Evidence map›Paper›PMID 40646604›Full record

ArticleArchives of public health = Archives belges de sante publique2025

Estimating county-level dental care utilization among adults in California using multilevel modeling with raking approach.

Yilan Huang, Honghu Liu

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yilan HuangDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA. yilanh19@ucla.edu.
Honghu LiuDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRegular dental visits are essential for oral health, yet disparities between regions exist due to socioeconomic and geographic factors. While national surveys provide valuable data on dental care utilization, they generally lack sufficient sample sizes at the local level to generate reliable county-level estimates. Small area estimation techniques, such as multilevel regression and post-stratification (MRP), can help address this gap by producing robust estimates for smaller geographic areas. However, the MRP approach relies on detailed population data in the form of joint distributions and cannot be applied when only marginal distributions are available.

methodsThis paper introduces a hybrid approach combining multilevel modeling with the raking procedure. We used individual-level data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) and census data from American Community Survey to estimate county-level dental care utilization among adults in California.

resultsThe county-level dental care utilization in California ranged from 52.5 to 73.1%, with a median of 63.1%. Our model-based estimates matched direct BRFSS estimates at metropolitan and micropolitan statistical area levels. Furthermore, we found significantly positive correlations between our model-based estimates and direct estimates from the California Health Interview Survey for 41 counties (Pearson coefficient: 0.801, P < 0.001).

conclusionsThe proposed approach accounts for individual- and area-level factors while overcoming data constraints that limit the application of MRP. The findings demonstrate the feasibility of this approach in generating county-level estimates, supporting public health planning and targeted interventions to reduce disparities in dental care utilization.

Indexed as

Dental care utilizationMultilevel modelRakingSmall area Estimation

Identifiers

PMID40646604
PMCPMC12247392

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.