Evidence map›Paper›PMID 40398951›Full record

ArticleBMJ open2025

Retrospective antimicrobial consumption surveillance at health facility level in Dodoma Region, Tanzania.

Romuald Mbwasi, Calvin Andeve Omolo, Eva Ombaka, Rajabu Mohamedi Kingo, Samuel Mungai, Karin Wiedenmayer

Abstract read
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

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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

3 citing papers in PubMed.

  1. 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

6 authors.

Romuald MbwasiSt John's University of Tanzania, Dodoma, Tanzania.
Calvin Andeve OmoloSchool of Pharmacy and Health Sciences, United States International University, Nairobi, Kenya andeve@gmail.com comolo@usiu.ac.ke.ORCID http://orcid.org/0000-0002-4421-3783
Eva OmbakaSt John's University of Tanzania, Dodoma, Tanzania.
Rajabu Mohamedi KingoDepartment of Pharmacy, The University of Dodoma, Dodoma, Tanzania.ORCID http://orcid.org/0000-0003-4408-0869
Samuel MungaiUnited States International University, Nairobi, Kenya.
Karin WiedenmayerSCIH, Swiss Tropical and Public Health Institute, Allschwil, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAntimicrobial resistance (AMR) is a major threat in sub-Saharan Africa (SSA), but assessments of antimicrobial consumption (AMC) are limited. This study aimed to investigate regional AMC and resistance patterns in a representative area of Tanzania and to introduce a method for determining AMC in low-resource settings. DESIGN AND

methodsWe conducted a retrospective study using prescription data collected over 5 years (2013-2017) from multiple hospitals and selected primary health facilities in the Dodoma Region of Tanzania. The study employed the WHO's Anatomical Therapeutic Chemical (ATC) classification and Daily Defined Dose (DDD) methodology to quantify antimicrobial use. Outpatient prescription records that met our inclusion criteria were analysed, while incomplete records were excluded. Sensitivity testing for frequently prescribed antimicrobials was performed against representative gram-negative and gram-positive bacteria, with resistance expressed as minimum inhibitory concentrations and resistance percentages. SETTING AND

participantsThis study was conducted across several high-volume healthcare facilities in the Dodoma Region, encompassing both hospital and primary care settings. The dataset comprises outpatient prescription records from these facilities, representing a significant proportion of the regional healthcare usage. The selected facilities were chosen based on their substantial catchment populations to maximise data volume and relevance. OUTCOME MEASURES: The primary outcome measure was the DDD per 1000 inhabitants per day (DID) for various antimicrobial classes. Secondary outcomes included the prevalence of specific drugs, such as amoxicillin and erythromycin, and their corresponding resistance profiles. Resistance data were quantitatively analysed, with particular attention given to penicillinase-sensitive penicillins and their resistance rates among gram-negative and Gram-positive bacteria.

resultsAnalysis revealed that single penicillins, particularly amoxicillin, dominated prescriptions, accounting for 25% to 60% of hospitals and 13% to 29% of primary health centres. Erythromycin was prescribed in 9.4% to 25.1% of cases across facilities. The overall AMC in the region ranged from 36.7 to 50.2 DID during the study period, with consumption patterns showing an initial increase of 4.3% from 2013 to 2014, a subsequent 29.0% decrease from 2014 to 2015, followed by a 34.1% increase from 2015 to 2016 and a further 37.9% increase from 2016 to 2017. Resistance testing demonstrated that penicillinase-sensitive penicillins exhibited an average resistance rate of 87.3%, with gram-negative and gram-positive bacteria showing resistance levels of 90.1% (±8%) and 83.6% (±8%), respectively, indicating a statistically significant association (p<0.05) between high consumption and elevated resistance.

conclusionOur findings reveal that AMC in the Dodoma Region is high and is linked to significant resistance against commonly used agents. This study presents a robust method for monitoring AMC and resistance in a resource-efficient manner, offering potential applicability in similar settings across SSA. The data, though representing only a fraction of total antimicrobial use, underscore the urgent need for targeted antimicrobial stewardship interventions, particularly at the primary healthcare level. Future research should further investigate demographic influences and evaluate intervention strategies to mitigate AMR effectively.

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialDrug UtilizationHealth FacilitiesGram-Negative BacteriaGram-Positive BacteriaHumansMicrobial Sensitivity TestsRetrospective StudiesTanzaniaAnti-Bacterial AgentsAntibioticsHospitalsInfection controlPUBLIC HEALTH

Identifiers

PMID40398951
PMCPMC12096996

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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.