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: Oliver.Nguyen@moffitt.org (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.

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