- Email: [email protected]
Assessing Retention in a Free Diabetes Clinic
The Journal for Nurse Practitioners 15 (2019) 301e305
Contents lists available at ScienceDirect
The Journal for Nurse Practitioners journal homepage: www.npjournal.org
Original Research
Assessing Retention in a Free Diabetes Clinic Katie C. Buys, DNP, FNP, Cynthia Selleck, PhD, RN, David R. Buys, PhD, MSPH a b s t r a c t Keywords: diabetes mellitus free clinic limited-resource patients lost to follow-up poverty retention
The PATH Clinic in Birmingham, Alabama, is a free clinic for uninsured patients with uncontrolled diabetes. In its first 22 months, more than 25% of patients were lost to follow-up. This report shares reasons for patients becoming lost to follow-up. Investigators used patient records for quantitative analyses and phone interviews for qualitative analysis. Sociodemographic factors were not associated with patients’ likelihood of becoming lost to follow-up. Qualitative results show that these patients had uncertainty regarding appointment dates, complicated life circumstances, and transportation barriers. Results indicate that social interventions may improve patient compliance and retention and should be tested. © 2019 Elsevier Inc. All rights reserved.
Introduction Diabetes rates have steadily increased over the past 2 decades. In the United States, diabetes affects more than 31 million people (10.5% of the population)1 and disproportionately affects African American, Native American, Latino America, and Asian American people; those who are obese or overweight; and those with a family history of diabetes.2-4 Furthermore, people below the federal poverty level and living with food insecurity are more likely to have diabetes, lower diabetes self-efficacy, and more difficulty managing the condition.5-7 Diabetes is a costly condition. Providing secondary and tertiary prevention across the sociodemographic spectrum to all persons with diabetes is important. Without it, patients may otherwise seek intervention at emergency departments at much higher costs to them and the health care system.8-10 One such opportunity for care exists in Birmingham, Alabama, at Providing Access To Healthcare (PATH) Clinic, a nurse practitionereled free clinic for continuing and transitional care that provides focused primary care for patients with diabetes. This report describes results from a mixed method study about reasons patients in a free diabetes clinic become lost to follow-up. Background and Significance Diabetes Epidemiology and Care Concerns In Alabama, the state with the third highest rate of diabetes, the prevalence rate of adults with diabetes increased from 3.4% in 1994 to 14.6% in 2016.1 Individuals with diabetes are at higher risk than the general population for developing microvascular disease (nephropathy, retinopathy, and neuropathy) and macrovascular events (coronary heart disease, cerebrovascular attack, and intermittent claudication).11-13 Diabetes also affects length of life: it is currently the 6th and 7th leading cause of death for men and https://doi.org/10.1016/j.nurpra.2018.12.003 1555-4155/© 2019 Elsevier Inc. All rights reserved.
women, respectively, as well as a contributing factor to other leading causes of death.12,14,15 Intense glycemic control has yielded a decrease in microvascular complications and cardiovascular events.12,16 In patients with chronic disease, teaching selfmanagement is more effective than giving information only.17,18 One goal of diabetes self-management is collaboration between the patient and provider to improve care, thus reducing morbidity and mortality and decreasing costs to the health care system.17,18 PATH Clinic The PATH Clinic is a joint effort between the University of Alabama at Birmingham (UAB) School of Nursing; UAB Hospital, an academic medical center and the nation’s third largest public hospital; and a local faith-based, nonprofit organization. This transitional and continuing care clinic for uninsured patients with uncontrolled diabetes opened in December 2012 and was initially funded by a Health Resources and Services Administration’s Nurse Education, Practice, Quality and Retention award from the Division of Nursing with additional funds provided by UAB Hospital. One of the primary goals of the clinic is to assist patients in adopting diabetes self-management approaches that lead to improved glycemic control. The interprofessional team includes a registered nurse, primary and acute care nurse practitioners, endocrinologist, psychiatric/mental health nurse practitioner, psychiatrist, optometrist, registered dietitian/certified diabetes educator, and public health professional who provides leadership for prescription assistance programs and provides medications and diabetes supplies at no cost to the patient. Social workers at the nearby academic medical center refer patients to the clinic if they have no insurance, no other access to a primary care provider, and uncontrolled diabetes. Providers at the PATH Clinic observed that a substantial number of patients were becoming lost to follow-up. However, when this
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observation emerged, the team found no scientific literature on factors associated with or predictive of becoming lost to follow-up in this type of clinic. Theoretical Framework: Health Services Utilization Model The Andersen-Aday Health Services Utilization Model is a framework first proposed in the late 1960s to help explain questions of health services use or related health outcomes.19 The model, which has undergone many iterations since that time, effectively informs the relationship between the environment, population characteristics, health behaviors, and outcomes. In the model, the environment includes factors related to both the health care system (including local, corporate, or national policy-related systems) and the external environment (physical, political, and economic components). Population factors include predisposing characteristics (demographics, position within the social structure, and beliefs of health services benefits), enabling characteristics (resources within a patient’s family and community such as economic status, living situation, access to health care facilities, and availability of social support), and need (perceived or objective need for care). Health behaviors include personal health practices (diet, exercise, and self-care) and the use of health services (accessing a health care provider). Finally, health outcomes include perceived health status, evaluated health status, and consumer satisfaction with obtained services. The model has been widely used to explain many outcomes. For this project, the model informs the investigation of reasons for use of and consumer satisfaction with the services provided at the clinic. Purpose This project sought to examine the question: “What factors are associated with patients becoming lost to follow-up at a free transitional and continuing care clinic for patients with uncontrolled diabetes?” The specific aims of this project were 1) to provide a sociodemographic description of patients in this free transitional and continuing care clinic for uninsured patients with diabetes, 2) to assess which proportion of the patients who made appointments at the clinic kept them and which of those continued care or became lost to follow-up, and 3) to quantitatively and qualitatively evaluate factors associated with patients becoming lost to follow-up after at least 1 visit to the clinic and their satisfaction with the clinic. An ancillary aim of the project was to assess patient satisfaction with the clinic. The project was reviewed and approved by the Institutional Review Board of UAB. Methods
in the chart was called, and that person was asked to provide the patient’s new phone number. Qualitative Survey Guide. Questions used to guide the phone interview included the following: Did you encounter any difficulties when interacting with staff at the clinic? Can you think of anything that would have enabled you to keep appointments and continue coming to the clinic? What are the reasons you stopped coming to the clinic? The entire set of interview questions is available upon request from the corresponding author. These questions are based on the Andersen-Aday Health Services Utilization Model and seek to assess ways that the environment, population characteristics, and health behaviors affect the outcome of becoming lost to follow-up (health behavior). Variables Lost to follow-up is defined as patients who attended at least 1 visit and had a follow-up scheduled but failed to attend their most recently scheduled visit at least 30 days prior; this included only patients who did not call to cancel their appointment. Kept last appointment is defined as patients who kept their last appointment; for comparative analysis with persons lost to follow-up, only individuals who kept at least 2 appointments, including their last appointment, are included. Age is reported as age in years at the initial visit. Gender is defined as female versus male. Non-Hispanic versus Hispanic are included. White versus Black are included. Total appointments made are included and are reported categorically as 1, 2, 3, and greater than or equal to 4 appointments. Analysis Analysis of qualitative data was completed using an open coding process according to Strauss and Corbin’s20 approach of breaking down, examining, comparing, conceptualizing, and categorizing data into themes. Quotes from the qualitative phone interviews were open coded by hand by two separate reviewers and themes were compared and reconciled. The themes were then fit to the Andersen-Aday model. Descriptive statistics are reported, including information about the outcome of patients’ most recent appointment with the clinic and their aggregate sociodemographic characteristics. Analysis of variance and chi-square statistics were used to assess sociodemographic differences in groups of patients by the outcome of their most recent appointment (appointment kept vs. lost to follow-up, etc.). Binary logistic regression was performed to assess for sociodemographic predictors of being lost to follow-up.
Data Sources Results Population. Initial data were obtained from records of the complete population of patients seen and treated at the PATH Clinic from December 12, 2012, through October 14, 2014, for a total of 22 months. Demographic data of all patients referred to the PATH Clinic from the local academic medical center were collected from the clinic’s electronic scheduling and electronic medical record (EMR) systems. Patients who dropped out/discontinued care (became lost to follow-up) after attending at least 1 visit (n ¼ 62) were recruited to participate in a phone interview to discuss their reasons for discontinuing care (Supplementary Figure). The last phone number recorded on the chart was used to contact the patient. If this number was nonworking, the emergency contact listed
Supplementary Figure shows the flow of patients from referral through discontinuation of care or active status, from December 12, 2012, through October 14, 2014. In its first 22 months, the PATH Clinic made appointments for 348 unique patients. Of those, 74 (21.2%) did not show for their initial appointment, 13 (3.7%) had their appointment canceled by clinic staff but were not rescheduled for a visit, and 15 (4.3%) had a new visit scheduled for some time in the future at the time of this report. Therefore, 246 of the 348 (70.7%) kept at least 1 appointment. Of the 246, 104 (42.3%) kept their last appointment; 18 (7.3%) did not keep their last appointment because they were hospitalized,
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deceased, or had a rescheduled appointment; 46 (18.7%) had a future return appointment scheduled at the time of this report; 4 (1.6%) called to cancel their last appointment; and 12 (4.9%) did not show for their appointment within the previous 30 days (these were not considered lost to follow-up because they were still within the window of opportunity to reschedule their appointments and return for care). Finally, 62 (25.2%) were lost to followup. The 348 unique patients were, on average, 44.5 years old, 44.5% female, 98% non-Hispanic, 71% Black, and 29% White. Among these patients, 32.2% kept 1 appointment, 19% kept 2 appointments, 12.4% kept 3 appointments, and 36.5% kept 4 or more appointments (Supplementary Table). Participants who were lost to follow-up (n ¼ 62) kept at least 1 appointment and missed at least 1 appointment; this means that they had at least 2 appointments scheduled. To better understand what may have caused these patients to discontinue care, we now compare them with active patients who had and kept at least 2 appointments (n ¼ 77) because both of these groups had at least 2 appointments scheduled. The Supplementary Table shows that although patients lost to follow-up were slightly younger, more often male, less White, and had a higher number of total appointments, these differences are likely due to chance. In binary logistic regression analysis, assessing for predictors of being lost to followup compared with being an active patient did not yield any significant findings; therefore, results are not presented here. Quantitative phone survey findings. Of the 62 who discontinued care, 24 (38.7%) were unreachable by phone; the investigators believe that they likely changed phone numbers. Ten (16.1%) had obtained insurance, 7 (11.3%) were noneEnglish speaking, and 4 (6.5%) had died (Supplementary Figure). There were 17 (27%) remaining who were eligible for participation in the phone interview and agreed to participate. Participants’ responses were fit to Andersen-Aday Health Services Utilization Model and details of that follow. Environmental factors. Forty-seven percent (n ¼ 8) felt that more could have been done to help them keep the appointment, and 65% (n ¼ 11) indicated that a phone call would have been useful. Fiftynine percent (n ¼ 10) said that a text message, e-mail, social media, or postal message would have been a helpful reminder, and 76% (n ¼ 13) felt that a calendar would have been helpful. Factors influencing participants becoming lost to follow-up included that they did not feel they knew their appointment dates: “I enjoyed coming down there. For some reason, it just stopped. I came 3 or 4 times, then I never got another appointment. Front desk said they would send me something in the mail, but I didn’t know what happened.” Regarding familiarity with appointment dates, a different participant said: “I’m not sure … I actually thought I was only referred by a doctor. Thought I had to go back through UAB to get another appointment. Right now, I feel that I am not under control. I was homeless when I came to clinic and misplaced my meter, and out of medications as well. I think my A1c will be quite high now.” Another participant said, “I didn’t know I had any more appointments for the clinic.” One participant said blatantly, “I never knew when my last appointment was.” Another said she believed she would have followed up if she would have “gotten a phone call back to know when it was.” Another individual indicated that even when he called he felt that he was unable to reach a staff member. To compensate for this problem, one participant indicated, “I would just go there and ask.” These participants represent a possible problem with communication from clinic staff that could be impeding patient follow-up; this is one factor that could be addressed to improve patients staying in care at the clinic.
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Despite these participants all becoming lost to follow-up, their mean satisfaction rating was quite high: 9.15 on a scale of 1 to 10. Population characteristics. In the Andersen-Aday model, population characteristics include enabling resources such as transportation and telephone communication. The more contemporary literature would refer to these factors as social determinants of health (SDOH). Forty-seven percent of the population (n ¼ 8) reported having difficulty with transportation during their time in care at the clinic. Reports included, “No, I just didn’t have no ride. I was having some issues with my girlfriend. She was my ride.” Another respondent said, “I tried my best. The only thing I had a problem with was back and forth transportation.” When asked what could be done to improve the clinic’s services, participants’ responses largely focused on transportation as well: “Maybe if y’all had bus passes or some help to get people back and forth, like a bus to get people to appointments, especially when the weather is really cold. The buses don’t run worth 2 cents. I did have to take the bus sometimes.” An additional respondent said, “Maybe bus passes and a van or something to pick people up who really can’t make it.” Thirty-five percent (n ¼ 6) indicated limited access to their phone or that they received a new number during their time in care. Furthermore, another indicated, “yeah, I did lose a phone, but I have a new number now,” and another person stated, “I changed my phone number about 3 times.” Still another said, “I had knew [sic] it, but I couldn’t make it because I had broken up with my girlfriend and couldn’t get there.” Health behaviors. Health behaviors include elements of personal health practices and use of health services. Respondents’ current health care status was assessed with the goal of linking them back to care if they needed it. Forty-one percent (n ¼ 7) indicated they had been receiving care since they stopped coming to the clinic. This does not necessarily mean the participants identified a new primary care provider; rather, they may be referring to having sought care at an emergency room. Sixty-five percent (n ¼ 11) had been to an emergency department since discontinuing care at the clinic, and 35% (n ¼ 6) had been hospitalized. Finally, 71% (n ¼ 12) still needed care and wanted to be reconnected to the clinic, indicating that the clinic was meeting an important need for these limited-resource patients. Health outcomes. In the Health Services Utilization Model, health outcomes include perceived and evaluated health status and consumer satisfaction. One participant reported a negative experience, saying: “I remember once feeling like I was homeless and people talked to me just any kind of way because so many homeless came there. I was asking about how to go about getting a bus pass or gas, and I was told very rude that, ‘If I knew, I would be getting it myself.’ … I have a lot of pride, and it took a lot for me to walk in there and ask for help.” In contrast, a respondent said, “I could never keep my appointment. That was the problem with me.” Participants overwhelmingly indicated that the challenges were their own and did not attempt to blame the clinic. One participant specifically said that the problem with becoming lost to follow-up “was really me that was working and trying to keep appointments. So it was really me.” Another patient said, “I can’t say that they didn’t [contact me] because at the time I wasn’t stable. I was no longer staying at that former address, so I can’t blame that on them. I was at a bad spot in my life at the time. I was in a near-fatal car accident and was disabled for 6 months.” Discussion More than 25% of the patients who kept at least 1 appointment with the PATH Clinic ultimately became lost to follow-up; none of
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the sociodemographic factors that were assessed were associated with this outcome. The investigators hypothesize that this may indicate that among a population of individuals who lack the social and economic resources to have health insurance, factors such as age, race, or gender have limited power to discriminate between why some subgroups of patients continue in care and others become lost to follow-up. Furthermore, it appeared that the patients who were more engaged with the clinic, as evidenced by having a higher total number of appointments made, were slightly more likely to become lost to follow-up, although the relationship may be due to chance (P ¼ 0.83). Results from the phone interview and qualitative analysis indicated that the key factors keeping patients from continuing in care with the PATH Clinic were uncertainty of appointment dates and unfamiliarity with navigating the health care system, complicated life circumstances, and transportation barriers. Researchers must continue to try to understand what factors affect patients’ likelihood of becoming lost to follow-up. This limited-resource population needs care for their own well-being and for the sake of the broader society due to the expense of unreimbursed emergency care, which is ultimately absorbed by taxpayers and health care consumers. For health care providers and practice administrators, this project underscores the need for more and closer care management to keep limited-resource patients in care. This is especially important considering findings from this project, which showed that once a patient missed an appointment, that patient was not going to call to make another one because most of these patients did not understand that calling to reschedule was an option. Although this may seem simple, it is a different way of thinking for patients who have previously only had access to the emergency department, even for routine care. Furthermore, this project highlights the need to educate all clinic staff regarding how to best support limited-resource patients. Although we often focus here on education for licensed professionals, front office staff are the patients’ first point of contact with the clinic, both at visits and when calling on the phone. We learned that one of our participants felt that the front office staff was jealous that the patient might receive additional resources to which she did not have access. Others reported that the clinic phone often went unanswered and that staff members were rude. Educating the office staff on the challenges experienced by limitedresource patients, as well as additional “hand-holding” they may need to stay in the clinic could be helpful. Limitations The limitations of this work include that investigators had to rely only on the administrative data and variables included in the database for quantitative analysis. These data limitations may be what led to no discernable differences by sociodemographic characteristics. Furthermore, the sample size was small in the final qualitative analysis due to the inability to reach many by phone. Also, this small sample size suggests that generalizability of the data may not be warranted. The strengths include that the full clinical population and nearly 2 years of data were available. Conclusion These findings do not yield any conclusive evidence that sociodemographic factors are associated with limited-resource patients with diabetes becoming lost to follow-up compared with those who remain active in care. This lack of significance may indicate that disadvantages associated with age, race, and gender are less powerful predictors of health care utilization for persons without health insurance and who have limited resources than they are for
those without such significant life challenges. That is, these patients already have such significant negative SDOH that demographic differences seem to have little effect. However, interviews with patients who did become lost to follow-up indicated that complicated life circumstances, transportation barriers, and uncertainty of appointment dates affected their ability to keep appointments. Toward this end, we recommend and intend to test these or similar questions about why limited-resource populations may become lost to follow-up using the Behavioral Model for Vulnerable Populations, which is an adaptation of the Andersen-Aday model tested with homeless populations.21 Although not all of the patients in this clinic are homeless, some are, and others may share similar characteristics. Also, further mixed methods investigation with similar populations is needed to validate these findings. Similarly, we also recognize the need for more sophisticated measures for determining who may be at risk for becoming lost to follow-up. As with being atrisk for medical conditions such as heart disease or diabetes, patients at risk for falling out of care likely do not know they are at an increased risk and are not able to verbalize to their health care provider that they need extra support to be able to keep appointments and remain active in the clinic. One helpful approach may be to incorporate a SDOH risk screening and referral process, as has been piloted by Page-Reeves et al22 in 3 New Mexico low-income clinics, one of which was a federally qualified health center. One of the authors on this project has also implemented a SDOH screening and referral project in family nurse practitioner education.23 Incorporating such a screening tool and referral process into the electronic medical record could help to identify patients who are at risk for becoming lost to follow-up so they can be supported more holistically from their first clinic visit onward. In the meantime, greater attention to transportation needs, care management between appointments, and follow-up reminders about appointments are all interventions that may improve patient adherence to appointments for limited-resource patients. These and other “personal touch” points between visits may help ensure that patients remain in care, and such services could be provided by office staff, nurses in the clinic, or community health workers. In summary, we must avoid falling into the Field of Dreams fallacy that “if we build it, they will come.” Although providing free care, medications, and supplies are vital to limited-resource patients living with chronic and complicated conditions such as diabetes, free services are not sufficient to keep some patients engaged in their care. Results of this project show that more support is needed to enable many limited-resource patients to continue to access ongoing care. Acknowledgments This project was supported in part by a U.S. Department of Health and Human Services, Health Resources and Services Administration, Nurse Education, Practice, Quality and Retention award (award # UD7HP25047) to the University of Alabama at Birmingham School of Nursing. The information or content and conclusions in this article are those of the authors and should not be construed as the official position or policy, nor should any endorsements be inferred by the U.S. Department of Health and Human Services. Supplementary Data Supplementary tables associated with this article can be found in the online version at https://doi.org/10.1016/j.nurpra.2018.12. 003.
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References 1. BRFSS Prevalence & Trends Data. 2015. https://www.cdc.gov/brfss/ brfssprevalence/. Accessed April 2, 2017. 2. Chow EA, Foster H, Gonzalez V, McIver L. The disparate impact of diabetes on racial/ethnic minority populations. Clin Diabetes. 2012;30(3):130-133. 3. Nguyen N, Nguyen X-M, Lane J, Wang P. Relationship between obesity and diabetes in a us adult population: findings from the National Health and Nutrition Examination Survey, 1999e2006. Obest Surg. 2011;21(3):351-355. 4. Mühlenbruch K, Jeppesen C, Joost H-G, Boeing H, Schulze MB. The value of genetic information for diabetes risk predictionedifferences according to sex, age, family history and obesity. PloS ONE. 2013;8(5):e64307. pez A, Tschann J, Fernandez A. Food insecurity and 5. Seligman HK, Jacobs EA, Lo glycemic control among low-income patients with type 2 diabetes. Diabetes Care. 2012;35(2):233-238. 6. Sims M, Diez Roux AV, Boykin S, et al. The socioeconomic gradient of diabetes prevalence, awareness, treatment, and control among African Americans in the Jackson Heart Study. Ann Epidemiol. 2011;21(12):892-898. 7. Smith BT, Lynch JW, Fox CS, et al. Life-course socioeconomic position and type 2 diabetes mellitus: the Framingham Offspring Study. Am J Epidemiol. 2011;173(4):438-447. 8. Zhuo X, Zhang P, Barker L, Albright A, Thompson TJ, Gregg E. The lifetime cost of diabetes and its implications for diabetes prevention. Diabetes Care. 2014;37(9):2557-2564. 9. American Diabetes Association. Economic costs of diabetes in the US in 2012. Diabetes Care. 2013;36(4):1033-1046. 10. Moheet A, Seaquist ER. Diabetes: hypoglycaemia, emergency care and diabetes mellitus. Nat Rev Endocrinol. 2014;10(7):384-385. 11. Zoungas S, Chalmers J, Ninomiya T, et al. Association of HbA1c levels with vascular complications and death in patients with type 2 diabetes: evidence of glycaemic thresholds. Diabetologia. 2012;55(3):636-643. € € m J, Svennblad B, Lohm L, Nilsson PM, Johansson G. As12. Ostgren CJ, Sundstro sociations of HbA1c and educational level with risk of cardiovascular events in 32 871 drug-treated patients with type 2 diabetes: a cohort study in primary care. Diabet Med. 2013;30(5):e170-e177. 13. Masharani U. Chapter 27. Diabetes mellitus & hypoglycemia. In: Papadakis MA, McPhee SJ, eds. Current Medical Diagnosis & Treatment 2014. New York, NY: McGraw-Hill; 2014.
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14. Santulli G. Epidemiology of cardiovascular disease in the 21st century: updated numbers and updated facts. J Cardiovasc Dis. 2013;1(1):1-2. 15. Centers for Disease Control and Prevention. QuickStats: Number of Deaths from 10 Leading CausesdNational Vital Statistics System, United States, 2010. 2013. https:// www.cdc.gov/women/lcod/2015/race-ethnicity/index.htm. https://www.cdc.gov/ healthequity/lcod/men/2015/race-ethnicity/index.htm. Accessed January 4, 2019. 16. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. 1998;352(9131): 837-853. 17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self-management of chronic disease in primary care. JAMA. 2002;288(19):2469-2475. 18. Haas L, Maryniuk M, Beck J, et al. National standards for diabetes self-management education and support. Diabetes Care. 2013;36(suppl. 1):S100-S108. 19. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior. 1995;36(1):1-10. 20. Strauss A, Corbin JM. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: SAGE; 1998. 21. Gelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273-1302. 22. Page-Reeves J, Kaufman W, Bleecker M, et al. Addressing social determinants of health in a clinic setting: the WellRx pilot in Albuquerque, New Mexico. J Am Board Family Med. 2016;29(3):414-418. 23. Buys KC, Somerall D. Social determinants of health screening and referral: innovation in graduate nursing education. J Nurs Educ. 2018;57(9):571-572.
Katie C. Buys, DNP, FNP, is an assistant professor and co-coordinator of the Family Nurse Practitioner Specialty Track, School of Nursing, University of Alabama at Birmingham, and can be reached at [email protected]. Cynthia Selleck, PhD, RN, is professor and associate dean for clinical and global partnerships at the School of Nursing, University of Alabama at Birmingham. David R. Buys, PhD, MSPH, is assistant professor and state health specialist at Mississippi State University, Mississippi State, MS. In compliance with national ethical guidelines, the authors report no relationships with business or industry that would pose a conflict of interest.
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Patients with ≥1 appointments scheduled (n = 348) 1. Did not show for appt: “never-shows” (n = 74) 2. Patient or clinic canceled appt (n = 13) 3. Has future “new” appt (n = 15) Kept ≥1 appointment (n = 246) 4. Kept last appt (n = 104) 5. Hospitalized, deceased, or clinic rescheduled (n = 18) 6. Future return appt (n = 46) 7. Patient canceled (n = 4) 8. No show <30 days (n = 12) 9) Dropped out/discontinued care (n = 62) Non–English-speaking (n = 7) Obtained insurance (n = 10) Unreachable (n = 24) Deceased (n = 4)
Participants enrolled in phone interview (n = 17) Supplementary Figure. Flow chart representing patients’ last appointment the PATH Clinic (as of October 14, 2014).
Supplementary Table Descriptive Statistics and Comparisons of Active vs. Lost to Follow-up Patients
Age, mean (SD) Female (vs. Male), n (%) Non-Hispanic (vs. Hispanic), n (%) White (vs. Black), n (%) Total appointments, n (%) 1 2 3 4 a b c d
Totala (n ¼ 348)
Kept 1 Apptb (n ¼ 246)
Active w/ >2 Apptsc (n ¼ 77)
Lost to Follow-Upd (n ¼ 62)
P value
44 155 341 101
(12.2) (44.5%) (98%) (29%)
45 104 240 72
(12.1) (42.3%) (97.6%) (29.3%)
46 40 76 23
44 28 61 15
0.22 0.27 0.70 0.29
112 66 43 127
(32.3%) (19.0%) (12.4%) (36.5%)
34 44 41 127
(13.8%) (17.9%) (16.7%) (61.6%)
d 21 (27.3%) 18 (23.4%) 38 (49.4%)
(11.4) (51.9%) (98.7%) (29.9%)
(10.4) (45.2%) (98.4%) (24.2%)
d 15 (24.2%) 13 (21.0%) 34 (54.8%)
0.83
All patients who had at least 1 appointment scheduled. Patients who kept 1 appointments, regardless of whether they were later lost to follow-up. Patients who kept at least 2 appointments, including last appointment. Patients who kept at least 1 appointment and had a follow-up scheduled but failed to attend or cancel their most recently scheduled appointment at least 30 days prior.
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Original Research
Assessing Retention in a Free Diabetes Clinic Katie C. Buys, DNP, FNP, Cynthia Selleck, PhD, RN, David R. Buys, PhD, MSPH a b s t r a c t Keywords: diabetes mellitus free clinic limited-resource patients lost to follow-up poverty retention
The PATH Clinic in Birmingham, Alabama, is a free clinic for uninsured patients with uncontrolled diabetes. In its first 22 months, more than 25% of patients were lost to follow-up. This report shares reasons for patients becoming lost to follow-up. Investigators used patient records for quantitative analyses and phone interviews for qualitative analysis. Sociodemographic factors were not associated with patients’ likelihood of becoming lost to follow-up. Qualitative results show that these patients had uncertainty regarding appointment dates, complicated life circumstances, and transportation barriers. Results indicate that social interventions may improve patient compliance and retention and should be tested. © 2019 Elsevier Inc. All rights reserved.
Introduction Diabetes rates have steadily increased over the past 2 decades. In the United States, diabetes affects more than 31 million people (10.5% of the population)1 and disproportionately affects African American, Native American, Latino America, and Asian American people; those who are obese or overweight; and those with a family history of diabetes.2-4 Furthermore, people below the federal poverty level and living with food insecurity are more likely to have diabetes, lower diabetes self-efficacy, and more difficulty managing the condition.5-7 Diabetes is a costly condition. Providing secondary and tertiary prevention across the sociodemographic spectrum to all persons with diabetes is important. Without it, patients may otherwise seek intervention at emergency departments at much higher costs to them and the health care system.8-10 One such opportunity for care exists in Birmingham, Alabama, at Providing Access To Healthcare (PATH) Clinic, a nurse practitionereled free clinic for continuing and transitional care that provides focused primary care for patients with diabetes. This report describes results from a mixed method study about reasons patients in a free diabetes clinic become lost to follow-up. Background and Significance Diabetes Epidemiology and Care Concerns In Alabama, the state with the third highest rate of diabetes, the prevalence rate of adults with diabetes increased from 3.4% in 1994 to 14.6% in 2016.1 Individuals with diabetes are at higher risk than the general population for developing microvascular disease (nephropathy, retinopathy, and neuropathy) and macrovascular events (coronary heart disease, cerebrovascular attack, and intermittent claudication).11-13 Diabetes also affects length of life: it is currently the 6th and 7th leading cause of death for men and https://doi.org/10.1016/j.nurpra.2018.12.003 1555-4155/© 2019 Elsevier Inc. All rights reserved.
women, respectively, as well as a contributing factor to other leading causes of death.12,14,15 Intense glycemic control has yielded a decrease in microvascular complications and cardiovascular events.12,16 In patients with chronic disease, teaching selfmanagement is more effective than giving information only.17,18 One goal of diabetes self-management is collaboration between the patient and provider to improve care, thus reducing morbidity and mortality and decreasing costs to the health care system.17,18 PATH Clinic The PATH Clinic is a joint effort between the University of Alabama at Birmingham (UAB) School of Nursing; UAB Hospital, an academic medical center and the nation’s third largest public hospital; and a local faith-based, nonprofit organization. This transitional and continuing care clinic for uninsured patients with uncontrolled diabetes opened in December 2012 and was initially funded by a Health Resources and Services Administration’s Nurse Education, Practice, Quality and Retention award from the Division of Nursing with additional funds provided by UAB Hospital. One of the primary goals of the clinic is to assist patients in adopting diabetes self-management approaches that lead to improved glycemic control. The interprofessional team includes a registered nurse, primary and acute care nurse practitioners, endocrinologist, psychiatric/mental health nurse practitioner, psychiatrist, optometrist, registered dietitian/certified diabetes educator, and public health professional who provides leadership for prescription assistance programs and provides medications and diabetes supplies at no cost to the patient. Social workers at the nearby academic medical center refer patients to the clinic if they have no insurance, no other access to a primary care provider, and uncontrolled diabetes. Providers at the PATH Clinic observed that a substantial number of patients were becoming lost to follow-up. However, when this
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observation emerged, the team found no scientific literature on factors associated with or predictive of becoming lost to follow-up in this type of clinic. Theoretical Framework: Health Services Utilization Model The Andersen-Aday Health Services Utilization Model is a framework first proposed in the late 1960s to help explain questions of health services use or related health outcomes.19 The model, which has undergone many iterations since that time, effectively informs the relationship between the environment, population characteristics, health behaviors, and outcomes. In the model, the environment includes factors related to both the health care system (including local, corporate, or national policy-related systems) and the external environment (physical, political, and economic components). Population factors include predisposing characteristics (demographics, position within the social structure, and beliefs of health services benefits), enabling characteristics (resources within a patient’s family and community such as economic status, living situation, access to health care facilities, and availability of social support), and need (perceived or objective need for care). Health behaviors include personal health practices (diet, exercise, and self-care) and the use of health services (accessing a health care provider). Finally, health outcomes include perceived health status, evaluated health status, and consumer satisfaction with obtained services. The model has been widely used to explain many outcomes. For this project, the model informs the investigation of reasons for use of and consumer satisfaction with the services provided at the clinic. Purpose This project sought to examine the question: “What factors are associated with patients becoming lost to follow-up at a free transitional and continuing care clinic for patients with uncontrolled diabetes?” The specific aims of this project were 1) to provide a sociodemographic description of patients in this free transitional and continuing care clinic for uninsured patients with diabetes, 2) to assess which proportion of the patients who made appointments at the clinic kept them and which of those continued care or became lost to follow-up, and 3) to quantitatively and qualitatively evaluate factors associated with patients becoming lost to follow-up after at least 1 visit to the clinic and their satisfaction with the clinic. An ancillary aim of the project was to assess patient satisfaction with the clinic. The project was reviewed and approved by the Institutional Review Board of UAB. Methods
in the chart was called, and that person was asked to provide the patient’s new phone number. Qualitative Survey Guide. Questions used to guide the phone interview included the following: Did you encounter any difficulties when interacting with staff at the clinic? Can you think of anything that would have enabled you to keep appointments and continue coming to the clinic? What are the reasons you stopped coming to the clinic? The entire set of interview questions is available upon request from the corresponding author. These questions are based on the Andersen-Aday Health Services Utilization Model and seek to assess ways that the environment, population characteristics, and health behaviors affect the outcome of becoming lost to follow-up (health behavior). Variables Lost to follow-up is defined as patients who attended at least 1 visit and had a follow-up scheduled but failed to attend their most recently scheduled visit at least 30 days prior; this included only patients who did not call to cancel their appointment. Kept last appointment is defined as patients who kept their last appointment; for comparative analysis with persons lost to follow-up, only individuals who kept at least 2 appointments, including their last appointment, are included. Age is reported as age in years at the initial visit. Gender is defined as female versus male. Non-Hispanic versus Hispanic are included. White versus Black are included. Total appointments made are included and are reported categorically as 1, 2, 3, and greater than or equal to 4 appointments. Analysis Analysis of qualitative data was completed using an open coding process according to Strauss and Corbin’s20 approach of breaking down, examining, comparing, conceptualizing, and categorizing data into themes. Quotes from the qualitative phone interviews were open coded by hand by two separate reviewers and themes were compared and reconciled. The themes were then fit to the Andersen-Aday model. Descriptive statistics are reported, including information about the outcome of patients’ most recent appointment with the clinic and their aggregate sociodemographic characteristics. Analysis of variance and chi-square statistics were used to assess sociodemographic differences in groups of patients by the outcome of their most recent appointment (appointment kept vs. lost to follow-up, etc.). Binary logistic regression was performed to assess for sociodemographic predictors of being lost to follow-up.
Data Sources Results Population. Initial data were obtained from records of the complete population of patients seen and treated at the PATH Clinic from December 12, 2012, through October 14, 2014, for a total of 22 months. Demographic data of all patients referred to the PATH Clinic from the local academic medical center were collected from the clinic’s electronic scheduling and electronic medical record (EMR) systems. Patients who dropped out/discontinued care (became lost to follow-up) after attending at least 1 visit (n ¼ 62) were recruited to participate in a phone interview to discuss their reasons for discontinuing care (Supplementary Figure). The last phone number recorded on the chart was used to contact the patient. If this number was nonworking, the emergency contact listed
Supplementary Figure shows the flow of patients from referral through discontinuation of care or active status, from December 12, 2012, through October 14, 2014. In its first 22 months, the PATH Clinic made appointments for 348 unique patients. Of those, 74 (21.2%) did not show for their initial appointment, 13 (3.7%) had their appointment canceled by clinic staff but were not rescheduled for a visit, and 15 (4.3%) had a new visit scheduled for some time in the future at the time of this report. Therefore, 246 of the 348 (70.7%) kept at least 1 appointment. Of the 246, 104 (42.3%) kept their last appointment; 18 (7.3%) did not keep their last appointment because they were hospitalized,
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deceased, or had a rescheduled appointment; 46 (18.7%) had a future return appointment scheduled at the time of this report; 4 (1.6%) called to cancel their last appointment; and 12 (4.9%) did not show for their appointment within the previous 30 days (these were not considered lost to follow-up because they were still within the window of opportunity to reschedule their appointments and return for care). Finally, 62 (25.2%) were lost to followup. The 348 unique patients were, on average, 44.5 years old, 44.5% female, 98% non-Hispanic, 71% Black, and 29% White. Among these patients, 32.2% kept 1 appointment, 19% kept 2 appointments, 12.4% kept 3 appointments, and 36.5% kept 4 or more appointments (Supplementary Table). Participants who were lost to follow-up (n ¼ 62) kept at least 1 appointment and missed at least 1 appointment; this means that they had at least 2 appointments scheduled. To better understand what may have caused these patients to discontinue care, we now compare them with active patients who had and kept at least 2 appointments (n ¼ 77) because both of these groups had at least 2 appointments scheduled. The Supplementary Table shows that although patients lost to follow-up were slightly younger, more often male, less White, and had a higher number of total appointments, these differences are likely due to chance. In binary logistic regression analysis, assessing for predictors of being lost to followup compared with being an active patient did not yield any significant findings; therefore, results are not presented here. Quantitative phone survey findings. Of the 62 who discontinued care, 24 (38.7%) were unreachable by phone; the investigators believe that they likely changed phone numbers. Ten (16.1%) had obtained insurance, 7 (11.3%) were noneEnglish speaking, and 4 (6.5%) had died (Supplementary Figure). There were 17 (27%) remaining who were eligible for participation in the phone interview and agreed to participate. Participants’ responses were fit to Andersen-Aday Health Services Utilization Model and details of that follow. Environmental factors. Forty-seven percent (n ¼ 8) felt that more could have been done to help them keep the appointment, and 65% (n ¼ 11) indicated that a phone call would have been useful. Fiftynine percent (n ¼ 10) said that a text message, e-mail, social media, or postal message would have been a helpful reminder, and 76% (n ¼ 13) felt that a calendar would have been helpful. Factors influencing participants becoming lost to follow-up included that they did not feel they knew their appointment dates: “I enjoyed coming down there. For some reason, it just stopped. I came 3 or 4 times, then I never got another appointment. Front desk said they would send me something in the mail, but I didn’t know what happened.” Regarding familiarity with appointment dates, a different participant said: “I’m not sure … I actually thought I was only referred by a doctor. Thought I had to go back through UAB to get another appointment. Right now, I feel that I am not under control. I was homeless when I came to clinic and misplaced my meter, and out of medications as well. I think my A1c will be quite high now.” Another participant said, “I didn’t know I had any more appointments for the clinic.” One participant said blatantly, “I never knew when my last appointment was.” Another said she believed she would have followed up if she would have “gotten a phone call back to know when it was.” Another individual indicated that even when he called he felt that he was unable to reach a staff member. To compensate for this problem, one participant indicated, “I would just go there and ask.” These participants represent a possible problem with communication from clinic staff that could be impeding patient follow-up; this is one factor that could be addressed to improve patients staying in care at the clinic.
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Despite these participants all becoming lost to follow-up, their mean satisfaction rating was quite high: 9.15 on a scale of 1 to 10. Population characteristics. In the Andersen-Aday model, population characteristics include enabling resources such as transportation and telephone communication. The more contemporary literature would refer to these factors as social determinants of health (SDOH). Forty-seven percent of the population (n ¼ 8) reported having difficulty with transportation during their time in care at the clinic. Reports included, “No, I just didn’t have no ride. I was having some issues with my girlfriend. She was my ride.” Another respondent said, “I tried my best. The only thing I had a problem with was back and forth transportation.” When asked what could be done to improve the clinic’s services, participants’ responses largely focused on transportation as well: “Maybe if y’all had bus passes or some help to get people back and forth, like a bus to get people to appointments, especially when the weather is really cold. The buses don’t run worth 2 cents. I did have to take the bus sometimes.” An additional respondent said, “Maybe bus passes and a van or something to pick people up who really can’t make it.” Thirty-five percent (n ¼ 6) indicated limited access to their phone or that they received a new number during their time in care. Furthermore, another indicated, “yeah, I did lose a phone, but I have a new number now,” and another person stated, “I changed my phone number about 3 times.” Still another said, “I had knew [sic] it, but I couldn’t make it because I had broken up with my girlfriend and couldn’t get there.” Health behaviors. Health behaviors include elements of personal health practices and use of health services. Respondents’ current health care status was assessed with the goal of linking them back to care if they needed it. Forty-one percent (n ¼ 7) indicated they had been receiving care since they stopped coming to the clinic. This does not necessarily mean the participants identified a new primary care provider; rather, they may be referring to having sought care at an emergency room. Sixty-five percent (n ¼ 11) had been to an emergency department since discontinuing care at the clinic, and 35% (n ¼ 6) had been hospitalized. Finally, 71% (n ¼ 12) still needed care and wanted to be reconnected to the clinic, indicating that the clinic was meeting an important need for these limited-resource patients. Health outcomes. In the Health Services Utilization Model, health outcomes include perceived and evaluated health status and consumer satisfaction. One participant reported a negative experience, saying: “I remember once feeling like I was homeless and people talked to me just any kind of way because so many homeless came there. I was asking about how to go about getting a bus pass or gas, and I was told very rude that, ‘If I knew, I would be getting it myself.’ … I have a lot of pride, and it took a lot for me to walk in there and ask for help.” In contrast, a respondent said, “I could never keep my appointment. That was the problem with me.” Participants overwhelmingly indicated that the challenges were their own and did not attempt to blame the clinic. One participant specifically said that the problem with becoming lost to follow-up “was really me that was working and trying to keep appointments. So it was really me.” Another patient said, “I can’t say that they didn’t [contact me] because at the time I wasn’t stable. I was no longer staying at that former address, so I can’t blame that on them. I was at a bad spot in my life at the time. I was in a near-fatal car accident and was disabled for 6 months.” Discussion More than 25% of the patients who kept at least 1 appointment with the PATH Clinic ultimately became lost to follow-up; none of
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the sociodemographic factors that were assessed were associated with this outcome. The investigators hypothesize that this may indicate that among a population of individuals who lack the social and economic resources to have health insurance, factors such as age, race, or gender have limited power to discriminate between why some subgroups of patients continue in care and others become lost to follow-up. Furthermore, it appeared that the patients who were more engaged with the clinic, as evidenced by having a higher total number of appointments made, were slightly more likely to become lost to follow-up, although the relationship may be due to chance (P ¼ 0.83). Results from the phone interview and qualitative analysis indicated that the key factors keeping patients from continuing in care with the PATH Clinic were uncertainty of appointment dates and unfamiliarity with navigating the health care system, complicated life circumstances, and transportation barriers. Researchers must continue to try to understand what factors affect patients’ likelihood of becoming lost to follow-up. This limited-resource population needs care for their own well-being and for the sake of the broader society due to the expense of unreimbursed emergency care, which is ultimately absorbed by taxpayers and health care consumers. For health care providers and practice administrators, this project underscores the need for more and closer care management to keep limited-resource patients in care. This is especially important considering findings from this project, which showed that once a patient missed an appointment, that patient was not going to call to make another one because most of these patients did not understand that calling to reschedule was an option. Although this may seem simple, it is a different way of thinking for patients who have previously only had access to the emergency department, even for routine care. Furthermore, this project highlights the need to educate all clinic staff regarding how to best support limited-resource patients. Although we often focus here on education for licensed professionals, front office staff are the patients’ first point of contact with the clinic, both at visits and when calling on the phone. We learned that one of our participants felt that the front office staff was jealous that the patient might receive additional resources to which she did not have access. Others reported that the clinic phone often went unanswered and that staff members were rude. Educating the office staff on the challenges experienced by limitedresource patients, as well as additional “hand-holding” they may need to stay in the clinic could be helpful. Limitations The limitations of this work include that investigators had to rely only on the administrative data and variables included in the database for quantitative analysis. These data limitations may be what led to no discernable differences by sociodemographic characteristics. Furthermore, the sample size was small in the final qualitative analysis due to the inability to reach many by phone. Also, this small sample size suggests that generalizability of the data may not be warranted. The strengths include that the full clinical population and nearly 2 years of data were available. Conclusion These findings do not yield any conclusive evidence that sociodemographic factors are associated with limited-resource patients with diabetes becoming lost to follow-up compared with those who remain active in care. This lack of significance may indicate that disadvantages associated with age, race, and gender are less powerful predictors of health care utilization for persons without health insurance and who have limited resources than they are for
those without such significant life challenges. That is, these patients already have such significant negative SDOH that demographic differences seem to have little effect. However, interviews with patients who did become lost to follow-up indicated that complicated life circumstances, transportation barriers, and uncertainty of appointment dates affected their ability to keep appointments. Toward this end, we recommend and intend to test these or similar questions about why limited-resource populations may become lost to follow-up using the Behavioral Model for Vulnerable Populations, which is an adaptation of the Andersen-Aday model tested with homeless populations.21 Although not all of the patients in this clinic are homeless, some are, and others may share similar characteristics. Also, further mixed methods investigation with similar populations is needed to validate these findings. Similarly, we also recognize the need for more sophisticated measures for determining who may be at risk for becoming lost to follow-up. As with being atrisk for medical conditions such as heart disease or diabetes, patients at risk for falling out of care likely do not know they are at an increased risk and are not able to verbalize to their health care provider that they need extra support to be able to keep appointments and remain active in the clinic. One helpful approach may be to incorporate a SDOH risk screening and referral process, as has been piloted by Page-Reeves et al22 in 3 New Mexico low-income clinics, one of which was a federally qualified health center. One of the authors on this project has also implemented a SDOH screening and referral project in family nurse practitioner education.23 Incorporating such a screening tool and referral process into the electronic medical record could help to identify patients who are at risk for becoming lost to follow-up so they can be supported more holistically from their first clinic visit onward. In the meantime, greater attention to transportation needs, care management between appointments, and follow-up reminders about appointments are all interventions that may improve patient adherence to appointments for limited-resource patients. These and other “personal touch” points between visits may help ensure that patients remain in care, and such services could be provided by office staff, nurses in the clinic, or community health workers. In summary, we must avoid falling into the Field of Dreams fallacy that “if we build it, they will come.” Although providing free care, medications, and supplies are vital to limited-resource patients living with chronic and complicated conditions such as diabetes, free services are not sufficient to keep some patients engaged in their care. Results of this project show that more support is needed to enable many limited-resource patients to continue to access ongoing care. Acknowledgments This project was supported in part by a U.S. Department of Health and Human Services, Health Resources and Services Administration, Nurse Education, Practice, Quality and Retention award (award # UD7HP25047) to the University of Alabama at Birmingham School of Nursing. The information or content and conclusions in this article are those of the authors and should not be construed as the official position or policy, nor should any endorsements be inferred by the U.S. Department of Health and Human Services. Supplementary Data Supplementary tables associated with this article can be found in the online version at https://doi.org/10.1016/j.nurpra.2018.12. 003.
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References 1. BRFSS Prevalence & Trends Data. 2015. https://www.cdc.gov/brfss/ brfssprevalence/. Accessed April 2, 2017. 2. Chow EA, Foster H, Gonzalez V, McIver L. The disparate impact of diabetes on racial/ethnic minority populations. Clin Diabetes. 2012;30(3):130-133. 3. Nguyen N, Nguyen X-M, Lane J, Wang P. Relationship between obesity and diabetes in a us adult population: findings from the National Health and Nutrition Examination Survey, 1999e2006. Obest Surg. 2011;21(3):351-355. 4. Mühlenbruch K, Jeppesen C, Joost H-G, Boeing H, Schulze MB. The value of genetic information for diabetes risk predictionedifferences according to sex, age, family history and obesity. PloS ONE. 2013;8(5):e64307. pez A, Tschann J, Fernandez A. Food insecurity and 5. Seligman HK, Jacobs EA, Lo glycemic control among low-income patients with type 2 diabetes. Diabetes Care. 2012;35(2):233-238. 6. Sims M, Diez Roux AV, Boykin S, et al. The socioeconomic gradient of diabetes prevalence, awareness, treatment, and control among African Americans in the Jackson Heart Study. Ann Epidemiol. 2011;21(12):892-898. 7. Smith BT, Lynch JW, Fox CS, et al. Life-course socioeconomic position and type 2 diabetes mellitus: the Framingham Offspring Study. Am J Epidemiol. 2011;173(4):438-447. 8. Zhuo X, Zhang P, Barker L, Albright A, Thompson TJ, Gregg E. The lifetime cost of diabetes and its implications for diabetes prevention. Diabetes Care. 2014;37(9):2557-2564. 9. American Diabetes Association. Economic costs of diabetes in the US in 2012. Diabetes Care. 2013;36(4):1033-1046. 10. Moheet A, Seaquist ER. Diabetes: hypoglycaemia, emergency care and diabetes mellitus. Nat Rev Endocrinol. 2014;10(7):384-385. 11. Zoungas S, Chalmers J, Ninomiya T, et al. Association of HbA1c levels with vascular complications and death in patients with type 2 diabetes: evidence of glycaemic thresholds. Diabetologia. 2012;55(3):636-643. € € m J, Svennblad B, Lohm L, Nilsson PM, Johansson G. As12. Ostgren CJ, Sundstro sociations of HbA1c and educational level with risk of cardiovascular events in 32 871 drug-treated patients with type 2 diabetes: a cohort study in primary care. Diabet Med. 2013;30(5):e170-e177. 13. Masharani U. Chapter 27. Diabetes mellitus & hypoglycemia. In: Papadakis MA, McPhee SJ, eds. Current Medical Diagnosis & Treatment 2014. New York, NY: McGraw-Hill; 2014.
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14. Santulli G. Epidemiology of cardiovascular disease in the 21st century: updated numbers and updated facts. J Cardiovasc Dis. 2013;1(1):1-2. 15. Centers for Disease Control and Prevention. QuickStats: Number of Deaths from 10 Leading CausesdNational Vital Statistics System, United States, 2010. 2013. https:// www.cdc.gov/women/lcod/2015/race-ethnicity/index.htm. https://www.cdc.gov/ healthequity/lcod/men/2015/race-ethnicity/index.htm. Accessed January 4, 2019. 16. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). The Lancet. 1998;352(9131): 837-853. 17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self-management of chronic disease in primary care. JAMA. 2002;288(19):2469-2475. 18. Haas L, Maryniuk M, Beck J, et al. National standards for diabetes self-management education and support. Diabetes Care. 2013;36(suppl. 1):S100-S108. 19. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior. 1995;36(1):1-10. 20. Strauss A, Corbin JM. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: SAGE; 1998. 21. Gelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273-1302. 22. Page-Reeves J, Kaufman W, Bleecker M, et al. Addressing social determinants of health in a clinic setting: the WellRx pilot in Albuquerque, New Mexico. J Am Board Family Med. 2016;29(3):414-418. 23. Buys KC, Somerall D. Social determinants of health screening and referral: innovation in graduate nursing education. J Nurs Educ. 2018;57(9):571-572.
Katie C. Buys, DNP, FNP, is an assistant professor and co-coordinator of the Family Nurse Practitioner Specialty Track, School of Nursing, University of Alabama at Birmingham, and can be reached at [email protected]. Cynthia Selleck, PhD, RN, is professor and associate dean for clinical and global partnerships at the School of Nursing, University of Alabama at Birmingham. David R. Buys, PhD, MSPH, is assistant professor and state health specialist at Mississippi State University, Mississippi State, MS. In compliance with national ethical guidelines, the authors report no relationships with business or industry that would pose a conflict of interest.
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Patients with ≥1 appointments scheduled (n = 348) 1. Did not show for appt: “never-shows” (n = 74) 2. Patient or clinic canceled appt (n = 13) 3. Has future “new” appt (n = 15) Kept ≥1 appointment (n = 246) 4. Kept last appt (n = 104) 5. Hospitalized, deceased, or clinic rescheduled (n = 18) 6. Future return appt (n = 46) 7. Patient canceled (n = 4) 8. No show <30 days (n = 12) 9) Dropped out/discontinued care (n = 62) Non–English-speaking (n = 7) Obtained insurance (n = 10) Unreachable (n = 24) Deceased (n = 4)
Participants enrolled in phone interview (n = 17) Supplementary Figure. Flow chart representing patients’ last appointment the PATH Clinic (as of October 14, 2014).
Supplementary Table Descriptive Statistics and Comparisons of Active vs. Lost to Follow-up Patients
Age, mean (SD) Female (vs. Male), n (%) Non-Hispanic (vs. Hispanic), n (%) White (vs. Black), n (%) Total appointments, n (%) 1 2 3 4 a b c d
Totala (n ¼ 348)
Kept 1 Apptb (n ¼ 246)
Active w/ >2 Apptsc (n ¼ 77)
Lost to Follow-Upd (n ¼ 62)
P value
44 155 341 101
(12.2) (44.5%) (98%) (29%)
45 104 240 72
(12.1) (42.3%) (97.6%) (29.3%)
46 40 76 23
44 28 61 15
0.22 0.27 0.70 0.29
112 66 43 127
(32.3%) (19.0%) (12.4%) (36.5%)
34 44 41 127
(13.8%) (17.9%) (16.7%) (61.6%)
d 21 (27.3%) 18 (23.4%) 38 (49.4%)
(11.4) (51.9%) (98.7%) (29.9%)
(10.4) (45.2%) (98.4%) (24.2%)
d 15 (24.2%) 13 (21.0%) 34 (54.8%)
0.83
All patients who had at least 1 appointment scheduled. Patients who kept 1 appointments, regardless of whether they were later lost to follow-up. Patients who kept at least 2 appointments, including last appointment. Patients who kept at least 1 appointment and had a follow-up scheduled but failed to attend or cancel their most recently scheduled appointment at least 30 days prior.