Evidence map›Paper›PMID 35442454›Full record

ArticleJAMA network open2022

Association of the 2020 US Presidential Election With Hospitalizations for Acute Cardiovascular Conditions.

Matthew T Mefford, Jamal S Rana, Kristi Reynolds, Omesh Ranasinghe, Murray A Mittleman, Jennifer Y Liu, Lei Qian, Hui Zhou, Teresa N Harrison, Alan C Geller and 4 more

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

14 authors at 4 institutions in 1 country.

Matthew T MeffordDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Jamal S RanaDepartment of Cardiology, Kaiser Permanente Oakland Medical Center, Oakland, California.
Kristi ReynoldsDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Omesh RanasingheDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Murray A MittlemanDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Jennifer Y LiuDivision of Research, Kaiser Permanente Northern California, Oakland.
Lei QianDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Hui ZhouDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Teresa N HarrisonDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena.
Alan C GellerDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Richard P SloanDepartment of Psychiatry, Columbia University Irving Medical Center, New York, New York.
Elizabeth MostofskyDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
David R WilliamsDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Stephen SidneyDivision of Research, Kaiser Permanente Northern California, Oakland.
Kaiser Permanente · USHarvard University · USBeth Israel Deaconess Medical Center · USColumbia University Irving Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Prior studies found a higher risk of acute cardiovascular disease (CVD) around population-wide psychosocial or environmental stressors. Less is known about acute CVD risk in relation to political events. Objective: To examine acute CVD hospitalizations following the 2020 presidential election. Design, Setting, and Participants: This retrospective cohort study examined acute CVD hospitalizations following the 2020 presidential election. Participants were adult members aged 18 years or older at Kaiser Permanente Southern California and Kaiser Permanente Northern California, 2 large, integrated health care delivery systems. Statistical analysis was performed from March to July 2021. Exposure: 2020 US presidential election. Main Outcomes and Measures: Hospitalizations for acute CVD around the 2020 presidential election were examined. CVD was defined as hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or stroke. Rate ratios (RR) and 95% CIs were calculated comparing rates of CVD hospitalization in the 5 days following the 2020 election with the same 5-day period 2 weeks prior. Results: Among 6 396 830 adults (3 970 077 [62.1%] aged 18 to 54 years; 3 422 479 [53.5%] female; 1 083 128 [16.9%] Asian/Pacific Islander, 2 101 367 [32.9%] Hispanic, and 2 641 897 [41.3%] White), rates of hospitalization for CVD following the election (666 hospitalizations; rate = 760.5 per 100 000 person-years [PY]) were 1.17 times higher (95% CI, 1.05-1.31) compared with the same 5-day period 2 weeks prior (569 hospitalizations; rate = 648.0 per 100 000 PY). Rates of AMI were significantly higher following the election (RR, 1.42; 95% CI, 1.13-1.79). No significant difference was found for stroke (RR, 1.02; 95% CI, 0.86-1.21) or HF (RR, 1.18; 95% CI, 0.98-1.42). Conclusions and Relevance: Higher rates of acute CVD hospitalization were observed following the 2020 presidential election. Awareness of the heightened risk of CVD and strategies to mitigate risk during notable political events are needed.

Indexed as

Cardiovascular DiseasesHeart FailureMyocardial InfarctionStrokeAcute DiseaseAdultFemaleHospitalizationHumansMaleRetrospective Studies

Identifiers

PMID35442454
PMCPMC9021908
OpenAlexW4224223639

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.