Disparities in Travel-Related Barriers to Accessing Health Care From the 2017 National Household Travel Survey
Notice bibliographique
Résumé
Importance: Geographic access, including mode of transportation, to health care facilities remains understudied. Objective: To identify sociodemographic factors associated with public vs private transportation use to access health care and identify the respondent, trip, and community factors associated with longer distance and time traveled for health care visits. Design, Setting, and Participants: This cross-sectional study used data from the 2017 National Household Travel Survey, including 16 760 trips or a nationally weighted estimate of 5 550 527 364 trips to seek care in the United States. Households that completed the recruitment and retrieval survey for all members aged 5 years and older were included. Data were analyzed between June and August 2022. Exposures: Mode of transportation (private vs public transportation) used to seek care. Main Outcomes and Measures: Survey-weighted multivariable logistic regression models were used to identify factors associated with public vs private transportation and self-reported distance and travel time. Then, for each income category, an interaction term of race and ethnicity with type of transportation was used to estimate the specific increase in travel burden associated with using public transportation compared a private vehicle for each race category. Results: The sample included 12 092 households and 15 063 respondents (8500 respondents [56.4%] aged 51-75 years; 8930 [59.3%] females) who had trips for medical care, of whom 1028 respondents (6.9%) were Hispanic, 1164 respondents (7.8%) were non-Hispanic Black, and 11 957 respondents (79.7%) were non-Hispanic White. Factors associated with public transportation use included non-Hispanic Black race (compared with non-Hispanic White: adjusted odds ratio [aOR], 3.54 [95% CI, 1.90-6.61]; P < .001) and household income less than $25 000 (compared with ≥$100 000: aOR, 7.16 [95% CI, 3.50-14.68]; P < .001). The additional travel time associated with use of public transportation compared with private vehicle use varied by race and household income, with non-Hispanic Black respondents with income of $25 000 to $49 999 experiencing higher burden associated with public transportation (mean difference, 81.9 [95% CI, 48.5-115.3] minutes) than non-Hispanic White respondents with similar income (mean difference, 25.5 [95% CI, 17.5-33.5] minutes; P < .001). Conclusions and Relevance: These findings suggest that certain racial, ethnic, and socioeconomically disadvantaged populations rely on public transportation to seek health care and that reducing delays associated with public transportation could improve care for these patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».