ORIGINAL ARTICLE
Factors affecting patients' adherence to orthodontic appointments Omair M. Bukhari,a Keyvan Sohrabi,b and Mary Tavaresc Boston and Cambridge, Mass, Mecca, Saudi Arabia, and Seattle, Wash
Introduction: Studies show that attendance at orthodontic appointments affects treatment outcomes, treatment duration, and the probability of side effects. The aim of this study was to predict factors that influence patients' attendance at orthodontic appointments. Methods: We conducted a face-to-face guided interview survey of 153 participants from orthodontic clinics in the Greater Boston area. Attendance at scheduled orthodontic appointments was self-reported as always, sometimes, or rarely. Participants' characteristics, including demographics, dental insurance, and oral hygiene practices, were self-reported. Moreover, from dental records, we collected the time that the participants spent undergoing active orthodontic treatment. Multivariable ordered logistic regression was used to report proportional odds ratios and attendance probabilities. A likelihood ratio test was performed to ensure that the proportional odds assumption held. Results: For overall appointment attendance, 76% of the participants reported always attending, 16% reported sometimes attending, and 8% reported rarely attending. Based on multivariable logistic regression (adjusted for age, race, and sex), the participants with optimal oral hygiene practices were almost 6 times (5.9) more likely to attend appointments than those who did not (P 5 0.002). The odds of attending appointments decreased significantly (by 23%) for every 6-month increase in treatment duration (P 5 0.008). Participants covered by nonMedicaid insurance were 4 times (P 5 0.018) more likely to attend appointments than were those with Medicaid insurance. Conclusion: Our findings indicate that adherence to orthodontic treatment follow-up visits was strongly correlated to insurance type, treatment duration, and oral hygiene practices. Unlike previous studies, sex was not a significant predictor of adherence. (Am J Orthod Dentofacial Orthop 2016;149:319-24)
A
challenging task facing a dental team is supporting patients in changing their oral health behaviors and maintaining those changes.1 According to the American Association of Orthodontists, because orthodontic treatment is seldom finished rapidly, the assumption would be that patients who want good-looking smiles and healthier occlusions would attend every appointment and comply with every treatment instruction to accomplish the desired outcome as rapidly as possible.2 In orthodontics,
a Resident, Harvard School of Dental Medicine, Boston, Mass; lecturer, Faculty of Dentistry, Umm Al-Qura University, Mecca, Saudi Arabia. b Resident, School of Dentistry, University of Washington, Seattle, Wash. c Senior clinical investigator, Forsyth Institute, Cambridge, Mass; program director, Dental Public Health Residency, Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, Mass. All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported. Address correspondence to: Omair M. Bukhari, Harvard School of Dental Medicine, Oral Health Policy and Epidemiology, REB 204, 188 Longwood Ave, Boston, MA 02115; e-mail, [email protected]. Submitted, April 2015; revised and accepted, July 2015. 0889-5406/$36.00 Copyright Ó 2016 by the American Association of Orthodontists. http://dx.doi.org/10.1016/j.ajodo.2015.07.040
adherence means attending appointments, maintaining good oral hygiene, wearing elastics or functional appliances as instructed, and avoiding foods that can loosen the brackets. In 2003, Trenouth3 found that the failure rate of patients who completed orthodontic treatment was 10.3%, and the failure rate of patients who discontinued orthodontic treatment was 21.4%. Therefore, we could say that attendance affected treatment success. In other studies, “no-show” rates for orthodontic appointments ranged from 13.6%4 to 23.3%.5 Patients who neglected orthodontic appointments during active treatment were likely to prolong their treatment durations6-9; as a result, they might experience more harmful side effects.10 Missed appointments decrease the possibility that orthodontic treatment will be completed successfully.3 The American Association of Orthodontists Insurance Company suggests the following possible causes for a patient's failure to keep orthodontic appointments: teenaged patients who are less than passionate about treatment; an unexpected illness or a crisis in the family; and adults who report interferences with work schedules 319
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Table I. Characteristics of respondents from the 3 orthodontic practices stratified by responses to attendance
question Characteristic/answer to the question Age category (y) \12 12 to \16 .16 Sex Male Female Race White Black Hispanic Other Insurance type Medicaid Non-Medicaid Brushing/flossing daily Yes No Mean time of active treatment (SD)* (mo) Mean age (SD)* (y)
Always (n 5 116)
Sometimes (n 5 25)
Rarely (n 5 12)
Total (n 5 153)
21 (81%) 58 (77%) 37 (71%)
5 (19%) 9 (12%) 11 (21%)
0 8 (11%) 4 (8%)
26 (100%) 75 (100%) 52 (100%)
48 (67%) 68 (84%)
17 (24%) 8 (10%)
7 (9%) 5 (6%)
72 (100%) 81 (100%)
37 (84%) 37 (69%) 31 (76%) 11 (79%)
2 (5%) 15 (28%) 6 (15%) 2 (14%)
5 (11%) 2 (3%) 4 (9%) 1 (7%)
44 (100%) 54 (100%) 41 (100%) 14 (100%)
64 (69%) 52 (87%)
18 (19%) 7 (12%)
11 (12%) 1 (1%)
93 (100%) 60 (100%)
101 (81%) 15 (52%) 8.8 (6.7) 14.6 (4)
15 (12%) 10 (34%) 7.3 (5.8) 14.8 (2.6)
8 (7%) 4 (14%) 10.8 (7.2) 15.4 (3.5)
124 (100%) 29 (100%) 21 (16) 14.7 (3.9)
P value (X2) 0.309
0.038
0.075
0.022
0.003
0.322* 0.740*
*Based on ANOVA test.
and emotional pressures.2 An additional cause, probably the most critical and frequent cause, is that the patient simply forgot.11,12 Forgetting indicates patient behavioral attitudes and oral health literacy. Although previous behavioral epidemiologic studies have tried to establish a connection between a patient's compliance with treatment, missed appointments, and oral hygiene, we could not find a study performed in private orthodontic offices in the United States. Although it is commonly thought that there is a correlation among elastic wear, showing up for appointments, and oral hygiene level, studies have shown contradictory results. Moreover, because of a lack of consensus about factors affecting attendance and the high percentage of malpractice claims against orthodontists who have frequent no-show patients, the American Association of Orthodontists Insurance Company recommends paying close attention to patient attendance deficiencies and addressing them as early as possible. Therefore, in this study, we predicted that attendance through a set of variables collected during the first visit would help to predict possible future attendance behavior, improve outcomes, and reduce the percentage of malpractice claims associated with no-show patients. MATERIAL AND METHODS
The study population was orthodontic patients in the Greater Boston area of Massachusetts. The participants
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were recruited from 3 private orthodontic offices in Boston, Cambridge, and Somerville. One hundred fifty-three participants were invited to participate in the study, and none refused or was unable to complete the questionnaire because of literacy problems. The subjects included 81 girls (53%) and 72 boys (47%). Their mean age was 14.7 years (SD, 3.9 years), and the mean average treatment time was 21 months (SD, 16 months). Demographics and participants' characteristics are shown in Table I. Overall, there were 54 African Americans (34.6%), 44 whites (28.8%), 41 Hispanics (27.6%), and 14 (9%) participants from other ethnic backgrounds. Medicaid insurance was used by 93 of the participants (60.9%). Patients with severe dentofacial deformities were excluded. Parents' consents and children's assents were obtained. This was a convenience sample of patients who agreed to take the surveys and signed the consent form. The study was approved by Committee on Human Studies of Harvard University Faculty of Medicine. The participants completed self-administered questionnaires guided by a face-to-face interview. The questionnaire was divided into 8 parts: (1) demographic data, (2) oral hygiene practices, (3) payment method, (4) attendance history, (5) patients' and parents' perceptions about the importance of braces, (6) treatment duration (actual time that the participant was undergoing active orthodontic treatment), (7) Oral Impact on Daily Performances scores, and (8) Peer Assessment Rating scores.
American Journal of Orthodontics and Dentofacial Orthopedics
Bukhari, Sohrabi, and Tavares
Before the actual data collection, the questionnaire sets were validated in a pilot study, conducted in waiting rooms of the Harvard dental clinic. This article will report and predict patients' attendance history. Patient attendance history was addressed by the question “Have you visited the orthodontist after having an appointment?” with possible responses of always, sometimes, and rarely. The data collection procedure had 2 main stages. First, the participants completed questionnaires that included questions about the parents' perceived need for orthodontic treatment, behavioral attitude, and sociodemographic information. An interviewer was available to clarify any questions. Second, the Oral Impact on Daily Performance and Peer Assessment Rating scores were calculated from the Oral Impact on Daily Performance questionnaire and the study casts, respectively. Statistical analysis
A descriptive analysis was performed for the demographic data to summarize the overall distribution of the characteristic variables, and bivariate analyses (chi-square and analysis of variance [ANOVA]) were performed to assess the associations between independent variables and attendance history. The first model used the multivariable ordered logistic regression to predict attendance history and examine the simultaneous association of independent and outcome variables. The associations between the independent and outcome variables were adjusted for age (continuous), sex, and race. To determine which additional variables needed to be adjusted, we used the purposeful selection method.13 The estimated attendance probabilities and odds ratios were reported. Finally, a likelihood ratio test was performed to ensure that the proportional odds assumption held. The second model used the multivariable logistic regression with the binary outcome attendance history (always vs sometimes or rarely) while adjusting for age (\12, 12 to \16, .16 years) to determine whether teenaged participants driving themselves attended their orthodontic appointments differently from other participants. We tested whether there were any significant differences in patient characteristics between the 3 orthodontic offices. Moreover, we added the variable of the 3 offices to our models. However, it was not significant and did not change the coefficients of other predictors. Consequently, to have a simpler model, easier interpretations, and better understanding by readers, we removed it from all models.
321
Table II. Results from multivariable logistic regres-
sions with attendance as a dependent variable adjusted for sex and race
Predictor Age category (y) \12 (reference) 12 to \16 Odds ratio 95% CI P value .16 Odds ratio 95% CI P value Insurance type Medicaid (reference) Non-Medicaid Odds ratio 95% CI P value Brush/floss daily No (reference) Yes Odds ratio 95% CI P value Treatment duration (mo) Odds ratio 95% CI P value
Model 1: logistic regression treating age categorically
Model 2: ordered logistic regression treating age continuously
0.7 0.12-4.20 0.703
1 0.85-1.20 0.951
1 0.15-6.79 0.995
3.6* 1.13-11.61 0.029
4.0* 1.26-12.47 0.018
6.9y 2.2-23.29 0.002
5.9y 1.93-17.85 0.002
0.8* 0.66-0.97 0.023
0.77* 0.64-0.94 0.008
*P \0.05; yP \0.01.
All analyses were conducted using a statistical package (version 12.0; Stata, College Station, Tex). All statistical tests were 2-sided, and a P value of \0.05 was deemed to be statistically significant. RESULTS
Among the girls, the proportions who reported they always, sometimes, and rarely attended were 84%, 10%, and 6%, respectively. On the other hand, the boys reported always, sometimes, and rarely attending at 67%, 24%, and 9%, respectively. Overall, the girls were more likely to attend than the boys (P 5 0.038; chi-square test). Among the Medicaid participants, the proportions who reported they always, sometimes, and rarely attended were 69%, 19%, and 12%, respectively. Among the non-Medicaid participants, the proportions who reported they always, sometimes, and rarely attended were 87%, 12%, and 1%, respectively. Overall, non-Medicaid participants were more likely to attend than Medicaid participants (P 5 0.022; chi-square test).
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Probabilities of different attendance categories 1
probability of A probability of S probability of R
Probability
.8
.6
.4
.2
0 0
20
40 Treatent Duration in Months
60
80
A: Always attend S: Sometimes attend R: Rarely attend
Fig. Probabilities of attendance over treatment duration.
Among participants with good oral hygiene practices, the proportions who reported they always, sometimes, and rarely attended were 81%, 12%, and 7%, respectively. In contrast, of the participants with suboptimal oral hygiene practices, the proportions who reported they always, sometimes, and rarely attended were 52%, 34%, and 14%, respectively. Those who practiced brushing and flossing daily were more likely to attend than were those who did not brush and floss daily (P 5 0.003; chi-square test). Overall, there was no statistically significant difference in attendance history among the different race or ethnic categories (P 5 0.075; chi-square test) or among the different age categories (P 5 0.309; chi-square test). Moreover, there were no statistically significant associations between attendance history and the duration of active orthodontic treatment (P 5 0.322; ANOVA) or age of the participants (P 5 0.74; ANOVA). The results of the multivariable analyses examining the simultaneous associations between attendance (dependent variable) and insurance type, oral hygiene behavior, and treatment duration are summarized in Table II. For non-Medicaid participants, the odds of always attending vs sometimes and rarely attending combined were 4 times higher than for Medicaid participants, adjusted for age, race, and sex (P 5 0.018). For participants with good oral hygiene (brush and floss daily), the odds of always attending vs sometimes and rarely attending combined were almost 6 times higher than for participants with suboptimal oral hygiene practices, adjusted for age, race, and sex (P 5 0.002).
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For every 6-month increase in treatment duration, the odds of always attending vs sometimes and rarely attending combined were 0.77 times lower, adjusted for age, race, and sex (P 5 0.008). The Figure shows the probability of the different attendance categories plotted over the treatment duration. According to the logistic regression, the age categories were not significantly associated with attendance. Table III includes the probabilities of attending. Girls had a higher probability of attending than did boys (82% and 72%, respectively). All race categories had similar attendance probabilities (range, 78%-83%) except for African Americans, who had a 68% probability of attending appointments. Medicaid participants had lower attendance probabilities than did non-Medicaid participants (69% and 87%, respectively). The probability of attending appointments was higher among participants who brushed daily than in those who did not (83% and 52%, respectively). DISCUSSION
In 2009, Lindauer et al14 conducted a study over a 6-week period where the last appointment of each active, non-Medicaid participant (n 5 538) was recorded as either kept or missed. They reported that male patients were more likely to miss their orthodontic appointments. Moreover, Qui~ nonez et al15 found that boys attended fewer follow-up visits in a study in which the parents of Medicaid children completed a questionnaire before their child's medical visit. The providers completed patient dental forms at each visit, recording
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Table III. Probability of attendance by participants'
characteristics
Characteristic Age (\12 y) Attendence probability 95% CI Age (12 to \16 y) Attendence probability 95% CI Age (.16 y) Attendence probability 95% CI Male Attendence probability 95% CI Female Attendence probability 95% CI White Attendence probability 95% CI African American Attendence probability 95% CI Hispanic Attendence probability 95% CI Other* Attendence probability 95% CI Medicaid Attendence probability 95% CI Non-Medicaid Attendence probability 95% CI Brush daily (yes) Attendence probability 95% CI Brush daily (no) Attendence probability 95% CI
Model 1: treating age categorically
Model 2: ordered logistic regression treating age continuously
81% 61%-93%
NA NA
74% 65%-84%
NA NA
79% 68%-90%
NA NA
73% 63%-82%
72% 63%-82%
82% 72%-91%
82% 73%-91%
82% 69%-96%
83% 69%-96%
67% 55%-80%
68% 55%-80%
83% 72%-94%
82% 70%-93%
78% 58%-98%
78% 58%-98%
70% 60%-80%
69% 59%-79%
87% 78%-96%
87% 78%-96%
83% 76%-90%
83% 76%-89%
50% 30%-69%
52% 35%-70%
NA, Not applicable. *Because of the small cell size, we used the exact method to estimate the probability.
dental services, caries risk, and dental disease. Questionnaires, dental forms, and Medicaid claims were connected to generate a database. Although our bivariate analysis results support these findings, the attendance by sex was not significantly different after adjusting for age, race, oral hygiene, and insurance type through an ordered logistic regression model. On the other hand, Horsley et al16 reported that female patients were more likely to miss their orthodontic appointments.
These differences may be due to different Medicaid participant proportions among the study populations: the sample of Lindauer et al consisted entirely of non-Medicaid subjects, and the sample of Horsley et al was dominated by non-Medicaid patients (74%). In our study, Medicaid participants were more common (61%). Our findings about the attendance behavior among Medicaid participants were similar to those reported in previous studies.16,17 In our study, 24% (18.7% Medicaid and 5.3% non-Medicaid) of the patients reported sometimes or rarely attending orthodontic appointments; among those, 78% were Medicaid insured. With elevated no-shows, it is clear why dentist participation in Medicaid dental coverage is so low. Lamberth et al18 specified that broken appointments by Medicaid participants affect a dentist's decision whether to participate in Medicaid insurance. Even though low reimbursement is considered the principal restriction to accepting Medicaid patients, a missed appointment generates no income. Additionally, a missed appointment could have been used for another patient. In 1998, Breistein and Burden19 found that dental health was a significant predictor for receiving orthodontic treatment: healthier patients (no caries and no oral hygiene problems) were more likely to receive orthodontic treatment. We found that patients who practiced good oral hygiene daily were more likely to attend appointments, and this increases their chances to receive and complete the orthodontic treatment. Obviously, in any treatment plan, the longer the treatment, the more likely it is for a patient to miss appointments. In our study, we found that active treatment duration was a significant predictor for patients' attendance. We could not perform ordered logistic regression with a model that contained age as a categorical variable (\12, 12 to \16, and .16 years) because of sample size issues when there were no participants in the category of less than 12 years old who reported rare attendance. However, to overcome this issue, we combined the sometimes attendance group with the rarely attendance group, and performed logistic regression with the binary dependent variable (always attend vs sometimes or rarely) and categorical age as predictors. Although we did not find a significant difference in attendance between the different age groups, we cannot be confident that age is not a predictor for participants' attendance. Conversely, the American Association of Orthodontists Insurance Company stated that teenaged patients who drive to appointments and are less than passionate about their treatment often have irregular attendance.
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More research should be conducted to answer this question. Additionally, to address whether the severity of malocclusion is associated with attendance, we adjusted for Peer Assessment Rating scores, and it had no significant effect. One other limitation of our study about attendance history was that we relied on self-reported questionnaires for attendance. The assessments of attendance and oral hygiene (with a question about brushing and flossing daily) might be more subjective. CONCLUSIONS
There were significant associations between oral hygiene practices (brush and floss daily), insurance type, treatment duration, and probability of attendance. Our findings support the concern among orthodontists that Medicaid participants have higher no-shows than do non-Medicaid participants. Future research should focus on exploring additional reasons that Medicaid patients have higher no-shows, and potentially develop and validate new behavioral treatment priority settings (incorporating quality of life, oral hygiene, and compliance) that can help to improve the current Medicaid screening systems that are based only on clinical or medical necessity, with a firm cutoff threshold. Furthermore, this can help to identify patients who need more help with compliance and eventually save orthodontic staff time and better allocate resources for Medicaid. ACKNOWLEDGMENTS
We thank the dentists who provided access to their patients: Patricia Brown, Robert Petrosino, and Mohamed Butt. REFERENCES 1. Asimakopoulou K, Daly B. Adherence in dental settings. Dent Update 2009;36:626-30. 2. Franklin E. Missed appointments often result in malpractice claims. Available at: https://www.aaoinfo.org/news/2014/01/ missed-appointments-often-result-malpractice-claims. Accessed August 31, 2014.
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3. Trenouth MJ. Do failed appointments lead to discontinuation of orthodontic treatment? Angle Orthod 2003;73:51-5. 4. Richardson A. Failed appointments in an academic orthodontic clinic. Br Dent J 1998;184:612-5. 5. Can S, Macfarlane T, O'Brien KD. The use of postal reminders to reduce non-attendance at an orthodontic clinic: a randomised controlled trial. Br Dent J 2003;19:199-201. 6. Haeger RS, Colberg RT. Effects of missed appointments and bracket failures on treatment efficiency and office productivity. J Clin Orthod 2007;41:433-7. 7. Fink DF, Smith RJ. The duration of orthodontic treatment. Am J Orthod Dentofacial Orthop 1992;102:45-51. 8. Beckwith FR, Ackerman RJ Jr, Cobb CM, Tira DE. An evaluation of factors affecting duration of orthodontic treatment. Am J Orthod Dentofacial Orthop 1999;115:439-47. 9. Jarvinen S, Widstrom E, Raitio M. Factors affecting the duration of orthodontic treatment in children. A retrospective study. Swed Dent J 2004;28:93-100. 10. Marcusson A, Norevall LI, Persson M. White spot reduction when using glass ionomer cement for bonding in orthodontics: a longitudinal and comparative study. Eur J Orthod 1997;19: 233-42. 11. Trenouth MJ, Hough A. Reasons for broken and canceled appointments in a British orthodontic clinic. J Clin Orthod 1991;25: 115-20. 12. Reekie D, Devlin H. Preventing failed appointments in general dental practice: a comparison of reminder methods. Br Dent J 1998;185:472-4. 13. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008;3:17. 14. Lindauer SJ, Powell JA, Leypoldt BC, T€ ufekc¸i E, Shroff B. Influence of patient financial account status on orthodontic appointment attendance. Angle Orthod 2009;79:755-8. 15. Qui~ nonez RB, Pahel BT, Rozier RG, Stearns SC. Follow-up preventive dental visits for Medicaid-enrolled children in the medical office. J Public Health Dent 2008;68:131-8. 16. Horsley BP, Lindauer SJ, Shroff B, T€ ufekc¸i E, Abubaker AO, Fowler CE, et al. Appointment keeping behavior of Medicaid vs non-Medicaid orthodontic patients. Am J Orthod Dentofacial Orthop 2007;132:49-53. 17. Brysh LS. “Where's my patient?”—a plan to decrease broken appointments in a predominantly Medicaid clinic. Spec Care Dentist 2001;21:126-8. 18. Lamberth EF, Rothstein EP, Hipp TJ, Souder RL, Kennedy TI, Faccenda DF, et al. Rates of missed appointments among pediatric patients in a private practice: Medicaid compared with private insurance. Arch Pediatr Adolesc Med 2002;156:86-7. 19. Breistein B, Burden DJ. Equity and orthodontic treatment: a study among adolescents in Northern Ireland. Am J Orthod Dentofacial Orthop 1998;113:408-13.
American Journal of Orthodontics and Dentofacial Orthopedics
Factors affecting patients' adherence to orthodontic appointments Omair M. Bukhari,a Keyvan Sohrabi,b and Mary Tavaresc Boston and Cambridge, Mass, Mecca, Saudi Arabia, and Seattle, Wash
Introduction: Studies show that attendance at orthodontic appointments affects treatment outcomes, treatment duration, and the probability of side effects. The aim of this study was to predict factors that influence patients' attendance at orthodontic appointments. Methods: We conducted a face-to-face guided interview survey of 153 participants from orthodontic clinics in the Greater Boston area. Attendance at scheduled orthodontic appointments was self-reported as always, sometimes, or rarely. Participants' characteristics, including demographics, dental insurance, and oral hygiene practices, were self-reported. Moreover, from dental records, we collected the time that the participants spent undergoing active orthodontic treatment. Multivariable ordered logistic regression was used to report proportional odds ratios and attendance probabilities. A likelihood ratio test was performed to ensure that the proportional odds assumption held. Results: For overall appointment attendance, 76% of the participants reported always attending, 16% reported sometimes attending, and 8% reported rarely attending. Based on multivariable logistic regression (adjusted for age, race, and sex), the participants with optimal oral hygiene practices were almost 6 times (5.9) more likely to attend appointments than those who did not (P 5 0.002). The odds of attending appointments decreased significantly (by 23%) for every 6-month increase in treatment duration (P 5 0.008). Participants covered by nonMedicaid insurance were 4 times (P 5 0.018) more likely to attend appointments than were those with Medicaid insurance. Conclusion: Our findings indicate that adherence to orthodontic treatment follow-up visits was strongly correlated to insurance type, treatment duration, and oral hygiene practices. Unlike previous studies, sex was not a significant predictor of adherence. (Am J Orthod Dentofacial Orthop 2016;149:319-24)
A
challenging task facing a dental team is supporting patients in changing their oral health behaviors and maintaining those changes.1 According to the American Association of Orthodontists, because orthodontic treatment is seldom finished rapidly, the assumption would be that patients who want good-looking smiles and healthier occlusions would attend every appointment and comply with every treatment instruction to accomplish the desired outcome as rapidly as possible.2 In orthodontics,
a Resident, Harvard School of Dental Medicine, Boston, Mass; lecturer, Faculty of Dentistry, Umm Al-Qura University, Mecca, Saudi Arabia. b Resident, School of Dentistry, University of Washington, Seattle, Wash. c Senior clinical investigator, Forsyth Institute, Cambridge, Mass; program director, Dental Public Health Residency, Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, Mass. All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported. Address correspondence to: Omair M. Bukhari, Harvard School of Dental Medicine, Oral Health Policy and Epidemiology, REB 204, 188 Longwood Ave, Boston, MA 02115; e-mail, [email protected]. Submitted, April 2015; revised and accepted, July 2015. 0889-5406/$36.00 Copyright Ó 2016 by the American Association of Orthodontists. http://dx.doi.org/10.1016/j.ajodo.2015.07.040
adherence means attending appointments, maintaining good oral hygiene, wearing elastics or functional appliances as instructed, and avoiding foods that can loosen the brackets. In 2003, Trenouth3 found that the failure rate of patients who completed orthodontic treatment was 10.3%, and the failure rate of patients who discontinued orthodontic treatment was 21.4%. Therefore, we could say that attendance affected treatment success. In other studies, “no-show” rates for orthodontic appointments ranged from 13.6%4 to 23.3%.5 Patients who neglected orthodontic appointments during active treatment were likely to prolong their treatment durations6-9; as a result, they might experience more harmful side effects.10 Missed appointments decrease the possibility that orthodontic treatment will be completed successfully.3 The American Association of Orthodontists Insurance Company suggests the following possible causes for a patient's failure to keep orthodontic appointments: teenaged patients who are less than passionate about treatment; an unexpected illness or a crisis in the family; and adults who report interferences with work schedules 319
Bukhari, Sohrabi, and Tavares
320
Table I. Characteristics of respondents from the 3 orthodontic practices stratified by responses to attendance
question Characteristic/answer to the question Age category (y) \12 12 to \16 .16 Sex Male Female Race White Black Hispanic Other Insurance type Medicaid Non-Medicaid Brushing/flossing daily Yes No Mean time of active treatment (SD)* (mo) Mean age (SD)* (y)
Always (n 5 116)
Sometimes (n 5 25)
Rarely (n 5 12)
Total (n 5 153)
21 (81%) 58 (77%) 37 (71%)
5 (19%) 9 (12%) 11 (21%)
0 8 (11%) 4 (8%)
26 (100%) 75 (100%) 52 (100%)
48 (67%) 68 (84%)
17 (24%) 8 (10%)
7 (9%) 5 (6%)
72 (100%) 81 (100%)
37 (84%) 37 (69%) 31 (76%) 11 (79%)
2 (5%) 15 (28%) 6 (15%) 2 (14%)
5 (11%) 2 (3%) 4 (9%) 1 (7%)
44 (100%) 54 (100%) 41 (100%) 14 (100%)
64 (69%) 52 (87%)
18 (19%) 7 (12%)
11 (12%) 1 (1%)
93 (100%) 60 (100%)
101 (81%) 15 (52%) 8.8 (6.7) 14.6 (4)
15 (12%) 10 (34%) 7.3 (5.8) 14.8 (2.6)
8 (7%) 4 (14%) 10.8 (7.2) 15.4 (3.5)
124 (100%) 29 (100%) 21 (16) 14.7 (3.9)
P value (X2) 0.309
0.038
0.075
0.022
0.003
0.322* 0.740*
*Based on ANOVA test.
and emotional pressures.2 An additional cause, probably the most critical and frequent cause, is that the patient simply forgot.11,12 Forgetting indicates patient behavioral attitudes and oral health literacy. Although previous behavioral epidemiologic studies have tried to establish a connection between a patient's compliance with treatment, missed appointments, and oral hygiene, we could not find a study performed in private orthodontic offices in the United States. Although it is commonly thought that there is a correlation among elastic wear, showing up for appointments, and oral hygiene level, studies have shown contradictory results. Moreover, because of a lack of consensus about factors affecting attendance and the high percentage of malpractice claims against orthodontists who have frequent no-show patients, the American Association of Orthodontists Insurance Company recommends paying close attention to patient attendance deficiencies and addressing them as early as possible. Therefore, in this study, we predicted that attendance through a set of variables collected during the first visit would help to predict possible future attendance behavior, improve outcomes, and reduce the percentage of malpractice claims associated with no-show patients. MATERIAL AND METHODS
The study population was orthodontic patients in the Greater Boston area of Massachusetts. The participants
March 2016 Vol 149 Issue 3
were recruited from 3 private orthodontic offices in Boston, Cambridge, and Somerville. One hundred fifty-three participants were invited to participate in the study, and none refused or was unable to complete the questionnaire because of literacy problems. The subjects included 81 girls (53%) and 72 boys (47%). Their mean age was 14.7 years (SD, 3.9 years), and the mean average treatment time was 21 months (SD, 16 months). Demographics and participants' characteristics are shown in Table I. Overall, there were 54 African Americans (34.6%), 44 whites (28.8%), 41 Hispanics (27.6%), and 14 (9%) participants from other ethnic backgrounds. Medicaid insurance was used by 93 of the participants (60.9%). Patients with severe dentofacial deformities were excluded. Parents' consents and children's assents were obtained. This was a convenience sample of patients who agreed to take the surveys and signed the consent form. The study was approved by Committee on Human Studies of Harvard University Faculty of Medicine. The participants completed self-administered questionnaires guided by a face-to-face interview. The questionnaire was divided into 8 parts: (1) demographic data, (2) oral hygiene practices, (3) payment method, (4) attendance history, (5) patients' and parents' perceptions about the importance of braces, (6) treatment duration (actual time that the participant was undergoing active orthodontic treatment), (7) Oral Impact on Daily Performances scores, and (8) Peer Assessment Rating scores.
American Journal of Orthodontics and Dentofacial Orthopedics
Bukhari, Sohrabi, and Tavares
Before the actual data collection, the questionnaire sets were validated in a pilot study, conducted in waiting rooms of the Harvard dental clinic. This article will report and predict patients' attendance history. Patient attendance history was addressed by the question “Have you visited the orthodontist after having an appointment?” with possible responses of always, sometimes, and rarely. The data collection procedure had 2 main stages. First, the participants completed questionnaires that included questions about the parents' perceived need for orthodontic treatment, behavioral attitude, and sociodemographic information. An interviewer was available to clarify any questions. Second, the Oral Impact on Daily Performance and Peer Assessment Rating scores were calculated from the Oral Impact on Daily Performance questionnaire and the study casts, respectively. Statistical analysis
A descriptive analysis was performed for the demographic data to summarize the overall distribution of the characteristic variables, and bivariate analyses (chi-square and analysis of variance [ANOVA]) were performed to assess the associations between independent variables and attendance history. The first model used the multivariable ordered logistic regression to predict attendance history and examine the simultaneous association of independent and outcome variables. The associations between the independent and outcome variables were adjusted for age (continuous), sex, and race. To determine which additional variables needed to be adjusted, we used the purposeful selection method.13 The estimated attendance probabilities and odds ratios were reported. Finally, a likelihood ratio test was performed to ensure that the proportional odds assumption held. The second model used the multivariable logistic regression with the binary outcome attendance history (always vs sometimes or rarely) while adjusting for age (\12, 12 to \16, .16 years) to determine whether teenaged participants driving themselves attended their orthodontic appointments differently from other participants. We tested whether there were any significant differences in patient characteristics between the 3 orthodontic offices. Moreover, we added the variable of the 3 offices to our models. However, it was not significant and did not change the coefficients of other predictors. Consequently, to have a simpler model, easier interpretations, and better understanding by readers, we removed it from all models.
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Table II. Results from multivariable logistic regres-
sions with attendance as a dependent variable adjusted for sex and race
Predictor Age category (y) \12 (reference) 12 to \16 Odds ratio 95% CI P value .16 Odds ratio 95% CI P value Insurance type Medicaid (reference) Non-Medicaid Odds ratio 95% CI P value Brush/floss daily No (reference) Yes Odds ratio 95% CI P value Treatment duration (mo) Odds ratio 95% CI P value
Model 1: logistic regression treating age categorically
Model 2: ordered logistic regression treating age continuously
0.7 0.12-4.20 0.703
1 0.85-1.20 0.951
1 0.15-6.79 0.995
3.6* 1.13-11.61 0.029
4.0* 1.26-12.47 0.018
6.9y 2.2-23.29 0.002
5.9y 1.93-17.85 0.002
0.8* 0.66-0.97 0.023
0.77* 0.64-0.94 0.008
*P \0.05; yP \0.01.
All analyses were conducted using a statistical package (version 12.0; Stata, College Station, Tex). All statistical tests were 2-sided, and a P value of \0.05 was deemed to be statistically significant. RESULTS
Among the girls, the proportions who reported they always, sometimes, and rarely attended were 84%, 10%, and 6%, respectively. On the other hand, the boys reported always, sometimes, and rarely attending at 67%, 24%, and 9%, respectively. Overall, the girls were more likely to attend than the boys (P 5 0.038; chi-square test). Among the Medicaid participants, the proportions who reported they always, sometimes, and rarely attended were 69%, 19%, and 12%, respectively. Among the non-Medicaid participants, the proportions who reported they always, sometimes, and rarely attended were 87%, 12%, and 1%, respectively. Overall, non-Medicaid participants were more likely to attend than Medicaid participants (P 5 0.022; chi-square test).
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Probabilities of different attendance categories 1
probability of A probability of S probability of R
Probability
.8
.6
.4
.2
0 0
20
40 Treatent Duration in Months
60
80
A: Always attend S: Sometimes attend R: Rarely attend
Fig. Probabilities of attendance over treatment duration.
Among participants with good oral hygiene practices, the proportions who reported they always, sometimes, and rarely attended were 81%, 12%, and 7%, respectively. In contrast, of the participants with suboptimal oral hygiene practices, the proportions who reported they always, sometimes, and rarely attended were 52%, 34%, and 14%, respectively. Those who practiced brushing and flossing daily were more likely to attend than were those who did not brush and floss daily (P 5 0.003; chi-square test). Overall, there was no statistically significant difference in attendance history among the different race or ethnic categories (P 5 0.075; chi-square test) or among the different age categories (P 5 0.309; chi-square test). Moreover, there were no statistically significant associations between attendance history and the duration of active orthodontic treatment (P 5 0.322; ANOVA) or age of the participants (P 5 0.74; ANOVA). The results of the multivariable analyses examining the simultaneous associations between attendance (dependent variable) and insurance type, oral hygiene behavior, and treatment duration are summarized in Table II. For non-Medicaid participants, the odds of always attending vs sometimes and rarely attending combined were 4 times higher than for Medicaid participants, adjusted for age, race, and sex (P 5 0.018). For participants with good oral hygiene (brush and floss daily), the odds of always attending vs sometimes and rarely attending combined were almost 6 times higher than for participants with suboptimal oral hygiene practices, adjusted for age, race, and sex (P 5 0.002).
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For every 6-month increase in treatment duration, the odds of always attending vs sometimes and rarely attending combined were 0.77 times lower, adjusted for age, race, and sex (P 5 0.008). The Figure shows the probability of the different attendance categories plotted over the treatment duration. According to the logistic regression, the age categories were not significantly associated with attendance. Table III includes the probabilities of attending. Girls had a higher probability of attending than did boys (82% and 72%, respectively). All race categories had similar attendance probabilities (range, 78%-83%) except for African Americans, who had a 68% probability of attending appointments. Medicaid participants had lower attendance probabilities than did non-Medicaid participants (69% and 87%, respectively). The probability of attending appointments was higher among participants who brushed daily than in those who did not (83% and 52%, respectively). DISCUSSION
In 2009, Lindauer et al14 conducted a study over a 6-week period where the last appointment of each active, non-Medicaid participant (n 5 538) was recorded as either kept or missed. They reported that male patients were more likely to miss their orthodontic appointments. Moreover, Qui~ nonez et al15 found that boys attended fewer follow-up visits in a study in which the parents of Medicaid children completed a questionnaire before their child's medical visit. The providers completed patient dental forms at each visit, recording
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Table III. Probability of attendance by participants'
characteristics
Characteristic Age (\12 y) Attendence probability 95% CI Age (12 to \16 y) Attendence probability 95% CI Age (.16 y) Attendence probability 95% CI Male Attendence probability 95% CI Female Attendence probability 95% CI White Attendence probability 95% CI African American Attendence probability 95% CI Hispanic Attendence probability 95% CI Other* Attendence probability 95% CI Medicaid Attendence probability 95% CI Non-Medicaid Attendence probability 95% CI Brush daily (yes) Attendence probability 95% CI Brush daily (no) Attendence probability 95% CI
Model 1: treating age categorically
Model 2: ordered logistic regression treating age continuously
81% 61%-93%
NA NA
74% 65%-84%
NA NA
79% 68%-90%
NA NA
73% 63%-82%
72% 63%-82%
82% 72%-91%
82% 73%-91%
82% 69%-96%
83% 69%-96%
67% 55%-80%
68% 55%-80%
83% 72%-94%
82% 70%-93%
78% 58%-98%
78% 58%-98%
70% 60%-80%
69% 59%-79%
87% 78%-96%
87% 78%-96%
83% 76%-90%
83% 76%-89%
50% 30%-69%
52% 35%-70%
NA, Not applicable. *Because of the small cell size, we used the exact method to estimate the probability.
dental services, caries risk, and dental disease. Questionnaires, dental forms, and Medicaid claims were connected to generate a database. Although our bivariate analysis results support these findings, the attendance by sex was not significantly different after adjusting for age, race, oral hygiene, and insurance type through an ordered logistic regression model. On the other hand, Horsley et al16 reported that female patients were more likely to miss their orthodontic appointments.
These differences may be due to different Medicaid participant proportions among the study populations: the sample of Lindauer et al consisted entirely of non-Medicaid subjects, and the sample of Horsley et al was dominated by non-Medicaid patients (74%). In our study, Medicaid participants were more common (61%). Our findings about the attendance behavior among Medicaid participants were similar to those reported in previous studies.16,17 In our study, 24% (18.7% Medicaid and 5.3% non-Medicaid) of the patients reported sometimes or rarely attending orthodontic appointments; among those, 78% were Medicaid insured. With elevated no-shows, it is clear why dentist participation in Medicaid dental coverage is so low. Lamberth et al18 specified that broken appointments by Medicaid participants affect a dentist's decision whether to participate in Medicaid insurance. Even though low reimbursement is considered the principal restriction to accepting Medicaid patients, a missed appointment generates no income. Additionally, a missed appointment could have been used for another patient. In 1998, Breistein and Burden19 found that dental health was a significant predictor for receiving orthodontic treatment: healthier patients (no caries and no oral hygiene problems) were more likely to receive orthodontic treatment. We found that patients who practiced good oral hygiene daily were more likely to attend appointments, and this increases their chances to receive and complete the orthodontic treatment. Obviously, in any treatment plan, the longer the treatment, the more likely it is for a patient to miss appointments. In our study, we found that active treatment duration was a significant predictor for patients' attendance. We could not perform ordered logistic regression with a model that contained age as a categorical variable (\12, 12 to \16, and .16 years) because of sample size issues when there were no participants in the category of less than 12 years old who reported rare attendance. However, to overcome this issue, we combined the sometimes attendance group with the rarely attendance group, and performed logistic regression with the binary dependent variable (always attend vs sometimes or rarely) and categorical age as predictors. Although we did not find a significant difference in attendance between the different age groups, we cannot be confident that age is not a predictor for participants' attendance. Conversely, the American Association of Orthodontists Insurance Company stated that teenaged patients who drive to appointments and are less than passionate about their treatment often have irregular attendance.
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More research should be conducted to answer this question. Additionally, to address whether the severity of malocclusion is associated with attendance, we adjusted for Peer Assessment Rating scores, and it had no significant effect. One other limitation of our study about attendance history was that we relied on self-reported questionnaires for attendance. The assessments of attendance and oral hygiene (with a question about brushing and flossing daily) might be more subjective. CONCLUSIONS
There were significant associations between oral hygiene practices (brush and floss daily), insurance type, treatment duration, and probability of attendance. Our findings support the concern among orthodontists that Medicaid participants have higher no-shows than do non-Medicaid participants. Future research should focus on exploring additional reasons that Medicaid patients have higher no-shows, and potentially develop and validate new behavioral treatment priority settings (incorporating quality of life, oral hygiene, and compliance) that can help to improve the current Medicaid screening systems that are based only on clinical or medical necessity, with a firm cutoff threshold. Furthermore, this can help to identify patients who need more help with compliance and eventually save orthodontic staff time and better allocate resources for Medicaid. ACKNOWLEDGMENTS
We thank the dentists who provided access to their patients: Patricia Brown, Robert Petrosino, and Mohamed Butt. REFERENCES 1. Asimakopoulou K, Daly B. Adherence in dental settings. Dent Update 2009;36:626-30. 2. Franklin E. Missed appointments often result in malpractice claims. Available at: https://www.aaoinfo.org/news/2014/01/ missed-appointments-often-result-malpractice-claims. Accessed August 31, 2014.
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3. Trenouth MJ. Do failed appointments lead to discontinuation of orthodontic treatment? Angle Orthod 2003;73:51-5. 4. Richardson A. Failed appointments in an academic orthodontic clinic. Br Dent J 1998;184:612-5. 5. Can S, Macfarlane T, O'Brien KD. The use of postal reminders to reduce non-attendance at an orthodontic clinic: a randomised controlled trial. Br Dent J 2003;19:199-201. 6. Haeger RS, Colberg RT. Effects of missed appointments and bracket failures on treatment efficiency and office productivity. J Clin Orthod 2007;41:433-7. 7. Fink DF, Smith RJ. The duration of orthodontic treatment. Am J Orthod Dentofacial Orthop 1992;102:45-51. 8. Beckwith FR, Ackerman RJ Jr, Cobb CM, Tira DE. An evaluation of factors affecting duration of orthodontic treatment. Am J Orthod Dentofacial Orthop 1999;115:439-47. 9. Jarvinen S, Widstrom E, Raitio M. Factors affecting the duration of orthodontic treatment in children. A retrospective study. Swed Dent J 2004;28:93-100. 10. Marcusson A, Norevall LI, Persson M. White spot reduction when using glass ionomer cement for bonding in orthodontics: a longitudinal and comparative study. Eur J Orthod 1997;19: 233-42. 11. Trenouth MJ, Hough A. Reasons for broken and canceled appointments in a British orthodontic clinic. J Clin Orthod 1991;25: 115-20. 12. Reekie D, Devlin H. Preventing failed appointments in general dental practice: a comparison of reminder methods. Br Dent J 1998;185:472-4. 13. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008;3:17. 14. Lindauer SJ, Powell JA, Leypoldt BC, T€ ufekc¸i E, Shroff B. Influence of patient financial account status on orthodontic appointment attendance. Angle Orthod 2009;79:755-8. 15. Qui~ nonez RB, Pahel BT, Rozier RG, Stearns SC. Follow-up preventive dental visits for Medicaid-enrolled children in the medical office. J Public Health Dent 2008;68:131-8. 16. Horsley BP, Lindauer SJ, Shroff B, T€ ufekc¸i E, Abubaker AO, Fowler CE, et al. Appointment keeping behavior of Medicaid vs non-Medicaid orthodontic patients. Am J Orthod Dentofacial Orthop 2007;132:49-53. 17. Brysh LS. “Where's my patient?”—a plan to decrease broken appointments in a predominantly Medicaid clinic. Spec Care Dentist 2001;21:126-8. 18. Lamberth EF, Rothstein EP, Hipp TJ, Souder RL, Kennedy TI, Faccenda DF, et al. Rates of missed appointments among pediatric patients in a private practice: Medicaid compared with private insurance. Arch Pediatr Adolesc Med 2002;156:86-7. 19. Breistein B, Burden DJ. Equity and orthodontic treatment: a study among adolescents in Northern Ireland. Am J Orthod Dentofacial Orthop 1998;113:408-13.
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