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Study Objectives: <p>Screening for early detection of sleep-disordered breathing in hospitalized patients has been shown to reduce readmission rates. However, postdischarge polysomnography for confirmation of diagnosis is required. We analyzed factors for “no-shows” using geospatial techniques.</p> Methods: <p>Data were obtained between September 2019 and September 2023. The outcome for the study was patients’ no-show rate (nonadherent for polysomnography) after hospital discharge. Predictors included the patient’s age, sex, body mass index, health literacy, Distressed Communities Index score, and distance to a sleep center for the patient’s zip code of residence. Logistic regression was applied to estimate odds of patients’ adherence at the patient level using a geospatial mapping technique. Geographically weighted logistic regression was applied to estimate the odds of a zip code’s including adherent patients.</p> Results: <p>Of the 1,318 hospitalized patients established as high-risk for sleep-disordered breathing and referred for an overnight sleep study who were able to be geocoded, 228 were adherent and 1,130 were nonadherent. In nonspatial regression analyses, health literacy (adjusted odds ratio = 1.06; 95% confidence interval = 1.03, 1.09), age (adjusted odds ratio = 0.99; 95% confidence interval = 0.98, 0.99), and drive time (adjusted odds ratio = 0.95; 95% confidence interval = 0.92, 0.97) were identified as statistically significant predictors of patients’ adherence. Spatial regression analyses identified areas that had high and low predictive probability of patients’ adherence, as well as which community-level factors were co-occurring in those areas.</p> Conclusions: <p>The findings suggest that both patient-level factors and the community where patients live may impact no-show rates. Health literacy was identified as a key modifiable predictor at the patient level. At the community level, we found that predicted probability of patient adherence varied throughout the state. Efforts should focus on enhancing patients’ education at the individual level and understanding geographical factors to improve adherence.</p> Citation: <p>Sharma S, Stansbury R, Rojas E, et&#xa0;al. Factors impacting sleep center no-show rates after hospital discharge using geospatial coding in Appalachia. <i>J Clin Sleep Med.</i> 2025;21(4):667–674.</p>

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Factors impacting sleep center no-show rates after hospital discharge using geospatial coding in Appalachia

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Sunil Sharma,
  • Robert Stansbury,
  • Edward Rojas,
  • Priyanka Srinivasan,
  • Kassandra Olgers,
  • Scott Knollinger,
  • Wes Kimble,
  • Brian Hendricks,
  • Timothy Dotson,
  • Brian A. Witrick

摘要

Study Objectives:

Screening for early detection of sleep-disordered breathing in hospitalized patients has been shown to reduce readmission rates. However, postdischarge polysomnography for confirmation of diagnosis is required. We analyzed factors for “no-shows” using geospatial techniques.

Methods:

Data were obtained between September 2019 and September 2023. The outcome for the study was patients’ no-show rate (nonadherent for polysomnography) after hospital discharge. Predictors included the patient’s age, sex, body mass index, health literacy, Distressed Communities Index score, and distance to a sleep center for the patient’s zip code of residence. Logistic regression was applied to estimate odds of patients’ adherence at the patient level using a geospatial mapping technique. Geographically weighted logistic regression was applied to estimate the odds of a zip code’s including adherent patients.

Results:

Of the 1,318 hospitalized patients established as high-risk for sleep-disordered breathing and referred for an overnight sleep study who were able to be geocoded, 228 were adherent and 1,130 were nonadherent. In nonspatial regression analyses, health literacy (adjusted odds ratio = 1.06; 95% confidence interval = 1.03, 1.09), age (adjusted odds ratio = 0.99; 95% confidence interval = 0.98, 0.99), and drive time (adjusted odds ratio = 0.95; 95% confidence interval = 0.92, 0.97) were identified as statistically significant predictors of patients’ adherence. Spatial regression analyses identified areas that had high and low predictive probability of patients’ adherence, as well as which community-level factors were co-occurring in those areas.

Conclusions:

The findings suggest that both patient-level factors and the community where patients live may impact no-show rates. Health literacy was identified as a key modifiable predictor at the patient level. At the community level, we found that predicted probability of patient adherence varied throughout the state. Efforts should focus on enhancing patients’ education at the individual level and understanding geographical factors to improve adherence.

Citation:

Sharma S, Stansbury R, Rojas E, et al. Factors impacting sleep center no-show rates after hospital discharge using geospatial coding in Appalachia. J Clin Sleep Med. 2025;21(4):667–674.