Evidence map›Paper›PMID 33718601›Full record

ArticleCities & health2020

A NEIGHBORHOOD-LEVEL POTENTIAL HEALTH IMPACT SCORING TOOL TO SUPPORT LOCAL-LEVEL HEALTH IMPACT ASSESSMENTS.

John D Prochaska, Robert N Buschmann, Daniel C Jupiter

Abstract read
In one paragraph

Article in Cities & health, 2020. 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

3 authors.

John D ProchaskaDepartment of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, Texas, USA.
Robert N BuschmannCommunity-University Partnership for the Study of Children, Youth and Families, Faculty of Extension, University of Alberta, Edmonton, AB, Canada.
Daniel C JupiterDepartment of Preventive Medicine & Community Health, Office of Biostatistics, University of Texas Medical Branch, Galveston, Texas, USA.

Funding

Synthetic Organic Chemistry CoreP30ES006676 · NIEHS · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI ELFERINK, CORNELIS JOHAN · 1995 to 2017
$21.7M
NIEHS NIH HHS P30 ES006676NIMHD NIH HHS L60 MD009841
6 · The paper itself

Abstract

Health Impact Assessments (HIAs) have quickly become a widely utilized tool for integrating health and health-related evidence and data into decision making processes across a range of projects and polices. Integrating and utilizing the wide range of available data can be daunting. To support communities seeking to engage in health impact assessments, we developed the Neighborhood Potential Health Impact Score (NPHIS) methodology. We present the NPHIS method's four step process, and how this process was applied to an HIA focusing on the rebuilding of public housing following a natural disaster. We discuss developing the boundary definition, selection and definition of indicators, calculation of the NPHIS, and interpretation and utilization of the scores. Findings were validated using feedback from a community stakeholder advisory board as well as through feedback collected from focus groups of community residents. NPHIS methodology has proven to be a useful resource in better understanding the complex sources of potential health impacts facing communities, and in being an evidence-based, data-driven resource for HIA decision-makers and their stakeholders in our specific application. Other groups seeking to integrate similar data into their decision-making processes could benefit from replicating the NPHIS in their efforts.

Indexed as

Cumulative RiskHealth impact assessment (HIA)mmunity Health AssessmentNeighborhood Health

Identifiers

PMID33718601
PMCPMC7953871

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.