Evidence map›Paper›PMID 37256896›Full record

ArticlePloS one2023

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018.

Fanghui Shi, Jiajia Zhang, Chengbo Zeng, Xiaowen Sun, Zhenlong Li, Xueying Yang, Sharon Weissman, Bankole Olatosi, Xiaoming Li

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Fanghui ShiSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.ORCID 0000-0002-5603-5398
Jiajia ZhangSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.
Chengbo ZengSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.
Xiaowen SunSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.
Zhenlong LiUniversity of South Carolina Big Data Health Science Center, Columbia, South Carolina, United States of America.
Xueying YangSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.ORCID 0000-0001-6788-0688
Sharon WeissmanUniversity of South Carolina Big Data Health Science Center, Columbia, South Carolina, United States of America.
Bankole OlatosiSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.
Xiaoming LiSouth Carolina SmartState Center for Healthcare Quality, Columbia, South Carolina, United States of America.

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
Patterns and predictors of viral suppression: A Big Data approachR01AI164947 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI OLATOSI, BANKOLE, ZHANG, JIAJIA · 2021 to 2025
$4.4M
NIAID NIH HHS R01 AI127203NIAID NIH HHS R01 AI164947
6 · The paper itself

Abstract

backgroundTimely linkage to care (LTC) is key in the HIV care continuum, as it enables people newly diagnosed with HIV (PNWH) to benefit from HIV treatment at the earliest stage. Previous studies have found LTC disparities by individual factors, but data are limited beyond the individual level, especially at the county level. This study examined the temporal and geographic variations of county-level LTC status across 46 counties in South Carolina (SC) from 2010 to 2018 and the association of county-level characteristics with LTC status.

methodsAll adults newly diagnosed with HIV from 2010 to 2018 in SC were included in this study. County-level LTC status was defined as 1 = "high LTC (≥ yearly national LTC percentage)" and 0 = "low LTC (< yearly national LTC percentage)". A generalized estimating equation model with stepwise selection was employed to examine the relationship between 29 county-level characteristics and LTC status.

resultsThe number of counties with high LTC in SC decreased from 34 to 21 from 2010 to 2018. In the generalized estimating equation model, six out of 29 factors were significantly associated with LTC status. Counties with a higher percentage of males (OR = 0.07, 95%CI: 0.02~0.29) and persons with at least four years of college (OR = 0.07, 95%CI: 0.02~0.34) were less likely to have high LTC. However, counties with more mental health centers per PNWH (OR = 45.09, 95%CI: 6.81~298.55) were more likely to have high LTC.

conclusionsFactors associated with demographic characteristics and healthcare resources contributed to the variations of LTC status at the county level. Interventions targeting increasing the accessibility to mental health facilities could help improve LTC.

Indexed as

Continuity of Patient CareHIV InfectionsAdultHumansMaleMass ScreeningSouth CarolinaUnited States

Identifiers

PMID37256896
PMCPMC10231826

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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.