Evidence map›Paper›PMID 37311276›Full record

ArticleHealth & place2023

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Fengrui Jing, Zhenlong Li, Shan Qiao, Huan Ning, Suhong Zhou, Xiaoming Li

Abstract read
In one paragraph

Article in Health & place, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 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

6 authors.

Fengrui JingGeoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA. Electronic address: fengrui@sc.edu.
Zhenlong LiGeoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
Shan QiaoBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.
Huan NingGeoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
Suhong ZhouSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China.
Xiaoming LiBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.

Funding

Multilevel Determinants of Racial and Ethnic Disparities in Maternal Morbidity and Mortality in the Context of COVID-19 PandemicR01AI127203 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI LI, XIAOMING, LIU, JIHONG · 2017 to 2021
$5.2M
NIAID NIH HHS R01 AI127203
6 · The paper itself

Abstract

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.

Indexed as

Emigrants and ImmigrantsMental Health ServicesBig DataEmigration and ImmigrationHumansMental HealthUnited StatesCOVID-19Geospatial big dataImmigrantsMental healthMental health service utilization disparities

Identifiers

PMID37311276
PMCPMC10527759

What OpenQuestion holds

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Registered trials

None linked

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.