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Predictors of intent to utilize the emergency department among a free clinic’s patients

a b s t r a c t

Objective: Primary care use helps reduce utilization of more expensive modes of care, such as the emergency de- partment (ED). Although most studies have investigated this association among patients with insurance, few have done so for patients without insurance. We used data from a free clinic network to assess the association between free clinic use and intent to use the ED. Methods: Data were collected from a free clinic network’s electronic health records on adult patients from January 2015 to February 2020. Our outcome was whether patients reported themselves as ‘very likely’ to visit the ED if the free clinics were unavailable. The independent variable was frequency of free clinic use. Using a multivariable logistic regression model, we controlled for other factors, such as patient demographic factors, social determi- nants of health, health status, and year effect.

Results: Our sample included 5008 visits. When controlling for other factors, higher odds of expressing ED interest were observed for patients who are non-Hispanic Black, older, not married, lived with others, had lower educa- tion, were homeless, had personal transportation, lived in rural areas, and had a higher Comorbidity burden. In sensitivity analyses, higher odds were observed for dental, gastrointestinal, genitourinary, musculoskeletal, or re- spiratory conditions.

Conclusions: In the free clinic space, several patient demographic, Social determinants of health and medical conditions were independently associated with greater odds of reporting intent on visiting the ED. Additional in- terventions that improve access and use of free clinics (e.g., dental) may keep patients without insurance from the ED.

(C) 2023

  1. Introduction

Primary care access and use offer several benefits to patients and health systems, such as lower mortality [1-4], lower hospitalization rates [5-8], and more timely preventive screenings [9-12]. However, the lack of health insurance is one of several barriers to primary care ac- cess and receiving its benefits [13-15]. After more than a decade since the signing of the Affordable Care Act, an estimated 27.5 million

Abbreviations: ED, emergency department.

* Corresponding author at: 12902 Magnolia Dr, Tampa, FL 32612-9416, USA.

E-mail address: [email protected] (O.T. Nguyen).

nonelderly adults (10.2%) continue to have no health insurance [16]. To help bridge this access gap, safety-net initiatives (e.g., student-run free clinics) have been formed to provide the benefits of primary care to these underserved populations.

Many academic medical centers staff student-run free clinics with volunteers from their clinical faculty and health profession student base [17,18]. These clinics offer multiple benefits to the community, including the provision of affordable and accessible health care for patients and medical education opportunities for students [17,19,20]. Researchers have also increasingly documented potential cost savings to health care organizations due to the free clinics addressing unmet medical needs that, in turn, reduce the number of uncompensated

https://doi.org/10.1016/j.ajem.2023.06.003

0735-6757/(C) 2023

emergency department (ED) visits and hospitalizations [21-25]. By inference, offering health care through student-run free clinics may reduce patients’ intent to utilize the ED for care. However, it is unclear which types of patients are less likely to express interest in visiting the ED due to free clinic availability. Consequently, additional study is needed to identify factors that are associated with decision-making to use the ED among patients without insurance.

To address this research gap, we used data from a free clinic network to assess factors associated with reporting intent on using the ED if the free clinics were unavailable when controlling for other factors. Free clinic leaders searching for solutions on optimizing primary care opera- tions may benefit from our findings.

  1. Methods

We based our reporting on this pooled, cross-sectional observational study using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement [26]. The University of Florida insti- tutional review board reviewed and approved the protocol.

    1. Setting and sample

This study occurred at the primary care arm of a student-run free clinic network that is affiliated with an academic medical center and

located in North Central Florida. The clinic network regularly provides preventative screening, chronic care management, and treatment for Acute conditions to local patients without insurance. Similar to other student-run free clinics in the United States [17], the clinic network operates on a limited schedule and serves patients for four weeknights each week. At all visits, patients complete an intake questionnaire during the check-in process, and volunteers enter those answers into the clinic network’s electronic health records (EHR) system. All study data were de- rived from this data source. We examined all visits dated from January 2015 to February 2020 inclusive because variables of interest were avail- able during this time period. Notably, all visits studied were in-person.

    1. Measures
      1. Outcome variable

Our outcome variable was whether patients self-reported that they would visit the ED if free clinic services were unavailable. Hereafter, we refer to our outcome as patients’ intent on using the ED. This ques- tion is originally posed to the patient as a five-option Likert-type item, and was asked of all patients as part of reporting requirements for con- tinued funding for the free clinics. We used the top-box proportion method and treated answers of ‘very likely’ as the only affirmative op- tion. This method has been used for analyzing patient-reported Likert- type data, including in health services research involving the ED. [27-29]

Table 1

Sample characteristics.

Characteristic, row (%)

Total Sample Size

No Reported Interest in Using ED Services

Reported Interest in Using ED Services

p

(n = 5008)

(n = 3484)

(n = 1524)

Demographic Factors

Race/Ethnicity

<0.001

Non-Hispanic White

1906 (38.1%)

1395 (73.2%)

511 (26.8%)

Non-Hispanic Black

1314 (26.2%)

768 (58.5%)

546 (41.6%)

Hispanic

1276 (25.5%)

922 (72.3%)

354 (27.7%)

Non-Hispanic Other b

512 (10.2%)

399 (77.9%)

113 (22.1%)

Age (in years), mean (SD)

44.2 (15.5)

43.5 (16.0)

45.6 (14.1)

<0.001

Sex

0.829

Female

2979 (59.5%)

2069 (69.5%)

910 (30.6%)

Male

2029 (40.5%)

1415 (69.7%)

614 (30.3%)

marital status

0.003

Not married

3539 (70.7%)

2418 (68.3%)

1121 (31.7%)

Married

1469 (29.3%)

1066 (72.6%)

403 (27.4%)

Number in Household

0.564

Lives with others

3951 (78.9%)

2741 (69.4%)

1210 (30.6%)

Lives alone

1057 (21.1%)

743 (70.3%)

314 (29.7%)

Socioeconomic Status

Educational Attainment

<0.001

Post-secondary education or higher

2689 (53.7%)

1994 (74.2%)

695 (25.9%)

High school/GED/Vocational school

1642 (32.8%)

1074 (65.4%)

568 (34.6%)

Less than high school

677 (13.5%)

416 (61.5%)

261 (38.6%)

Housing Status

0.001

Not homeless

4929 (98.4%)

3443 (69.9%)

1486 (30.2%)

Homeless

79 (1.6%)

41 (51.9%)

38 (48.1%)

Has Personal Car

0.001

Yes

2133 (42.6%)

1428 (67.0%)

705 (33.1%)

No

2875 (57.4%)

2056 (71.5%)

819 (28.5%)

Urbanicity

0.009

Urban

4952 (98.9%)

3454 (69.8%)

1498 (30.3%)

Rural

56 (1.1%)

30 (53.6%)

26 (46.4%)

Health Status

van Walraven Comorbidity Score, mean (SD)

1.1 (2.8)

1.0 (2.7)

1.3 (2.9)

0.007

Time

Year

0.028

2015

432 (8.6%)

281 (65.1%)

151 (35.0%)

2016

878 (17.5%)

600 (68.3%)

278 (31.7%)

2017

1040 (20.8%)

708 (68.1%)

332 (31.9%)

2018

1042 (20.8%)

723 (69.4%)

319 (30.6%)

2019

1367 (27.3%)

991 (72.5%)

376 (27.5%)

2020

249 (5.0%)

181 (72.7%)

68 (27.3%)

  1. Abbreviations used include emergency department (ED), standard deviation (SD), and General Educational Development (GED).
  2. “Non-Hispanic Other” includes all patients reporting their race as Native American, Asian, Pacific Islander, or other.
  3. Percentages may not add to 100% due to rounding.
      1. Covariates

We used Andersen’s Behavioral Model to guide covariate selection for the analysis. This model suggests that patients’ health care utilization behaviors are guided by three groups of factors: (1) Predisposing factors represent factors that affect the decision to use health care services;

(2) enabling factors represent factors that facilitate the use of health care services; and (3) need factors represent factors that represent the need for health care services [30,31]. We controlled for predisposing factors through race/ethnicity, age, sex, marital status, and whether patients lived alone. We also controlled for enabling factors through educational attainment, housing status, having personal transportation, and urbanicity. To determine urbanicity, we mapped postal zip codes to the 2010 rural-urban commuting area (RUCA) codes [32]. We also controlled for need factors through comorbidity burden as measured by the weighted van Walraven comorbidity scoring system. Using diag- nosis codes, we determined whether a patient had any of the disease concepts covered by this system. Further details on Diagnosis codes, disease concepts, and design are described elsewhere [33].

    1. Analytic approach

We reported the sample characteristics and the results of the stu- dent t-tests and Pearson chi-square tests that were conducted to com- pare our studied characteristics between observations that reported intent on using the ED versus those that did not report intent on visiting

the ED. We then conducted a visit-level multivariable logistic regression model to examine for factors associated with our outcome variable when controlling for other factors. We report the adjusted odds ratio (OR), 95% confidence interval, (CI) and p-value for all variables in our final model. To study how different covariates influenced our outcome variable, we took a hierarchical modelling approach and reported both our intermediate and final models. To assess if certain groups of clinical conditions were associated with our outcome variable, we conducted a sensitivity analysis where we replaced the van Walraven score with several dichotomous (yes-no) variables representing several medical conditions, using a taxonomy adapted from another study on a free clinic [34]. We utilized the complete case analysis approach to handle missing data. We also tested for multicollinearity to assess for high cor- relation between independent variables. P < 0.05 was treated as signif- icant. We used Stata SE 16.0 (StataCorp LLP, College Station, TX) to conduct all analyses.

  1. Results

Our final sample contained 5008 visits among 2301 unique patients. Of these, patients in 1524 visits (30.4%) self-reported intent on using the ED if the free clinics were not available (Table 1).

In our final model (model 4), we found several predisposing, enabling, and need factors that were associated with reporting intent on visiting the ED when controlling for other factors. Higher odds of

Table 2

Adjusted odds ratios for factors associated with expressing interest in emergency department services (n = 5008 visits).

Characteristic

Model 1

Model 2

Model 3

Model 4

OR (95% CI)

OR (95% CI)

OR (95% CI)

OR (95% CI)

Predisposing Factors

Race/Ethnicity

Non-Hispanic White

Ref

Ref

Ref

Ref

Non-Hispanic Black

1.94 (1.66-2.25)***

1.83 (1.57-2.13)***

1.88 (1.61-2.19)***

1.86 (1.59-2.17)***

Hispanic

1.06 (0.90-1.25)

1.00 (0.85-1.19)

1.04 (0.87-1.23)

1.06 (0.89-1.26)

Non-Hispanic Other b

0.76 (0.60-0.97)*

0.74 (0.58-0.94)*

0.75 (0.59-0.97)*

0.76 (0.59-0.97)*

Age (in years)

1.01 (1.01-1.02)***

1.01 (1.01-1.01)***

1.01 (1.01-1.01)***

1.01 (1.00-1.01)**

Sex

Female

Ref

Ref

Ref

Ref

Male

0.99 (0.88-1.13)

0.98 (0.86-1.11)

0.97 (0.85-1.10)

0.97 (0.86-1.11)

Marital Status

Not married

Ref

Ref

Ref

Ref

Married

0.76 (0.66-0.89)**

0.77 (0.66-0.90)**

0.78 (0.67-0.91)**

0.78 (0.67-0.90)**

Household Number

Lives with others

Ref

Ref

Ref

Ref

Lives alone

0.83 (0.71-0.97)*

0.83 (0.71-0.98)*

0.85 (0.72-1.00)*

0.84 (0.72-1.00)*

Enabling Factors

Educational Attainment

Post-secondary education or higher

Ref

Ref

Ref

High school/GED/Vocational school

1.35 (1.18-1.55)***

1.36 (1.19-1.57)***

1.36 (1.19-1.57)***

Less than high school

1.59 (1.32-1.91)***

1.60 (1.33-1.93)***

1.61 (1.33-1.94)***

Housing Status

Not homeless

Ref

Ref

Ref

Homeless

1.72 (1.08-2.74)*

1.70 (1.07-2.71)*

1.66 (1.04-2.65)*

Has Personal Transportation

Yes

Ref

Ref

Ref

No

0.85 (0.75-0.97)*

0.85 (0.75-0.96)*

0.85 (0.75-0.96)*

Urbanicity

Urban

Ref

Ref

Ref

Rural

2.05 (1.19-3.52)*

2.02 (1.17-3.48)*

2.06 (1.19-3.55)**

Need Factors

van Walraven Comorbidity Score

1.03 (1.01-1.06)**

1.03 (1.01-1.06)**

Time

Year

2015

Ref

2016

0.86 (0.67-1.10)

2017

0.89 (0.70-1.14)

2018

0.82 (0.64-1.05)

2019

0.75 (0.59-0.95)*

2020

0.75 (0.53-1.07)

  1. p < 0.05*, p < 0.01**, p < 0.001***.
  2. “Non-Hispanic Other” includes all patients reporting their race as Native American, Asian, Pacific Islander, or other.

reporting intent on visiting the ED were observed among patients who were non-Hispanic Black (OR = 1.86; 95% CI:1.59-2.19) compared to non-Hispanic White, older (OR = 1.01, 95% CI:1.00-1.01), had less than high school (OR = 1.61, 95% CI:1.33-1.94) or high school, general education development or vocational school (OR = 1.36, 95% CI:1.19-1.57) education compared to post-secondary education or higher, were homeless (OR:1.66, 95% CI:1.04-2.65), lived in rural areas (OR = 2.06, 95% CI:1.19-3.55) compared to urban areas, and had a higher van Walraven comorbidity score (OR = 1.03, 95% CI:1.01-1.06). Lower odds were observed among patients who were married (OR = 0.78, 95% CI:0.67-0.90), lived alone (OR = 0.84, 95%

CI:0.72-1.00), and lacked personal transportation (OR = 0.85, 95% CI:0.75-0.96). Lower odds were also observed in 2019 (OR = 0.75, 95% CI:0.59-0.95) compared to 2015 (Table 2).

In our sensitivity analyses, the fully adjusted model revealed several medical conditions associated with reporting intent on visiting the ED. Higher odds of reporting intent on visiting the ED were observed among patients who had dental (OR = 1.69, 95% CI:1.20-2.39), gastro- intestinal (OR = 1.39, 95% CI:1.08-1.80), genitourinary (OR = 1.23, 95% CI:1.00-1.52), musculoskeletal (OR = 1.24, 95% CI:1.05-1.47), and re- spiratory conditions (OR = 1.28, 95% CI:1.04-1.57). All other findings found in our main analysis were also reflected in this model (Table 3).

Table 3

Sensitivity analysis results on medical condition and odds of expressing interest in emergency department services (n = 5008 visits).

Characteristic

Model 1

Model 2

Model 3

Model 4

OR (95% CI)

OR (95% CI)

OR (95% CI)

OR (95% CI)

Predisposing Factors

Race/Ethnicity

Non-Hispanic White

Ref

Ref

Ref

Ref

Non-Hispanic Black

1.94 (1.66-2.25)***

1.83 (1.57-2.13)***

1.89 (1.61-2.22)***

1.88 (1.60-2.20)***

Hispanic

1.06 (0.90-1.25)

1.00 (0.85-1.19)

1.04 (0.88-1.24)

1.07 (0.89-1.26)

Non-Hispanic Other b

0.76 (0.60-0.97)*

0.74 (0.58-0.94)*

0.77 (0.60-0.99)*

0.77 (0.60-0.99)*

Age (in years)

1.01 (1.01-1.02)***

1.01 (1.01-1.01)***

1.01 (1.01-1.01)***

1.01 (1.01-1.01)***

Sex

Female

Ref

Ref

Ref

Ref

Male

0.99 (0.88-1.13)

0.98 (0.86-1.11)

0.98 (0.86-1.11)

0.98 (0.86-1.12)

Marital Status

Not married

Ref

Ref

Ref

Ref

Married

0.76 (0.66-0.89)**

0.77 (0.66-0.90)**

0.77 (0.66-0.89)**

0.77 (0.66-0.90)**

Household Number

Lives with others

Ref

Ref

Ref

Ref

Lives alone

0.83 (0.71-0.97)*

0.83 (0.71-0.98)*

0.84 (0.71-0.99)*

0.83 (0.70-0.98)*

Enabling Factors

Educational Attainment

Post-secondary education or higher

Ref

Ref

Ref

High school/GED/Vocational school

1.35 (1.18-1.55)***

1.35 (1.17-1.55)***

1.35 (1.17-1.55)***

Less than high school

1.59 (1.32-1.91)***

1.60 (1.32-1.93)***

1.60 (1.33-1.94)***

Housing Status

Not homeless

Ref

Ref

Ref

Homeless

1.72 (1.08-2.74)*

1.73 (1.08-2.78)*

1.69 (1.05-2.71)*

Has Personal Transportation

Yes

Ref

Ref

Ref

No

0.85 (0.75-0.97)*

0.84 (0.74-0.96)*

0.84 (0.74-0.96)**

Urbanicity

Urban

Ref

Ref

Ref

Rural

2.05 (1.19-3.52)*

1.98 (1.14-3.43)*

2.03 (1.17-3.52)*

Need Factors

Cardiovascular

1.16 (0.99-1.36)

1.17 (1.00-1.37)

Dental

1.64 (1.16-2.31)**

1.69 (1.20-2.39)**

Endocrine

0.84 (0.71-0.99)*

0.84 (0.71-0.99)*

Gastrointestinal (Non-Dental)

1.37 (1.06-1.77)*

1.39 (1.08-1.80)*

Genitourinary

1.21 (0.98-1.49)

1.23 (1.00-1.52)*

Head, Eyes, Ears, Neck, Throat

1.05 (0.79-1.39)

1.07 (0.81-1.41)

Integumentary

1.05 (0.81-1.37)

1.07 (0.82-1.39)

Musculoskeletal

1.23 (1.04-1.45)*

1.24 (1.05-1.47)*

Neurological

1.11 (0.85-1.43)

1.11 (0.86-1.45)

Psychiatric

1.04 (0.88-1.24)

1.05 (0.89-1.25)

Respiratory

1.27 (1.04-1.57)*

1.28 (1.04-1.57)*

Substance Use

1.49 (0.82-2.71)

1.50 (0.82-2.74)

Trauma

1.23 (0.86-1.77)

1.24 (0.87-1.79)

Wellness Visit

0.63 (0.46-0.88)**

0.65 (0.47-0.90)*

Medication Refill

0.75 (0.60-0.94)*

0.77 (0.61-0.96)*

Other Chief Complaint

0.89 (0.78-1.02)

0.91 (0.80-1.04)

Time

Year

2015

Ref

2016

0.82 (0.64-1.05)

2017

0.87 (0.68-1.12)

2018

0.81 (0.63-1.04)

2019

0.74 (0.58-0.95)*

2020

0.68 (0.48-0.97)*

  1. p < 0.05*, p < 0.01**, p < 0.001***.
  2. “Non-Hispanic Other” includes all patients reporting their race as Native American, Asian, Pacific Islander, or other.
  3. Discussion

Our study assessed for factors associated with reporting intent on using the ED if the free clinics were unavailable. Overall, we found several predisposing (e.g., race/ethnicity, marital status), enabling (e.g., educational attainment, having personal transportation, housing status), and need factors (e.g., comorbidity burden) that were associated with patients reporting visiting the ED if the free clinics were unavailable. We also identified several medical conditions (e.g., dental) that were associated with higher odds of ED intent. We discuss implications for clinical practice and research below.

Several factors associated with reporting intent on using the ED if the free clinics were unavailable echo the findings found in studies looking at predictors of general and frequent use of the ED, such as non-Hispanic Black race [35], lower education [36], ease in traveling to the ED [37], not being married [36], and being homeless [38]. The over- lap in findings is encouraging, as this suggests that access to and use of free clinics may prevent potential downstream ED visits. Accordingly, health systems might develop pathways to refer patients without insur- ance to free clinics for follow-up and management. Additional prospec- tive research is needed to confirm whether these patients’ utilization patterns significantly change after being referred to free clinics.

Our sensitivity analysis suggests that certain medical conditions, such as dental concerns, were associated with reporting interest in vis- iting the ED if the free clinics were unavailable. In the local county area, dental concerns were named a leading reason for ED utilization among patients without insurance [39,40]. On a broader level, this finding has been observed in other parts of Florida [41], other states [42,43], and na- tionally [44-46]. Unfortunately, treatment in EDs for dental concerns is limited (e.g., pain management, antibiotics) [42]. Furthermore, ED use for dental issues may be more pronounced among patients without in- surance compared to those with insurance [42]. Although our free clinic network offers a free dental clinic on select evenings that has absorbed some of the local unmet dental demand, there continues to be a large number of patients in need of dental care as evidenced by our long waitlists. Although visiting the ED for dental concerns is known to be a multifactorial issue [42], one small-scale intervention may be to expand the number of free dental clinics. Free medical clinics have ob- served financial and material support from academic medical centers and external agencies due to, in part, downstream cost savings to health systems from reduced numbers of uncompensated visits [21-25]. How- ever, the impact of free dental clinics on cost savings for EDs is less clear. Future research quantifying these cost savings is needed and may provide motivation for supporting free dental clinics in a community.

Our results should be interpreted with some limitations. First, our outcome measure came from self-reported data, which may be influ- enced by social desirability bias [47]. Second, intent to use the ED may be an indirect proxy of the decision-making for ED use. Thus, other un- measured variables may influence whether a patient reports intent on using the ED, such as the number of prior ED visits made and patient sat- isfaction with primary care services rendered [35,48-50]. Future re- search should examine these additional factors when studying factors that affect a patient’s decision to go to the ED. Lastly, our results come from one free clinic network, which may limit generalizability to other free clinics and regions. For instance, regional differences in availability of free health care services may affect the likelihood of using the ED. [51,52] Further research using multi-center or state-level designs may be needed to confirm our findings. Notwithstanding these limitations, our results add to the literature on types of conditions that, if left unad- dressed by the safety-net system, may result in future ED visits.

  1. Conclusion

In this study of patients from free clinics, we found that several pa- tient demographic, social determinants of health and medical condi- tions were independently associated with greater odds of reporting

intent on visiting the ED. Additional interventions that improve access and use of free clinics (e.g., dental) may minimize ED use.

Funding

Research reported in this publication was supported by the Univer- sity of Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Transla- tional Sciences under award number UL1TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Authors’ contributions

This work represents the original research of the authors. This work has not been previously published. OTN and YRH conceptualized the study. OTN analyzed the data. All authors interpreted the data. OTN drafted the manuscript. All authors provided critical revisions to the manuscript. All authors approved the submission.

CRediT authorship contribution statement

Oliver T. Nguyen: Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. SriVarsha Katoju: Writing – review & editing. Erick E. Pons: Writing – review & editing. Kartik Motwani: Writing – review & editing. Gabriel M. Daniels: Writing – review & editing. Austin C. Reed: Writing – review & editing. Joanne Alfred: Writing – review & editing. David B. Feller: Writing – review & editing. Young-Rock Hong: Writing – review & editing, Methodology, Conceptualization.

Declaration of Competing Interest

None.

Acknowledgement

None.

References

  1. Shi L. Primary care, specialty care, and life chances. Int J Health Serv. 1994;24: 431-58. https://doi.org/10.2190/BDUU-J0JD-BVEX-N90B.
  2. Franks P, Fiscella K. Primary care physicians and specialists as personal physicians. Health care expenditures and mortality experience. J Fam Pract. 1998;47:105-9.
  3. Basu S, Berkowitz SA, Phillips RL, Bitton A, Landon BE, Phillips RS. Association of pri- mary care physician supply with population mortality in the United States, 2005-2015. JAMA Intern Med. 2019;179:506-14. https://doi.org/10.1001/jamainternmed.2018. 7624.
  4. Baker R, Freeman GK, Haggerty JL, Bankart MJ, Nockels KH. Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract. 2020;70: e600-11. https://doi.org/10.3399/bjgp20X712289.
  5. Parchman ML, Culler S. Primary care physicians and avoidable hospitalizations. J Fam Pract. 1994;39:123-8.
  6. Parchman ML, Culler SD. Preventable hospitalizations in primary care shortage areas. An analysis of vulnerable Medicare beneficiaries. Arch Fam Med. 1999;8: 487-91. https://doi.org/10.1001/archfami.8.6.487.
  7. Godard-Sebillotte C, Strumpf E, Sourial N, Rochette L, Pelletier E, Vedel I. Primary care continuity and potentially avoidable hospitalization in persons with dementia. J Am Geriatr Soc. 2021;69:1208-20. https://doi.org/10.1111/jgs.17049.
  8. Oh NL, Potter AJ, Sabik LM, Trivedi AN, Wolinsky F, Wright B. The association be- tween primary care use and potentially-preventable hospitalization among dual el- igibles age 65 and over. BMC Health Serv Res. 2022;22:927. https://doi.org/10.1186/ s12913-022-08326-2.
  9. Roetzheim RG, Pal N, Gonzalez EC, Ferrante JM, Van Durme DJ, Ayanian JZ, et al. The effects of physician supply on the early detection of Colorectal cancer. J Fam Pract. 1999;48:850-8.
  10. Lyu W, Wehby GL. The impacts of the ACA Medicaid expansions on cancer screening use by primary care provider supply. Med Care. 2019;57:202-7. https://doi.org/10. 1097/MLR.0000000000001053.
  11. Benarroch-Gampel J, Sheffield KM, Lin Y-L, Kuo Y-F, Goodwin JS, Riall TS. Colonoscopist and primary care physician supply and disparities in colorectal cancer screening. Health Serv Res. 2012;47:1137-57. https://doi.org/10.1111/j.1475-6773. 2011.01355.x.
  12. Ferrante JM, Gonzalez EC, Pal N, Roetzheim RG. Effects of physician supply on early detection of breast cancer. J Am Board Fam Pract. 2000;13:408-14. https://doi.org/ 10.3122/15572625-13-6-408.
  13. Stevens GD, Seid M, Halfon N. Enrolling vulnerable, uninsured but eligible children in public health insurance: association with health status and primary care access. Pediatrics. 2006;117:e751-9. https://doi.org/10.1542/peds.2005-1558.
  14. Szilagyi PG, Dick AW, Klein JD, Shone LP, Zwanziger J, McInerny T. Improved access and quality of care after enrollment in the New York State Children’s Health Insur- ance Program (SCHIP). Pediatrics. 2004;113:e395-404. https://doi.org/10.1542/ peds.113.5.e395.
  15. Burstin HR, Swartz K, O’Neil AC, Orav EJ, Brennan TA. The effect of change of health insurance on access to care. Inquiry. 1998;35:389-97.
  16. Tolbert J, Drake P, Damico A. Key facts about the uninsured population. https:// www.kff.org/uninsured/issue-brief/key-facts-about-the-uninsured-population/; 2022. (accessed February 3, 2023).
  17. Darnell JS. Free clinics in the United States: a nationwide survey. Arch Intern Med. 2010;170:946-53. https://doi.org/10.1001/archinternmed.2010.107.
  18. Gertz AM, Frank S, Blixen CE. A survey of patients and providers at free clinics across the United States. J Community Health. 2011;36:83-93. https://doi.org/10.1007/ s10900-010-9286-x.
  19. Simpson SA, Long JA. Medical student-run health clinics: important contributors to patient care and medical education. J Gen Intern Med. 2007;22:352-6. https://doi. org/10.1007/s11606-006-0073-4.
  20. Meah YS, Smith EL, Thomas DC. Student-run health clinic: novel arena to educate medical students on systems-based practice. Mt Sinai J Med. 2009;76:344-56. https://doi.org/10.1002/msj.20128.
  21. Hutchison J, Thompson ME, Troyer J, Elnitsky C, Coffman MJ, Lori Thomas M. The ef- fect of North Carolina free clinics on hospitalizations for ambulatory care sensitive conditions among the uninsured. BMC Health Serv Res. 2018;18:280. https://doi. org/10.1186/s12913-018-3082-1.
  22. Trumbo SP, Schuering KM, Kallos JA, Baddour N, Rakhit S, Wang L, et al. The effect of a student-run free clinic on hospital utilization. J Health Care Poor Underserved. 2018;29:701-10. https://doi.org/10.1353/hpu.2018.0053.
  23. Kramer N, Harris J, Zoorob R. The impact of a student-run free clinic on reducing ex- cess emergency department visits. Journal of Student-Run Clinics. 2015:1.
  24. Thakkar AB, Chandrashekar P, Wang W, Blanchfield BB. Impact of a student-run clinic on emergency department utilization. Fam Med. 2019;51:420-3. https://doi. org/10.22454/FamMed.2019.477798.
  25. Wallace S, Johnson TJ, Hendel E, Chakravarthy V, Leanos L, Ansell DA. The Financial impact of a partnership between an academic medical center and a free clinic. Am J Med. 2021;134. https://doi.org/10.1016/j.amjmed.2021.06.011. 1389-95.e4.
  26. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335:806-8. https://doi.org/10.1136/bmj.39335.541782.AD.
  27. Lee MO, Altamirano J, Garcia LC, Gisondi MA, Wang NE, Lippert S, et al. Patient age, race and emergency department treatment area associated with “Topbox” press Ganey scores. West J Emerg Med. 2020;21:117-24. https://doi.org/10.5811/ westjem.2020.8.47277.
  28. Lang SC, Weygandt PL, Darling T, Gravenor S, Evans JJ, Schmidt MJ, et al. Measuring the correlation between emergency medicine resident and attending physician pa- tient satisfaction scores using press Ganey. AEM Educ Train. 2017;1:179-84. https://doi.org/10.1002/aet2.10039.
  29. Hoonpongsimanont W, Sahota PK, Chen Y, Nguyen M, Louis C, Pena J, et al. Emer- gency department patient experience: same location, same provider, different scores by different survey methods. World J Emerg Med. 2019;10:138-44. https:// doi.org/10.5847/wjem.j.1920-8642.2019.03.002.
  30. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1-10.
  31. Andersen R, Newman JF. Societal and individual determinants of medical care utili- zation in the United States. Milbank Mem Fund Q Health Soc. 1973;51:95-124.
  32. United States Department of Agriculture. Rural-urban commuting area codes. n.d. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx. (accessed October 31, 2021).
  33. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using

administrative data. Med Care. 2009;47:626-33. https://doi.org/10.1097/MLR. 0b013e31819432e5.

  1. Tran T, Briones C, Gillet AS, Magrath J, Mayer S, Brug A. “Knowing” your population: who are we caring for at Tulane University School of Medicine’s student-run free clinics? Z Gesundh Wiss. 2022;30:1087-93. https://doi.org/10.1007/s10389-020- 01389-7.
  2. Giannouchos TV, Washburn DJ, Kum H-C, Sage WM, Ohsfeldt RL. Predictors of mul- tiple emergency department utilization among frequent emergency department users in 3 states. Med Care. 2020;58:137-45. https://doi.org/10.1097/MLR. 0000000000001228.
  3. Sun BC, Burstin HR, Brennan TA. Predictors and outcomes of frequent emergency de- partment users. Acad Emerg Med. 2003;10:320-8. https://doi.org/10.1111/j.1553- 2712.2003.tb01344.x.
  4. Uscher-Pines L, Pines J, Kellermann A, Gillen E, Mehrotra A. Emergency department visits for nonurgent conditions: systematic literature review. Am J Manag Care. 2013;19:47-59.
  5. Kushel MB, Perry S, Bangsberg D, Clark R, Moss AR. Emergency department use among the homeless and marginally housed: results from a community-based study. Am J Public Health. 2002;92:778-84. https://doi.org/10.2105/ajph.92.5.778.
  6. WellFlorida Council. Alachua County community health assessment. http://alachua.

floridahealth.gov/_files/_documents/publications/_documents/2016-cha.pdf; 2016.

(accessed November 24, 2021).

  1. WellFlorida Council. Alachua County community health needs assessment. http:// alachua.floridahealth.gov/programs-and-services/community-health-planning- and-statistics/data-and-reporting/_documents/cha-2020.pdf; 2020. (accessed

November 24, 2021).

  1. Tomar SL, Carden DL, Dodd VJ, Catalanotto FA, Herndon JB. Trends in dental-related use of hospital emergency departments in Florida. J Public Health Dent. 2016;76: 249-57. https://doi.org/10.1111/jphd.12158.
  2. Sun BC, Chi DL, Schwarz E, Milgrom P, Yagapen A, Malveau S, et al. Emergency de- partment visits for nontraumatic dental problems: a mixed-methods study. Am J Public Health. 2015;105:947-55. https://doi.org/10.2105/AJPH.2014.302398.
  3. Anderson L, Cherala S, Traore E, Martin NR. Utilization of hospital emergency depart- ments for non-traumatic dental care in New Hampshire, 2001-2008. J Community Health. 2011;36:513-6. https://doi.org/10.1007/s10900-010-9335-5.
  4. Amen TB, Kim I, Peters G, Gutierrez-Sacristan A, Palmer N, Simon L. Emergency de- partment visits for dental problems among adults with private Dental insurance: a national observational study. Am J Emerg Med. 2021;44:166-70. https://doi.org/ 10.1016/j.ajem.2021.02.001.
  5. Lee HH, Lewis CW, Saltzman B, Starks H. Visiting the emergency department for dental problems: trends in utilization, 2001 to 2008. Am J Public Health. 2012; 102:e77-83. https://doi.org/10.2105/AJPH.2012.300965.
  6. Kelekar U, Naavaal S. Dental visits and associated emergency department-charges in the United States: Nationwide emergency department sample, 2014. J Am Dent Assoc. 2019;150. https://doi.org/10.1016/j.adaj.2018.11.021. 305-12.e1.
  7. Donaldson SI, Grant-Vallone EJ. Understanding self-report bias in organizational be- havior research. J Bus Psychol. 2002;17:245-60. https://doi.org/10.1023/A: 1019637632584.
  8. Coster JE, Turner JK, Bradbury D, Cantrell A. Why do people choose emergency and urgent care services? A rapid review utilizing a systematic literature search and nar- rative synthesis. Acad Emerg Med. 2017;24:1137-49. https://doi.org/10.1111/acem. 13220.
  9. van den Berg MJ, van Loenen T, Westert GP. Accessible and continuous primary care may help reduce rates of emergency department use. An international survey in 34 countries. Fam Pract. 2016;33:42-50. https://doi.org/10.1093/fampra/cmv082.
  10. Young GP, Wagner MB, Kellermann AL, Ellis J, Bouley D. Ambulatory visits to hospital emergency departments. Patterns and reasons for use. 24 hours in the ED study group. JAMA. 1996;276:460-5. https://doi.org/10.1001/jama.276.6.460.
  11. Grumbach K, Keane D, Bindman A. Primary care and public emergency department overcrowding. Am J Public Health. 1993;83:372-8. https://doi.org/10.2105/ajph.83. 3.372.
  12. Huntley A, Lasserson D, Wye L, Morris R, Checkland K, England H, et al. Which fea- tures of primary care affect unscheduled secondary care use? A systematic review. BMJ Open. 2014;4:e004746. https://doi.org/10.1136/bmjopen-2013-004746.