Evidence map›Paper›PMID 28545417›Full record

ArticleBMC public health2017

Community-level characteristics and environmental factors of child respiratory illnesses in Southern Arizona.

Nathan Lothrop, Khaleel Hussaini, Dean Billheimer, Paloma Beamer

Abstract read
In one paragraph

Article in BMC public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Spatio-temporal trends in caries: A study on children in Berlin-Mitte.Clinical and experimental dental research · 2021
    Article
  5. Article
  6. Article
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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

4 authors.

Nathan LothropMel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., PO 245210, Tucson, AZ, 85724, USA. lothrop@email.arizona.edu.ORCID http://orcid.org/0000-0003-3076-6755
Khaleel HussainiMel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., PO 245210, Tucson, AZ, 85724, USA.
Dean BillheimerMel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., PO 245210, Tucson, AZ, 85724, USA.
Paloma BeamerMel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., PO 245210, Tucson, AZ, 85724, USA.

Funding

SOUTHWEST ENVIRONMENTAL HEALTH SCIENCES CENTERP30ES006694 · NIEHS · UNIVERSITY OF ARIZONA · PI Nathan J Cherrington · 1994 to 2026
$36.7M
Relating Diesel Exhaust Exposure to Respiratory and Immune Outcomes in Early LifeK25HL103970 · NHLBI · UNIVERSITY OF ARIZONA · PI BEAMER, PALOMA I · 2011 to 2015
$666k
NHLBI NIH HHS K25 HL103970NIEHS NIH HHS P30 ES006694
6 · The paper itself

Abstract

backgroundLower respiratory illnesses (LRIs) and asthma are common diseases in children <5 years of age. Few studies have investigated the relationships between multiple, home-based social and environmental risk factors and asthma and LRIs in children. Of those that have, none have focused exclusively on children <5 years of age, who are more physiologically vulnerable and spend more time at home compared to older children. Further, no studies have done so at the community level.

methodsWe modeled relationships between emergency department visits and hospitalization rates for asthma and LRIs for children <5 years and geographic risk factors, including socio-economic and housing characteristics, ambient air pollution levels, and population density in Maricopa and Pima Counties, Arizona, from 2005 to 2009. We used a generalized linear model with a negative binomial observation distribution and an offset for the population of very young children in each tract. To reduce multicollinearity among predictors, socio-economic characteristics, and ambient air pollutant levels were combined into unit-less indices using the principal components analysis (PCA). Housing characteristics variables did not exhibit moderate-to-high correlations and thus were not included in PCA. Spatial autocorrelation among regression model residuals was assessed with the Global Moran's I test.

resultsFollowing the regression analyses, almost all predictors were significantly related to at least one disease outcome. Lower socio-economic status (SES) and reduced population density were associated with asthma hospitalization rates and both LRI outcomes (p values <0.001). After adjusting for differences between counties, Pima County residence was associated with lower asthma and LRI hospitalization rates. No spatial autocorrelation was found among multiple regression model residuals (p values >0.05).

conclusionsOur study revealed complex, multi-factorial associations between predictors and outcomes. Findings indicate that many rural areas with lower SES have distinct factors for childhood respiratory diseases that require further investigation. County-wide differences in maternal characteristics or agricultural land uses (not tested here) may also play a role in Pima County residence protecting against hospitalizations, when compared to Maricopa County. By better understanding this and other relationships, more focused public health interventions at the community level could be developed to reduce and better control these diseases in children <5 years, who are more physiologically vulnerable.

Indexed as

Air PollutionArizonaAsthmaChild, PreschoolEmergency Service, HospitalEnvironmental ExposureHospitalizationHousingHumansInfantLinear ModelsPopulation DensityRegression AnalysisResidence CharacteristicsRespiratory Tract DiseasesRisk FactorsAir pollutionAsthmaChildrenHousingLower respiratory illnessNeighborhoodSocio-economic status

Identifiers

PMID28545417
PMCPMC5445507

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.