Current Research in Clinical Diabetes and Obesity

Article / research article

"Consideration of Peripheral Artery Disease as an Ambulatory Care Sensitive Condition: Examining Incidence in U.S. Emergency Departments"

Omar B. Saeed1, Kristin A. Schuller2*, Shannon E. Nicks2

1Heritage College of Osteopathic Medicine, Ohio University, USA

2Department of Social & Public Health, Ohio University, USA

*Corresponding author: Kristin A. Schuller, Department of Social & Public Health, Ohio University, Grover Center W357, Athens, OH 45701, USA

Received Date: 31 March, 2021; Accepted Date: 07 April, 2021; Published Date: 12 April, 2021,

Abstract

Background: Peripheral artery disease (PAD) affects approximately 10% of the U.S. adult population. Those with lower income and education are more likely to develop PAD. The purpose of this study was to analyze the patient characteristics associated with PAD-related emergency department (ED) utilization.

Methods: The 2015 Nationwide Emergency Department Sample (NEDS) was used to model the incidence of PAD in U.S. emergency departments based on various patient demographic and socioeconomic data and the presence of diabetes.

Results: Results of multivariate logistic regression indicate that patients with PAD were significantly more likely to be older, male, a Medicare beneficiary, and have a lower household income. Patients with PAD were also significantly more likely to be hospitalized for longer lengths of time and have diabetes.

Conclusions: This study provides evidence of the disparities surrounding PAD management, access to care, and general population health. PAD management should be focused on targeted preventative interventions.

Keywords

Peripheral artery disease; Diabetes; Atherosclerosis; Health insurance; Rural; Access to care; Emergency department, Disparities; Survey data; Primary care; Low income

Introduction

Ambulatory care-sensitive conditions (ACSCs) are those in which hospital admission could be prevented by primary care interventions [1]. Acute and preventative management of ACSCs act as a buffer to reduce hospital emergency department (ED) utilization rates for these conditions. In the United States, ACSCs can represent a measure of health care system performance by assessing access to primary care resources. These conditions typically respond well to primary care level interventions and management to prevent hospital ED admission.

Despite the potential preventative management from primary care providers, an increasing number of ACSCs are being treated in ED settings [2]. The disproportionate use of the ED for ACSCs are typically highest for underserved groups, indicating differential primary care access or quality care for these populations [3]. Previous studies have identified discrepancies in the nonemergent use of EDs among black, Hispanic, uninsured and low-income patients [4,5]. Johnson et al. (2012) specifically found disparities in ED visits for ACSCs among black, Hispanic, Medicaid insured, those older than 50 and low socioeconomic status [3].

Peripheral artery disease (PAD) is a condition often caused by atherosclerosis and leads to many symptoms and complications including claudication, ischemic rest pain, ischemic ulcerations and limb loss [6]. The diagnosis of PAD is critical as potential complications include myocardial infarction, stroke, cardiovascular disease and all-cause mortality are elevated among those with PAD [7,8]. PAD has a strong relationship with cardiovascular disease as well and shares key risk factors [8]. Diabetes is a major risk factor for vascular disease as well and the PAD course can even be accelerated in those with diabetes. The prevalence of PAD in those with diabetes has been estimated to be as high as 30% [9]. PAD effects approximately 10% of the U.S. adult population [10]. Socioeconomic status, an important contributor to overall health, has been demonstrated to have a strong relationship with PAD [11]. Specifically, those with lower income and education are more likely to develop PAD [12]. Low socioeconomic status is also associated with increased risk of hospitalization from PAD [12].

The purpose of this study was to analyze the patient characteristics associated with PAD-related ED utilization. This study fills a gap in the current knowledge since limited research has been conducted on the patient demographic and socioeconomic characteristics and comorbidities associated with PAD. To our knowledge there is limited information on the association between low income patients with PAD and diabetes.

Methods

The 2015 Nationwide Emergency Department Sample (NEDS) was used to model the incidence of PAD in U.S. EDs based on various patient demographic and socioeconomic data and the presence of diabetes. The NEDS is one database in a collection offered through the Healthcare Cost and Utilization Project (HCUP) sponsored by the Agency for Healthcare Research and Quality. The NEDS contains a stratified sample of all-payer, patient-level ED utilization in the U.S. Weighted results can be used to estimate national trends in U.S. ED visits. ED visits were based on patient demographics (age, gender, location), socioeconomic indicators (primary payer, household income), and health status (discharge status, length of stay, and presence of diabetes). For location, urban was classified as center or fringe metropolitan area, suburban is classified as having a population between 50,000-999,999, and rural has a population less than 50,000. PAD was analyzed using ICD-9 and ICD-10 codes since the transition from the ninth to tenth edition occurred during the third quarter of 2015. The ICD-9 and ICD-10 codes used for PAD and diabetes are as follows: PAD 443.9 and I739, Diabetes 250.00 and E119, respectively. Data was analyzed using bivariate frequencies to determine incidence of PAD. Multivariate logistic regression was used to analyze a binary outcome variable for PAD to calculate odds ratios of the likelihood of occurrence based on patient demographics, socioeconomic status, and health status.

Results

Of the 95,524,715 weighted ED visits in the 2015 NEDS, there were 972,117 (1.02%) incidences of PAD. Results of bivariate analysis (Table 1) indicate that patients who present at the ED for PAD tend to be 65 or older (70.56%), male (54.50%), Medicare beneficiaries (77.33%), have a lower median household income, and reside in a large metropolitan area. Sixty-six percent of patients in the ED for PAD were admitted to the hospital as an inpatient and 26.23% experienced a routine discharge from the ED. Forty-one percent of patients had a length of hospitalization less than two days. Approximately 26% of those with PAD also had diabetes.

Results of multivariate logistic regression (Table 2) indicate that patients with PAD were significantly more likely to be 65 and older, male, a Medicare beneficiary, and have a lower household income. Patients living in urban and suburban locations were significantly more likely to experience PAD compared to rural patients. Patients with PAD were significantly more likely to be admitted as an inpatient (OR: 4.946; CI: 4.607, 5.309), discharged to home health care (OR: 4.406; CI: 4.016, 4.834), die in the ED (OR: 1.471; CI: 1.296, 1.668), or transferred to another acute care hospital or other short-term hospital (OR: 1.745; CI: 1.644, 1.852) compared to a routine discharge. Patients with PAD were significantly more likely to be hospitalized for longer lengths of time and were more likely to have diabetes.

When stratified by diabetes, results of multivariate logistic regression (Table 3) indicate that the demographic characteristics of patients with both PAD and diabetes are comparable to those of patients with just PAD. However, there was no significant difference between suburban and rural patients. Furthermore, the only significant difference in discharge status was that patients with PAD and diabetes were significantly less likely to be admitted as an inpatient compared to having a routine discharge (OR: 0.902; CI: 0.844, 0.963). Finally, patients with PAD and diabetes experienced significantly shorter lengths of hospitalization.

Discussion

In the present study, we found a strong positive association between PAD and lower household income. There was also a strong positive association between those with PAD and diabetes and lower household income. Several potential mechanisms behind PAD and lower household income are plausible. Primarily, cardiovascular disease, which shares key risk factors with PAD, is highly prevalent in low socioeconomic groups [13]. Additionally, access to healthcare is a potential barrier for routine care. Individuals with low income are more likely to lack adequate health insurance and thus refrain from routine medical care [12]. Other plausible factors linking income to PAD includes chronic psychological stress and limited health literacy. Chronic psychological stress is often higher in low socioeconomic and is shown to be associated with atherosclerosis, the key pathology in PAD [14]. Limited health literacy in the low-income groups might also influence health‐seeking behaviors like when and where to seek care and adherence to medication. This could also explain an increased risk for PAD among those with low income.

Our study shows the presence of diabetes impacts utilization of health care services for PAD. Patients with PAD and diabetes were more likely to experience a routine discharge. This is likely because hospital discharge instructions for patients with PAD include lifestyle modifications, risk factor reduction therapies and medications, weight control, knowledge of symptoms of disease and maintaining good blood sugar control to reduce diabetes complications [15]. Once the patient’s beliefs and knowledge about the disease are better acquainted, effective discharge education can be accomplished with individualized health care interventions. Compliance with lifestyle modifications can then be maintained throughout the treatment process, including follow-up visits with general practitioners, laboratory tests like fasting glucose levels and hemoglobin A1c and continued patient-caregiver education in the primary care setting.

Findings indicate that 17% of patients with PAD live in rural communities. This can be due to a lack of physical resources in rural areas leading to reduced management and exacerbation of complications. One study found a negative association between primary care physicians per capita and the likelihood of hospital admissions for ambulatory conditions [16]. This implies that when fewer primary care physicians are available to the community, a greater number of patients must seek treatment as an inpatient. These unnecessary admissions are avoidable if appropriate medical services are available in the community. Because PAD affects a larger number of people, performing health screenings in rural health clinics may help prevent morbidity and mortality through early diagnosis and management. One study in a rural context observed how specialized training of primary care providers increased their skill and competence in PAD screening [17].

The current study has several limitations. The cross-sectional design precludes the temporal nature of the association between peripheral artery disease and the respective covariates. The study also utilized secondary data which limited the number of variables, preventing further data manipulation. Furthermore, during the 3rd quarter of 2015 the U.S. healthcare industry transitioned from using ICD-9 codes to ICD-10 codes. This transition could have led to coding errors in data analysis and may explain the variation in results over time. However, the current study has several strengths including a large and diverse national sample and availability of several potential confounders for adjustment in multivariable models. These strengths allow for generalizability of the results.

Conclusions

Our findings raise important questions about the preventative care of PAD among vulnerable demographics. The lack of access to quality health care resources may place those with low income and those living in rural areas at a significant disadvantage regarding disease prevention. Evidence for community-based health care interventions targeting specific ACSCs has showed to result in reduced hospital admissions for these conditions [18]. This suggests that similar interventions should be targeted towards patients with PAD to reduce preventable complications.

Our findings point to areas in which PAD management is susceptible to the influence of other health conditions and insurance status. Our study adds to the literature by identifying vulnerable populations within patients who have PAD. To address the quality of care gap, we need to start by providing underserved communities with appropriate education, awareness and quality improvement programs.


Patient Demographics

n

%

Age

 

 

65+

685,910

70.56%

45-64

263,587

27.11%

27-44

22,621

2.33%

Gender

 

 

Male

539,766

54.50%

Female

442,244

45.50%

Payer

 

 

Medicare

751,274

77.33%

Medicaid

79,756

8.21%

Private

104,610

10.77%

Uninsured

18,066

1.86%

Other government

17,771

1.83%

Median Household Income

 

 

0-25%

334,175

34.92%

26-50%

230,831

24.12%

51-75%

223,177

23.32%

76-100%

168,756

17.64%

Patient Location

 

 

Urban

488,541

50.40%

Suburban

315,242

32.52%

Rural

165,512

17.08%

Discharge Status

 

 

Routine

255,020

26.23%

Transferred to another facility

31,635

3.25%

Home health care

7,599

0.78%

Against medical advice

4,007

0.41%

Admitted as inpatient

642,134

66.06%

Died in ED

1,851

0.19%

Unknown

29,869

3.07%

Length of stay

 

 

0-1 day

404,680

41.63%

2-3 days

222,659

22.90%

4-6 days

200,508

20.63%

7+ days

144,269

14.84%

Diabetes

 

 

No

712,443

73.29%

Yes

259,673

26.71%


Table 1: Bivariate analysis of the patient demographics associated with PAD.

Effect

Point Estimate

95% Confidence Limits

p-values

Age

 

 

 

 

65+

11.697

10.876

12.578

<.0001

45-64

7.616

7.187

8.071

<.0001

Gender

 

 

 

 

Male

1.507

1.485

1.528

<.0001

Payer

 

 

 

 

Other

0.505

0.458

0.558

<.0001

Uninsured

0.317

0.285

0.353

<.0001

Private

0.477

0.452

0.503

<.0001

Medicaid

0.576

0.543

0.611

<.0001

Median Household Income

 

 

 

 

0-25%

1.312

1.229

1.401

<.0001

26-50%

1.183

1.114

1.257

<.0001

51-75%

1.130

1.073

1.189

<.0001

Patient Location

 

 

 

 

Urban

1.095

1.009

1.188

0.0303

Suburban

1.091

1.008

1.180

0.0311

Discharge Status

 

 

 

 

Unknown

1.262

0.985

1.617

0.0662

Died in ED

1.471

1.296

1.668

<.0001

Admitted as inpatient

4.946

4.607

5.309

<.0001

Against medical advice

0.912

0.826

1.007

0.0686

Home health care

4.406

4.016

4.834

<.0001

Transfer to another facility

1.745

1.644

1.852

<.0001

Length of stay

 

 

 

 

7+ days

1.076

1.037

1.117

0.0001

4-6 days

1.165

1.133

1.199

<.0001

2-3 days

1.087

1.062

1.114

<.0001

Diabetes

 

 

 

 

No

0.671

0.657

0.687

<.0001


Table 2: Logistic Regression of Patients with PAD.

Effect

Point Estimate

95% Confidence Limits

p-value

Age

 

 

 

 

65+

1.524

1.367

1.699

<.0001

45-64

1.564

1.405

1.742

<.0001

Gender

 

 

 

 

Male

1.049

1.024

1.075

0.0001

Payer

 

 

 

 

Other

0.969

0.889

1.057

0.4803

Uninsured

0.740

0.627

0.873

0.0004

Private

0.893

0.855

0.934

<.0001

Medicaid

0.921

0.866

0.979

0.0077

Median Household Income

 

 

 

 

0-25%

1.365

1.266

1.471

<.0001

26-50%

1.210

1.123

1.304

<.0001

51-75%

1.134

1.061

1.213

0.0002

Patient Location

 

 

 

 

Urban

1.071

1.005

1.140

0.0340

Suburban

1.012

0.951

1.078

0.7011

Discharge Status

 

 

 

 

Unknown

1.222

1.026

1.456

0.246

Died in ED

1.203

0.947

1.528

0.1294

Admitted as inpatient

0.902

0.844

0.963

0.0021

Against medical advice

0.907

0.767

1.072

0.2507

Home health care

0.864

0.741

1.007

0.0613

Transfer to another facility

1.067

0.999

1.140

0.0527

Length of stay

 

 

 

 

7+ days

0.690

0.655

0.728

<.0001

4-6 days

0.830

0.793

0.869

<.0001

2-3 days

0.932

0.890

0.976

0.0030


Table 3: Logistic Regression of Patients with PAD and Diabetes.

References

  1. Purdy S, Griffin T, Salisbury C, & Sharp D. (2009). Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health.123: 169-173.
  2. Kellermann A. (2006). The Future of Emergency Care in the United States. MedGenMed. 48: 115-120.
  3. Johnson PJ, Ghildayal N, Ward AC, Westgard BC, Boland LL, et al., (2012). Disparities in potentially avoidable emergency department (ED) care: ED visits for ambulatory care sensitive conditions. Med Care. 50: 1020-1028.
  4. Hong R, Baumann BM, Boudreaux ED. (2007). The emergency department for routine healthcare: race/ethnicity, socioeconomic status, and perceptual factors. J Emerg Med. 32: 149-158.
  5. Oster A, Bindman, AB. (2003) Emergency department visits for ambulatory care sensitive conditions: insights into preventable hospitalizations. Med Care. 41: 198-207.
  6. Olin JW, Sealove BA. (2010). Peripheral Artery Disease: Current Insight Into the Disease and Its Diagnosis and Management. Mayo Clinic Proceedings. 85: 678-692.
  7. Criqui MH, Langer RD, Fronek A, Feigelson HS, Klauber MR et al. (1992). Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med. 326: 381-386.
  8. Fowkes FGR, Rudan D, Rudan I, Aboyans V, Denenberg JO et al. (2013). Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet. 382: 1329-1340.
  9. Thiruvoipati T, Kielhorn CE, Armstrong, EJ. (2015). Peripheral artery disease in patients with diabetes: Epidemiology, mechanisms, and outcomes. World J Diabetes. 6: 961-969.
  10. Dhaliwal G, Mukherjee, D. (2007). Peripheral arterial disease: Epidemiology, natural history, diagnosis and treatment. Int J Angiol. 16: 36-44.
  11. Pande RL, Creager MA. (2014) Socioeconomic Inequality and Peripheral Artery Disease Prevalence in US Adults. Circ Cardiovasc Qual Outcomes. 7: 532-539.
  12. Vart P, Coresh J, Kwak L, Ballew SH, Heiss G, et al., (2017). Socioeconomic Status and Incidence of Hospitalization With Lower‐Extremity Peripheral Artery Disease: Atherosclerosis Risk in Communities Study. J Am Heart Assoc. 6
  13. Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelsonet DE et al., (2006) Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971-2002. Arch Intern Med. 166: 2348-2355.
  14. Bostock S, Steptoe A. (2012). Association between low functional health literacy and mortality in older adults: longitudinal cohort study. The British Medical Journal. 344: e1602.
  15. Wiltz-James LM, Foley J. (2019). Hospital Discharge Teaching for Patients with Peripheral Vascular Disease. Crit Care Nurs Clin North Am. 31: 91-95.
  16. Basu J, Friedman, B, Burstin H. (2002). Primary care, HMO enrollment, and hospitalization for ambulatory care sensitive conditions: a new approach. Medical Care. 40: 1260-1269.
  17. Coe ER. (2014). Screening for peripheral arterial disease in a rural community health setting. J Vasc Nurs. 32: 137-138.
  18. Bird S, Noronha M, Sinnott H. (2010). An integrated care facilitation model improves quality of life and reduces use of hospital resources by patients with chronic obstructive pulmonary disease and chronic heart failure. Aust J Prim Health. 16: 326-333.

Citation: Saeed OB, Schuller KA, Nicks SE. (2021) Consideration of Peripheral Artery Disease as an Ambulatory Care Sensitive Condition: Examining Incidence in U.S. Emergency Departments. Curr Res Clin Diab Obes 2: 104. DOI: 10.29011/CRCDO-104.100004

free instagram followers instagram takipçi hilesi