Evidence map›Paper›PMID 35592541›Full record

ArticleInternational journal of general medicine2022

Is Travel Time Associated with Health Service Utilization in Northwest China? Evidence from Shaanxi Province.

Ruhai Bai, Jianmin Gao, Wanyue Dong

Abstract read
In one paragraph

Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ruhai BaiSchool of Public Affairs, Nanjing University of Science and Technology, Nanjing, People's Republic of China.ORCID 0000-0001-5426-6291
Jianmin GaoSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Wanyue DongSchool of Elderly Care Services and Management, Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.ORCID 0000-0001-5502-3857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Northwest China has a large area, low population density, and few health resources, which makes the utilization of health resources in this region difficult. The objective of this study was to assess utilization of health services and its association with travel time in Shaanxi Province. Patients and Methods: Data were obtained from the fifth Household Health Service Survey of Shaanxi Province conducted in 2013. Binary logistic regression was used to assess the relationship between travel time and health service utilization, and negative binomial regression was conducted to assess the relationship between travel time and the frequency of health service utilization. Results: A total of 42.6% of patients used health services, with a higher use rate among rural residents than among urban residents (47.0% and 27.4%, respectively). A total of 30.9% of patients traveled more than 15 min to the nearest medical facility (33.3% in rural areas and 22.6% in urban areas). A total of 12.4% of patients traveled more than 30 min to the nearest medical facility (15.1% in rural areas and 3.0% in urban areas). Urban residents living farthest from health care facilities (more than 30 min) had a 2.12-fold higher probability of health service utilization and expected to have a health service utilization rate 1.77 times greater than that of residents with a travel time of less than 5 min. Among the rural population, there was no significant correlation between travel time and health service utilization. Conclusion: Urban patients living farthest from hospitals were more likely to use health services and used health services more frequently. This suggests that more attention should be given to urban patients who live far away from health service providers in Shaanxi Province.

Indexed as

accessibilityhealth equityhealth service utilizationtravel time

Identifiers

PMID35592541
PMCPMC9113551

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.