Evidence map›Paper›PMID 26407973›Full record

ArticleBMC pharmacology & toxicology2015

Prevalence and predictors of prior antibacterial use among patients presenting to hospitals in Northern Uganda.

Moses Ocan, Yukari C Manabe, Hannington Baluku, Esther Atukwase, Jasper Ogwal-Okeng, Celestino Obua

Open access · goldAbstract read
In one paragraph

Article in BMC pharmacology & toxicology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Journal of public health in Africa · 2025
    Article
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  6. Article
  7. Article
  8. Antimicrobial Resistance inFrontiers in medicine · 2021
    Review
  9. Article
  10. Article
  11. Review
  12. 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

6 authors at 4 institutions in 2 countries.

Moses OcanDepartment of Pharmacology & Therapeutics, Makerere University, P.O. Box 7072, Kampala, Uganda. mss_ocan@yahoo.co.uk.
Yukari C ManabeDivision of Infectious Diseases Department of Medicine, Johns Hopkins School of Medicine, Baltimore, USA. ymanabe@jhmi.edu.
Hannington BalukuDepartment of Clinical Microbiology, Makerere University, P.O. Box 7072, Kampala, Uganda. hanzbtasi@gmail.com.
Esther AtukwaseMild may Uganda, P. O. Box 24985, Kampala, Uganda. atukesie@gmail.com.
Jasper Ogwal-OkengFaculty of medicine, Gulu University, P.O. Box 166, Gulu, Uganda. jogwal.okeng@gmail.com.
Celestino ObuaDepartment of Pharmacology & Therapeutics, Makerere University, P.O. Box 7072, Kampala, Uganda. cobua1953@gmail.com.
Makerere University · UGGulu University · UGJohns Hopkins University · USMildmay Uganda · UG

Funding

Medical Education for Services to All Ugandans (MESAU)R24TW008886 · FIC · MAKERERE UNIVERSITY COLLEGE OF HEALTH SCIENCES · PI SEWANKAMBO, NELSON K · 2010 to 2015
$9.2M
FIC NIH HHS 5R24TW008886FIC NIH HHS R24 TW008886
6 · The paper itself

Abstract

backgroundHuman antibacterial exposure occur in different ways including consumption of animal and agricultural products as well as use of prescribed and non-prescribed agents. We estimated the prevalence and explored the predictors of antibacterial use among patients presenting to hospitals in northern Uganda.

methodsFour hundred fifty (450) patients were randomly selected and antibacterial use prior to hospital visit measured using a questionnaire and urine antibacterial activity assay. Urine antibacterial bioassays were performed using American type culture collections of Escherichia coli, Bacillus subtilis and Streptococcus pyogenes. Data were analysed using STATA 12.0 at 95% confidence level.

resultsOf 450 patients interviewed, 62.2% had used antibacterial agents. Urine antibacterial activity was detected in 30.4% of the samples tested. Of the 85 patients who reported not taking any antibacterial at home, 16 (18.8%) had urine with antibacterial activity. Most test bacteria, E. coli (74.5%), B. subtilis (72.6%) and S. pyogens (86.7%) were sensitive to urine of patients who reported using antibacterial drugs before hospital visit. From the interview, metronidazole 15.6% (70/450), amoxicillin 12% (54/450), and ciprofloxacin 10.4% (47/450) were the most used antibacterial agents. Patient age (OR, 2.45: 95% CI: 1.02-5.91: P = 0.024), time-lag between last drug intake and hospital visit (OR: 3.18: 95% CI: 1.44-7.0: P < 0.0001), and time-lag between illness onset and hospital visit (OR: 1.89: 95% CI: 0.38-5.1: P = 0.027) predicted the use of antibacterial agents before hospital visit. DISCUSSION: Community antibacterial use continues to take place in an unregulated manner. In addition, physiciansrarely seek to ascertain prior use of antibacterial agents among patients presenting to hospitals. This couldhave a bearing on patient treatment outcomes.

conclusionKnowledge of prior antibacterial use among patients presenting to hospitals is useful to physicians in ensuring antibacterial stewardship.

Indexed as

AdolescentAdultAge FactorsAnti-Bacterial AgentsBacillus subtilisBacteriaBacterial InfectionsDrug UtilizationEscherichia coliFemaleHospitals, PublicHumansLogistic ModelsMaleMicrobial Sensitivity TestsMiddle AgedAnti-Bacterial Agents

Identifiers

PMID26407973
PMCPMC4583731
OpenAlexW2137303522

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.