Evidence map›Paper›PMID 28593116›Full record

ArticlePreventive medicine reports2017

Bridging the gap between clinical practice and public health: Using EHR data to assess trends in the seasonality of blood-pressure control.

Aurora O Amoah, Sonia Y Angell, Hannah Byrnes-Enoch, Sam Amirfar, Phoenix Maa, Jason J Wang

Open access · goldAbstract read
In one paragraph

Article in Preventive medicine reports, 2017. 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
1.8field-weighted citation impact, top 14% 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, 11 citations in OpenAlex.

  1. Article
  2. Artificial Intelligence in Public Health and Epidemiology.Yearbook of medical informatics · 2018
    Review
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

6 authors at 1 institution in 1 country.

Aurora O AmoahPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
Sonia Y AngellPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
Hannah Byrnes-EnochPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
Sam AmirfarPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
Phoenix MaaPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
Jason J WangPrimary Care Information Project (PCIP) Division of Prevention and Primary Care New York City Department of Health and Mental Hygiene (NYCDOHMH), United States.
New York City Department of Health and Mental Hygiene · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic health records (EHRs) provide timely access to millions of patient data records while limiting errors associated with manual data extraction. To demonstrate these advantages of EHRs to public health practice, we examine the ability of a EHR calculated blood-pressure (BP) measure to replicate seasonal variation as reported by prior studies that used manual data extraction. Our sample included 609 primary-care practices in New York City. BP control among hypertensives was defined as systolic blood pressure of 140 or less and diastolic blood pressure of 90 or less (BP < 140/90 mm Hg). An innovative query-distribution system was used to extract monthly BP control values from the EHRs of adult patients diagnosed with hypertension over a 25-month period. Generalized estimating equations were used to compare the association between seasonal temperature variations and BP control rates at the practice level, while adjusting for known demographic factors (age, gender), comorbid diseases (diabetes) associated with blood pressure, and months since EHR implementation. BP control rates increased gradually from the spring months to peak summer months before declining in the fall months. In addition to seasonal variation, the adjusted model showed that a 1% increase in patients with a diabetic comorbidity is associated with an increase of 3% (OR 1.03; CI 1.028-1.032) on the BP measure. Our findings identified cyclic trends in BP control and highlighted greater association with increased proportion of diabetic patients, therefore confirming the ability of the EHR as a tool for measuring population health outcomes.

Indexed as

Blood pressure controlChronic diseaseElectronic health recordsHealthcare qualityPopulation healthPrimary carePublic healthQuality measuresSeasonal variation

Identifiers

PMID28593116
PMCPMC5443962
OpenAlexW2608171964

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.