Abstract
Objective
To identify preoperative, intraoperative, and postoperative variables that predict higher short- and long-term patient satisfaction with Mohs surgery.
Design
Prospective cohort study.
Setting
A university-based dermatology practice and the affiliated Veterans Affairs medical center dermatology clinic.
Patients
A total of 339 consecutive patients treated with Mohs surgery in 1999 and 2000.
Main Outcome Measures
Short-term satisfaction at 1 week and long-term satisfaction at 1 year. We used directed acyclic graphs to determine appropriate confounding adjustment for preoperative, intraoperative, and postoperative variables that influence satisfaction with Mohs surgery in logistic regression models.
Results
Better preoperative skin-related quality of life (measured using Skindex) and more intraoperative Mohs stages were the most salient predictors of higher short- and long-term satisfaction; these odds ratios (ORs) were 2.33 (95% CI, 1.01–5.35) and 5.19 (1.66–16.29), respectively, for preoperative skin-related quality of life and 7.06 (2.02–24.67) and 5.30 (1.24–22.64), respectively, for more intraoperative Mohs stages. Patients not bothered by postoperative bleeding were more likely to be satisfied short term (OR, 2.25; 95% CI, 1.25–4.05), as were those who considered themselves involved in decision making about their treatment (3.05; 1.52–6.10). Higher long-term satisfaction with Mohs surgery was observed among patients who were married (2.36; 1.10–5.09).
Conclusions
Higher short- and long-term satisfaction with Mohs surgery is predicted by better preoperative skin-related quality of life and by more intraoperative Mohs stages. The effect of postoperative variables wanes over time, suggesting that factors influencing satisfaction can vary depending on the time frame when satisfaction is measured. Our results may help clinicians identify patients who are at higher risk of dissatisfaction following Mohs surgery.
Mohs surgery is often used to treat nonmelanoma skin cancer (NMSC), and its use among Medicare patients over the past decade has increased more than 200%.1 Mohs surgery has the unique ability to couple tissue conservation with complete microscopic margin control. However, most NMSC can be treated using various modalities, with varying recurrence rates, costs, and other outcomes, such as patient satisfaction. In an increasingly patient-oriented health care system, patient satisfaction is an important outcome for cancer therapies, especially for typically nonfatal conditions like NMSC. Patient satisfaction can be influenced by numerous variables, including patient demographics (age, gender, and race),2–5 socioeconomic variables (education status, income, marital status, and site of care),6,7 health status (physical health, mental health, skin-related quality of life [QOL], and worry),2,8–10 tumor characteristics (size, type, location, and invasiveness),11 and previous experience with the disease. Satisfaction may also be influenced by intraoperative variables (number of stages, defect diameter, and repair type),12,13 as well as by postoperative variables (complications, time lost for treatment, and perceived involvement in care). As an outcome measure, patient satisfaction has been associated with several other important outcomes, including health status, QOL, adherence to medical advice, and initiation of complaints.2,9,10,14,15 To improve patient satisfaction, it is important for clinicians to understand the factors that affect it.
Little is known about what preoperative, intraoperative, and postoperative characteristics influence patient satisfaction in the treatment of NMSC with Mohs surgery. Using a prospective cohort of patients treated with Mohs surgery at 2 academic medical clinics, we sought to identify preoperative, intraoperative, and postoperative variables that predict short-term (measured at 1 week after treatment) and long-term (measured at 1 year after treatment) patient satisfaction.
METHODS
STUDY DESIGN
Data were derived from a prospective cohort of consecutive patients with NMSC diagnosed in 1999 and 2000 at a university-based dermatology practice and a nearby affiliated Veterans Affairs medical center dermatology clinic. The study was approved by the institutional review board at both institutions. Potential study participants were identified by daily review of all pathology records at both institutions. Nonmelanoma skin cancer was defined as a final histopathologic diagnosis of basal cell carcinoma or squamous cell carcinoma (primary or recurrent). Potential participants were excluded if they were younger than 18 years or if their medical records were protected because they were employees. Individuals were considered ineligible if they were physically or mentally unable to complete surveys, did not speak English, or had no current address. Patients were recruited after initial biopsy results were obtained but before definitive treatment. Patients were enrolled if they responded to a pretreatment questionnaire about their health and QOL. If a patient had multiple tumors, he or she was asked to respond only about therapy for the most bothersome tumor. Only tumors treated with Mohs surgery (N=339) were included in the data set.
DATA COLLECTION AND MEASURES
Data were obtained from medical records and from patient surveys. Using structured data forms, trained research staff collected data from clinical notes and from pathology records. Socioeconomic and demographic characteristics, comorbidities, tumor-related QOL, and health status were measured before therapy by patients’ answers to a mailed survey. Comorbidities were measured using an adapted version of the Charlson Comorbidity Index.16–18 Preoperative skin-related QOL was measured using the 16-item version of Skindex,19 which was modified to be tumor specific. We used a composite score calculated as the mean of the 3 Skindex-16 subscale scores. We also assessed pretreatment worry about the scar and about the treatment. Health status was measured using an adapted version of the 12-Item Short-Form Health Survey,20 which reported a Physical Component score and a Mental Component score. In evaluating these scores, higher scores denote better health, and 50 is the mean norm-based standardized score. Invasive tumors included squamous cell carcinoma and nodular and morpheaform basal cell carcinoma. Superficial tumors included multifocal squamous cell carcinoma and squamous cell carcinoma in situ (Bowen disease). Postoperative variables were factors that could influence satisfaction that arise or are measured after completion of the Mohs procedure. These were ascertained at 1 week following Mohs surgery using items from a second self-reported survey that inquired about time lost for treatment, bother from bleeding (“How bothered were you by the bleeding from this skin problem after the treatment was done?”), and perceived involvement in treatment selection (“If there had been a choice between treatments, do you think this doctor would have asked you to help make the decision?”). Although these postoperative survey items had face validity, they were not derived from validated instruments. Temporal ordering of these postoperative variables still allows for the possibility of a causal link to both satisfaction outcomes.
Short-term patient satisfaction was measured at 1 week after Mohs surgery using the 18-item version of the Patient Satisfaction Questionnaire,21 adapted for the treatment of skin cancer. The Patient Satisfaction Questionnaire Short-Form assesses general satisfaction. For each question, scores varied from 1 to 5; for overall satisfaction and the domains of care, higher scores denoted greater satisfaction with medical care. A validated composite score consisting of 2 items from the Patient Satisfaction Questionnaire Short-Form was used to measure general satisfaction. Long-term patient satisfaction was measured at 12 months after Mohs surgery using a single global question (“I am completely satisfied with the treatment of my skin problem”), which was derived from the general satisfaction items of the Patient Satisfaction Questionnaire Short-Form. Scores varied from 1 (strongly disagree) to 5 (strongly agree). Short-term and long-term satisfaction was dichotomized (scores of 1–3 indicate unsatisfied; scores of 4–5 indicate satisfied).
STATISTICAL ANALYSIS
We used standard logistic regression models to estimate unadjusted associations between exposures (preoperative, intraoperative, and postoperative variables) and the 2 binary satisfaction outcomes (satisfied or very satisfied vs not satisfied). Such crude associations may not be causal because of confounding. Traditional statistical approaches to confounding, which involve adjusting for all possible variables that significantly change the coefficient estimate in logistic regression models, do not incorporate causal subject matter knowledge and may lead to incorrect adjustment for confounding.21 To properly adjust for confounding, we used causal directed acyclic graphs (DAGs),22,23 which are diagrams composed of arrows and nodes that represent a system of causal relationships (arrows) between measured or unmeasured variables (nodes) in a study.24–26 By explicitly depicting causal assumptions, DAGs can guide confounding adjustment in standard regression models. This strategy helps avoid drawing incorrect causal inferences due to the following: (1) confounding (spurious associations between the exposure and the outcome that result from a lack of adjustment for common causal risk factors), (2) blocking (adjustment for a covariate on a causal pathway between the exposure and the outcome), and (3) colliding (eg, spurious associations between the exposure and the outcome that result from adjustment for variables affected by both the exposure and the outcome). After the DAG is developed, graphic rules allow identification of covariates whose adjustment, in a standard regression model, may permit a causal interpretation.22,27,28 The correct identification of such “adjustment sets” relies on the correctness of the topology of the graph that is drawn and on the implicit assumption that all common ancestors of any 2 nodes in the diagrams are included in the DAG.29 If these assumptions do not hold, then causal inferences from the standard regression models specified based on such a DAG may be biased. Examples of applications of causal DAGs in other epidemiological research have been previously published.26,30,31
The causal DAG used in this study is shown in the Figure. We hypothesized causal relationships among variables based on previously published literature about patient satisfaction relative to age,2 gender,3 race/ethnicity,4,5 socioeconomic variables (education status, income, marital status, and site of care),6,7 mental health,8,9 comorbidities,2,10 preoperative skin-related QOL,2,10 tumor-related factors (size, location, invasiveness, recurrence, and type),11 and postoperative variables.11 Also hypothesized to influence patient satisfaction and included in the model were intraoperative variables, such as the number of stages, defect size, and repair type, some of which been shown to be associated with long-term cosmesis.11–13 Postoperative variables, such as time lost for treatment, bother from bleeding, and perceived involvement in treatment selection, were included in the model because they were deemed likely to influence patient satisfaction.11 Although it may be possible to draw different DAGs based on varying causal assumptions, we believed that this DAG best captured the causal relationships among the factors based on known temporal ordering of the variables, the existing literature on patient satisfaction, and clinical judgment.
Figure.
Directed acyclic graph for hypothesized relationships among measured variables. MCS-12 indicates mental component score on the 12-Item Short-Form Health Survey; NMSC, nonmelanoma skin cancer; PCS-12, physical component score on the 12-Item Short-Form Health Survey; and QOL, quality of life.
According to the assumptions in the DAG, we determined a set of adjusting variables for each exposure and used standard logistic regression analysis of the outcome on the exposure, including all covariates in the adjustment set (no interaction terms were considered). The associations between the exposure and the outcome revealed by our models (ie, the coefficient in front of the exposure variables) rely on the aforementioned causal DAG assumptions, as well as on correct specification of the parametric logistic regression model (eg, the absence of interaction terms between exposures and covariates).
Because many exposures were of interest in this analysis, we also corrected the list of exposures deemed to have a significant effect on the outcome (2-sided P<.05) for multiple comparisons using the false discovery rate method.32 All statistical analyses were performed using commercially available software (STATA, version 10; StataCorp LP, College Station, Texas).
RESULTS
Characteristics of the patients, tumors, and their care are summarized in Table 1. Most patients were of white race (91.7%) and male (69.0%), with a mean age of 65.6 years at baseline. Tumors were most often basal cell carcinoma (83.2%) on the face (80.8%) and were histologically invasive (72.0%). Most tumors were nonrecurrent (83.5%) and small (≤1 cm in diameter) (74.0%). Most tumors cleared after 1 intraoperative Mohs stage (40.4%)or after 2 intraoperative Mohs stages (42.2%). The resulting defects were most often repaired using a primary closure consisting of a simple, intermediate, or complex linear closure (45.4%) or a flap (30.4%). Some defects were allowed to heal fully or partially by secondary intention (15.3%). A full-thickness skin graft for reconstruction was used in 5.9% of the sample. Based on the DAG, models were chosen to estimate causal associations between the different exposures and the 2 satisfaction outcomes. Given that 68.4% of the original cohort responded to the long-term satisfaction items, we compared the characteristics of the full cohort with those who responded at 1 year and did not detect any significant differences in characteristics between initial responders and nonresponders.
Table 1.
Characteristics of the Cohort
| Variable | Full Cohort (N=339) |
Short-term Satisfaction Cohort (n=301) |
Long-term Satisfaction Cohort (n=232) |
|---|---|---|---|
| Preoperative | |||
| Age at baseline, mean (SD), y | 65.6 (14.4) | 66.0 (14.3) | 66.3 (14.1) |
| Male gender, No. (%) | 234 (69.0) | 209 (69.4) | 164 (70.7) |
| Race, No. (%) | |||
| White | 311 (91.7) | 278 (92.4) | 214 (92.2) |
| Other | 18 (5.3) | 14 (4.7) | 12 (5.2) |
| Missing | 10 (2.9) | 9 (3.0) | 6 (2.6) |
| Education, No. (%) | |||
| >High school | 192 (56.6) | 173 (57.5) | 134 (57.8) |
| ≤High school | 136 (40.1) | 119 (39.5) | 90 (38.8) |
| Missing | 11 (3.2) | 9 (3.0) | 8 (3.4) |
| Annual income, $, No. (%) | |||
| <30 000 | 149 (44.0) | 128 (42.5) | 92 (39.7) |
| 30 000–75 000 | 69 (20.4) | 63 (20.9) | 53 (22.8) |
| >75 000 | 94 (27.7) | 85 (28.2) | 66 (28.4) |
| Missing | 27 (8.0) | 25 (8.3) | 21 (9.1) |
| Marital status, No. (%) | |||
| Married | 162 (47.8) | 144 (47.8) | 113 (48.7) |
| Not married | 170 (50.1) | 150 (49.8) | 114 (49.1) |
| Missing | 7 (2.1) | 7 (2.3) | 5 (2.2) |
| Site of care at Veterans Affairs medical center, No. (%) | 115 (33.9) | 101 (33.6) | 74 (31.9) |
| Previous nonmelanoma skin cancer, No. (%) | 181 (53.4) | 161 (53.5) | 128 (55.2) |
| Charlson Comorbidity Index score, No. (%) | |||
| 0 | 137 (40.4) | 127 (42.2) | 100 (43.1) |
| 1–3 | 134 (39.5) | 113 (37.5) | 87 (37.5) |
| >3 | 67 (19.8) | 60 (19.9) | 44 (19.0) |
| Missing | 1 (0.3) | 1 (0.3) | 1 (0.4) |
| 12-Item Short-Form Health Survey, mean (SD) | |||
| Physical Component score | 46.4 (11.0) | 46.6 (10.8) | 46.8 (10.6) |
| Mental Component score | 48.4 (10.8) | 48.7 (10.7) | 49.1 (10.5) |
| Skindex composite score, mean (SD) | 27.2 (18.8) | 26.7 (18.7) | 26.1 (18.3) |
| Pretreatment worry, mean (SD) | |||
| About scar | 36.9 (36.8) | 36.3 (36.5) | 34.3 (36.1) |
| About treatment | 45.5 (36.9) | 43.9 (36.8) | 42.4 (36.9) |
| Combined worry | 82.7 (67.6) | 80.6 (67.2) | 77.3 (67.0) |
| Tumor | |||
| Basal cell carcinoma, No. (%) | 282 (83.2) | 249 (82.7) | 195 (84.1) |
| Diameter, mean (SD), mm | 9.5 (7.4) | 9.4 (7.3) | 9.9 (8.2) |
| Facial location, No. (%) | 274 (80.8) | 246 (81.7) | 183 (78.9) |
| Invasiveness, No. (%) | |||
| Invasive | 244 (72.0) | 216 (71.8) | 164 (70.7) |
| Superficial | 61 (18.0) | 53 (17.6) | 43 (18.5) |
| Missing | 34 (10.0) | 32 (10.6) | 25 (10.8) |
| Recurrence, No. (%) | 56 (16.5) | 50 (16.6) | 40 (17.2) |
| Intraoperative | |||
| No. of intraoperative Mohs stages, No. (%) | |||
| 1 | 137 (40.4) | 124 (41.2) | 101 (43.5) |
| 2 | 143 (42.2) | 126 (41.9) | 87 (37.5) |
| ≥3 | 55 (16.2) | 48 (15.9) | 42 (18.1) |
| Missing | 4 (1.2) | 3 (1.0) | 2 (0.9) |
| Defect diameter, mean (SD), mm | 17.8 (11.5) | 17.4 (10.4) | 18.4 (12.2) |
| Repair type, No. (%) | |||
| Secondary intention | 52 (15.3) | 43 (14.3) | 32 (13.8) |
| Primary closure | 154 (45.4) | 134 (44.5) | 104 (44.8) |
| Flap | 103 (30.4) | 96 (31.9) | 72 (31.0) |
| Graft | 20 (5.9) | 19 (6.3) | 16 (6.9) |
| Missing | 10 (2.9) | 9 (3.0) | 8 (3.4) |
| Postoperative | |||
| Time lost for treatment, d, No. (%) | |||
| <1 | 120 (35.4) | 115 (38.2) | 87 (37.5) |
| ≥1 | 184 (54.3) | 174 (57.8) | 135 (58.2) |
| Missing | 35 (10.3) | 12 (4.0) | 10 (4.3) |
| Bother from bleeding, No. (%) | |||
| Not bothered | 191 (56.3) | 183 (60.8) | 146 (62.9) |
| Bothered | 119 (35.1) | 113 (37.5) | 80 (34.5) |
| Missing | 29 (8.6) | 5 (1.7) | 6 (2.6) |
| Perceived involvement in treatment selection, No. (%) | |||
| Not involved | 54 (15.9) | 49 (16.3) | 38 (16.4) |
| Involved | 254 (74.9) | 244 (81.1) | 185 (79.7) |
| Missing | 31 (9.1) | 8 (2.7) | 9 (3.9) |
FACTORS AFFECTING SHORT-TERM PATIENT SATISFACTION
At 1 week after Mohs surgery, 301 patients (88.8%) responded about their satisfaction following treatment. General satisfaction was high, with 229 (76.1%) rating their overall satisfaction at 4 or higher on the 5-point scale. Unadjusted and adjusted models revealed that higher short-term patient satisfaction was predicted by better preoperative skin-related QOL, more intraoperative Mohs stages, no bother from bleeding, and perceived involvement in treatment selection (Table 2).
Table 2.
Unadjusted and Adjusted Predictors of Short-term and Long-term Patient Satisfaction After Mohs Surgery
| Satisfied or Very Satisfied, Odds Ratio (95% CI) | ||||
|---|---|---|---|---|
| Unadjusted | Adjusteda | |||
| Variable | Short Term (n=301) |
Long Term (n=232) |
Short Term (n=301) |
Long Term (n=232) |
| Preoperative | ||||
| Age at baseline, y | ||||
| ≤65 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| >65 | 1.49 (0.87–2.54) | 0.64 (0.32–1.27) | 1.52 (0.89–2.60) | 0.63 (0.32–1.27) |
| Gender | ||||
| Female | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Male | 0.92 (0.51–1.64) | 1.16 (0.57–2.35) | 0.86 (0.48–1.56) | 1.15 (0.56–2.37) |
| Race | ||||
| White | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Other | 0.41 (0.14–1.22) | 0.30 (0.09–1.00) | 0.40 (0.13–1.20) | 0.30 (0.09–1.01) |
| Education | ||||
| >High school | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≤High school | 1.36 (0.78–2.37) | 1.20 (0.60–2.43) | 1.51 (0.80–2.84) | 1.23 (0.56–2.68) |
| Annual income, $ | ||||
| <30 000 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| 30 000–75 000 | 0.86 (0.43–1.72) | 0.77 (0.34–1.78) | 1.11 (0.52–2.34) | 0.73 (0.29–1.80) |
| >75 000 | 0.97 (0.51–1.84) | 1.27 (0.54–2.98) | 1.30 (0.59–2.89) | 0.86 (0.30–2.47) |
| Marital status | ||||
| Not married | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Married | 1.43 (0.83–2.45) | 2.13 (1.07–4.24) | 1.39 (0.77–2.50) | 2.36 (1.10–5.09) |
| Site of care | ||||
| Private hospital | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Veterans Affairs medical center | 1.10 (0.62–1.94) | 0.61 (0.31–1.21) | 0.98 (0.49–1.95) | 0.47 (0.20–1.13) |
| Previous nonmelanoma skin cancer | ||||
| No | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Yes | 1.20 (0.71–2.04) | 1.62 (0.84–3.13) | 1.07 (0.61–1.87) | 1.75 (0.85–3.57) |
| 12-Item Short-Form Health Survey Physical Component score | ||||
| <Median | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≥Median, better | 1.12 (0.65–1.93) | 1.41 (0.71–2.80) | 1.55 (0.79–3.05) | 1.73 (0.74–4.05) |
| Charlson Comorbidity Index score | ||||
| 0 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| 1–3 | 1.55 (0.85–2.82) | 0.97 (0.47–2.00) | 1.63 (0.82–3.26) | 1.51 (0.62–3.72) |
| >3 | 1.58 (0.75–3.32) | 1.06 (0.42–2.63) | 1.78 (0.75–4.24) | 1.56 (0.52–4.75) |
| 12-Item Short-Form Health Survey Mental Component score | ||||
| <Median | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≥Median, better | 1.23 (0.71–2.13) | 1.77 (0.88–3.53) | 1.09 (0.60–1.96) | 1.73 (0.82–3.65) |
| Skindex composite score | ||||
| ≤Median | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| <Median, better | 2.00 (1.08–3.70) | 2.83 (1.25–6.39) | 2.33 (1.01–5.35) | 5.19 (1.66–16.29) |
| Combined pretreatment worry | ||||
| ≤Median | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| >Median, more worry | 0.71 (0.39–1.30) | 0.83 (0.39–1.77) | 1.27 (0.53–3.01) | 2.11 (0.66–6.73) |
| Tumor | ||||
| Squamous cell carcinoma | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Basal cell carcinoma | 1.71 (0.89–3.29) | 1.47 (0.64–3.39) | 1.61 (0.77–3.36) | 1.29 (0.50–3.36) |
| Location | ||||
| Other | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Facial | 1.39 (0.72–2.68) | 1.32 (0.61–2.84) | 1.25 (0.60–2.57) | 1.12 (0.46–2.75) |
| Invasiveness | ||||
| Invasive | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Superficial | 0.90 (0.45–1.82) | 0.60 (0.27–1.33) | 1.10 (0.51–2.37) | 0.48 (0.19–1.22) |
| Tumor diameter, mm | ||||
| ≤10 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| >10 | 0.71 (0.40–1.28) | 0.91 (0.44–1.89) | 0.80 (0.42–1.55) | 0.91 (0.39–2.14) |
| Recurrence | ||||
| No | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Yes | 0.87 (0.44–1.75) | 0.92 (0.39–2.17) | 0.79 (0.37–1.71) | 0.77 (0.27–2.18) |
| Intraoperative | ||||
| No. of intraoperative Mohs stages | ||||
| 1–2 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≥3 | 4.03 (1.39–11.64) | 3.63 (1.07–12.33) | 7.06 (2.02–24.67) | 5.30 (1.24–22.64) |
| Defect diameter, mm | ||||
| <15 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≥15 | 0.89 (0.52–1.52) | 1.79 (0.89–3.57) | 0.85 (0.41–1.73) | 2.17 (0.80–5.89) |
| Repair type | ||||
| Secondary intention | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Primary closure | 0.57 (0.23–1.40) | 1.17 (0.47–2.96) | 0.67 (0.26–1.73) | 1.27 (0.46–3.48) |
| Flap | 0.58 (0.23–1.48) | 2.33 (0.81–6.75) | 0.62 (0.23–1.71) | 2.00 (0.62–6.47) |
| Graft | 0.42 (0.12–1.49) | 1.00 (0.25–4.00) | 0.25 (0.06–1.09) | 0.37 (0.07–2.02) |
| Postoperative | ||||
| Time lost for treatment, d | ||||
| <1 | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| ≥1 | 1.08 (0.62–1.87) | 1.36 (0.69–2.68) | 1.21 (0.65–2.23) | 1.35 (0.65–2.80) |
| Bother from bleeding | ||||
| Bothered | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Not bothered | 2.58 (1.49–4.47) | 1.41 (0.71–2.77) | 2.25 (1.25–4.05) | 1.57 (0.73–3.39) |
| Perceived involvement in treatment selection | ||||
| Not involved | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] | 1.00 [Reference] |
| Involved | 3.71 (1.95–7.07) | 1.14 (0.48–2.71) | 3.05 (1.52–6.10) | 0.81 (0.31–2.15) |
Models for each demographic variable were adjusted for the remaining demographic variables. Models for each socioeconomic factor variable were adjusted for the remaining socioeconomic factor variables and all demographic variables. Model for the site-of-care variable was adjusted for all socioeconomic factor variables. Model for previous nonmelanoma skin cancer was adjusted for all demographic and socioeconomic factor variables. Models for each physical health variable were adjusted for the remaining physical health variables and all demographic and socioeconomic factor variables. Model for the 12-Item Short-Form Health Survey mental component score was adjusted for all demographic and socioeconomic factor variables. Models for the preoperative skin quality-of-life variables were adjusted for the remaining preoperative skin quality-of-life variables plus demographic, socioeconomic factor, mental health, and tumor characteristic variables. Models for each tumor characteristic variable were adjusted for the remaining tumor characteristic variables plus demographic, socioeconomic factor, and previous nonmelanoma skin cancer variables. Models for each intraoperative event variable were adjusted for the remaining intraoperative event variables plus tumor characteristic variables. Models for each postoperative variable were adjusted for the remaining postoperative variables plus intraoperative event variables.
FACTORS AFFECTING LONG-TERM PATIENT SATISFACTION
At 1 year after Mohs surgery, 232 patients (68.4%) responded to a global item about their overall satisfaction with care. Once again, general satisfaction was high, with 188 (81.0%) rating their satisfaction at 4 points or higher. Unadjusted univariate analysis revealed that higher long-term satisfaction was predicted among patients who were married (compared with single, divorced, or widowed), had better preoperative skin-related QOL, and underwent 3 or more intraoperative Mohs stages. Postoperative variables were not significantly associated with long-term patient satisfaction (Table 2). In adjusted models based on the DAG, all significant variables in the unadjusted models remained significant predictors of long-term patient satisfaction. Correction for multiple testing revealed that no variables crossed P>.001, the false discovery rate–adjusted threshold value.23
COMMENT
Clinicians may have preconceived notions about what factors influence patient satisfaction with Mohs surgery. For example, they may believe that satisfaction is related to age, with younger patients being more difficult to satisfy following a surgical procedure that can result in a visible scar. Or, they may believe that satisfaction is related to the size of the tumor, with bigger tumors leading to larger scars and less satisfied patients. Few data systematically identify patient, tumor, and care characteristics that influence satisfaction with Mohs surgery. We found that short-term satisfaction following Mohs surgery for the treatment of NMSC is predicted by few variables, namely, better preoperative skin-related QOL, more than 3 intraoperative Mohs stages, no bother from bleeding, and perceived involvement in care. At 1 year, preoperative skin-related QOL and the number of intraoperative Mohs stages continued to be strongly associated with higher long-term satisfaction, but the postoperative variables were no longer important.
Preoperative skin-related QOL significantly influences short-term and long-term patient satisfaction. This finding is consistent with previous work by our group9 and with other studies2,8 of patient satisfaction among dermatologic outpatients. The difficulty for a clinician lies in identifying patients with low preoperative skin-related QOL. Studies2,33 showed that a dermatologist’s assessment of skin-related QOL does not always reflect a patient’s perception of skin-related QOL. Yet, it is important to accurately identify patients with low skin-related QOL because they are often the least satisfied with overall care and with physicians’ interpersonal skills.2 Including a brief skin-related QOL scale may be a useful adjunct to clinical practice because it not only enables a clinician to understand patients’ perception of their skin-related QOL but also may help identify patients at risk for lower satisfaction after treatment.
Receipt of 3 or more intraoperative Mohs stages was also independently associated with high short- and long-term patient satisfaction. Explanations include that patients who underwent more stages may have perceived themselves more cared for overall, thought their tumors were treated with more attention, believed their tumors were more severe and merited Mohs surgery, or had opportunities to spend more time with the clinicians and in the medical treatment. Deciphering the underlying reasons for this salient variable should be investigated in future studies.
We found that being married predicted higher long-but not short-term patient satisfaction. Marital status has been previously shown to be associated with overall satisfaction among patients with cancer.34 It may be that long-term satisfaction is more highly correlated with global measures of QOL, such as the social support that arises from a marital relationship, rather than with short-term measures, such as bother from bleeding.
We also found that perceived involvement in decision making about treatment choices was associated with higher short-term patient satisfaction, but the effect seemed to wane over time. Among women with breast cancer, data on shared decision making showed that a higher participatory treatment decision-making score was associated with greater patient satisfaction at 6 months.6 In our cohort, perceived involvement in care no longer significantly influenced patient satisfaction at 1 year after Mohs surgery.
Only 18 individuals in our cohort self-identified themselves as being of nonwhite race, a finding consistent with the typical patient at high risk for NMSC (fair skin phenotype). Yet, these individuals had noticeably different satisfaction scores, both short and long term, compared with individuals of white race. Of 14 individuals of nonwhite race who responded to the short-term satisfaction question, only 8 reported being satisfied. This low proportion of satisfied patients persisted at 1 year, and the difference in satisfaction vs that among patients of white race approached statistical significance. In adjusted models, nonwhite cohort members seemed less satisfied in the short term and in the long term, with the long-term results approaching statistical significance. Nonwhite race has previously been shown to be a significant predictor of poorer satisfaction among patients with cancer, including head and neck cancer.5 We did not explore the underlying reason for this disparity; perhaps nonwhite patients have increased risk of postinflammatory pigmentation or hypertrophic scar formation. Further studies of predictors and outcomes should focus on this important issue.
Results of previous investigations showed that among patients with cancer increasing depressive symptoms are associated with decreased patient satisfaction.35 Although patients in our cohort with worse physical and mental health tended to be less satisfied, the results were not statistically significant in adjusted or unadjusted models.
Strengths of this study include the prospective assembly and follow-up care of the cohort, the fact that satisfaction was measured more than once after treatment, and the detailed information on preoperative, intraoperative, and postoperative variables. A limitation is that our study participants were drawn from an academic dermatology practice and a Veterans Affairs medical center, which may limit the generalizability of our findings. Also, our analysis used a general measure of overall satisfaction that may be too coarse to measure subtle variations in domains of satisfaction. Another limitation is that the proportions of dissatisfied patients in our sample were small for short- and long-term satisfaction. Our effect estimates had wide CIs. Therefore, we may have been underpowered to detect a true effect. Also, our findings are based on the DAG that we thought best represented the causal relationships among our variables of interest based on the published patient satisfaction literature and on clinical insight. It is possible that other DAG models could be used to map these causal associations. Finally, questions about bother from bleeding and perceived involvement in care were asked at the same time as assessment of short-term satisfaction. Our DAG assumes that a patient’s response about these past events is unaffected by short-term satisfaction (ie, not prone to recall bias). However, our estimates may be the result of reverse causality, and apparent causal relationship (as outlined in the DAG) may not exist.
In summary, we found that better preoperative skin QOL and more intraoperative Mohs stages predicted higher short- and long-term patient satisfaction. Additional significant predictors of short-term satisfaction were bother from bleeding and perceived involvement in treatment choice. An additional predictor of long-term satisfaction was being married. The temporal changes in the variables that predict patient satisfaction suggest that attention should be paid to the time frame during which measures of satisfaction are ascertained.36 For patients who undergo surgical treatment, questions about satisfaction asked at different time points may yield slightly different outcomes. It may be important to measure preoperative skin-related QOL in patients undergoing Mohs surgery, as it is a potentially modifiable factor that is associated with patient satisfaction. Further studies may help determine which preoperative skin-related QOL factors are amenable to interventions and possible improvement. If potentially modifiable preoperative skin-related QOL factors can be targeted with intervention, clinicians may be able to improve short- and long-term patient satisfaction with Mohs surgery.
Acknowledgments
Funding/Support: This study was supported in part by grants K23 AR 051037 (Dr Asgari) and K24 AR052667 (Dr Chren) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases and by grant IIR 04-043-3 (Dr Chren) from the Health Services Research and Development Service, Department of Veterans Affairs.
Role of the Sponsors: The sponsors had no role in the design or conduct of the study; in the collection, analysis, or interpretation of data; or in the preparation, review, or approval of the manuscript.
Footnotes
Author Contributions: All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Asgari and Chren. Acquisition of data: Chren. Analysis and interpretation of data: Asgari, Warton, Neugebauer, and Chren. Drafting of the manuscript: Asgari, Warton, Neugebauer, and Chren. Critical revision of the manuscript for important intellectual content: Asgari, Warton, Neugebauer, and Chren. Statistical analysis: Warton and Neugebauer. Obtained funding: Asgari and Chren. Administrative, technical, and material support: Asgari and Chren. Study supervision: Chren.
Financial Disclosure: None reported.
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