Social Networks Are Associated with Healthcare Utilization Among Taxi and For-Hire Vehicle Drivers: a Latent Class Analy
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Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center , New York , NY , USA; 2Department of Psychology, Fordham University , New York , NY , USA.
J Gen Intern Med DOI: 10.1007/s11606-019-05456-y © Society of General Internal Medicine 2019
INTRODUCTION
Taxi and for-hire vehicle (FHV) driving is a growing occupation, with over 185,000 licensed taxi/FHV drivers in New York City (NYC) alone and well over 300,000 in the USA.1, 2 Most licensed NYC drivers have racial/ethnic minority backgrounds and are authorized immigrants.1 Because of their sedentary occupation, poor diet, pollution exposure, discrimination, stressors, and ethnic backgrounds, drivers are at an elevated risk for cardiovascular disease (CVD), cancer, hypertension, obesity, and diabetes.3 Most NYC drivers are independent contractors without workplace-sponsored health insurance.1, 3 Hence, many are uninsured (34% in a recent study) and lack a primary care provider (PCP).3 According to Andersen’s widely acknowledged behavioral model of health services use, social context predisposes individuals to use services or not.4 In low socioeconomic status and immigrant populations, social networks are important conduits for health information.5 Subgroups of medically underserved ethnic minority individuals share healthcare utilization habits,6 and it is likely that the heterogeneous taxi/FHV driver population can be classified according to at least three healthcare utilization profiles.6 This is the first study to test whether social networks relate to healthcare utilization profiles in taxi/FHV drivers.
METHODS
An Institutional Review Board-exempted, cross-sectional needs assessment was conducted with NYC taxi/FHV drivers from March 2013 to April 2014, assessing demographics; healthcare access and utilization (having health insurance, a PCP, a pastThe results have previously been presented at the Society of Behavioral Medicine’s 40th Annual Meeting. Received July 9, 2019 Accepted September 27, 2019
year routine checkup, and dental visit); and drivers’ social networks, by asking with whom (Balters^) the driver (Bego^) had discussed health-related advice over the past year. Latent class analysis grouped drivers by their healthcare access and utilization patterns. Descriptive egocentric social network analysis tested whether driver healthcare access and utilization patterns related to their social network characteristics. Bivariate associations between social network variables or demographic covariates with latent class membership were tested, and social network variables and covariates were included in the multivariable model, based on statistically significant associations with latent class membership.
RESULTS
Table 1 describes the 211 participants and shows each latent class’s healthcare access and utilization item response probabilities. Model-fit diagnostics identified three latent classes: Buninsured past-year non-utilizers^ (lowest item response probabilities for all items), Binsured past-year utilizers^ (high item res
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