 1. RUCC county classification.Rural-Urban Continuum Codes (RUCC) based on 2010 census population data and 2013 National Center for Health Statistics scheme. Counties are divided into six levels: four metropolitan (large central metro, large fringe metro, medium metro, and small metro) and two nonmetropolitan (micropolitan and noncore).

Table 1

County classification and hospital stratification.

Category name

Population definition

# of counties

# of hospitals

Metropolitan RUCC-1

Large centrala

MSAs >=1,000,000

68

773

RUCC-2

Large fringeb

MSAs >=1,000,000

368

502

RUCC-3

Medium

MSAs 250,000-999,999

373

691

RUCC-4

Small

MSAs 50,000-249,999

358

686

Non-Metropolitan

RUCC-5

Micropolitan

Urban cluster of 10,000-49,999

641

1068

RUCC-6

Noncore

Nonmetropolitan counties that did not qualify as micropolitan

1341

1078

Total

3149

4798

MSA = metropolitan statistical areas.

a Large central counties must either contain the entire population of the largest principal city of the MSA, or have their entire population contained in the largest principal city of the MSA or contain at least 250,000 inhabitants of any principal city of the MSA.

b Large fringe counties are large metropolitan counties that do not qualify as large central.

of the MSA, or have their entire population contained in the largest principal city of the MSA, or contain at least 250,000 inhabitants of any principal city of the MSA) and Large Fringe counties (large metropolitan counties that do not qualify as Large Central).

Data analysis

Variations in outcomes and quality of care were analyzed using rank-based Kruskal-Wallis H tests, to determine if there were sta- tistically significant differences between two or more RUCC groups, and Spearman’s rank-order correlations, to determine the strength and direction of associations. These nonparametric statistical tests were necessary given the RUCC’s ordinal classification. Correlations between quality of care metrics and outcomes were analyzed using univariate linear regression. Longitudinal changes were analyzed using paired-samples t-tests, when hospital-level data was avail- able for both 2012 and 2018 reporting periods. All data analysis was performed using SPSS (IBM, Armonk, NY), with p < 0.05 con- sidered statistically significant.

Results

County classification and reporting

A total of 3149 United States counties were categorized using the six-level urban-rural classification scheme of the National Cen- ter of Health Statistics data system. Within these counties, 4798 Medicare-certified hospitals were identified in the 2018 dataset. [Table 1] Only a fraction of hospitals had performance variables available from the CMS public-domain report, with significantly higher availability among more urban hospitals (p < 0.001). Vari-

ables were unavailable because the number of cases was too few to report (<=11/year), the hospital did not participate in the Inpa- tient Quality Reporting program, or results were not available for the specified reporting period.

Variations in outcomes

In the 2018 dataset, among all hospitals, mean risk adjusted 30- day mortality was 13.6% [IQR 12.8-14.3%] and readmission rate was 16.3% [IQR 15.8-16.8%]. [Table 2] Kruskal-Wallis H tests showed statistically significant differences between RUCC classifi- cations in both mortality (v2(5) = 25.75, p < 0.001) and readmis- sion rates (v2(5) = 21.44, p = 0.001). Further evaluation with Spearman’s rank-order correlation revealed a weak, but statisti- cally significant, correlation between more rural RUCC classifica- tion with increased mortality (rs = 0.095, p < 0.001).

Variations in quality of care

In the 2018 dataset, Kruskal-Wallis H tests showed statistically significant differences between RUCC classifications in median time to ECG (v2(5) = 33.50, p < 0.001), aspirin at arrival (v2(5) = 44.88, p < 0.001), statin prescribed at discharge (v2(5)

= 16.68, p = 0.005), median time to fibrinolysis (v2(5) = 12.23,

p = 0.032), and fibrinolytic therapy received within 30 min of ED arrival (v2(5) = 12.29, p = 0.031). [Table 2] Further evaluation with Spearman’s rank-order correlation revealed a weak, but statisti- cally significant, correlation between more rural RUCC classifica- tion with decreased statin prescribed at discharge (rs = –0.066, p = 0.004). Univariate linear regression of the quality of care met- rics revealed only increased aspirin prescribed at discharge

Table 2

Variations in outcomes and quality of care (2018 dataset).

# of hospitals reporting National average Rural-urban continuum code

1

2

3

4

5

6

Outcome metric

30-day mortality (%)

2363

13.6

13.4

13.4

13.6

13.6

13.6

13.8

30-day readmission rate (%)

2150

16.3

16.4

16.4

16.3

16.1

16.3

16.4

Quality metric

Median time to ECG (minutes)

2616

8

9

7

9

8

8

8

Aspirin at arrival (%)

2575

95

93

96

95

95

96

94

Aspirin prescribed at discharge (%)

1957

99

99

99

98

99

98

99

Statin prescribed at discharge (%)

1935

98

98

98

97

98

97

98

PCI w/in 90 min of arrival (%)

148

94

94

97

94

95

91

95

Fibrinolytic w/in 30 min of arrival (%)

88

66

86

84

72

61

61

78

Values are expressed as means. Bolded variables indicate significant Spearman’s rank-order correlations between more rural RUCC classification and poorer hospital performance. Significant p-values and Correlation coefficients are reported in the manuscript text (Sections 3.2 and 3.3).

Fig. 2. Longitudinal changes in hospital performance.Mean differences (with 95% confidence intervals) in hospital performance between 2012 and 2018 for (A) all hospitals, and (B) metropolitan verse nonmetropolitan hospitals. Median time to ECG could not be analyzed because it was not available in to 2012 data archive.

(R2 = 0.003, p = 0.024) and increased statin prescribed at discharge (R2 = 0.006, p = 0.001) were correlated with improved 30-day mortality.

Longitudinal changes

Among all hospitals, between 2012 and 2018, there were signif- icantly improved 30-day mortality (15.4% +- 1.5% vs 13.6% +- 1.2%,

p < 0.001) and 30-day Readmission rates (19.7% +- 1.6% vs 16.3% +- 0.8%, p < 0.001). [Fig. 2A] During the same period, there was also an increased utilization of aspirin prescribed at discharge (net change: +0.47%, p < 0.001), statin prescribed at discharge (net change: +2.83%, p < 0.001), PCI within 90 min of arrival (net change: +4.06%, p < 0.001), and fibrinolytic within 30 min of arrival (net change: +4.94%, p = 0.135). Only aspirin at arrival (net change:

–1.10%, p < 0.001) had decreased utilization. When comparing metropolitan (RUCC 1-4) verse nonmetropolitan (RUCC 5-6) hospi- tals, the nonmetropolitan hospitals had larger improvements in all Performance metrics, except for fibrinolytic within 30 min of arrival [Fig. 2B].

Discussion

This study reports that, when stratified by Rural-Urban Contin- uum Codes, Medicare-certified hospitals had only modest, but per- sistent, differences in risk-adjusted 30-day mortality following hospitalizations for acute myocardial infarction. Specifically, the most rural hospitals had a 0.4% higher risk-adjusted mortality rate compared to their urban counterparts. Although the percent differ- ence is small, its consequence is magnified when multiplied by the frequency of AMI in the United States. An average of 1.5 Americans will have an AMI every minute [3]. Admittingly, the observation

that variations exist between rural and urban hospitals is not a novel finding and a plethora of literature demonstrates higher mortality among rural AMI hospitalizations dating back to the 1990s [5,10]. These earliest studies found 2-6% differences in mor- tality between urban and rural hospitals. Although opportunities for improvement still exist, this current analysis shows that the rural/urban performance gap has almost entirely disappeared.

Additionally, this study reports that since 2012 Medicare- certified hospitals have had a 1.8% reduction in mortality and 3.4% reduction in readmissions. These gains were driven primarily by enhanced care within rural hospitals. The most rural hospitals (RUCC 6 classification) now achieve >94% scores for aspirin at arri- val, aspirin prescribed at discharge, statin prescribed at discharge, and PCI within 90 min of arrival. Furthermore, the only two vari- ables correlated with improved 30-day mortality (aspirin and sta- tin prescribed at discharge) score > 98%. This contrasts with earlier studies showing slow implementation of American Heart Associa- tion and American College of Cardiology guidelines within commu- nity hospitals [24].

With hospitals now achieving near perfect scores on many existing quality of care metrics, performance has likely plateaued given that their use is not indicated in 100% of patients. Generally, the metrics used by national registries are most relevant to patients with type 1 (atherothrombotic and thrombosis) myocar- dial infarctions [21,25]. For example, aspirin is indicated in type 1, but may be contraindicated in a subset of type 2 (oxygen Supply and demand imbalance) myocardial infarctions and may result in critical Bleeding complications. Increased understanding of the pathological differences of AMI may explain the observed 1.1% declined in aspirin given at arrival between 2012 and 2018. Mov- ing forward, strategies to improve care should focus on new and underutilized metrics such as fibrinolytics, immediate angiogra- phies, noninvasive Stress testing before discharge, early cardiac troponin measurements, and hospital participation in national reg- istries [18,19]. These metrics represent where hospitals still have the greatest opportunities for improvement and are of growing importance to healthcare payers developing incentive-based qual- ity improvement programs.

Despite the limitations of administrative data, we chose to use the CMS Hospital Inpatient Quality Reporting Program database because the Medicare population has been the focus of many prior studies, therefore facilitating longitudinal comparisons. The Medi- care population is also particularly pertinent given that the age of an average American’s first AMI is 65.6 years for males and

72.0 years for females [3]. Coincidentally, our study period over- laps with the implementation of the Hospital Readmissions Reduc- tion Program (HRRP), launched under the Patient Protection and Affordable Care Act of 2010. Beginning in October 2012, financial penalties were applied to Medicare-certified hospitals with higher-than-expectED readmission rates for patients with acute myocardial infarctions and other common medical conditions. Readmission rates are an especially important outcome measure- ment because patients who suffer an initial AMI and survive have substantially higher risk for future recurrent AMI, heart failure, and stroke. These HRRP financial incentives, estimated at between $227 million and $524 million in reduced payments by Medicare per year, certainly contribute to the 3.4% reduction in hospital read- missions observed between 2012 and 2018. The simultaneous 1.8% reduction in mortality also disproves previous concerns that the HRRP would inadvertently increase AMI mortality by disincen- tivizing hospitals from performing indicated readmissions, as has been reporting among Medicare beneficiaries hospitalized for heart failure or pneumonia [26].

Another key feature of our study is the use of the National Cen- ter for Health Statistics’ Urban-Rural Classification Scheme [23]. This six-level classification is uniquely suitable for analyzing

demographic variations in healthcare because it differentiates large metropolitan counties into ”central” (analogous to inner cities) and ”fringe” (analogous to suburbs) based on the size of each county’s principal city. It also includes medium, small, micropolitan, and noncore counties. Unlike dichotomous classifica- tions, this scheme allows precise characterization of the entire rural-urban spectrum.

This study has several limitations. Foremost, the analysis focused on only 6 performance variables captured by the CMS database. Undoubtedly, there are numerous socioeconomic, infras- tructure, and organizational influences on mortality and readmis- sion outcomes that are unaccounted in our current study. For example, Rural patients travel further to reach their nearest hospi- tal, causing critical delays between onset of symptoms and first medical contact. Delayed access to medical care, particularly reper- fusion therapy, increases myocardial damage and mortality [27]. Once admitted, rural patients are more often treated by generalist physicians, as opposed to cardiologists, which correlate with increased mortality [28]. Rural admitting hospitals also have less percutaneous coronary intervention volumes and experience, which correlates with increased mortality [29,30]. Because the cur- rent CMS risk-adjustments incorporate only age, past medical his- tory, and comorbidities, accounting for unmeasured confounding variables would produce a more suitable indication of hospital quality [31].

The second main limitation of this study is the low percentage of hospitals with performance variables available from the CMS public-domain report. Mortality and readmission rates were only available for 49% and 45% of hospitals, respectively. Quality of care metrics were only available for between 2% and 55% of hospitals. Moreover, rural hospitals were significantly more likely to have unavailable or incomplete reporting, presenting significant non- response bias. Although sensitivity analysis for data not missing at random was not performed, we do report that variables were most often unavailable because the number of cases was too few to report (<=11/year), and these low-volume hospitals likely have little impact on national AMI outcomes. Nonreporting hospitals account for <1.5% of hospital admissions nationally [32].

In conclusion, increased hospital rurality is now associated with only modest increases in risk-adjusted mortality rates following AMI. As contemporary treatment guidelines are being uniformly implemented in rural hospitals, disparities in AMI care have nar- rowed over time. Efforts to alleviate residual cardiovascular health- care disparities in rural communities must rely on both new strategies to improve care and new metrics to track outcomes, complications, and performance.

Funding information

None.

Disclosures

The authors declare no conflicts of interest.

References

  1. Singh GK, Siahpush M. Widening rural-urban disparities in life expectancy, US, 1969-2009. Am J Prev Med 2014;46(2):e19-29.
  2. Kulshreshtha A, Goyal A, Dabhadkar K, Veledar E, Vaccarino V. Urban-rural differences in coronary heart disease mortality in the United States: 1999- 2009. Public Health Rep 2014;129(1):19-29.
  3. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart disease and stroke statistics–2018 update: a report from the American Heart Association. Circulation 2018;137(12):e67-e492.
  4. Torio CM, Moore BJ. National inpatient hospital costs: The Most Expensive Conditions by Payer, 2013: Statistical Brief 204. Rockville, MD: Healthcare Cost

    and Utilization Project (HCUP) Statistical Briefs, Agency for Healthcare Research and Quality; 2016.

    Baldwin LM, MacLehose RF, Hart LG, Beaver SK, Every N, Chan L. Quality of care for acute myocardial infarction in rural and urban US hospitals. J Rural Health 2004;20(2):99-108.

  5. Baldwin LM, Chan L, Andrilla CHA, Huff ED, Hart LG. Quality of care for myocardial infarction in rural and urban hospitals. J Rural Health 2010;26 (1):51-7.
  6. Abrams TE, Vaughan-Sarrazin M, Kaboli PJ. Mortality and revascularization following admission for acute myocardial infarction: implication for rural veterans. J Rural Health 2010;26(4):310-7.
  7. Bhuyan SS, Wang Y, Opoku S, Lin G. Rural-urban differences in acute myocardial infarction mortality: evidence from Nebraska. Journal of cardiovascular disease research 2013;4(4):209-13.
  8. Cai M, Liu E, Li W. Rural versus urban patients: benchmarking the outcomes of patients with acute myocardial infarction in Shanxi, China from 2013 to 2017. Int J Environ Res Public Health 2018;15(9):1930.
  9. Sheikh K, Bullock C. Urban-rural differences in the quality of care for Medicare patients with acute myocardial infarction. Arch Intern Med 2001;161 (5):737-43.
  10. Svensson L, Karlsson T, Nordlander R, Wahlin M, Zedigh C, Herlitz J. Safety and delay time in prehospital thrombolysis of acute myocardial infarction in urban and rural areas in Sweden. Am J Emerg Med 2003;21(4):263-70.
  11. Keil JE, Saunders Jr DE, Lackland DT, Weinrich MC, Hudson MB, Gastright JA, et al. Acute myocardial infarction: period prevalence, case fatality, and comparison of black and white cases in urban and rural areas of South Carolina. Am Heart J 1985;109(4):776-84.
  12. Yeh RW, Normand S-LT, Wang Y, Barr CD, Dominici F. Geographic disparities in the incidence and outcomes of hospitalized myocardial infarction: does a rising tide lift all boats? Circ Cardiovasc Qual Outcomes 2012;5(2):197-204 [CIRCOUTCOMES. 111.962456].
  13. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. New England Journal of Medicine 2010;362(23):2155-65.
  14. Burwen DR, Galusha DH, Lewis JM, Bedinger MR, Radford MJ, Krumholz HM, et al. National and state trends in quality of care for acute myocardial infarction between 1994-1995 and 1998-1999: the Medicare health care quality improvement program. Arch Intern Med 2003;163(12):1430-9.
  15. Roger VL, Weston SA, Gerber Y, Killian JM, Dunlay SM, Jaffe AS, et al. Trends in incidence, severity, and outcome of hospitalized myocardial infarction. Circulation 2010;121(7):863-9.
  16. Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B, et al. 2015 ACC/AHA/SCAI focused update on primary percutaneous coronary intervention for patients with ST-elevation myocardial infarction: an update of the 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention and the 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction. J Am Coll Cardiol 2016;67(10):1235-50.
  17. Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, et al. 2014 AHA/ACC guideline for the management of patients with non-ST- elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;64(24):e139-228.
  18. O’gara PT, Kushner FG, Ascheim DD, Casey DE, Chung MK, De Lemos JA, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;61(4):485-510.
  19. Bechtold D, Salvatierra G, Bulley E, Cypro A, Daratha KB. Geographic variation in treatment and outcomes among patients with AMI: investigating urban- rural differences among hospitalized patients. J Rural Health 2017;33 (2):158-66.
  20. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth Universal Definition of Myocardial Infarction (2018). J Am Coll Cardiol 2018;72 (18):2231-64.
  21. Centers for Medicare & Medicaid Services. Hospital Compare, https://www. medicare.gov/hospitalcompare/search.html.
  22. Ingram DD, Franco SJ. NCHS urban-rural classification scheme for counties. Vital and health statistics Series 2, Data evaluation and methods research 2012;154:1-65.
  23. Larson DM, Sharkey SW, Unger BT, Henry TD. Implementation of acute myocardial infarction guidelines in community hospitals. Acad Emerg Med 2005;12(6):522-7.
  24. Jneid H, Addison D, Bhatt DL, Fonarow GC, Gokak S, Grady KL, et al. 2017 AHA/ ACC Clinical performance and quality measures for adults with ST-elevation and non-ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures. J Am Coll Cardiol 2017;70(16):2048-90.
  25. Wadhera RK, Maddox KEJ, Wasfy JH, Haneuse S, Shen C, Yeh RW. Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. Jama 2018;320(24):2542-52.
  26. Luepker RV, Raczynski JM, Osganian S, Goldberg RJ, Finnegan Jr JR, Hedges JR, et al. Effect of a community intervention on patient delay and emergency medical service use in acute coronary heart disease: the Rapid Early Action for Coronary Treatment (REACT) Trial. Jama 2000;284(1):60-7.
  27. Jollis JG, DeLong ER, Peterson ED, Muhlbaier LH, Fortin DF, Califf RM, et al. Outcome of acute myocardial infarction according to the specialty of the admitting physician. New England Journal of Medicine 1996;335(25):1880-7.
  28. McGrath PD, Wennberg DE, Dickens Jr JD, Siewers AE, Lucas FL, Malenka DJ, et al. Relation between operator and hospital volume and outcomes following Percutaneous coronary interventions in the era of the coronary stent. Jama 2000;284(24):3139-44.
  29. Magid DJ, Calonge BN, Rumsfeld JS, Canto JG, Frederick PD, Every NR, et al. Relation between hospital primary angioplasty volume and mortality for

    patients with acute MI treated with primary angioplasty vs thrombolytic therapy. Jama 2000;284(24):3131-8.

    James PA, Li P, Ward MM. Myocardial infarction mortality in rural and urban hospitals: rethinking measures of quality of care. The Annals of Family Medicine 2007;5(2):105-11.

  30. Landon BE, Normand S-LT, Lessler A, O’Malley AJ, Schmaltz S, Loeb JM, et al. Quality of care for the treatment of acute medical conditions in US hospitals. Arch Intern Med 2006;166(22):2511-7.

Leave a Reply

Your email address will not be published. Required fields are marked *