Neighborhood Effects on Missed Appointments in a Large Urban Academic Multispecialty Practice

Chou, Edgar; Moore, Kari; Zhao, Yongfeng; Melly, Steven J.; Payvandi, Lily; Buehler, James W.

doi:10.1007/s11606-021-06935-x

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…On multivariate logistic regression, those with Medicaid insurance had more than twice the odds of missing their appointments compared with those who had other insurance types. These results are consistent with previous studies that also found younger age and Medicaid insurance were associated with reduced appointment attendance in various health care settings 1,5,8,10,11 . Black race, Hispanic ethnicity, and inability to speak English have also been identified by others as potential risk factors for missed health care appointments 4,5,8–11 …”

Section: Discussionsupporting

confidence: 91%

“…These results are consistent with previous studies that also found younger age and Medicaid insurance were associated with reduced appointment attendance in various health care settings. 1,5,8,10,11 Black race, Hispanic ethnicity, and inability to speak English have also been identified by others as potential risk factors for missed health care appointments. 4,5,[8][9][10][11] Language and/or cultural barriers may contribute to missed health care appointments.…”

Section: Discussionmentioning

confidence: 99%

“…1,5,8,10,11 Black race, Hispanic ethnicity, and inability to speak English have also been identified by others as potential risk factors for missed health care appointments. 4,5,[8][9][10][11] Language and/or cultural barriers may contribute to missed health care appointments. Limited English proficiency can increase the risk of miscommunication about appointment dates and times or about the need for follow-up care especially in health care settings where staff members only speak English.…”

Section: Discussionmentioning

confidence: 99%

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Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

Miranne,

Courtepatte,

Schatzman-Bone

et al. 2023

UROGC

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Importance Missed appointments lead to decreased clinical productivity and poor health outcomes. Objectives The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments. Study Design We conducted an institutional review board–approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments. Results Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47–72 years] vs 69 years [IQR, 59–78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5–55.5 days] vs 30.5 days [IQR, 12.8–47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04–4.48], P = 0.044). Conclusions Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

Miranne,

Courtepatte,

Schatzman-Bone

et al. 2023

UROGC

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“…Worse neighborhood safety is associated with missing medical appointments. 42 Individuals residing in neighborhoods with the greatest number of violent crimes versus the least number of violent crimes were found to have 27% increased odds of missing a medical appointment (OR = 1.27; 95% CI, 1.19–1.35) after adjusting for the same patient-level factors previously mentioned. The level of crime and the perception of lower safety in a neighborhood have an impact on attendance at medical appointments.…”

Section: Neighborhood and Built Environment Srfsmentioning

confidence: 83%

Spatial Vision Inequalities: A Literature Review of the Impact of Place on Vision and Eye Health Outcomes

Hicks,

Simmons,

Newman-Casey

et al. 2024

Trans. Vis. Sci. Tech.

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“Neighborhood and built environment” is one of the five domains of social determinants of health that has been outlined by Healthy People 2030, and this domain impacts an individual's well-being, health, and quality of life. Social risk factors (SRFs) in the neighborhood and built environment domain include unstable or unsafe housing, poor access to transportation, lack of green spaces, pollution, safety concerns, and neighborhood measures of inequity. In this narrative literature review, we assess the relationship between neighborhood and built environment SRFs and eye health and vision outcomes. We explain how mapping neighborhood-level SRFs may be used to advance health equity in the field of eye health and vision care.

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“…Residential areas differ in terms of ease of access to primary health care providers, walkability, access to healthy foods, literacy about symptoms of an ACS, and access to EMS in case of a perceived health emergency. For instance, a recent study showed that neighborhood-related metrics, such as rate of violent crime and perceived neighborhood safety, are strong predictors of missed medical appointments in a large urban society (Chou et al, 2021).…”

Section: Clinical and Policy Implicationsmentioning

confidence: 99%

Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

Faramand

Alrawashdeh

Helman

et al. 2021

Research in Nursing & Health

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Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community.This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ 2 = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.

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Neighborhood Effects on Missed Appointments in a Large Urban Academic Multispecialty Practice

Cited by 11 publications

References 33 publications

Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

Spatial Vision Inequalities: A Literature Review of the Impact of Place on Vision and Eye Health Outcomes

Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

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