Determinants of Clinic Absenteeism: A Novel Method of Examining Distance from Clinic and Transportation

Abstract

Delayed treatment and non-adherence are associated with inferior prostate cancer (CaP) outcomes. Missed clinic appointments (MA) are one form of non-adherence that may be preventable. We conducted a retrospective cohort study of 1341 scheduled clinic encounters for men referred to an academic urology clinic for evaluation of known or suspected CaP. Driving distance and public transit times were calculated using a Google Distance Matrix API algorithm. Zip code level data regarding socioeconomic status was obtained from the 2013 American Community Survey. Logistic regression multivariate analysis was used to identify MA predictors. Of scheduled clinic encounters, 14% were missed. Public health insurance was associated with MA (Private insurance 10%, Public insurance 19%), (p < 0.01) Calendar month was associated with MA with December showing the highest rate (21.2%) and June the lowest (5.3%) rates. (p = 0.02) Appointments for suspected CaP were more likely to be missed (19.3%) than those for known CaP (10.5%), p < 0.01. Driving distance was inversely associated with rate of MA (CA median 11.8 miles, MA median 10.4 miles, p = 0.04) while public transit times were not (66.7 min for CA, 65.3 min for MA, p = 0.36). Men that missed appointments were from areas with lower household incomes and educational attainment. Patient encounter type, insurance status, and reason for referral remained significantly associated with MA after multivariable adjusted analysis. By computing public transit time to the clinic using a mapping engine, we present a novel way to measure this parameter for studies of urban health care.

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

“…indicating that past no-show behavior is carried onto future appointments, as observed in previous studies [3,24]. Likewise, the number of no-shows in consultations in the previous year was found statistically signi cant and positively associated to no-show in previous works [13,15,25].…”

“…There is consensus in the literature regarding the non-random nature of no-shows, and research has been published proposing statistical analyses of factors that may impact no-show rates [6,7]. Previous studies have shown that factors associated with no-show include patients' sociodemographic conditions such as age [4]; gender [7,8]; distance between home and healthcare unit [9,10]; race [11]; marital status [12,13]; level of schooling [14]; appointment characteristics -time [2], day of the week [9], and month [15]; elapsed time between appointment request and actual consultation [2]; and patient's clinical diagnosis [16].…”

Implications of Regionalizing Care in the Developing World: Impact of Distance to Referral Center on Compliance to Biopsy Recommendations in a Brazilian Prostate Cancer Screening Cohort

“…While distance to care is a potential barrier restricting access, data evaluating the association between distance and compliance with cancer screening are limited. ough our study is the first to analyze the association between distance and PC in the developing world, few studies addressed this issue in the US [4,[14][15][16]. One single-center study examined the association between distance and missed clinic appointments among men suspected of or being treated for PC [14].…”

“…ough our study is the first to analyze the association between distance and PC in the developing world, few studies addressed this issue in the US [4,[14][15][16]. One single-center study examined the association between distance and missed clinic appointments among men suspected of or being treated for PC [14]. Among 1,341 scheduled clinic encounters for 576 patients, greater distance was associated with improved compliance (compliant median � 11.8 mi vs. missed appointment � 10.4 mi, P � 0.04).…”

The use of geographical analysis in assessing the impact of patients’ home addresses on their participation in outpatient cardiac rehabilitation: a prospective cohort study

“…The direct distance of 30 km from home to the hospital was the best cut-off point for participating in OCR. As geocoding, which provides geographical coordinates corresponding to a location on the earth, becomes easier to calculate [6], in the present study, we performed geographical analysis using this geocoding to investigate the impact of the distance from home to the hospital on participation in OCR. Subgroup analysis was performed according to a variety of clinical backgrounds.…”