Evidence map›Paper›PMID 31703586›Full record

Observational studyInternational journal of health geographics2019

The population randomization observation process (PROP) assessment method: using systematic habitation observations of street segments to establish household-level epidemiologic population samples.

Mieka Smart, Richard Sadler, Alan Harris, Zachary Buchalski, Amber Pearson, C Debra Furr-Holden

Abstract readObservational Study
In one paragraph

Observational study in International journal of health geographics, 2019. 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

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

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

6 authors.

Mieka SmartDivision of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA. smartmie@msu.edu.ORCID 0000-0002-5939-5575
Richard SadlerDivision of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA.
Alan HarrisDivision of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA.
Zachary BuchalskiDivision of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA.
Amber PearsonDept of Geography, Environment & Spatial Sciences, College of Social Science, Michigan State University, 673 Auditorium Road, Room 115, East Lansing, MI, 48824, USA.
C Debra Furr-HoldenDivision of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA.

Funding

The Flint Center for Health Equity SolutionsU54MD011227 · NIMHD · MICHIGAN STATE UNIVERSITY · PI SADLER, RICHARD CASEY · 2016 to 2020
$10.7M
National Institutes of Health National Institute on Minority Health and Health Disparities U54MD011227NIMHD NIH HHS U54 MD011227
6 · The paper itself

Abstract

backgroundIdentifying and intervening on health disparities requires representative community public health data. For cities with high vacancy and transient populations, traditional methods of population estimation for refining random samples are not feasible. The aim of this project was to develop a novel method for systematic observations to establish community epidemiologic samples.

resultsWe devised a four-step population randomization observation process for Flint, Michigan, USA: (1) Use recent total population data for community areas (i.e., neighborhoods) to establish the proportional sample size for each area, (2) Randomly select street segments of each community area, (3) Deploy raters to conduct observations about habitation for each randomly selected segment, and (4) Complete observations for second and third street segments, depending on vacancy levels. We implemented this systematic observation process on 400 randomly selected street segments. Of these, 130 (32.5%) required assessment of secondary segments due to high vacancy. Among the 130 primary segments, 28 (21.5%) required assessment of tertiary (or more) segments. For 71.5% of the 400 primary street segments, there was consensus among raters on whether the dwelling inhabited or uninhabited.

conclusionHouses observed with this method could have easily been considered uninhabited via other methods. This could cause residents of ambiguous dwellings (likely to be the most marginalized residents with highest levels of unmet health needs) to be underrepresented in the resultant sample.

Indexed as

EcosystemFamily CharacteristicsPopulation DensityResidence CharacteristicsCitiesHealth Status DisparitiesHumansMichiganPopulation SurveillanceRandom AllocationCensus block groupHabitationPopulation sampleRandom sample

Identifiers

PMID31703586
PMCPMC6842250

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Registered trials

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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.