Geographic Proximity Affects Adherence to Primary Care Facility Appointment in New York City Open Access

Ishikawa, Genta (2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/n870zr590?locale=en
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Abstract

Background: A missed primary care appointment is an independent predictor of acute care utilization and suboptimal primary care outcomes. However, the relationship between geographic proximity to the primary care facility (PCF) and the risk of no-shows has not been fully investigated in New York City. Objective of the study was (1) to investigate the relationships of geographic proximity to PCF with the risk of a no-show and (2) to explore/predict no-shows based on a host of environmental and health system factors. Methods: This was a single-center, retrospective study at General Medical Associates (GMA)/Mount Sinai Beth Israel, a large, urban, general internal medicine outpatient practice in lower Manhattan, from January through December 2015. We calculated street network distance to GMA in miles and transit time by public transportation to GMA in minutes. A multivariable generalized estimating equation regression model was used to analyze the relationships of geographic proximity with missed facility appointments (i.e., no-show). Results: A total of 11,881 patients over 18 years old who had appointments (total appointment = 36,144) at GMA from January through December 2015 were included. Missed facility appointments accounted for 21.2% of the total appointments. Median street network distance and transit time were 4.4 miles and 23.9 minutes, respectively. Fully adjusted models by multiple covariates showed positive coefficient values in primary exposure variables ([transit time] and [network distance]; 0.003 and 0.006, respectively) and negative coefficient values in quadratic terms ([transit time]2 and [network distance]2: -0.0001 and -0.0003, respectively) despite the lack of statistical significance but trend of positive correlation between transit time and risk of a no-show (p-values for coefficients of [transit time], [network distance], [transit time]2, and [network distance]2 in the full models were 0.052, 0.225, 0.053, and 0.581, respectively). The other significant predictors of no-shows were younger age, male, black race, Medicaid, resident/intern physician appointment, snowy weather, and low annual household income. Conclusions: As the transit time by public transportation to a PCF increases in New York City, patients are more likely to have no-shows to facility appointments.

Table of Contents

Introduction page 1-2 Materials and methods Patients and other clinical variables page 2-3 Statistical methods page 3-4 Results Patient and appointment characteritics page 4-5 Main exposures of no-shows page 5-6 Other predictors of no-shows page 6 Discussion page 6-10 Conclusions page 10-11 Acknowledgement page 12 References page 13-14 Table 1 page 15 table 2 page 16 Figure legends page 17 Figure 1 page 18 Figure 2 page 19 Figure 3 page 20 Figure 4 page 21

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