Evidence map›Paper›PMID 20559697›Full record

ArticleMaternal and child health journal2010

Perinatal periods of risk: phase 2 analytic methods for further investigating feto-infant mortality.

William M Sappenfield, Magda G Peck, Carol S Gilbert, Vera R Haynatzka, Thomas Bryant

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In one paragraph

Article in Maternal and child health journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  7. Contributors to excess infant mortality in the U.S. South.American journal of preventive medicine · 2014
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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

5 authors.

William M SappenfieldDivision of Reproductive Health, Centers for Disease Control and Prevention, Atlanta, GA, USA. Bill_Sappenfield@doh.state.fl.us
Magda G Peck
Carol S Gilbert
Vera R Haynatzka
Thomas Bryant

Funding

MATERNAL, INFANT &REPRODUCTIVE HEALTH: NATIONAL/STATE COALITION CAPACITYU65DP724969 · DP · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI SIMPSON, PATRICK S · 2005 to 2009
$2.7M
NCCDPHP CDC HHS 5 U65 DP724969-05PHS HHS U50/CCU300860
6 · The paper itself

Abstract

The perinatal periods of risk (PPOR) methods provide a framework and tools to guide large urban communities in investigating their feto-infant mortality problem. The PPOR methods have 11 defined steps divided into three analytic parts: (1) Analytic Preparation; (2) Phase 1 Analysis-identifying the opportunity gaps or populations and risk periods with largest excess mortality; and (3) Phase 2 Analyses-investigating these opportunity gaps. This article focuses on the Phase 2 analytic methods, which systematically investigate the opportunity gaps to discover which risk and preventive factors are likely to have the largest effect on improving a community's feto-infant mortality rate and to provide additional information to better direct community prevention planning. This article describes the last three PPOR epidemiologic steps for investigating identified opportunity gaps: identifying the mechanism for excess mortality; estimating the prevalence of risk and preventive factors; and estimating the impact of these factors. While the three steps provide a common strategy, the specific analytic details are tailored for each of the four perinatal risk periods. This article describes the importance, prerequisites, alternative approaches, and challenges of the Phase 2 methods. Community examples of the methods also are provided.

Indexed as

Fetal MortalityInfant MortalityRisk AssessmentBirth WeightFemaleFloridaGestational AgeHealthcare DisparitiesHealth Status DisparitiesHumansInfantInfant, NewbornInfant WelfareMaleMaternal WelfarePerinatal Care

Identifiers

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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.