Evidence map›Paper›PMID 28693036›Full record

ArticleAnnals of internal medicine2017

Association of Coffee Consumption With Total and Cause-Specific Mortality Among Nonwhite Populations.

Song-Yi Park, Neal D Freedman, Christopher A Haiman, Loïc Le Marchand, Lynne R Wilkens, Veronica Wendy Setiawan

Open access · greenAbstract read
In one paragraph

Article in Annals of internal medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 4 pooled it
17.0field-weighted citation impact, top 1% of its field
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

135 citing papers in PubMed, 4 syntheses or guidelines pooled it, 302 citations in OpenAlex.

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75 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Song-Yi ParkFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
Neal D FreedmanFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
Christopher A HaimanFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
Loïc Le MarchandFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
Lynne R WilkensFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
Veronica Wendy SetiawanFrom University of Hawai'i Cancer Center, Honolulu, Hawaii; National Cancer Institute, Bethesda, Maryland; and Keck School of Medicine of the University of Southern California, Los Angeles, California.
University of Hawaii System · USCancer Center of Hawaii · USUniversity of Hawaiʻi at Mānoa · US

Funding

Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
NCI NIH HHS U01 CA164973
6 · The paper itself

Abstract

backgroundCoffee consumption has been associated with reduced risk for death in prospective cohort studies; however, data in nonwhites are sparse.

objectiveTo examine the association of coffee consumption with risk for total and cause-specific death.

designThe MEC (Multiethnic Cohort), a prospective population-based cohort study established between 1993 and 1996.

settingHawaii and Los Angeles, California.

participants185 855 African Americans, Native Hawaiians, Japanese Americans, Latinos, and whites aged 45 to 75 years at recruitment. MEASUREMENTS: Outcomes were total and cause-specific mortality between 1993 and 2012. Coffee intake was assessed at baseline by means of a validated food-frequency questionnaire.

results58 397 participants died during 3 195 484 person-years of follow-up (average follow-up, 16.2 years). Compared with drinking no coffee, coffee consumption was associated with lower total mortality after adjustment for smoking and other potential confounders (1 cup per day: hazard ratio [HR], 0.88 [95% CI, 0.85 to 0.91]; 2 to 3 cups per day: HR, 0.82 [CI, 0.79 to 0.86]; ≥4 cups per day: HR, 0.82 [CI, 0.78 to 0.87]; P for trend < 0.001). Trends were similar between caffeinated and decaffeinated coffee. Significant inverse associations were observed in 4 ethnic groups; the association in Native Hawaiians did not reach statistical significance. Inverse associations were also seen in never-smokers, younger participants (<55 years), and those who had not previously reported a chronic disease. Among examined end points, inverse associations were observed for deaths due to heart disease, cancer, respiratory disease, stroke, diabetes, and kidney disease. LIMITATION: Unmeasured confounding and measurement error, although sensitivity analysis suggested that neither was likely to affect results.

conclusionHigher consumption of coffee was associated with lower risk for death in African Americans, Japanese Americans, Latinos, and whites. PRIMARY FUNDING SOURCE: National Cancer Institute.

Indexed as

CoffeeMortalityAgedAsianBlack or African AmericanBlack PeopleCause of DeathDrinkingEthnicityFemaleHispanic or LatinoHumansMaleMiddle AgedProspective StudiesRisk FactorsCoffee

Identifiers

PMID28693036
PMCPMC7494322
OpenAlexW2735828659

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.