Evidence map›Paper›PMID 34299923›Full record

ArticleInternational journal of environmental research and public health2021

Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys.

Marina Christofoletti, Tânia R B Benedetti, Felipe G Mendes, Humberto M Carvalho

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Marina ChristofolettiDepartment of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0002-5672-6869
Tânia R B BenedettiDepartment of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0002-2035-5082
Felipe G MendesDepartment of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0001-9552-754X
Humberto M CarvalhoDepartment of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0002-2855-0296

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population.

objectiveWe developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018).

methodsWe used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested.

resultsMRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates.

conclusionThe MRP is a promising strategy to predict subpopulations' physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations.

Indexed as

ExerciseCross-Sectional StudiesHealth SurveysHumansMultilevel AnalysisSample SizeBayesian analysispublic health surveillanceselection biasstatistical modelssurvey methods

Identifiers

PMID34299923
PMCPMC8304573

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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.